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FIFRA SCIENTIFIC ADVISORY PANEL (SAP)

OPEN MEETING

 

SCIENTIFIC REVIEW OF ISSUES ASSOCIATED WITH 

THE PROPOSED ENDOCRINE DISRUPTOR SCREENING 

PROGRAM (EDSP) TIER 2 ECOTOXICITY TESTS

DOCKET NUMBER: EPA-HQ-OPP-2013-0182

FIFRA SAP WEB SITE 

http://www.epa.gov/scipoly/sap/

OPP Docket Telephone: (703) 305-5805

UNITED STATES ENVIRONMENTAL

PROTECTION AGENCY

CONFERENCE CENTER LOBBY LEVEL

ONE POTOMAC YARD (SOUTH BUILDING)

2777 SOUTH CRYSTAL DRIVE

ARLINGTON, VIRGINIA 22202

JUNE 25-28, 2013 
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1 DR. SHARLENE MATTEN: Good morning. My name

2 is Sharlene Matten. I am the designated federal official for

3 this FIFRA Scientific Advisory Panel Meeting. It will be a

4 four-day meeting to consider and review a set of scientific

5 issues associated with validation of the Endocrine Disruptors

6 Screening Program Tier 2 Ecotoxicity test. 

7 As the DFO for this meeting, I am the liaison

8 between the Panel and Agency and am responsible for ensuring the

9 provisions of the Federal Advisory Committee Act are met. I

10 want to thank Dr. Martha Sandy to my left for serving as the

11 Session Chair for this SAP meeting. I also want to thank all of

12 the members of the Panel who have traveled near and far to

13 participate in this very important meeting. And I want to thank

14 the public for attending this meeting and offering public

15 comment. I would also like to thank the entire EPA team from

16 all of its locations for their hard work in preparing for this

17 FIFRA SAP meeting.

18 The FIFRA Scientific Advisory Panel is a

19 legally mandated Federal Advisory Committee. It provides

20 independent scientific peer review and advice to the agency on

21 pesticides and pesticide related issues regarding the impact of

22 proposed regulatory actions on human health and the environment.

23

24 Only the EPA makes policy and regulatory

25 decisions. The FIFRA SAP's peer review of the science enhances

26 the scientific integrity, credibility and transparency of the
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1 Agency's policy and regulatory decisions.

2 As a designated federal official for this

3 meeting, a critical responsibility is to work with appropriate

4 agency officials to ensure that all appropriate ethic

5 regulations are satisfied. In that capacity, Panel members are

6 briefed, take ethics training on the provisions of the federal

7 conflict of interest laws and other ethics regulations. In

8 addition, each participant has filed a government confidential

9 financial disclosure report. 

10 I, along with our Deputy Ethics Officer for

11 the Office of Science Coordination and Policy and in

12 consultation with the Office of General Council, Ethics Advisor

13 have reviewed these reports to ensure all ethics requirements

14 are met.

15 Members of the public are offered an

16 opportunity to make oral comments during this meeting. Please

17 limit your comments to five to ten minutes, unless prior

18 arrangements have been made. 

19 At this time, I have five public commenters in

20 total, each representing the Endocrine Policy Forum. Each

21 presenter is scheduled to speak for approximately 15 minutes. 

22 For those who have not preregistered and would like to make

23 public comments, please contact me or one my colleagues on the

24 FIFRA Scientific Advisory Panel staff. 

25 The charge questions and all background

26 documents related to this meeting are available by going to the
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1 public e-docket located at http://regulations.gov and entering

2 the docket number for this meeting; EPA-HQ-OPP-2013-0182. That

3 number is found on the top of your roster and agenda. 

4 EPA's presentations and any public comments

5 made during this meeting will be available in the public

6 e-docket within approximately three to five business days

7 following the conclusion of the meeting. After the meeting, the

8 Scientific Advisory Panel will prepare a written report of their

9 responses to the Agency's charge questions. Only those comments

10 or specific issues made during the meeting may be included in

11 the report as part of the open and transparent process

12 requirement under the Federal Advisory Committee Act.

13 The report serves as the meeting minutes and

14 will be completed within 90 days following the conclusion of the

15 meeting. The meeting report is not a verbatim transcript

16 although a transcript will be made available in approximately

17 two weeks following the meeting.

18 In closing, I again would like to thank all

19 the members of the Panel, the public and the members of the EPA

20 staff who have participated in preparing for this meeting, as

21 well as my colleagues on the FIFRA Scientific Advisory Panel for

22 their preparations in this meeting. I am looking forward to a

23 very challenging and interesting meeting in the next four days. 

24

25 Now I will turn the microphone over to Dr.

26 Martha Sandy, who will introduce herself and the rest of the
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1 members of the Scientific Advisory Panel. Thank you very much.

2 DR. MARTHA SANDY: Thank you Sharlene, and

3 good morning. As Session Chair of this meeting, I will start by

4 saying my name is Martha Sandy. I am a Toxicologist. I am

5 Chief of the Reproductive and Cancer Hazardous Assessment Branch

6 of the Office of Environmental Health Hazard Assessment, which

7 is part of the California EPA.

8 DR. BARRY DELCLOS: I am Barry Delclos from the

9 FDA's National Center for Toxicological Research. I am a member

10 of the permanent Panel. I work in Reproductive and General

11 Toxicology.

12 DR. JAMES MCMANAMAN: Good morning. I am Jim

13 McManaman. I am a Professor at the University of Colorado,

14 Reproductive Biologist and a member of the permanent Panel.

15 DR. DANIEL SCHLENK: My name is Dan Schlenk. 

16 I am the current SAP Chair and a Professor of Environmental

17 Toxicology from the University of California, Riverside and a

18 member of the permanent Panel.

19 DR. STEPHEN KLAINE: Hi. I am Steve Klaine. 

20 I am a Professor at Clemson University and I direct the

21 Institute of Environmental Toxicology and I am a member of the

22 permanent Panel.

23 DR. MARION EHRICH: Marion Ehrich from

24 Virginia Tech, Teacher of Pharmacology and Toxicology and I am a

25 member of the permanent Panel.

26 DR. GEOFFREY SCOTT: Geoff Scott, Director of
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1 the National Oceanic and Atmospheric Administration Center for

2 Coastal Environmental Health, Bimolecular Research. I am a

3 Marine Ecotoxicologist.

4 DR. VICKI BLAZER: Vicki Blazer. I am with

5 the U.S. Geological Survey, Leetown Science Center. I do

6 primary fish pathology work. 

7 DR. REYNALDO PATINO: I am Reynaldo Patino. I

8 am also with the U.S. Geological Survey with the Texas

9 Cooperative Fish and Wildlife Research Unit at Texas Tech

10 University. And I am an Aquatic Toxicologist Comparative

11 Endocrinologist.

12 DR. CATHERINE PROPPER: I am Cathy Propper. I

13 am from Northern Arizona University in Flagstaff. I am an

14 Environmental Endocrinologist and Comparative Endocrinologist,

15 and I am an ad hoc member of the Panel.

16 DR. EDWARD PERKINS: I am Ed Perkins. I am

17 Senior Scientist with the U.S. Army Corps of Engineers. I am a

18 Molecular Toxicologist. I am an ad hoc member of the Panel.

19 DR. ROBERT DENVER: Good morning. I am Bob

20 Denver. I am Professor of Molecular Cellular Developmental

21 Biology from the University of Michigan and I am an

22 Endocrinologist.

23 DR. JOHN MCCARTY: I am John McCarty. I am

24 Professor of Biology and Director of Environmental Studies at

25 University of Nebraska at Omaha, and I am an Avian Ecologist.

26 DR. DALLAS JOHNSON: My name is Dallas Johnson
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1 and I am Professor Emeritus from Kansas State University. I am

2 a Statistician.

3 DR. COLIN SCANES: Colin Scanes. I am a

4 Professor of Biological Science at the University of Wisconsin,

5 Milwaukee with expertise in Avian Endocrinology.

6 DR. SETH KULLMAN: Good morning. I am Seth

7 Kullman, North Carolina State University and Environmental and

8 Molecular Toxicology, ad hoc member of the Panel, expertise in

9 Molecular Endocrinology Developmental Biology.

10 DR. SHEA TUBERTY: I am Shea Tuberty,

11 Associate Professor of Biology at Appalachian State University. 

12 I have expertise in Invertebrate Zoology and Reproductive

13 Physiology and Ecotoxicology, ad hoc member.

14 DR. JENNIFER FREEMAN: I am Jennifer Freeman. 

15 I am from the School of Health Sciences at Purdue University. I

16 am Assistant Professor of Toxicology and I am an ad hoc member

17 of the Panel.

18 DR. MARY CHRISTMAN: Hello. I am Mary

19 Christman. I am a private statistical consultant and courtesy

20 professor at University of Florida. My area of expertise is

21 environmental and ecological statistics.

22 DR. GEORGE BENTLEY: Good morning. I am

23 George Bentley. I am a Professor at U.C. Berkeley in the

24 departments of Integrated Biology and Neuroscience, and I am an

25 Avian Endocrinologist.

26 DR. MARTHA SANDY: Thank you. And if I could
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1 ask the members of the Panel to pull your microphones closer to

2 you and talk directly into the microphone. The audio, we are

3 having a little bit of breakup in the sound. 

4 DR. SHARLENE MATTEN: Just one other note. 

5 Dr. Ken Portier is on the Panel. He had a sudden death in his

6 family and is at a funeral today. But his comments have been

7 sent to Dallas Johnson and Mary Christman, and those comments

8 will be summarized and synthesized for the variety of questions

9 and at a variety of points before the meeting is closed. Ken

10 has agreed and has wanted to review some of the summary

11 comments, so he will be included in the meeting and reviewing

12 the report. Thank you.

13 DR. MARTHA SANDY: So now I would like to turn

14 it over to Dr. Steven Knott, who is Deputy Director of the

15 Office of Science Coordination and Policy and Dr. Steven

16 Bradbury, who is the Director of the Office of Pesticide

17 Programs.

18 STEVEN KNOTT: Okay. Thank you, Dr. Sandy. 

19 And it is actually just Steve Knott, no doctor associated. So

20 my name is Steve Knott. I am the Deputy Director of the Office

21 of Science Coordination and Policy within EPA's Office of

22 Chemical Safety and Pollution Prevention. Our Acting Office

23 Director, Dr. David Dix had hoped to be here today to welcome

24 the Panel. He had a conflicting engagement and asked me to make

25 some remarks on his behalf.

26 I want to begin by thanking the Panel for your
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1 work in preparation for this meeting. As a former DFO with the

2 SAP, I know first-hand the work that is required to prepare for

3 these meetings and to participate in them and work on the report

4 afterwards, so we really appreciate your efforts.

5 We are very much looking forward to the

6 discussions that will take place over the next several days and

7 to the advice that we will receive from the Panel. In addition,

8 I would like to thank the public for participating in this

9 important meeting and for providing comments for the Panel's

10 consideration. I also want to thank Dr. Sharlene Matten, the

11 Designated Federal Official for this meeting, and all the SAP

12 staff who make these meetings possible.

13 There is a tremendous amount of work that goes

14 into organizing an SAP meeting. Much of it takes place behind

15 the scenes. And the SAP staff does an outstanding job of

16 putting together a seamless meeting process so that the Panel,

17 the public and the Agency can fully participate in the

18 proceedings and focus on topics under consideration.

19 This week's SAP addresses the third Endocrine

20 Disrupters Screen Program related topic in this calendar year. 

21 It is focused on the validation of proposed Tier 2 ecotoxicity

22 test. This is an important milestone for the EDSP building on

23 advice from previous advisors in representing years of

24 collaborative efforts from several offices across the Agency and

25 with our international partners. 

26 The role of the Scientific Advisory Panel in
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1 terms of providing a publicly transparent peer review is key to

2 ensuring that we will move forward using the best available

3 science. The Office of Science Coordination and Policy provides

4 leadership and coordination on scientific issues such as

5 endocrine disruptors and has had a lead role in the validation

6 of the test systems that will be under review this week.

7 In a few minutes, we will move into the

8 scientific presentations beginning with an overview provided by

9 Mary Manibusan, the Director of our Exposure Assessment

10 Coordination and Policy Division.

11 I want to acknowledge the efforts of Mary, the

12 scientist in her division and across OCSPP and also a scientist

13 in the Office of Research and Development and others who have

14 worked tirelessly to move the EDSP forward and bring us to the

15 point where we have reached today. 

16 So in closing, I want to thank you again for

17 your efforts in reviewing this important topic and we look

18 forward to the discussions over the next several days. And I

19 will turn the mic over to Dr. Steven Bradbury, the Office of

20 Pesticide Programs.

21 DR. STEVEN BRADBURY: Thank you and good

22 morning. Just a couple of brief statements. I definitely want

23 to echo Steve's acknowledgements of the DFO and the staff that

24 helps prepare for the meetings, and thank all of you on the

25 Panel for your hard work in providing us input and advice as we

26 move forward, as well as the public and the comments that we
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1 will hear later on today. All that information is critical for

2 giving the advice to the Agency as we implement the Endocrine

3 Disruptor Screening Program.

4 Just to backup again, just want to stress how

5 important the Scientific Advisory Panel is to not only the

6 business of the Office of Pesticide Programs, but more broadly

7 across the Office of Chemical Safety and Pollution Prevention. 

8 As you may know, within FIFRA, the statue that

9 sets our business path forward, the Scientific Advisory Panel

10 was put by congress right into the day-to-day operations of our

11 organization. I think it really underscores the importance of

12 scientific peer review and an open and transparent process as we

13 go forward on many fronts. And clearly, the endocrine program

14 is a very challenging area. And from our meeting in January, we

15 were talking about 21st Century toxicology techniques and

16 genomics and expert systems and QSAR to understand what kind of

17 methods could be used to help prioritize chemicals for

18 investigation. 

19 Then the meeting we had very recently looking

20 at the Tier 1 battery and getting some insights on what kind of

21 methods and the data that will come from those methods to give

22 us insights into the potential for a chemical to interact with

23 the estrogen, androgen or thyroid system, and then coming to

24 this week's meeting to get feedback on the methodology that can

25 be used to provide additional insights on the mechanistic

26 attributes of the chemical and to get that dose-response
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1 information that could inform an ecological risk assessment,

2 incremental steps that are important to build the foundation and

3 to build the building of our program of screening chemicals for

4 their potential to interact with the endocrine system, and if

5 they do, to have the kind of information necessary to inform our

6 risk assessments. Going through this incremental process and

7 getting the feedback step-by-step we think is really critical so

8 that we can get feedback and input as we move forward. 

9 So I want to thank the Panel, the permanent

10 members, especially, since this has been quite a path we have

11 been going down over the last several months, and one more to go

12 next month. But we think it is really important to have these

13 incremental steps so that we can get feedback as we go and as we

14 move forward. So again, I just want to thank all of you for

15 your time and effort in preparing for the meeting and during the

16 efforts this week and as you prepare the report. I want to

17 thank the public for the comments we will be hearing later on

18 today, and we are really looking forward to a very in-depth set

19 of discussions this week and valuable advice from the Panel. So

20 with that, I will turn it back over to the Chair. Thanks.

21 DR. MARTHA SANDY: And thank you Dr. Bradbury

22 and Mr. Knott. And now I believe we are ready to have a

23 presentation from Mary Manibusan.

24 MS. MARY MANIBUSAN: Thank you. Good morning

25 everybody. And let me start by thanking Mr. Knott and Dr.

26 Bradbury for their opening remarks. Thank the permanent Panel
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1 as well as the ad hoc Panel for all the work that you have

2 already done in preparation for today's meeting, this week's

3 meeting, and over the course of this week, engaging with the

4 agency as we move this very important program forward, and then

5 after this meeting, to help prepare the final report for the

6 Agency to consider.

7 I want to especially thank the DFO, Dr.

8 Sharlene Matten for all of the work that she has put in to

9 making this meeting happen. Certainly a meeting such as this is

10 not something easy to pull together, so logically and

11 content-wise, Dr. Matten has been at the forefront of

12 leadership.

13 So my job this morning and the next few

14 minutes is to introduce you to the Endocrine Disrupters

15 Screening Program; to give you a feel and sense of the program

16 of where we are going, where we are headed, where we have been

17 and where we would like to be.

18 As Mr. Knott had indicated, the Endocrine

19 Disrupters Screening Program is housed in the Office of Science

20 Coordination and Policy, and that is in fact what we do. We

21 coordinate across our sister offices and the Office of

22 Pesticide, Pollution and Prevention and our Office of Pesticide

23 Programs reaching out to Office of Water and certainly engaging

24 with our colleagues in the Office of Research and Development to

25 bring to you and to the public advanced technologies to push our

26 program forward.
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1 With that in mind, with my overview slide

2 here, what I would like to address are a few components,

3 building the house as Dr. Bradbury had indicated. Starting from

4 the top laying down what our mission and vision for the program

5 is, following the congressionally mandate statutes of the

6 Federal Food, Drug and Cosmetic Act as well as the Safe Drinking

7 Water Act and how that informs our universe of chemicals that we

8 have to screen and test.

9 We will also point back to the FACA that was

10 put together focusing on the Endocrine Disrupters Screen Program

11 back in 1998 and what they cast forward as their conceptual

12 framework of what the program should look like in terms of a

13 Tier 1 and Tier 2 screening that we are here to talk about.

14 And then I would like to go through and just

15 talk briefly with you our validation process that we have

16 followed for not only the Tier 1 test methodology, but in

17 particular, for the Tier 2 and how we have walked through each

18 of those processes very carefully. And what we bring today to

19 you for consideration is an incremental amount of work that

20 everyone has put together for your review.

21 And lastly, I have been asked many times in

22 past SAPs to put together a roadmap. And I hope what I can do

23 within our talk today is just to talk you through where we have

24 been, and in doing so, laying down that roadmap for you to begin

25 to understand where we have been, how we have incrementally laid

26 down these SAPs to help inform the Agency, and then closing out
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1 with our last SAP that is coming in the next month. But I would

2 like to begin with the end in mind.

3 Here is our mission statement. It is to

4 protect public health and wildlife by screening and testing

5 chemicals and, more importantly, taking appropriate action for

6 those chemicals that are found to have endocrine effects. So we

7 are not in the business of just identifying which chemicals have

8 endocrine effects and which do not, but where they do have

9 effects we learn on our risk assessment paradigm to fold that

10 into our risk assessment so that we can assure that we are being

11 public health and wildlife protective of chemicals with those

12 effects. So we are indeed a risk-based program.

13 We operate under two specific legislative

14 mandates. We are congressionally mandated under the 1996 FFDCA

15 under 408P. I point you to this particular language because it

16 helps guide the program. It requires the Agency to put together

17 the EDSP focusing on utilization of validated test systems and

18 other scientifically relevant methods to in particular focus on

19 chemicals that have similar effects to naturally occurring

20 estrogens in the human population and opens the door for the

21 administrator to expand that coverage to include other pathways.

22 And we will talk a little bit about that.

23 Under 408P we are required to screen all

24 pesticide chemicals actives and inert ingredients. Very

25 different from the 1996 Safe Drinking Water Act Amendment that

26 was issued; under Section 1457 it stipulates that the Agency can
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1 test chemicals only if we find them in sources of drinking water

2 to which a substantial human population may be exposed. So

3 there is an exposure component there that we need to meet before

4 we screen those chemicals.

5 Shortly after in 1996, we formed a Federal

6 Advisory Committee, the EDSTAC Committee. This EDSTAC Committee

7 had a wide range of representation across federal agencies,

8 state agencies as well as experts in their environmental fields

9 in the endocrine area as well as research scientists. This

10 committee deliberated between 1996 and 1998. They had issued

11 their final report which really helped inform the Agency of what

12 it looks like today. 

13 So here are some of their key recommendations;

14 one of which is to expand the protection to not only include

15 human health but also to wildlife. This was in recognition that

16 wildlife was inherently valuable in the element of the

17 ecosystems and is a good indicator of the overall health of the

18 environment, which humans live in. It also expanded the

19 coverage beyond estrogenic chemicals to be inclusive of the

20 androgen and thyroid pathways, because those were the most

21 common pathways at that time. But even so, the EDSTAC had

22 recognized that science was advancing very rapidly and soon

23 there would be additional validated methods coming online,

24 additional information in terms of other endocrine pathways that

25 we will need to cover. And that recognition is evident in the

26 Tier 1 as well as in the Tier 2 assays.
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1 Perhaps the most critical recommendation was

2 the formation of this two tiered screening and testing program,

3 and that was their conceptual framework. The Tier 1 screen is

4 comprised of about 11 assays, five in vitro assays and six in

5 vivo assays. And it sought to answer the question of whether

6 that chemical has a potential to interact with regards to the

7 estrogen, androgen and thyroid hormone pathways. If chemicals

8 are deemed to interact, have the potential to do so, and if

9 weight of evidence considering all of its biological

10 information, that we pull together a weight of evidence that

11 warrants additional testing and it be the Tier 2 test design,

12 those Tier 2 tests have a higher expectation in terms of

13 answering that question of whether that chemical does interact,

14 and if it does, then at what dose. What is that dose-response

15 relationship on a quantitative level, and that will help inform

16 the risk assessment. So that is the conceptual framework.

17 Here is a visual depiction of the conceptual

18 framework translated into today's language, what we call

19 strategic testing approach. We think about this in terms of a

20 big funnel. What we have from the Safe Drinking Water Act as

21 well as from FFDCA is a universe of 10,341 unique chemicals. 

22 That is the chemical list that we brought to the SAP in January

23 for consideration of prioritization. So when we have

24 prioritized that list of chemicals and should they be ranked to

25 have a higher probability of having potential to interact with

26 the endocrine system, we would advance those forward for Tier 1
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1 screening.

2 And again, after Tier 1 screening, we build

3 the weight of evidence assessment alongside with other

4 scientifically relevant information and move that through if the

5 data warrants Tier 2 testing. Not all chemicals will

6 automatically go forward for Tier 2 testing. There could be

7 other opportunities, other interim studies that could be done to

8 address a particular pathway. But if those chemicals warrant

9 additional multigenerational studies, they would be advanced for

10 our Tier 2 testing.

11 Alongside on the arrow to the left, I

12 highlight that we are not only interested in human health

13 protection but also ensuring that we are protecting against

14 ecological impacts for chemicals that may have the ability to

15 interact with the endocrine system.

16 Here is just a quick timeline flash we moved

17 from vision. Here is a slide that highlights operationally how

18 we have put together that vision. 

19 So starting from 1999, the EPA had established

20 the endocrine program at that time. We had spent about a decade

21 validating 11 Tier 1 assays, and I will show you a list of those

22 in a few minutes. But surely after validation of those assays,

23 we moved very quickly in putting forward our initial list of

24 pesticide chemicals for test order issuance, and that was

25 comprised of 67 pesticide chemicals. 58 were active ingredients

26 and nine were high production volume inert ingredients.
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1 In 2009, what we also brought before the SAP

2 was our attempt at putting together a solid method based on the

3 QSAR validation principles, and that was the estrogen receptor

4 expert QSAR system that at that time was focused on food use

5 inert ingredients as that was our universe of focus.

6 Shortly after in 2013, we brought back that ER

7 expert system but expanded that domain of coverage to be

8 inclusive of non-pesticide inert ingredients. What we also

9 brought for the first time is some consideration of P-chem

10 properties, such as whether a chemical is too acidic or too

11 basic or too large in terms of being a polymer that would not

12 interact with the endocrine system, certainly would not become

13 bio-available enough to trigger any endocrine effects, and so

14 therefore would be excluded from Tier 1 testing.

15 We also brought forward for the first time

16 high throughput technology to help expand that ER expert system

17 in terms of its chemical domain of coverage. And that certainly

18 was a big step for the Agency in order to do so. 

19 As Dr. Bradbury and Mr. Knott had indicated,

20 it was just a month ago that we brought to the Panel our

21 evaluation of the Tier 1 assays in battery performance bringing

22 forward 21 of the 52 chemicals that submitted Tier 1 data to the

23 Agency, and we really appreciate those comments and

24 recommendations. 

25 And here we are today moving forward in that

26 completion of the conceptual framework and bringing forward our
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1 ecological Tier 2 test methods for validation. And we will talk

2 with you in a lot more detail over the course of today with our

3 experts here with regards to each of those assays. 

4 And lastly, we have an SAP focused on weight

5 of evidence analysis bringing forward the Agency's case studies

6 on the Tier 1 data and how we have melted that information

7 together with other scientifically relevant information to

8 determine whether a chemical interacts with E, A or T.

9 Here is another look at our 2013 year for

10 scientific advisory panels. This is my attempt at putting

11 together a roadmap, so we look to the four discrete steps that

12 are laid out here. The first step for a chemical as it moves

13 through the endocrine program for evaluation is the

14 determination that it meets the prioritization goals of the

15 program so it does not have even a higher probability of

16 interacting with E, A or T. The second step would be initiating

17 the test order issuance for Tier 1 assay data review, and then

18 conducting that full weight of evidence determination. And we

19 have our guidance as we had issued on our public website in

20 September of 2011 to help illustrate the guidance in which we

21 will be following to do so.

22 And what we are here today to do is to

23 complete the validation effort for the Tier 2 assays. And these

24 are, again, longer term reproduction studies across multiple

25 taxa and consistent with the EDSTAC recommendations to provide

26 that larger coverage.
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1 Before I begin talking in a little bit more

2 detail about the Tier 2 assays, I wanted to remind the Panel of

3 the 11 Tier 1 screening battery that was brought to the SAP in

4 May for review. Again, it includes five in vitro assays and six

5 in vivo assays.

6 The in vitro assays are specific to an AOP

7 concept in adverse outcome pathway and looking at some earlier

8 molecular initiating events to help secure our understanding of

9 whether that chemical has a specificity to interact with the

10 endocrine system.

11 The in vivo studies are intended to give us a

12 sense of what the toxico-dynamics, as well as the toxicokinetics

13 in combination would do in a whole animal system. Here, the

14 coverage is larger than those specific AOPs that the in vitro

15 can cover, but it can cover other modes of action that are not

16 yet know or identified.

17 Here is a look at the proposed Tier 2 test

18 methods. We have coverage for the mammalian system, the

19 Mammalian Two-Generation Reproduction Study. And we provide as

20 an alternative, the Extended One-Generation Reproduction Study. 

21 And this will help fit that data into the human health risk

22 assessment. For the ecological risk assessment, we have four

23 taxa here represented and we will talk a little bit more in

24 detail about that.

25 I just want to use this slide to remind

26 everyone of the comparison between Tier 1 and Tier 2. These are
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1 very different assays answering very different questions with

2 different levels of acceptable uncertainty. 

3 So for the Tier 1 screening on the left, these

4 are of course shorter termed lower cost assays. They are

5 intended to screen thousands of chemicals. So our acceptance of

6 false negatives is going to be a lot lower, but our

7 understanding and acceptance of false positive is equally as

8 high. So we need to make sure that that is at an acceptable

9 level, or else it does not serve the impact of what a screen

10 should be.

11 The question we are answering again is whether

12 that chemical interacts with the endocrine system, so it is a

13 binary answer; it is a yes/no. We are looking at limited doses

14 because these are short-term studies we are going to push the

15 kinetics high. So you will see oftentimes in Tier 1 screening,

16 we will have high doses for the animal because we don't have a

17 lot of animals per dose, so the power is not there.

18 And again, the combination is looking at the

19 balance between the in vitro and in vivo assays and clicking

20 them back together like a puzzle piece and giving us a sense of

21 what that totality of information is telling us in terms of its

22 interaction. And the battery stand as a whole. It is 11 assays

23 large, and it maintains its structure because of the ability to

24 inform us across the adverse outcome pathway and across

25 different levels of biological organization. Very different from

26 the Tier 2 testing, here we are answering a question of whether
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1 that chemical interacts, so a lot more certainty. We need a lot

2 more refinement, and that means a lot more animals. 

3 There are longer term studies, repeat doses,

4 we have a wider coverage of dose response because we want to get

5 at what the NOEL or LOEL value is and what the NOEC and LOEC

6 values are. We are also not restricted from looking at

7 particular modes of action. This is an in vivo system so we are

8 going to get everything from compensatory mechanisms to adaptive

9 effects to apical endpoints and that really is the bases for

10 risk assessment.

11 These are in vivo studies and we are cutting

12 across multiple taxa, all the way from rat to frog to fish and

13 invertebrates, so we will cover that over the course of today. 

14 And different from Tier 1, these are not 11 assays built

15 together as a battery. We are going to go and ask for Tier 2

16 testing if we need that information, and if so, we will be

17 selective about which Tier 2 that we will be issuing test orders

18 for.

19 So starting from the mammalian side, we have

20 already two validated test methods, the two-generation

21 reproduction study and then the extended F1 generation. The F1

22 generation is a mere streamlining of the multi-generation study.

23 It includes a direct exposure to post weanling of F1 offspring

24 after lactation until termination, but there is no mating of the

25 F1 animals to produce the F2 offspring. 

26 A distinction here is that there is still the
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1 flexibility of going forward with the second generation, if data

2 from the first generation indicates that you need to do so.

3 We moved from the two-generation to extended

4 one-generation based on a retrospective analysis of a rich

5 dataset of 300 chemicals, in which it informed the Agency that

6 without that second generation study, we would not be making

7 changes in our risk assessment. That is, we would not be less

8 protective. That case is a case that we will be looking to make

9 across remaining Tier 2 test methods, as you will see in a few

10 minutes, but here is our baseline. 

11 Our baseline is this; that these are very

12 complex studies. They are not to be taken lightly. So even for

13 the mammalian reproduction study, our advice to those who are

14 initiating study is to come in early to the agency, ask for

15 consultation, and along the way as you move forward in that test

16 design, make sure you are staying connected with the Agency so

17 that if something comes up, some incident happens, we are able

18 to move quickly and address the issue. Because these are very

19 expensive studies, we want to make sure that we are doing them

20 right and we are doing them in a way that can inform the Agency

21 to be health protective. 

22 So there is that tight connection already

23 build-in to the extended one-generation as well as the

24 two-generation study, and we will look for that close connection

25 as we move forward with the remaining ecological toxicity test

26 guidelines.
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1 The Avian Tier 2 test method, again, this is

2 proposed for evaluation and for inclusion in our tool box. The

3 Japanese quail was selected for Tier 2 testing based on its

4 ability, its robustness in a laboratory setting as well as the

5 rate in which it matures. So it really fits what we want it to

6 do. It is fit for purpose.

7 This is designed to detect chemical disruption

8 of hormonally controlled sexual differentiating birds. And

9 specific to the study, it enables us to determine the longer

10 term effects of maternal transfer and in ovo exposure. 

11 Like many of the studies that you will hear

12 about today, we have not done these validation efforts alone. 

13 For this particular test, we collaborate very closely with the

14 U.S. Army as well as USGS. My next speaker Dr. Touart will be

15 providing a lot more detail on this test method.

16 The Fish Tier 2 test method, the medaka

17 species was selected again because they are smaller, easier to

18 handle and has been deemed robust for this particular assay and

19 for this particular purpose. What we provide in the ISR are two

20 methods; the Medaka multi-generation toxicity test as well as

21 the streamline Medaka reproduction test. And that MRT is our

22 preferred test method as we move forward because it enables us

23 to get at the answer that we are seeking to ask, and that is

24 whether that chemical has the ability to cause adverse outcome

25 in this particular species across many doses.

26 It also allows us more specifically to
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1 determine the genetic sex by obtaining fish samples. So again,

2 this species was picked, selected for a particular reason. Like

3 the previous study, this particular validation effort could not

4 have happened without the collaboration with the US Japan

5 Bilateral. The US and the Japanese Ministry of Environmental

6 really work together to really refine this particular test

7 method and has demonstrated its ability to perform.

8 The amphibian Tier 2 test method, we selected

9 the xenopus laevis, and this was selected in particular to

10 correspond back to the Tier 1 AMA assay. And this again has the

11 ability to be genetically sexed, which is an advancement in

12 technology. It was designed to characterize perturbations of

13 normal development and growth, especially as we focus on the

14 larval stage that is the sensitive stage for the amphibian.

15 And this inter-laboratory collaboration effort

16 was also done under the US Japanese Bilateral, and you will hear

17 more from Dr. Sigmund Degitz later on today about the validation

18 effort.

19 And lastly, the Mysid. The Mysid is a very

20 important species for ecological risk assessment. It certainly

21 has been used for quite a number of decades. This particular

22 test method is a mere adaptation from the existing 850 test

23 method, life-cycle test that is already on the books for 158. 

24 This particular test method was selected with this species

25 because of the assumption that chemicals that affect

26 invertebrate hormones may indeed affect invertebrate hormones
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1 resulting in altered reproduction development and growth. 

2 And again, we have collaborated with NOAA on

3 this particular validation effort, and you will hear more from

4 Dr. Touart as we close the day with the EPA presentations.

5 Of course, all these test methods could not be

6 incorporated into the endocrine program without it being

7 validated. And here is a definition that I pulled from ICCVAM

8 that a validation is a scientific process for which the

9 reliability and relevance of an assay method are evaluated for

10 the purpose of supporting a specific use; so does it fit the

11 purpose of its intention.

12 Reliability as defined as the reproducibility

13 of results from assays within and between laboratories. The

14 relevance is whether a test is meaningful and useful for a

15 particular purpose. 

16 Here is a five-step validation process that we

17 have followed. The first step involves a lot of published

18 literature research and looking at what has been already done so

19 that we can glean and build from that. That is a method

20 development and preparation of this detailed review paper that

21 lays out the structure of the study protocol. The second step

22 is the pre-validation step that is really the intra-laboratory

23 testing in which we look to demonstrate relevance to the

24 question that we are asking as well as looking to optimize the

25 protocols. So we have selected a number of key reference

26 compounds for each particular assay to really challenge that
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1 protocol so that we can learn and we can address those issues

2 and further improve the protocol as we move forward through the

3 validation process.

4 We are also in this step looking to determine

5 its readiness for the inter-lab validation effort, which is

6 right after we have demonstrated the reliability across labs. 

7 But we have done this inter-lab seeking to, again, learn as we

8 go. And I hope that is the message you received when you read

9 the ISRs, is that as we move through this validation process, we

10 have highlighted areas of deficiencies and we have noted those. 

11 And we have learned from them and we are looking to build upon

12 all of that intelligence and all of that experience in putting

13 together the final test guideline, because that is where the

14 information will be really, really important. And that

15 assurance is what our speakers here are to provide today, as

16 well as in the ISRs.

17 The ISRs are the subject of today's review and

18 we look for your recommendations and comments on how those

19 efforts have been and whether they have demonstrated that

20 relevance and reliability that we seek to answer before we

21 accept these test methods as regulatory protocols.

22 So with that, I would like to end my

23 presentation with where it all began. And where it began was in

24 a little retreat called Wingspread in Racine, Wisconsin. And

25 this was in the summer, July 1991 that a group of

26 multidisciplinary scientists, much like yourselves sitting here
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1 today, had talked through the issue of endocrine disrupting

2 chemicals and its prevalence in the environment as well as for

3 human health exposure.

4 And I pull out this particular comment because

5 as I read through the consensus statement that was signed in

6 1991, it read very much as if it had been written today; so here

7 is the quote: "The testing of products for regulatory purposes

8 should be brought in to include hormonal activity in vivo." And

9 it was recognition 20 years ago that we were lacking these test

10 methods in order to answer the question that we have before us. 

11 And I hope with this meeting that we will come away with

12 utilization of test methods so that we can get back to the

13 mission and to the vision that we stated earlier and end the

14 program with all the advancements of test methodology so that we

15 can answer the question. 

16 This slide is just to acknowledge the hard

17 work as Dr. Matten had indicated. This is a cross-Agency

18 effort, so we have our representatives from OSCP as well as from

19 our Office of Research and Development here today to provide

20 their presentations. We will hear from Dr. Touart on the

21 Japanese Quail Two-Gen. Following behind is Dr. Johnson for the

22 Medaka assay; Dr. Degitz for the LAGDA assay. And then finally

23 Dr. Touart will close us out with the Mysid Two-Generation

24 Study. With that, I thank you for your attention and I am open

25 to any questions you might have.

26 DR. MARTHA SANDY: Thank you very much, Ms.
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1 Manibusan. And I will ask the Panel if you have any questions

2 of clarification?

3 DR. DANIEL SCHLENK: Hi Mary. It is nice to

4 be down on this end of the table for a change. I have got a

5 bunch of questions for Rodney later but I thought I would hit

6 you with this one first. 

7 So as I understand this, the Tier 2 test is

8 supposed to demonstrate a dose response relationship and that is

9 very important in the processes, and some of the assays, that is

10 what we are hoping to see.

11 I am curious how the Agency is defining dose

12 response relationship. Is that going to be a monotonic

13 dose-response relationship? And particular, with the LOEC

14 measurements, if that is a single point on a dose-response

15 relationship that is statically significant, would that still be

16 good enough in a risk assessment for the next step? Does that

17 question make sense? Do you know what I am saying?

18 MS. MARY MANIBUSAN: So I can start and pass it

19 to others who might want to comment. That is a really relevant

20 question because, as stated, the Tier 2 test is to inform us of

21 whether we can identify the apical endpoint, and more

22 importantly, at what dose should we be regulating, so not

23 necessarily a dose-response but a dose that we understand to be

24 protective of endocrine activity.

25 Having said that, I think it is important to

26 delineate the issues of non-monotonic dose-response curves and
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1 monotonic dose-response curves. And it is very publicly made

2 aware that the Agency had put forward our Save the Science

3 document on non-monotonic dose-response curves for the NAS

4 review this summer over the course of this year.

5 Having said that, whether it be monotonic or

6 non-monotonic, the two-generation or multi-generation studies

7 within the Tier 2 test toolbox should inform the Agency of

8 indeed what dose we should be regulating, if we are not already

9 regulating at a lower dose to be protective of endocrine

10 activity. And so, to that extent, different from Tier 1, the

11 Tier 2 test are inclusive of a larger range of doses for

12 testing, and additionally, more animals per dose for testing, so

13 to increase the statistical power to find those subtle effects

14 and then to regulate a dose that is protective, but is also

15 environments reflective of what the current exposure is.

16 DR. DANIEL SCHLENK: Yes. I guess one

17 specific question I had was does the Agency consider a

18 dose-response relationship more than a single dose that is

19 statistically significant from the previous dose or does that

20 dose -- for example, there are many data in the documentation,

21 there is a lot of data that presents an LOEC, but it is the

22 highest dose that is statistically significant. But it is not

23 different from the previous dose, statistically, and it is not

24 different from the dose above that. So the sequential doses are

25 not statistically significant. There is a trend sometimes

26 towards dose dependence but at least there is not -- that I can
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1 see a lot of times -- a statistically significant dose-response

2 relationship. So would an LOEC of a single dose count as a

3 valid Tier 2 test? I guess that is what I am after.

4 DR. STEVE BRADBURY: Good line of questioning.

5 I think as we go through the different assays, y'all can kind

6 of delve into that a bit more. I think, just standing back in

7 terms it is the risk assessment process and information that

8 informs a risk assessment. Certainly we look at all the doses

9 and try to discern what kind of trends we are seeing. Obviously

10 some designs are setup for different kinds of statistical

11 approaches, but a risk assessment does not operate in isolation

12 from other information that is available. 

13 So I foresee if chemical X had gone through

14 the prioritization, had gone through Tier 1, had gone into Tier

15 2, we are going to be looking not only at the Tier 2

16 information, we will be taking a look at the Tier 1 information,

17 there may be mechanistic information, there may be other

18 information along different parts of the adverse outcome pathway

19 in understanding what those dose relationships look like. As

20 Mary said, the Tier 1 does not give you a full dose-response but

21 it gives you a sense of what could be happening at different

22 doses and different tissues or different levels of biological

23 organization. So I think it is looking at the totality of

24 information.

25 I think as you go forward, and perhaps on a

26 case-specific situation where you may know something about the
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1 mechanism and some of the toxico-kinetics and dynamics, that may

2 influence some of the dose selection for, say, a Tier 2 assay

3 and gets back to the point -- you know, with mammalian studies,

4 we encourage the registrants to come in, let's talk about all

5 the information we know about that chemical so that we set the

6 doses and hopefully get a well informed set of data that comes

7 out. 

8 I think another aspect of ecological risk

9 assessments is, if not quantitatively, at least qualitatively. 

10 And I think more and more, moving towards quantitative based on

11 the NAS report on ESA, Endangered Species Act and consultation

12 with the services, how do you integrate dose-response curves,

13 say for reproductive effects in the population model? So I

14 think all of that information will help come together to sort of

15 think about the dose selection and dose interpretation.

16 So I am not giving you a specific answer

17 because when you move into a risk assessment process you are

18 doing more of a case-specific analysis and you are not going to

19 be going in blind 99 times out of a hundred. You are going to

20 have a lot of information to help guide you in that dose

21 selection and interpretation of dose-response curve.

22 DR. DANIEL SCHLENK: Okay. So I define

23 dose-dependence for this particular SAP as being a single point

24 that is statistically different from the rest of the doses. 

25 That is how dose-dependence is defined here. Is that correct?

26 DR. STEVE BRADBURY: Yes. 
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1 DR. DANIEL SCHLENK: And is sounds like a

2 weight of evidence will probably be utilized in this after Tier

3 2 as well, in terms of putting this together as well, right?

4 DR. STEVE BRADBURY: Correct.

5 DR. DANIEL SCHLENK: Okay. 

6 DR. MARTHA SANDY: Any other questions from

7 the Panel? Yes?

8 DR. ROBERT DENVER: I am just curious how the

9 Tier 2 assays will be selected from the hits from the Tier 1

10 assay? You said that the Tier 2 is not a battery, but there is

11 overlap in the information that you can get from the different

12 Tier 2 assays. So has that been thought about? Is there any

13 guidelines for that?

14 MS. MARY MANIBUSAN: So the Agency has been

15 very clear in the Tier 1 assays being -- to answer the question

16 of whether that chemical has the potential. Once we declared

17 whether a chemical has the potential and needs to advance

18 forward, it's not an automatic that we move forward to Tier 2. 

19 And as we have stated the Tier 2 to be a targeted testing

20 approach -- meaning that it would be judged on a case-by-case

21 basis, as Dr. Bradbury indicated -- when we do a risk

22 assessment, we would bring together the totality of the

23 information not only looking at the hazard but looking at

24 exposure, exposure pathways to help inform us of whether we need

25 additional Tier 2 data, and if so, which among the taxa that we

26 are presenting today do we need that information in order to
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1 complete our risk assessment.

2 But the focus of this particular SAP is not on

3 the policy determinations of how the Agency plans to select

4 among the Tier 2, but whether the Tier 2 can be performed

5 adequately and replicated across the laboratories and can be

6 used in a regulatory setting.

7 DR. ROBERT DENVER: Right. But just to

8 follow-up on that, our evaluation of the Tier 2's will be

9 evaluating what they can tell you also. And so I presume that

10 you are going to use that information in deciding which Tier 2's

11 to move to.

12 DR. LESLIE TOUART: I think one of the context

13 that we are looking for is that the Tier 2's will be able to

14 identify adverse consequence. So if we have identified a

15 potential endocrine activity, we want to understand what is the

16 adverse consequence of that. And so if the determination based

17 upon the available information that we seek, that we would need

18 to address that adverse consequence in the particular taxa, then

19 that particular Tier 2 test would be needed and required. And

20 so we would evaluate that on the context of what the consequence

21 was and maybe what the point of departure or effect

22 concentration would be what we would need to be concerned about,

23 and that gets more into the risk assessment realm.

24 DR. MARTHA SANDY: Dr. Christman?

25 DR. MARY CHRISTMAN: I have a point of

26 clarification. We were talking about the dose-response earlier,
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1 and the question that I have is, is the LOEC -- I thought the

2 LOEC was the lowest concentration of the test substance that was

3 statistically different from the control, not from all other

4 levels, which is what I thought I heard. But what we mean is it

5 is the lowest level that is statistically different than the

6 control state, correct? It does not need to be different from

7 the others, it just needs to be the first concentration, the

8 smallest concentration.

9 DR. LESLIE TOUART: Yes, that is correct. I

10 mean the lowest observed effect concentration would be the

11 highest concentration above a no-observed effect concentration

12 compared to controlled.

13 DR. MARY CHRISTMAN: That's correct. 

14 DR. MARTHA SANDY: Dr. Scanes.

15 DR. COLIN SCANES: I want to follow-up on Bob

16 Denver's question. If we look at the Tier 1 test, they are

17 predominantly rodent test. Now does that mean when we are

18 considering Tier 2, the need to look at a variety of taxa come

19 into play?

20 MS. MARY MANIBUSAN: I will start and I will

21 pass it to Dr. Touart. So the Tier 1 assays are 11 assays

22 large. The question again is whether that chemical interacts

23 for E, A or T by pathway. So they are not distinguished by

24 species, they are fitted together because they are informative

25 to answering that question for an AOP. So that is very

26 different from the Tier 2, which are different across taxa.
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1 DR. COLIN SCANES: By reasonably there are

2 going to be differences in pathways across taxa. And so the

3 question I am raising is, when one has the Tier 1 battery, which

4 is predominantly a rat battery, if there are differences with,

5 first of all, across taxa buds, amphibians, et cetera -- that is

6 where the Tier 2 comes into play -- but there is no test early

7 on across taxa. Am I correct?

8 DR. LESLIE TOUART: Let me try to explain in

9 the context of the battery. Again, there are in vitro assays in

10 which are identified to look at some mostly initiating events

11 like receptor binding or transcriptional activation or effects

12 in something like steroidogenesis. There are the mammalian

13 assays that are included but we also have fish, and there is an

14 amphibian that is represented on the thyroid side. And these

15 assays work in concert, in a sense the battery is an integrated

16 concept and the general thinking is that we are covering

17 somewhat bookends within the vertebrate taxa where we have the

18 rats as well as the fish kind of looking at both of those.

19 But the context is that there is conservation

20 across the, and in the validation effort of the Tier 1, we look

21 at a variety of at least the known or at least the suspected

22 endocrine active materials to determine if we were able to

23 detect all known mechanisms at a time. And so we looked across

24 a variety of estrogenic/androgenic and thyroid active mechanisms

25 to determine, "Okay, we can identify those." But the outcome of

26 those interactions is not going to be determined until we get
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1 into more of Tier 2 where we are able to characterize whether

2 there is an adverse effect. But the intent was that Tier 1

3 would be able to identify somewhat comprehensively the potential

4 to interact with estrogen/androgen or thyroid activities, not a

5 particular life-stage or not just that the hormonal action would

6 be interacted.

7 DR. COLIN SCANES: If I could just follow it

8 up. And that is even at receptive binding level, you can get

9 mark species differences. 

10 DR. MARTHA SANDY: Any other questions from

11 the Panel? I see none. I think it is time for us to take a

12 break. It is just about 10 o'clock. Sharlene, how about we

13 come back at 10 after 10? Thank you.

14 (Whereupon a break was taken)

15 DR. MARTHA SANDY: Coming back. If we can get

16 the Panel members to take their seats. 

17 So I have been asked to remind everyone to

18 speak clearly and into the microphone. Get as close as you can

19 because they are having trouble hearing the speakers, Panel

20 members and the speakers making presentations for the recording.

21 So I believe we are now going to hear from Dr.

22 Touart. Is that right?

23 DR. LESLIE TOUART: Again, good morning to the

24 Panel and thank you for the time and effort having been involved

25 in putting together the integrated summary reports and a lot of

26 the supporting material. I know that there is a lot to try to
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1 look through and I do not envy your task at all. And hopefully

2 I can be helpful in trying to walk through right now the

3 Japanese Quail Two-Generation test and some of the information

4 to try to touch on some of the highlights that we have with this

5 particular test methodology.

6 The outline of my talk is basically to

7 consider the purpose and basis of an Avian Two-Generation test

8 and what is it intended to try to do. Then I want to go into a

9 little bit of the history of the validation effort -- the things

10 that we did -- and try to accomplish as part of demonstrating

11 the utility of this methodology.

12 Give a brief summary of the protocol in terms

13 of the general design and some of the endpoints that are

14 included. I will talk briefly about some egg injection studies

15 that were done as part of the effort to gain some insights --

16 not really part of that test itself, but just in terms of some

17 other associated information -- then discuss some of the

18 demonstration and inter-laboratory studies, some of the results

19 that we saw, and then talk advantages/limitations that were

20 evident and then end up with kind of the value added of a

21 multi-generation test in the context of the birds.

22 Briefly, the purpose of the test is to

23 characterize adverse effects and to define the concentration

24 response to the extent, if possible, for any potential endocrine

25 disrupting compound for birds.

26 We want the method to be responsive and
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1 sensitive to these endocrine effects in birds. The method being

2 comprehensive would evaluate adverse effects through other

3 mechanisms, but we are interested in ensuring that any endocrine

4 mechanisms or effects that may manifest would be covered by the

5 test itself. The test is to provide a multi-generation of

6 protocol that contains basically all phases of lifecycle so that

7 we can somewhat isolate the exposure within those phases to

8 understand which life-stages are sensitive and what cumulative

9 effect may be occurring.

10 In terms of, you know, look at avians and what

11 is the basis here, hormonal control and sexual differentiation

12 in birds differs from that of mammals and other vertebrates. 

13 The hormonalcomedic sex in birds is the male, which is different

14 from others. Oviparity in birds is quite large in terms of the

15 size of the eggs compared to the body size of the bird itself. 

16 And this affects retention and transfer of compounds both in

17 terms of detoxifying material by moving it into the egg but also

18 for what the embryo may see. And the context here too is the

19 embryo may see actual transfer of test material but also of

20 maternal factors that could carry over.

21 Existing protocols that we have, primarily the

22 avian reproduction test that is part of the established

23 guidelines in the OCSPP, these are not considered sufficiently

24 robust to define all reproductive and development effects for

25 EDCs. They are not designed to determine the long-term effects

26 of an in ovo exposure and fitness to the offspring. These tests
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1 are just exposure of the parents and production of eggs to

2 14-day chicks. So it is not carrying forward in terms of the

3 development or in terms of the maturation phases that may occur

4 or indications of effects due to transfer.

5 So they do not assess effects on all four

6 critical life-stages that could be sensitive to endocrine

7 disruption. So the concept of using a two-generation test would

8 ensure that all life-stages have been included and any adverse

9 consequence manifest within the context of the test itself.

10 Each portion of a multi-generation test

11 protocol then provides a unique treatment in that the F0 or

12 parental generation, their maturation and exposure as adults. 

13 The F1s are exposed embryonically and through a full lifelong

14 development process. And then their offsprings in the F2s would

15 be solely an embryonic exposure. Together, all of these

16 life-stages are considered and would be expected to express any

17 potential adverse consequence.

18 So the other context is there is suspicion

19 that some chemicals may exert effects that would have long-term

20 impacts on populations over multiple generations without

21 continued EDC exposure. This is still somewhat hypothetical but

22 the consideration is that if this is indeed the case, we would

23 need some type of test methodology to evaluate that potential

24 outcome.

25 The history of the effort goes back to the

26 adoption of the Endocrine Disrupters Screening Program and
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1 recommendations from EDSTAC. One of the initial evaluations

2 that were done was a species comparison study using a

3 two-generation methodology, somewhat the forwarner of the

4 protocol that we are using today. And this included both

5 bobwhite and Japanese quail, was a test done with methoxychlor.

6 And the outcome of that really demonstrated

7 bobwhite quails an impractical species that carried forward

8 through two generations because of its extended life-cycle in

9 terms of its development and growth and the reproductive phase,

10 whereas Japanese quail, given its much shorter maturation

11 development and more consistent reproduction in terms of the

12 egg-laying was a more practical choice.

13 A detailed review paper considering avian

14 toxicology and the available methodologies and recommendations

15 for testing was completed in 2005, and really formed somewhat of

16 an outline for the protocol that we ultimately adopted.

17 In the same time period there was what we

18 refer to as the avian dosing study. This was done with

19 estradiol. And the purpose of this test was to evaluate some of

20 the differences in terms of exposure, whether one has continuous

21 exposure or whether one just exposes the parental generation,

22 leaves the offspring unexposed and then exposes later. And the

23 outcome of that really was to determine that really continuous

24 exposure would be the most appropriate because all stages would

25 have some level of exposure to be evaluated.

26 There were also some egg injection studies
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1 that were done. These were done because they are somewhat

2 shorter term and I will talk a little bit more about these

3 later. But the context was that this was to provide some

4 additional information in terms of how a particular

5 endocrine-active material when exposed at an embryo stage may

6 manifest an effect later in life.

7 In terms of demonstrating the protocol that we

8 have now, there were two studies done sequentially at the Army,

9 and this was their Public Health Command out at Aberdeen,

10 Maryland. And they did an endosulfan test followed by

11 trenbolone. 

12 We also had collaboration with USGS where they

13 also did a trenbolone study in roughly the same time period as

14 the Army but somewhat following onto what the Army had done. 

15 And then we entered an inter-laboratory trial where we had three

16 contract laboratories involved in testing a compound,

17 vinclozolin.

18 In giving kind of a brief summary of the

19 protocol itself, again, the recommended species that we

20 supported was Japanese quail. This seemed to be the only

21 practical species that we could carry through a multi-generation

22 test paradigm. Based on this avian dosing study results, a

23 pre-egglaying treatment continued through with treatment to

24 chicks was considered the appropriate dosing regimen that would

25 be utilized as part of these multiple generations. 

26 The test is to be initiated with sufficient
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1 pairs to ensure a minimum of 12 in the parental generation

2 control group at the end of treatment. In practice, this

3 usually works in 14, maybe up to 16 pairs being utilized. With

4 these, it is intended that one set a minimum of six eggs per

5 pair, that is 40 per level, from the 6th and 7th weeks of

6 exposure, and these would be hatched and those offspring would

7 be characterized later. And from these, one would develop a

8 minimum of 12 pairs per test level to establish the F1

9 generation.

10 The one context here is that, although eggs

11 are laid and counted and maybe the fertility and the embryo

12 viability is identified through the egg-laying regime. Only

13 eggs from the 6th and 7th weeks are collected to be set and

14 hatched, so the test is terminated with the 14-day survival

15 observation of these 6th or 7th week offspring in the F2. There

16 is option to continue these to interpret potential maturation

17 effects if there are indications that there would be some

18 subsequent effect that may occur.

19 I should mention that as part of the

20 validation exercise, the laboratories did carry these further to

21 evaluate what additional information might be there. But from

22 our viewpoint, the amount of information collected post this

23 time period in terms of the 14-day survival would be minimal in

24 context.

25 In terms of the kinds of endpoints that are

26 included in the method, I have lumped these into what I am
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1 referring to as fitness endpoints. These are somewhat the usual

2 characters or endpoints that are present in, say, existing avian

3 reproduction test or egg production, fertility, the eggshell

4 integrity of both the thickness and strength are measured,

5 embryo viability, the hatchability in terms of a hatching

6 success, sex ratio in terms of the offspring outcome, chick

7 health in terms of any signs of toxicity, and so these would be

8 identified.

9 In terms of another group of endpoints, which

10 I have lumped as kind of endocrine endpoints, these would be

11 ones that would be somewhat more susceptible or indicative of

12 some kind of an endocrine disruption or activity. And these are

13 within the morphology/histopathology organ/gland weights, the

14 histology of these glands, spermatid counts, morphology and

15 other gross anomalies that one might identify in organs,

16 especially if they are related to, say, the sex organs. 

17 Developmental landmarks, feather dimorphism,

18 which are generally under hormonal control as well as the

19 cloacal foam gland in the Japanese quail. Sexual maturation and

20 behavior are also included. And then actual hormone measures,

21 both plasma and fecal/urate were steroid and thyroid hormones.

22 For histopathology there are a number of

23 tissues that have been looked at in terms of tissues that are

24 collected, preserved and then processed through Histotech and

25 then read by a pathologist. That will be one of the charge

26 questions for the Panel later in terms of is this list overly
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1 comprehensive and are there ways of maybe economizing.

2 But primarily, we are looking at brain

3 kidneys; liver, adrenal glands, the reproductive tissues, the

4 cloacal gland, bursa. And we use the right thyroid gland for

5 histopath. The left is taken for actual hormonal measures and

6 things. But the context is that looking at the pathology is

7 considered to be perhaps indicative of any disruption that may

8 be occurring in the particular organism as indicators of the

9 overall health and integrity of that organism.

10 This is kind of a diagrammatic schematic. It

11 is not exactly to scale, but diagrammatically it tries to

12 represent what is being done in the different generations. And

13 treatment begins at approximately six weeks of age in terms of

14 post-hatch time. And from that six-week time period the egg

15 laying is anticipated to really begin in earnest within about

16 two weeks.

17 For Japanese quail, there may actually be some

18 birds that are producing eggs even at six weeks, but the context

19 is that they are expected to be all pairs producing by the 8th

20 to 9th week. And then in terms of progression, the egg-laying

21 would be observed and collected for a six-week time period and

22 those sixth and seventh week eggs would then be transferred,

23 set, hatched. And these would form the bases for the F1

24 generation and these would then grow, you know, the egg laying

25 produces. And then at their six-week of egg laying, eggs

26 collected to form the F2 generation, in terms of those eggs set
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1 and hatched, and these F2 birds would be untreated. 

2 So these later birds are not treated with diet

3 and they are going to be observed for at least 14 days. And

4 this potentially could be extended to evaluate some maturation

5 levels, if it is indicated by effects that may have been seen

6 in, say, F1 generation maturation.

7 Moving now to a little bit of discussion of

8 these egg injection studies that were summarized within the

9 integrated summary report.

10 These tests were designed to simulate maternal

11 deposition of EDCs into eggs prior to the oviposition by the

12 female. These were done because it is relatively short term and

13 we would be evaluating the compound effect on the embryo stages

14 and the outcomes post-hatch. It targets at embryo stage. And

15 the thinking at the time was that maybe these could be

16 potentially used in range-finding so one would not have to do a

17 several-week reproduction study to try to establish what doses

18 to set in this multi-generation test. So the context was we

19 want to try and see if that was going to be possible.

20 In terms of the kind of results, this chart is

21 to kind of give an indication of some of the things that we are

22 seeing. This is comparing some studies that were done with

23 estradiol and trenbolone, an estrogen and androgen. 

24 The context here, in terms of those arrows

25 that are up or down would indicate an increase or decrease. If

26 they are not parentheses, those were significant increase or
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1 decrease. If there is a parentheses around that, that means

2 that there may be a trend but it was not considered significant,

3 or that if the parentheses around double arrows, that means a

4 non-linear dose-response was observed. So the ED represents the

5 embryo day. 

6 So the context here is that the exposure at a

7 particular time point -- the embryo day-4 -- may have some

8 effects seen in estradiol up here where you do not have a

9 response if the exposure is at embryo day 11. So there would be

10 some sensitivity at that particular point. But then you have

11 some effects seen in embryo day 11 that you do not see manifest

12 at embryo day 4. And same on the trenbolone side; you see some

13 differences although the embryo day 4 and embryo day 11 seem to

14 match pretty well, except kind of contradictory in the sense of

15 the bursa mass being decreased at embryo day 4 treatment, but

16 with an embryo day 11 treatment, it increases. 

17 So really the main point here is just this is

18 an example of the kinds of information that one can glean to

19 understand that in endocrine activity, the actual embryo stage

20 would be important, and so what factors that embryo sees would

21 be an effect.

22 In general, I think one of the things that we

23 determined was that there is not an easy transfer of the egg

24 injection data in terms of the concentration injected into the

25 egg to translate that into a dietary exposure. So the utility

26 of egg injection or establishing range finding information is
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1 really somewhat limited or suspect.

2 The information that one can glean more from a

3 research standpoint, I think it probably would be helpful but

4 not as part of the actual test methodology that we take forward.

5 In the actual two-generation test itself, one

6 of the first iterations that we did was the endosulfan that the

7 Army had done. 

8 Endosulfan was chosen for a couple of reasons.

9 One was that the Army had an interest since endosulfan was

10 being used on lots of bases and they needed to understand the

11 potential risk that it posed on their responsible areas. 

12 But the other context was that there is

13 literature on endosulfan that suggested it may have some week

14 estrogenic activity, but also suspect in terms of some other

15 androgenic thyroid type level. So the context was that this was

16 somewhat a win-win for both Army and us to evaluate this

17 particular compound.

18 In the actual conduct of the test there really

19 were not very many real effects seen. They were sporadic

20 effects across all the endpoints but nothing that was consistent

21 or considered dose-related. 

22 In terms of the table here, just looking at

23 reproduction, which would be one of our apical summary endpoints

24 for determining an adverse effect. In terms of a risk

25 assessment, the Agency generally considers survival, growth and

26 reproduction as key elements for adverse consequence, so trying
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1 to translate particular endpoints into one of these is

2 important. 

3 But in this case for the egg production

4 itself, there were no statistical differences. And in looking

5 at the average number of eggs produced per week by groups, there

6 were really no marked significant differences either between

7 concentrations or across the generations that were concluded. 

8 So endosulfan was re-demonstrated. There were very little or no

9 adverse effects within the test as we proceeded.

10 Now this is the next compound that was used,

11 and this test was one of the trenbolone studies. This was done

12 at USGS. And this was presented because -- and it may be a

13 little difficult to read but I wanted to make sure that I had

14 all three generations here to look at eggs laid per day. 

15 The different colors would be with increasing

16 concentrations. And what is evident in the parental generation

17 is their highest concentration, the 40 ppm, really shutdown

18 reproduction so there was not sufficient production to even make

19 individuals into the F1 generation, so F1 generation only goes

20 up to the 20 ppm level. And while there is some reduction at

21 the 20 ppm level in the F1 generation, there is more reduction

22 across in that concentration. In terms of the other

23 concentration, there is somewhat of a trend in terms of

24 decreasing production.

25 When we get to the F2s, these are again

26 untreated, and most of the concentrations they are no different
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1 from control, but the 20 ppm still is reduced here, in terms of

2 some carryover response.

3 When we look at the trenbolone study that the

4 Army did, it has similar results, although I do not have a

5 graphic of their levels. But the 20 ppm had significant effects

6 as well as the 10 ppm in the parental generation. In the F1s,

7 the 20 ppm virtually shut down reproduction so there were no

8 individuals carrying over to the F2s. But again, the 10 ppm

9 would be an effect concentration which would be consistent with

10 what was seen at USGS in terms of an effect. But in F2, there

11 are really no differences in terms of the production.

12 One thing to note here with the F2s is these

13 were not carried as long in terms because this is averaged over

14 the course of the egg production in both the F0 and the F1. But

15 in the F2, it is just the intimal seven weeks. So part of the

16 reason, the production here is lower is the initial weeks of egg

17 laying is somewhat less than when the animals reached their

18 stride. With the Japanese quail it is not uncommon for a

19 productive pair to be producing six or even seven eggs on a

20 weekly basis.

21 This is another response that was seen in the

22 trenbolone. This is, again, back at the USGS where they were

23 monitoring some of the hormone levels. And with this, you see

24 with the blue, this is the parental generation. The red is the

25 F1 generation, and then this kind of green color is the F2. But

26 with this model androgen, there is an increase in estradiol
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1 level with eggs with increasing concentrations.

2 Within the F1, there is an increase also. The

3 line is a little bit less, but with the variations that we have

4 here, they are probably close to the same. But with the F2s

5 where there is no more exposure, then they fall back and compare

6 them closely to controls. So this is a curious indication of

7 what may be going on, but the indication was in terms of the

8 adverse effects. Remember, reproduction was really affected at

9 the 10- and 20-part per million but the indications of some

10 hormonal deviations were starting to show at somewhat lower

11 concentration.

12 Now in terms of the vinclozolin inter-lab

13 where we had three different laboratories, and again, looking at

14 average number of eggs laid per hen per day, the context here

15 across all three laboratories and in the parental and the F1

16 generations, the production was consistent that there was no

17 significant difference that we saw with vinclozolin in this

18 particular study. There were little significant effects seen in

19 this inter-laboratory exercise with the vinclozolin, which is

20 considered an androgen antagonist.

21 The egg production is fairly consistent and

22 between laboratories. And this is a key point, is that there is

23 consistency in terms of the egg production that is made

24 generally close to .9 eggs a day, so that is almost an egg a day

25 in all cases. They may trail here or there but not

26 statistically different. So the context is that, in terms of
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1 reproducibility, the Japanese quail is very consistent in its

2 ability to produce eggs.

3 And the next slide, I think this gets into

4 vinclozolin in terms of the effect that was observed, which was

5 based on histopathology. And there were potential lesions

6 attributed to vinclozolin that included lesions in the

7 epididymides of the parental and F1 generation adult male quail

8 in all three laboratories. So this was seen in all three,

9 although the histopathology that was observed did not manifest

10 into any other adverse effects in the other endpoints that were

11 evaluated.

12 Background lesions, particularly in liver and

13 ovary, were identified in different tissues but these lesions

14 were unrelated to vinclozolin exposure so there is no

15 consistency in terms of what those might have been. And clearly

16 there were no vinclozolin related lesions in any of the other

17 tissues that were evaluated in the adult male/females or

18 hatchlings.

19 This is just to show some of the differences. 

20 I do not think this slide shows up as well when it is projected

21 but the context is that there were clear effects, in terms of

22 actualization and the tissues between control and treatment.

23 Now in terms of the inter-lab, we did some

24 further statistical analyses to look at the range of the CVs

25 across laboratories by category. And the context here is that,

26 really there is fair consistency within the Japanese quail where
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1 our CVs are predominantly less than 20 percent in most cases,

2 and 20 percent is considered pretty good. And in terms of the

3 reproduction, which is one of our key endpoints of concern, the

4 median CV was close to 10 percent in the parental generation and

5 5 percent in the F1s, which would indicate there are consistency

6 between generations as well.

7 When we come down to mating behavior that was

8 observed, this is pretty high. So mating behavior as an

9 endpoint was highly variable. I would believe this is due

10 somewhat to the actual technique or method for identifying. In

11 the Japanese quail, the mating behavior was observed primarily

12 by evaluating the onset of the population when naive pairs are

13 brought together, exposed naive pairs. So the time to first

14 copulation and the number amount of attempts that are made,

15 these were used to quantify mating behavior. 

16 And again, this was a fairly highly variable

17 in terms of the measure itself. So it raises the question as to

18 how useful that would be. Although we feel behavior should be

19 an appropriate methodology, but at least in Japanese quail we

20 did not identify a more appropriate method to utilize. So

21 again, any recommendations the Panel may have on how better to

22 do that or how better to improve the measure itself would be

23 useful.

24 This slide which is fairly busy, again, this

25 one was done to look at the ability to -- at least the power of

26 the test -- to have 80 percent power of detecting a difference
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1 from control. And the point here again -- and I just

2 highlighted it in green for reproduction -- we feel that we

3 would have sufficient power at 22 percent change difference to

4 observe in the reproduction. 

5 But again, coming down with mating behavior as

6 the extreme, this does not give us a lot of confidence that we

7 would be able to detect an effect unless it was very gross in

8 context. But overall, for other effects like in development

9 growth, these are very good in terms of our ability to determine

10 some differences in context.

11 So trying to summarize a little bit of the

12 inter-lab comparison, I think one thing we also recognize is

13 there were some clear differences in strains that were used,

14 basically in terms of size.

15 Japanese quail are somewhat semi-domesticated

16 and there are strains that are produced for different reasons. 

17 Some for, say, the meat-market, which would be a larger sized

18 body mass would be selected for, and those that are selected

19 more for, say, the egg market where egg production, and so body

20 size is not as important. But there is a range in size. There

21 are some different strains that do exist.

22 And strain differences may be a factor in rate

23 of maturation in some reproductive characteristics, and some

24 variables also show some variability and it is not clear whether

25 that is necessarily indicative of an unreliable measurement of

26 the endpoint or just the method of measurement that requires
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1 definement; behavior being one of the context in terms of is

2 there a better way of trying to measure that, that would be a

3 little bit more consistent. 

4 The one thing with the differences in strains

5 though, across the laboratories, there was sufficient

6 consistency as seen in our power to detect that we would be able

7 to observe effects if they were to occur.

8 Again, the primary measures indicative of

9 endocrine disruption would required somewhat a protocol that

10 would be able to discern the vulnerability across the different

11 stages. We feel all of that is consistent here and that the

12 laboratories were able to carry fourth these measures in their

13 tests.

14 This is just to try to summarize some of

15 advantages that Japanese quail bring in context. And, you know,

16 Japanese quail precocial model matures rapidly and chicks can be

17 treated by dietary administration. 

18 The Japanese quail is relatively well

19 characterized with a great deal of information on the particular

20 species. A multi-generation test with this species can be

21 completed in seven months for the in-life portion of the test,

22 and that is just not possible with other bird species that we

23 commonly would utilize within the laboratory setting. 

24 A potential to develop straight-forward tests

25 that elucidate mechanisms is prominent for this species. It

26 appears to be moderately sensitive to EDCs that would be
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1 somewhat more broadly applicable to other avian species. A wild

2 type strain retains the photoperiodicity and other

3 characteristics of wild populations, and so we recommend that

4 one steer more towards wild type individuals.

5 The other point is just that the immune,

6 adrenal and thyroid system were also pretty well understood in

7 the Japanese quail. There are limitations though, is that being

8 precocial, they differ from altricial birds in pattern. And

9 Song Birds are of interest in how well one can translate from

10 the Japanese quail to these is somewhat suspect, but we

11 currently do that in context of the avian tests that are

12 currently utilized by the Agency would use precocial birds. 

13 They are initiatives to try to develop some altricial birds, but

14 they pose many challenges to doing any kind of long-term study

15 since there is a lot of parental care for the hatchlings that

16 would be necessary. 

17 Japanese quail do vary by strains and there

18 are some differences there and so it may need to be a minimal

19 set of strains recommended in terms of protocol for moving

20 forward. 

21 Finally, I guess Japanese quail provide a

22 model species that does not completely emulate all birds,

23 especially long-lived birds, but they are a practical species to

24 utilize.

25 For multi-generation exposures, the data

26 indicate that adult birds are generally less vulnerable to
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1 adverse effects of EDCs whereas the embryonic exposure or the

2 offspring might be more susceptible. 

3 It is felt that it is critical to have an F1

4 generation and to observe the capability of those F1s to

5 successfully reproduce. So exposure outcomes maternal transfer

6 that may take place, both of chemical as well as other modified

7 backers being deposited into the egg, one would want to carry

8 that forward to determine what is the consequence in terms of

9 those individuals to mature and reproduce successfully. 

10 In a multi-generation test, each life-stage is

11 included and can be evaluated. And it is considered important

12 to incorporate some measures of these endocrine perturbations or

13 behavioral perturbations that may be in place. Including, an F2

14 generation provides a test for assessing the possible

15 trans-generational effects if these were to occur, and that is

16 another consideration in terms of an advantage of maintaining

17 something of a multi-generational test paradigm. 

18 So to summarize, I think the Japanese quail

19 two-generation tests, the protocol that we utilized was

20 successfully performed in five laboratories, meaning they were

21 able to meet the performance criteria and collect information to

22 be utilize. The CVs and statistical power relevant for avian

23 fitness and endocrine integrity have been determined in terms of

24 how well certain endpoints play out. And it would appear then

25 that the test is reasonably reliable for its intended purpose to

26 be able to characterize potential adverse effects when
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1 challenged by a compound that would affect one or more of these

2 endpoints. 

3 And with that, I am finished with my

4 presentation and would take any clarifying questions or comments

5 the Panel might have at this time.

6 DR. MARTHA SANDY: Thank you very much for

7 that summary of the Japanese quail two-generation toxicity test.

8 Do we have comments, questions of clarification from the Panel?

9 Yes, Dr. Patino?

10 DR. REYNALDO PATINO: I am just curious if you

11 could provide the rationale for the inter-laboratory validation,

12 the rationale for using the compound that you chose,

13 vinclozolin. Why was that particular one chosen for this test?

14 DR. LESLIE TOUART: I think in hindsight we

15 might wanted to have used something different. But the thinking

16 at the time in terms of trying to use vinclozolin, there was

17 some information from egg injection studies that indicated that

18 vinclozolin would potentially have activity. We knew that it

19 was an androgen antagonist and so there was some consideration. 

20 Well we have already looked at an estrogen and a model of

21 androgen of sorts. And given the cost and time associated with

22 these tests, we did not really have a lot of time to do multiple

23 chemicals, so the context of what might be an appropriate one to

24 utilize. 

25 We looked at the vinclozolin and recognized

26 that, well, we were also using vinclozolin in terms of some of
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1 the other Tier 2 tests, so we felt that would be useful in terms

2 of comparison. It was really somewhat unknown how this would

3 play out in the test. We knew that it potentially would be

4 challenging compound because vinclozolin also requires a

5 metabolic step to go through, since there are some metabolites

6 that are somewhat more active as the anti-androgen type

7 responses. So we felt that would be useful too, since the birds

8 have somewhat different metabolism, or at least they have a

9 higher metabolic rate than other species. So we felt, well this

10 one should be useful.

11 And in the context of the test, based upon

12 evaluating the trenbolone and ensulfan results was we felt that

13 the test could be performed fairly easily in terms of being

14 successful. And we felt that any effects that would be seen, we

15 would be able to quantify those, and so we selected that

16 compound on that kind of bases and the results were what we got.

17 I think that in comparing across controls

18 gives us indication that the method is reasonably reproducible

19 in that context in terms of the effects seen, we cannot say so

20 much about that in context since vinclozolin -- the only

21 consistent response was this histo-effect in the epididymides,

22 so that was consistent in that context.

23 So in terms of quantitative comparison for

24 this particular method and how the, say, a NOEC might be

25 observed, we would rely upon the trenbolone results to

26 demonstrate that with the USGS and the Army, that the NOEC level
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1 would have been identified at roughly the same concentration in

2 that context.

3 DR. MARTHA SANDY: Dr. Ehrich?

4 DR. MARION EHRICH: You mentioned the egg

5 injection test and sort of like you dropped it. And I am just

6 asking for clarification. That is just out of the picture,

7 right?

8 DR. LESLIE TOUART: Yes, that is correct. I

9 mean the egg injection was done early on because we felt we

10 could look at some different endocrine activities more quickly. 

11 But the other driver in terms of why we entertained it at all

12 was that, potentially, this could be useful as a range-finding

13 if we were able to figure out a way to convert those injection

14 concentrations into some dietary level so that that would save

15 time and animals in doing range-finding to establish the

16 concentrations that would go into a two-generation test later

17 on. 

18 But we did not realize that particular

19 advantage so the egg injection studies were dropped in terms of

20 any continuation, so they are not part of the guideline in terms

21 of potential range-finding exercise. And they really returned

22 more towards an academic level test to evaluate different

23 effects and how they might manifest later. And there are public

24 studies that are referenced within the document to demonstrate

25 some of those kinds of outcomes.

26 DR. MARTHA SANDY: Dr. Johnson?
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1 DR. DALLAS JOHNSON: You have indicated that

2 there were differences in the strains of Japanese quail that

3 were used, and I am just wondering how those strains varied

4 across laboratories. Did different laboratories use a

5 particular strain or were the strains sort of selected and

6 assigned to different laboratories, or questions about how

7 randomization took place and so on, things that we statisticians

8 worry about?

9 DR. LESLIE TOUART: Right, just to respond,

10 there were no specific directions given to the laboratories in

11 terms of selecting strains. That was left to the particular

12 laboratories, and so they would collect the animals that were

13 available from whatever supplier that they may have. 

14 I believe one of our contract laboratories

15 maintains somewhat of an in-house culture, so they maintained an

16 in-stock crew. But for the others, they were going outside and

17 it was just what was available at the time providing some

18 different strains. I do not recollect specifically which labs

19 had what particular source of birds that were utilized. 

20 I am also trying to remember -- and I can find

21 this information out for later -- whether like for the Army they

22 had done the endosulfan and the trenbolone test, whether for

23 both of those tests, it was the same strain utilized or whether

24 they had a different strain. I do not recall at current. But I

25 believe there were, again, strain differences through the

26 laboratories, but they were not specified or they were not
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1 established in any kind of context to evaluate what those were.

2 In looking at the different strains and the

3 differences in terms of the actual size of the individuals, some

4 of the responses endpoints in terms of what the actual measure

5 was, but in terms of comparisons and especially in terms of,

6 say, the egg production that was seen. Those still were

7 comparable between the different strains to indicate that they

8 did not make that much of a difference in context. But how much

9 that strain difference might make, understanding how strain

10 differences affect toxicity in other taxa groups, one would

11 anticipate, yeah, that is a potential factor that could affect

12 the outcomes and one would have to pay attention. But there is

13 no information to suggest one strain being more sensitive than

14 the other or more susceptible for certain kinds of activities.

15 DR. MARTHA SANDY: Dr. Colin Scanes? 

16 DR. COLIN SCANES: A couple of questions for

17 you. First of all, at the beginning you mentioned that the

18 current avian protocol is not sufficiently robust. Could you

19 provide a little bit of clarification about that? And secondly,

20 when you look at the trenbolone studies and the vinclozolin

21 studies, there is a different bases for the dose-response scope.

22 One is logged to the base 2, one is to the base 2.5. And I

23 think the third study in the report, which is logged to the base

24 3. What is the thinking as to what log-base should the

25 dose-response curve be done at? 

26 DR. LESLIE TOUART: Could you repeat the first
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1 question again?

2 DR. COLIN SCANES: The first question was

3 about the current avian protocol is not sufficiently robust, and

4 then you did not really give any more information. So if you

5 could provide a little bit more clarity on that.

6 DR. LESLIE TOUART: Right now the existing

7 avian product tests that are done, long-term test are bobwhite

8 quail or mallard duck reproduction test and those tests are

9 exposed prior to the onset of egg laying. 

10 So the test has continued treatment through

11 egg-laying for several weeks while the eggs are collected and

12 the hatchlings are produced, and they carry these two 14-day

13 hatchlings. So there is just that single partial generation of

14 a maturing bird and adult reproduction, the egg and embryo

15 survival in 14-day chicks, there is no histopathology, there is

16 no biochemical that is measured as a routine course, so it is a

17 subset, I think, of the range of life-stages that would exist. 

18 So that is the context of why it is felt that it is not

19 necessarily as comprehensive in terms of what is looked at.

20 Now to respond to your second question, which

21 I am not sure I can at the present, in terms of why there were a

22 different log-based that were utilized for the comparisons, I am

23 not sure of that. It is something that I would have to go back

24 and look at. It may just be the way the data were presented but

25 I would have to investigate to determine why that may be the

26 same. 
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1 I do know that in terms of how different

2 laboratories recorded information, they sometimes use some

3 different reference point or how the time course for when the

4 eggs were collected are counted. But in terms of the log-bases

5 for the evaluation, I have to check and see what that was.

6 DR. MARTHA SANDY: Dr. Bentley?

7 DR. GEORGE BENTLEY: You mentioned large

8 variations in mating behavior. I wonder if you could clarify

9 which aspects of behavior were measured, because measuring

10 behavior is quite complex.

11 DR. LESLIE TOUART: And what I said was that

12 the mating in the Japanese quail in terms of the information

13 that was collected, it was based on mating behavior, meaning the

14 time to (inaudible) birds are brought together, how soon the

15 male mounts the female; so that time period would be measured as

16 one indication. And then the number of mounts over a time

17 period would be identified, so how often the male may mount the

18 female would be observed. 

19 So that is pretty much the extent of what

20 would be evaluated. And across the different laboratories in

21 terms of how the birds were handled before being brought into

22 that mating condition in terms of the birds being brought

23 together for the first time and how they had been kept separate

24 prior to that and what socialization that may impact how well

25 they have done in context.

26 I think in the reports, if I am not mistaken,
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1 I believe it was the Army's trenbolone study or the mating

2 behavior was evaluated, there was sufficient observation to be

3 able to determine an actual effect occurring there, but the

4 other laboratories that attempted to make these evaluations were

5 not able to measure anything because of the high variability

6 across those, so there was no ability to discern what might be

7 going on. 

8 So the concern is that, well, behavior is

9 something that may be affected by the endocrine, especially in

10 birds, because that is a critical element. And so to the extent

11 that we can exploit some aspect of Japanese quail would be

12 useful in trying to do so. So that is the context.

13 DR. GEORGE BENTLEY: So the focus was on

14 consumatory mating behavior versus repetitive. And also the

15 focus was on male sexual behavior, and there was no measure of

16 female interest in the male?

17 DR. LESLIE TOUART: That is correct. There

18 may be some aggression shown. Sometimes the female would be

19 more aggressive to the male, depending upon the situation, but

20 most often the measures here would be somewhat more male

21 specific. 

22 DR. MARTHA SANDY: Dr. Propper?

23 DR. CATHERINE PROPPER: So along those lines

24 one of the other areas where you identified a lot of variability

25 across the control groups especially was in reproduction in the

26 table on slide 44. And I am wondering what went into that value
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1 as reproduction.

2 DR. LESLIE TOUART: In terms of the

3 reproduction value in terms of the measure, that was based on

4 the number of egg laid per hen, so that reproduction measure is

5 eggs laid. It did not include like fertility measures

6 associated with that. It was primarily the variability around

7 the eggs laid per hen.

8 DR. CATHERINE PROPPER: Okay. And as I recall

9 with the vinclozolin study, there was no difference across

10 treatments in the number eggs laid but there was a huge amount

11 of variability then across labs in the control groups? I am

12 trying to put together the summary data for eggs laid and the

13 variability and the controls that we are seeing in slide 44.

14 DR. LESLIE TOUART: Let me see if I can go

15 back. I am just going to try and go back to slide 44 so I can

16 see. Now which were you identifying?

17 DR. CATHERINE PROPPER: Under Reproduction,

18 which is number of eggs laid.

19 DR. LESLIE TOUART: Right. The power to

20 detect that effect was 22 -- the level of effect that one is

21 able to differentiate was at 80 percent power from control, is

22 22 percent here. So I am not sure of what your question is in

23 terms of the variability across the species. Like, 17 percent

24 in the initial generation in 22 here -- and this is across all

25 three laboratories that were part of the inter-lab evaluation,

26 so the variability would have been fairly small. 
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1 And I go back to the previous slide; this is

2 the one that looks at the actual CV percent with the range and

3 the median. And for reproduction, the median is 10 percent and

4 9 percent. The range was what was large from .54 to 103 across

5 those. But again, the median was fairly small, so there were

6 outliers, meaning there were likely a pair that was

7 nonproductive or minimally productive in context. So one pair,

8 given that we are only dealing with maybe 12 pairs optimally,

9 that has a big influence on the ultimate CV value that one might

10 have from that. But in terms of the median response across

11 those, it is somewhat narrow.

12 DR. CATHERINE PROPPER: Okay. Thank you.

13 DR. MARTHA SANDY: Dr. Denver?

14 DR. ROBERT DENVER: Thank you for the

15 presentation. In the interest of understanding endocrine modes

16 of action using the Tier 2 assays predicted from the Tier 1

17 assays, can you comment on whether any of the data allow for a

18 clear diagnoses of an endocrine mode of action that fits within

19 the known or predicted modes of action? For example, the

20 trenbolone clearly affected plasma estradiol in the males, but I

21 think it had no affect on aromatase. So I mean how do we

22 interpret that kind of result?

23 DR. LESLIE TOUART: Thanks for that question,

24 I think. I think it is a difficult kind of thing when you get

25 into the Tier 2 because these are designed to be comprehensive

26 and we are including apical level endpoints that are -- being
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1 apical, you know, they are a summary of a lot of other things

2 that are going on.

3 In terms of our ability to interpret what

4 endocrine manifestations there are, there would be some

5 endpoints that are a little bit more, I guess you would say

6 diagnostic or at least endocrine-relevant than others, meaning

7 like the hormone measures that one would have or some

8 histopathology that one would see. 

9 But clearly, other endpoints, which would be

10 the growth or reproductive which are the important apical

11 endpoints from a risk assessment context, these do not tell us

12 very much in terms of whether that is strictly through an

13 endocrine mechanism or secondary to some other systemic response

14 that may be occurring. So we would be evaluating the additional

15 information that we have based upon the Tier 1 outcomes in terms

16 of any initiating event or indications that it may be operating

17 through an estrogen pathway more than an androgen pathway or

18 not. So I think that is probably the best I can try to

19 characterize.

20 Because of the number of endpoints that we

21 have, there is somewhat of a pattern that would be evaluated and

22 you would want to try to interpret that in context. But I do

23 not want to suggest that there are easy clear-cut answers that

24 this is indeed the activity or that it is definitively

25 androgen-active or not. 

26 For a lot of these materials, there can be
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1 multiple areas it can go in and there can be somewhat

2 conflicting information. And that is going to be a challenge in

3 interpretation because, given the large number of endpoints and

4 that there likely could be some that are statistically

5 significant just by chance, trying to interpret is that "by

6 chance" or is there some way of connecting that to real outcome

7 is a consideration. So hopefully you can provide some

8 additional context that we can use in assisting in the

9 interpretation.

10 But our ability to identify kind of a

11 point-of-departure or like a no-observed or low-observed

12 concentration or some adverse effect is really the context that

13 would be carried forward in terms of interpreting what the

14 effect of a material might be.

15 DR. MARTHA SANDY: Dr. McManaman?

16 DR. JAMES MCMANAMAN: Thank you for the

17 presentation. So I have a couple of questions just for

18 clarification. On slide 37, I thought you said that there was

19 no statistical difference in egg production in the F2 generation

20 at 20 ppm on trenbolone. But is that true, because it looks

21 like there should be -- in the F2s? So on the bottom panel, the

22 light blue bar, it looks like it is much lower than the other,

23 the F2 generation.

24 DR. LESLIE TOUART: Yes. In the F2, the 20

25 ppm still had a significant effect. 

26 DR. JAMES MCMANAMAN: It was significant. 
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1 Okay. 

2 DR. LESLIE TOUART: I think what I was

3 mentioning was that that seemed to sustained into the F2. You

4 know these are untreated. I think what might have been

5 confusing is I was talking about the other effects. These, you

6 know, there appear to be no trend associated with these other

7 concentrations, whereas in the parental and the F1 generations,

8 there appears to be (inaudible); these were not statistically

9 significant, but at least looking at the bars, there is a trend.

10 But in the F2s, there is not. But the 20 ppm, there was

11 effect.

12 DR. JAMES MCMANAMAN: There was at 20 ppm,

13 okay. And then the next slide, just a question about

14 interpretation here. If you look at the effect on egg

15 production at the 20 ppm and compare that at the 10 -- so for

16 the F0 and the F1, it looks like there is statistical

17 differences at the 10 and 20 ppm doses for the F0 and the F1. 

18 But the magnitude of the effect at the 20 ppm looks to be much

19 greater than one would expect from a simple dose relationship. 

20 I mean it looks like there might be

21 sensitization going on and I just had a question about what is

22 your interpretation of that and has that question been built

23 into the apical endpoint analyses?

24 DR. LESLIE TOUART: I will try to explain. I

25 do not know that it is necessarily built-in. And this was a

26 question in terms of the F1. The magnitude of that difference
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1 was much larger there and whether that is due to maternal

2 transfer of the trenbolone and the effect that that might have

3 on the individual later is possible. And it is not just within

4 the bird studies, but I think it is what is seen with the other

5 taxa as well. And it is something that we would want to be

6 careful in our interpretations of is that, oftentimes where

7 there are effects -- especially if you have a major effect in

8 terms of something like reproduction, and you have got fewer

9 individuals that are carried into the next generation, those

10 individuals might be less sensitive than the original generation

11 because they are the ones that have demonstrated -- they have

12 survived enough to be able to get there. So we have to be

13 careful for those situations where you see a reduced effect in

14 the subsequent generation as much as if you have an enhanced

15 effect in the next generation. 

16 So trying to evaluate to determine is that a

17 simple linear additive response into the next generation or

18 whether there is something that is modified may be more

19 exponentially different between one and the other would need to

20 be considered. And one can evaluate generational effects that

21 may occur.

22 In the case of the birds, there was not enough

23 chemicals to be able to evaluate in context of the generational

24 effects that might occur. I think later today when we talk

25 about the invertebrate side where we did have many more of those

26 kinds of situations, the generational effects were evaluated
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1 statistically to determine how much one generation differs from

2 the other generation kinds of thing. 

3 But here that was not the case, and it is

4 something that one does have to pay attention to and consider in

5 terms of the interpretation, you know, whether it is really a

6 generational effect or whether there is no difference between

7 what might have occurred or not. And I think when you are

8 dealing with the oviparous animals because of the embryo

9 exposure is so dependent upon the material exposure and

10 transfer, it is important to recognize how that then ultimately

11 manifests. 

12 DR. JAMES MCMANAMAN: And then the final

13 question is what will be evaluated on the F2 -- I was not clear

14 about that -- on the F2 generation? And with that, are you

15 planning on including positive controls for reproductive effects

16 in the assays themselves? 

17 DR. LESLIE TOUART: To that last question,

18 there would be no positive controls added to these tests. They

19 are logically difficult as they are just with the compounds that

20 would be evaluated. But in terms of the F2s, the way the

21 guideline as we are proposing it, it really just takes the F2

22 chicks to 14 days with an option to continue if you have

23 identified some maturation effects that you may want to carry

24 forward. 

25 As I mentioned for the validation exercise,

26 the F2s were carried longer into maturation and actual
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1 reproduction so we do get some numbers from those. And that

2 becomes a question of, well, should the test continue to that

3 point for those potentials to occur, or in terms of our

4 interpretation, the level of effect that had been seen in, say,

5 the F1s would be sufficient to identify what your level of

6 concern of point of departure would be in terms of how that

7 would feed into somewhat of a risk assessment. 

8 But just to reiterate, the F2s are intended

9 only to evaluate the chicks and the F2s are untreated at this

10 point. So we are just looking at the hatching success and

11 viability of the chicks with the possible option of carrying

12 that into maturation observation.

13 DR. JAMES MCMANAMAN: Okay. So based on the

14 USGS data showing an F2 effect on egg-laying, you think that you

15 will be able to discern potential effects? Because you will not

16 be looking at F2, as I understand what you have just explained

17 to me about the F2 assay. So doing it that way would eliminate

18 what the USGS found with theirs?

19 DR. LESLIE TOUART: Right. I think it was

20 based on those observations that we felt that the concentration

21 that pose an effect were seen in the F1s, so we would be able to

22 identify the NOEC in terms of what that would carry on. 

23 So whether that would be additional

24 information that would be of value and worth the additional

25 effort in terms of length of test and collecting that additional

26 information in terms of the reproduction of those individuals,
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1 if that is necessary for something like a population model, that

2 would be in question. But the general sense was we felt that

3 there was enough information to be able to have confidence that

4 the level of concern based upon the lowest observed effect

5 concentration would not have changed from the F2 versus the F1

6 generation.

7 DR. MARTHA SANDY: I think we are ready to

8 move on. Thank you very much. We will move from quail to fish,

9 so we will now hear from Dr. Rodney Johnson.

10 DR. RODNEY JOHNSON: Hello. I am Rodney

11 Johnson and I am pleased to be here to present this information.

12 And I think for expedience, we will just get started. I am

13 with the Mid-Continent Ecology Division of ORD of EPA and I will

14 be presenting the fish multi-generation toxicity test

15 information.

16 First of all, just to give you a real quick

17 overview of what the presentation is intended to do, I will

18 first of all introduce the Research Medaka Multigeneration Test

19 protocol. And then I am going to go through and review some of

20 the Medaka multigeneration test data describing some of the

21 control outcomes, chemical control data and some inter-lab

22 outcomes. And then from there I am going to introduce a test

23 that we are proposing as a recommended Tier 2 test, fish test

24 which is a subset of the Medaka multigeneration test. We call

25 it the Medaka reproduction test. 

26 And after I present that information, I will
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1 present some conclusions and then we will go into conversation

2 about implementation of a test protocol.

3 First of all, I would like to review the

4 purpose of the Tier 2 fish test. I am going to just very

5 quickly read it. We have all read this if we read the summary

6 reports, but in any case, it is to determine whether a test

7 substance adversely affects a test organism through

8 endocrine-mediated pathways. It is to quantify those effects

9 with respect to the estrogen, androgen and thyroid systems. It

10 is to include exposure during potential life-stages that may be

11 more sensitive than those that were tested in Tier 1. It is to

12 characterize the dose-response relationship and it is intended

13 to include exposure during gametogenesis to allow for potential

14 effect of parental transfer of chemical and other endogenous

15 factors across generations.

16 I would like to discuss why was Medaka chose

17 as the test species. First of all, it is a small fish. In

18 fact, it tends to be quite a bit smaller than, often, some of

19 the fish that are used in environmental testing paradigms. It

20 is generally about 300 milligrams or up to 500 milligrams in

21 adult size. Small size allows them to be exposed and tested in

22 relatively small aquaria which then, because of that, also have

23 relatively smaller amounts of test chemical to use in the test

24 protocol. 

25 They are sexually dimorphic, so the phenotype

26 is expressed dimorphically fairly easily in young adult through
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1 adult fish. 

2 One important aspect that we chose for Medaka

3 was that the sex-determining gene for Medaka has been identified

4 and was identified some years ago by the Japanese, and we use

5 that to our advantage insofar as identifying the genetic gender

6 of each fish in the test protocol, so that we can look at

7 relating what would be the expressed phenotype with the gender

8 of each organism. 

9 It has a relatively short life-cycle. That

10 short life-cycle allows for keeping the test reasonably shorter

11 than it would be if it had fish with longer life-cycles. That

12 being said, it is not exactly really short either. 

13 The fish is cultured easily. It is fairly

14 hardy, it is fairly disease-resistant, and its basic biology has

15 been well characterized, especially by Japanese scientists. It

16 is a fairly well understood fish.

17 Some of the work that we have done is in

18 cooperation with the Japanese. The fish is a Southeast Asian

19 fish. The Japanese has extensive experience understanding its

20 biology, studying its biology, studying its culture, and we have

21 capitalized on that information and cooperated with them to try

22 and develop protocols for testing EDCs that would be harmonized

23 at least across the two countries, but also more globally than

24 that. 

25 And we did not start this work without some

26 previous work having been done by the Japanese. The Japanese
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1 had, at the beginning of our work, had already developed a full

2 life-cycle fish test with the Medaka. And that full life-cycle

3 test did not allow one to test for transgenerational effects, so

4 we used that as the beginning point but we added other

5 generations so that we could look for effects across

6 generations.

7 But the Japanese had developed all the

8 baseline endpoints or most of the baseline endpoints that are

9 employed. They certainly had developed good methods for

10 breeding and culture, and information on how to optimize growth,

11 and they also had good information regarding gonadal development

12 timelines. So that gave us a good kick-start when we began this

13 work.

14 I am going to discuss what we are calling the

15 Research MMT Protocol. The Research MMT Protocol was intended

16 to be a protocol from which Tier 2 test could be derived. It

17 includes three generations but not three complete generations. 

18 It starts out with an F0 generation of adults that are exposed. 

19 The embryos from that are used to begin the F1 generation. The

20 F1 generation then goes through hatch development and the

21 adulthood. And then from that, the F2 generation is derived,

22 and that too then is taken, at least in the original MMT

23 protocols, all the way through reproduction. 

24 The test took 29 weeks in most of the

25 iterations that we did. Some of this was variable. This is

26 like what the very first research protocol was. As we learned
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1 things and as we worked with the Japanese, these protocols are

2 not exactly identical between tests because we took the

3 information derived to change the protocol to try and improve

4 it. 

5 So for instance, you will see here that --

6 well F1 and F2 are in fact what we would call rubber stamps of

7 each other. They are intended to be, as much as possible,

8 identical, or all the endpoints are measured at the same time in

9 both generations. And both generations were exposed for their

10 full life history.

11 The generations overlapped by three weeks. 

12 That was intended to try and reduce the total duration of the

13 test. One of the consequences of that is that it took quite a

14 bit more infrastructure to hold one generation while you were

15 starting the next generation. We worked with the Japanese on

16 how to do that. They did not like that, so we have some of the

17 later tests; the overlap was not three weeks, it was one week,

18 so that you did not have to have two full exposure systems going

19 at one time to keep the two generations going.

20 So what I am really getting at is this is our

21 research protocol for which we intended to try and derive a Tier

22 2 protocol. It measures lots and lots of endpoints across all

23 generations. So we can see that in reproduction, I just want to

24 emphasize, was a five-week assessment in each generation where

25 the egg production -- the fecundity in this case -- that is what

26 we are calling fecundity -- and the fertility were evaluated
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1 four days per week across five weeks for 20 measurements. So

2 from Tuesday through Friday of each week, it was assessed. 

3 So there was pathology assessed in each

4 generation, and the other AOP endpoints like vitellogenin, the

5 secondary sex characters, were all again all measured at each

6 generation with the intention to make inter-generational

7 comparisons.

8 I want to mention this one part about the test

9 protocol that we think is a very, very strong attribute for the

10 protocol, and that is that the genetic sex of each fish is used

11 in the protocol to set up breeding pairs. 

12 As we know, when animals grow up in an

13 androgen or an estrogen fish, a certain concentration sex

14 reversal can often occur, and one cannot identify which fish was

15 reversed unless you know the genotype.

16 So to set up, breeding pairs and all those

17 sorts of aspects of the actual conducting the test, as well as

18 to evaluate the endpoints of the test, the genetic sex of the

19 fish is determined in real time during the test. So the

20 animals' genetic sex is determine nondestructively by taking a

21 small fin clip -- anesthetizing the fish -- taking a small fin

22 clip and then returning the fish to the tank and then doing PCR

23 to identify the presence of the gene called DMY, which is the

24 sex determining gene for Medaka. 

25 So all the fish that have DMY are genetic

26 males. All the fish that do not have DMY are genetic females. 
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1 So all outcomes in the test are measured against that particular

2 individual's genetic sex. Because it is so important to the

3 test, we feel any test laboratory has to be capable of doing

4 that part of the test to adequately perform the test. 

5 So now I am going to just review the endpoints

6 in the multi-generation test. I have organized them by what we

7 would consider apical endpoints, reproduction. And fecundity,

8 which consists of fecundity and fertility -- in this case

9 fecundity again is total eggs laid by the female. And then of

10 course fertility is the number of fertile eggs laid by the pair,

11 embryos produced. 

12 We also histologically determined sex

13 reversal, again, comparing against the genetic sex of the

14 animals, and intersex conditions. 

15 Now I did not say it here but obviously growth

16 is an important endpoint, but I don't consider it in general to

17 be an endpoint that we would consider necessarily to be involved

18 with endocrine action. I mean I know that is a generalization

19 that may not be true, but the most important thing is we

20 consider growth effects to be most important in the context of

21 toxic effects. 

22 So the idea is that we evaluate whether or not

23 some kind of an impact is perhaps due to a non-endocrine

24 mediated AOP that results in growth impairment, and it sort of

25 raises a flag that there is toxicity overlaid perhaps on

26 endocrine activities or reductions in fecundity, for instance,
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1 or would be due to a toxic AOP independent of an endocrine AOP.

2 We also have what we would call biomarker AOP

3 endpoints. Almost all of these, although not entirely all of

4 these, are taken at the sub-adult stage of the bioassay at the

5 same time that we would prepare or set up breeding pairs. So

6 that when we set up breeding pairs, we setup the breeding pairs

7 and then the animals that are left are sampled for these AOP

8 biomarker endpoints, which includes secondary sex characters,

9 which is primarily in this case, the anal fin of the fish, which

10 in the male has papillary processes. So in the phenotype of the

11 male in the secondary sex is to have papillae on the anal fin

12 that can be counted. You can just simply count how many there

13 are on the anal fin.

14 We also measure the level of liver

15 vitellogenin mRNA there is in each fish, and that is done at a

16 PCR reaction. And then we also do gonadal pathology in this

17 MMT. And in this case, the primary gonadal pathology was

18 isolated to the kidney and to the gonad itself. And it was

19 primarily looking for gonadal phenotypes, things like

20 sex-reversals, intersex conditions, but for instance, not other

21 tissue pathology. So it did not include liver in all cases,

22 although in some cases it did. It does not include spleen. I

23 mean lots of other tissues are not involved in that.

24 Then the last endpoints I want to discuss will

25 be these toxicity endpoints where we have hatching success,

26 growth as I mentioned earlier, survival at several points of the
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1 test, but the most important survival endpoints actually occur

2 in our experience very early in the test, so primarily early

3 life-stage survival. 

4 Then liver pathology; and liver pathology in

5 this case is intended primarily to indicate, as I said, toxicity

6 as distinct from an endocrine type endpoint. So for instance,

7 if vitellogenin goes down and there is liver pathology and there

8 was an effect on growth, you would be very suspicious that this

9 particular toxicant was acting through a toxic mode of action

10 and not an endocrine mode of action.

11 I will just review the histology endpoints --

12 well not really the endpoints very much. But what I really want

13 to say is that we have also developed guidance for doing

14 histopathology, and this guidance is still being developed in

15 the online form of it. There are paper versions of this, but we

16 feel an online version is very, very useful, amongst other

17 things, because it provides guidance to any pathologist that

18 wishes to be engaged in this process. It also provides an

19 opportunity for consistency in pathology evaluation in different

20 pathology laboratories. 

21 So this guidance provides SOPs for doing

22 necropsy, histotec work, data structure, literally how to handle

23 data and organize data as pathology is being done. So it allows

24 efficient statistical analysis. There are some diagnoses of the

25 pathology that you would expect and then there are examples of

26 images that are annotated that show normal histology of tissues.
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1

2 For instance, this is a normal testis in a

3 controlled medaka. And this for instance is a testis that has

4 some testicular oocytes in it. And this would be what we would

5 consider a severity score of two, and this is the same diagnoses

6 with a severity score of three. So the point being that you can

7 develop consistency in how you would score a pathology outcome

8 by using this tool. First of all, it gives you the diagnostic

9 character, and then it also provides you with a uniformed

10 scorning tool.

11 The intention in this case also is that this

12 be able to be updated online so that as information accrues on

13 effects, that it can be entered into this guidance and the

14 guidance can become more mature.

15 And I mentioned about liver pathology as an

16 indication of toxicity. This is just a panel that shows what

17 happens to the liver exposed to a chemical called dichloroacetic

18 acid, which in this case did reduce the fecundity and the growth

19 of the fish that were exposed to it. We did not measure

20 vitellogenin in this case, but as you go from control to higher

21 concentrations, the liver gets increasingly damaged due to the

22 exposures; another example then of toxicity and not endocrine

23 activity.

24 One can identify limited endocrine activity in

25 the liver also, but it tends to be not as sensitive as some of

26 the other endpoints.
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1 So the diagnostic endpoints that we have found

2 useful in the MMT -- so what we would call primary EDC diagnoses

3 in the testis would be the intersex condition and sex reversal,

4 so in this case intersex being what we would call the testicular

5 oocytes with various severity scores. And then the worse

6 condition are, let's say, once when the exposure is high enough

7 and it is done early enough in the developmental stages of the

8 animal, instead of getting an intersex condition, you end up

9 with a sex reversed animals.

10 In the ovary, the same. Sex reversal is an

11 endpoint that we identify. We also see increases in oocyte

12 atresia, decreases in the yolk formation within each egg, and

13 then we also see what is called perifollicular cell hypertrophy

14 in response to this steroidogenesis inhibitors. And then -- I

15 am beating a dead horse here -- we also look at liver,

16 especially as a diagnostic endpoint for toxicity.

17 I want to go through now some of the typical

18 control values that we get in an MMT, and this data is compiled

19 from a number of MMTs, and it was also compiled in some cases

20 with fish that were sub-adults. And as I mentioned earlier, the

21 tests that we are comparing, in some cases, the sub-adult

22 sampling stage occurred at different times within their --

23 sometimes it was seven weeks, sometimes it was eight weeks,

24 sometimes it was nine weeks as we were developing these tests. 

25 Consequently, some of the variance that you

26 see is not necessarily as high as it looks because of the time
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1 course difference in when they were measured. But I just want

2 to have some representative information for you to look at that

3 allows you to evaluate as the test is progressing in some cases

4 whether the test is on track. So let's just go through a few

5 things.

6 The typical fecundity output for adults is

7 around 25. It certainly in some cases is higher than that. 

8 This is by 25 eggs per pair per day. And we consider an

9 acceptable range to be something greater than 20 at this stage.

10 Fertilities, control values tend to be 80

11 percent and higher. We consider values of 75 percent and higher

12 as acceptable.

13 Sex reversal also happens in control animals,

14 and it happens in what would be the XX genotype. And in the XX

15 genotype, there is a relatively low prevalence, but it seems to

16 be relatively constant of less than four percent in I would say

17 most cultures of fish we see at being less than two percent, but

18 it gets pretty hard to evaluate what that percentage really is

19 when it is down at that level. It takes a lot of animals to

20 actually put a good number on that. 

21 So what we are basically saying is we would

22 prefer that labs actually check their cultures and be sure that

23 the prevalence of this in their cultures is less than five

24 percent.

25 And then the intersex condition seems to be

26 actually less prevalent. And I would say, in most cases, we see
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1 it fairly rarely and put our proportion of that at less than two

2 percent. So these biomarker endpoints, we have secondary sex

3 characters, which I mentioned before. In a sub-adult fish,

4 meaning before it is like 10 weeks old, typical numbers are 70. 

5 We have seen them in some cases to be quite a bit lower than

6 that in other laboratories, so to be conservative, we feel like,

7 in this case an acceptable range is greater than 20. That would

8 be number of papillae on the anal fin of a male at seven weeks

9 and older. But we like to measure it now. We are proposing

10 that it be measured at nine weeks, in which case, I think in

11 most cases it tends to be quite a bit larger than 20.

12 Vitellogenin mRNA numbers: These are measured

13 in both the males and in the females. And the thing to note is

14 that this number to some extent is defined by your PCR method. 

15 And to tell you what an absolute value of what that ought to be

16 is, we feel probably not appropriate, but we certainly can tell

17 you that in the males there is vitellogenin mRNA, so it is not

18 zero. And in the females, it is quite a bit higher; three

19 orders of magnitude tends to be. And probably more importantly,

20 within any particular study, the ratio of male to female mRNA

21 copy number in your control should be several hundred fold. 

22 So then this would talk about toxicity hatch

23 success. In the embryos, we feel it tends to be around 80

24 percent or greater, and we feel that an acceptable range is

25 about 70 percent as kind of a minimum. Growth, once again,

26 depending on the time exactly, but we do feel that growth is
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1 actually very important to getting good fecundity measures. So

2 we actually encourage very strict feeding regimens that will

3 result in the fish growing to an acceptable and optimal size for

4 reproduction by the time that starts in the assay. 

5 We feel that control males of 170 milligrams

6 at the sub-adult stage is about where we like to see it, 95 in

7 the females. However, anything greater than 100 in the males

8 and greater than 150 in the females will likely produce good

9 test results.

10 Once again, early life-stage survival, 80

11 percent tends to be an acceptable control value or a control

12 value that we find, and greater than 65 is acceptable. Once

13 again, liver pathology in adults tends to be very low in control

14 animals, and that will be the prevalence of pathology in the

15 liver. Once in a while you get liver pathology due to some

16 infectious diseases and that is a good hallmark to tell you that

17 your test might be in trouble or have been in trouble.

18 I think I have gone through this enough that

19 we are all on the same page. So unless there are some

20 questions, I am just going to move on. 

21 I want to talk to you a little bit about the

22 statistics that we have used for evaluating the data. We have

23 been using a program that is called StatCHARRMS that was

24 developed for us to use to be able to allow us to systematically

25 and effectively test statistical effects in a systematic way

26 across all the tests that were run. It is a SAS-based program
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1 that was developed for us. And it is our version currently in

2 development in beta form that we would expect to be available

3 shortly for anyone who wish to have it. 

4 It basically has three modules. There is an

5 open module that is used for the data that continuous response

6 is measured once, so most of the data that was taken at the

7 sub-adult stage can be evaluated in this program. And it

8 basically tests for abnormality, tests for homogeneity and the

9 variances, and then depending on the outcomes, it goes through

10 different protocols, if it passes or fails. What you will see

11 is different statistical tests that are performed, given that

12 the meeting acceptance criteria are met. 

13 There is another module that is for the

14 reproductive data which is, in this case, a repeated measures

15 ANOVA. And it is of course for a continuous response measured

16 multiple times in the same pair of animals. So I am not going

17 to go through reading all of these details.

18 And for the pathology, the severity score,

19 which is an ordinal response, is used in a statistical fashion

20 using a test that was developed by Springer, Green and Saulnier,

21 and it is a variation on a Rao-Scott/Cochran-Armitage

22 nonparametric test protocol. And the slices what was added by

23 these folks, where basically it goes through a step-down

24 procedure and looks for significance in a particular level

25 compared to a control response. And not only does it look for

26 the differences in the prevalence of the observation, but also
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1 the severity score of the observation. So any significance that

2 is highlighted is highlighted so that you know at what severity

3 that particular outcome is significant at, at what treatment. 

4 And that is what is probably the most unique of this StatCHARRMS

5 program.

6 Now we probably are all aware of the errata

7 regarding the statistical analysis of the data, and I will go

8 through that quickly here.

9 As I mentioned, StatCHARRMS consist of three

10 modules. There is a one-way ANOVA module, and that is the one

11 that we had the error in, and the error was in how we

12 implemented the one-way ANOVA module. And that is that the

13 replication data was not considered in the analysis. The

14 statistical unit of the analysis was the individual and it was

15 not the tank. 

16 So there were erroneous estimates for

17 biological variance, treatment variance and variance estimates

18 of the biological means in that data analysis within the MMT. 

19 So it entirely involved the sub-adult AOP biomarker endpoints,

20 vitellogenin secondary sex characteristics and growth.

21 Turns out that when we recognized this error,

22 which was last week, we went through and we reanalyzed the data,

23 but we did not have the option to reanalyze the Japanese data. 

24 And when they provided us with the data in the first place, they

25 provided us with their own significant testing outcomes, so all

26 the data that we present of the Japanese data following,
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1 basically substituted what would have been the StatCHARRMS

2 outcome with the Japanese data outcome. So all the data

3 presented from Japan is with their analysis of the AOP biomarker

4 endpoints. 

5 The adult reproduction data, fecundity and

6 fertility were not affected and the pathology analysis was not

7 affected. 

8 The power analysis which I will be discussing

9 later, which discusses the fecundity by number of reproductive

10 pairs was also not affected.

11 And lastly, I just want to state that "R"

12 version of StatCHARRMS correctly uses replicate data to identify

13 the statistical sampling unit as the tank. So let's move on. 

14 I want to just review some data expectations. 

15 This information is primarily derived from literature and Tier 1

16 test with fish. The point being, we don't have a lot of

17 experience with these outcomes in the MMT because we do not have

18 all of these modes of action in the studies that we are

19 presenting. 

20 But real quickly, an ER agonist in the males

21 should show an increase in vitellogenin, a decrease in the

22 secondary sex score and tends to produce sex reversals and

23 intersex. And the gonads tends not to have an effect on growth.

24

25 And the females, those arrows that have

26 parenthesis basically indicate that the vitellogenin levels in
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1 the females may go up but it is not a defining character. There

2 is no effect on the secondary sex of the females because they

3 don't have any, no effect on the gonad to speak of unless the

4 doses are very high. But the outcome is a reduction in

5 fecundity by ER agonist and no apparent effect on fertility.

6 AR-agonist cause increase in the male's

7 secondary sex characters, but not necessarily a large one and it

8 may not be real heavily noticed. But in the female it

9 definitely causes a female which does not have any sex

10 characters to show anal fin papillae, and typically a reduction

11 also in vitellogenin in the female. 

12 Once again, we also see sex reversal and we

13 see perifollicular cell hypertrophy and hyperplasia. And the

14 most important thing I wanted to mention there, with the

15 AR-agonist, we do not see intersex conditions. So basically it

16 is either sex reversal or nothing, and once again, reduced

17 reproduction.

18 The AR-agonist, we don't have a very good

19 record here on what to expect there. Steroidogenesis

20 inhibitors, we definitely know that we have effects and

21 reductions in vitellogenin. We also get the perifollicular cell

22 hyperplasia and hypertrophy and again, reductions in fecundity.

23 Now with the toxicity, we see reductions in

24 vitellogenin. Oftentimes we see a poorly developed gonad in

25 both the male and the female, reduced growth. And then other

26 kinds of pathology would be liver pathology and once again,
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1 reductions in fecundity and perhaps fertility but almost always

2 fecundity. So the idea being that at least we can try and

3 identify the difference between toxicity and other endocrine

4 modes of action.

5 So I am going to just review some of the

6 summaries from the multi-gen test. These are the data that I

7 can summarize. And what we have here are tests from our

8 Japanese colleagues, tests at the EPA lab in Duluth, and we have

9 a set of inter-lab tests, three.

10 

11 And also, I mentioned earlier that some of the

12 protocols were full protocols. However, as we moved towards the

13 end of this process we, along with the Japanese, discussed ways

14 to abbreviate the protocols so that later on we did not go

15 through what would be full MMTs. We reduced the F2 generation

16 to sub-adult sampling for some of these tests. So in some

17 cases, we do not have F2 reproduction that we can compare to the

18 F1 reproduction. But in any case, this is the series of test

19 chemicals that we have to evaluate. 

20 What I have here, I have highlighted the

21 places where the data changed due to the reanalysis. Anytime

22 there is a yellow highlight, it is explaining that the LOEC that

23 we had reported in the Integrated Summary Report -- the ISR --

24 if it changed what was in the ISR or in the supporting

25 appendixes -- we did of course change it in those, but I am

26 highlighting what changed here in what we are discussing. So
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1 what am I getting at? 

2 The Japanese data, for instance, at one time,

3 the LOEC and the biomarker endpoint for 17-beta estradiol was

4 2.8 nanograms per liter, and the sub-adult XY fish where there

5 was a reduction in the secondary sex characters.

6 Well after the reanalysis, that was no longer

7 statistically significantly different than the controls and this

8 became the LOEC for the biomarker endpoint which is 8.9

9 nanograms per liter, or in this case, the sub-adult XX fish

10 where there was an increase in vitellogenin. 

11 And what I am trying to show you basically is

12 there were some changes and what the nature of the changes was. 

13 But probably more importantly what we really should be looking

14 at would be the LOECs in this case. So the LOEC for fecundity

15 for 17-beta estradiol was 28 nanograms per liter. It was in the

16 F1 generation. 

17 The biomarker LOEC was 8.9 and that was in the

18 sub-adult females with increased vitellogenin in the F2

19 generation. And the published literature data that supports

20 these as being appropriate and reasonable or comparable is that

21 fecundity was reduced in Japanese Medaka and was 17-beta

22 estradiol at 28 nanograms per liter, 27.9 published.

23 Octylphenol; reporting octylphenol outcomes. 

24 One study done in the EPA laboratory in Duluth and the other one

25 done by the NIES laboratory in Japan. We can see that the

26 fecundity LOEC in the Duluth lab was 51 micrograms per liter in
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1 the F2 generation and was 100 micrograms per liter in the F2/F1

2 generation in the Japan study.

3 Once again, supporting biomarker endpoints in

4 the EPA study, 13 micrograms per liter caused a reduction in the

5 male fish's secondary sex characteristics in the F2 generation;

6 13 micrograms per liter adult XY intersex in the F2 generation. 

7 And once again, reported information for 4-t-Octylphenol is

8 reduction in vitellogenin at 11.4 micrograms per liter.

9 The Japanese study showed again, as I said,

10 100 micrograms per liter is the LOEC and three different

11 biomarkers all at the 6.5 microgram per liter in one case F1

12 generation and in another case the F2 generation. Once again,

13 similar concentrations in the literature published.

14 DDT study performed in Japan; the LOEC for

15 fecundity 1.9 micrograms per liter in the F2 generation, and the

16 biomarker endpoints, .07 micrograms per liter for both the

17 sub-adult females with an increase in vitellogenin and adult

18 females with an increase of vitellogenin. 

19 And in this increase of vitellogenin in the

20 sub-adults was both in the F1 and the F2 generation whereas the

21 increase observed in the adult females was in the F2 generation.

22 And the published literature shows vitellogenin response at the

23 .1 microgram per liter for DDT, so similar again. 

24 This is 4-chloro-3-methylphenol. This is a

25 study that we did at the EPA lab in Duluth and we ran that

26 chemical because an in vitro assessment indicated that that
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1 chemical bound to the estrogen receptor. And we felt that it

2 would be a very interesting chemical to see what the

3 relationship was between the in vitro response, so it would have

4 been one of the screening assays in Tier 1 and compare it to

5 what would be an outcome in Tier 2. 

6 And the most important thing I think to show

7 is that at our highest concentration there was no effect on

8 fecundity or fertility, but in any case I want to go back and

9 then show that there were slight increase in female vitellogenin

10 expression, liver vitellogenin mRNA at the 88 microgram per

11 liter level, and there is no data with respect to this in the

12 literature, but I think it is a good example of a negative

13 control, to some extent. We know that it is an estrogen binder

14 and we know that with pretty good information. 

15 We also know that what we did just to try and

16 confirm that our result made sense was that we ran a study to

17 look for what might happen at or near the LC-50 of that

18 chemical, and then we measured the effect on vitellogenin

19 production. And as you reach the LC-50, vitellogenin production

20 goes to zero. You can't induce it. You get toxic effects

21 before you get EDC effects. So I guess what I am suggesting is

22 that for CMP, while it may potentially have been an endocrine

23 disruptor in vivo, it is toxic before it is an endocrine

24 disruptor. 

25 Tamoxifen outcome; we have a study done in

26 Japan and the fecundity LOEC was 20 micrograms per liter and the
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1 biomarkers were 1.3 micrograms per liter. And they were effects

2 on vitellogenin, so it caused an increase in vitellogenin in the

3 male sub-adult. It caused a decrease in vitellogenin in the

4 female in F1 and F2. And in the sub-adult males, it caused

5 decreases in the secondary sex characters of the F2.

6 Published information again, 625 micrograms

7 per liter affecting fecundity. So the point being that we are

8 showing in this case quite a bit greater sensitivity with the

9 MMT than the published literature.

10 17-beta trenbolone; 32 nanograms is the LOEC

11 for reproduction or for fecundity in the F1 generation. The

12 same level for the biomarkers, 32 nanograms per liter for

13 sub-adult female increases in secondary sex characters, adult

14 decreases in males for vitellogenin and, once again,

15 sex-reversal in adult females. And once again very similar

16 concentrations listed in the literature for vitellogenin

17 effects.

18 Prochloraz, LOEC fecundity 25 micrograms per

19 liter, F2 generation. We see nine micrograms per liter as a

20 sub-adult concentration reducing secondary sex characters and

21 this became statistically significant in the -- this is the same

22 as listed by the Japanese in their statistical analysis and once

23 again, 30 micrograms per liter for fecundity in the literature

24 comparable to 25 in the MMT. 

25 Vinclozolin; the EPA vinclozolin study shows

26 that the fecundity was at the greater than 136 microgram per
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1 liter level and no information available at this time for the

2 generation. We also are showing that the evaluation of

3 secondary sex characters was changed by one treatment level; it

4 went from 17 to 33 and that was in the reduction of the number

5 of papolary processes in XY males in the F1 generation. And

6 that moved up one level, so it went from 17 to 33. And this

7 particular observation became significant and that was at the 33

8 microgram level for these sub-adult females with an increase in

9 vitellogenin.

10 The published information regarding effects on

11 spermatogenesis is very, very high, as you can see. It is like

12 2.5 at 250 milligrams per liter. It is very high. And we think

13 that this study was running vinclozolin at higher than

14 solubility. 

15 And then just real quickly, the one study

16 where we increased the replication in the reproductive part, the

17 MRT, and that was at 4-t-Octylphenol, once again, the same

18 chemical we present here. And in that case, we show the

19 fecundity LOEC at 23 micrograms per liter in both the F1 and the

20 F2 generation. And the published literature shows effects in

21 vitellogenin at 11 micrograms per liter. That was a long slide

22 but it does in fact truly, I think, summarize the essential data

23 that we have for looking at this test protocol.

24 Summary Control Data. Now this is the one

25 where I wanted to mention to you that -- I did mention to you --

26 that there is quite a bit of variance or differences in the
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1 values in the sub-adult biomarker data partly because, as I

2 mentioned, these studies were changing between the beginning

3 ones and the end ones. And the changes included not only the

4 sampling time for the sub-adults but also the feeding regimens

5 and the temperature regimens that the studies were run at. And

6 that was intended, as I've mentioned, to try and optimize the

7 test for fecundity, especially for the fecundity endpoints.

8 So let me just real quickly go through and

9 just show some of the data with respect to reproduction. 

10 17-beta estradiol, controlled data in fecundity 27 eggs per day,

11 fertility 26 eggs. I am going to just go down. This is all

12 going to be fecundity first. So 27, 17, 29, 16, 39, 23. 

13 The lowest is 14 on the prochloraz and that is

14 probably lower than what we would consider to be acceptable

15 today. You can see that in the interlabs most of them were

16 quite good. This one was quite bad, but this fish also were

17 very, very small. They were not adequately sized for good

18 reproduction.

19 You can see that in most cases, the control

20 fertility is very high. Growth; you know, we have in some cases

21 with the Japanese data they did not discriminate between males

22 and females in the sub-adult, so that is combined. So this is

23 just simply a fish weight rather than a male weight and female

24 weight. You can see that the Japanese had tended to have larger

25 fish early on. We learned a lot from the Japanese. This is our

26 first full MMT and our fish were much smaller. 
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1 As we got further on, we were able to develop,

2 first of all, better fish and plus we also sampled later. This

3 was sampled in seven weeks and most of these others were sampled

4 in nine weeks. The fish are in their linear growth phase at

5 that time, so a difference in about a week makes a huge

6 difference in the size in the sub-adult weights.

7 Anyhow, let us get back to talk about

8 secondary sex characters tend to be related to how big the fish

9 are during this sub-adult sampling. You can see that -- I

10 mentioned the MED, the Duluth octylphenol study, small fish, low

11 secondary sex numbers. As we started sampling later, they tend

12 to be larger values.

13 And once again, vitellogenin reported in

14 different ways. We have been reporting vitellogenin as mRNA

15 copy number. And the Japanese data is measuring vitellogenin

16 through ELISA techniques and reporting the amount of

17 vitellogenin per unit weights. So they are not necessarily real

18 comparable but I just wanted to literally put the data in so

19 that you can see it.

20 Now I want to look at a little bit about the

21 reproducibility in the MMT by chemicals. We do not have a lot

22 of comparisons because there were not a lot of chemicals that

23 were run multiple times. But octylphenol was run three

24 different times; twice in the EPA lab in Duluth. In the EPA,

25 this was very, very early in the study protocol and this was

26 recent, so these two studies are separated by about four years. 
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1

2 And the Japan study was done in the middle and

3 we can see that the fecundity outcome is comparable in terms of

4 the LOEC in the Japan and the U.S. study. And then you get to

5 the last U.S. study with increased replication and the LOEC

6 dropped to two treatment levels, and that is primarily I think

7 because of statistical power not because of actual changes in

8 the LOEC. Fertility, very similar, and sort of the same story,

9 I think. 

10 Growth; let us look at those values. The LOEC

11 for growth was 102 micrograms per liter in the EPA study and in

12 the NIES study.

13 Secondary sex, 51 micrograms per liter at the

14 EPA study, substantially lower at the NIES/Japan study with

15 octylphenol. 

16 Vitellogenin, once again, the same at the 51

17 micrograms per liter level, and with the Japan study, 25

18 micrograms per liter, so a difference of one treatment level

19 again.

20 Vinclozolin; this chemical was run both at EPA

21 and in three inter-lab studies. And we see that the LOEC for

22 fecundity was, in most cases, the highest treatment level, so it

23 was greater than the highest treatment level. The point is they

24 are all comparably high in terms of their LOECs. And the

25 fertility LOECs are essentially the same as the fecundity LOECs.

26 And let us look at effects on growth; what we
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1 can see is that the LOEC for growth is, once again, very high in

2 the EPA study and somewhat lower in the two inter-lab studies,

3 and then in the third inter-lab study, very high again, the

4 highest treatment level.

5 Effects on secondary sex 17 micrograms per

6 liter EPA study, 197 micrograms per liter in the lab one of the

7 inter-lab and higher than the highest dose in the -- so greater

8 than the highest dose in lab two and not measurable in lab three

9 because of primarily problems in genetic sexing and other

10 problems. 

11 Let us go to vitellogenin; once again, the

12 high treatment level of vitellogenin, no change. In other

13 words, not statistically significant at the highest treatment

14 level, same here and 360 in this one. So the point was

15 vitellogenin was not reactive to vinclozolin in all the

16 laboratories.

17 I want to just summarize the inter-lab data. 

18 We have basically -- I mean this is a very broad summary -- the

19 control data from two of the three interlabs. The studies were

20 very similar. Fish from laboratory number two were very small

21 compared to the other labs and we have tried to understand some

22 of the reasons why and we don't know why. So basically, the

23 fish in lab 2 were very small. 

24 And the consequence of that is their

25 reproduction was low and lots of other things were affected. A

26 lot of data was affected because of the small fish. In all
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1 cases, there were no effects on reproduction of vinclozolin, in

2 all labs.

3 The treatment related decreased in secondary

4 sex characteristics were observed in one of the three

5 laboratories and that was consistent with the same biomarker

6 effect that we observed in the EPA study with vinclozolin. So

7 in other words, there was a treatment related decrease in the

8 anal fin papillae of male fish.

9 Vitellogenin was not affected by treatment and

10 essentially the effect outcomes are consistent between the

11 inter-lab and the EPA study with vinclozolin and there were

12 problems determining genetic sex across all the inter-lab

13 laboratories. So genetic sexing was a problem in the inter-lab

14 studies.

15 So what are our lesions learned in the

16 inter-lab? We feel like we did not adequately transfer the

17 information, the techniques to allow successful execution of the

18 MMT protocol, the labs with limited experience with testing

19 Medaka.

20 Examples of this inadequacy include some

21 issues with Medaka culture and growth. At least one of the labs

22 clearly did not rear fish in a way that led to fish that were

23 adequate to study the endpoints in. Genetic sex determination

24 was problematic across all the labs. 

25 There were problems in sample archiving for

26 the secondary sex characters and for vitellogenin evaluation and
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1 there were also problems with sample tracking and QA/QC. And I

2 feel like there are, for instance, there were some things that

3 one would consider very simple, and that is naming conventions

4 for samples across generations cannot be the same. You just

5 simply cannot do that because you cannot tell which generation

6 you are looking at when you evaluate the outcome. 

7 And because some of the labs that were doing

8 these kinds of tests had not experienced those requirements in

9 QA in multigeneration tests they just simply overlook some of

10 the things that would seem obvious. So I am just saying,

11 something that would seem as obvious with sample tracking turned

12 out to be problematic and we just have to work on developing

13 better tools for the labs to use.

14 More explicit guidelines; training to provide

15 information to the labs interested in performing a Tier 2 test

16 protocol. Make sure that they can do it and make sure that they

17 can do it before they start the test, genetic sexing being

18 essential. And I believe that as labs gain experience, that we

19 can expect that they will be able to perform a Tier 2 test

20 successfully.

21 I am going to move on now real quickly I hope

22 to some comparisons of the F1 and F2 generations in the MMT.

23 DR. MARTHA SANDY: Excuse me, Dr. Johnson. 

24 Maybe, given the late time, we take a break here for lunch and

25 come back.

26 DR. RODNEY JOHNSON: That is just fine for me.
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1 I am sure it is fine for everybody else too.

2 DR. MARTHA SANDY: Sure. If we can come back

3 at 1:30 promptly, then we will start again. And that will give

4 you a little bit of a break. Thank you so much.

5 (Whereupon a break was taken)

6 DR. MARTHA SANDY: So good afternoon. I think

7 we are going to try to get the Panel members in their seats. I

8 think we are missing just a few and then we will get started. 

9 Good afternoon. This is Martha Sandy and we are about ready to

10 kick off where we left off with Dr. Johnson. Are you on slide

11 72? We are. Please go ahead.

12 DR. RODNEY JOHNSON: All right. Everybody is

13 nourished. So I wanted to discuss with you some information

14 regarding comparing the reproductive output in F1 compared to F2

15 and just very quickly look in the context of the lowest observed

16 effect concentration measured in F1 and F2 within the same test

17 where we had that kind of data.

18 So here we have got estradiol where we showed

19 in the study in Japan that the LOEC for fecundity was 28

20 nanograms per liter in F1 and it was greater than 28 nanograms

21 per liter in F2. And because of reproductive failure, that is

22 the highest concentration that was in F2. So does that make

23 sense? So reproductive failure in F1 eliminates the higher dose

24 in F2, so the LOEC automatically has to be there or higher,

25 unless it is lower, of course, but the point is you cannot go up

26 higher than what you have tested. 
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1 The octylphenol study at the Duluth lab or the

2 EPA lab showed 102 as being the LOEC in F1, and the next lower

3 dose of 51 micrograms per liter in F2. And the NIES study show

4 100 micrograms per liter as the LOEC in F1, and then once again,

5 same as in the estradiol study, the highest dose with which

6 there was information is not the hundred anymore so the LOEC is

7 greater than 50 micrograms per liter.

8 The EPA octylphenol study in the last one

9 where we increased the replication where it was 12 replicates,

10 we show 23 micrograms per liter as the LOEC in both F1 and F2. 

11 DDT, the Japan study, shows the LOEC in F1 as

12 being greater than 1.9 micrograms per liter and in F2 it was 1.9

13 micrograms per liter. In the trenbolone EPA study it shows 32

14 nanograms per liter being the LOEC for fecundity in both F1 and

15 F2.

16 Now this is the biomarker comparisons. In

17 this case, I have been through this first study with estradiol

18 once before. Basically, we show the LOEC in F1 as in papillae

19 goes away. In other words, it is not the lowest LOEC anymore. 

20 And the lowest LOEC is in fact liver vitellogenin, and it is at

21 28 nanograms per liter is the LOEC in F1, 8.9 nanograms per

22 liter in F2, so it is lower in F2 than in F1. 

23 Octylphenol shows 51 micrograms per liter in

24 F1 and a lower LOEC of 13 milligrams per liter in F2, and that

25 is for secondary sex or papillae. 

26 Octylphenol in Japan shows equal LOECs for
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1 liver vitellogenin endpoints of 6.5 milligrams per liter in both

2 F1 and F2. 

3 DDT, same, F1/F2 again for vitellogenin. 

4 Tamoxifen, same LOEC again for liver vitellogenin and in F1 and

5 F2. For trenbolone, the same LOEC for the biomarker of both

6 papillae and liver vitellogenin at 32 nanograms per liter. 

7 So I am going to try and summarize these

8 outcomes in the context of just simply that LOECs F1/F2 by

9 reproductive endpoints. And simply F1 was more sensitive than

10 F2 that has a lower LOEC for 17-beta estradiol, and F2 has a

11 lower LOEC for octylphenol at EPA and DDT for Japan, and they

12 are equal for three different studies, octylphenol Japan,

13 trenbolone and octylphenol at EPA.

14 Biomarkers F1, lower biomarker LOEC than F2 in

15 none of the studies whereas F2 is lower than F1 in two of the

16 studies, and they are equal in four of the studies.

17 These outcomes with the research Medaka

18 multigeneration Test suggest the following to us and that is

19 that the F2 generation does not provide sufficient value added

20 to warrant inclusion in the Tier 2 test protocol. 

21 And to realize that the test that we are

22 proposing is called the Medaka Reproduction Test rather than the

23 multigeneration test because it does not span multiple

24 generations anymore and it is a subset. The timeline of the MRT

25 is a subset of the MMT, so it is not an entirely different study

26 or test protocol, it is a test protocol that is derived from the
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1 research MMT.

2 The differences that we are proposing are

3 primarily that the exposure would stop at F2 hatch and so the

4 study duration would be 19 weeks. Endpoints are measured

5 primarily in F1 and pathology is measured in F1 adults only. 

6 And we are recommending an increase in the replication in F1

7 reproduction for the fecundity and the fertility endpoints.

8 So this is the protocol. You have seen these

9 types of diagrams before. It starts with exposure to F0. The

10 embryos are used to populate F1. We take the F1 all the way

11 through reproduction. We take embryos into F2 through hatch and

12 the study is terminated.

13 The endpoints are measured are hatched in F1,

14 survival F1, biomarker endpoints, week nine of F1. There

15 survival, growth, vitellogenin and secondary sex characters. 

16 Reproduction now is three weeks long continuous measuring

17 reproduction, fecundity and fertility for 21 days in succession.

18

19 We measure hatch with pathology then as

20 occurring at the end of reproduction and survival growth

21 vitellogenin is not measured. Vitellogenin is only coming at

22 the sub-adult point, and then we measure hatch in F2. So it is

23 a 19-week study, reproduction, 21 days continuous. The

24 generations do not overlap, and that is primarily for

25 infrastructure reasons. Basically allows you to do the study

26 with the same number of tanks throughout the full study.
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1 I want to just review a little bit about how

2 the experimental design works here. What we have is three

3 different generations; F0, F1 and F2. I want to show that this

4 is sort of the replication structure that we are recommending. 

5 There are six replicate pairs in F0. We collect eggs from those

6 six pairs. We pull them and we distribute them into six

7 separate incubators, each in a tank by itself.

8 After the hatch, data is collected. All the

9 hatchlings are pulled into one tank and they are all

10 redistributed into six separate tanks, 12 fish per tank. 

11 As they are reared and grow up, we reach

12 finally week nine. At week nine all the fish are genetically

13 sexed. Four males and four females from each replicate are

14 pooled into this set of tanks; one tank is male, one tank is

15 female. All the remaining fish are sampled for the biomarker

16 endpoints, and their information with respect to replicate is

17 retained. So all of the information regarding the biomarker

18 endpoints are analyzed and understood in the context of the

19 replicate from which they came. 

20 These pooled males and females then are paired

21 up randomly into 12 replicate breading pairs and reproduction is

22 evaluated and we set up F2 incubators and they are sampled for

23 pathology and for secondary sex, and that is the design.

24 So we have the endpoints across the top hatch,

25 survival, growth, vitellogenin, secondary sex, reproduction,

26 pathology, secondary sex and hatched. So this is F1 hatch data;
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1 this is F2 hatch data. Survival, growth, vitellogenin,

2 secondary sex all taken on six replicate pairs, reproduction,

3 pathology secondary sex all taken on 12 replicate pairs. 

4 So that is the experimental design overview. 

5 The rationale for going to 12 replicate pairs for reproduction

6 follows. This is representing a Monte Carlo power analysis

7 derived from the control reproductive data that we got in our

8 previous last two MMT studies. And what these lines represent

9 are basically different levels of reduction in fecundity

10 compared to the control values. Each line represents a

11 different reduction in fecundity, starting with -- 70 percent

12 reduction is this bottom line, 70 percent all the way down to 20

13 percent. 

14 The point being that if this is true, that

15 amount of reduction, the probability that you will detect that

16 is affected by the number of pairs that you have in your test

17 design. So at the highest level of reduction, there is not a

18 whole lot of difference between the power of detecting an effect

19 with 12 pairs compared to six pairs. 

20 If you go to that particular line, which is

21 the 30 percent reduction in fecundity, you can see that the

22 power changes very dramatically by the number of pairs and goes

23 all the way from about .45 with six pairs to .83 with 12 pairs.

24 Basically, I think what we feel is that this

25 line here, which represents the 40 percent reduction in

26 fecundity is a fairly reasonable amount to be able to detect. 
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1 And with six replicate pairs, you can see that the probability

2 of detecting a 40 percent reduction in fecundity is about 75

3 percent.

4 And the other thing to recognize is that

5 replicate loss has a fairly serious consequence in the power. 

6 All the way from up to 12 coming down to six goes from nearly

7 one to .75. 

8 With 12 treatment pairs, the probability

9 increases to nearly one. The replicate loss does not affect

10 power seriously. So in other words if you lose replicates here

11 you are in this part of the curve. If you lose replicates here,

12 you are in this part of the curve. And it is not unusual to

13 lose replicates during this study.

14 So I guess what I am arguing is that if we

15 specified 12 replicate pairs as the recommended amount and you

16 lose some, you still have quite a bit of power to detect effects

17 on your apical endpoint.

18 Recognize also that this is what we are

19 calling the two/one experimental design, and that experimental

20 design is one where there are two times the number of control

21 replicates compared to each treatment. So for each treatment,

22 there are 12 replicate pairs. And for the controls, there are

23 24 replicate pairs for a total number of cells in this study of

24 84 cells.

25 This just demonstrates the difference in two

26 different octylphenol studies that we did; one of them with 12
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1 replicate pairs, the other with 6 replicate pairs. And I think

2 the most important thing was that, compared in the 12 replicate

3 set, first of all, the LOEC for fecundity is 23 in F1, it is 23

4 in F2. And with six replicate pairs in another octylphenol

5 study, this treatment LOEC is the highest treatment in the

6 study, which is 100. And in this case it is 51. So I guess

7 what I am saying is that increasing the replication really

8 substantially improved the ability to detect effects with

9 confidence.

10 So in conclusion, EPA asserts that the

11 proposed MRT protocol fulfills the EDSTAC-defined purpose of a

12 Tier 2 test. It can determine whether a test substance

13 adversely affects a test organism through endocrine-mediated

14 pathways. It can quantify those effects with respect to the

15 estrogen and the androgen systems. It includes exposure during

16 potential life stages that may be more sensitive than those

17 tested in Tier 1. It characterizes the dose response and it

18 includes exposures during gametogenesis to allow for potential

19 effects of parental transfer of chemical and other endogenous

20 factors.

21 And I have one more slide and I want to just

22 go through some conversation regarding implementation. 

23 We have learned a lot as we have tried to

24 develop these tests and transfer the information to other people

25 so that they can do these tests. We feel that support and the

26 improved guidance in pathology and for data reporting is an
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1 important part of tech transfer that we feel can be improved and

2 that we are dedicated to working on. We feel that there needs

3 to be training in genetic sex and other molecular methods.

4 Now, I want to sort of emphasize that

5 laboratories that do these kinds of tests will have to use

6 modern molecular biology tools and they will have to have them

7 in their laboratories or they cannot do the test. If they

8 expect to do these tests by shipping samples and getting data

9 back by which to make decisions on what they are doing, the time

10 turn-around and the QA problem is enormous. They simply have to

11 have the infrastructure within their laboratories to form these

12 tests. And we have been working with tech transfer of these

13 methods and I think they are very doable. 

14 One of the things I have not mentioned at all

15 is that, one of the most challenging parts of doing a study like

16 I have described is the exposure and the exposure chemistry

17 part. And all of these studies are done with flow-thru exposure

18 systems. And in order to keep the concentration of chemicals

19 that you are intending to dose real, you have to make sure that

20 your exposure systems are clean. You have to go through

21 prophylactic cleaning of your test systems while they are on

22 tests. We have methods for doing that and we have trained

23 people on how to do that and we have transferred methods to do

24 that, but it is essential.

25 Hederaltrophic growth within tubing systems,

26 aquarial systems will absolutely destroy the chemical that you
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1 expect to be testing if you do not ever clean the system during

2 the test. It has to be done.

3 The other thing is that we have methods to

4 expose and delivery what we think we are. We have very good

5 quality diluter system and dilution delivery systems. And labs

6 that do these tests should have good systems. Once again, we

7 are wiling to provide all the information necessary for these

8 apparatuses to be used by test labs.

9 We would provide statistic programs based at

10 "R" for people who wish to use them. We have lots of guidance

11 and specimen tracking, QA/QC, which we are also willing to

12 share. We even have something that seems as trivial as

13 calendars on how to do daily protocols, but they are very, very

14 helpful. They help us out and they will be helpful for anybody

15 else.

16 I guess I am trying to emphasize that I do

17 think these tests are runable by other labs besides us and

18 Japan, but it will take diligence and a lot of training to pull

19 it off. I think in time, the labs we will get, we will be able

20 to. And I appreciate your time and I am sorry I was so

21 long-winded.

22 DR. MARTHA SANDY: Thank you very much, Dr.

23 Johnson. So now is the time for the Panel to ask any questions

24 of clarification, and I would like to go first. Could you go

25 back to slide 69 for us? 69 is tilted MMT Reproducibility by

26 Chemical. 
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1 So I had a question because it looks like here

2 for the 4-t-octylphenol on the fecundity for the F1s, the EPA

3 values 102 micrograms per liter and the NIES is 100 micrograms

4 per liter. If you go back two more slides, if we look at the

5 fecundity for the EPA 4-t-octylphenol, it looks to me like it is

6 51 micrograms per liter. Am I misreading these slides? The

7 previous slide was 100.

8 DR. RODNEY JOHNSON: And it is between F1 and

9 F2, that is why.

10 DR. MARTHA SANDY: So you are only presenting

11 the F2.

12 DR. RODNEY JOHNSON: Yeah, yeah.

13 DR. MARTHA SANDY: Okay. Thank you. And Dr.

14 Schlenk?

15 DR. DANIEL SCHLENK: Okay. I have got a

16 couple of questions, just a couple. 

17 So again, going back to sort of Tier 2 and

18 what you would really like to have in a Tier 2 model. You would

19 really like something that has a population metric or endpoint. 

20 And I was just curious. I did some literature search just to

21 see, but is there an equivalent model like you have with the

22 Miller model with fathead minnows? Is there an equivalent model

23 for Japanese Medaka that actually has an estimate of population

24 change connecting reproduction to population that you know of? 

25 I could not find anything.

26 DR. RODNEY JOHNSON: No. I don't know of any.
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1 We have not pursued anything like that recently with using the

2 Miller type models. At the same time, we are working with a

3 different group within EPA to try and develop what we would

4 call, instead of population level models, what we are calling

5 toxicity translator models where we are trying to get what would

6 be a way to translate the data that comes out of an MRT or an

7 MMT, literally growth data, fecundity data, and get those into

8 what would be models that could go into iterative population

9 models where you literally have -- and in this case, it would be

10 not even a population model as much as a growth and fecundity

11 model for a year class, and its probability of surviving into

12 the next year to reproduce. 

13 But they are not what you would think of as

14 classical population models. We are trying to put one set of

15 models in between them and that is where we are working right

16 now. And actually we are working with fathead minnows right now

17 to develop those models, but we think that they are applicable

18 to the Medaka. And we have not been working them yet into the

19 endocrine disruptor pathways. So far we have been working with

20 them in the growth toxicity pathways.

21 DR. DANIEL SCHLENK: Okay. Thanks. One of

22 the things, as you are aware of, is water quality is a very

23 unique issue between laboratories. And I noticed in the

24 document that the Lake Superior waters which you guys use, the

25 MED -- I am curious. In Japan, any comparison of water quality

26 parameters between what you have there and what is in Japan and
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1 any recommendations for that, in particularly sort of nutrient

2 level type of things, micronnutrients, any sort of movement in

3 that direction at all?

4 DR. RODNEY JOHNSON: Well we will just send

5 that Lake Superior water to everybody and they can have as much

6 as they want. No, that is maybe not true. 

7 So we have not done that in particular. We

8 know that Medaka are fairly capable of reproducing through a

9 major range of water quality. They tend to be more tolerant of

10 your (inaudible) conditions than probably most fish. In fact, I

11 think when you get into really, really soft waters, that is when

12 they tend to be stressed. 

13 So I guess I would just off-the cuff, I mean

14 there is nothing formal put together yet in this case, recommend

15 using, if at all possible, harder waters. And I would recommend

16 in most cases, if at all possible, de-gased well waters and not

17 surface waters. We use Lake Superior water because it is

18 available to us and we can sterilize it and degas it and things

19 like that. But I think in most cases, it would be simpler for

20 any laboratory to use degassed well water that is reasonably

21 hard, not super soft. 

22 The Japanese as well as us have never seemed

23 to have see big differences in our outcomes, and I honestly

24 cannot tell you how similar our waters are. I don't know. 

25 DR. DANIEL SCHLENK: I am curious also. I

26 mean the other three labs that ran those as well, there was not
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1 any information regarding what their water was. Because I do

2 know from personal experience that even well water can be quite

3 soft and it took us about six months to figure out that we had

4 to add Instant Ocean to get the two parts per thousand just to

5 get reproduction to take place. I mean those are issues -- I

6 don't know if those are going to be in the guidance documents

7 but it's something to be working on. 

8 DR. RODNEY JOHNSON: Well I would certainly

9 want you to recommend something to that effect and we can

10 clearly look into it more carefully. It is just simply

11 something that we have not studied carefully.

12 DR. DANIEL SCHLENK: Sure. Also, on your

13 slide 60 which compares all of the control values for gender,

14 the gender change to me was quite interesting with the

15 temperature. I did not know that actually. That was quite

16 interesting. I am wondering did the Japanese have the same

17 issue with regard to gender change and temperatures that has

18 been sort of well characterized there?

19 DR. RODNEY JOHNSON: That is well

20 characterized. It is well characterized by temperature studies.

21 I think we have done the most extensive temperature study but

22 we have also compared our results with the Japanese results, and

23 they are very similar. 

24 So it does turn out to be apparently a strain

25 dependent and we are recommending a specific strain of fish to

26 use. You know, it is the derivative of what they call the
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1 carbio, which is probably what you have and most of us in the US

2 have. But they are not like the bread strains that are

3 available in Japan. In fact, we exchange fish with Japan with

4 an intention to keep, if there is such as a thing, as an

5 out-bread strain. I mean it is an oxymoron in a sense, but we

6 try and make sure that our strain is consistent and not

7 changing.

8 DR. DANIEL SCHLENK: Okay. Moving to your

9 steroidogenesis AOP with prochloraz; so in the fathead minnow

10 Tier 1, their steroid measurements in those particular assays,

11 any reason why steroid measurement is not a part of the Tier 2?

12 DR. RODNEY JOHNSON: The fish are really

13 small.

14 DR. DANIEL SCHLENK: Yes, I know. 

15 DR. RODNEY JOHNSON: They are very difficult

16 -- we tried to get blood out of them and it is consistently

17 difficult for us to get blood -- and felt that we could give

18 that up. I mean to be honest, just not capable of getting that

19 information, the steroid information.

20 DR. DANIEL SCHLENK: So you have tried it and

21 the QA/QC is such that your signal to noise is low enough that

22 you do not feel that is minimal to a guidance, sort of; is that

23 right?

24 DR. RODNEY JOHNSON: The most important thing

25 is we seriously tried to get consistent amounts of bloods.

26 DR. DANIEL SCHLENK: Well what about a whole
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1 tissue though?

2 DR. RODNEY JOHNSON: We never tried it with

3 whole tissue, never did. We just have not. And if we did do it

4 with whole tissue, it would probably have to be with heads,

5 because that is probably the only tissue we have available to

6 work with after the secondary sex sample.

7 DR. DANIEL SCHLENK: Yes, unless you allocate

8 it.

9 DR. RODNEY JOHNSON: Unless we allocated it

10 certain numbers of fish for that. And we have been pretty

11 careful in streamlining the design as much as possible to not

12 have excess animals, and that has been part of -- part of the

13 calculus is sort of how can we do this and keep animal use low

14 but still get the information that we felt we needed. And that

15 was one that -- it was something that fell to the side quite a

16 while ago and we have not reconsidered that in the last several

17 years.

18 DR. DANIEL SCHLENK: Okay. And you mentioned

19 the flow-thru system. One of the things that I was a little

20 unclear about was, you take measured values periodically

21 obviously throughout this process that the egg chambers that you

22 are exposing the animals to, is that still flow-thru or is that

23 a static system? It appeared from the documentation that was a

24 static system but I was not sure if you have that submerged with

25 a flow-thru component that allows that to maintain the flow-thru

26 characteristic.
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1 DR. RODNEY JOHNSON: The incubator is a small

2 test tub that we cut off and put a screen in it. And then that

3 is suspended in the aquarium that the water is delivered to. 

4 And then it is agitated with air so that there is exchange with

5 the tank through the screen system.

6 DR. DANIEL SCHLENK: Okay. So it is

7 flow-thru, it is just not -- 

8 DR. RODNEY JOHNSON: Yeah, yeah.

9 DR. DANIEL SCHLENK: Okay. I just wanted to

10 make sure that was there. So with the vinclozolin studies in

11 lab two with the low weight, it was recommended that you

12 normalize according to weight. Was that just for that one lab

13 or is that something that you are proposing to do across the

14 board?

15 DR. RODNEY JOHNSON: We do not recommend

16 normalizing data by weight. We think there is no really good

17 justifiable biological well defined relationship between weight

18 and fecundity. We know that they are correlated but we

19 certainly do not have an understanding of the correlation at

20 all.

21 DR. DANIEL SCHLENK: Okay. I just want to

22 make sure that was not in the guidelines or anything. And it

23 was stated -- and I kind of got this impression in reading --

24 that some of those labs did not follow particular guidelines

25 that you had given to them. And I think you may have alluded to

26 a few things in terms of feeding and other issue. Were those
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1 primarily the main things, temperature and feeding where the

2 things that were not followed or -- were guidelines provided

3 that everybody had? I guess I am just kind of curious as to why

4 would one lab not do what you told them to do and the other two

5 do it? I mean is that essentially what happened? 

6 DR. RODNEY JOHNSON: We've asked ourselves

7 that. I am not sure why there were deviations from the

8 recommendations. I do believe that our recommendations were not

9 as clear-cut emphatic as they need to be and should have been. 

10 I mean I think there are certain places where you say, there is

11 no discretion here unless you can show that whatever it is that

12 you propose meets at least the minimum standard. And I honestly

13 do not care if they deviate but they have to demonstrate that

14 they can meet some minimum. And for starts, you would think at

15 least they would maybe try the guideline and then deviate, but

16 they did not. 

17 And the other thing I had emphasized early on

18 in the talk was that demonstrated capacity of genetic sexing

19 should be a priori; do not even start the test until you can do

20 that.

21 DR. DANIEL SCHLENK: Next question actually. 

22 So the DMY method, is this the gel run basically where you look

23 for the ban? I mean it was not clear in the documentation which

24 method you are actually proposing, if it is the gel

25 visualization method or is it actually just qPCR.

26 DR. RODNEY JOHNSON: qPCR method.
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1 DR. DANIEL SCHLENK: So whether it is there or

2 not.

3 DR. RODNEY JOHNSON: Yeah.

4 DR. DANIEL SCHLENK: So a fairly

5 straight-forward method, I mean just in terms of just the

6 thermocycler essentially, and sterile techniques.

7 DR. RODNEY JOHNSON: Thermocycler and good

8 probs.

9 DR. DANIEL SCHLENK: Okay. And I am curious

10 too, just noticing your paper that just came out not too long

11 ago, how would you compare the DMY versus the (inaudible) or any

12 of the other probes that you guys saw in that particular study? 

13 I mean what gave you the best -- I know that the DMY is probably

14 more robust.

15 DR. RODNEY JOHNSON: It is definitive. I mean

16 it does in fact tell you which one is a male and which one is a

17 female genetically. Now the other markers are more phenotypic

18 actually, and they are not genotypic. Those would be the

19 expression phenotype of a male or a female independent of DMY. 

20 And we are quite certain that some chemicals can trigger that

21 expression pattern of a male or a female gonad independent of

22 DMY. And chemicals can do it and temperature can do it and some

23 things that we do not understand can do it. And it is the

24 things that we don't understand that we want to keep below two

25 percent in our controls.

26 DR. DANIEL SCHLENK: So I am just curious. 
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1 Would you be proposing those as particular endpoints that might

2 function in that capacity?

3 DR. RODNEY JOHNSON: Absolutely. They very

4 much could be, yes.

5 DR. DANIEL SCHLENK: Okay. And on slide 65,

6 which is going to be the Data Expectation slide -- it kind of

7 segways into the last question I have here. So you have for

8 males in toxicity in the bottom line, that toxicity, you would

9 see a decrease in male vitellogenin. Wouldn't you also see that

10 in female vitellogenin as well?

11 DR. RODNEY JOHNSON: Yes. Absolutely.

12 DR. DANIEL SCHLENK: Okay. I wanted to check

13 that, which gets to the next question. So in the MRT, you are

14 proposing essentially to eliminate any sort of growth or

15 measurements in F0, is that correct?

16 DR. RODNEY JOHNSON: Well it is certainly

17 prudent to measure growth anytime you can. I mean I would just

18 simply say that.

19 DR. DANIEL SCHLENK: So growth is still going

20 to be measured in F0.

21 DR. RODNEY JOHNSON: I think it should be, but

22 we don't have very much time in their adults in the first place,

23 so you could see probably some toxicity reductions perhaps.

24 DR. DANIEL SCHLENK: Because you are not going

25 to be doing histopath in those, correct?

26 DR. RODNEY JOHNSON: No histopath in F0, no.
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1 DR. DANIEL SCHLENK: So the only metric you

2 would actually have to determine an acute non-endocrine based

3 toxicity would be growth if you are not going to be doing

4 histopath on those guys, right?

5 DR. RODNEY JOHNSON: You would have growth. 

6 And more than likely, if it is serious toxicity, you would see

7 pathology. But if we are not doing pathology in that group,

8 which we are not, growth would be the best one for F0 toxicity.

9 DR. DANIEL SCHLENK: And you think that is

10 strong enough to give you toxicity maternal -- 

11 DR. RODNEY JOHNSON: I think we will get it in

12 F1. I think we will get toxicity understanding in F1 even

13 though we may not have it in F0. 

14 DR. DANIEL SCHLENK: So the F2s that would be

15 resulting from F1 as a result of F1 exposure -- I guess what I

16 am concerned about is maternal effects that would manifest

17 themselves in developmental abnormalities but it would be

18 indirect. It would be because of acute toxicity. 

19 So for example, if you had hepatic toxicity

20 with some agent that targets the liver that has no endocrine

21 response whatsoever, but you are not going to be making

22 vitellogenin for example. If you are not making vitellogenin,

23 you are going to have reduced fecundity. And then that may lead

24 itself to things in F1.

25 DR. RODNEY JOHNSON: And at the end of F1,

26 there is histopathology.
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1 DR. DANIEL SCHLENK: Right. But I guess at

2 that point, the F1s may not have any endocrine response, per se,

3 but it is all because of what happened at F0. Does that make

4 sense? It is the maternal effects that happened at F0 that

5 could lend themselves to something that is not really there, I

6 guess.

7 DR. RODNEY JOHNSON: Perhaps. I guess I am

8 not sure. 

9 DR. MARTHA SANDY: Dr. Schlenk, I think maybe

10 you are bordering a little bit into getting into the charge

11 question.

12 DR. DANIEL SCHLENK: No, I am not.

13 DR. MARTHA SANDY: No, you are not, okay.

14 DR. DANIEL SCHLENK: I am asking if you think

15 that the maternal effects at F0 basically can manifest

16 themselves or -- the bottom line is, what you are saying is you

17 think the assay as it stands, MRT, would pick up any effects at

18 F0 in the F1s. That is what you are saying. Is that what I

19 hear?

20 DR. RODNEY JOHNSON: I think that if there is

21 maternal transfer of contaminant of chemical or maternal factors

22 that are caused by exposures in F0 that they will be expressed

23 in F1 and they will see them in F1.

24 DR. DANIEL SCHLENK: Okay. And then last,

25 this gets back to the Jonckheere-Terpstra for the stats. So can

26 you just help me out with that? That is a stepwise measure. So
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1 when I see a JT in the figures, what does that mean? Does that

2 mean that that point is different from the point preceding it or

3 it is different from -- 

4 DR. RODNEY JOHNSON: It is the one that is

5 statistically different from the controls.

6 DR. DANIEL SCHLENK: So it is the same thing

7 as a Dunnett. 

8 DR. RODNEY JOHNSON: Yes. It is a different

9 procedure, but the JT -- 

10 DR. DANIEL SCHLENK: And Dunnett's are

11 basically the same thing. 

12 DR. RODNEY JOHNSON: Right, right. Well

13 again, different procedure, and it is a trend test which means

14 that -- 

15 DR. DANIEL SCHLENK: Is it a trend for

16 dose-dependents is really what I am after.

17 DR. RODNEY JOHNSON: There are several

18 requirements for it to be a legitimate statistical test. One of

19 them is that the dose-response has to be monotonic. And then

20 secondly, what the procedure does if it is doing it is that you

21 look for the statistically significant dose, take off the high

22 dose, run it again and look for the lowest treatment that is

23 statistically different from the controls as you take off the

24 higher steps, and that becomes the treatment at which there are

25 significant difference from the control.

26 DR. DANIEL SCHLENK: So is it a test to
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1 determine dose-dependence?

2 DR. RODNEY JOHNSON: Yes. 

3 DR. DANIEL SCHLENK: It is?

4 DR. RODNEY JOHNSON: Yes. 

5 DR. DANIEL SCHLENK: Okay. I am done.

6 DR. MARTHA SANDY: Dr. Patino?

7 DR. REYNALDO PATINO: I have some follow-up

8 questions to the DMY or genetic sex determination of fish. You

9 have shown that you can have some, was it XY female in these

10 matches with temperatures, is that right?

11 DR. RODNEY JOHNSON: XX males.

12 DR. REYNALDO PATINO: And that that was

13 strain-dependent?

14 DR. RODNEY JOHNSON: The prevalence of that

15 within a population is strain-dependent.

16 DR. REYNALDO PATINO: Yes. Do you have

17 similar information just generally speaking on whether there may

18 be some strain differences in the background mismatches of XX

19 males and XY females using in any population or any strains of

20 medaka?

21 DR. RODNEY JOHNSON: There is a very low

22 prevalence -- in the studies that have been done with what would

23 be XY females, there are a few studies that have been done with

24 wild fishes in Japan where they have looked at the relationship

25 between DMY and the expressed gender, and they have shown that

26 in about -- it is very, very low prevalence. One in a thousand
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1 kinds of numbers that they get what would be XY females. 

2 And then what they have done is they have

3 sequenced the DMY gene, and they have shown that in those fishes

4 that express that phenotype that there is mutations in the DMY

5 gene preventing it from being expressed. So it is like a stop

6 code on them. And then those fish, instead of becoming males,

7 become females. So it is an XY female that is very rare, XX

8 males is not as rare and seems to show a strain dependency. 

9 And so the prevalence of XX males is

10 detectable. It is likely to show up once in a while in the

11 controls of an MRT study or an MMT study. Any study that is

12 looking for genotype/phenotype matching, it is not going to be

13 unusual. It is going to be low but it is not unusual to find XX

14 males.

15 DR. REYNALDO PATINO: Okay. In your study

16 with vinclozolin, if I read correctly the report, you did find

17 some XY female mismatches and you only found them in the

18 treatments, is that correct, not in the controls?

19 DR. RODNEY JOHNSON: I think they were also in

20 controls but I am not certain. I would have to do some checking

21 on that. I did not study that data as carefully because of some

22 reasons where -- I think the great majority of problems that we

23 found in the inter laboratory studies had to do with QA/QC

24 problems. So we tended to take that data with a major grain of

25 salt.

26 DR. REYNALDO PATINO: Yes. So I am more
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1 interested in the XY female mismatches, and I remember in the

2 report you only found them in the treatments. But please do

3 check and correct me if I am wrong. But my question is, you

4 know, the Japanese study -- they did a similar study. They did

5 not do the vinclozolin, did they?

6 DR. RODNEY JOHNSON: No. 

7 DR. REYNALDO PATINO: I guess you already

8 answered that question in part. But in the controls of any of

9 the studies, other than the inter-laboratory validations, the

10 incidence of XY females is very rare or rarely found?

11 DR. RODNEY JOHNSON: Can I ask a question? 

12 Have we ever seen that, XY females in our study? No. 

13 DR. REYNALDO PATINO: All right. Thank you.

14 DR. MARTHA SANDY: Dr. Johnson, can you tell

15 us who you were conversing with?

16 DR. RODNEY JOHNSON: Oh. I spoke with Kevin

17 Flynn. Kevin Flynn is the coauthor on the talk and Kevin has

18 done a gazillion genetic sexing studies compared to me. 

19 DR. REYNALDO PATINO: Thanks. Follow-up

20 questions. So in your assessment of endpoints in the EPA study,

21 you used a genotype to determine males/females and then do the

22 calculations on the endpoints, is that correct?

23 DR. RODNEY JOHNSON: Yes.

24 DR. REYNALDO PATINO: Yes. But if I read the

25 report correctly, in at least one of the laboratories because of

26 the problems of incorrect sexing genotypically, it appears that
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1 they used either the gonadal sex or the secondary sex.

2 DR. RODNEY JOHNSON: Right. 

3 DR. REYNALDO PATINO: I guess my question was

4 what is the rationale for using the secondary sex to sex the

5 fish as opposed to the gonadal sex and why either or?

6 DR. RODNEY JOHNSON: I think it was what was

7 most readily available to the laboratory to use and what seemed

8 to jive. 

9 I mean because there were so many problems

10 with the genetic sexing, there was an attempt to try and

11 understand the data with what would be concordance amongst the

12 phenotypic sexes that they had. And they had secondary sex and

13 they had gonadal sex, and then tried to see if they were

14 concordant. And then if they were, try and say well then that

15 probably was the genetic sex of the animals, especially if it

16 was a control. 

17 Now if you start asking that in the context of

18 the treatments, then you start getting into trouble because then

19 you have to start looking at population responses and the sample

20 sizes are not big enough to do that kind of analysis of effects

21 on the gender phenotype. It just simply does not have the power

22 to do that. So quite frankly, I think those parts of those

23 studies were flawed sufficiently that to take home a real

24 serious story of what did vinclozolin do to these fish is

25 probably not possible.

26 Except for the things that I did mention in my
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1 summary -- which was that there were sort of some consistencies

2 in some growth effects -- the one thing that we saw that looked

3 to be a treatment related effect was an effect on the secondary

4 sex characters of the male, and they did show some tendencies

5 towards having that same response. And we did not show any

6 other effect in our study either and we were pretty careful for

7 at least the exposure part of that study. I mean we obviously

8 were careful with the whole study. 

9 The most important thing I want to say is we

10 tried to be really careful with keeping our flow dynamics of our

11 exposure system relatively brisk so that we were testing parent

12 and not degradence because vinclozolin is a very, very quickly

13 degrading chemical in aqueous solution, and not only that, some

14 of the degradence are apparently endocrine-active. So in order

15 to test the parent and not the degradence, you have to try and

16 turn over your tank volumes very quickly. And we did measure

17 parent and degradence in our studies, and in the inter-lab

18 studies, the degradence were not measured. 

19 I mean some of the differences I cannot

20 account for. I do think there were similarities to say well, we

21 did kind of show somewhat the same result.

22 DR. REYNALDO PATINO: Yes. So that leads me

23 to another question on the test chemical used for the

24 inter-laboratory validation. Why vinclozolin? I ask that

25 question in regards to the avian studies, but why did you pick

26 that one for the study?
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1 DR. RODNEY JOHNSON: Well I think the primary

2 reason was to see the response in the fish study to an

3 antiandrogen, extensively an antiandrogen. If we could do it

4 again, I think we would certainly use a chemical that is much,

5 much more simple to deal with, especially as it relates to the

6 dynamics of chemistry. We just wouldn't pick a chemical like

7 that. At the same time, I understand the interest in looking

8 for another mode of action, but I do not think it was the right

9 place for that. So it was something that is a mistake, should

10 not have done that chemical.

11 DR. REYNALDO PATINO: Okay, thank you. And

12 one last simple question. In your results -- I think it was

13 trenbolone -- what is your interpretation of the finding that

14 you found that would use vitellogenin expression in females?

15 DR. MARTHA SANDY: Can you repeat the name of

16 the chemical?

17 DR. REYNALDO PATINO: Trenbolone. I was

18 trying to match the expected outcome versus what you found.

19 DR. RODNEY JOHNSON: I do not have at the top

20 of my head a good AOP explanation, and certainly I can converse

21 with my colleagues and we can talk about that, but I honestly do

22 not know for sure if that is derived from the data. 

23 I mentioned when I showed that slide that some

24 of that data has been shown in the Tier 1 test with the fish and

25 I honestly cannot tell you for sure whether that is the

26 rationale for some of those arrows on that or whether it was our
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1 test explicitly, so I just would have to talk to my people about

2 that.

3 DR. REYNALDO PATINO: Well thank you.

4 DR. MARTHA SANDY: Before I take anymore, I

5 just want to remind, it is very important that the Panel get a

6 chance to ask the clarifying questions, but remember it is 2:30

7 and we are a little behind. So if we can keep the questions and

8 the answers short, it would be great. Jim, I think you are

9 next.

10 DR. JAMES MCMANAMAN: I think the answer is it

11 acts as an androgen.

12 DR. RODNEY JOHNSON: Well it sure should. 

13 Good. 

14 DR. JAMES MCMANAMAN: So on slide 66 you show

15 a summary of several EDR compounds and prochloraz appears to be

16 the only steroidogenic inhibitor that you tested, and it appears

17 to be primarily acting as an aromatase inhibitor. 

18 So my question is, is this a concern for

19 evaluating effectiveness of the MMT versus the MRT assays as a

20 T2 because you really did not take that steroidogenesis

21 inhibitor all the way through the F2s and you did not look at

22 its effectiveness as well as you did some of the anti-estrogens

23 or anti-androgens.

24 DR. RODNEY JOHNSON: I guess I do not know

25 what I would have expected at the end of an F2 with an aromatase

26 inhibitor. I certainly do think we got the kind of response
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1 that we would have expected out of an aromatase inhibitor in the

2 context of its effect on the pathology and effect on the

3 vitellogenin production. And it also did have effect on

4 fecundity. And it is not a real strong indicator of the mode of

5 action or the AOP, and I do not think the MRT or the MMT are

6 strong supporters of modes of action, per se.

7 I think they do a good job of identifying

8 estrogen agonist. I think they do a really good job of showing

9 androgen receptor agonist and I do not think we have a large

10 dataset to tell you how it behaves with other modes of action. 

11 And I just simply do not think we can use our data to give you

12 strong expectations of those types of modes of action.

13 DR. JAMES MCMANAMAN: That answer partially

14 addresses my question, but I was also thinking about compounds

15 that could have more of a global steroidogenic disrupting effect

16 rather than just an anti-aromatase.

17 DR. RODNEY JOHNSON: I do not know what they

18 might be. 

19 DR. JAMES MCMANAMAN: Maybe it would be assay

20 pick up, something like that; that is my question.

21 DR. RODNEY JOHNSON: Well I do think it would,

22 yes. Yes, I think it would.

23 DR. LESLIE TOUART: I think though that with

24 prochloraz, is it not just an aromatase inhibitor. It does

25 affect other enzymes within the steroidogenesis pathway, so

26 maybe not as much as the aromatase but there are some of the
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1 enzymes that are affected. So I think the context is that the

2 test method would still detect adverse consequences. The

3 specifics of what it has interfered or modulated through a given

4 AOP or steroidogenesis pathway may not be as elucidated but you

5 could still pick up the adverse effects. 

6 DR. JAMES MCMANAMAN: Well I was thinking

7 about multigenerations having an effect on F1 that could be

8 magnified at F2 if it had a larger global effect, but just

9 whether it has been considered or not.

10 DR. RODNEY JOHNSON: I do not know of any

11 evidence that I have read in the literature -- and certainly we

12 have not done it -- where we show exacerbation of effects in F2

13 compared to F1. 

14 DR. JAMES MCMANAMAN: Right. So the last

15 question is what pathology assays are you proposing, or is it

16 just standard histopath? And given the problems with getting

17 labs to do simple PCR assays correctly, what plans do you have

18 to train people to do the histopath assays?

19 DR. RODNEY JOHNSON: Well it actually turns

20 out that there are several histopathology labs in the US that do

21 a good jobs on fish, and they have been in fact part of sort of

22 our training programs already and they have been working with us

23 on guidance documents. And they have also been involved in the

24 OECD guidance documents that have been developed. So I think,

25 actually we are doing better with the pathology than we are with

26 some of the other parts of the tech transfer. So I think
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1 actually pathology tech transfer is reasonably on track.

2 DR. JAMES MCMANAMAN: Is there a biochemical

3 assay for patotoxicity that you could use, like in mammals use

4 ALT?

5 DR. RODNEY JOHNSON: Perhaps. 

6 DR. JAMES MCMANAMAN: I mean I am just

7 wondering if that might simplify it.

8 DR. RODNEY JOHNSON: Simplify it, yes.

9 DR. MARTHA SANDY: I think next we have Dr.

10 Christman and then Kullman.

11 DR. MARY CHRISTMAN: I will try to make this

12 quick. I want to ask questions about the statistics, so

13 StatCHARMMS. You sent out the information saying that the

14 one-way ANOVA had been corrected. Could you explain

15 specifically how it was corrected; I did not quite get that.

16 DR. RODNEY JOHNSON: Okay.

17 DR. MARY CHRISTMAN: I mean, to me there is

18 one of two ways to go. One is you simply average to the tank

19 and use those means; and the other ways is you added a random

20 effect for the nesting.

21 DR. RODNEY JOHNSON: And what we ended up

22 doing was we reran the statistics in another part of the module,

23 in fact, were ran it in the repeated measures module with one

24 measurement rather than a series of measurements. And then that

25 particular module incorporates the replication component and

26 there is a standard ANOVA with a Dunnett. So we did not correct
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1 that particular module.

2 Now I know that, instead -- I mean in other

3 words -- how do I say this? The repeated measures module of

4 StatCHARRMS takes the replicate information into account

5 properly, so we used that part of StatCHARRMS which has an ANOVA

6 which is an ANOVA, and instead of having repeated values, it was

7 one value.

8 DR. MARY CHRISTMAN: At one point in time.

9 DR. RODNEY JOHNSON: Yes, one point in time. 

10 And that was how that analysis was rerun.

11 DR. MARY CHRISTMAN: Okay. Which leads to the

12 question about the repeated measures analysis or the fecundity

13 and fertility. What was the covariant structure used for the

14 repeated observations on the same pair? 

15 DR. RODNEY JOHNSON: I cannot answer that. I

16 do not know.

17 DR. MARY CHRISTMAN: Okay. And finally, for

18 the power analysis, did you do pair-wise T tests or did you

19 actually do the power analysis based on the idea that it would

20 be a step-down procedure for the LOEC?

21 DR. RODNEY JOHNSON: It was based on the

22 step-down procedure. It used the repeated measures module of

23 StatCHARRMS procedure.

24 DR. MARY CHRISTMAN: No, not the repeated

25 measures but the testing of where the LOEC was, not the time

26 problem. Because a description that I saw in another study said
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1 that the power analysis was done based on a T test; and so I am

2 wondering whether this was that same power analysis or whether

3 this used the JT procedure and we would have had to have done a

4 Monte Carlo type analysis.

5 DR. RODNEY JOHNSON: And I will have to check

6 on that too.

7 DR. MARY CHRISTMAN: Okay, thank you.

8 DR. MARTHA SANDY: Dr. Kullman?

9 DR. SETH KULLMAN: Okay Rodney, last but not

10 least. So a couple of things concerning your QA/QC protocol in

11 the SOP in general. I think it has been alluded to that it

12 appeared that there were several aspects of the SOP that did not

13 appear to be present. And I was wondering if that might have

14 some relation to some of the variability of some of the data

15 that you saw. 

16 So for example, just simple PCR; I know you

17 have a protocol for sex determination. But also things like

18 vitellogenin, you know, the amount of RNA, the storage of the

19 liver, how the liver is. The RNA is extracted, primers used,

20 all those can have really large impacts on an output of a qPCR. 

21

22 Do you guys have an SOP for that? And I guess

23 the question sort of extends beyond that as well, is, are there

24 SOPs that you could establish similar guidance that you put

25 together for the histology that would sort of allow the

26 laboratories to sort of coordinate their processes in these
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1 endpoints?

2 DR. RODNEY JOHNSON: We have internal SOPs. 

3 We have not put them into the guidelines explicitly and we

4 certainly can. And obviously Kevin would be willing to and with

5 no problem, absolutely.

6 DR. SETH KULLMAN: Yes. I mean the simple

7 thing is primary design for vitellogenin, so it is it not clear

8 if you are going for vitellogenin 1, vitellogenin 2, you know,

9 which vitellogenin you are after in that. 

10 Okay. So we have touched on a couple of these

11 but I want to get a little bit more clarification. You also

12 have shown that you are using a genetic gender to identify

13 breeding pairs, and I think that is a great way to go about

14 doing that. But it seems like, in many of the assays, that you

15 are not fully exploiting the capability of making ratios for sex

16 determination, so I did not see in any of the data numbers of

17 males, numbers of females, and certainly no ratios between how

18 well the genetic sex correlates to phenotypic sex. 

19 And it seems like it might be a very

20 interesting output to have where you are looking at your

21 secondary sex characteristics such a papillae or even a split in

22 the anal fin for the males when comparing that to your genetic

23 sex. Is there any reason why that type of comparison is not

24 done in the assay?

25 DR. RODNEY JOHNSON: I think every case, I

26 think -- if this true, I may be wrong -- but the gender
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1 phenotype as expressed by the gonad has been, I think in all

2 cases, the same as the gender phenotype expressed by the anal

3 fin. So it does not give you an independent different answer

4 than comparing the outcome of the gonad phenotype with the

5 genotype.

6 DR. SETH KULLMAN: Okay. In the introduction

7 of the ISR, you talk about organizational versus activational

8 effects. Can you clarify that a little bit more on what exactly

9 do you mean about activational versus organizational and how the

10 modifications in the MRT might sort of in essence not allow you

11 to really make those determinations for those differences?

12 DR. RODNEY JOHNSON: Well I think in general

13 the interpretation of activational versus organizational effects

14 is generally thought of in the context of a developmental effect

15 being what we would consider an organizational effect as

16 compared to activation where we are primarily talking about the

17 endocrine functions that lead towards the development of actual

18 reproductive process in behavior and the outcome of production

19 of viable embryos. 

20 So think of that as being primarily an

21 activational process. It is clear though that those

22 distinctions are somewhat fuzzy. I mean to imply that

23 activation does not result in some kind of an organizational

24 effect in some tissue I think is naive. 

25 But I think, in general, the idea of being

26 that there is sort of a developmental effect, which we think of
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1 as organizational, activational being sort of those kinds of

2 events that are occurring later on in development that lead to

3 the actual successful reproduction. 

4 Like for instance, the Tier 1 fathead minnow

5 reproductive test primarily is measuring effects on activation,

6 for the most part, because those animals that are being used in

7 that study have already developed all of their gonadal

8 structures, all of their secondary sex characters. Everything

9 about them that makes the male and female has already happened

10 before they start the study. So when you treat those fish with

11 a chemical to try and look for an impact, what you are really

12 looking for is what I think would be an activational effect. 

13 When we go into the MMT or the MRT, what we

14 are doing is measuring both. We are literally looking for

15 effects on embryo development and early stage development on

16 gonad structure, gonad function, and ultimately its capacity to

17 be activated also, and of course brain involvements through that

18 whole process. 

19 So to some extent, I think it is simplistic to

20 say we are studying organization and activation, and then

21 instead it is sort of a continuum; and in the MRT and in the MMT

22 what you are really doing is looking at the whole progression of

23 development in the context of reproductive competence.

24 DR. SETH KULLMAN: Right, I agree with that. 

25 And I am curious though, if you are looking at for

26 organizational, why you do not include any sort of developmental
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1 checkpoints within that.

2 DR. RODNEY JOHNSON: Right. I mean we truly

3 are looking for what would be an apical outcome of ostensibly an

4 organizational process that is disrupted. Yes. 

5 DR. SETH KULLMAN: Okay, let's see, just a

6 few more things. Go to slide 76, if you will. It is your

7 illustration of the new MRT protocol. Okay. 

8 So my question here is, if you look at the

9 nine-week in the F1 generation, you are not conducting any

10 histopathology. And if you look at the 18-week of the same

11 animals, you are not conducting any vitellogenin. I am curious

12 of why you have not coordinated histopathology and vitellogenin.

13 So I imagine, in the F1 generation, having histopathology sort

14 of at the same time that the animals are vitellogenic might

15 produce a nice concordance of any potential effects on

16 vitellogenin with gonad morphology. 

17 And then vice-versa, you are doing

18 histopathology in the F1 at 18 weeks but you are not doing any

19 vitellogenin. I would imagine that if you coordinated both your

20 histopathology and your vitellogenin you are going to sort of

21 maximize your output for that data by correlating histopath in

22 the gonad and/or liver with vitellogenin production.

23 DR. RODNEY JOHNSON: I agree in concept. I

24 mean I do not disagree with you at all. What we have done is we

25 have sort of made a choice to correlate the pathology with the

26 fecundity endpoint rather than correlate the pathology directly
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1 with the biomarker vitellogenin endpoint. So as it truly is a

2 decision as to what would be the best use of the tissue because

3 of the fact that all of the pathology is done with all the

4 tissues in situ, so it is a whole animal histopathology

5 analysis.

6 And to do the vitellogenin, you remove some of

7 the liver or part of the liver, and we have done that. In fact,

8 that is what we have done at the nine-week sacrifice. We have

9 also taken some fish for vitellogenin and some fish for

10 histopathology. 

11 But we felt especially in the MRT where we

12 have reduced the histopathology effort to what we consider to be

13 a bare minimum, which is 12 animals of each gender per

14 treatment, and then tried to correlate it directly with the

15 reproductive output of those animals. So we know that this pair

16 had this reproductive output and this is the pathology

17 associated, we feel like we are better able to correlated

18 pathology with reproduction and essentially give it up, if you

19 will, for the biomarker endpoint of vitellogenin, which I do

20 believe is an important one, and I am not denying that.

21 DR. SETH KULLMAN: Yes. I actually do not

22 know if it needs to be exclusive. I mean we personally run

23 studies where we cut the liver in half and use half for histo

24 and half for RNA.

25 DR. RODNEY JOHNSON: We did. We have done

26 that too, but the difficulty has been with the histological
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1 method that we have chosen to use, which in this case, we used

2 to do a method where we did a transfer sort of a frontal type

3 sectioning with the sub-adult fish, which pretty much leaves you

4 working with the gonad and the kidney, and I did mention that.

5 Because we wanted to have comprehensive

6 histopathology across all possibilities of tissue, we are doing

7 sagittal sectioning now at the end of the reproductive endpoint,

8 and that allows us to get gonad, liver, pituitary, thyroid, all

9 the other tissues that may or may not be associated with an

10 explanation of responses. 

11 So it is a more comprehensive histopathology

12 in that it samples more tissues, fewer animals but animals that

13 we have better case data for. So I think it works in the end to

14 the advantage of the assay.

15 DR. MARTHA SANDY: I do not want to keep Dr.

16 Johnson here too long. I see we have two more people here, and

17 realize we are running an hour behind, so why don't we just move

18 up the table. So that is Dr. Scanes.

19 DR. COLIN SCANES: Continuing on the

20 histopath, a number of questions. Did you take sample in a

21 random way if you are looking at the liver or the gonads or was

22 this representative samples or how did you define -- what were

23 you sampling? 

24 DR. RODNEY JOHNSON: It varied between

25 studies. In most of the studies that we did at EPA, we took all

26 the samples we had. And on occasion, I think the Japanese
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1 subsampled, and they subsampled a prescribed number per

2 treatment of males and females and did some histopathology on

3 those.

4 DR. COLIN SCANES: I am thinking of the

5 sections. Did you do that in a systematic way or random way or

6 a judgment based?

7 DR. RODNEY JOHNSON: We do that in a judgment

8 way. The SOP is specific in the sense that you cut, for

9 instance, certain place into the specimen and then take four

10 sections, 200 microns apart and mount them on one slide and read

11 all slides, all sections. 

12 So it is a systematic with a not highly

13 specified start point, but it is a recommended approximation of

14 where to start. 

15 So the point, I think, is that if there is a

16 big animal or a small animal, there is some variance in that

17 it's like exactly what part of the gonad are you looking. But

18 we do step sections through the tissue and we start with

19 landmarks -- on the sagittal sectioning, we start with landmarks

20 within the animal. 

21 So on the medial margin of the eye is where

22 the first section comes, and we know that we are going to get

23 liver and spleen and the different tissues there; then take two

24 sections at the midline and they are 200 microns a part and then

25 another one at the other side of the lateral median of the other

26 eye, so there are five samples taken for every specimen at the
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1 adult stage.

2 DR. COLIN SCANES: And a follow-up to that;

3 and that is what is basically your thinking to do this simply by

4 a scoring system rather than say a morphometric analysis where

5 you could look at number of oocytes per unit area, for instance,

6 or number of vacuoles per unit area in the liver?

7 DR. RODNEY JOHNSON: The morphometric methods

8 are arduous in terms of time, and they are difficult to do

9 consistently across laboratories. It is possible and it is

10 possible to do it with image analysis help. I think, if we

11 review the methods with the pathologists, they tend to reject

12 those notions and would prefer to do -- Vicky will tell me I am

13 wrong maybe, I do not know. But all that to say, I think having

14 good references does a good job. 

15 DR. COLIN SCANES: On the other hand, you

16 could make the point that if you are using a morphometric

17 analysis, image analysis, you do not have to get into very

18 different ways of doing statistics. You have got a much more

19 normal distribution.

20 DR. RODNEY JOHNSON: Yes, I agree.

21 DR. COLIN SCANES: And finally, what is the

22 basis for the histopath doing this simply by hematoxylin and

23 eosin staining as opposed to say in situ or immunasotic

24 chemistry?

25 DR. RODNEY JOHNSON: The primary rationale is

26 that we do not have a good set of particular things that we
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1 would want to look for. I mean it is a very generalized but

2 powerful method to detect lots of general outcomes, but

3 obviously it does not give you the capacity to pursue a

4 particular mode of action in great detail. And it could

5 probably be done almost for sure, but in a sense that is out of

6 the range of the capacity of the test system to -- I mean it is

7 sort of out of its design to some extent. I agree with you

8 completely in the context of the research agenda and mode of

9 action, I agree. I agree, yeah.

10 DR. MARTHA SANDY: We will go to Dr. Dallas

11 Johnson and then Dr. Scott.

12 DR. DALLAS JOHNSON: On the same slide that is

13 showing up there on the screen right now, the words that you did

14 not talk about on the right, experimental design consist of

15 seven blocks, five blocks for chemical treatment and two blocks

16 for control treatments. And I have the feeling that you are

17 using blocks differently than I use blocks, and I wondered if I

18 just could have you clarify your use of the word blocks there.

19 DR. RODNEY JOHNSON: Almost for sure I am

20 using the words differently than you do and I probably should be

21 careful. So what I do mean? What I mean is that, essentially,

22 the experimental design is parsed out into seven blocks where

23 two of them -- and each block, if you will -- boy, I don't know

24 how to say this -- group of replicates, let's say, which are

25 multiples of six, so seven groups of replicates that are

26 multiples of six.
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1 DR. DALLAS JOHNSON: For each dose group?

2 DR. RODNEY JOHNSON: And there are six of

3 those allocated to each dose group, except the controls which

4 have 12 or two blocks. So if there is a block of six, it would

5 be six for the high treatment, six for the next one until you

6 get down to the controls, and then in that case it is two blocks

7 of six each or a total of 12.

8 DR. DALLAS JOHNSON: Okay, thank you. And

9 then on the next slide after that one, your experimental design

10 overview, I want to make sure I understand. So on the left you

11 have six here, but this would generally be 12, is that right?

12 DR. RODNEY JOHNSON: On the left side, it is

13 six, and it is six replicate pairs of fish, okay? F0; this is

14 describing what I called in the previous slide a block.

15 DR. DALLAS JOHNSON: Okay. So this is one

16 block?

17 DR. RODNEY JOHNSON: This is one block. I am

18 sorry. It is not clear enough. This is one block, one of 7.

19 DR. DALLAS JOHNSON: Okay. So there would be

20 seven of these. Two of them would be control and five would be

21 for each dose level?

22 DR. RODNEY JOHNSON: One for each dose level. 

23

24 DR. DALLAS JOHNSON: Okay. And then as I look

25 at this, you take all the eggs that are produced by the first

26 column. And do I understand that you are taking all of those
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1 eggs and you are putting them in -- I'll call it a basket, I am

2 sure it is not a basket -- you throw them in the basket and you

3 are mixing them all up, and then you are taking 20 eggs out and

4 putting them in this first little deal at the top and then 20 in

5 the next little deal and so on.

6 DR. RODNEY JOHNSON: Yes.

7 DR. DALLAS JOHNSON: And then they go in the

8 incubators then?

9 DR. RODNEY JOHNSON: Yes.

10 DR. DALLAS JOHNSON: And then they come out

11 with hatchlings. And then again you do the same thing. You put

12 all the hatchlings in a bigger basket.

13 DR. RODNEY JOHNSON: A bigger basket, right.

14 DR. DALLAS JOHNSON: And you mix them all up

15 again and then you again redistribute them. Okay. And is this

16 the type of thing that was done in the studies that are reported

17 in here, or is this just the thing that you are planning to do

18 in the future?

19 DR. RODNEY JOHNSON: This was done in most, if

20 not all the studies that we report, and it is what we recommend

21 for the future.

22 DR. DALLAS JOHNSON: Okay, thank you.

23 DR. MARTHA SANDY: Dr. Scott?

24 DR. GEOFFREY SCOTT: Yes. Slide 69, just a

25 quick comment and just want to make sure I understand where EPA

26 is coming from in terms of expectations on multiple lab testing
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1 for the vinclozolin. 

2 If we look at the fecundity and fertility

3 results there, the range of effects between the labs, the

4 variation appears to be somewhere between a factor of two or

5 three, somewhere in that range. 

6 And I guess I would just go back to a lot of

7 inter-lab calibration exercises with a lot less eloquent test

8 just looking at LC-50s. Seems like to me the range of variation

9 was three to four, four to five. 

10 And I guess what I am trying to find out is

11 how do you set your expectations on what you expect out of

12 inter-lab comparisons? And particularly, since you are looking

13 at a LOEC that is at the bottom of the curve towards the tail as

14 opposed to the mid point where, like on an LC-50 where you are

15 going to be in the center of the data. The point I am trying to

16 make is, it would seem there should be some value in going back

17 and looking -- and this is not just true for Medaka but for any

18 species -- historical inter-lab testing to set some of that. 

19 Are you all doing that, or can you enlighten us a little bit as

20 to what is your standard?

21 DR. RODNEY JOHNSON: I do not have very much

22 experience with inter-lab studies but -- all that to say, so I

23 have not, and perhaps our colleagues in OSCP have. So I do not

24 have an answer with respect to a standard to expect because it

25 is somewhat new to me. We can talk about that and try and come

26 up with some answers.
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1 DR. GEOFFREY SCOTT: I am only saying it could

2 be inherent to the species.

3 DR. RODNEY JOHNSON: Yes. 

4 DR. GEOFFREY SCOTT: And I am saying it could

5 be redundant across endpoints, so it may be worth looking back

6 at, was my suggestion to you to do as you said that.

7 And then the last one and you do not have to

8 look at the slide, this is really going back and looking at the

9 new proposed test. You mentioned your collaborations with the

10 Japanese in trying to do this concordant. I just was curious,

11 are they moving to modify their test or is this the first

12 attempt to modify the test? I just was kind of curious for

13 standardization.

14 DR. RODNEY JOHNSON: I sent them copies of the

15 ISR and asked them to comment and they did comment. And

16 basically what they said was that they agreed with some parts of

17 it and they were not sure about some other parts and that they

18 wanted to discuss it further. And the parts that are concerning

19 to them honestly was the replication structure in the sense that

20 they were concerned of the infrastructure required to run a

21 test, and they said it takes a large lab. And I agree in a

22 sense, but I also believe that there are labs in the U.S. that

23 can take this on and have in fact considered their

24 infrastructure in a way that they probably can take this on.

25 DR. GEOFFREY SCOTT: One last clarification. 

26 What were their comments regarding shortening the F2 endpoint? 
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1 I just was curious of that.

2 DR. RODNEY JOHNSON: They were circumspect on

3 that. They did not come down either way.

4 DR. GEOFFREY SCOTT: Fair enough, thank you.

5 DR. MARTHA SANDY: I would like to propose

6 that we move on. So we are going to take a 10-minute break. We

7 have two more presentations by EPA staff, so please be back at

8 3:15 by this clock. Thank you.

9 (Whereupon a break was taken)

10 DR. MARTHA SANDY: Okay. So as the Panel

11 takes their seats, I believe we have one comment or question

12 from Dr. Christman.

13 DR. MARY CHRISTMAN: Hi Rodney, this is Mary

14 Christman again. I just won't let StatCHARRMS go away.

15 The specific questions that I would like to

16 have answers to, because you had said you would look into some

17 things for me. 

18 In the one-way ANOVA, is the experimental unit

19 the individual with a random effect per tank to acknowledge the

20 clustering or is it in fact the individual observations are

21 averaged over the tank and then the tank becomes the

22 experimental unit with no random effect? So that is one

23 question. 

24 And then the second question was, in the

25 repeated measures design, what that means is if you have

26 multiple time points, you are observing the same data on the
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1 same experimental units through time. So what I would like to

2 know is how did they adjust for the covariance of the fact that

3 I am measuring it from the same pair of individuals? 

4 So if you can find out that covariance, how

5 that was accounted for, that would be great. And in the absence

6 of all that, I will take the SAS code, so thank you.

7 DR. MARTHA SANDY: Thank you. It is 3:20. I

8 believe today we are only going to accomplish running through

9 the last two presentations from the EPA. I think we will be

10 starting the public comments tomorrow morning. So that everyone

11 knows that. Unless we get surprised and move through this

12 really fast. So I would now like to turn it over to Dr. Degitz

13 where he will be talking about the larval amphibian growth and

14 development assay. Thank you.

15 DR. SIGMUND DEGITZ: Now this is not the first

16 time I have come to D.C. and followed Rodney's presentations. I

17 had one occasion I came to give a presentation which I never

18 gave. So I came all the way to Washington, D.C. to never give

19 that presentation because Rodney has consumed most of the day. 

20 So I am kind of use to this in trying to catch up and that sort

21 of stuff.

22 So what we are here to do is, I am going to do

23 a walk-thru the integrated summary report. There are a lot of

24 details in the slides. I wrote those slides such so that they

25 would be a guide to you in the absence of my narration so that

26 you could take them back to your hotel room, go through them. 
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1 And I hope I have provided enough information there that the

2 story is told completely. Thank goodness I did because now we

3 might be able to jump the rocks across the river and get their

4 quicker.

5 I will start out by saying the outline. We

6 are going to talk about rationale of the test, test species,

7 test duration, test overview. I will spend a majority of the

8 time talking about the inter-laboratory and then lessons

9 learned. You will never go through an inter-laboratory and not

10 learn things.

11 So the purpose of the test is to characterize

12 the adverse consequences of aquatic exposures in an amphibian

13 species. The intent is to cover development maturation. And

14 the initial intent was to cover reproduction, although that is

15 not currently possible given the state of science for these

16 frogs.

17 There is only one other test method that has

18 been validated for amphibians and that is the amphibian

19 metamorphosis assay, which was developed in Duluth about nine or

20 10 years ago and went through. We have heard some discussion

21 about that already, but there is no standardized test for

22 evaluating chronic effects of chemicals on amphibians.

23 So ideally, the test would be capable of

24 genetically sexing animals and capable of encompassing all the

25 period of development through reproduction. We will get into it

26 more. We fell short of that.
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1 So ideally, the test species should be robust

2 in a laboratory setting, capable of determining genetic sex and

3 must develop to maturity in a reasonable amount of time. Given

4 those criteria, we are limited to two amphibian species

5 currently in our minds. That is xenopus laevis and xenopus

6 tropicalis.

7 Both species are widely cultured now, although

8 the tropicalis has not been in the lab nowhere near as long as

9 the laevis; and laevis is considerably more robust in the

10 laboratory in general. 

11 Both species, we have the ability to

12 genetically sex both. One is with a W which is analogous to the

13 Medaka DMY. And then out of the Duluth lab we developed a

14 polymorphism, a marker that is linked to the female phenotype,

15 but unfortunately that came from a single individual female and

16 it was not possible to develop the strain up enough to be able

17 to transfer that strain broadly for testing, although we have

18 done that at this point.

19 Although they are closely related, the one

20 thing that is a very important difference is tropicalis reach

21 sexual maturity much sooner, so it would be the ideal species

22 over laevis, which is one and a half to two years. 

23 But given the lack of the animals, we were

24 forced to chose xenopus laevis to move forward into

25 inter-laboratory evaluation. So we had basically developed this

26 test with both species, but when it was identified that we would
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1 not have the animals, we made the move for laevis.

2 Test duration; due to the lack of ability to

3 measure fecundity in the laboratory, basically the husbandry and

4 the inability of these animals to reproduce in a laboratory

5 without intervention with hormone injections, we do not have the

6 luxury they do in fathead minnows in medaka. 

7 So then we move to the point where the intent

8 was to go to surrogate measures to look at sperm count, oocyte

9 numbers, things like that. We embarked on several studies; one

10 was published in 2009 by my Olmstead, and those endpoints were

11 incredibly variable. 

12 And it was clear to us after doing some

13 simulations that we could not build a study large enough to

14 robustly test toxicants, given the variability that existed in

15 these populations in the laboratory. 

16 To prove that to ourselves, we conducted our

17 40-week octylphenol study and basically, again, proved to

18 ourselves that there was very little value in running that type

19 of a study. And then as we have moved to laevis, we were

20 talking more in terms of 78-week study. I do not think anybody

21 really would want to do that study.

22 So since it is impractical to go to sexual

23 maturation, it was decided we would start with freshly

24 fertilized eggs or stage eight eggs just about as soon as you

25 can get them on test, given when they are laid and what you

26 would have to do to get them prepared to go on test, to when
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1 significant hallmarks of development have occurred in the

2 organism, superficially in the gonads.

3 So here I show you two images of cross

4 sections of an ovary. And on the left is a female gonad at

5 stage 66 which is just at the completion of metamorphosis. We

6 explored the possibility of ending the study here. Then we did

7 a developmental series study from zero through 12 weeks post

8 metamorphosis and here we are at 10 weeks post metamorphosis

9 with basically complete ovary development and pre-vitellogenic

10 oocytes.

11 And then the male, we have the male stage 66

12 testis on the left and the 10-week on the right. We have cystis

13 formation, spermatogonia, Sertoli cells, so development is

14 fairly well advanced.

15 Based on these studies, we concluded that the

16 stage 66 gonads were not differentiated enough for us to be able

17 to make confident histological analysis of perturbation of the

18 germs cells so we chose 10 weeks post, and then here is a

19 summary of things that we identified as having completed

20 development. 

21 I am going to pass over this because this

22 figure it much better. The previous slide gives you more of the

23 details, but the figure here -- so if I narrate this, on our

24 left, this is when the study is initiated. This study proceeds

25 for approximately 16 weeks. So at stage 62, the subset of

26 animals are sampled and measured for thyroid endpoints
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1 histology, T4 measurements and time two stage 62. And then

2 basically, these are rough numbers. It takes about another week

3 to two weeks to get to stage 66. 

4 So what we have done is we have added eight

5 weeks or 10 weeks onto 62, eight weeks onto post metamorphosis,

6 and this corresponds to those histological sections that I

7 showed you in terms of the development.

8 To remind everyone, this is the period of

9 thyroid hormone mediated development here. In the early phase,

10 we have gonad differentiation in this period, and then this is

11 post metamorphosis when the maturation process starts.

12 So here we have the endpoints. I have divided

13 it out. It is very important for us to have a suite of measures

14 of overt toxicity. Specifically if we are interested in

15 labeling chemicals an endocrine disruptors; we want to be

16 certain that that would be occurring at concentrations where

17 overt toxicity is not occurring. 

18 So for that, we basically observed the animals

19 daily, look for mortalities, look for signs of toxicity,

20 disease, those types of things. At stage 62, we make assessment

21 at growth. At test termination we also make an assessment of

22 growth. And then at test termination we also look at liver

23 somatic index, liver histology and kidney pathology.

24 So then we have thyroid endpoints, and these

25 are fairly straight-forward and I already mentioned them; time

26 to NF stage 62; thyroid histopathology and T4 measurements. And
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1 at the end of the test, in addition to gonad histopathology, we

2 gave a determination of genetic sex, compared that to the

3 determination of phenotypic sex as well as look at vitellogenin

4 levels.

5 So we conducted an inter-laboratory study

6 utilizing ourselves, the U.S. Army Center for Environmental

7 Health and Research. That was interagency agreement. 

8 IDEA is a consulting company in Japan that is

9 actually funded by the Japanese Ministry of Environment to do

10 this work. And then we contracted to Battelle who then

11 subcontracted to two laboratories, one was ABC and the other was

12 Fort Environmental Laboratories.

13 So I want to point out here that these

14 laboratories were provided a protocol. It was not quite as

15 complete as the protocol in the integrated summary report that

16 you have, although we did participate in a number of conference

17 calls with them and provided them all the protocols that you see

18 in there, the SOPs, as the process was unfolding. 

19 So as everyone already knows, all the

20 inter-laboratory data was analyzed by the EPA. There was an

21 error discovered and it was a way in which there was a

22 misunderstanding in how StatCHARRMS was handling the data input.

23

24 We had a replicate column. We had assigned

25 replicates; control 1, 2, whatever. It was our understanding

26 that StatCHARRMS was taking that and generating a tank mean and
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1 moving forward with it. We discovered that it was not. 

2 So what we did to solve this problem is we

3 generated tank means, taking into account the individuals per

4 tank. And the ANOVA was run weighted and the end was the tank. 

5 So that was the solution. So what happened was, we got to the

6 point of tank means and all of that prior to going into the

7 ANOVA module for StatCHARRMS. 

8 To give you a sense of the endpoints that may

9 have been impacted by this era, mainly growth, that would be

10 length and weight and LSI, time to stage 62 and vitellogenin

11 measurements. All these other measurements, the histopathology,

12 the thyroid hormone measurements were not impact by this

13 data-entry error.

14 So in general, there were only minor changes

15 from my perspective, and the reported LOECs in the original

16 integrated summary report. In most but not all cases, the

17 difference was we observed an increase in the LOEC. And in two

18 cases we observed a decrease, and in one other case we saw a

19 non-monatomic response popping up, but it did not have a

20 significant impact on the overall analysis of the dataset.

21 So here is the four test chemicals that we

22 tested, prochloraz, octylphenol, trenbolone. Rodney has

23 mentioned these as well as I believe Les as well. We tested in

24 collaboration with the Japanese a different chemical called

25 benzophenone-2. That was a very interesting chemical.

26 So this table is to try to give a sense of the
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1 scope of the inter-laboratory. So in the case of prochloraz, we

2 had four labs participating; octylphenol, three. In the case of

3 trenbolone, NBP-2 where only one lab ran the studies.

4 I guess the one point I will make is,

5 prochloraz is also known to be an antagonistic androgen

6 receptor, and in mammals it produces abnormalities in rat

7 reproductive tract that are consistent with an anti-androgenic

8 effect.

9 Okay. I am going to take a minute to talk

10 about this because all my tables are like this. I do not plan

11 to go through all this data, trying to show more patterns here

12 rather than data. 

13 So down here, the highlight, it says if it is

14 yellow it means it changed when we redid the statistical

15 analysis. And a dash line is -- did they come out on your copy,

16 because here we do not see them? There are dashes. All those

17 blank areas should have dashes and maybe I just cannot see them.

18 I cannot see them. Okay. 

19 So that meant it was not statistic in either

20 of the first analysis or the second analysis. And then there

21 are situations where we have highlighted a dash, and that means

22 that we had got a value in the first analysis which would have

23 been, in this case, 180. And if we go over to lab C and we look

24 at LSI, what we are saying here is in the reanalysis, we lost a

25 concentration that showed significance, so all the tables are

26 going to be oriented this way.
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1 The red arrows have nothing to do with

2 statistics but the directionality of the change. Did it go up

3 or did it go down? So a red arrow is down and a green arrow is

4 it went up.

5 Here, what we observed with prochloraz is --

6 and these are the growth parameters for the most part, larval as

7 well as juvenile males and females. And lab D showed an impact

8 on growth as well as lab B. And there was an impact on LSI in

9 lab A. And then lab C did not show very much. But what I will

10 talk about later in the presentation is that lab C had a number

11 of issues, we could say.

12 And then we here have, in all laboratories, we

13 saw effects on male and female kidney and liver. So basically

14 prochloraz was pretty toxic in these studies. 

15 And I think many of the studies that have been

16 published previously with prochloraz have not had a whole lot of

17 data that looked at overt toxicity. So some of these chemicals

18 that everyone is reporting the endocrine effects, they are not

19 looking at the underlying toxicity that is occurring as well. 

20 And I think that, as we do these studies, we are starting to

21 realize that some of these endocrine disruptors have their

22 endocrine activity about where they are also overtly toxic.

23 Thyroid endpoints, in this case we saw effect

24 on T4 and pathology, and we saw effects on pathology in three of

25 the four labs. And what that means is there was glandular

26 change consistent with the disruption of the thyroid axes.
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1 Here we are on the reproductive endpoints. 

2 And I guess this is a good a time as any to bring this up. It

3 is a charge question and we have pulled it out. Basically

4 vitellogenin is not present in males or females at this stage of

5 development because estrogen is not present at this stage of

6 development. So it is only an endpoint that we expect to be

7 observed when there is an estrogen involved. 

8 So then here we are going to the gonad

9 pathologies where pathologies reported across all four

10 laboratories for males and females. There were duct pathologies

11 reported in the males across all laboratories, although the LOEC

12 varied from lab to lab. And several labs showed duct changes in

13 females. 

14 This is an example of the high treatment. 

15 What I am showing you here is a control test that is on the top,

16 and I just showed these to give you an indication of how

17 dramatic these effects were. So these are the same treatment

18 level, and this is very severe. Probably the section is fraught

19 with artifact due to the germinal epithelium breaking down and

20 being filled with macrophages and infiltrate that is there to

21 try to repair the tissue.

22 So then we also tested octylphenol, and again,

23 I do not think octylphenol needs much of an introduction other

24 than to say it has probably been tested in every aquatic model

25 there is, at this point.

26 The one thing I guess would be noteworthy is
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1 Kloas et al. (1999) reported sex reversal with OP, and in that

2 he was using sex ratios. And we are fortunate enough now to

3 have the newly discovered sex determining gene and have the

4 ability to do genetic sex and do not have to rely on ratios. 

5 And we did a study in tropicalis where we could not reproduce

6 that, so just to point that out.

7 You know octylphenol was fairly -- and I do

8 not recall to what extent we pulled this out in the integrated

9 summary report -- octylphenol was fairly toxic to the organisms

10 during our organogenesis. 

11 So concentration, we tested 50. We tested 60

12 in a range-finder and the animals did not make it for longer

13 than five days. So this test concentration is much lower than

14 what you will commonly see tested in adult fish where those

15 organisms are not developing and are much more capable of

16 handling high test concentrations, so it had very little effects

17 on growth. 

18 Lab C, again, showed some effect on LSI but

19 they had some feeding issues. Their animals were really small

20 but we will talk about that later. Not really sure what to make

21 of lab C's finding when it comes to the growth parameters. Here

22 we did see a consistent impact on the female kidney in all three

23 laboratories. 

24 Here is more what we are looking at stage 62;

25 lab A, who I know did really good work found a thyroid effect in

26 all three parameters. Lab B and lab C did not. I do not know
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1 how to explain that. Lab C did see something but it was

2 actually an acceleration in the time to get to stage 62 as

3 opposed to a delay in the time to get -- I am sorry. I said

4 that wrong. They saw an acceleration of metamorphosis and we

5 saw a delay because green means it took more time to get there. 

6

7 So then T4, throughout these studies the

8 contractors were to make an effort to optimize procedures for

9 measuring T4. They attempted, were not successful. So as a

10 result we went back through -- we tend to use this ion coupled

11 plasma mass spect approach, but it does require a fairly

12 sophisticated piece of equipment. 

13 We went back to one of the commercially

14 available kits and developed an ethanol extraction procedure,

15 and I was even accused of ginning this up. Here is the two

16 different methods of analysis side-by-side. Granted it is on

17 the same samples but it is remarkably consistent. And this

18 method is now in the guidance document.

19 Reproductive endpoints; I guess the main

20 consistency here was, we saw an impact in the ducts of females

21 which is consistent with advancement, which would be an

22 estrogenic response. Here we summarized those effects and you

23 guys can take a look at that. I would never be able to read it

24 anyway. 

25 Okay. So then we turned our attention to

26 trenbolone and, again, that was only run in one laboratory. We
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1 chose this chemical because this chemical had been tested

2 previously in multiple fish species as well as tested by

3 ourselves in xenopus tropicalis. And the test concentrations

4 that we used were based on the test concentrations from the

5 Olmstead 2012 paper. 

6 So here we have observed some degree of

7 effects on male and female liver and kidney pathologies. And

8 here we are looking at thyroid endpoints. It was an observed

9 effect on thyroid gland pathology. Now the reproductive effects

10 were not terribly dramatic. What we had learned is that we had

11 to keep the trenbolone concentrations at 100 or less because in

12 the tropicalis study we killed the animals by advancing their

13 larynx development and shutting off their airway prior to

14 metamorphosis. So we held the concentration in the high to 100.

15

16 We saw some pathology in the male gonad. We

17 had duct pathology in the males. The most striking thing was

18 duct pathology in the females. 

19 So here is an oviduct. This is the duct that

20 will carry the oocyte from the ovary to the external

21 environment. And this is a female and this is a trenbolone

22 treated female. So this structure forms and then regresses to

23 this. And in this instance, trenbolone results in the

24 regression of this duct. So in this case there would be no

25 ability for an oocyte to pass to the external environment for

26 fertilization. 
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1 Benzophenone-2 may not be as well known. It

2 is a UV filter used in cosmetic suntan lotions, products of that

3 nature. It has been shown to be estrogenic as well as has been

4 shown to be a thyroid peroxidase inhibitor, in essence,

5 preventing the synthesis of thyroid hormone. 

6 We thought it was a unique opportunity to look

7 at two modes of action at once. And I will just go ahead and

8 say that this was done by our Japanese collaborators. They were

9 limited by the exposure systems that they had, so we allowed for

10 a modification to the experimental design. And as recommended

11 in the guideline, you have eight controls replicates. You have

12 four replicates for treatments with four treatments. Let me say

13 that again; eight control replicates. You have four replicates

14 per treatment at four treatments. In the case of the Japanese

15 study, they reduced the control from eight to four. And here

16 are the findings. 

17 So these are the test concentrations, 1.5, 3

18 and 6 milligrams per liter. And basically the high treatment

19 causes a significant amount of mortality at the larval phase,

20 and then an additional amount of mortality from the larval phase

21 to the juvenile phase, so that was a fairly hard-hitting test

22 concentration.

23 There were effects obviously at the high and

24 the growth parameters in both males and females. Yes, there

25 were effects on LSI in females.

26 In the case of liver pathologies, it is not
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1 surprising we saw effects on both the liver and kidney at

2 basically three and above. But in the case females, we saw

3 effects in the kidney at 1.5. I want to point out that that is

4 not necessarily overt toxicity. These kidneys were swimming in

5 vitellogenin with no place to put it because they did not have

6 vitellogenic oocytes. You will see later that the vitellogenin

7 levels are screaming high and it is not uncommon to see kidney

8 pathologies in fish when vitellogenin levels get elevated.

9 In this case, you will see that there was an

10 effect on Time to NF62 and there was dramatic effects on thyroid

11 gland pathology. 

12 Here are the thyroid glands of a control and

13 this is not a camera trick. So this cavity right here is where

14 they reside, so this is lower mg than this. But you see the

15 same cavity the glands have now basically taken it over, so this

16 is extreme hyperplasia.

17 In the case of vitellogenin, we saw an

18 increase at all the test concentrations. We see effects on male

19 gonads at all test concentrations. There were effects on duct

20 pathologies and likewise, there were effects on all the

21 reproductive endpoints in females.

22 At the 1.5 we saw some intersects. At the 3

23 and the 6, they were all completely sex-reversed and we could

24 make that determination based on genetic sex. And we also had

25 at the 1.5, we had both sex-reversal and intersex. 

26 So here is an example of control male testis
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1 right here, and this is an example of an intersex male testis. 

2 And you see potentially a lumen, you see oocytes but then there

3 is also cystis formation over here, so it is a mix-sexed. 

4 So what are the take-home messages? And you

5 are going to have to look at this. So this table attempts to

6 compare where there were consistencies observed across the

7 laboratories. I indicate here the number of laboratories that

8 participated in each of the studies, so any inter-laboratory

9 comparisons are only limited to prochloraz and octylphenol. 

10 In the case of prochloraz, we saw growth

11 effects in liver pathology and kidney pathology in all labs, so

12 the high concentration was fairly toxic. We saw thyroid effects

13 in three of the four labs and we saw reproductive effects in

14 three of the four labs. 

15 In terms of octylphenol, we had thyroid

16 effects and delays only in one laboratory and we had impacts on

17 vitellogenin induction in two laboratories. And in terms of

18 gonad and reproductive ducts, we had effects in all

19 laboratories. That is mainly what that table has been

20 constructed for.

21 So diet formulation and feeding rates have

22 been shown to be correlated with organismal growth and

23 development rates in the LAGDA. 

24 What we had was a laboratory that chose to

25 increase their feeding by 30 percent. So they participated in

26 both studies, prochloraz and octylphenol. 
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1 Prochloraz was their first study here, and

2 they fed the formulation and the quantity that we recommended. 

3 Here they were concerned about potential for early mortality

4 during earlier organigenesis and they added a few extra

5 organisms, I think it was five. And they increased their food

6 by 30 percent, and that increase in 30 percent resulted in it

7 doubling in the mean weight for their larvae. This is not that

8 uncommon. Feeding rates are more dictated by tank flow rates

9 than the number of organisms that are in the tank. We were not

10 consulted about this and actually it did not come to our

11 attention until the end. And then here is an overall mean at

12 the bottom of 1.

13 The other issue was that these animals also

14 reported a high frequency of scoliosis or bent-tail or spinal

15 curvature. We know from work in our laboratory that over-feeding

16 commonly produces this effect, so these are images of scoliosis

17 in severity.

18 I was asked to bring some attention to this

19 because the previous SAP, in terms of the AMA, recognized this

20 and brought attention to it, so I am here to kind of tell you

21 what I know. I know it is a controllable phenomenon. 

22 Now is this not laevis, it is tropicalis. Now

23 we have never done any work with laevis and scoliosis because we

24 do not get it. We have a diet, we use it and we do not get

25 scoliosis with xenopus laevis. 

26 With tropicalis, we get a lot of scoliosis,
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1 because that diet that worked for laevis did not work for

2 tropicalis. So we have developed a diet that works for

3 tropicalis as you can see here. But I want to make the point

4 that we have had some fairly high frequencies with certain types

5 of diets. It is a controllable phenomenon and it can be

6 controlled by diet.

7 We now have a diet for this species that

8 produces zero scoliosis and provides for good growth and

9 development. And the diet recommended for the LAGDA did not

10 produce scoliosis in our hands. To our knowledge, it only

11 produced scoliosis in one laboratory and that is when they

12 increased the feeding rate.

13 Lab C; remember I kept saying lab C? There is

14 a problem with lab C. This is the problem with lab C. They fed

15 their own formulation and that was against my insistence, but I

16 lost that argument. And if you see their mean weight -- I mean,

17 basically, in the octylphenol study, their juveniles were half

18 the size of the mean weights of everyone else's. We also saw

19 some pathologies in the livers that were unique for this

20 laboratory, and it is in the integrated summary report. We do

21 not know if this laboratory was fighting disease issues, and in

22 conjunction with diet was providing for some fairly poor growth

23 rates. And they also had the smallest juveniles as well.

24 So our take-home is this lab fed a feed

25 formulation -- its own -- and that it can be concluded that that

26 is inadequate, and that we recommend that anyone who performs
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1 this assay adhere fairly strictly to the feeding recommendations

2 that we have, because we do know that they are flow-rate

3 dependent, not necessarily individuals in the tank dependent,

4 which is kind of what you would think intuitively, the more

5 individuals, more food. 

6 The simple diet; Trout Starter,

7 algae/TetraFin, you probably feed your fish at home that; brine

8 shrimp, which is readily obtainable and easy to culture. It

9 takes 24 hours. 

10 Just to point out the level of detail at which

11 we provided in the protocol, we were telling them on a biweekly

12 basis how much to increase the food in this. So we have to

13 understand that -- and even in light of this level of guidance,

14 contract labs will give you what we got.

15 So lessons learned; overall, the vitellogenin

16 was not really measurable in the control males and females, and

17 it is questionable what was really going on in terms of the

18 prochloraz and the octylphenol studies, were those measures

19 actually real?

20 In the case of BP-2 there was no question that

21 there was clearcut induction of vitellogenin. Yes, so BP-2

22 shows you what could happen when you are actually working with

23 an estrogen.

24 Inconsistencies in pathology findings; I am

25 not going to read this directly. Because of contract situations

26 and resources, in the midst of the prochloraz study, the
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1 pathology contract was changed. And a two of the studies were

2 read by different -- so we had two studies read by one, two

3 studies read by the other, and there were inconsistencies in the

4 findings. 

5 I will be honest with you. The glass coming

6 out of one of the contract laboratories was in my opinion

7 inferior to the other laboratory and probably substandard. I am

8 not sure I could have read anything in those slides. 

9 So we have to ensure that -- and in the same

10 case with Rodney who keeps harping that it is going to be

11 implementation of these things that are going to be the

12 challenge to instill in these folks that they have to make thin

13 sections and we have to have diagnostic criteria. And Rodney's

14 group has developed some of these tools for fish and we are also

15 working on developing them for the frog as well. Hopefully we

16 can move through these types of inconsistencies we see.

17 Serum T4; the take-home on serum T4 is, in

18 terms of having inter-laboratory comparisons, it was a bust. 

19 The two contract labs did not have a successful technique for

20 measuring T4, and as a result, the only place that T4

21 measurements were made were at our laboratory. We used two

22 different techniques, one that we published on; the ICP-MS and

23 then the ethanol extraction. They were very comparable. And we

24 also know that that is very comparable to some work coming out

25 of Dow using MSMS analysis to measure T4. So the absolute

26 amounts at stage 62 are very similar across the three methods.
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1 So when the guidelines are adhered to, the

2 contributing laboratories were able to conduct the test and the

3 controlled performance was consistent across all three

4 laboratories. 

5 Stage 62 is optimal stage for evaluating

6 thyroid-related endpoints. And I guess this is a question. It

7 is not necessarily a charge question but it is a question that I

8 have, and that is, currently we have three thyroid endpoints. 

9 We have time to metamorphosis, pathology and T4 measurements. 

10 Given the robustness of the thyroid histopathology, do we really

11 need T4? That is a good question, I think.

12 Ten weeks post-stage 62; I think the gonads

13 were developed well enough and we got a very good indication of

14 chemical-induced abnormalities in the gonads. 

15 The ducts, I really had not paid all that much

16 attention to the ducts prior to this inter-laboratory. And then

17 it turns out that the ducts seem to be one of the more sensitive

18 endpoints in terms of their reproductive system. And you know

19 the ducts are an incredibly important structure given that

20 failure in their development basically would result in no

21 reproduction. 

22 And histopathology tended to be inconsistent

23 between the two contract laboratories that we utilized. And

24 again, we need to dig into why that was that way and provide

25 guidance that corrects that issue.

26 So control vitellogenin levels are below
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1 detection limits and measurable only when induction occurs. And

2 to be honest with you, I think that is okay. You may decide if

3 vitellogenin is useful or not, but do not let the fact that it

4 is not measurable in controls steer you away from the fact that

5 it could be valuable. And with that, I am done. I will take

6 questions.

7 DR. MARTHA SANDY: Thank you very much. Mary?

8 DR. MARY CHRISTMAN: To your very last slide,

9 the reason that you say you think it is okay is because of your

10 earlier statement that it should not be expressed.

11 DR. SIGMUND DEGITZ: Yes. 

12 DR. MARY CHRISTMAN: And therefore any

13 expression is evident.

14 DR. SIGMUND DEGITZ: Yes. Right. 

15 DR. MARY CHRISTMAN: Okay. I agree with you. 

16 I was just wondering.

17 DR. SIGMUND DEGITZ: Yes. We struggled with

18 this data in terms of statistics. We make an estimate, and then

19 that gives you a number that really is not real.

20 DR. MARY CHRISTMAN: You mean when it is below

21 the detection limit?

22 DR. SIGMUND DEGITZ: Yes. And I don't really

23 know how to handle when you have 10 individuals and eight are

24 not there and two are. You know, do we need statistics?

25 DR. MARY CHRISTMAN: Thank you.

26 DR. SIGMUND DEGITZ: Yes.
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1 DR. DALLAS JOHNSON: Yes, but we need

2 statisticians here. 

3 DR. MARTHA SANDY: Yeah. I have a follow-up

4 on that. So benzophenone-2, is that a strong estrogen agonist

5 or a weak or a moderate compared to estradiol? Can you give us

6 a sense?

7 DR. SIGMUND DEGITZ: I think if you look at

8 the test concentrations, you know we are pretty high up there. 

9 The Japanese -- the low is 1.5 milligrams per liter. Estradiol

10 you would test at about three orders of magnitude less than that

11 to produce a similar type of effect. Does that answer your

12 question?

13 DR. MARTHA SANDY: Yes, thank you. So Dr.

14 Scanes?

15 DR. COLIN SCANES: Can we flip to slide 125?

16 DR. SIGMUND DEGITZ: Thank you. You will have

17 to tell me when we get there.

18 DR. COLIN SCANES: The one with the oviduct. 

19 Yes. You talk about this as a histopathology endpoint. It is

20 either a score 2 or a 1. Well the one is very small. And if

21 you decided to actually use some quantitative measures, you

22 could get an area for the duct and you may have much finer

23 discrimination.

24 DR. SIGMUND DEGITZ: I agree, yes.

25 DR. COLIN SCANES: And I think the same

26 applies to the thyroid where if you simply look at the area of
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1 the thyroid gland, you are going to get some very -- as long as

2 you are systemically section, you are going to get much more

3 fine ways of looking at a potential endocrine disruption. Would

4 you like to comment on that?

5 DR. SIGMUND DEGITZ: Well for the sake of

6 responding, you know this staging system is something that is an

7 inherited thing. You know we have not put much time and effort

8 into thinking about a staging system that would have better

9 resolution as what you are saying, and I agree.

10 DR. COLIN SCANES: But you can simply take the

11 area of the oviduct here. 

12 DR. SIGMUND DEGITZ: You know and the oviduct

13 is probably amenable to that as well.

14 DR. COLIN SCANES: So it is the thyroid.

15 DR. SIGMUND DEGITZ: Well the problem with the

16 thyroid, the thyroid is shaped like a football in the frog. It

17 tends to be football shaped. And you get on the ends and the

18 diameter is much smaller.

19 DR. COLIN SCANES: But that is why systematic

20 sectioning comes in. And you could equally well look at the

21 size of the total area of the colloid.

22 DR. SIGMUND DEGITZ: Yes. I would hate to pay

23 the histo bill on that.

24 DR. COLIN SCANES: It might be done much more

25 with using images.

26 DR. SIGMUND DEGITZ: Yes, yes.
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1 DR. MARTHA SANDY: I see many hands. Vicki

2 Blazer?

3 DR. VICKI BLAZER: I just wanted some

4 clarification on the contract labs. So were they both actually

5 pathology contract labs?

6 DR. SIGMUND DEGITZ: Yes, they were.

7 DR. VICKI BLAZER: And you said that, in one

8 case the slides were really pale. So it wasn't necessarily the

9 pathologist? 

10 DR. SIGMUND DEGITZ: No. That is what I am

11 saying, is the glass was not very good. So we were provided

12 images of the sections. And if you have experience with this,

13 you would know when you are seeing nice fine detail and when you

14 are seeing basically a purple something. 

15 DR. VICKI BLAZER: So you read the slides.

16 DR. SIGMUND DEGITZ: I did not read the

17 slides. What I did was I reviewed images that we were provided

18 by the histopath contractor.

19 DR. VICKI BLAZER: Okay. Thanks.

20 DR. MARTHA SANDY: Dallas Johnson?

21 DR. DALLAS JOHNSON: Just a question. Both

22 you and the previous presenter talked about correcting the

23 analyses for the statistical program that you are using, but you

24 both corrected it different ways. 

25 DR. SIGMUND DEGITZ: Yes. 

26 DR. DALLAS JOHNSON: One of you corrected it
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1 using your repeated option; one of you corrected it by using

2 weights. 

3 DR. SIGMUND DEGITZ: Weights?

4 DR. DALLAS JOHNSON: Well I thought you said

5 you did a weighted analysis.

6 DR. SIGMUND DEGITZ: We did a weighted

7 analyses, yes. I think that the way we did it is appropriate.

8 DR. DALLAS JOHNSON: And that previous author

9 thinks the way they did it is appropriate? Or maybe you do not

10 want to speak for them.

11 DR. SIGMUND DEGITZ: I do not want to speak

12 for them, no. I think what we did was appropriate.

13 DR. MARTHA SANDY: Dr. Bentley?

14 DR. GEORGE BENTLEY: Yes. Just a quick

15 follow-up from Dr. Scanes' comment on the thyroid

16 histopathology; in measurement of the song control system areas

17 of the brain in the songbirds, you can routinely measure

18 strangely shape of the brain and measure them relative to the

19 rest of the brain, so you could quite easily measure the volume

20 of the thyroid gland, for example, relative to the rest of them

21 for that part of the body.

22 DR. SIGMUND DEGITZ: Okay.

23 DR. MARTHA SANDY: Dr. Propper?

24 DR. CATHERINE PROPPER: I will try not to

25 monopolize the conversation here, but with regards to the

26 imaging, I do agree. There is, in fact, some really new and
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1 cool technology where they can just slide slides by and get

2 images very quickly, and then the program automatically can

3 measure things like area of the individual slides and then

4 compile it into volume data, and it involves little or less,

5 let's say, human contact. 

6 So there are technologies out there that are

7 available and are being upgraded probably nearly on a daily

8 basis at this point to start getting some really nice histo data

9 that could be useful for a number of these studies.

10 And now I wanted to just ask for the T4 and

11 the vitellogenin assays, did you go into anymore debt with the

12 validation? Was there any parallelism to a standard done, and

13 did you do any evaluation of inter and intra-assay coefficients

14 of variation?

15 DR. SIGMUND DEGITZ: The vitellogenin actually

16 has already been published. That approach was published in the

17 Olmstead et al. It was not the '12 paper because that was

18 trenbolone. I think it was the '09 or '10 paper, so we covered

19 that there.

20 DR. CATHERINE PROPPER: Okay. 

21 DR. SIGMUND DEGITZ: The T4 is literally weeks

22 old and we are working through getting that nailed down, so no

23 other lab had an opportunity to use it. And we have been trying

24 to as quickly as we can run some studies to produce samples for

25 perturbations so that we can confirm its utility. The hard part

26 is in running the 21-say assays to get samples, so we are
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1 actually trying to validate it ourselves by running studies and

2 confirming that we get dose response.

3 DR. CATHERINE PROPPER: Okay. And it is not

4 so much dose response I am looking for but just even dilution of

5 your samples and see that it is running parallel to the curve.

6 DR. SIGMUND DEGITZ: Yes. 

7 DR. CATHERINE PROPPER: Yes. Okay. So that

8 is in progress?

9 DR. SIGMUND DEGITZ: Yes. Well in terms of

10 the T4, because of the ethanol perception, so you could have a

11 -- let me state this correctly. It is a matrix issue, is why

12 the contract labs could not make it work. 

13 As you move to the ethanol precipitation, you

14 take the matrix effect out of it. And we are testing to what

15 extent we do that by looking for a change in the slope with

16 these that occur. That is where you can see it.

17 DR. CATHERINE PROPPER: Okay. Thank you. And

18 then I have got a few others here. 

19 So this gets back to the stats. And for both

20 studies, I get a little concerned about just the loss of power

21 with going to the tank effects. And I am not a statistician so

22 please step in statisticians if I am going down a different path

23 or an invalid path. But is there a way in with those tank

24 effects can be taken into account without losing the power that

25 you get with the individual animals?

26 DR. SIGMUND DEGITZ: You did not get power
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1 with the individual animals; you were using an inappropriate

2 statistic. I mean you only get power when you use the same

3 statistic and get the result you want. 

4 DR. CATHERINE PROPPER: Neither one of us

5 might be wording this correctly.

6 DR. SIGMUND DEGITZ: Right. But the point is,

7 do not be mislead; there wasn't power in the first analysis. It

8 was an incorrect analysis. 

9 DR. CATHERINE PROPPER: Oh, that I understand,

10 so I do understand that. 

11 DR. SIGMUND DEGITZ: So I would not say that

12 we loss power; I think we found the right power.

13 DR. CATHERINE PROPPER: So I understand why

14 you have to include the tank effect in there. I do worry when

15 you start going down to -- and maybe it is because I do not

16 understand quite how you switched your stats when you start

17 going to the tank mean.

18 DR. SIGMUND DEGITZ: Okay. First off, we did

19 not want to do our statistics the way we did it. We made an

20 assumption that we were loading columns. The SAS code had been

21 written to take those and turn them into tank means. That had

22 not been done. So using the individuals as a treatment would

23 not be appropriate to run ANOVA that way, so we cannot even go

24 down that road of talking about what happened when we did do

25 that.

26 DR. CATHERINE PROPPER: Okay. All right. 
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1 Well I will go with that for the moment. I have a few other

2 questions but I will let other folks jump in while I organize my

3 thoughts. 

4 DR. MARTHA SANDY: So I have three other

5 people, I see their hands; Dr. Kullman, Scott and Denver. Dr.

6 Kullman?

7 DR. SETH KULLMAN: So just a couple of quick

8 questions. So if you look through the literature there is a

9 number of other sort of messenger RNAs or proteins that seem to

10 be mediated by the thyroid hormone receptor. So I am curious

11 why maybe those are not incorporated into the assay, you know,

12 things like looking at thyroid releasing hormone, thyroid

13 stimulating hormone, in particular TPO activity, as such. Those

14 are all well-known and well reported throughout the literature

15 to respond, and maybe pretty decent biomarkers for having

16 effect.

17 DR. SIGMUND DEGITZ: We do all that. So when

18 we do TPO assays we have approaches for measuring TSH. The

19 question becomes what is appropriate for contract testing and

20 what are we going to do with those endpoints in a risk

21 assessment if we have them. That really is what it comes down

22 to. 

23 I mean it probably would be best served to

24 hand that off to my colleagues to speak to but, you know, they

25 are still questioning whether we are going to risk assess off

26 the histopath, let alone gene expression.
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1 DR. SETH KULLMAN: But we tend to use

2 vitellogenin as a pretty prominent biomarker for endocrine

3 disrupting effects. I do not see a whole lot of difference

4 using mRNA expression using a vitellogenin versus an mRNA

5 expression of the transporter, like in one of the other thyroid

6 hormone biomarkers. 

7 DR. SIGMUND DEGITZ: It is a biomarker for --

8 agreed, it tends not to be the kind of endpoint that we would

9 make regulatory decisions on.

10 DR. LESLIE TOUART: Just to try to explain or

11 to follow-on. I mean biomarker, a physiological bio-chemical,

12 even pathological type effects, are not on their own a regulated

13 endpoint in terms of something that we would determine to be an

14 adverse consequence needing some type of regulatory

15 intervention. 

16 But the context is that these things are

17 indicative of what may be adverse consequences. So following

18 adverse outcome pathways and these things, one might determine

19 that the end result of a biomarker like Vtg may give an early

20 indication that reproduction may be affected later. And if your

21 concentration response study that you have demonstrates a

22 reproductive effect at a higher concentration and a Vtg change

23 at a lower concentration but concomitant at that higher

24 concentration, one might then start to determine is the level of

25 the departure a concern level, should that be based on that

26 lower level; recognizing on the ecological side which is
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1 different from the human health side where health of organisms

2 are a driver. On the eco side, it is more population level

3 effects.

4 So unless we identify that particular adverse

5 effect may have some consequence to population or to maybe like

6 an endangered species in terms of its survival or ability to

7 continue, those could be questions.

8 But the risk assessment component part, it is

9 a little bit further out of line. And I think Sig's comment

10 that what is practical from contract laboratory, what the cost

11 effectiveness of some of these measures, these are some things

12 to be considered. But I think any advice that you have or

13 others on the Panel in terms of endpoints that we might want to

14 try to add in without toppling the Christmas tree, so to speak,

15 those are things to be considered, but again, recognizing that

16 with each of these the interpretations might become a little bit

17 more complex.

18 DR. SETH KULLMAN: I hear what you are saying.

19 I do not see a true distinction between the vitellogenin as a

20 marker of reproduction effects other than its estrogenic

21 mediator. 

22 In the same way, I sort of do not see these as

23 possibly impacting reproduction other than that it is a thyroid

24 effect and it will impact growth and development, so on and so

25 forth. 

26 So I am having a hard time in making the
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1 distinction of why use a biomarker like vitellogenin in one

2 instance and maybe not a biomarker in another instance. 

3 Certainly do not have to discuss it in detail now but it is

4 something that should be discussed.

5 DR. SIGMUND DEGITZ: Well I could give you a

6 very quick answer to that, and that is, in terms of the thyroid,

7 we have so much better diagnostic capabilities there. We have

8 T4 measurement. We have glandular change. I mean we are really

9 strong there. It is actually in the reproductive end where we

10 are a little softer. So that is why you are reaching to

11 vitellogenin to pull it in, in terms of a weight of evidence.

12 DR. MARTHA SANDY: Next we have Dr. Scott.

13 DR. GEOFFREY SCOTT: On slide 148 you proposed

14 a question about T4 and I just was trying to come back and

15 understand that a little bit. In the Tier 1 screen with the

16 amphibian assay, we measured T4, right?

17 DR. SIGMUND DEGITZ: No, we do not.

18 DR. GEOFFREY SCOTT: We do not?

19 DR. SIGMUND DEGITZ: We do not.

20 DR. GEOFFREY SCOTT: Okay. I was just trying

21 to remind myself.

22 DR. SIGMUND DEGITZ: At the time that that

23 went forward, we did not have a readily transferrable

24 methodology. And plus, that assay stops a little sooner so we

25 have not reached the peek that works to our advantage in this

26 case to make the measurement.
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1 DR. GEOFFREY SCOTT: Okay. Well I am glad you

2 clarified that. I could not remember. I knew we were

3 discussing that in the fish test the last time and I could not

4 remember which was which.

5 I would only comment on the T4. That is a

6 universally measured metric in humans and (inaudible). 

7 And if we are going down the adverse outcome

8 pathway, I will just use an example. A panel or two back where

9 we had an example with triclosan and you all showed how we could

10 go down that adverse outcome pathway. You all were using T4, I

11 believe. I am pretty sure it was T4. 

12 We measure that in dolphins. And because we

13 had triclosan measurements, we could actually go back and look

14 at that in our dolphins. And I really think, where we have

15 these readily established methods where we can do that, that are

16 good reliable methods, that is going to help us where we do have

17 some benchmarks. And since you are asking the question, at

18 least I just thought I would mention our experience of being

19 able to piggy-back off of where you all were going in other

20 species.

21 The other question I had related to

22 histopathology, and not so much the sectioning and the

23 processing of samples but in the reading of samples. In the

24 current guidelines, are there positive controls sent to folks

25 reading the slides or some set of slides where they should be

26 telling you the same thing?
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1 DR. SIGMUND DEGITZ: Not in the current

2 guideline. But as Rodney mentioned in his presentation, we are

3 building a web-based tool, so all this glass feeds that

4 web-based tool at this point.

5 DR. GEOFFREY SCOTT: Could we just say it

6 would be good to have those benchmarks. 

7 DR. SIGMUND DEGITZ: Here is what an estrogen

8 looks like and here is what an androgen looks like, I agree. 

9 DR. GEOFFREY SCOTT: I just thought I would

10 make that comment.

11 DR. MARTHA SANDY: Okay. And now we will hear

12 from Dr. Touart.

13 DR. LESLIE TOUART: And this is just kind of a

14 follow-up on the point that Dr. Scott made.

15 We are planning to have a histopathology

16 workshop similar to what we had done for the Tier 1 in frogs to

17 go over some of these aspects to bring on some of the work being

18 done to try to develop these web-based guidances, but also to

19 try to look a little bit more in terms of the histo techniques

20 to be utilized as well as the pathologist themselves to be

21 utilized. And this would include pathologists that had been

22 part of the validation studies in terms of the readings, but

23 others that would have expertise on the taxa that we would be

24 looking at, which would include fish, frogs and birds. And the

25 tentative plans for this would be later this summer or early

26 fall, but actual dates have not been set just yet.
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1 DR. GEOFFREY SCOTT: Well very good. You all

2 are thinking in the right way. Thank you.

3 DR. MARTHA SANDY: And now Dr. Denver.

4 DR. ROBERT DENVER: Say, were you also unable

5 to measure T4 using the ELISA before ethanol extraction in your

6 lab? I am just wondering.

7 DR. SIGMUND DEGITZ: Yes, we could measure it,

8 but what was happening to us, it was incredibly inconsistent. 

9 So the CVs were huge. And by precipitating out a lot of protein

10 during that process, we were able to get the values to be much

11 more reproducible. 

12 So we basically got the same results that the

13 contract labs did when we tried to use the kit. We went through

14 a lot of perturbations to clean that approach up and it turned

15 out the first thing we tried was ethanol; went on to a lot of

16 other things but that ethanol was still the best.

17 DR. ROBERT DENVER: And given the difficulties

18 of obtaining blood from tadpoles, and also the variability that

19 you see at stage 62, why don't you just measure it in the

20 juvenile frogs? I mean you can get plenty of serum from them. 

21 They have -- xenopus compared to a lot of -- and urines has

22 detectible T4.

23 DR. SIGMUND DEGITZ: You know, in our hands --

24 maybe in your hands it is different -- we do not detect T4 in

25 xenopus blood of juveniles.

26 DR. ROBERT DENVER: Really?
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1 DR. SIGMUND DEGITZ: No. So we have used the

2 ICP-MS method pretty extensively with that. Yes. 

3 DR. ROBERT DENVER: I imagine you would in

4 those thyroid hyperplasic animals, possibly.

5 DR. SIGMUND DEGITZ: And then the other reason

6 to get the T4 measure at stage 62 is for consistency, in terms

7 of taking all three of those and considering them

8 simultaneously. But you are right. 

9 DR. ROBERT DENVER: Right. 

10 DR. SIGMUND DEGITZ: But again, and something

11 very interesting, I mean we have not found in trops or laevis

12 post metamorphosis; it is below our detection limits and we have

13 some fairly low detection limits with the ICP-MS. 

14 DR. ROBERT DENVER: Yes, we have used

15 radioimmunoassay in adult xenopus but not juvenile.

16 DR. SIGMUND DEGITZ: Yes. 

17 DR. ROBERT DENVER: The other question I have

18 is about replication. And is four replicate tanks sufficient

19 given that you know you can lose tanks pretty easily if it is

20 diseased.

21 DR. SIGMUND DEGITZ: Yes. Yes. 

22 DR. ROBERT DENVER: And a related question is

23 why the dis-balance in the design? Why eight control and why

24 four?

25 DR. SIGMUND DEGITZ: Well I will take that

26 question first. And that is, if you lose a control you lose a
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1 study, in terms of everybody, all of your doses. That is why we

2 are balanced on the controls. 

3 You know you lose a middle of the dose

4 response, you lose one tank and you are left with three, yeah,

5 your power is reduced but you have not lost the others. So that

6 is why the control is stat. We did some power analysis -- I do

7 not have it with me -- and we looked -- and, you know, you

8 really have to start to get those replicates up pretty high

9 before you are gaining a whole lot. 

10 Now I understand your idea about insurance

11 policy, is that can you work with three -- you know, and four is

12 okay. You know, if you cannot work with three -- and we did

13 some looking at that and it is really a compromise between how

14 complex the exposure system gets because these are not the small

15 Medakas, you know, where you have got multiple animals per tank.

16 So that is why we are weighted for the controls. And you could

17 always have more replicates but how doable is it? Does that

18 answer your question? I do not mean to skirt around it. I mean

19 I am trying to answer it.

20 DR. ROBERT DENVER: Yes. I mean you said that

21 you have sufficient data to evaluate the power in that four is

22 sufficient.

23 DR. SIGMUND DEGITZ: Yes. What we have found

24 is it took quite a bit of increase to get you -- you did not

25 make gains with one or two replicates, which is what we are

26 talking about. So at this point we are talking 28 tanks in
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1 these studies. Then the next jump would be 32, 36.

2 DR. ROBERT DENVER: Yes. I just have one last

3 question.

4 DR. SIGMUND DEGITZ: Okay. 

5 DR. ROBERT DENVER: Why can't the EPA hold

6 contract labs accountable for following protocol? I am just

7 wondering who has that authority?

8 DR. SIGMUND DEGITZ: These guys will answer

9 that question. 

10 DR. LESLIE TOUART: I can try to answer that a

11 little bit. And within the contract framework, there is some

12 accountability that is there and there is performance reviews

13 that are done of contractors when things come in. 

14 We try to hold them, but with any protocol, as

15 we found, individual labs sometimes like to do things and

16 interpret thing in their own way, in a sense, and it is just the

17 reality of how things are dealt with. What we do not have is as

18 big a hammer to use against them if they are starting to color

19 outside the lines or something like that. It is even more

20 difficult when we deal with some of our cooperators where we

21 have an interagency agreement, another federal partner or other

22 collaborator like the Japanese. 

23 We try to work and communicate what is

24 important about assays, but in terms of the ability that we have

25 to actually control a contractor to follow everything is a

26 little difficult. We get what we get. And that is part of the
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1 validation exercise itself, because the contract labs that we

2 are using are the contract labs that are likely to be the ones

3 foremost in conducting these tests in routine basis.

4 So the results that we see are likely the

5 things that they would be doing and they point out where we need

6 to modify our guidance and specifications in terms of the test

7 methodologies that we give them in terms of the test guidelines

8 and specifics; what things we feel are really must, what things

9 are more recommended kind of things. So it is a little

10 difficult and that is the best I can say.

11 DR. STEVEN BRADBURY: As Les was saying, and

12 Sig and Rodney, as we are going through the development of the

13 protocols, labs are trying it out. I think some of the

14 reflections are some things that we need to describe perhaps

15 more clearly or directly. This is a really key critical

16 component.

17 But indirectly speaking as we go through the

18 test guidelines generically, sort of the test requirements that

19 come in, the register test that we already got in play, when we

20 lay out the test guidelines, we lay out what we expect to see in

21 terms of certain performance criteria to be met.

22 And if a study comes in and it does not have

23 certain quality characteristics, we will not accept the study

24 and we will require the study to be rerun. And we try to be

25 very clear about what are the decision points and we try to make

26 sure we are looking at all the information to make sure we are
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1 just not doing check box because a lot of animals are being

2 scarified to do these studies and we want to make sure we're

3 getting all the information that we can.

4 I think the incentive, once you start moving

5 into a regulatory program is clarity over what it takes to have

6 an acceptable study. You have to have acceptable data to

7 maintain the registration. If you do not provide any data that

8 meets our quality criteria, you will have to do it again, and

9 there is a cost to doing it again. 

10 So I think you are just sort of seeing the

11 evolution of what we are learning about, development of a method

12 versus documenting what it takes to have a method work. And it

13 is reasonable that that method can work if you follow key

14 protocols properly, and then that is the expectations for the

15 data when it comes in to actually make our regulatory

16 determinations.

17 DR. MARTHA SANDY: And Dr. Touart? 

18 DR. LESLIE TOUART: Again, just a quick

19 follow-up. But one of the things with the test guidelines that

20 we have before test methods, you know, we have the proposed test

21 guidelines and there are some performance criteria that are

22 listed within that.

23 So I would really ask the Panel to look at

24 those and determine should we have some more stringent criteria

25 listed or should they be presented in a different way because

26 there are some difficulties as Dr. Bradbury pointed out, in
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1 terms of our ability to accept studies in terms of the quality

2 factors that we think are appropriate. 

3 So we would hopefully be able to look at those

4 and provide some comments in terms of, you know, are these too

5 laxed or do we need to specify some more. And it really is part

6 of the laboratory exercises that we do. These are to allow us

7 to test to see what things really matter and what things that

8 seem to be the ability to have a little bit more flexibility.

9 DR. MARTHA SANDY: Dr. Perkins?

10 DR. EDWARD PERKINS: Sig, can you comment on

11 the reproducibility in these other labs and the other obviously

12 histopathology/pathology problems? It seems like there was a

13 lot of variability in growth endpoints and a lot of other stuff

14 like that. I guess, is this really reproducible?

15 DR. SIGMUND DEGITZ: Well I guess that is the

16 question that we are here to talk about at the end, that is the

17 take-home. 

18 I think in terms of the contract laboratories

19 electing to sort of go on their own in terms of feeding, like

20 the one laboratory that increased their feeding by 30 percent

21 and did not tell us, or the other laboratory that uses a

22 different diet formulation. 

23 If you look at some of those things, you know,

24 when you start to manipulate diet and you start to affect growth

25 to an extent that you see being effected, that is also

26 associated with the devil is in the details; but a fairly
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1 protracted period of time that it takes that group of

2 individuals to reach stage 62. So now we start to see it all

3 wrinkling through all of our endpoints then. You know,

4 something that starts at feed. You know, I guess if you can

5 read and follow directions, I think that this protocol is

6 reproducible. 

7 DR. EDWARD PERKINS: But you did not have that

8 requirement from the beginning?

9 DR. SIGMUND DEGITZ: We forgot to state it, I

10 guess.

11 You know, the problem is that the labs -- one

12 of the labs was very experienced with frogs, and it was probably

13 the worse performance we saw. The other lab was fairly new to

14 it and they did pretty darn good, except for the extra feed by

15 30 percent. Aside from that, their first study was really nice.

16 But what ends up happening is -- so you

17 increase feed relative to what everyone else does, and all of a

18 sudden those animals are on a different growth rate. That

19 changes your sensitivity to be able to pick up the chemicals

20 effect on growth. So if you are producing excessive growth, you

21 are going to be probably way more sensitive than another

22 laboratory that is not producing excessive growth. Does that

23 make sense?

24 DR. EDWARD PERKINS: No, it does. So as a

25 result of what you are saying, do you think you need to repeat

26 the whole inter-laboratory studies with more stringent criteria
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1 for actual people that read the protocols to really show this

2 reproducibility?

3 DR. SIGMUND DEGITZ: I guess that is what the

4 SAP is here to judge. You know, it is reproducible in our

5 hands, and reproducible in some of the people we work with. The

6 key is that more contact is better. The folks that we had

7 collaborations with were much more successful than the contract

8 laboratories.

9 DR. MARTHA SANDY: Okay. I have seen two

10 other hands, Dr. Propper, then Dr. Klaine and then Dr. Scott. 

11 DR. CATHERINE PROPPER: So along these lines

12 then, because the variability was there -- and I was actually

13 surprised because there were places where there was more

14 consistency than I would have expected, but there is variability

15 both in overall outcome and especially in LOECs. How do you

16 then interpret your data to actually set regulatory LOECs? And

17 maybe this is a question for -- 

18 DR. MARTHA SANDY: I am sorry. We are

19 supposed to be asking questions of clarification.

20 DR. CATHERINE PROPPER: Clarification? Okay. 

21 How do you interpret the different LOECs?

22 DR. SIGMUND DEGITZ: Okay, start over. I was

23 following you and I was anticipating you and then we got

24 changed.

25 DR. CATHERINE PROPPER: Right; and I apologize

26 I think I went beyond the scope of the question period. The
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1 different LOECs that you get, do you find that that makes

2 interpretation also the inter-laboratory variation tricky?

3 DR. SIGMUND DEGITZ: Well it depends on to

4 what degree you are making that interpretation. You know, when

5 you have multiple endpoints such as -- okay. So if you have

6 four labs and one is the odd lab out, it is a pretty easy

7 situation to be in, right? You have got two labs and which one

8 is the odd lab out? And in regulatory context, most of the time

9 there is one lab, one study, and we do not know what the

10 variability is. And beyond that, I do not know what else to

11 say. 

12 DR. MARTHA SANDY: Mary Manibusan?

13 MS. MARY MANIBUSAN: So just for clarification

14 because it falls into a regulatory domain, typically as Dr.

15 Degitz had indicated, the Agency received one study, and that

16 one study will be judged based on its performance. 

17 So those proficiency criteria, as Dr. Touart

18 had indicated is going to be really critical to nail down and be

19 very specific in order to judge the quality of that study. When

20 that quality of that study meets our acceptance for regulatory

21 determination, that one study will give us, perhaps, the point

22 of departure we need for risk assessment. You will not,

23 usually, typically have multiple labs running the same study for

24 submission to an agency. But again, the quality of that study,

25 as we have learned through our inter-laboratory evaluations,

26 will help us nail down that test guideline so that it speaks to
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1 the conductor of that study so they can demonstrate the

2 performance that we need. And we will not be in a situation

3 where we have such a wide variability in our evaluation in our

4 LOECs. So that is our goal here. 

5 And so it is important to split out the

6 difference between laboratory performance that we are seeing in

7 the inter-lab. And quite frankly you are right, we have picked

8 a variety of labs from inexperienced all the way to experienced

9 laboratories, but that information is very important to the

10 Agency to, again, helping us to achieve a test guideline that

11 can speak to a range of laboratories so that they can all begin

12 to submit acceptable test guideline studies.

13 DR. MARTHA SANDY: Dr. Klaine?

14 DR. STEPHEN KLAINE: So following up on that. 

15 So when a laboratory submits a report and you deem it

16 unacceptable; so in an inter-laboratory comparison test, you

17 don't make them repeat it? Because it seems like you accepted

18 the test but just did not count it in the analyses.

19 DR. SIGMUND DEGITZ: Are you talking about for

20 the inter-laboratory?

21 DR. STEPHEN KLAINE: Yes. 

22 DR. SIGMUND DEGITZ: So she is talking in

23 terms of -- 

24 DR. STEPHEN KLAINE: I know what she is

25 talking about. But you were talking about comparison between a

26 regulatory laboratory submission versus the submissions for what
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1 we are dealing with now.

2 DR. SIGMUND DEGITZ: Right. As I understand

3 it -- and I will just cut to the chase on this -- is the way

4 these contracts are established. They are paid for the work

5 they do, not for the product they produce.

6 DR. STEPHEN KLAINE: Okay. 

7 DR. SIGMUND DEGITZ: Yes. 

8 DR. STEPHEN KLAINE: So if you get complete

9 control failure, you just toss it out -- pay them anyway.

10 DR. SIGMUND DEGITZ: I was going to say that

11 earlier, but I did not think it was my place, but I do not think

12 you guys got the direct answer that you were looking for so I

13 just did that.

14 DR. STEPHEN KLAINE: Thanks.

15 MS. MARY MANIBUSAN: But these are

16 subcontractors.

17 DR. MARTHA SANDY: Yes, Dr. Touart?

18 DR. LESLIE TOUART: Just a clarification as

19 Sig said. I mean the contract vehicles that we utilize with the

20 government are somewhat limited. And in the most part, it is a

21 level of effort type contract. So when the work is performed,

22 it would be concluded as you see time limitations that are

23 involved as well. And so you could run into some situations

24 that where if you needed the study to be done a different way

25 you are going to have to pay for a new study in a sense. Often

26 there is not time to do that. 
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1 So the context is that in an inter-laboratory

2 exercise, we have established certain performance that we feel

3 would make the study scientifically readable, in some sense. 

4 And so the context is that, if that study fails to meet those

5 quality factors, then we can deem it as unacceptable if there is

6 too much control survival or some other factors that we use, and

7 some of those are performance criteria that we have established

8 within the proposed guidelines that we have. So if it fails

9 those, we cannot compare that. 

10 We cannot use a bad study and then compare

11 that in terms of its CVs to good studies. We want to try to

12 identify how -- with what we are perceiving as good studies --

13 how these compare with each other. And so that is why some

14 laboratories would fall out of an evaluation.

15 DR. STEPHEN KLAINE: Just real quick here. I

16 was just expecting to see the test repeated. You are asking him

17 to repeat the test, that is all, but now I know why you do not

18 ask him.

19 DR. MARTHA SANDY: So Dr. Scott?

20 DR. GEOFFREY SCOTT: Just a couple of quick

21 comments for clarification. In all of the assays you all have

22 presented today, the fish and everything, the frog as well; I am

23 particularly thinking of slide 60 that you showed on the fish,

24 Rodney. You gave what were the control metrics for a successful

25 -- you need to put a lot of emphasis on what that is because in

26 some instances we are doubling the number of controls to compare
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1 with, in some instance. 

2 So you really need to focus on are the labs

3 producing that in the absence of those. And then it would seem

4 to me everything else then falls into compliance with the dosing

5 in other aspects of the metrics you are measuring. But if you

6 know your controls are good, that is at least the starting point

7 of quality assurance. So it would seem to me in defining this

8 that the emphasis or the lab should be focused on the control

9 metrics. And we have not seen that clarity on every

10 presentation today, but I just was taken by slide 60. I thought

11 that was really nice, so I would go back -- if you all do this

12 -- focusing on that control metric to me would seem appropriate.

13 DR. MARTHA SANDY: So Dr. Scott, thank you. 

14 Hold that thought for the charge questions. Okay. Dr. Scanes?

15 DR. COLIN SCANES: I want to follow-up Dr.

16 Scott's question, and that is, Dr. Scott talked about the issue

17 of controls. And I wonder if one of the lessons learned might

18 be there needs to be a positive control used, so that if you

19 have got different labs doing tests you want to see if they get

20 a response. If they get no response to the positive control

21 that equally may be a criteria to be considered.

22 DR. MARTHA SANDY: And I would like to remind

23 the Panel that those are excellent comments, but those are more

24 along the lines of answering charge questions, I think, than

25 clarifying questions. 

26 Okay. Do we have any other clarifying
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1 questions from the Panel? Yes, Dr. Bentley?

2 DR. GEORGE BENTLEY: Going back to the thyroid

3 histopathology in your T4 assay -- I should have asked this

4 earlier, but I am curious; do you ever run any correlations

5 between the size of the thyroid and the circulated T4? And then

6 following on from that, do you ever consider looking for changes

7 in expression in the enzymes in the liver?

8 DR. SIGMUND DEGITZ: I would answer yes to all

9 of your questions in our research life, okay? Not in our

10 regulatory life. I mean the ORD does that future looking type

11 of research to understand relationships. You know, can volume

12 predict? Could the other things predict? You know, we are

13 always looking to the future that some day we could shelf these

14 archaic approaches we are using today and come up with something

15 better. 

16 So yes, we do look at that. It is hard at

17 this point in time to implement that and bring that into the

18 regulatory context. 

19 DR. COLIN SCANES: The reason I ask is I am

20 thinking about biologically meaningful endpoints of what you are

21 measuring, and these would be quite simple things to measure. 

22 It could actually make life a lot easier from a regulatory

23 standpoint.

24 DR. SIGMUND DEGITZ: Agreed. The key is going

25 to be for ORD to work through these approaches and work with the

26 program office so they understand how they can assist in
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1 implementation.

2 DR. COLIN SCANES: Okay, thank you.

3 DR. MARTHA SANDY: At this point, I would like

4 to thank you very much Dr. Degitz for your presentation. We

5 need to take a short five-minute break, just five minutes. 

6 Please be back. We have one more presentation to get through

7 today. Thank you.

8 (Whereupon a break was taken)

9 DR. SHARLENE MATTEN: Mary Christman and

10 Dallas Johnson have written down in plain English the more

11 specific questions they wanted to ask about clarifications of

12 the reanalysis, if that is the appropriate word, of the

13 statistical StatCHARRMS package, and I am typing those. For

14 those who are listening, I am typing those because it is in

15 handwritten form. 

16 I am sending it to Mary Manibusan who is

17 sitting across from me and she is going to get it to the team

18 and they will provide some clarifications to the best of their

19 ability in the morning sometime or sometime in the near future,

20 not at 6 a.m. or -- it does not have to be that. But they are

21 going to consider the questions and try to clarify them. Is

22 that clear?

23 DR. MARTHA SANDY: Thank you, Dr. Matten. And

24 now we are ready for our fourth presentation today by Dr. Touart

25 on the Mysid. 

26 DR. LESLIE TOUART: Okay. Thank you, Chair. 
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1 And I know it has been a long day and I know people are starting

2 to get weary, so I will try to walk through this as quickly as I

3 can and try to cover the key points. 

4 And what I am going to do is talk about the

5 Mysid two-generation test as well as the copepod development and

6 reproduction test, which was kind of an additional aspect that

7 we evaluated. And I will talk a little bit about why we did

8 that.

9 The basic outline of this presentation is just

10 to go back and cover a little bit of the purpose and overview of

11 an invertebrate test as part of the EDSP Tier 2, you know, why

12 is it fitting in here. A little bit of the history of the

13 validation process for both the mysid copepod and then talk

14 specifically about the mysid in terms of the protocols, some

15 example test results, some generation comparisons and lessons

16 learned. And similarly, with the copepod, to talk about its

17 protocols, some basic results seen and some advantages,

18 limitations. And then talk about a little bit of the

19 recommendation that we have in terms of why we feel we prefer

20 just to pursue the mysid.

21 In terms of overview -- and again we are not

22 talking about the more important taxa to be considered and that

23 is the invertebrate. 

24 The estuaries are an important ecosystem and

25 recipient of many EDCs. And I mention estuaries because the

26 species, both the mysid as well as the copepod are estuarine in
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1 a sense. They are crustaceans and these are often among the

2 more sensitive organisms and they are important individuals

3 within the ecosystems themselves.

4 There are many insecticides that are really

5 putative EDCs in some context. There are some that are

6 exploited specifically to interact with the insect hormone

7 systems. And crustaceans are also effected by these, so we

8 understand the context of how endocrine disruption can happen

9 because that is used to try to eradicate some species in a

10 sense.

11 But in the context, the invertebrates have

12 hormones. They have endocrine systems that are very well

13 developed. And ecdysteroids, which is a molting hormone, is

14 also important for reproduction in these individuals, these

15 occur.

16 In context the endocrine systems of

17 invertebrates differ from that of vertebrates in some

18 fundamental ways, but the response of invertebrates to an EDC

19 are kind of expressed differently but they can also be affected

20 by compounds that will affect vertebrate hormones. 

21 Crustaceans have true endocrine glands, but we

22 have seen, at least in some cases were are vertebrate active

23 materials -- and I just give a couple of examples here like

24 testosterone interferes with ecdysteroid regulation, at least

25 suggested. And aromatase inhibitor can interfere with

26 ecdysteroid synthesis because it is just, you know, steroid
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1 production kinds of things. 

2 So these are just some examples. But the real

3 key here is not so much that would cure their invertebrate

4 active compounds will have similar effects like on estrogens,

5 androgens and thyroids, which are not active in these

6 individuals, but that these activities, if we have identified an

7 invertebrate active compound, that this may have an adverse

8 effect within the invertebrate. 

9 And with some of the validation work, we will

10 show that these invertebrates are sensitive to some of these

11 compounds because some of these vertebrate compounds that we

12 have here have been active. 

13 So the context is, when we have identified a

14 particular material as having some potential endocrine activity,

15 at least vertebrate endocrine activity, there is a question as

16 to how this is going to adversely effect invertebrates. And to

17 do that we need a test that would comprehensively evaluate some

18 of the adverse effects that are plausible before that, and that

19 is what the purpose of the test really is.

20 And so with both mysids and copepods, these

21 have been used for a number of years now in toxicity tests. 

22 Mysids especially have been used in regulatory testing both

23 acute and life-cycle tests for over 30 years now. There are

24 standard test protocols for these mysids, American mysids bahia.

25 And the Amphiascus Tenuiremis as a copepod exist as like ASTM

26 standard methods for conducting tests. But the copepods are not
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1 used as much as mysid, but there are methods that have been

2 established.

3 In terms of a history for the mysids, the

4 initial event was an evaluation of fenoxycarb. You know this is

5 a growth regulating type compound and this was evaluated in a

6 multigeneration mysid test and was a basis for an initial draft

7 test guideline back in 2001. 

8 There was an invertebrate detailed review

9 paper that was initiated and completed in 2004 to look at a

10 variety of invertebrate methodologies and recommendations in

11 terms of what may be appropriate to evaluate in terms of an

12 invertebrate test.

13 Through the course of time from 2004 through

14 2009 there were a number of demonstration tests that were done

15 with the particular protocol that we have that we are proposing

16 and these are a variety of compounds that have been used. Some

17 of these are not specifically a vertebrate-active material, at

18 least we know of, in terms of like the dichlorophenol and

19 compounds like fipronil, but clearly some of these are ones that

20 we have already talked about today in the validation of some of

21 our other vertebrate test with things like prochloraz, compounds

22 like flutamide, ketoconazole which is a steroidogenesis. We

23 have atrazine and a perfluorodecanoic acid as another compound.

24 And then in our inter-laboratory evaluation,

25 we had labs look at lindane as a compound as kind of a standard

26 toxicant, but also octylphenol. And we had a laboratory
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1 evaluate vinclozolin. 

2 For the copepods, the efforts with copepods

3 initiated more as an international effort through the auspices

4 of the OECD, that is the Organization of Economic Co-operation

5 and Development. There was a proposal by Sweden to development

6 a copepod test methodology, and so they proposed a particular

7 protocol. And the initial protocol really included three

8 different harpacticoid copepod species as well as a calanoid

9 species to investigate.

10 Subsequent, the detailed review paper that was

11 completed that considered mysids as well as copepods as well as

12 some other crustacean, you know, these were considered finalized

13 in this 2004 time period. But within the OECD there were two

14 phases that were completed, one with a DCP and then a second

15 phase with lindane and atrazine that were completed.

16 But the outcomes of these left a couple of

17 questions lingering and it was considered that copepods may

18 offer some advantages over the mysids in context of maybe being

19 a more logistically easy or a shorter study to do. It takes

20 less space, and I will talk about that a little bit later when

21 we talk about copepod methodology itself. 

22 But EPA decided that we would include the

23 copepod as part of our inter-laboratory effort where we were

24 doing lindane and vinclozolin for the mysid. So we felt, well

25 we will take a look at this and perhaps copepod might be

26 considered an alternative or even a replacement for the mysids
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1 in context.

2 So just a brief overview in context of what

3 these two methodologies were like. With the mysid, this is a

4 two-generation method. The copepod is just a single life-cycle.

5 With the mysid, we are dealing with Americamysis bahia. We are

6 dealing with the flow-through system. We recommend solvent-free

7 continuous flow. The duration of this test is 60 days or a

8 little bit longer, depending on the development rate of the

9 species. Design is basically 30 animals per tank with 15 per

10 chamber. There is a reproductive chamber and a growth chamber

11 that is utilized, and each tank would be a replicate. There are

12 three of these in each treatment group. There would be five

13 treatments in seawater control, and if one was not using a

14 solvent-free system, one would need to add a solvent control as

15 well.

16 The exposure duration begins with individual

17 offsprings that are less than 24 hours, so that is less than 24

18 hours post-release from the brood pouch of their parent. And

19 this goes through the median second brood release in controls

20 plus seven days, and this is within the second generation.

21 For the copepod, it is semi-static. These are

22 done in really the microwell plates. It is 36-day test, time

23 definite. There are 30 animals used per plate, three rows of

24 10. There are three replicates of the treatments of 90 animals

25 per treatment level. There would be five concentrations solvent

26 control and seawater control. And these are also 24 hours
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1 post-release through day 36, and that should include two brood

2 releases within the time period.

3 So the mysid two-generation test is really

4 just an adaptation and continuation of the existing OCSPP

5 850.1350 Mysid Life-Cycle Test. The mysid life-cycle test is

6 currently a 28-day time definite study where it begins the same

7 with less than 24-hour old individuals and carried for 28 days. 

8 Typically there are two brood releases within that time, but

9 depending upon developmental rates, it could be one or more,

10 even as much as three within the context of that test.

11 And what we are doing in the two-generation is

12 you initiate the life-cycle test, but you take the offspring

13 from this second brood of this life-cycle test and initiate the

14 second generation with that and grow those into adulthood and

15 reproduction. 

16 So the purpose, again, to evaluate adverse

17 reproductive and developmental effects of vertebrate endocrine

18 active compounds that would effect an invertebrate, and

19 characterize a dose response that would be relevant for

20 ecological risk assessment.

21 There are multiple endpoints that allow for

22 diagnostic and definitive evaluation of these compounds but they

23 are basically evaluating endpoints of survival, growth and

24 reproduction.

25 This is just a schematic that talks a little

26 bit about evaluating potential endocrine effects and
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1 considerations of the activation of reversible type effects. 

2 These are considered those that occur post-developmental. These

3 are adults that have been developed. So effects that would

4 occur here are really more of these activational and reversible

5 where you would anticipate that removing exposure would return

6 the individual to a healthy state.

7 On the organizational level, these are

8 generally developmental exposures that would occur at

9 developmental stages like embryo, larval or juvenile. And these

10 may be organizational and considered maybe irreversible if I

11 have affected something within, say, larval development that is

12 latent and manifest later in terms of its reproductive

13 viability.

14 This is a schematic of the two-generation test

15 where the test would initiate with these new released

16 offsprings. These would mature and grow to adulthood. 

17 A common name for mysid is opossum shrimp

18 because they carry their brood in a pouch. The embryos will

19 develop until they are really a fully metamorphosed juvenile

20 that are then released at a time. So once it has gone through

21 one life-cycle, which would be consistent to the existing mysid

22 life-cycle test, the offspring from the second brood would be

23 used to initiate the second generation and carry those through

24 adulthood and reproduction.

25 This is a little bit more of a specific

26 overview in terms of species delivery. We talk about the
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1 endpoints that are included or survival. There is dry weight,

2 time to maturation, time to brood release, inter-brood

3 durations, number of young per female, total number, offspring

4 viability, sex ratio, and also percent reproductively active

5 female. 

6 And test validity criteria that we utilized in

7 this test really relate to the purity delivery of the chemical

8 ensuring that we are maintaining the expected exposure

9 concentrations to a level that would be appropriate. 

10 We are looking for an average production in

11 the controls greater than eight young/control female through the

12 first and second broods to demonstrate enough reproduction so we

13 have enough power to detect reproductive events. Water quality,

14 looking at greater than/equal to 75 percent survival and greater

15 than 75 percent females producing young in controls.

16 So this gets into some of the results that we

17 have seen. In this case, this is the lindane comparison with

18 mysid. Three laboratories participated in this test. Two

19 laboratories achieved the performance criteria but one lab did

20 not and so it was dropped from further inclusion in the

21 analyses.

22 In terms of effects, what we are seeing is

23 effects on length, days to brood and offspring production were

24 affected within one laboratory. In the other lab, the length

25 and days to brood release were affected, but some of the other

26 endpoints like reproduction and survival were not as affected to
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1 the same extent. 

2 But in context, the LOEC based upon a more

3 sensitive endpoint like length were somewhat comparable in

4 context. So in this case, the LSE comparisons were somewhat

5 similar.

6 This is, again, inter-lab, and this is the

7 number of offspring in the two broods. And the interesting

8 thing here -- this is the SMV lab and this is the UNT lab. The

9 context is that there is somewhat of a U-shaped response curve

10 that is occurring in the first generation, and this is repeated

11 in the second generation with somewhat lower effects, or at

12 least more significant effects from the control.

13 The interesting thing also is that in the

14 other lab, although we were not seeing significant effects on

15 reproduction, there were similar trends in terms of the pattern,

16 you know, that we see with some declines, but also a kick-up in

17 the number of offspring at the higher concentrations. And so

18 that is repeated. 

19 So the context here is that we are seeing

20 something similar. The actual significant concentrations were a

21 little bit different.

22 This is another case for octylphenol where two

23 laboratories participated and the nature of the effects seen. 

24 And again, the most sensitive effects seen here were in length

25 and in offspring production. And the LOECs are very close in

26 context between the two laboratories.
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1 In this case for the days to brood, the

2 concentration, which was equivalent to the more sensitive LOEC,

3 this occurred only at the lower concentration so that the higher

4 concentrations did not have the significant effect, except, I

5 think it is one of the highest. 

6 This is, again, the table that demonstrates

7 this with the up arrow is significant, the down arrow is the

8 decrease that is occurring. And of course all concentrations

9 for some of these endpoints we were seeing effects. This is

10 within the SMV laboratory which had the, I guess, greater

11 magnitude of effects that were seen.

12 This table which is a little hard to see, but

13 I will try to point out a couple of things. This was done to

14 evaluate significant treatment effects, and so this is looking

15 at is there a difference between treatment and control for the

16 variety of endpoints for the F0 and the F1. 

17 And the next slide kind of highlights one of

18 the examples and this is for the 14-day length. And the context

19 here is that, yes, there was a difference seen here. These are

20 the different laboratories. The S and U are different

21 laboratories for the lindane.

22 But the next slide kind of points out, well

23 what are we talking about in terms of a significant trend that

24 we are seeing between control and the treatments. And here you

25 see the, from the lower concentration to the higher, a trend

26 that would have effects. So these are seen across many of the
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1 various endpoints and characterized in contents.

2 And along similar lines, we looked at

3 significant treatment effects comparing generations, the

4 difference between the F0 versus the F1 generation for different

5 endpoints, again between the laboratories that were involved

6 where we had two generations to look at. 

7 And again, pulling one of these in terms of

8 the time to first brood where, yes, we see a generational effect

9 in both laboratories but at least more pronounced within this

10 one laboratory. The context here is that, within the F0

11 generation you see the responses across the different

12 concentrations. But in the F1, the duration higher, so there is

13 a significant difference between the F1 generation, brood

14 duration versus the F0.

15 So trying to summarize a little bit more of

16 effect differences between generations, this is another example

17 using ketoconazole with the F0 mysids and looking at the second

18 brood release, where in the F0s, the significant effect was seen

19 at the 33.7 micrograms per liter and in the F1 mysids, much

20 lower in terms of the lowest concentrations.

21 And the context as I get into some of these

22 others, is, for a variety of these chemicals, some of the

23 vertebrate active compounds, the actual effect concentrations in

24 the mysids are very low. Like octylphenol, we are seeing .36

25 micrograms per liter or 3.9. Both of these are lower than the

26 lowest test concentrations say in the frogs or fishes in
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1 context. So the mysids are more sensitive, and so if we are not

2 including these then you may miss information in terms of

3 potential adverse effects in these type of species.

4 What I have done here is highlighted in red

5 the more sensitive endpoint between like F0 and F1, so you can

6 see that in some cases the F0 generation is more sensitive. In

7 other cases the F1 generation is more sensitive. And in others,

8 there really is not much of a difference between the

9 concentrations.

10 Just a point of clarification with atrazine

11 and the mysid, the highest concentration was 193, and there were

12 no significant effects that were seen, so LOEC would be greater

13 than that. But the question mark is that there was a trend that

14 was identified in the F1s. Because of the variabilities, it was

15 not deemed significant, so that is why the question marks.

16 And this is going to be more difficult to see

17 because I cannot really see it myself. The context here was the

18 numbers -- and this was ranges of coefficient of variation in

19 the F0s and the F1s. 

20 And the reds are those where the coefficient

21 of variation was greater than 20. All the blacks are those

22 where the coefficient of variations for those endpoints are less

23 than 20. But in many of these, even though it is greater than

24 20, they are not much greater; I mean, you were dealing with

25 things like 24, 26 or 21, they are some of the endpoints that

26 the coefficients of variation did get up to a little bit higher
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1 level. But by and large, these are -- at least in my experience

2 of aquatic test -- CVs at 20 percent or that neighborhood are

3 very respectable in context. 

4 So limitations of the mysid studies; again,

5 there are difficulties in evaluating your dose selection

6 methodologies in terms of how you set your concentrations so you

7 can determine actual no effects. In some of the tests that we

8 had, we had effects down at the lowest concentrations tested, so

9 those tests would need to be repeated to identify a real NOEC to

10 be evaluated.

11 There are problems with food limitations in

12 the studies. Food availability for mysids is an important thing

13 to do and one has to ensure that they are getting adequate feed

14 and that feed is made available to them multiple times during

15 the day. 

16 Few endpoints were considered responsive based

17 on signal to noise. Also blocking design was often ineffective

18 and may have resulted in some erroneously high errors in ANOVA

19 because of the blocking that was done. Sometimes the F1

20 generation is too small to test endpoints, meaning that the

21 effects in the F0 were so great that we did not have enough

22 individuals to fully populate the F1 to evaluate potential

23 effects.

24 Again, difficult to test substances are

25 difficult to test in any aquatic system. And with some of these

26 difficult materials where we had high CVs of measured
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1 concentrations, these may overlap which confounds ones ability

2 to detect effects later on.

3 In terms of lessons learned with the mysid,

4 first are these test designs. Again we prefer a completely

5 randomized test unless some form of blocking would have been

6 required. And it just cautions in terms of how the blocking

7 would be utilized.

8 In some of the earlier studies, they employed

9 self-starting siphons and it was found that we get a lot of

10 impingement with the mysids. The mysids in their systems, they

11 are usually kept within little screened compartments. And as

12 the water levels in these self-starting siphons would deplete,

13 the mysids would get impinged or attached to the side of these

14 containers and so they would dry out and then be lost. So

15 avoiding the self-starting siphons and just having a direct

16 overflow seem to reduce the numbers of individuals lost through

17 the impingement.

18 Feeding; what we found out was that brine

19 shrimp alone was insufficient nutrition for these individuals. 

20 And what we began to use were enriched brine shrimp where these

21 brine shrimp are grown with an additional enriching media that

22 adds more fatty acid content to the brine shrimp themselves. 

23 And these are commercially available enrichment materials that

24 can be used.

25 We also found from the initial study where we

26 were using just mysids that we need to increase those to 20 to
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1 have sufficient numbers to carry through the test. 

2 In our dry weight measures, initially we had

3 0.1 micrograms which requires a very sensitive balance to

4 detect. But we found that perhaps one microgram would be

5 sufficient to utilize for that measure.

6 Exposure duration we feel should be terminated

7 at second brood release or the seven days median time post the

8 control brood release. And the context here is that once an

9 individual has, like in controls or treatment, has had its

10 second brood release, that individual could be sacrificed and

11 utilized at that particular time. You do not want to

12 necessarily carry those individuals until you have actually

13 achieved the seven-day median. 

14 So once that brood release is made it is done.

15 If they have not released brood within seven days of the median

16 control release, then the whole test is terminated and any

17 individuals left over would be captured. And it is felt that if

18 they had not released, that is an endpoint within itself in

19 terms of the duration per release. So it would be a

20 reproductive effect.

21 For survival, we found out that sometimes if

22 we lost one of the individuals in one of the reproducing pairs

23 that could be replaced by a like individual within the growth

24 chambers. So if we lost a male in a reproductive chamber and

25 replaced that with a male from the growth chamber that is in the

26 same replicate that would be satisfactory. It is just we have
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1 to reconcile that in terms of how the survival calculation is

2 made in terms of individuals that are lost and where those go.

3 And for reproduction, we felt that we need the

4 cumulative measure of number of offsprings per female per day

5 would be the best summary statistic to utilize for comparisons.

6 Now switching to the copepods; again, this is

7 a single generation method originally proposed through OECD. 

8 The purpose is, again, similar to the mysid to evaluate adverse

9 effects and characterize dose response.

10 The attraction of the copepod was that there

11 were fewer endpoints in the mysid, but it still includes some

12 development reproductive endpoints and has shorter duration and

13 less lab space requirement. And so the thought was maybe this

14 might be more logistically practical. 

15 The mysid tests, for those that are familiar

16 with them have a history of concerns that are difficult,

17 rejection rates could be relatively high compared to some of the

18 other standard methodologies. And it is a difficult test

19 because, again, in marine system, trying to maintain sufficient

20 marine water, especially for inland-based laboratories where

21 they have to use artificial seawater and stuff, it can be

22 complicated. But with copepods being a much smaller critter,

23 there was thought that we could have some advantages here.

24 And this is a little bit of a description of

25 what goes on through the development. So there are discrete

26 life stages beginning with embryos; two days to hatch to
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1 nauplii, and then going through six nauplius stages to develop

2 into a copepodite, there are five stages, and then developing

3 into adults and reproducing in context. So there are

4 development that can occur.

5 The copepod has sexual dimorphism in that

6 males have characteristics that separate them from female, for

7 instance antennae for clasping can be seen.

8 And this is another kind of schematic of the

9 life-cycle and the context of using like 96 well plates where

10 you have individuals within each particular plate to evaluate

11 and carry forward.

12 Overview of the method; again, semi-static

13 systems with 250 microliter per well, one animals per well,

14 three rows of 10, three plates and survival going on, number of

15 offsprings and clutches. And then validity criteria, again,

16 similar where you have got delivery of test compound, water

17 quality, maintaining survival and controls and survival within

18 any given plate that is utilized, an average of eight produced

19 within the clutches.

20 In terms of the test that were performed, this

21 is just to identify concentrations, you know, differences that

22 were kind of -- these were the nominal concentrations; for 3,

23 5-DCP, three laboratories; atrazine, three labs; lindane, there

24 were overall seven laboratories and these are representing some

25 of the more sensitive effect concentrations that were seen

26 across these.
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1 And the next slide looks at an inter-lab

2 comparison with the lindane. And this was the last comparison

3 with the lindane that was done with EPA with the three contract

4 laboratories that were utilized. And again, with the

5 concentrations tested, the lowest effect concentration was five

6 micrograms in two of these and 10 micrograms across all three

7 laboratories, and higher concentrations having effect. So that

8 is reasonably fair concordance across the laboratories in terms

9 of the copepod comparison.

10 Advantages of copepod, again, laboratories are

11 generally able to culture the species. They can harvest gravid

12 copepods and hatch healthy nauplii to conduct the test. 

13 Microplate testing allows for individual-based

14 tracking. And this species has a rapid life-cycle, which the

15 time is money in the context of these types of tests. 

16 There are limitations though, and we ran into

17 substantial technical difficulties in the laboratories in terms

18 of misidentifying copepodites in adults with gender

19 misidentifications and increased handling time, these kinds of

20 problems. And that may obscure some of the treatment-related

21 effects. 

22 The misidentification of copepodites as adults

23 decreases reliability. There is some questionable sensitivity

24 but I will talk about that a little bit more. Largely that is

25 due to the number of compounds that we looked at to really

26 understand what is the copepod sensitivity. 
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1 Chemical exposures are problematic because

2 these are done in semi-static and in microwell plates so that we

3 are not able to reverify exposure concentrations in the actual

4 test chambers themselves as part of the plate. I only would be

5 able to monitor it in the actual media that would be used to

6 fill those. There might be some solvent-related toxicity that

7 could also obscure effects.

8 So trying to consider the rationale for mysid

9 versus copepod, while both mysids and copepod require

10 significant laboratory experiences, there seems to be a little

11 bit more substantial experience with the mysids and copepods. 

12 There are more labs that utilize that primarily because it is a

13 standard species on pesticide testing for estuarine

14 invertebrates.

15 There is a large amount of published toxicity

16 data with the mysid. And the available evidence suggests that

17 mysids are generally more sensitive in general of the test

18 species. 

19 We may have a little bit more understanding of

20 the biology of mysids than of the copepods. And mysids have

21 been used for evaluating reproductive toxicity for a long time

22 so we are familiar with that. And it has potential to

23 development some more mechanistic endpoints, although we have

24 not explored those specifically for our method. Similar to

25 copepod, mysids are of ecological importance and would be

26 important there. 
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1 The mysid system allows for a continuous

2 intermitted flow exposures so we can maintain the exposure

3 concentrations to a better extent. The greater body size of the

4 mysids makes them more amenable to other measures but also

5 easier to handle than the copepods. The mysid test includes

6 well-established endpoints, and the copepod does not include

7 growth measurements, per se, just the difference between their

8 developmental stages. Although data obtained through both the

9 mysid and copepod can be used for population modeling, the mysid

10 has data through two generations, which might be an advantage.

11 So in considering sensitivity and in those

12 compounds that we had similar studies for the mysid and copepod,

13 it is a little bit mixed where for the DCP, the copepod was a

14 factor to more sensitive. For atrazine, the copepod did have

15 significant effects, whereas the mysid we did not see the

16 significance at the concentrations testing.

17 For lindane, you know there -- or is the

18 magnitude more sensitive for the mysid in this case. And for

19 vinclozolin, an anti-androgen, the copepod mysid are of really

20 equivalent sensitivities.

21 So trying to summarize the results is that

22 laboratories were generally able to complete both the mysid and

23 copepod. The inter-laboratory, lowest observed effect

24 concentrations were comparable for some but not all endpoints in

25 terms of the results individually for mysids and individually

26 for copepods.
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1 The copepod exhibited substantial technical

2 difficulties, which required some additional time and effort to

3 overcome. In conversations with the laboratory personnel, the

4 amount of effort is fairly substantial for those individuals. 

5 So the context that we have a logistical improvement is

6 questioned in that sense. 

7 So based on limitations with the copepod, that

8 leads us to somewhat prefer the mysids since it is an extension

9 of an existing life-cycle test, and that commercial laboratories

10 are already familiar with the method and so they would be able

11 to apply that. 

12 So with that, that is the conclusion of the

13 presentation. So if there are some clarifying questions, I am

14 happy to respond.

15 DR. MARTHA SANDY: Thank you very much for

16 that presentation. And we will go with Dr. Christman first.

17 DR. MARY CHRISTMAN: Quick question. You

18 mentioned blocking but I never saw a definition of what a block

19 was in the design. And it could be I was just blanking out at

20 the time, I apologize, but I am having a hard time understand

21 what was your block.

22 DR. LESLIE TOUART: In the blocking, this was

23 more considered in terms of -- because this was in relation to

24 the mysid test and it is in the context that, in the laboratory

25 setup where the individual controls or experimental units are

26 placed, they may be in different locations, so that your
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1 blocking would be associated with where they occur. 

2 There is a context that within each replicate

3 chamber, meaning tank, you have a reproductive chamber where you

4 have got individually paired mysids in context.

5 DR. MARY CHRISTMAN: Right, you add those two

6 baskets.

7 DR. LESLIE TOUART: And then you have got your

8 growth chambers that are utilized. And so if one is selecting

9 individuals in a purely random form you could have individuals

10 that may be subject to other, I guess you would say experimental

11 influences that would not be seen by others. So if you are

12 blocking you are trying to identify some of those. But I think

13 what we found out that any blocking schemes that we used really

14 did not provide any advantage to what was actually seen.

15 DR. MARY CHRISTMAN: So let me repeat back to

16 you if I understand this correctly. What you are saying then is

17 that, within the tank -- because you had two blocks. You had

18 the reproductive and then you had the growth, I guess it was,

19 the other part?

20 DR. LESLIE TOUART: Growth, where as the

21 length measure from the individuals would be sacrificed at

22 different times.

23 DR. MARY CHRISTMAN: Right. So you had them

24 separated out, although I supposed there was some movement. So

25 what you are saying is that if you identified individuals as

26 belonging to a basket as opposed to a tank, then you found that
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1 that was too noisy. Because it repeatedly says that it just

2 made the variance too high.

3 DR. LESLIE TOUART: Correct. In context, as

4 far as I understand, that is correct in terms of how that was

5 employed and how that increased the variance that was seen. So

6 I think I am thinking clearly right now. I think that is

7 correct from your observation.

8 DR. MARY CHRISTMAN: So the blocking then

9 refers to baskets within a tank?

10 DR. LESLIE TOUART: Correct. I can check that

11 to verify to make sure.

12 DR. MARY CHRISTMAN: But like I said, I could

13 not find a good definition that is why I asked.

14 DR. LESLIE TOUART: I think it is explained a

15 little bit more clearly within the ISR itself.

16 DR. MARY CHRISTMAN: I did a "find" and -- 

17 DR. LESLIE TOUART: Okay. I will check on

18 that for you and provide further clarification.

19 DR. MARY CHRISTMAN: Okay. Thank you.

20 DR. MARTHA SANDY: Thank you. Dr. Klaine?

21 DR. STEPHEN KLAINE: Could we go back to slide

22 12? It is always good to start at the beginning. 

23 And sorry for this, but I am trying to get

24 back at where we started today, which was building this house. 

25 And this is Mary's slide actually. And so there is a connecting

26 between Tier 1 and Tier 2 tests. My understanding is, if I
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1 interpreted what you said and read the slide correctly that when

2 you do Tier 1 testing, you are going to get some indication as

3 to prioritizing Tier 2 testing. 

4 And I can see a link between Tier 1 testing

5 and Tier 2 testing for the other Tier 2 tests, but I am not

6 finding a link for this one. And I just thought do you have a

7 link or an idea of what data you would get in Tier 1 that would

8 cause you to pull up the mysid test in Tier 2?

9 DR. LESLIE TOUART: Let me try to explain. 

10 Again, this goes back to the EDSTAC recommendations in terms of

11 consideration that invertebrates make up 98 percent plus of the

12 animal (inaudible). And the consideration that if we have a

13 chemical that is demonstrated to be endocrine active in

14 vertebrate systems because we are focused on estrogens,

15 androgens and thyroids, the concern is that, okay, if it is

16 active there and we want to do a comprehensive risk assessment,

17 well is there a likelihood that that might influence an

18 invertebrate?

19 And there are some suggestions. I mean it

20 extends in context but there are some studies in the literature

21 that demonstrate that there are activities that do cross over,

22 you know, where you have materials that, even though it may be

23 active on, say, estrogen pathway, it may influence something in

24 an ecdysteroid pathway in the compound. How it does that, we do

25 not fully understand in context, but it has that potential. 

26 Now when you look at steroidogenesis
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1 inhibitor, then clearly that might inhibit some steroidogenesis

2 process in terms of the ecdysteroid. There are some similar

3 pathways that are involved in those syntheses. But the context

4 is that it potentially could have some effect. 

5 So if we see an activity, then the question is

6 well what does it do to an invertebrate that may be sensitive? 

7 And so that is the rationale, I guess, in terms of why that

8 might then trigger the Tier 2 tests, since invertebrates would

9 be exposed, and the concern is, well what is that consequence?

10 And if you remember the side for the mysids in

11 terms of evaluating some of these compounds, you see the

12 sensitivity of these as yeah, these concentrations are fairly

13 low and lower than what we might see in fishes or birds or

14 something. And so the invertebrate could be a driver in terms

15 of a risk assessment.

16 Now, trying to understand what is going on, I

17 do not think is the intent because the estrogen/androgen thyroid

18 pathways at least in crustacean are not so prevalent. For other

19 invertebrates there are suggestions that there might actually be

20 some connection, but in crustaceans that is not the case. But

21 there is potential cross-talk that exist, and so that

22 determination would likely be made if we anticipate that there

23 is likely environmental exposure and concern what that material

24 might pose to invertebrates.

25 DR. STEPHEN KLAINE: Okay. And I certainly

26 agree with everything you said, in terms of the sensitivity and
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1 the need for some invertebrate testing. I guess in my mind, I

2 was trying to figure out just where this test would fit, Tier 1

3 or Tier 2? So in your mind, if I am interpreting what you have

4 said, is that you expect to call up the invertebrate test every

5 time you see a positive in the Tier 1 test; is that correct?

6 DR. LESLIE TOUART: Well I mean that decision

7 has not been made. I mean we are looking at more of other

8 information that draws into those types of conclusions in terms

9 of how one may decide what test within Tier 2 are actually

10 selected.

11 There are some considerations. I mean if you

12 go back to EDSTAC recommendations, I mean they had kind of a

13 default concept that, everything else being equal, you would ask

14 for all of your Tier 2 tests if you have positive in Tier 1. 

15 But they also said that you could consider

16 other targeted methodologies based on exposure, based on some

17 other considerations, other information that you might have

18 available to you. And so we were working through exactly what

19 those other criteria might be in terms of how it can make that

20 particular determination. 

21 But I think the context is that when you have

22 identified endocrine potential, the next question is, well what

23 is the adverse consequence. And if you have other pathways to

24 try to determine what that is, then you can use those. If you

25 do not, then the question is well how do I evaluate that in

26 terms of the Tier 2 tests which were developed to try to address
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1 the adverse effects that may occur.

2 DR. STEPHEN KLAINE: Okay. 

3 MS. MARY MANIBUSAN: So I just want to maybe

4 shorten Dr. Touart's response into maybe a couple of points. 

5 What I am hearing is the assumption that, if I have a Tier 1 rat

6 positive, like a pubertal positive, then that would correlate to

7 a Tier 2 and I would call up the rat 2-gen or the extended

8 1-gen. And similarly, if I see the fish or the frog lighting up

9 in Tier 1, I would ask for the corollary Tier 2.

10 And this slide, although I have probably

11 failed in the attempt, was meant to clarify the distinction

12 between Tier 1 and Tier 2 in that they are answering very

13 different questions, and they are allowing for different levels

14 of uncertainty. 

15 So the Tier 1 is meant to only answer the

16 question, again, whether a chemical has a potential to interact

17 with E, A or T pathways. And pathways would start from an AOP

18 concept of having a molecular initiating event or early key

19 event like ER-binding, ERTA, uterotropic lighting up and then

20 you run into the in vivo cells of the pubertals, the fish and

21 the fish study. So that is a pathway analysis. 

22 And we are not looking at Tier 1 in terms of

23 species-specific types of interaction, we are looking at them

24 altogether and just answering that blunt question; yes, no, does

25 it have a potential, not whether it is yes only in rat or yes

26 only in fish or whatever. You see what I am saying? 
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1 And once you have answered that question that

2 it does have the potential to interact, then you make the

3 decision, "Okay. So now do I need Tier 2 testing? And if I do,

4 which ones would they include?" And as Dr. Touart indicated,

5 there are different sets of conditions or criteria for making

6 that decision. One could be that we ask for all five. That is

7 a default that EDSTAC allow for. 

8 The other condition is we ask, well what is a

9 use patterns? What is exposure pattern? Is it

10 residential-only, for example, where the only concern about the

11 human health risk assessment, and therefore we naturally call in

12 the two-generation or extended one-generation, or does it have

13 an environmental exposure pathway? And there, it is split up

14 across the ecological test in answering the question, well which

15 species do I need to call up to again answer the question that

16 we are posing in the risk assessment framework.

17 So again, Tier 1 is a screen. We are allowing

18 for a false positive, so it is not directly that, oh you see

19 something lighting up in Tier 1, therefore I must see something

20 in Tier 2. That is not the question, right? So Tier 1 we're

21 allowing for some chemicals to fall through the sieve -- if you

22 think about this as a filter because we want to be conservative,

23 it is meant to be a screen. It is not meant to have that higher

24 level of statistical power to get at the questions that Tier 2

25 was intended to answer. So I hope that clarifies that. I do

26 not know if I have muddied it a little bit more.
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1 DR. STEPHEN KLAINE: That does; thanks.

2 DR. GEOFFREY SCOTT: Thanks Les, for that

3 great presentation. 

4 On slide 173, which is the one where you are

5 looking at the coefficient of variance in the mysid metrics. 

6 And I was struck by your comment where we were looking at over

7 20 percent. In many of those instances, I noticed it may be the

8 duration of exposure endpoints in some instances. Sometimes it

9 is not. 

10 But in going through that as you look at those

11 ones that are in red, you know, if you were to go back and look

12 at the variation, say, within 10 to 20 percent of that

13 coefficient of variation, I assume like you said, probably some

14 of those would go away, and that is actually pretty good

15 concordance; just your comment to make sure I am understanding

16 what you are saying.

17 DR. LESLIE TOUART: I think that is correct. 

18 And the other point I was trying to make -- and again, this

19 slide is not easily read. 

20 In context, for many of the endpoints, also,

21 even though they are greater in 20 percent, they are not that

22 much greater so they are still kind of in the same ballpark. 

23 And how much that differs, and given the small number of tests

24 that have been utilized, I am sure those can change. 

25 But the main context here was just to indicate

26 that, yes, we have evaluated sort of the variety of endpoints
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1 that we have coefficient of variations, and they are not so bad

2 in terms of being able to determine what may be going on.

3 For a couple of these, those CVs might be

4 fairly large that they would cause some difficulty in trying to

5 interpret, but some of that might be cleared up with more

6 experience or understanding. But for the key endpoints that we

7 are concerned about with reproduction and growth measures, I

8 believe that they are satisfactory.

9 I would say though that something like dry

10 weight -- and we have some very high CVs for dry weight -- that

11 is a difficult thing to measure in these individuals because

12 they are so small. And usually, especially for the day 0s and

13 day 7s, you are pooling new individuals.

14 The context of whether dry weight gives you

15 anymore information, because length is an easier measure for

16 these critters, so the context is that perhaps length might be

17 more appropriate to use then you could drop the dry weight

18 measure, in context.

19 DR. GEOFFREY SCOTT: Yes. That is kind of

20 interesting because you would think weight is a more -- you

21 know. If you take a pair of calipers out or however you are

22 doing it, you would think it is a more consistent measurement

23 with a metric, a balance, to have a balance or something as you

24 do this. But that is what the data says. That surprised me.

25 In slide 163 you mentioned in the mysid assay

26 that one lab was excluded, and I was just curious as to which of
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1 those metrics on 173 were not met. I was just curious as to

2 what led to that exclusion.

3 DR. LESLIE TOUART: I am trying to recall. I

4 think it primarily was control reproduction that was affected

5 because the laboratory was having some difficulty in being able

6 to get sufficient numbers produced. And this was one of the

7 lessons learned in terms of being able to modify, because they

8 were using just a straight brine shrimp diet and they were not

9 fortifying that with the additional enrichment, you know, to add

10 the fatty acid, what the other labs had keyed onto to improve

11 their production of numbers. 

12 And so that laboratory was dropped here for

13 the lindane, but we utilized them again in the octylphenol and

14 they had sufficient performance there so that the changes that

15 they made were sufficient so that they could actually complete

16 the test correctly as well.

17 DR. GEOFFREY SCOTT: Slide 174, you mentioned

18 high coefficient of variance in dose levels for the mysid assay

19 as limitation. You also mentioned that in the copepod assay as

20 a rationale for problems with it. So I was trying to understand

21 the magnitude of those two statements, given that it peers in

22 some instances, both assays has problems delivering dose, so if

23 you could just clarify that.

24 DR. LESLIE TOUART: Yes. In this particular

25 side, the high CVs is in the actual maintaining concentrations

26 to ensure that they were consistent. Typically, we would like
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1 to see exposure concentration with CVs of less than 20 percent

2 to be able to have high confidence in the effect.

3 On the copepods, I am not sure if I

4 understood. 

5 DR. GEOFFREY SCOTT: Well in your slide, I

6 forget which one it was. 

7 DR. LESLIE TOUART: Because I do not think for

8 the copepod it was CVs in exposure. It was more CVs in terms of

9 -- 

10 DR. GEOFFREY SCOTT: No, it was not CVs, it

11 was chemical exposure less consistent from the static; just

12 trying to clarify that.

13 DR. LESLIE TOUART: Again, I think for the

14 copepod, remember these are done in these microwell plates, and

15 so you are adding material in those and you really do not get

16 good verification of what the actual exposure concentration was

17 because the volumes are too small. You are usually doing it by

18 a relative sense, in terms of the media that you have prepared

19 to add to those, but it declines over time. I mean it is being

20 replaced every three days or so, but the context is that the

21 actual exposure is a little bit more different. 

22 Typically it is just going to be expressed in

23 terms of a nominal exposure and that may be difficult for a

24 compound. And the difficulty that we saw with the copepod was

25 for the vinclozolin as an example that has a fairly short

26 hydrolytic half-life understanding, well what did they actually
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1 see in terms of exposure was question.

2 But in the bottom line when you look at the

3 actual results that were seen in terms of comparing the LOEC of

4 the copepod to the mysid, they were close. It may not have

5 mattered that much in context, but in our ability to project

6 effects and things, it is difficult from a risk assessment

7 standpoint if you have uncertainty in your exposure metric.

8 DR. MARTHA SANDY: And Dr. Scott, I am sorry. 

9 Given the late hour, I would like to suggest that we stop here

10 today and we pick up again the morning, all right? So we will

11 be starting at 9 a.m. tomorrow morning. 

12 I would really like to thank all the

13 presenters from EPA for hanging in there, and all the co-authors

14 that are in the audience. I would also like to thank all the

15 Panel members for sticking with these presentations and being so

16 diligent in their questions. Dr. Matten has something to say

17 before we adjourn.

18 DR. SHARLENE MATTEN: I just want to meet with

19 the Panel members in the back room for about five minutes just

20 to get a game plan for everyone.

21 And also, I sent Mary Manibusan the questions

22 from the statistical duo/trio via email so they can -- well Ken

23 Portier says he is still going to check his email. 

24 So the expectation is you will write

25 something, say something. And I will put those questions, since

26 it is formally written, in the public docket for the public and
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1 today's presentations. 

2 And with that, if anyone who is still here has

3 not signed up for public comments, just think about it and you

4 can make more public comments tomorrow or tell me tomorrow. And

5 with that, we will be adjourned for today and we will start

6 again at 9. Thank you very, very much.

7 (Whereupon the meeting was adjourned for the day)

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1 DR. SHARLENE MATTEN: Good morning. My name

2 is Sharlene Matten. I'm the designated federal official for

3 this FIFRA Scientific Advisory Panel meeting. This is the

4 second day of the Scientific Advisory Panel meeting to review

5 and consider the scientific issues associated with the

6 environmental Endocrine Disruptor Screening Program's proposed

7 Tier 2 ecotoxicity tests. It's my job to maintain the

8 regulatory part of the process here under the Federal Advisory

9 Committee Act, to make sure all the ethics requirements are met.

10 I have already loaded into our Docket

11 Management System for quality assurance, quality control the EPA

12 presentations made yesterday, the panel's question that was

13 given to EPA, the public comment that's going to be read into

14 the record that we have. And after that, we will have the

15 public comments from the group that's representing the Endocrine

16 Policy Forum.

17 If there's anyone here who hasn't registered

18 with myself or the EPA Scientific Advisory Panel staff for

19 public comments, please see Laura Bailey and the FIFRA

20 Scientific Advisory Panel staff at this table, over to my right.

21 With that, I'm going to turn the microphone

22 over to Dr. Martha Sandy, and the panel will go around and

23 introduce themselves. Thank you.

24 DR. MARTHA SANDY: Good morning. I am Martha

25 Sandy, and I'm chairing this session. I'd like to welcome

26 everyone back to this meeting. And we're just going to go
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1 around quickly and introduce ourselves. I'm Martha Sandy. I'm

2 a toxicologist, and I'm with the Office of Environmental Health

3 Hazard Assessment of California EPA.

4 DR. BARRY DELCLOS: Barry Delclos from the

5 FDA's National Center for Toxicological Research.

6 DR. JAMES McMANAMAN: Jim McManaman,

7 University of Colorado.

8 DR. DANIEL SCHLENK: Dan Schlenk, University

9 of California, Riverside.

10 DR. STEPHEN KLAINE: Steve Klaine, Clemson

11 University.

12 DR. MARION EHRICH: Marion Ehrich, Virginia

13 Tech.

14 DR. GEOFFREY SCOTT: Geoff Scott, NOAA.

15 DR. VICKI BLAZER: Vicki Blazer, U.S.

16 Geological Survey.

17 DR. REYNALDO PATINO: Reynaldo Patino, U.S.

18 Geological Survey, Texas Cooperative Fish & Wildlife Research

19 Unit.

20 DR. CATHERINE PROPPER: Cathy Propper,

21 Northern Arizona University.

22 DR. EDWARD PERKINS: Ed Perkins, U.S. Army

23 Corps of Engineers.

24 DR. ROBERT DENVER: Bob Denver, University of

25 Michigan.

26 DR. JOHN McCARTY: John McCarty, University of
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1 Nebraska, Omaha.

2 DR. DALLAS JOHNSON: Dallas Johnson, Professor

3 Emeritus, Kansas State University.

4 DR. COLIN SCANES: Colin Scanes, University of

5 Wisconsin, Milwaukee.

6 DR. SETH KULLMAN: Seth Kullman, North

7 Carolina State University.

8 DR. SHEA TUBERTY: Shea Tuberty, Appalachian

9 State University.

10 DR. JENNIFER FREEMAN: Jennifer Freeman,

11 Purdue University.

12 DR. MARY CHRISTMAN: Mary Christman, MCC

13 Statistical Consulting. 

14 DR. GEORGE BENTLEY: George Bentley, U of C,

15 Berkeley.

16 DR. MARTHA SANDY: Okay. I would like to

17 remind all panel members who are -- we need to speak directly

18 into the microphone so that the webcast and audio recording is

19 clear.

20 So as we left off yesterday evening, we had

21 just heard the last presentation from EPA staff on the mysid and

22 copepod assays. And we hadn't quite finished with some

23 questions. And I think we were in the middle of Dr. Scott's

24 question. So I'll turn to him.

25 DR. GEOFFREY SCOTT: Thank you, Dr. Sandy. 

26 Good morning. Geoff Scott, NOAA. Good morning, Les. Just a
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1 couple more questions, and again, thank all of you all for the

2 great presentations yesterday.

3 I just had a couple of last final questions,

4 and that really gets to the addition of the additional lifecycle

5 to the conventional mysid assay. And I say this not lightly

6 because I do think if you look at the use of the conventional

7 mysid assay, it has already been used to regulate known

8 endocrine disruptors, IGRs, things such as that.

9 So one of the questions that I have is in

10 Rodney's presentation he had a slide, I think it was like 74 --

11 you don't need to put it up. All it was doing was comparing his

12 F0, F1, which is more sensitive, the number of tests and the

13 compounds. 

14 And I guess as we were looking at your data,

15 it seems to me a summary like that would be helpful because it

16 appeared to us that only in two or three instances did we

17 actually see the F -- I'm particularly thinking of Slide 172. 

18 There only seemed to be a couple instances where that additional

19 lifecycle provided additional protection.

20 And I'm going to kind of go back and draw an

21 analogy, Les, to lifecycle testing. We can't predict with --

22 whether it's grass shrimp or mysids -- whether larvae or

23 post-larvae stages are most sensitive. Some compounds it's one,

24 some it's the other. 

25 And I kind of used that analogy of not being

26 able to predict that. It's sort of like looking at this, an
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1 analogous situation here to the multigen testing because it

2 appears just like we can't predict whether larvae or post-larvae

3 are most sensitive, I mean the good thing about the assays is

4 you have it. You're embracing all those life stages and in some

5 instances multiple times.

6 So I guess just make sure we understand the

7 data and get your comments on that for clarification.

8 DR. LESLIE TOUART: Yeah. Thanks, yeah, for

9 the observation. I think the context with the two-generation,

10 the concern was as presented, you know, there was concern how

11 may this play out into another generation, and how valuable

12 would that be in terms of population modeling or subsequent

13 analysis of that sort. And it's all well and good, but the

14 question still comes down is well, what is that value of the

15 additional time and effort into the next generation? And so

16 that was one of the efforts to try to evaluate, you know, the

17 performance of this test.

18 And in the mysid case, it's I think a good

19 case in a sense that the first generation, you know, the F0, is

20 really identical to the existing lifecycle test. So you

21 contained that or incorporated that in the context of the

22 two-generation to say okay, well what other information do I

23 glean from that next generation? And as you point out, and we

24 did in this Slide 172, there were a couple instances where, you

25 know, there was a little bit more sensitivity, I guess, in that

26 next generation. 
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1 Whether that's a statistical anomaly, you

2 know, based on the results in terms of okay, this one was

3 significant, that maybe the other one was close but not quite,

4 if you had redone it. But the context is that this is, you

5 know, likely to happen, as you've indicated, in terms of

6 particular life stages might be sensitive sometimes, maybe not

7 the other time.

8 So the question comes in terms of when is it

9 really a question? I mean we could live with the existing mysid

10 lifecycle test because that's already on the books, I mean it's

11 standard. But the question comes is well, would this carry

12 forth into another generation? And in that context, you know,

13 there may be occasions when it would be lower. 

14 Now what are the kinds of compounds that would

15 likely do that? Well, potentially they could be a material that

16 is endocrine active. And if it's a vertebrate active would that

17 do the same? The one thing we did notice for the mysids were

18 that the sensitivity of mysids for these vertebrate active

19 compounds was generally, you know, more sensitive than the

20 vertebrates themselves, maybe not for the same reason, but at

21 least there's indication. 

22 So I think there's utility in that sense, but

23 in terms of ultimate value I think that's where we would seek

24 additional guidance from the panel, you know, given that if

25 you've only got a few instances where you've got some

26 improvement, you know, where your NOEC or your level of concern
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1 might change slightly; is that sufficient to, you know, for

2 everybody to go into one of these tests? 

3 When you test just a small number of

4 compounds, I mean with mysids we have, I don't know, maybe a

5 dozen, but the, you know, a few of them were more sensitive than

6 the others in that next generation. So that's a question as to,

7 you know, whether we would want to go forward.

8 One thing I would add, though, is that in the

9 development and optimization for the two-gen, we were able to

10 refine certain aspects that would improve the existing lifecycle

11 test as it goes in terms of being more consistent in proving the

12 production of offspring so that you have a little bit more

13 ability to detect where we are, you know, setting performance

14 criteria of at least eight mysids through the two broods.

15 The other context is that one thing we notice

16 is that we really want to focus the test more on the actual

17 biological performance, not on a time dependent like the

18 existing lifecycle test is 28 days. And it's stopped whether

19 you had two broods or not. But if you key it into a brood

20 production and brood duration as endpoints, then that gives you

21 another indication of all the effects that may be occurring. So

22 there would be improvements in that context.

23 But I think in our view, there would be some

24 value in that next generation. The question is, is that value

25 sufficient to warrant this as an additional, you know, guideline

26 that could be utilized in terms of Tier 2 for endocrine-active
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1 materials.

2 DR. GEOFFREY SCOTT: Thank you for that. And

3 I did not realize the addition -- I'm glad you clarified that

4 improvement in -- that can be also put forward to the, you know,

5 the conventional test through this process. And that's really

6 important.

7 I have just two more questions. When you made

8 the slide at the end with the comparison between the copepod

9 assay and the mysid assay at the end, were you surprised at how

10 similar some of the results were, and in some instances, there

11 were differences, but were you surprised that both assays for

12 those compounds you had listed were as close as they were, I

13 guess is what I'm saying, going in? Just your perspective on

14 that.

15 DR. LESLIE TOUART: I think I was a little

16 surprised only in the sense that we had done that little bit of

17 literature review because there are a lot of published studies

18 done on the copepod, the Amphiascus. Tom Chandler at South

19 Carolina has done a lot of work for a lot of different

20 compounds. And for many of those we have mysid data as well

21 that have been done based on just mysid lifecycle type

22 information. And from those comparisons we've seen that mysids

23 tend to be more sensitive.

24 But as with any species comparison, sometimes

25 one's more than the other. But for the few compounds that we

26 had matched with the mysid and copepod in our exercise, the
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1 results were as we saw. Really only I think lindane you had a

2 dramatic difference. The others, you know, were in the same

3 ballpark.

4 And that was a little surprising in one

5 context. The lindane wasn't so surprising because mysids

6 typically have been a lot more on the more sensitive side.

7 The other thing that I think, for me, that was

8 surprising about the mysids was through, well, two generations

9 where the copepod was still within its lifecycle component and

10 to be in the same ballpark for the compounds that we could match

11 up, you know, said a lot about the copepod itself. 

12 But again, our rationale for saying, well,

13 let's just stick with the mysid was that the thought that the

14 copepod could be an easier, quicker, you know, cheaper type test

15 to proceed with kind of fell apart when we went into inter labs

16 and found the difficulties that laboratories had trying to apply

17 the method. Even though it would conceptually seem simpler, it

18 had its own difficulties and problems that would make it a

19 little bit more cumbersome to try to deal with in terms of

20 routine type testing.

21 DR. GEOFFREY SCOTT: Slide 171, if you could

22 turn to that just for clarification, Les; the asterisk there

23 referred to differences with what?

24 DR. LESLIE TOUART: In this case, these are

25 significant difference, but in the statistics that were done on

26 this test, this was to the vehicle control. So they would
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1 compare it --

2 DR. GEOFFREY SCOTT: To the vehicle -- within

3 each generation?

4 DR. LESLIE TOUART: Right.

5 DR. GEOFFREY SCOTT: Okay. 

6 DR. LESLIE TOUART: The treatment effect, you

7 know, so that in the F0s it would be the seven offspring, you

8 know, per second brood compared to the vehicle control of 9.87.

9 DR. GEOFFREY SCOTT: So it's not comparing

10 among generations? 

11 DR. LESLIE TOUART: In this slide, no. This

12 is just looking for where we had significant difference, and in

13 this case, the 33.7 was significant for the F0s and for the

14 others, the concentrations down to 2.15 with the 4.19, not

15 statistically different.

16 DR. GEOFFREY SCOTT: Okay. One last question

17 and it will be the last one and same question I asked Rodney. 

18 Since we're trying to look at international harmonization on

19 these criteria and some of the information you provided with

20 some of the OECD parallel trials, what's the current status in

21 terms of OECD in terms of these assays? Could you kind of give

22 us an update on where they are so that -- and have you informed

23 them of your, you know, just like Rodney I asked are you getting

24 comments back on moving forward with just the mysid?

25 DR. LESLIE TOUART: And thanks for that

26 question. As it happens, the OECD invertebrate expert group did
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1 have a meeting this past week, last Monday, and I

2 teleconferenced in to give them a little bit of a briefing where

3 I showed them basically the same slides that I've shown the

4 panel here today to give them some indication or update in terms

5 of how the mysid and copepod have been developed. 

6 And I informed them that this panel will be

7 providing comments, and so they're interested in knowing the

8 outcomes of that within the OECD process where expert groups

9 provide advice and information in terms of test guideline

10 development.

11 It then goes to their -- what they have now

12 with validation management groups, and there's one for

13 ecotoxicity testing, and the validation management group kind of

14 reviews the data that supports the test guidelines that have

15 been, you know, I guess consulted through the expert groups. 

16 And, you know, then they would make a recommendation as to

17 whether the guidelines should go forward to international

18 acceptance where the national coordinators within the test

19 guideline program across multiple nations would consider. 

20 And to be adopted by the OECD test guideline

21 has to be unanimously approved. If there's one member country

22 that's not accepting of a particular guideline then it doesn't

23 move forward.

24 In the development of the invertebrate assays

25 for the copepod and the mysid, the copepod was one that was

26 initially proposed by Sweden and has been going through the
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1 process as I kind of outlined in the historical perspective, and

2 so that one has some interest. 

3 From the European standpoint, they are also

4 interested in having an estuary test guideline. They currently

5 don't have a method similar to the mysid to evaluate, so they

6 would like to have that even, you know, regardless of endocrine

7 activities or not. And so that was part of the original

8 interest that the copepod had. But they're interested in seeing

9 endocrine methodology as well. 

10 In their discussions, they're also concerned

11 about invertebrate relevant endocrine disruption, not just

12 vertebrate, but the context as they can understand and there is

13 some interest.

14 On the mysid side, I would say that there is

15 not as much international interest on the mysid as has been

16 shown on the copepod, but they're looking to see what are

17 recommendations. If the U.S. ultimately adopts a particular

18 guideline they would likely want to pursue it. They haven't

19 pursued the mysid lifecycle to date, but that doesn't mean that

20 it may be something that they would like to see. 

21 But the outcome of this meeting will have

22 influence because that would get discussed at their validation

23 management group meeting which is tentatively scheduled for this

24 fall in the October timeframe. And at that meeting, they would

25 then decide whether to advance the method into the test

26 guideline, you know, to be adopted by OECD.
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1 DR. GEOFFREY SCOTT: Just a quick follow-up. 

2 In their validation, are they looking at those assays as a Tier

3 1 or Tier 2, I mean relative to your schema?

4 DR. LESLIE TOUART: With the OECD, they have a

5 different framework. They have five levels within their

6 endocrine testing scheme where a first level is really more

7 computational in-vitro moving on through a level five which

8 would be considered a definitive test. So the mysid tests as

9 proposed and the copepod tests would fall within their level

10 five, so this would be their definitive test. 

11 They don't consider Tiers in a concept, they

12 feel you can enter and exit in any different point, so they have

13 a little bit different context than the U.S. program, but we've

14 got kind of a two-tiered approach. So for them, they built a

15 toolbox of assays so one can pick and choose where within the

16 framework one might want to apply a particular tool to make the

17 determination.

18 DR. GEOFFREY SCOTT: Well, thank you for that

19 clarification because that wasn't clear in the OEC -- I'm not

20 familiar with that whole process and that was really helpful to

21 understanding. Thank you, Les.

22 DR. MARTHA SANDY: I saw Dallas Johnson first.

23 DR. DALLAS JOHNSON: Thank you. While we're

24 on this particular slide, some of the slides that we've been

25 presented have the standard error. This one looks like it's

26 standard deviation, and I guess I'm curious as to which it
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1 really is in this case because I think if that were standard

2 deviations there would be a lot more significant differences

3 there than what are shown. So I wonder if this isn't standard

4 errors that are on this slide as well.

5 DR. LESLIE TOUART: It's reported as standard

6 deviation, so my assumption is that's what it is. But I didn't

7 do the stats for this. I would have to go back and review that

8 particular study to see if perhaps it was just transcribed

9 differently in terms of what came up here.

10 DR. DALLAS JOHNSON: I guess the point is that

11 if you were comparing two means controlled to one other mean,

12 then the standard error of the difference would sort of be

13 involved with sample size. So if the sample size were to go in

14 as a denominator, which would make those standard errors, what

15 are reported as standard deviations, they'd be a whole lot

16 smaller because you're going to divide them by the number of

17 samples or N, number of replicates, I guess, depending on what

18 the sampling unit is. And so I'm just -- it might be worth

19 checking on that. I just noted that when I saw that particular

20 slide a little bit ago.

21 DR. MARTHA SANDY: Dr. Tuberty.

22 DR. SHEA TUBERTY: Les, in Slide 172, the next

23 slide, if you come to that; looking through this list of which

24 of the generations were more sensitive, 3 of the 11 times there,

25 the F1 was more sensitive or protective than the F0. Are these

26 based on the brood or any of the outcomes that were measured at
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1 about 20 different endpoints that were measured, the mysids? 

2 Was this just looking at one, any one of them that might have

3 been more protective or was it the outcomes of the F1 brood

4 reproduction?

5 DR. LESLIE TOUART: For all these except the

6 ones noted, it was on the most sensitive endpoint identified. 

7 And that could have been growth, it could have been first brood

8 or inter-brood duration. 

9 For the ketoconazole and then I think for

10 vinclozolin, we separate out those that were on the

11 reproductive, so these would be offspring production, and so

12 those were based on that particular endpoint. 

13 For the vinclozolin, what we did was looked at

14 the reproduction endpoint but also considered the others because

15 there was a big difference between the sensitivity of some of

16 the other endpoints that were growth related or in that context.

17

18 So in this sense, I'd have to go back to look

19 at the specific endpoint that was responsive. But my guess is

20 it would probably be like a 7-day length or a 14-day length on

21 say the vinclozolin and for ketoconazole this would have been

22 the second brood production because the slide previous to that

23 showed the 2.15 as the lowest effect concentration.

24 DR. SHEA TUBERTY: All right. So for

25 clarity's sake, yesterday when we were looking through some of

26 the other assays and the results and the decision-making
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1 process, there were more or less equal numbers of more

2 protective in second generation than first. And in this case,

3 like I said, 3 of the 11 were actually more protective going

4 through the second brood all the way to the second brood of the

5 F1 generation. 

6 Since two of them show that the F0 generation

7 was actually more protective and all the rest of the seven or

8 eight there were actually equal in both, it really seems like

9 the -- going all the way to the second brood of the F1

10 generation is, you know, using the thinking from yesterday it

11 wouldn't have been the decision-making process to go forward

12 with that. Does the EPA feel that in the big scheme of things

13 that going all the way through the F1 generation is going to be

14 more protective?

15 DR. LESLIE TOUART: I think that's somewhat of

16 a big question. But in terms of would that next generation be

17 more protective, potentially in some cases that would be the

18 case. 

19 The actual risk assessments and decisions that

20 are made carry other, you know, considerations and things as

21 well. We want to look at magnitude of a particular fact and

22 numbers of endpoints and stuff that it would look like.

23 But I think the context is that one could make

24 an argument that it is more protective, but how much more

25 protective, you know, might it be? 

26 Are you sufficiently protective if you could
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1 use just F0 information, you know, kinds of things, I think that

2 is the question. We see that with these numbers how much of a

3 difference in a risk assessment would the difference between say

4 13 part per billion versus 32 part per billion. That would come

5 into play depending up on where your exposures might be in terms

6 of whether you would exceed a level of concern or not. So in

7 some cases there could be a difference that could occur.

8 In the case of like the octylphenol where we

9 had a much more sensitive endpoint in the F0 where your F1 was

10 higher is an indication that there had been some type of maybe

11 compensation. Those individuals that survived to the F1 were

12 more, you know, less -- or at least less susceptible in a

13 context. So in that context, the next generation isn't giving

14 you anything other than maybe confounding some information if

15 you're adding that into a population model because it might give

16 you a different sense of security.

17 It's somewhat I think a value judgment in

18 terms of what one would see. We are interested in the Panel's

19 thinking. You know, my thinking and speaking personally is that

20 yeah, there is some value. How much of a value, it's debatable.

21 But I would be concerned that that question would still exist

22 if I only have the one generation. What would happen if I can

23 carry it on into the next generation, would that have lowered

24 effects or not? Not knowing that, how much that weighs into

25 decisions is a different consideration

26 I wish there was an easier way to say or be
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1 able to predict when that next generation would be more

2 sensitive. If that were a case, then you may be -- given the

3 nature of the kinds of compounds that we were exploring, which

4 were primarily vertebrate-active not invertebrate-active, the

5 consideration that we actually did see some instances that were

6 more sensitive in the next generation makes me think, well, how

7 often does that need to take place before I would want to see

8 that more universally? You know, 10 percent of the time, 20

9 percent of the time, you know, 50 percent? How many times is

10 that appropriate to try to look at? So that's the

11 consideration.

12 I think one could evaluate it in a couple of

13 different ways, but I think that we have demonstrated there is

14 some value. It is just does that value offset the additional

15 time and cost and potential complications of maybe having to

16 repeat the test if you fail in that next generation because of

17 some happenstance in the laboratory?

18 DR. SHEA TUBERTY: I appreciate you sharing

19 your personal views on that. I feel the same. I think there

20 seems to be some complication there from the loss of sensitivity

21 or increased tolerance in the second generation which we

22 wouldn't expect unless there was some culling out of the first

23 generation.

24 Another question I have is in looking at

25 copepods versus mysids and the types of endpoints that were

26 chosen to look for endocrine disruption specifically -- and this
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1 goes back to some of the comments that were being made yesterday

2 that when endpoints seem to be more growth related, they were

3 weighed less important for endocrine disruption. And certainly

4 you've commented several times and many of the endpoints that

5 were listed as the most sensitive for mysids was, in fact,

6 growth.

7 Now as an invertebrate zoologist and

8 physiologist, I'll give you that molting processes controlled by

9 ecdysteroids ultimately control growth. But the amount of

10 growth between the molting steps, the ecdysis events, could, in

11 fact, be controlled by many other things including just

12 nutritive state and the amount of resources available. And

13 we've talked in all the assays about the importance of good

14 nutrition and food stocks during the assays.

15 So back to my point, given that the copepod

16 tests are actually looking at timing between molting events,

17 which is a direct measure of the active effects of ecdysteroids,

18 rather than in just growth per se; in mysids you're just looking

19 at the final size of your animals or lengths of your animals,

20 not the number of ecdysal events that have happened during that

21 period. Would you say that the copepod assays are actually

22 better at measuring specific ecdysteroid events or

23 endocrine-mediated processes?

24 DR. LESLIE TOUART: And that's an interesting

25 question in context. I think the ecdysteroid effects would show

26 up apically in the mysid. But the copepod would have a little
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1 bit more diagnostic capability of being able to link that. 

2 Just to go back in terms of the purpose for

3 the mysid and/or copepod is to evaluate adverse consequence of

4 vertebrate-active material. So even though it's an endocrine

5 test, we are not so focused on the endocrine activities to be

6 able to measure because the method does not have direct measures

7 of those. These could be added in with extra complications, but

8 they could be in terms of being more specific to molting stages

9 and things like that which can be measured.

10 But the context is that we're concerned about

11 what are the adverse effects to the endpoints like growth or

12 reproduction. 

13 For growth with mysids as other crustacea, a

14 measure like length is going to go up more stepwise because with

15 each molt the length if pretty well set until the next molt kind

16 of thing. 

17 So that's an indication of, you know, how that

18 individual has fared in that sense, but recognizing there can be

19 other factors in strictly endocrine. When we look at a compound

20 like DCP, you know, this inhibits oxidative phosphorylation in

21 mitochondria. So the context is that yeah, this is likely

22 something that would reduce energy, metabolism and uptake. 

23 So it's not likely directly an

24 endocrine-active material, but we do see effects carry into

25 another generation that are resulting from that. So the adverse

26 effect is of importance even though there's no direct indication
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1 that that was disrupting the endocrine process of the

2 individual.

3 But I think the context again is that if one

4 were interested in the actual endocrine mechanisms and being

5 able to diagnose the disturbances of those endocrine processes,

6 whether the ecdysteroids or molt-inhibiting hormones or others

7 that would be present, one would have to add some additional

8 endpoints to observe those and be confident of that. But if

9 it's strictly apical concern, then the reproductive growth

10 endpoints are appropriate.

11 DR. SHEA TUBERTY: Thank you. Another point I

12 wanted to make related to growth was on 168, Slide 168. You

13 highlighted this data here and showed that in the inset dataset

14 there, and I'm not sure if this was from lindane or octylphenol

15 exposure.

16 DR. LESLIE TOUART: It's lindane.

17 DR. SHEA TUBERTY: Okay. So this is looking

18 at dry weights of I guess adults in response to lindane exposure

19 in the F0 generation. And with increased exposure you show

20 significantly increased growth of the animals. How would EPA

21 judge this as positive or negative in this adverse effects

22 assessments?

23 DR. LESLIE TOUART: And just for

24 clarification, this is the Day 14, so these would be individuals

25 that would be approaching their first brood release in a

26 context. Mysids will typically begin releasing brood from about
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1 Day 14 through maybe 18 depending upon the particular strength. 

2 But in this case, as you see, there is a significant size

3 increase.

4 In combination since there were some other

5 effects seen with inter-brood duration being increased and

6 things like that that, these are linked in a context that the

7 longer the individuals have not released brood, you know, you do

8 have an effect on dry weight and things like that. So there are

9 some indications.

10 In terms of whether just by size alone, like

11 weight alone, if that's increasing with concentration, that may

12 not be perceived as adverse unless it starts to be reflected in

13 other endpoints. A decrease in growth would be considered a

14 little bit more adverse. But biological changes, as I'm sure

15 you're aware, are changes if it's a statistically significant

16 change there may be a biological significance to that change if

17 it's different in terms there is energy input to grow and what

18 is at the expense of that, so one would have to judge that.

19 If it's an endpoint like this that stands

20 alone, it would be difficult to make a regulatory judgment on

21 that by itself and one would have to evaluate whether that's

22 really a true response or a statistical anomaly. 

23 So when we see a dataset like that, that tends

24 to indicate that that's more of a real response. And it would

25 garner concern, and likely we would set a NOEC/LOEC around that

26 particular endpoint, but it would be referenced as such so when
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1 it carries into a risk assessment one would be considering that

2 aspect, that it would be a growth stimulant in a sense and

3 whether that plays out or makes any other determinations would

4 depend on other factors within risk assessment.

5 DR. SHEA TUBERTY: Thanks, Les. And was

6 there, in fact, concomitant decrease in reproduction in this

7 group, or was there increased reproduction in this group with

8 the increased growth? I only ask because in many -- there was a

9 comment yesterday, somebody was saying that they didn't know of

10 a clear link between size and fecundity. But in my experience,

11 it's always been the largest females by far the largest

12 fecundity, and I thought there was a direct relationship there. 

13 So is that true in the mysids a well?

14 DR. LESLIE TOUART: I'm trying to move the

15 slide back. But if you go back on Slide 163, there's a lindane

16 comparison that was done where survival, weight, length, days to

17 brood release and offspring production are included for the two

18 laboratories, and you can see from that that the weight wasn't

19 the more sensitive endpoint. Length was in this context. But

20 there is connection to offspring production and others in that

21 particular laboratory.

22 DR. MARTHA SANDY: Dr. Scanes.

23 DR. COLIN SCANES: Can we flip to Slide 171

24 for a second? You said that the data with the stars and the

25 highlight are significantly different from control. But you've

26 got two controls, vehicle control and control. Which are they
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1 different from? And then I'll come back.

2 DR. LESLIE TOUART: As I mentioned before, the

3 comparisons for the treatments in this particular case was the

4 treatment groups compared to the vehicle control.

5 DR. COLIN SCANES: In which case, you seem to

6 have quite a marked effect to the vehicle in the F1 generation. 

7 How do you explain that?

8 DR. LESLIE TOUART: I think the context is

9 there was not a significant difference between the dilution

10 control and the solvent control, but in terms of differences,

11 I'm not exactly, you know, clear in the sense that the vehicle

12 seems to, you know, just numerically have greater root in the

13 next generation. Why that is occurring would be speculative on

14 my part. I don't know that that's always the case in terms of

15 these particular studies.

16 DR. COLIN SCANES: It does make you start

17 wondering whether one shouldn't be doing the statistical

18 comparison against both controls because if you've got a 30

19 percent increase in the vehicle control compared to the control,

20 that's not seen in the F0 generation. And that would, if you

21 did it against both controls, my betting is the 2.15 would drop

22 out.

23 DR. LESLIE TOUART: That's possible. I think

24 the context, and there are differing considerations in terms of

25 how one is more appropriately to analyze, you know, studies in

26 terms of comparing to vehicle control, if a vehicle's used,
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1 comparing to dilution water control, cooling controls in a

2 context. 

3 You know, typically what we want to be able to

4 do is to evaluate to ensure that there is no difference between

5 the controls in terms of the vehicle versus control. If there's

6 not a statistical difference, then we would consider that the

7 vehicle likely didn't have an effect.

8 In cases where the vehicle control is

9 different from your dilution water, that raises the question of

10 the vehicle then imposing some effects that you are aware of,

11 and so how you deal with that, whether you want to invalidate a

12 study and repeat it or whether you would accept it somehow by

13 pooling. Pooling control does add some additional power to your

14 test. But there are questions as to how you would deal with

15 that.

16 There is a guidance document that was produced

17 by the OECD, Statistical Approaches to Analyzing Ecotoxicity

18 Tests. And it discussed some of these kinds of concepts and

19 recommendations in terms of how one would utilize it. The

20 current practice within the Office of Pesticide Programs is to

21 basically compare to dilution water control and to keep that

22 somewhat consistent. But that's, you know, depending upon what

23 would be, you know, most appropriate in the context of a

24 particular test.

25 The best approach is to avoid solvents. And I

26 know that the EPA Duluth laboratory has gone to great extent to
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1 development methodologies to solvent-free systems. And they're

2 pretty much solvent free now, even for very difficult test

3 materials like the vinclozolin material able to do in

4 solvent-free type systems. 

5 So these are what's recommended, but many

6 contract laboratories have not developed their techniques or

7 skills to the point of being completely solvent free, though

8 there has been movement in that direction. And I would

9 anticipate in the future that we see more solvent-free type

10 systems where this becomes more of a historical consideration

11 than something in the future.

12 DR. MARTHA SANDY: Dr. Scott.

13 DR. GEOFFREY SCOTT: Just a follow-up on that

14 comment, Les. I think that is one of the advantages of the

15 mysid system is those solvent-free systems have been developed

16 with those flow-through diluters, right? I mean I think that's

17 one of the advantages, right?

18 DR. LESLIE TOUART: Yeah. That's correct in a

19 sense that one would be able to adapt that. 

20 There are some added difficulties with, you

21 know, mysids because it's seawater system, there at 20 parts per

22 thousand. So that makes some materials less soluble and maybe a

23 little bit more difficult to deal with in the solvent-free

24 system. But it is adapted to that, and that could be developed

25 on the maybe extreme cases what solvents you could pursue for

26 that. 
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1 But again, contract labs currently would still

2 opt for a solvent-based system because that's how they're set

3 up, and their diluter systems are designed with syringe

4 injectors and things to be able to do that. But going forward

5 they recognize that there are advantages in going solvent free

6 and we're seeing those kinds of adaptations being made by some

7 contractors. And we would anticipate, more or less, that that

8 will become more commonplace.

9 DR. MARTHA SANDY: Any other questions of

10 clarification from the panel?

11 Seeing none, I would like to give the panel a

12 chance, with Dr. Touart here, are there any questions that came

13 to mind overnight on the quail assay?

14 Yes, Dr. Scanes.

15 DR. COLIN SCANES: A couple of questions, and

16 I'll go through one by one. First of all is the issue of the

17 end, the number of replicates. For a number of the endpoints

18 the number of end -- the end was either five or, in some cases,

19 even three. What was the rationale behind that?

20 DR. LESLIE TOUART: Can you be more specific

21 upon which endpoints you would be referring to in terms of the

22 ends? I mean primarily with the avian study, we're dealing

23 with, you know, 12 pairs for collection and the ends are going

24 to be fairly large in a lot of cases. But in some of the

25 sub-sampling primarily when we get into some of the biochemical

26 or histopathology we are sub-sampling and it may be male only or
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1 female only so there would be just a few in some of those

2 instances.

3 DR. COLIN SCANES: Being more specific then,

4 for the hormonal and the histopath data, the end is down to five

5 and in some cases we've lost samples and it gets much lower than

6 that.

7 DR. LESLIE TOUART: Yeah. Again, for the

8 samples, what we were trying to do is, you know, select and

9 we're basically sampling from the individuals that have survived

10 to the end. So for some of the treatments there may not be, you

11 know, sufficient pairs of individual. In controls, what we

12 would be trying to do would be collect from the particular sex

13 and one's making tradeoffs between what goes to histopath, what

14 may be going to hormone analyses.

15 DR. COLIN SCANES: Related question. The

16 report makes a lot of the histopath data and yet no statistics

17 was done. What's the rationale for the lack of statistical

18 analysis?

19 DR. LESLIE TOUART: I think the context is we

20 didn't do any statistics on the pathology on the bird side. On

21 StatCHARRMS statistics program that had been identified and had

22 been utilized for the fish and frogs has the capability of doing

23 some statistical analyses of that. We didn't apply that on the

24 bird side because we're still developing and for the datasets

25 that we had they just were not conducive to the adaptations and

26 StatCHARRMS was developed more for the fish test paradigm.
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1 With the pathologists, they're generally used

2 to evaluating their information and making their judgments

3 through nonstatistical methodologies because of the nature of

4 the kinds of responses that are seen. So we didn't do the

5 analyses because it was not deemed to be necessary for the

6 results that we were observing.

7 DR. COLIN SCANES: Looking at your data, if

8 you're putting histopath data into a scoring system of either 1,

9 2, 3, 4 and 5 or 0, 1, 2, 3 and 4, that is then amenable to

10 statistical analysis.

11 DR. LESLIE TOUART: Yeah. I mean I would

12 agree that one could be able to evaluate, you know, some of

13 that. But in the context and judgment of the pathologists,

14 especially when we went through the vinclozolin study, there

15 weren't very many remarkable responses to try to evaluate in

16 such a manner. And the nature of the responses that were seen

17 for the epididymis, you know, these were observed and concluded

18 to be legitimate effects.

19 Now the context in terms of whether that might

20 be appropriate in retrospective or moving forward to apply some

21 statistical techniques to evaluate that, I think that the

22 further advancement and development of the StatCHARRMS program

23 and as that is converted to the "R" statistic base, then I think

24 it would be useful to apply such a technique, you know, to the

25 evaluations of birds or really any other datasets that we would

26 have.
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1 DR. MARTHA SANDY: Could I remind panel

2 members to keep their questions to clarifying questions and not

3 a back and forth discussion. I don't mean to cut people off, so

4 any other clarifying questions on the avian assay?

5 No. Thank you very much, Dr. Touart. I'd

6 like to now give the panel a chance, if you've thought of any

7 questions of clarification you'd like to ask Dr. Rodney Johnson

8 about the Medaka assay.

9 Dallas Johnson.

10 DR. DALLAS JOHNSON: Yes. I'm still confused

11 with blocks and stuff and hopefully -- let me see if I can pull

12 the slides up. Got too many files open here.

13 I guess the reason I'm confused is we talked

14 yesterday what a block was, and the block was one of those nice

15 pictures that you showed. Yeah, if you know where it is.

16 DR. RODNEY JOHNSON: I think I can find it. 

17 DR. DALLAS JOHNSON: Slide 85 or something

18 around in there, I think.

19 DR. RODNEY JOHNSON: Am I going in the right

20 direction?

21 DR. DALLAS JOHNSON: No. You're going the

22 wrong direction.

23 MALE SPEAKER: Wrong direction.

24 DR. LESLIE TOUART: It's Slide 77.

25 DR. DALLAS JOHNSON: You can do a search on

26 blocks, the way I usually find it.
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1 DR. RODNEY JOHNSON: Okay. Yeah.

2 DR. DALLAS JOHNSON: Yeah. Now I guess the

3 reason I'm getting confused again is back on the previous slide

4 you talked about the experimental design consisting of seven

5 blocks, five for chemical and two for control. And then the

6 next slide you indicated that this represented a block which had

7 -- and I asked you about the fact that there were 6 pairs there,

8 and I thought there was going to be 12. And the reason I

9 thought that there was 12 is on the next slide, you talked about

10 using 12 treatment pairs rather than 6 treatment pairs. So I'm

11 all mixed up with respect to the number of treatment pairs and

12 anything that you can say that would help clarify that in my

13 mind would be appreciated.

14 DR. RODNEY JOHNSON: Okay. So the first thing

15 I think I would say is that I'm concerned about using the word

16 "block" but let's say that this is a representation of the

17 replication structure of any particular treatment, let's say,

18 and let's not even talk about it in the context of the controls

19 right now.

20 All right. As we go from left to right, in F0

21 we start with six replicate breeding pairs. And the embryos

22 produced by those six pairs are all pooled.

23 DR. DALLAS JOHNSON: Yeah. I'm okay with that

24 part.

25 DR. RODNEY JOHNSON: Okay. Then we go to what

26 would be incubation in F1, which has six replicates.
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1 DR. DALLAS JOHNSON: My question is mostly

2 about the F0, the first column.

3 DR. RODNEY JOHNSON: Okay. 

4 DR. DALLAS JOHNSON: You have six pairs there.

5 Excuse me. Turn my microphone.

6 You have six pairs there, and this six pairs

7 are going to be -- let's say they're treated with the 100-level

8 dose.

9 DR. RODNEY JOHNSON: Correct.

10 DR. DALLAS JOHNSON: Okay. Then are those the

11 only six pairs that are in the 100-level dose, or is there

12 another six pairs somewhere else that's in the 100-level dose,

13 or are there six of these blocks that are within the 100-level

14 dose?

15 DR. RODNEY JOHNSON: Okay. This would be on

16 the left-hand side where there are six breeding pairs that would

17 be six breeding pairs per treatment. So in let's say 100 -- a

18 dose of a 100, level of 100, there are going to be six breeding

19 pairs at dose level 100. 

20 There will be six breeding pairs at the next

21 lower dose, which let's say is 50, et cetera, until we get to

22 the controls. And at the controls, there would be two times

23 that or 12.

24 DR. DALLAS JOHNSON: And then how does that

25 relate to the next slide where you did the power analysis and I

26 thought I interpreted from that that you were going to use 12
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1 treatment pairs or recommending use of 12 treatment pairs rather

2 than 6?

3 DR. RODNEY JOHNSON: Okay. So in F1, first of

4 all, you should recognize or I want to state that there are no

5 data collected here. They simply are the brood stock, if you

6 will, for starting the exposure within the next generation which

7 then, of course begins at this line or in here where we have

8 embryos being incubated within the F1 generation, or they are

9 the beginning of the F1 generation.

10 Now at Week 9, where we measure the effects,

11 the diagnostic or the AOP effects, we measure that at Week 9,

12 but we also at Week 9 take those six replicate tanks, and we

13 take two males and two females from each tank, and we produce a

14 common tank of males and a common tank of females which are then

15 distributed into 12 reproductive pairs, and those 12

16 reproductive pairs are the data by which reproduction is

17 assessed.

18 DR. DALLAS JOHNSON: Yeah. I think that

19 clarifies it for me.

20 DR. MARTHA SANDY: Dr. Blazer.

21 DR. VICKI BLAZER: I was curious, with the MTT

22 and the fact that you're doing the whole fish histopathology,

23 why there was nothing with thyroid? Do you actually look at the

24 thyroid at all?

25 DR. RODNEY JOHNSON: The protocol specifies to

26 explicitly look at the gonads, explicitly look at the kidneys,
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1 explicitly look at the liver and to evaluate other organs for

2 effects. But it does not specify which organs and what to

3 evaluate them for. So the thyroid is implicitly in the

4 guideline, but it is not explicitly called out to be evaluated.

5 The principal rationale for that is that the

6 -- as of course you know, the thyroid of fishes is not an

7 encapsulated organ. It tends to be rather diffuse and it is not

8 an easy organ to evaluate in sagittal section in particular

9 because it is not real predictable where you're going to get

10 good sections of the thyroid. 

11 You will almost always get sections of

12 follicles in parasagittal sections, almost always. The most

13 important caveat would be that it's difficult to establish a

14 subjective, sort of qualitative evaluation of extent of changes.

15 Now it is clear that one can evaluate things like follicular

16 cell hypertrophy and some things like that, but it's quite

17 difficult to definitively and subjectively provide a good

18 assessment of extent. So it would be more, I think, a hallmark

19 of a remark rather than something that would be defined as a

20 definitive endpoint in the fish.

21 DR. MARTHA SANDY: And Dr. Jennifer Freeman.

22 DR. JENNIFER FREEMAN: Hi. Thank you. I just

23 had a clarifying question on whether or not dose was analyzed if

24 there's any tissue analysis that was done in the assay?

25 DR. RODNEY JOHNSON: I'm sorry. I didn't hear

26 the last phrase of the question clear.
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1 DR. JENNIFER FREEMAN: Yeah. Was tissue

2 analysis completed at all during the assay to determine dose?

3 DR. RODNEY JOHNSON: No tissue analyses are

4 done to determine dose. Only the water concentration is

5 measured for dose.

6 DR. JENNIFER FREEMAN: All right. In light of

7 that, I have a follow-up quick question about your thoughts on

8 how the revised MRT without assessing the F2 reproductive end

9 points, whether or not the assay would be able to assess if the

10 chemical is bio-accumulating in the F1?

11 DR. RODNEY JOHNSON: There's quite a bit of

12 literature describing determinates of bioaccumulation. So I

13 think one may be able to ascertain whether or not

14 bioaccumulation is something to be considered based on the

15 physical chemical properties of the chemical in question. 

16 But there is sort of one sort of check, if you

17 will, and it's sort of an indirect check, and that would be

18 looking at the toxicity with respect to hatching in F1 compared

19 to F2. And with those chemicals that would not bio-accumulate,

20 you would expect those values to be similar.

21 On the other hand, if there was a major

22 difference in the hatching success rate in F1 compared to F2,

23 especially if F2 was quite a bit lower or was lower, I think one

24 would be very concerned that it perhaps was due to

25 bioaccumulation and would make you consider your adequacy of the

26 protocol on that basis.
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1 DR. MARTHA SANDY: Any other questions of

2 clarification?

3 Seeing none, thank you very much, Dr. Johnson.

4 I'd now like to have Dr. Degitz come here, and

5 anyone have any questions for him on the amphibian assay? Looks

6 like we have all our questions answered. 

7 I think we should take a break. We're

8 scheduled for a break now. Ten minutes or fifteen? I'm not

9 sure. Let's go with a ten-minute break. Let's give ourselves

10 12 minutes. Let's come back at 10:15. Thank you.

11 (Whereupon a break was taken)

12 DR. MARTHA SANDY: Mary is seated. For those

13 who are listening. Okay. 

14 MS. MARY MANIBUSAN: Thank you. I want to

15 thank Dr. Christman and Dr. Johnson for their clarification

16 questions on the statistical analyses that were conducted in

17 part of the reanalysis that was issued from the errata. 

18 Because of time differences and receiving

19 those questions late yesterday, we're having a slight challenge

20 with time zone differences. But we anticipate bringing before

21 you the final responses sometime today. We'll be checking in

22 around the noon hour to help solidify when that will be done. 

23 We want to make sure that we are firm in our response and clear

24 and accurate. Thank you.

25 DR. MARY CHRISTMAN: Thank you, Mary. 

26 Appreciate that a lot. Fortunately, since it relates to
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1 StatCHARRMS, and they don't come up until probably tomorrow and

2 the following day, we're good.

3 MS. MARY MANIBUSAN: Thank you. But we'd like

4 to make sure we provide those responses as swiftly as possible

5 for your consideration. So thank you again.

6 DR. MARTHA SANDY: Thank you very much, Mary.

7 All right. I think now we're ready to turn to

8 the public comments. And we have received comments from two

9 groups of individuals. One set of comments the person that

10 submitted them has asked that that be read into the record. So

11 I've been asked to read this. So I think we'll start with this

12 public comment and then we'll move to comments that have been

13 submitted by the Endocrine Policy Forum, and there we have

14 presenters that are here physically in the room.

15 So if you'll bear with me, I've been asked to

16 read these comments. They're submitted by Patricia Bishop, and

17 she's providing these comments on behalf of the People for the

18 Ethical Treatment of Animals, the Physicians Committee for

19 Responsible Medicine and the Humane Society of the United

20 States.

21 These comments are a summary of written

22 comments that these groups have submitted for the panel's

23 consideration. They're in the Docket, and we've all been sent

24 them and read them. Okay. So here are the comments.

25 The three vertebrate ecotoxicity assays,

26 the Japanese quail two generation, the Medaka
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1 Multigeneration and the Larval Amphibian Growth and

2 Development Assay are complex tests of long duration. 

3 They use large numbers of animals. The fish study uses

4 about 3,000 animals, the bird study uses about 1,500

5 animals, and the amphibian test uses at least 750 animals.

6 These numbers do not include animals used in optimization

7 and range-finding studies or cold animals. Thus, we must

8 have assurance that these tests are fit for purpose and

9 will definitively characterize a chemical's action as an

10 endocrine disruptor in a clear dose response fashion.

11 The results of the validation studies

12 indicate that the reliability as definitive tests is

13 suspect because numerous areas exist for introduction of

14 error and variability that could confound interpretation

15 of findings. Thus, we believe that there is a high

16 potential for inexperienced laboratories to generate poor

17 quality data which will result in the repeating of tests

18 that will kill even more animals. 

19 While it is important to show the

20 consistency and use of a protocol as achievable, this

21 should follow careful development of the test. The main

22 focus during development and evaluation of the Tier 2

23 assays must be the identification of endocrine-sensitive

24 endpoints and characterization of sensitivity as well as

25 specificity of these endpoints. It is not clear in the

26 case of the three tests discussed here that there has been
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1 sufficient, or any, characterization of a sensitivity

2 and/or specificity of these endpoints. It is critical

3 that careful attention be paid to the endocrine

4 sensitivity of endpoints included in any new reproductive

5 tests that are not measured in the standard suite of

6 toxicity tests used in pesticide registration.

7 With respect to the inter-laboratory

8 comparisons, one of the most obvious issues noted in all

9 of the inter-laboratory comparisons was a lack of

10 consistency across labs in identifying effects. The JQTT

11 inter-laboratory study using a single reference chemical

12 test substance, the androgen receptor antagonist

13 vinclozolin, showed little in the way of treatment-related

14 effects. However, there were several sources of variation

15 such as the strain of the bird that could lead to serious

16 problems with interpretation of results. 

17 The MMT inter-laboratory comparison also

18 used vinclozolin as the single reference substance for the

19 inter-laboratory study and the only significant conclusion

20 regarding effects across labs seems to be that all three

21 labs did not see decreased reproduction. In the case of

22 the LAGDA inter-lab comparison, where two different

23 chemicals were used, there also were few instances where

24 all labs consistently reported the same significant

25 finding.

26 Use of vinclozolin as a reference
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1 chemical in two of the three studies and the ambiguous

2 responses reported seems to have raised more questions

3 than it answered, since even EPA noted that it posed a

4 challenge as a test substance in a validation exercise,

5 leading to uncertainty in the expected response. It

6 clearly did not result in a well defined

7 concentration-response relationship, the development of

8 which is one of the primary aims of the Tier 2 tests and

9 essential to the assessment of inter-laboratory validity.

10 One of the stated objectives of the JQTT

11 study was to show that the endpoints added to this assay

12 beyond the ones in the standard avian reproduction

13 toxicity assay were, in fact, sensitive to endocrine

14 disruptors. It seems counterintuitive to select a

15 reference chemical such as vinclozolin to test this

16 hypothesis when there were questions about whether effects

17 would even be seen. Use of vinclozolin as a reference

18 chemical did not appear to adequately demonstrate the

19 usefulness of the assay or the appropriateness of the

20 selective endpoints for detecting endocrine disruptors.

21 During the fish studies, there were

22 issues of labs being unable to demonstrate consistent

23 control responses. This does not lead to confidence in

24 the ability of commercial labs to successfully conduct

25 this test or confidence in the results produced. Cases of

26 erroneous sexing of fish occurred as well which obviously
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1 introduces substantial error into the results. 

2 Genotypic, phenotypic sex mismatching

3 was noted by EPA to be a problem by labs in performance of

4 the Tier 1 fish short-term reproduction assay as well. 

5 Very few significant effects were noted in the MMT

6 inter-lab comparison. One of the three labs showed no

7 significant effects at all, and there was little

8 consistency among labs in the effects that were observed. 

9 

10 The failure of the three labs to discern

11 effects of vinclozolin in a consistent manner raises a

12 question as to whether this is an artifact of the method's

13 inability to detect weak endocrine disruptors or if too

14 many sources of variability introduced into the

15 experiments masked any significant results.

16 With respect to the LAGDA validation,

17 several significant sources of variability introduced by

18 the labs were indentified in the study. Inter-lab

19 comparisons demonstrated how slight differences in feeding

20 regimens alone can alter the study results. Also, it was

21 noted that interpretation of pathology results by

22 different pathologists could significantly alter study

23 findings. During the prochloraz study, one lab reported

24 treatment-related liver pathologies that were vastly

25 different from what was reported by the other three labs. 

26 
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1 EPA speculated that this may be

2 explained by disease in the fish, but the cause was never

3 determined. The fact that one lab could have such

4 dramatically different results from the other three

5 suggests that this assay may also not be ready for use in

6 the Tier 2 Endocrine Disruptor Screening Program.

7 We conclude from the studies provided

8 that none of the three vertebrate assays appears ready for

9 use as a valid Tier 2 test. More critically, the tests

10 have not been shown to include endpoints that are

11 sufficiently sensitive to endocrine-active substances to

12 definitively characterize the suspected endocrine

13 disruptors. 

14 In addition, inconsistencies among labs

15 in reporting significant findings, variability introduced

16 into the tests by strains of animals used, feeding

17 regimens, pathology interpretation and other unknown

18 factors indicate that more evaluation and refinement of

19 the methods are necessary.

20 Therefore, neither the relevance nor the

21 reliability of these tests has been sufficiently shown. 

22 These tests are lengthy and complex new protocols

23 requiring a high level of expertise to perform properly

24 and due to lack of reproducible significant effects

25 amongst labs participating in the inter-laboratory

26 validation studies, validation has not been adequately
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1 demonstrated.

2 That said, we are extremely concerned by

3 the announcement yesterday that EPA has initiated the

4 process for collecting Tier 2 data from some of the List 1

5 chemicals by issuing an information collection request for

6 Tier 2 testing. This action appears to be premature for

7 the reasons I have already described. Most importantly,

8 it is not assured at this time that the Tier 2 ecotoxicity

9 tests will be positively reviewed and supported by the

10 SAP.

11 So again, I have just read into the record, by

12 request, the comments submitted by Patricia Bishop on behalf of

13 the People for the Ethical Treatment of Animals, the Physicians

14 Committee for Responsible Medicine and the Humane Society for

15 the United States.

16 DR. SHARLENE MATTEN: So the sound system for

17 the webcast works, but some folks, it's individual specific, so

18 I'm demonstrating how you need to speak into the microphone. 

19 Martha is heard. Mary Manibusan is heard. I'm heard. Dallas

20 was heard. You basically have to go like this. And I don't

21 have a singing career or acting career, but in radio land, my

22 husband works for radio, you do hold the microphone really close

23 to your mouth. And if you have to move, move the microphone

24 with you. All right. That applies to the panel, too. So I

25 don't have as booming as Mary Manibusan, but I do my best.

26 That's a little laughter here. That's always
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1 good. And now Martha will state where we are going next.

2 DR. MARTHA SANDY: Thank you, Dr. Matten.

3 So now we'll turn to the next group of public

4 commenters and these are comments made on behalf of the

5 Endocrine Policy Forum. I'll let you sit down and introduce

6 yourself, please.

7 DR. ELLEN MIHAICH: Good morning. Thank you

8 for letting me speak. My name's Ellen Mihaich and I am the

9 scientific advisor for the Endocrine Policy Forum. I know I did

10 this initial slide last month, since many of us were together. 

11 But since there's some new faces, I just wanted to be able to do

12 this again to set the stage for what you're going to be hearing

13 next. I also want to thank you for being here and being willing

14 to participate in this review. It is a huge amount of

15 information and we feel for you for having to try to get through

16 it.

17 The next four presenters will actually take

18 each one of the tests separately and go through some of the high

19 points that we saw in going through the data and then I will be

20 back up again to do a final sort of wrap up.

21 Just a brief introduction to who the Endocrine

22 Policy Forum is. We're a consortium of about 95 percent of the

23 List 1 test order holders so the recipients, the people that

24 have been doing this testing. So for the SAP last month, we

25 were here with all of the experience of actually having to do

26 these in a regulatory forum of the 11 screens. So clearly we
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1 have a pretty significant interest in how the Tier 2 tests are

2 going to move forward because we're going to be the ones that

3 have to do it in a regulatory setting.

4 So the other thing that I want to point out to

5 you is we're a group that the List 1 is predominantly, not all,

6 but predominantly from the crop protection industry. And if you

7 know anything about what it takes to get a pesticide registered,

8 it's millions and millions of dollars of testing, regulatory

9 testing, and we do a lot of these chronic studies. 

10 So we have a lot of expertise in running these

11 in contract labs and being able to evaluate them. So we came at

12 this from looking at that, trying to look at the data that's

13 here and thinking about having to do this in a regulatory

14 setting. But I think, again, as we go through this, you know,

15 you're being charged with deciding whether or not these tests

16 are valid for their purpose, and that's where we were looking

17 at, too.

18 So with that, I was just setting that up, and

19 then the next four, as I said, will take each of the four tests

20 separately, and then I'll come back to fill in a few overarching

21 comments. Thank you.

22 DR. MARTHA SANDY: Thank you. And I gather

23 the first presenter will be Tim Fredericks, and there will be

24 four presentations of about 15 minutes each. Right?

25 DR. TIM FREDERICKS: Yes. This is Tim

26 Fredericks. Okay. I'll be commenting on the Japanese Quail
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1 Two-generation Test.

2 Can I advance the slides?

3 So we're going to start with some conclusions,

4 just to give everything a background of what I'm going to

5 explain and we'll come back to why these are important

6 throughout the next about 15 slides. So as was mentioned

7 yesterday, there's no guidance on when this test design would be

8 triggered from Tier 1 testing to Tier 2 testing. This is fairly

9 important. This test system has not been validated and thus is

10 not appropriate for regulatory studies. We would consider this,

11 what's happened, a pre-validation.

12 So poor performance in ring testing, too many

13 measurement endpoints, limited test sensitivity to

14 endocrine-mediated effects. So it includes both population

15 level, relevant endpoints for risk assessment, but it's limited

16 in its ability to definitively link these to endocrine mechanism

17 of action. So the big question is how does the Japanese Quail

18 Two-generation Test enhance our understanding of population

19 level effects in birds versus a one-generation test that's

20 available for a lot of these compounds?

21 So the background, quail and duck have been

22 used in regulatory testing for greater than 20 years following

23 the one-generation test guideline. So routinely conducted for

24 pesticides, the F generation, most F0 and F1 generation are

25 pretty much the basis for the endpoints that are in the

26 two-generation test. All of these are in the two-generation
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1 test, and then two-generation test builds on these.

2 So the specific rationale for selecting the

3 Japanese quail is well presented for the -- in the ISR. Again,

4 there needs to be specific guidance on when this would be

5 required based on the Tier 1 testing. And although

6 theoretically there are reasons presented in the ISR as to why

7 an avian species should be tested in the Tier 2 test, these are

8 mostly theoretical and have not been proven based on the

9 validation testing and the pre-validation work that's been done.

10 So specifically, Charge Question 2,

11 toxicological relevance of this test design; so there were 106

12 endpoints analyzed statistically, and a lot of these were

13 analyzed over multiple time points during the test. So you

14 ended up at almost 155 statistical comparisons in this one test

15 design. And on top of that, there was endpoints that were

16 collected and actually not analyzed statistically or not

17 analyzed at all. They were just archived, these samples. So

18 there were more endpoints then were actually presented.

19 The diversity of endpoints selected for this

20 test monitor wide variety of adverse effects to birds and the

21 connection to endocrine-related effects has not been made. 

22 Specific endpoints have not been determined to give definitive

23 answer to endocrine activity versus overt toxicity. And this is

24 particularly important in regards to how this will be used in

25 the risk assessment which is the goal of Tier 2 testing.

26 So the assay endpoints. I've already listed
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1 that there's probably too many endpoints included that have not

2 been validated in responses for endocrine-active pathways. So

3 pretty much all of these endpoints can be influenced by overt

4 toxicity and the difference between overt toxicity and

5 endocrine-mediated effects has not been clearly made.

6 There's a large degree of interrelatedness

7 between a lot of these endpoints. You may measure eggs

8 collected, but then you're going to measure the egg set of eggs

9 collected. You're going to measure the egg set of the eggs laid

10 and eggs laid per hen, per day. So although there may be 106

11 endpoints, a lot of those rely on other endpoints to make those

12 endpoints. And again, a lot of these endpoints are included in

13 the current guideline for one-gen reproduction study.

14 So one of the endpoints that was collected and

15 not analyzed were fecal steroids. And to quote the ISR on page

16 54, "Fecal samples reflect more consistent effects than

17 biochemical analyses." So biochemical analyses were analyzed

18 but fecal steroids weren't. So this is probably a major limiter

19 for this.

20 And then it's difficult to collect blood, so

21 fecal steroids could be collected from the 15-day-old chicks

22 before they hatched, so they'd be actually 15-day-old embryos. 

23 So those embryos will have a fecal sack that will be easily

24 collected. What was currently done is blood was taken from that

25 15-day-old embryo, and a lot of the samples did not have the

26 quantity to be analyzed for the biochemical analyses, and you
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1 could do a correlation between the fecal sack and blood

2 chemistry and determine if that relationship exists and then

3 have a more simple sampling technique.

4 And then fecal steroids for the adults,

5 there's a lot of sample. These birds are in cages for 10, 12,

6 14 weeks. They're defecating every day, and you could separate

7 it by males and females, but it has not been correlated between

8 blood steroid levels and fecal steroid levels, but this could

9 easily be done.

10 Histopathological endpoints have not been well

11 validated for avian species, as some of the commenters mentioned

12 today. Sample size was an issue here, and the statistical and

13 the interpretation of these endpoints was limited by the number

14 of samples that were collected and analyzed. And from the

15 histological report, there were a lot of cases of missing,

16 insufficient or not submitted tissues, especially for the

17 embryos. They didn't have the tissue available for a lot of

18 these to be able to do a valid histological comparison.

19 And then biochemical analyses, as was pointed

20 out yesterday, these are highly variable. It was one of the

21 most variable endpoints measured across the adults, the chicks

22 or the embryos. And again, there's a couple of issues with the

23 sampling that happened for both the histopath and the

24 biochemical. These samples were selected, not selected

25 randomly; they were selected as the best samples that were

26 submitted. So there seems to be a sampling disconnect there and
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1 a statistical design disconnect there as well.

2 And specifically for the histopath, we

3 mentioned yesterday the epididymis as the one endpoint that was

4 noted as statistically different across the study designs

5 through the labs. And so I looked last night and that

6 epididymis was statistically dose response at the one lab, and

7 when you looked at those individual pens for those individual

8 males that had valcuolization or dilation in the epididymis,

9 they all produced chicks at 14 days in the second generation or

10 the first generation if they were the F0s or F1s. 

11 So the reproductive endpoints were all okay. 

12 They were able to produce chicks, which is the main goal of this

13 test is to see if we're going to have population level effects. 

14 So despite having a potential histopathological effect, it was

15 not realizing a population level effect.

16 So again, the background studies, a lot of

17 these are one generation and this would be what we would

18 consider pre-validation testing. Most of these were developing

19 the endpoints that would have gone into this ring test. But in

20 reality, we were still pre-validating in the ring test because

21 we don't have validated methods for these endpoints or

22 responses.

23 And just to point out the trenbolone that was

24 talked about yesterday that was used in the two-generation

25 design, and there were effects in both the F1 and the F2 or

26 there was just effects in the F1 in each of those tests that
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1 were run. So the second generation didn't add anything to the

2 interpretation of these results. The results were the same

3 based on the F1 or the F2. So you would expect a similar if you

4 ran one-generation test instead of a two-generation test.

5 So statistical methods in the ISR were not

6 well defined but were generally appropriate. Again, you run --

7 oh so there are two things -- you're running multiple

8 statistical tests on a single endpoint. So you're running a

9 Dunnett test and JT test for trend. And really, you shouldn't

10 analyze the same endpoint with two statistical tests.

11 So there needs to be some correction for false

12 discovery rate. It was discussed in the ISR, and just based on

13 the sheer number of variables analyzed when you take into

14 account the different days that food consumption, body weights

15 and the different generations that each of these were measured

16 for, you end up with over 155 endpoints instead of the 106 that

17 you started with. And so just sheer five percent alpha value,

18 you're looking at quite a few false detections with 155

19 variables.

20 So the standard one-generation avian repro

21 data is very robust and reliable. These studies have been done

22 for over 20 years. The additional endocrine-specific endpoints

23 have not been rigorously evaluated. So a; the endpoint list

24 needs to be refined, and b; these specific endpoints haven't

25 been linked to endocrine-mediated effects. 

26 No endpoints were consistently statistically
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1 significant across all three laboratories following vinclozolin

2 exposure. And when you compare across the labs for some of the

3 endpoints that were flagged, you see at some labs that endpoint

4 may have been up regulated, and some labs that may have been

5 down regulated. 

6 So when you're running this test as part of a

7 Tier 2 test design, how are you to interpret your results if it

8 changes between the three labs that were running the validation?

9 So could that result that was maybe tempered by the fact that

10 you had two other studies to compare it to result in a

11 regulatory decision if there was a trend at one lab but not the

12 other two or the other two trended the opposite way?

13 So transferability between labs; so strain

14 selection needs to be addressed and this is probably a positive

15 and the negative for the assay because yes, you could choose a

16 specific strain to temper this variability, but is that really

17 appropriate at the larger population level?

18 So refinement to the protocol; the protocol

19 hasn't been well refined, and that's why this should be

20 considered pre-validation work. There's large inner and

21 intra-laboratory variability across many of the variables,

22 especially the new variables that aren't in the standard

23 one-generation test design.

24 So dose response relationships were not

25 present, especially for vinclozolin. And then a lot of the

26 other compounds that were run through the one-gen and two-gen
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1 study designs didn't have multiple comparisons of the same

2 chemicals. So how valid are those responses at a single lab? 

3 Endpoint responses were generally not present

4 or consistent when present.

5 Strengths of this study design; species

6 selection is well justified in the ISR. It uses a precocial

7 species that matures rapidly, and this is important for a test

8 that runs as long as this test does. So includes both apical

9 endpoints for overt toxicity, and these are the endpoints that

10 are really useful for risk assessment at the population level. 

11 So there's the potential to detect endocrine-mediated effects

12 fairly well, but this hasn't been well validated.

13 So the limitations to the Japanese Quail

14 Two-generation Test. There's large variability in endpoint

15 response, and a lot of this could be based on the sample size

16 and not having adequate sample size. And actually, it's

17 probably not the sample size it's the analysis of the samples. 

18 So the samples could have been there, and they may have decided

19 to not analyze these for cost reasons. 

20 So there's limited data to justify the

21 necessity of a second generation. Poor reproducibility between

22 labs and non-endocrine-mediated toxicity may complicate test

23 interpretation for determining endocrine-specific mechanisms of

24 action, but this would not affect risk assessment.

25 So histopathological expertise and experience

26 in avian tissues are limited. This is a fairly new analysis, it
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1 hasn't been traditionally included in the test designs as it has

2 for the mammalian systems. 

3 And one thing to consider is when the

4 mammalian two-gen versus one-gen test design was discussed there

5 wasn't an overwhelming response that the two-generation was

6 necessary even in a well-validated system like the mammalian

7 system. So limited value is added by that second generation

8 which is why that's staggered as a potential one-gen/two-gen

9 study for the Tier 2 test.

10 So one-gen avian data required for pesticide

11 registrations. So there's extensive experience with the

12 one-generation guideline. So the variability tended to increase

13 from the F0 to the F1 to the F2. It was a slight increase, but

14 it was there and really if you saw something at F1, you might

15 not see it at F2 and the same thing is if you saw it at F1 in

16 the studies we've seen, you saw it at F2. So necessity for the

17 second generation is still questionable. 

18 So added value for risk assessment from

19 additional generations has not been well established.

20 This has been highlighted before, but it feels

21 like there was just a major opportunity missed with test

22 substance selection. There were other compounds that were run

23 through one-generation test designs and it would have been nice

24 to see a ring test on a compound that was run through a

25 one-generation test so you can make a comparison from

26 one-generation to two-generation and if the endpoints were
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1 helpful in the second generation.

2 So histopathology was not analyzed in previous

3 validation work for a lot of the tests. So how are you to

4 compare and build a dataset of control values that you're going

5 to look for in these two-generation tests?

6 So the low number of samples in the analysis

7 of the ring test data for vinclozolin, and again, no compound

8 was run through the entire battery for the Tier 1 and Tier 2

9 assays.

10 So back to our conclusions and why all of that

11 leads to these. Again, there's no guidance on when we're going

12 to use this assay. So it has not been validated, adequately

13 validated. This would be considered more pre-validation work. 

14 And again, it's not appropriate for regulatory studies as it

15 currently sits. 

16 Poor performance, too many measurement

17 endpoints, limited assay sensitivity to endocrine-mediated

18 effects. So the Japanese quail includes population relevant

19 endpoints for risk assessment but is limited in its ability to

20 definitively link these to endocrine-mediated mechanism of

21 actions.

22 DR. MARTHA SANDY: Thank you, Dr. Fredericks.

23 I think I'd like to have all the commenters

24 present first, and then we'll open it up to panel questions. So

25 Katie Coady.

26 DR. KATIE COADY: I'll just take a moment to
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1 introduce myself. I'm an ecotoxicologist at the Dow Chemical

2 Company. And I'll be providing some comments today on both the

3 Medaka Multigeneration Test and the Medaka Reproduction Test on

4 behalf of the Endocrine Policy Forum.

5 I'm starting also with a summary slide just to

6 show everybody the major points I'm going to be making. One is

7 that it really does seem that the revised protocol, the Medaka

8 Reproduction Test, seems to be an improvement over the multigen

9 medaka test that the Agency started with the validation effort.

10 The second point, however, is that this Medaka

11 Reproduction Test is this new optimized protocol that we're

12 coming out with at the end of the validation effort, and it

13 really has not been adequately ring tested, hasn't been ring

14 tested. So we need to take a step back and look at doing this.

15 Also, both assays, the 2-1/2-generation Medaka

16 Multigen and the Medaka Reproduction Test do include endpoints

17 that are population-level relevant and useful for risk

18 assessment. But as some of the speakers before me, I'll just be

19 making the point that they are limited in their ability to

20 definitively link to an endocrine mode of action. And I'll

21 discuss that a little bit more as I go along here.

22 I think actually this first bullet point was

23 elucidated a little bit better yesterday by Dr. Johnson. There

24 was some more rationale provided, I think, for why the medaka is

25 used as the test species. I just want to point out that it is a

26 different test species that was used in Tier 1. So if there's
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1 going to be a need to incorporate data from Tier 1 and Tier 2

2 together and look at both of those analyses from both of those

3 studies, there's an extra variable now that we have a different

4 species in Tier 2.

5 I think we saw yesterday that the

6 2-1/2-generation Medaka Multigen Test, the MMT, that test design

7 did not contain sufficient replication to adequately assess

8 reproduction. 

9 However, the MRT does seem to be designed

10 appropriately to really look at the biological significance of

11 reproduction. That seems to be an enhanced design. It also

12 includes looking at growth, survival and development, all of

13 these things that are important for performing a risk

14 assessment. But again, there has been no demonstration of this

15 particular study design, the Medaka Reproduction Test, for

16 repeatability, reliability or transferability.

17 Okay. This is really specific to Charge

18 Question 2 where it's asking what do the endpoints tell us

19 specifically about endocrine disruption? And the point I want

20 to make with this slide is that some of the endpoint responses

21 that you can see in these assays are not specific to an

22 endocrine mode of action. 

23 If a fish is stressed out, there's a lot in

24 the literature that shows us that a fish that's stressed is just

25 not going to reproduce as well. A fish that's stressed will

26 have lower circulating levels of sex steroid hormones, will have
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1 lower secondary sex characteristics and have lower vitellogenin

2 and this is really an ecological principle that goes back to

3 having a finite energy reserve. 

4 So if something's stressed, it just doesn't

5 have the energy to put into things associated with reproduction

6 that are energetically costly. I think this was mentioned

7 yesterday, and I'm just reiterating it as well, that decreases

8 in these parameters are not necessarily endocrine specific.

9 For Charge Question 3, the question that's

10 around just the assay endpoints, there's selection, optimization

11 and demonstration; we've heard multiple comments just about the

12 genotyping of the fish. And what we see in the inter-laboratory

13 studies is there's some sort of problem going on here with this

14 and really we just need better guidance to make sure that this

15 important endpoint is done correctly.

16 Again, not all the data may be present in the

17 Tier 2 assays to definitively determine if the adverse effect on

18 population-level endpoints, this is the data you'd use in a risk

19 assessment like reproduction, growth, development, these are

20 apical endpoints meaning that they can be influenced by a lot of

21 different things. 

22 So you might see something like a change in

23 vitellogenin at a lower concentration, let's say, but you cannot

24 be absolutely certain that biochemical change is also the same

25 mode of action that's causing your effect on your apical

26 endpoint. You can't make that definitive link. So that's a
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1 limitation that we have in these Tier 2 assays.

2 With this slide, I just do want to point out

3 that there were several different kinds of reference chemicals

4 evaluated in both the full MMT and the abbreviated MMT. 

5 The point I want to make here is that, as we

6 heard yesterday, this protocol changed throughout the validation

7 effort. It was being optimized, as it should be, and at the end

8 we have a better, I think, test design, the Medaka Reproduction

9 Test proposed. 

10 But I just want to point out that this study

11 design, the MRT, has not been ring tested, hasn't been tested in

12 various laboratories. As you see here on the screen, the

13 increased replication design was tested in one lab with a weak

14 estrogen agonist at one time. So we just have to keep that in

15 mind that that has not been ring tested.

16 The other point I want to make is that it

17 would actually be interesting to run a non-endocrine-active test

18 material through the assay in the validation effort. And this

19 would be potentially something that has another known mode of

20 action, and the reason for doing this would be able to get an

21 evaluation of what these supposedly diagnostic endpoints in the

22 assay, how they would respond to something that has a mode of

23 action that is not primarily endocrine but has some other known

24 mode of action, for instance, a hepatotoxin, what does that do

25 to vitellogenin? Let's put that out there so we can understand

26 the endpoints and how specific they really are for endocrine. 
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1 I also again want to point out that there is

2 only one assay with the increased replication as proposed in the

3 MRT that's been tested with only one mode of action.

4 The statistical methods seemed appropriate,

5 but there was some confusion about is it okay to run a Dunnett

6 test and a Jonckheere-Terpstra to determine treatment-related

7 differences from controls, to run both of those tests. 

8 So just to solidify that that decision should

9 be made a priori looking at your dataset, if it's monotonic or

10 not, which is the appropriate statistic to employ, and then just

11 one statistical test should be employed to determine

12 significance of your treatment versus your control. I also want

13 to point out that Appendix E in the materials provided which was

14 the MRT protocol, the statistical section didn't match that in

15 the Integrated Summary Report for the MMT, so that just needs

16 some clarification.

17 As brought up before, the solvent control

18 issue needs also clarification. When a solvent control is used,

19 and you do a test to see is your solvent control different from

20 your negative control, if the answer is yes, there's some

21 confusion about what you do there.

22 In the Integrated Summary Report, it was

23 indicated that treatments that should then be compared to your

24 dilution control, your negative control. However, in the OECD

25 guidance document for Statistical Approaches For Ecotoxicity

26 Tests, the guidance is actually to compare to your solvent
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1 control. And that guidance document is also cited in the

2 Integrated Summary Report as something to follow. So again,

3 this is an issue that needs some clarification so that labs are

4 performing the statistics appropriately.

5 I think this has been brought up before, so

6 I'll just go over this very quickly, that the control data and

7 the treatment-related data with vinclozolin, these were all

8 different between labs, and it really seemed that this was due

9 to the differences in the feeding regimen and the technical

10 problems of the genotypic sexing. 

11 Also in addition with vinclozolin, there were

12 some issues with test material delivery and just the fact that

13 it didn't provide a really strong response in the assay and

14 therefore it made it hard to compare assay results. So those

15 are some of the issues with repeatability and reproducibility.

16 And as has been acknowledged, a quote from the

17 Integrated Summary Report was that the expectation for any

18 specific response to the AR antagonist vinclozolin is uncertain.

19

20 So unfortunately, the ring test that was done,

21 not on the protocol that's being proposed but on the

22 2-1/2-generation MMT, that abbreviated MMT, really didn't give

23 us a lot of information. So the MMT is not transferrable. The

24 newly proposed MRT, which is shorter in duration and has

25 increased replication and also includes specific guidance when

26 the sub-adults are to be examined at week nine, this has not
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1 been ring tested. This specific design doesn't seem to have

2 even been conducted. So this is a shortcoming in the validation

3 effort.

4 Also, testing with this proposed design should

5 include more than one mode of action. It should be evaluated

6 with various other modes of action. We need to work out the

7 genotypic-phenotypic match problem. There's specific

8 refinements in the proposed MRT protocol needed that have been

9 provided in the written comments. And again, a correlation

10 between the fathead minnow and medaka would be useful if we're

11 going to at any time be thinking about looking at Tier 1 and

12 Tier 2 together with the fish.

13 The strengths of the Medaka Reproduction Test,

14 this is the protocol outlined to move forward. It has a lot of

15 strengths. It's a chronic test. It includes sensitive life

16 stages. It includes apical endpoints that are useful in risk

17 assessment. It does include enough concentrations that you

18 could theoretically do a dose response. It does have additional

19 replication that seems to be much better for assessing

20 biologically significant effects on reproduction. It uses a

21 fairly well-studied fish species, the medaka. It does actually

22 have the potential to detect estrogen agonists and androgen

23 agonists fairly well. 

24 And the point I want to make here is that the

25 diagnostic endpoints here are a little bit more specific for

26 these modes of action. For instance, if there's an increased
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1 vitellogenin in males, well, this is likely more specific to an

2 estrogen receptor mode of action. Also, if you do see the

3 presence of male secondary sex characteristics in females, that

4 is likely pretty specific to an androgen receptor agonism. So

5 there's several modes of action that could potentially be picked

6 up fairly well in these assays, we just need to test further to

7 see this.

8 The limitations, these other modes of action,

9 endocrine activity, the anti-androgen, the anti-estrogen and the

10 steroidogenesis inhibitors, because the endpoint responses here

11 are very similar to what you'd see with a generalized stressed

12 response, these are more nonspecific. So it's a little bit

13 harder to nail down these modes of action with this assay. 

14 We have some limitations in correlating Tier 1

15 to Tier 2 if we're going to do that at any time because we have

16 a different test species. Just coming from the lab in my own

17 personal experience, any time you run a long assay you're afraid

18 because there's just going to be a chance with a longer assay

19 that random events will occur, especially in your mechanical

20 stuff, to deliver a waterborne exposure. So that can be a

21 limitation.

22 The route of exposure is potentially

23 difficult, and I think Dr. Johnson addressed this yesterday,

24 indicating, you know, there's some compounds that have low water

25 solubility that are very hard to deliver, also compounds that

26 can biodegrade over time, and this makes it very tricky to keep
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1 those concentrations at the appropriate level over a chronic

2 test.

3 Again, non-endocrine mediated toxicity, that

4 overt toxicity that could be occurring, that could cloud the

5 interpretation and make it more difficult for determining

6 endocrine-specific mode of action. However, overall, this

7 doesn't affect the endgame which is to get an endpoint that can

8 be used in an environmental risk assessment.

9 In closing, there's not good reproducibility

10 or transferability right now of the MMT design. And another

11 limitation is that laboratories are going to need not only

12 expertise in environmental toxicology but analytical, genomic,

13 histopathological expertise as well. And the laboratories that

14 could do this under one roof are few and far between, so that

15 can be a limitation for getting these conducted in a reasonable

16 timeframe.

17 This is particularly to Charge Question 8,

18 what's the concern potentially with the loss of the F2, and in

19 really looking at the LOEC values for the F1 and the F2 on the

20 whole. In the MMTs that have been conducted to date, there

21 doesn't seem to be a great loss with that not including the F2

22 up through reproduction. The data indicate that lower NOECs and

23 LOECs values would not routinely be expected in the F2. So

24 terminating the assay after F2 hatch appears to be supported in

25 the current dataset.

26 So just in conclusion, I think the Medaka
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1 Reproduction Test holds promise as an improvement over the

2 Medaka Multigeneration Test for all of the reasons listed there.

3 However, we're sort of at the pre-validation stage. We now

4 have a study protocol that's been optimized but not ring tested,

5 so we need to ring test it before it's ready for use. We need

6 to ring test it with a compound that's easier to work with, has

7 a stronger response in the assay that's to be expected and we

8 need to ring test including this revised protocol.

9 So the MMT and the MRT, in closing, they have

10 population-level relevant endpoints useful for risk assessment,

11 but they can be limited in their ability to definitively link to

12 endocrine mode of action. This is just something to keep in

13 mind as interpretation goes forward. Thank you.

14 DR. MARTHA SANDY: Thank you very much. I'd

15 like to ask Katie Holmes come up for the next presentation.

16 MS. KATIE HOLMES: Hello. My name is Katie

17 Holmes, and I work for BASF, and I also serve as the Endocrine

18 Policy Forum regulatory affairs chairperson. I'm commenting

19 today on the Larval Amphibian Growth and Development Assay, and

20 that's the last time you'll hear me call it that. From here on

21 it will be the LAGDA because it's just a lot faster, and I will

22 certainly run out of time if I say Larval Amphibian Growth and

23 Development Assay every time.

24 Thought I'd start with just a little bit of

25 background. The amphibians are slightly different than some of

26 the organisms. They're still a fairly new organism to our
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1 little world of environmental toxicology testing in the

2 regulatory arena. There is no existing standard guideline EPA

3 study right now. Under the current risk assessment scenario,

4 the fish data are usually used in lieu of the amphibian data. 

5 There's a critical review paper that's just getting ready to

6 come out from Velcha, (phonetic) et al., and it looks

7 specifically at the sensitivity between fish and frogs and has

8 concluded that amphibians are equally or less sensitive than

9 fish in most cases. So I would -- it's in press right now in

10 ET&C.

11 I want to make clear during these comments, we

12 are not questioning the scientific validity of this amphibian

13 assay. What we are questioning a little bit is if it is fit for

14 purpose for what we're looking for in the EDSP program which is

15 a really different question. I think the assay itself has a lot

16 of scientific validity. There's a lot of good work that's been

17 put into it. It's just how does it fit into the EDSP program

18 which is the overall question.

19 Let's see. According to the ISR, the LAGDA is

20 intended to serve as a higher-tier test with amphibian for

21 collecting definitive concentration response information on

22 adverse effects suitable for use in the ecological risk

23 assessment. 

24 And the key there is ecological risk

25 assessment. Ecological risk assessment has traditionally been

26 done on population-level effects, and that's an important thing
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1 to think about when we're looking at these biomarkers and things

2 like that. We're really looking for endpoints that we can use

3 in ecological risk assessment. 

4 If we are going to spend the financial and

5 animal resources necessary for this chronic test, the data

6 generated really must add value to the EDSP assessment and

7 inform the risk assessment.

8 I'm going to go now through the different test

9 endpoints. Mortality, growth and development delay are apical

10 endpoints that have clear relevance to the environmental risk

11 assessment. As was mentioned yesterday and has come up several

12 times, these endpoints may or may not be related to endocrine

13 modulation. Hopefully, by the time we're at Tier 2, we've

14 determined the mode of action in Tier 1 or the proposed mode of

15 action and we can see if they're related.

16 The other thing I want to make a point of here

17 is that these studies are being run in a way that will be

18 protective without the knowledge of mode of action. You all

19 were talking a little bit a while ago about the protective

20 nature of these studies. These are studies that are conducted

21 over the entire lifecycle or more to the lifecycles of an

22 organism where the organism is exposed to concentrations 100

23 percent of the time, 24 hours a day, all day, every day over the

24 life cycle and often multiple lifecycles. And they're often

25 exposed at concentrations that are above anything that's

26 environmentally relevant. So it's hard to imagine that that
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1 won't be protective when we get the responses to those studies. 

2 I'm trying to be respectful of the time. 

3 Due to the linkages with adverse outcomes at

4 the population level, we're questioning the use of the

5 histopathology in the liver somatic indices. They're not

6 traditionally been used in environmental risk assessment. We're

7 anxious to see what the proposal is. Will those be endpoints in

8 the risk assessment, or are they just information as to mode of

9 action in the end? So that's a question that's sort of still

10 lagging out there. I think that's enough for that one.

11 The vitellogenin and T4 are used as biomarkers

12 in this assay. The endpoints are least likely to inform a

13 traditional ecological risk assessment. The vitellogenin

14 induction is already well measured in the Tier 1 FSTRA, so we're

15 wondering if it might be redundant here and if it's something

16 that really is going to inform the long-term goals of this

17 study. T4 measurements seem to maybe be more appropriate as a

18 determinative mode of action in Tier 1. And so if we're going

19 to be measuring T4, perhaps should we consider moving that back

20 to the frog study in Tier 1 where we're specifically tasked to

21 look at mode of action.

22 The HPG axis, we're looking at -- well, what

23 we're thinking, sorry about that, is that the medaka test looks

24 -- does a much better job at looking at HPG axis issues than

25 does the frog study. Medaka includes measures of fecundity,

26 while the LAGDA endpoints are not directly related to
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1 reproduction.

2 We would like to request or suggest that

3 because of this that the EPA consider doing a comparison between

4 the amphibian and fish sensitivity to alterations and gonad

5 differentiation and development and then decide, based on that

6 assessment, if both assays are justified. If they're redundant,

7 they're both taking a lot of time, energy, money and animal

8 resources, do we really need them both?

9 In terms of the HPT axis, there are three

10 endpoints in the LAGDA that are looked at. The first two, T4

11 and thyroid histopathology, don't usually inform risk

12 assessment. They don't quantitatively inform for sure. The

13 delay in development is the only HPT axis ecological risk

14 assessment relevant endpoint.

15 I thought the presentation yesterday did a

16 great job at looking at some of the vulnerability of the study

17 and some of the issues with the transferability of the

18 endpoints. Obviously, some of these things still need work. 

19 Hopefully, you've all had a chance to look at our paper. We go

20 into a little bit more detail in the written comments that we've

21 provided. 

22 There was a wide variability in the growth and

23 mortality of the control individuals. The vitellogenin ELISAs

24 that were run, EPA had ended up needing to supply the antibody

25 which is something need to be -- it's not something that can't

26 be cleared up, but it's something that needs to be finalized
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1 before industry is asked to run many of these studies.

2 Based on the information that came out just

3 Friday in the ICR, looking at Tier 2; there are 26 of our

4 compounds maybe will be moving forward to Tier 2 testing. And

5 if that's true, and the frog study is one of those that's based

6 on exposure, the study itself, I'll mention later, is going to

7 be somewhere around $500,000 and PETA said 750 organisms.

8 So we better be justified in running the study

9 by the time we get there. Even if only half of those, that's

10 almost $10 million' worth of studies, and I can't do the math

11 fast enough on the organisms. But we need to make sure that we

12 really know what we want and that the data are going to be used

13 before we get to the end of the process.

14 T4 was only measured adequately in two out of

15 the five labs. None of this is unsolvable, but these things

16 should be addressed prior to asking people to run these studies,

17 and that's really our point, not that they're a bad idea or they

18 can't be done, but it's not ready if we can't do that yet.

19 And then finally, just a reality check. There

20 are exactly four laboratories in the U.S. right now that can do

21 the frog histopath. That's it, that industry knows of. Now

22 there may be some academics, but they may not be working under

23 GOP. We won't be able to use that data. There are four. 

24 That's not a lot of labs. We have Tier 1 studies that have both

25 fish and frog, and now we have the Tier 2 tests that will also

26 have fish and frog histopath, four labs. 
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1 There's not a lot of experience in this, and

2 that's one of the reasons I think we saw so much variation. So

3 we need time to come up to speed and practice.

4 I think you've heard enough about the

5 statistical message. You can take what you heard for the fish,

6 restate it, and we'll move on.

7 In terms of test reproducibility and

8 repeatability, the way that the ISR was written, it was very

9 difficult for us to look at individual studies themselves. When

10 we're working in a regulated arena, we only get one study. We

11 don't get three or four or two and a half to look at. And so we

12 need to be able to rely on the results of that one study to

13 inform our decision making. 

14 And things being generally consistent across

15 studies aren't good enough. We need a study that can stand

16 alone, and right now, there are enough endpoints within the

17 context of this study that aren't quite defined that that would

18 be difficult to trust that you have a good study at the end.

19 This particular table was really interesting

20 to me. I pulled it out and put it in here. First, I want you

21 to notice this is an NOEC table. We're looking at the growth of

22 the amphibians in the prochloraz study. Because it's an NOEC

23 table, what that really means is where we see the 170 and the

24 164 in Lab A and Lab C, the LOEC which is what most people have

25 been talking about through this meeting are actually above those

26 levels. So your LOEC is really greater than 170.
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1 If you look at Lab D and Lab B, those are both

2 effects studies. Lab A and C are no effects studies. The

3 difference between greater than 170 and 20 or 59 is very

4 different than looking at this data.

5 Having been in the lab for a long time, can

6 say that the dose setting on these -- and this is an issue that

7 goes across all four, and I think it's really important -- dose

8 setting is going to be very hard. We are looking at bracketing

9 both biomarkers and the lifecycle development of an organism. 

10 In this study, the concentrations were set in

11 a way that would be acceptable to OECD but generally not

12 acceptable to EPA unless you had some extenuating circumstance. 

13 The dilution factor here is based on .3 which means it goes 180

14 to 60 to 20, the numbers. We are for our chronic studies,

15 unless it's going to be changed, really obligated to use .5

16 which would mean the only way that we could cover this same

17 range would be to add additional concentrations which would be

18 additional frogs, additional cost, additional time.

19 So I understand this is a validation and when

20 the rubber hits the road, and we're running these studies not

21 very long from now, the ICR is out. We're going to need to set

22 the doses for these and anything you all can have to say about

23 that would be really useful for us. It's going to be difficult

24 to cover both ends of the spectrum unless we can do a wider

25 concentration range, but when we do that we lose some of the

26 ability to come up with reasonable NOECs and LOECs in our risk
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1 assessment because our range gets too broad.

2 The strengths of the LAGDA are that it

3 provides chronic toxicity in a class of organisms that are not

4 typically tested in environmental toxicology. The measures in

5 survival and growth are particularly relevant for environmental

6 risk assessment. The mechanistic data that's generated, while

7 potentially of limited use for the ecological risk assessment,

8 may help resolve issues with specific chemicals with concerns in

9 amphibians.

10 The limitations, we don't believe that

11 transferability between labs was demonstrated with respect to

12 husbandry or some of the endpoints. Many of the endpoints

13 required are not necessarily relevant to the ecological risk

14 assessment. 

15 This is likely going to be the most expensive

16 single ecological test that we're required to perform under the

17 standard test guidelines right now. There is no final protocol.

18 But what there is right now suggests that the cost of the

19 biology without any of the histopath and things is around 300-

20 to $350,000. When you add to that the histopath and the

21 biomarker measurements, it could easily get to $500,000. And

22 that's for one of the studies. So it needs to be defined, and

23 we need to have a good study before we start running it.

24 It's unclear if the labs can adequately

25 perform some of the more technically challenging measurements. 

26 It's not that they can't we just need practice and to make sure
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1 that those guidelines are out there and then it's been tried

2 before we start. And then the lack of experience with histopath

3 with amphibians will happen as we get experience with histopath

4 and amphibians.

5 Finally, our conclusions, we don't believe the

6 LAGDA has been adequately validated. Inconsistent husbandry

7 across the labs needs to be resolved. While the T4 and

8 vitellogenin measurements have potential to inform mode of

9 action, if they can't be inform reliably their measurement

10 provides little value and should be reconsidered as required

11 endpoint.

12 Medaka is likely protective of HPG effects,

13 eliminating the need to run LAGDA past metamorphosis. Just

14 again, those two tests need to be put together and see which

15 provides value where. 

16 And finally, there's limited value in the

17 ecological risk assessment of relevant thyroid-related

18 endpoints.

19 And that is all I've got. Thank you very

20 much.

21 DR. MARTHA SANDY: Thank you very much.

22 And I'd like to call up John Brausch, please.

23 DR. JOHN BRAUSCH: Good morning. Thank you. 

24 My name is John Brausch, and I'll be presenting the public

25 comments regarding the Tier 2 invertebrate studies once I get

26 slides.
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1 Thank you.

2 So as we've seen before, I'm going to start

3 with the conclusions. And this will really allow us to focus on

4 some of the important points with an understanding of where it's

5 all going to lead in the end.

6 So Dr. Klaine brought up yesterday that

7 there's really very little guidance on when this invertebrate

8 study would be needed, as there's no E, A or T pathways in

9 invertebrates. We've got some feedback on how this study is

10 going to be implemented in the Tier 2, but we need a lot more

11 information, and then these decisions are going to need to be

12 thoroughly vetted, so we all have confidence that doing this

13 study is actually going to enhance the EDSP program or

14 protecting aquatic invertebrates.

15 I'm going to abbreviate the studies as MTTT,

16 which is the Mysid Two-gen, or the HCDRT, which is the copepod. 

17 And we feel that neither has been appropriately validated, and

18 thus with the current status they're likely not suitable for

19 regulatory studies.

20 In the ISR, it pretty clearly states that the

21 mysid shrimp are the species that we want to move forward with,

22 and we would agree with this. So as we move forward, I'm going

23 to focus primarily on the mysid but just point out little things

24 with the copepod when appropriate. And also, I hope we get to

25 the point where we realize that the mysid chronic studies that

26 are currently conducted for pesticide registration and
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1 re-registration might be a suitable replacement for the MTTT if

2 we're looking to replace or just to further enhance our

3 understanding.

4 The one question I kind of want to remind

5 everybody to think about the entire presentation, and as you're

6 going through the ISRs, really how does this study enhance our

7 understanding of population-level effects? And even more so,

8 how does it enhance the EDSP program as stated?

9 So as we've heard, mysid have been used for a

10 long time in regulatory testing. One of the common studies is

11 the mysid chronic study which is 28 days. What's important

12 about this is the endpoints that are looked at. We look at

13 survival, growth and reproduction already for the first

14 generation, and then there's also the possibility to look at

15 second generation survival and length, and if you take it a

16 couple extra days even the sex ratio. 

17 A lot of these endpoints are the exact same

18 endpoints that we're looking at in the MTTT, but rather than

19 being a 60-day assay we're looking at a closer to 28-day assay. 

20 So this could be beneficial for a lot of the problems that have

21 been observed thus far.

22 I want to follow kind of the charge questions,

23 and I'll kind of run through them quickly. But before we do, I

24 want to remind everyone what EDSTAC's envision was for the Tier

25 2 studies. The Tier 2 studies are to characterize the dose

26 response relationship of endocrine disruption of the E, A and T
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1 pathways in humans and wildlife. So as we think about that, how

2 does this study actually fit into that initial definition?

3 Well, there are some positives of using an

4 invertebrate. Invertebrates are very common throughout the

5 world, and 95 percent of the species worldwide. An aquatic

6 invertebrate even furthermore, as aquatic systems tend to be

7 syncs for anthropogenic compounds. 

8 But there's also a number of negatives using

9 an invertebrate and an aquatic invertebrate. As a whole,

10 invertebrates' endocrine systems are really poorly understood,

11 and this could lead to many difficulties in trying to determine

12 the potential for endocrine disruption. Additionally,

13 invertebrates have no estrogen, androgen or thyroid system. So

14 this is almost contrary to what the Tier 2 studies were

15 initially to look even though we've got some further information

16 yesterday on how the invertebrate could potentially add to the

17 system. 

18 I think what this shows is that invertebrates

19 and aquatic invertebrates are definitely justified for chemical

20 regulation. We know they're sensitive. We know they're common.

21 But this doesn't actually justify its use in the EDSP studies

22 specifically.

23 Real quickly, hormone systems in invertebrates

24 are generally very different than vertebrates. There's two main

25 types or sets of hormones in invertebrates, and those would be

26 similar to vertebrates such as serotonin, for example, or the
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1 invertebrate specific such as ecdysone and the ecdysteroids. 

2 Ecdysone and similar compounds are responsible

3 for all growth, reproduction and embryogenesis in invertebrates,

4 very important class of steroids. In the detailed review paper

5 from 2004, they mention this, and they talk about it, and they

6 say that they're even analogous to a number of vertebrate

7 hormones. But it's very important to remember they're

8 structurally, functionally and metabolically different from the

9 vertebrate hormones themselves, and thus, they very likely have

10 very different responses than what would be seen in invertebrate

11 study.

12 I think it's clear that mysids are

13 ecologically relevant and sensitive. But this alone does not

14 actually justify a multigeneration exposure for them. We'd have

15 to take into account what that multiple-generation exposure

16 actually enhances in our understanding.

17 One example I'd like to give was from the ISR,

18 and that's for methoprene. Methoprene would be a characteristic

19 endocrine modulator in invertebrates. It is an analog to

20 juvenile hormone.

21 The results from this study demonstrate that

22 numerous endpoints were affected, but all of these endpoints are

23 observed in the chronic mysid study, once again, further

24 illustrating how this mysid chronic study could be beneficial.

25 There are greater than 20 endpoints analyzed

26 in the mysid two-gen for both generations. None of the
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1 endpoints are specifically endocrine specific, and all endpoints

2 are influenced by overt toxicity. 

3 One of the problems with these endpoints is

4 there's a large degree of interrelatedness. Things such as

5 female body length and offspring produced per female are

6 interrelated. Therefore, if you see an effect on offspring per

7 female, you'd have to almost do your own weight of evidence

8 approach to try to determine where that effect is coming from. 

9 And once again, virtually all these endpoints

10 can be assessed in the mysid chronic study, and the ISR even

11 hints at that when it says that additional information obtained

12 in this second generation was not obvious.

13 Here's a table similar to what we've seen, but

14 I want to point something out. We're going to be looking at

15 NOECs here versus LOECs. So that's why you're going to see

16 differences in numbers. The reason why I'm presenting the NOEC

17 is because that's actually the regulatory value of concern. 

18 That's the number that the chemical industry gets analyzed on,

19 so I want to present those because they give a much better

20 understanding of what the impacts of these studies could

21 potentially be. 

22 In general, the first-generation NOEC was

23 lower than the two-generation NOEC, indicating that a

24 shorter-term study would be appropriate to determine effects and

25 to promote safety of aquatic invertebrates.

26 For the inter-laboratory validation, only two
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1 of the three labs appropriately conducted the different studies

2 for the different compounds. And I would suggest only one

3 compound was actually used for validation, and that would be

4 lindane. 

5 We saw octylphenol results from yesterday. 

6 The one issue with the octylphenol results is that although it

7 met the performance criteria in that the controls in the second

8 generation produced sufficiently, none of the exposed

9 individuals reproduced, and therefore, no results from that

10 second generation can be used. So basically, for one lab we're

11 comparing a two-generation study, and in the other lab we're

12 comparing a two-generation for the controls and no -- a

13 one-generation for the exposed. So it's not quite an

14 apples-to-apples comparison.

15 The one compound that was adequately validated

16 was lindane. When you look at the actual results, only one of

17 the 25 endpoints that showed an effect -- so there were a lot of

18 endpoints that did not show any effect. But for the response

19 endpoints, only one of the 25 endpoints showed definitive

20 concurrence. 

21 There were trends that lined up. There was a

22 trend in one and a definitive statistical difference in one. 

23 But for ones that actually had statistical significance, there

24 was only a single endpoint. And to kind of show what that

25 means, it was actually for the second generation, the Day 14

26 length but only at the second lowest concentration. I would
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1 suggest that's not very good concurrence between the labs.

2 Once again, there's transferability issues

3 between the labs that we saw major differences in the control

4 groups specifically. And when you take it even past the control

5 groups, if you look at this regulatory value of concern, the

6 NOECs actually differed by almost twentyfold for lindane. That

7 would be a very large difference if you're doing a risk

8 assessment.

9 For the test substances, we feel that the test

10 substance selection was not optimal. Only one compound that was

11 tested had known endocrine effects in invertebrates, and that

12 was fenoxycarb. That was done very, very early on in the

13 process. 

14 But more so, we feel a major opportunity was

15 missed. I mentioned this mysid chronic study that is routinely

16 done for registration and re-registration. However, only two of

17 the compounds that they looked at actually had this mysid

18 chronic data. 

19 There's a whole list of compounds that could

20 be investigated, and then that way you could compare between the

21 mysid chronic study and the two-gen to really see what value was

22 added. However, the only two compounds, atrazine and fipronil,

23 unfortunately had non-definitive endpoints in the mysid two-gen,

24 so no real comparisons could be made.

25 For invertebrates it's also really important

26 that compounds are run through the entire battery. We don't
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1 know how mysids are going to respond to vertebrate hormones or

2 vertebrate endocrine-active compounds. The only way to

3 determine that is additional research. This would be a great

4 opportunity to kind of retroactively go back and run compounds

5 through the Tier 1 battery and then the additional Tier 2 test,

6 and then you could really get a better feel of how the mysid

7 comes into the EDSP and really what it adds to it.

8 The number of strengths and limitations, some

9 of the strengths of the mysid specifically is they've got a long

10 history regulatory testing. Analytical verification is possible

11 with the mysid two-gen. That's one thing we didn't hear. The

12 copepod, currently there's no way to do analytical verification

13 of the test compounds due to the minimal amount of water used. 

14 So that's something to keep in mind.

15 Also, the mysid also provides the needed

16 information for population modeling. But this is also true for

17 the mysid chronic study. The same authors that really came out

18 with ways to use the two-generation for population modeling, if

19 you'd look at their other papers that they have come up with,

20 they've also used it for the mysid chronic study. So these are

21 basically similar between the two. 

22 There are a number of limitations with the

23 mysid and the copepod assays, and that would be just the limited

24 understanding of endocrine function and really the negligible

25 value of the additional generations. The large variability in

26 endpoint responses between the labs is definitely concerning,
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1 especially at the large degree we saw.

2 And that kind of takes us back to where we all

3 started 15 minutes or so ago, I hope. Mysid shrimp are probably

4 the most appropriate species, but really, are they appropriate

5 for the EDSP? We would suggest currently probably not. 

6 However, at some point they might be, but additional research

7 would need to be done, and really this system would need to be

8 understood. We also feel there's really no need for a second

9 generation, as the first generation would be sufficient for

10 protection goals and to protect aquatic invertebrates.

11 So I'd like to just leave with a couple last

12 words, just some quotes from the ISR directly. But I asked one

13 thing in the beginning was to remember the question what does

14 the MTT add to both aquatic risk assessment for invertebrates as

15 well as the EDSP? From my point of view, I don't think it

16 really adds a whole lot, as the mysid chronic study likely

17 incorporates all the necessary endpoints as well as provides the

18 necessary safety for aquatic invertebrates. Thank you.

19 DR. MARTHA SANDY: Thank you very much.

20 The last speaker is Dr. Mihaich. Welcome.

21 DR. ELLEN MIHAICH: Thanks. Slides? Sorry.

22 So what I want to do is just talk a little bit

23 about some of the overarching themes I think that we've been

24 hearing over the last four talks. And again, I'm Ellen Mihaich,

25 and I am the scientific coordinator for the Endocrine Policy

26 Forum.
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1 So I wanted to start with sort of questions to

2 be asked. You know, you're here to determine the validity of

3 these four ecotoxicity tests, and then after that, we're the

4 ones that are going to have to use them. So we have come up

5 with, in these last four presentations, and in our fairly

6 extensive 60 pages of written comments, which I'm sure you've

7 read cover to cover since you've only had a couple days to do it

8 in; I'm sorry about that. 

9 But we really feel like there's insufficient

10 validation to determine if the studies are reliable and relevant

11 which means they're fit for purpose.

12 In Appendix A of each of the studies, you

13 know, you've got a description of what validity means, and this

14 is a regulatory program that there is a mandate for valid tests.

15 So in asking the questions then, will sufficient

16 inter-laboratory testing with more modes of action be allowed to

17 assess the reliability, sensitivity and specificity of these

18 studies? 

19 Is one of these species or two of these

20 species more sensitive than others? That's going to be the most

21 relevant for risk assessment, you know, in that can we look at

22 the endpoints that there, you know, I know the EPA has talked

23 about which -- this is not a battery. They'll be picking and

24 choosing.

25 Well, is there something that we can look at

26 to decide which ones would be, if we had an idea of a particular
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1 mode of action, which ones would be the most relevant to be

2 choosing to help us? Could some endpoints be removed? I mean

3 unfortunately, I don't think that there's enough information to

4 date yet to look at somewhat of a sensitivity analysis, look at

5 the CVs around some of these endpoints to know whether or not

6 they're useful. I mean because every endpoint you choose to do

7 is just one more test you have to evaluate and one more way to

8 sort of muddy the waters because you might have inconsistent

9 responses.

10 And does testing in the second generation

11 really provide any additional information that would impact the

12 risk assessment? Remembering that Tier 2 data is for risk

13 assessment, and given that ecological risk assessment, we look

14 at population-level effects of survival, growth and

15 reproduction. Is it really critical to define a particular or

16 specific mode of action? I mean I think that's a good question

17 to ask.

18 And these are, again, these are going to be

19 guideline studies in the end, so 870 or 890 series guidelines. 

20 And for those of you that were here last month for the last SAP,

21 I had brought up some of the challenges we had in the Tier 1

22 guidelines which were that they weren't -- we wanted to have

23 some public comment, and so I'm just asking again will public

24 comment be sought on the draft test guidelines. I mean you have

25 protocols in the ISR, not necessarily guidelines.

26 So I'm just going to go separately through
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1 some of these if I can. 

2 Okay. So looking at the question of

3 validation; really you're being asked a very important point

4 here, and that is because since Tier 2 results are for risk

5 assessment, we need to make sure that we understand scientific

6 validity of these studies. I mean this is important. They're

7 going to be used to determine whether or not a chemical is going

8 to be a problem in the environment or not. And so we need to

9 have the confidence, we all need to have the confidence of the

10 scientific validity of these studies. 

11 So that means we have to understand fully the

12 sensitivity, specificity, reliability and relevance. This needs

13 to be determined before test orders are sent out to be able to

14 do it. I think there's been a lot of question about why

15 couldn't labs do things. Well, I think we've been working as --

16 and this is not a problem, this is just the way the process

17 goes. We're still learning a lot, but it shouldn't happen after

18 it's for a regulatory program. It should happen before.

19 And I think that we've all seen that there's

20 been some inconsistent and sometimes contradictory results in

21 the studies that we've been presented with. There's no common

22 test substance that's gone through Tier 2 or across both tiers

23 to really determine sensitivity and specificity. 

24 And as I think Katie brought up, Katie Coady,

25 no negative or non-endocrine toxic chemical has been run in

26 these to try to establish the actual specificity of the
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1 endpoints. So you know, it's not going to be telling us very

2 much if you're trying to look at these biomarker endpoints but

3 they're still impacted.

4 And we know that many of them are impacted by

5 overt, systemic type of toxicity and that's okay, we just need

6 to understand. And it's also important for -- we talk a lot

7 about adverse outcome pathways. Well, if that language is going

8 to keep coming up, we really need to understand what that

9 biomarker means to an adverse effect so that we can get there. 

10 We're not there yet.

11 And I have to say that, you know,

12 unfortunately, and I know we're all under time pressure and all

13 of that. But in this case, a data evaluation appeared to be

14 somewhat rushed. I know the whole discussion about the

15 StatCHARRM challenge, and luckily it was caught. It was

16 actually one of our scientists that caught it and then discussed

17 it with the agency, but these things happen. It's not a

18 problem. You go, you find it and you fix it. 

19 But if you're trying to rush through an

20 evaluation to meet some timeline, then the scientists involved

21 don't get the opportunity to really sit back and make sure it

22 makes sense. You know, and that's all we're saying is we need

23 to make sure that if this is going to be something for risk

24 assessment, and we're going to rely on it, you need to have

25 enough time to really look at the information.

26 And of course, then there's this whole issue
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1 of, you know, this requirement for Tier 2 and remembering, we've

2 heard and it's true, unlike Tier 1 the Agency has been very

3 clear that Tier 2 is not a battery, and we appreciate that. But

4 we don't really understand yet what that's going to mean. What

5 are the criteria? What's the rationale for the requirement of

6 these specific tests. 

7 And you have an opportunity here to help weigh

8 in on what seems to make sense, what might be the endpoints that

9 in a particular mode of action and what studies might be the

10 ones that would be the most relevant to look at.

11 And as been said by the previous speakers, we

12 need to know that there is value added for running these in the

13 assessment. So you know, having again an understanding of the

14 sensitivity of endpoints to meet their relevance and the

15 sensitivity of the species is, I think, important.

16 Again, we've said it over and over, Tier 2

17 data is for risk assessment. But these studies have both

18 mechanistic and apical endpoints. And the apical endpoints are

19 good for the risk assessment. The mechanistic endpoints can

20 help us understand what's going on, to some extent. It's not

21 necessarily -- I mean there's -- we just don't have the length

22 that the mechanistic endpoints are the cause of the adverse

23 effect, and we have to always keep that in mind, but that's

24 okay. I mean that helps us in this whole weight of evidence to

25 try to understand this.

26 There is a little bit of a concern to me,
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1 though, and that's what was in the EDSTAC report and was also

2 said in the Federal Register Notice, and that's that quote

3 there. "Tier 2 has the purpose to characterize the nature and

4 likelihood in dose response relationship with endocrine

5 disruption of estrogen, androgen and thyroid in humans and

6 wildlife." 

7 Now I'm not a mammalian toxicologist. I'm not

8 going to speak to the rat study. Maybe there you feel a little

9 more comfortable with what those endpoints mean. But again, if

10 you look at these four ecotox studies, we don't have that link

11 to say it's endocrine disruption, and we need to be very careful

12 about what any biomarker endpoint means to an adverse effect. 

13 So identifying that lead mode of action is

14 going to be quite challenging in these. Again, that's okay, in

15 my opinion as an environmental toxicologist, because those

16 population-level endpoints of survival, growth and reproduction

17 integrate toxic responses from all modes of action that are

18 going on. And those are the things that we care about because

19 that's what we're going to be, you know, we want to know that

20 when we use this chemical in the environment, it's going to be

21 safe. 

22 So just please keep in mind that most of these

23 biomarker endpoints are correlative, not causative, and that

24 hopefully, as we move forward, and this is the plan for the

25 Agency, and I think we'll, you know, I'm hoping someday we will

26 get there, that we will determine some causal links; we'll get
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1 there to the adverse outcome pathways. It's still a work in

2 progress. Okay.

3 And I was disappointed by seeing adverse

4 outcome pathway listed all through the ISRs because I think

5 unfortunately sometimes things can take on a life of their own

6 if you keep reading about it; oh, it must be so. We're not

7 there yet, and I think we just have to be careful about that. 

8 The causal links are not necessarily established, not in every

9 case. I think they can be. We're just not there yet.

10 And in the end, what we're going to be doing

11 is looking at a weight of evidence using Tier 1 data, Tier 2

12 data and other scientifically relevant information so that we

13 can make strong, robust conclusions on what this chemical is

14 doing. It may include the ability to have some understanding of

15 the endocrine activity, but I think it's going to be very

16 difficult to necessarily pin it down. So again, just trying to

17 keep that in mind.

18 It's a bit of a soapbox for me, this guideline

19 development thing. Again, you have protocols, there are

20 protocols and there are guidelines. Okay. So you saw protocols

21 for the most part, even though they had kind of the word

22 "guideline" on some of them in the ISRs. 

23 In Tier 1, the 11 assays, there was no public

24 review of the guidelines before they were put out. And again,

25 thinking about this whole question of well, why can't a lab do

26 what you're telling them to do? Well, if it's not real clear in
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1 the guideline, or if there are some things in there that really

2 aren't quite worked out, that's why a lab can't necessarily do

3 it because they go about doing what they think is the best thing

4 forward. So we need to be very careful about what guideline

5 goes out.

6 In the case of the Tier 1's, many were overly

7 prescriptive. There were errors in the Tier 1 guidelines that

8 needed to be corrected, and the Agency has done that on the

9 website, not necessarily in the guideline, but they have really

10 worked that way. However, if we again had had the opportunity

11 to look at that ahead of time, we might have been able to fix it

12 before they were finalized.

13 And we did notice some similar problems in

14 those draft two protocols, some errors in things that were in

15 there. And again, let's just fix it up front, and then we can

16 be done with it. Some of the specific things are this issue of

17 the solvent pooled the negative controls. I think you've seen

18 that there was some inconsistency there where it said we're

19 following the OECD 2006 guidelines. Those guidelines say

20 compared to the solvent control if you can't pool, but yet the

21 policy typically is to look at the negative controls. So we

22 just need some clarity there.

23 Requirements for multiple statistical tests. 

24 It's not good statistical -- I'm not a statistician, but I hear

25 from my friends that it's not good statistical practice to

26 evaluate the same response two different ways because you can
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1 come up with inconsistencies. We need to answer the question on

2 the need for multiple generations, and I think it's a good

3 opportunity to do some sensitivity analysis on some of the

4 endpoints.

5 So in conclusion, again, all stakeholders need

6 confidence in these Tier 2 tests, and we need to know that

7 they're validated for purpose, and that purpose is for use in

8 risk assessment. 

9 The risk relevant apical endpoints; really

10 we've got to be very careful not to just link them to an

11 endocrine pathway by default just because they're in there. We

12 need to be very careful not to do that. We need to have some

13 good understanding there. 

14 We need to make sure that the Tier 2 tests and

15 the guidelines are well vetted, and they need to be finalized. 

16 We need to have an opportunity for stakeholder input before

17 they're finalized and before being required in Tier 2 testing. 

18 And we really do need to take the time to thoroughly evaluate

19 the data and heed the advice of the stakeholders as well as the

20 panel.

21 And I was, just as the earlier comments from

22 PETA and the Humane Society, we were pretty surprised to see

23 that release of the information collection request the day

24 before the Science Advisory Panel. And in that information

25 collection request, that we now have 60 days to comment on, it

26 has all five of the studies just like you're looking at today,
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1 including the MMT, doesn't even talk about the MRT.

2 Now I know we can, you know, we'll have time

3 to comment on them, but this will be, for the most part, the

4 last time you get to really comment on them. And given the fact

5 that you haven't even had a chance to give your input, that's a

6 little disappointing.

7 So with that, I am finished.

8 DR. MARTHA SANDY: Thank you very much --

9 DR. ELLEN MIHAICH: Sure.

10 DR. MARTHA SANDY: -- for your comments.

11 If I could ask all your fellow presenters to

12 come up as well, and we'll give the panel a chance, if they have

13 any clarifying questions, to ask them now. If you have any,

14 this is an opportunity. Do I see any?

15 Yes, Dr. Scott.

16 DR. GEOFFREY SCOTT: Thank you all for that

17 really excellent presentation. And I just want to go to a

18 couple of points that I think were very interesting in your

19 comments.

20 And Ellen, I'll start with yours since it was

21 the last. If we're going to go to a weight of evidence

22 approach, h we have to have, in my opinion, some mode of action

23 assessment. I don't know how else we distinguish between overt

24 toxicity and endocrine disruption. It seems like you all are

25 wrestling with that and we are, too. And I know we'll have a

26 panel in a few weeks. We'll get into that, but in the absence
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1 of that right now, I just wanted to have any more clarification

2 on what you think we need to do to remedy that dilemma or

3 address that dilemma.

4 DR. ELLEN MIHAICH: Geoff, I have to say we

5 are wrestling with that. I mean this is a really tough thing to

6 talk about because from the standpoint we see this as important

7 for risk assessment. I mean what we're doing here is we want to

8 know that if we get through, you know, I don't have a problem

9 with doing the testing, okay. But you need to know what the

10 endgame is and is it meaningful. Otherwise, it's a lot of

11 wasted resources on everybody's part. So it becomes really hard

12 when you see the purpose being to identify endocrine activity,

13 but yet you know full well that what we're looking, and

14 especially from ecotox, is population-relevant endpoints,

15 survival, growth and reproduction.

16 My opinion? I don't think you need to know

17 exactly the specific mode of action. I think you need to know

18 what that number is that when you look at the risk assessment

19 you're being protective.

20 Now I think that, you know, you -- I look at

21 Tier 1 as saying okay, well, maybe we were going to work on what

22 could be a mode of action. And then I think we just need to

23 make sure that the studies in Tier 2 are making sure that you're

24 going to pick up anything that might be impacted by that mode of

25 action. So I think we've seen some good -- I mean we've seen

26 some -- definitely some good work out of EPA Duluth in possibly
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1 moving forward with some changes or some amendments, some

2 improvements to current protocols that maybe do a better job of

3 picking some of these things up.

4 The same thing happened in the rat study, the

5 rat two-gen. Now interestingly, in there you see the two-gen is

6 now being a one, you know, a one-gen, extended one-gen. Being a

7 little more focused on some of the reproductive, anything that

8 could be more endocrine. I think we can do that on some of

9 these, too. But I think that to me it's more important to make

10 sure that we know that we're being protected.

11 Does anybody else need to -- want to weigh in?

12 Okay. 

13 DR. GEOFFREY SCOTT: Well, I think that's

14 really good because EPA has successfully regulated, at least on

15 the invertebrate side of known endocrine disruptors safely. I

16 think John pointed out methoprene, for example, in his slides.

17 If I could ask a second question, and John, I

18 really enjoyed your presentation. One of the things that I

19 found most interesting was your NOEC comparison and I don't --

20 I'm not saying this is what it implies, but when you see higher

21 NOECs in second gen, that may suggest resistance, the

22 development, and we know particularly with insecticides the

23 development of resistance is a common phenomena. 

24 And I guess one of the questions I see that

25 may be helpful, and I know this gets into proprietary

26 considerations, but I'd certainly like to know industry's view
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1 on this, you all gather data for the target test on some of that

2 type of information and we're not -- right now, that's

3 proprietary, and we're not using it. But it seems like for

4 weight of evidence that could or could possibly be considered. 

5 I don't know whether industry would be willing to share any of

6 that kind of data, but it certainly seems like if we see that

7 evidence in the screening, it would be worth looking at that

8 kind of information and I don't -- you all may not have an

9 answer for that today, but I just simply throw that out as an

10 observation and something to think about.

11 MS. KATIE HOLMES: Mode of action of

12 registered compounds is not a secret. So I don't guess that

13 there would be -- I mean I can't speak for everybody. I think

14 that that would not necessarily be a big concern. But again, I

15 can't speak for the industry overall.

16 DR. GEOFFREY SCOTT: Well, I'm not going to

17 put you on the spot and ask you to say -- I'm only asking to see

18 if you think that's within the realm of consideration in getting

19 all the information out without having to spend more money is

20 all I'm thinking.

21 Yeah. There was one other question, John, I

22 had. Let me just real quickly go through your slides because

23 you had some really good points on the invertebrate test. Just

24 real quickly. I have them written down. 

25 Okay. Yeah. The present mysid test you guys

26 mentioned, and John in particularly paying attention to that, I
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1 happen to agree with that because it has served us well. And I

2 guess I just wanted to agree and clarify the process as we go

3 forward in looking at those EDSTAC charges of looking at A, E

4 and T pathways for the invertebrates, do you think consulting

5 that information where we have available one-gen mysid would be

6 a first step in that weight of evidence analysis? I'm just

7 trying to make sure I follow in your logic.

8 DR. JOHN BRAUSCH: Yeah. Yes. I completely

9 agree. I think we have sufficient information on a wide range

10 of pesticides specifically, and if we can look at that data with

11 what we've got for vertebrate systems, I think that would be a

12 good first step to try to elucidate which compounds may or may

13 not function similarly between inverts and verts.

14 DR. GEOFFREY SCOTT: Thank you.

15 DR. MARTHA SANDY: Dr. Ehrich.

16 DR. MARION EHRICH: It seemed there was an

17 underlying theme that went through all these talks that I need

18 some clarification on is that you're looking for

19 population-definitive endpoints, and there was discussion of

20 fecal steroids, blood things and histopathology as individual

21 points. Am I hearing you right? Is that the overall theme that

22 all of you seem to go for? You need the population things for

23 Tier 2?

24 DR. ELLEN MIHAICH: Typically from a

25 regulatory perspective on the eco side of things, we care more

26 about the populations than an individual. Now that's not saying
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1 that we don't still, even in the current guideline studies have

2 things that we would consider to be biomarkers or something like

3 a biomarker that might help tell us something. 

4 But we typically do not regulate, we're not

5 regulated on those. For example, we're not regulated on the

6 vitellogenin concentration. Vitellogenin may be helpful in

7 understanding what else might be going on, but no, you get

8 regulated on survival, growth, reproduction kinds of things

9 because that's the most important for the survival of the

10 population.

11 DR. MARTHA SANDY: Dr. Scanes.

12 DR. COLIN SCANES: I was very much struck by

13 John Brausch's comments about the need for value added for

14 multigeneration tests. And I have a question for Tim

15 Fredericks. On one of your slides, you talked about the use of

16 quail and ducks being used for 20 years. We talked about there

17 are measures that include the F0 reproduction. Now how would a

18 compound that had an endocrine disruptor effect be detected with

19 the one-generation test?

20 DR. TIM FREDERICKS: I think the point, and it

21 was made by Marion's comment recently, is we're looking for

22 population-level effects. And so whether it is endocrine

23 mediated or not, if you see a decrease in chicks that are

24 produced, whether it's at the F1 or the F2, if you do to the

25 two-gen test, that's where you'll pick it up. 

26 So you may have an endocrine-mediated effect
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1 in the epididymis in the two-gen test. And that wasn't picked

2 up in chicks produced at the F1 or in chicks produced at the F2

3 from those specific males that had the potential

4 histopathological effect in the adults.

5 DR. COLIN SCANES: However, if one looks at

6 the effect of trenbolone on the F2 generation when picked up an

7 effect on reproduction in terms of a marked decrease in

8 reproduction, wouldn't that test have been missed in the present

9 one-tier test?

10 DR. TIM FREDERICKS: So you're saying there

11 was an endpoint picked up from trenbolone in the F2 that was

12 presented yesterday by Les, in that study, but it was on a chick

13 production, right? It was number of chicks at the F2?

14 DR. COLIN SCANES: Number of eggs.

15 DR. TIM FREDERICKS: So number of eggs at the

16 F2. That endpoint was also statistically significant at the F1.

17 So that's an endpoint that's measured in a one-generation test.

18 DR. COLIN SCANES: But it wasn't significant

19 at F0.

20 DR. TIM FREDERICKS: Okay. So when you say F0

21 --

22 DR. COLIN SCANES: F0 is the --

23 DR. TIM FREDERICKS: Eggs produced to make the

24 F1.

25 DR. COLIN SCANES: Correct.

26 DR. TIM FREDERICKS: So that would be
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1 considered the F1 endpoint. So eggs produced by the F0 is an F1

2 endpoint. So that if you look at --

3 DR. COLIN SCANES: No --

4 DR. TIM FREDERICKS: -- eggs produced by the

5 F1, that happens in the F2. That's considered the second

6 generation, the F1 eggs. The F0 eggs are the F1.

7 DR. COLIN SCANES: I don't think so but --

8 DR. ELLEN MIHAICH: I think it's just an

9 understanding of how the study goes, and I have to admit, I

10 don't know the two-gen as well. But you start with adults. 

11 Okay. 

12 So you're exposing adults, and in the current

13 for Bobwhite quail and mallard duck, you expose them for ten

14 weeks, and then you start egg collection. Okay. And so then

15 that egg collection is then what makes the next generation. And

16 what I think Tim is saying is that the eggs were reduced in the

17 way we would currently do the study, and it would have been

18 picked up at the same level as in the new study that goes on,

19 you know, raises those chicks up and does it again. Okay. So

20 the F0 are the adults.

21 DR. COLIN SCANES: I think what we're

22 concerned about is the possibility of either sensitization or an

23 effect that is only seen on the embryo in terms of development

24 of the reproductive system.

25 DR. TIM FREDERICKS: And I would say that

26 there isn't a study that's presented that shows that.
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1 MS. KATIE HOLMES: The F0 generation is

2 adults. They have eggs that are then exposed through their

3 whole -- the adults have been exposed the entire time those eggs

4 are being made. Those eggs are laid. That's your F1

5 generation, and what Tim is saying is that we saw an effect in

6 that F1 generation that's the same as what you saw. That F1

7 generation went on to have an effect in the eggs it laid also. 

8 But F0 starts as adults, so there are no eggs exposed in F0. Is

9 that -- no? Okay.

10 DR. MARTHA SANDY: Yes. Dr. Tuberty.

11 DR. SHEA TUBERTY: John, back to you again. 

12 What tier -- well, now, let's get away from the EDC testing. 

13 What in the regulatory realm is a requirement of you for

14 invertebrate testing that would be related to some of the mysid

15 endpoints?

16 DR. JOHN BRAUSCH: Okay. So for invertebrate

17 testing, we are required to conduct two different species,

18 daphnid and mysid. 

19 For the daphnid we've got acute as well as

20 chronic lifecycle study. For mysids we've got an acute study. 

21 We also have the chronic study and how the requirements are

22 currently written, it's a conditional study. So certain

23 toxicological or (inaudible) parameters have to be met, and if

24 they're met then you have to continue on with the mysid chronic

25 study. 

26 However, recently, over the last couple years,
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1 the mysid chronic studies have been conducted whether or not

2 they meet those guidelines. And as we move forward, those are

3 becoming almost a nonofficial required study, I guess is the

4 best way to put it.

5 DR. SHEA TUBERTY: Okay. Thank you. And I

6 guess what I'm thinking about is would those suffice to serve as

7 a Tier 1 in your opinion? 

8 DR. ELLEN MIHAICH: Sorry. Let me field that

9 one, if you don't mind. So you're asking if it goes into what

10 is already the Tier 1 battery?

11 DR. SHEA TUBERTY: No. I know there's no

12 invertebrate Tier 1 requirement.

13 DR. ELLEN MIHAICH: Right. Okay. 

14 DR. SHEA TUBERTY: What I'm saying is are the

15 current requirements for registration, in your opinion,

16 fulfilling the requirements of a Tier 1 for EDCs? Are you going

17 to find the sensitive measures of reproduction, development and

18 growth in those assays?

19 DR. ELLEN MIHAICH: Okay. If that study was

20 -- and we need to be a little careful here that these are

21 requirements --

22 DR. SHEA TUBERTY: I understand. I

23 understand.

24 DR. ELLEN MIHAICH: -- for pesticides, not

25 safe drinking --

26 DR. SHARLENE MATTEN: Ellen, excuse me.
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1 DR. ELLEN MIHAICH: Sorry. Yeah.

2 DR. SHARLENE MATTEN: Before we go down

3 further in this road, just let me remind the panel that we're

4 looking at scientific issues. You're putting our colleagues in

5 the public in a bit of a predicament because they're speaking to

6 policy matters and regulatory matters that for the point of the

7 panel are not relevant and would potentially be very confusing. 

8

9 So if you could talk amongst yourself over

10 lunch or a beer, just to speculate, but just -- it would be

11 prudent just to stick to the slides that they mentioned or any

12 of the background documents if there's any understanding

13 questions, and the same goes for colleagues over in the corner

14 because it's great to have a little chitchat, but really we're

15 looking for clarifying questions. 

16 And any comments that you make for charge

17 questions, great, and so I just -- I know this is very exciting.

18 I really do. And I appreciate the excitement, how hard you've

19 worked to get here. 

20 DR. SHEA TUBERTY: All right. I apologize. 

21 So you mentioned that you feel that the mysid assay was more

22 appropriate than the copepod assay. Can you give me some

23 specific reasons for your opinions on that?

24 DR. JOHN BRAUSCH: Yeah. Probably the major

25 one is the mysids have been used in the lab for so long that we

26 have a very good idea of husbandry and everything that needs to
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1 be done. None of the current CROs currently have the copepod in

2 house. So all new husbandry methods would need to be developed,

3 and it's just a lot of back work that would need to be conducted

4 before we could ever even get to the point of being able to use

5 a copepod in an assay.

6 Also, the problem with analytical verification

7 or the lack of analytical verification in the copepod assay

8 would be a major concern moving forward, especially if you were

9 to see an effect, and you wouldn't actually be able to relate it

10 to an actual concentration other than what you'd expect to be

11 there.

12 There's also some concern -- the copepod that

13 was chosen is typically a benthic copepod. So we are now

14 exposing a copepod that is usually associated with sediment in a

15 96-well plate, water only. So this could also add some

16 uncertainty to the results you'd observe. I believe

17 reproduction is one of those endpoints that when looking at the

18 copepod in a water sediment situation, there's very good

19 reproduction. As soon as you take it to a water-only situation,

20 the reproduction drops off dramatically.

21 DR. MARTHA SANDY: Dr. Perkins.

22 DR. EDWARD PERKINS: I have a question for

23 John. On your chart on comparing the one-gen NOEC to the

24 two-gen NOEC, can you clarify how you derive this and how it

25 relates to the comparison by generation in mysids that Leslie

26 presented yesterday?
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1 DR. JOHN BRAUSCH: I can't really compare to

2 what Les presented. I never looked at the NOEC values, so I

3 actually have no relation -- I have no way to speak on the

4 difference between the two.

5 One thing I will point out for the NOEC, there

6 was a bit of -- looking at the endpoints, there were a lot of

7 endpoints that had effects at one concentration, then no effects

8 for a couple concentration, and then an effect for another

9 concentration. There was no typical dose response observed. 

10 In situations like that, it's usually looked

11 at as you try to find the actual dose response and then take

12 those endpoints. So some of these endpoints, they might be a

13 bit different because the LOEC could be established at one point

14 where there was no dose response and simply just a response at a

15 single endpoint. These NOECs were derived from where a dose

16 response trend was observed.

17 DR. EDWARD PERKINS: Okay. Thank you.

18 DR. MARTHA SANDY: Okay. I think we've

19 exhausted our questions. Thank you very much to the presenters,

20 public commenters. I think this closes our public comment

21 period. And we will now go to lunch. I'd like people to return

22 in an hour, that would be 1:15, and we'll start on the first set

23 of charge questions. Thank you.

24 (Whereupon a lunch break was taken)

25 DR. MARTHA SANDY: 3:00:42 Good afternoon. 

26 I'd like all the panel members to take their seats please. So
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1 thank you all. I think we'll get started now. Thank you for

2 getting back on time. Dr. Matten has a few things to say.

3 DR. SHARLENE MATTEN: I just wanted to let

4 everyone know that Mary Manibusan, Les Touart and Patience Brown

5 provided a description from a summary study in the invertebrate

6 area to address the consideration of block effects or blocking

7 effects. And I have then two things. 

8 I extracted out the highlighted pages that Les

9 Touart pointed out to me and distributed that to the panel. And

10 then I also provided the full summary study if you want to see

11 the whole details. And this is in response to a question that

12 Mary Christman asked after Les Touart's talk on the invertebrate

13 assays.

14 Oh and it will also be put into the public

15 Docket. I probably will put that in later this evening for QA

16 and QC. 

17 DR. MARTHA SANDY: Thank you for providing

18 that. Now we're ready, I believe, to start the series of charge

19 questions on the quail assay. I don't know who is going to be

20 reading them. Dr. Touart.

21 DR. LESLIE TOUART: Okay. I can go through

22 these. And there is some preamble information that just talks

23 about, you know, general purpose of what to see the Tier 2s go

24 and talk about the integrated summary reports.

25 DR. MARTHA SANDY: And could I ask you to

26 speak clearly into the microphone. Close to the microphone.
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1 DR. LESLIE TOUART: Okay. Sorry about that. 

2 I'll try to keep the microphone in front of me. Moving,

3 basically to the first charge question and reading through; a

4 rationale for the test method should be available including a

5 clear statement of scientific basis in the regulatory purpose in

6 need of the test method. The EDSTAC described the Tier 2 test

7 as having the purpose to characterize the nature likelihood and

8 dose response relationship of endocrine disruption of estrogen,

9 androgen and thyroid in humans and wildlife.

10 Tier 2 tests were designed to be definitive

11 tests that generates sufficient data to characterize a specific

12 hazard of the substance and provide sufficient information on

13 dose response and adverse effects to permit risk decisions. So

14 given that please comment on the rationale and purpose of the

15 assay as part of the Tier 2 testing in the EDSP as described in

16 Sections 2.5 and 2.6 of the Japanese Quail Two-generation Test

17 ISR.

18 DR. MARTHA SANDY: And Dr. McCarty is the lead

19 discussant on this?

20 DR. JOHN MCCARTY: Okay so the group of us

21 assigned to the avian protocol included myself, George Bentley,

22 Colin Scanes and Dallas Johnson, Ed Perkins and then Ken Portier

23 also provided some input to this. And so we'll be the lead and

24 associate discussants on these eight questions.

25 After the presentations yesterday and then

26 this morning Drs. Bentley, Scanes, Johnson and I met to compare
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1 our reviews of the protocol documents and discuss issues raised

2 in the presentations and so we've attempted to produce some

3 synthesis of what we thought were the most important points that

4 each lead person will read and then after that associates will

5 provide additions, corrections, disagreements, et cetera.

6 So first we are asked is there a clear

7 statement of the scientific basis in regulatory purpose and in

8 general we found the rationale and purpose behind the principle

9 of an avian two-generation test to be well supported. It's well

10 supported in Section 2 of the report as well as elsewhere in the

11 meeting documents. 

12 The science is clear that birds may react in a

13 fundamentally different way than mammals and the endocrine

14 disrupting chemicals both because of the mechanism of sex

15 determination as we've heard with males being homogametic sex,

16 females heterogametic as well as because of other differences in

17 hormone pathways among distantly related Taxa.

18 In terms of the goal of using the results of

19 the Tier 2 tests as a component of risk assessments, it makes

20 sense to have Taxa on specific information for birds which will

21 be an important focus of possible ecological risk assessments.

22 Next we were asked about the need for the test

23 method and we also thought the need for an avian specific test

24 method was well explained in the documents. The OECD document

25 in Appendix D goes into great detail on this and the arguments

26 outlined convinced us of the need for additional endocrine
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1 relevant endpoints and for protocols that expose a greater range

2 of life stages than our founding current approved test

3 protocols.

4 I'd like to emphasize in our discussions that

5 we overall agreed that the modifications might be called for. A

6 strong case has been made for an Avian specific test protocol as

7 part of the Tier 2 testing program. So we don't want that

8 message to be lost in our recommendations for improvement that

9 follow.

10 That said the group did have some concerns as

11 we moved from the principle behind the test approach to some of

12 the specifics of the currently proposed test. 

13 In particular, if we look at the wording on

14 the screen there that the Tier 2 tests were designed to be

15 definitive tests that generates sufficient data to characterize

16 the specific hazard of the substances and provide sufficient

17 information on dose response and adverse effects for risk

18 assessments sets a pretty high bar for this test. 

19 And members of the avian group thought some of

20 the decisions behind the details of the current protocol needed

21 additional justification if this goal was to be met.

22 In particular, a general theme that will run

23 through many of our comments has to do with the choice of the

24 Japanese quail as the sole subject of these tests. The detailed

25 OECD report in Appendix B does a good job of summarizing the

26 rationale for choosing Japanese quail over Bobwhite for this
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1 test. That review clearly lays out the advantages of Japanese

2 quail and that they're strictly practical in nature.

3 They're a domesticated species, easy to raise

4 in captivity, they have an usually short generation time, they

5 have precocial development that allows for production of large

6 numbers of eggs that can be artificially incubated. 

7 Now the group is well aware of the importance

8 of ensuring that any test protocol is logistically practical to

9 implement if these tests are to be successfully used on a large

10 scale. So we don't want to discount the importance of the

11 practical factors and the attractiveness of using Japanese

12 quail.

13 However, the reliance on Japanese quail needs

14 more justification if these tests are to be the definitive tests

15 providing sufficient information to do an ecological risk

16 assessment. Virtually everything that makes Japanese quail

17 uniquely easy to work with in the lab also makes it less easy to

18 extrapolate to wildlife populations that may be the subject of

19 risk assessments.

20 This issue will probably come up several times

21 in our comments and we want to emphasize we're not suggesting

22 that these concerns mean we're recommending dropping the

23 Japanese quail. Instead it's a concern that the tests relying

24 solely on this for input are unlikely to fully characterize

25 adverse effects in birds.

26 We'd recommend that as part of the protocol
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1 the agency carefully outline the areas where additional

2 information about other groups of birds may be needed in order

3 to apply results of the Tier 2 tests to risk decisions. 

4 As an example, one of the practical advantages

5 of Japanese quail is that they're young and precocial and can be

6 raised with little intervention from parents or lab managers. 

7 However, the majority of bird species and all passerines are

8 altricial. They hatch earlier in development and are dependent

9 on extensive parental care.

10 Now there is no evidence to support the idea

11 that either precocial or altricial species will be more

12 sensitive to the effects of endocrine disrupting chemicals but

13 they are likely to respond differently to endocrine disrupting

14 chemicals. 

15 And obvious an example of the differences

16 would be in the importance of parental care in altricial

17 species. A set of behaviors crucial to successful reproduction

18 therefore relevant in ecological risk assessment and known to be

19 vulnerable to endocrine disruption and so that makes a direct

20 link between EPAs focus on population level responses and

21 wildlife for risk assessment.

22 This difference between Japanese quail and a

23 majority of birds runs more deeply. Recent phylogenies of birds

24 place quail and their relatives in a lineage distinct from the

25 vast majority of birds. Likewise there is concern about

26 evolutionary and ecological differences between Japanese quail
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1 and bird species of greatest concern for risk assessment.

2 The gap between the proposed tests that rely

3 on only Japanese quail and the goal of being a definitive test

4 and fully characterizing adverse effects could be partly

5 addressed by incorporating supplemental data from an altricial

6 species such as Zebra finches, at least in some cases.

7 The zebra finch is a passerine, it's got

8 altricial development, it's also a model lab organism with many

9 of the same practical advantages that Japanese quail have,

10 including a very short generation time. 

11 Now overall while the basis for the choice of

12 Japanese quail over Bobwhite is clearly articulated, the zebra

13 finch has advantages of its own, including the recent

14 publication of the species genome, which may facilitate future

15 advances in testing.

16 It will be helpful in future application of

17 Tier 2 tests if the protocol included explicit guidance on when

18 additional information from other groups of birds will be needed

19 before going forward for ecological risk assessments. 

20 DR. MARTHA SANDY: And then I'll turn to Dr.

21 Bentley. Any additional comments?

22 DR. GEORGE BENTLEY: No. Dr. McCarty has

23 summarized the discussion quite well as far as I'm concerned.

24 DR. MARTHA SANDY: Thank you and Dr. Scanes?

25 DR. COLIN SCANES: I fully agree with all

26 points John raised.
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1 DR. MARTHA SANDY: Dr. Johnson.

2 DR. DALLAS JOHNSON: This item is probably

3 outside my area of expertise and so I have no comments to add.

4 DR. MARTHA SANDY: Dr. Perkins.

5 Dr. Edward Perkins: I have no further

6 comments to add.

7 DR. MARTHA SANDY: Any comments from other

8 members of the panel?

9 DR. DALLAS JOHNSON: I might just add that

10 there were some comments from Ken Portier but I think they've

11 been incorporated in the discussion as been given so far.

12 DR. MARTHA SANDY: Thank you. No other

13 comments? I think we're ready for the next charge question. Oh

14 I'm sorry. So EPA do you have any questions of clarification?

15 DR. LESLIE TOUART: Yes. Just a point of

16 clarification because I understand, I appreciate your comments

17 on the Japanese quail being relevant but -- with a big but in

18 terms of other species to be considered -- now typically within

19 a test guideline we focus on the actual test methodology that

20 would be utilized in that context. And given the suggestion

21 that now we consider some criteria of sorts as to when to go to

22 maybe another species for additional information, I think that's

23 relevant. I'm not sure how that fits within a test guideline

24 per se. 

25 So, you know, two things, one do you have

26 suggestions of how that might be suggested in terms of the say
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1 data interpretation and additional information to be sought and

2 second in considering a recommendation such as you have with the

3 zebra finch and we have looked into zebra finch as well as some

4 other species like canary altricials to evaluate, but these pose

5 big difficulties for long term tests especially carry on through

6 reproduction. 

7 So do you have suggestions that potentially

8 could be made in terms of whether there is available methods

9 that we could adapt or whether this would be more of a research

10 developmental program that considered zebra finch as an

11 auxiliary species in future testing?

12 DR. MARTHA SANDY: Dr. Scanes?

13 DR. COLIN SCANES: I was actually signaling

14 for John to take the question.

15 DR. JOHN MCCARTY: Yes, thanks. Thanks for

16 that. I'm going to ask for input from the other members. So

17 one thing to clarify the wording in the charge that this is

18 definitive and that this will be sufficient is what motivated us

19 to emphasize this. I realize you're designing a protocol, but

20 you're setting a high bar if you say this is the definitive and

21 sufficient test. 

22 That said, I'd say the first step one of our

23 recommendations is there is a lot known about the endocrinology

24 of zebra finches and to outline the pathways that are likely to

25 be most vulnerable.

26 So for example, parental care is an obvious
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1 one. Song is another obvious one where these are different and

2 if given the other information that is going through the whole

3 screening program here perhaps that would provide the evidence

4 of when there is going to be need for additional information. 

5 And then outside the realm of this particular

6 screening program, if the focus of an ecological risk assessment

7 is likely to target species that are passerines that are

8 altricial that would be another motivation. But I defer to the

9 endocrinologists as well.

10 DR. MARTHA SANDY: Dr. Bentley.

11 DR. GEORGE BENTLEY: If I remember what you've

12 asked correctly you asked for recommendations on how a passerine

13 species could be included within this test? Could I ask you a

14 question back, you said you had considered the uses of zebra

15 finches and canaries, has that actually been attempted and if so

16 can you tell me what happened?

17 DR. MARTHA SANDY: I think I need to interrupt

18 a little bit. We don't want -- this is not a back and forth. 

19 It's really you've answered the charge question and then we're

20 asking EPA if they understood the answer and if they got -- if

21 you've fully answered the question. We don't want to have a

22 long back and forth.

23 DR. GEORGE BENTLEY: Okay let me answer in a

24 different way. Zebra finches, as I'm sure you are aware, are

25 quite easy to breed in captivity, pretty much as easy as

26 Japanese quail housed in the correct conditions. So it is my
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1 opinion that if it were deemed suitable to test one of these

2 chemicals in a passerine species to help with this question of

3 whether this test is definitive in terms of risk assessment,

4 then it should be fairly straight forward with slight

5 modification of the protocol described in here.

6 DR. MARTHA SANDY: Dr. Scanes.

7 DR. COLIN SCANES: I want to come back to your

8 question and I'm agreeing with my colleagues but suggesting or

9 encouraging EPA to consider some research using zebra finch and

10 the same compounds that have been applied in other situations.

11 DR. MARTHA SANDY: And Dr. Perkins.

12 DR. EDWARD PERKINS: Yes, I second Colin's

13 comments and I would add that one other way to extend this test

14 is potentially include genomics where you can look at

15 fundamental pathways in the brain that might be related to the

16 song and learning across species. 

17 I know there is -- genomics are available in

18 Japanese quail also. We've done studies comparing quail and

19 zebra finch correctly. 

20 So looking at -- that can establish a

21 commonality of impacts on function that would provide some

22 weight-of-evidence to pursue a targeted -- is there an impact on

23 song development or is there developmental impact in passerine

24 birds. So that would be more of an extension in of the test

25 rather than a whole new prescribed series. And do it more in a

26 hypothesis testing manner consistent with the general approach
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1 of risk assessment.

2 DR. MARTHA SANDY: Okay. For the discussants

3 on Charge Question 1 if you can make sure you include some of

4 the suggestions you just discussed in your written comments so

5 that EPA has them in front of them and I'm asking you Dr. Touart

6 is that enough clarification there?

7 DR. LESLIE TOUART: Yes I understand the

8 responses and it's helpful thank you.

9 DR. MARTHA SANDY: I think we're ready for the

10 next question.

11 DR. LESLIE TOUART: Going to Charge Question

12 2. Test methods and their associated endpoints should be

13 scientifically relevant to the biological processes of interest

14 and should be demonstrated to be responsive to the specific type

15 of effect/toxicity of interest. Each species presents unique

16 characteristics from a biological perspective and allows for

17 specialized endpoints to address a specific toxicological mode

18 of action. 

19 Please comment on the biological and

20 toxicological relevance of the assay in regards to the stated

21 purpose of characterizing endocrine disruptors, as described in

22 Sections 3 and 4 of the ISR.

23 DR. MARTHA SANDY: Dr. Scanes.

24 DR. COLIN SCANES: I'm going to start with the

25 overview. The test methods and their associated endpoints are

26 clearly scientifically relevant to the biological processes of
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1 interest. The available evidence on the effects on

2 environmental toxicants in birds is highly supportive of

3 including eggshell thickness as an endpoint. Indeed its absence

4 would impact the credibility of the Agency. There is a clear

5 consensus that the behavioral indices should be maintained and

6 were justified. 

7 Then we'll come on to some less major points

8 but nonetheless important in terms of areas where the protocol

9 could be improved. And it was often areas where a little bit of

10 extra clarification could be helpful. 

11 An example would be to ensure that there were

12 at least 12 pairs remaining in the study. Another example would

13 be the information on water quality for the birds should be

14 included. Another example would be to ensure that the dose

15 response curve is done in the same way between different service

16 labs, contract labs.

17 We've seen in some of the materials that we've

18 had use of Log to the base 2, Log to the base 2.5, log to the

19 base 3 and sometimes with jumps. I think the protocol needs to

20 be clear whether it's Log to the base 2, 2.5, 3. Whatever

21 figure the top dose there needs to be a clear consensus as to

22 how does one determine what is the top dose in the dose response

23 curve. 

24 The use of vehicle to dissolve the test agent.

25 At least one of us was concerned that use of acetone and then

26 not blowing off the acetone could be a confounding factor. 
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1 The issue of number of replicates we will be

2 coming back to on a number of occasions. But I think that it's

3 clear that we're viewing the number of replicates for the

4 hormonal assay, for the histopath should be much greater. Maybe

5 a minimum of 8. We're very concerned on the loss of sample. 

6 One study they only found one pineal out of eight birds. Now I

7 just collect a lot of pineals in my life, but I've never found a

8 bird without a pineal.

9 The review which was very well done, talked

10 about some of the quail models that the Japanese are using. 

11 There was some concern that without full genome data it's not

12 possible to know if this is really a very unusual model that a

13 Japanese worker is using, Oshima and colleagues 2012. I'll come

14 back to the histopathology when on Question 3. 

15 And the other areas in terms of looking at

16 this are concerns whether we should be also looking at any other

17 hormones. And I know my colleague George Bishop will be coming

18 back to that. 

19 DR. MARTHA SANDY: So George Bentley.

20 DR. COLIN SCANES: George Bentley, I'm sorry.

21 DR. GEORGE BENTLEY: That's okay. I know you

22 got me muddled up with another person from the same lab years

23 ago. You wanted me to come back to that now or later?

24 DR. COLIN SCANES: I thought later.

25 DR. GEORGE BENTLEY: Okay.

26 DR. MARTHA SANDY: Dr. Scanes are you
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1 finished?

2 DR. COLIN SCANES: Yes.

3 DR. MARTHA SANDY: So now I am turning to the

4 other people on this question and the first one is Dr. Bentley

5 if he has anything to add to Question 2 response.

6 DR. GEORGE BENTLEY: Nothing else to add apart

7 from this is going to be a recurrent theme throughout the

8 questions is that the behavioral analyses we feel are very

9 important and we need to make sure that they're carried out

10 adequately. But we will get back to that too.

11 DR. MARTHA SANDY: Dr. McCarty.

12 DR. JOHN MCCARTY: Yes. I just would agree

13 with both what Colin said and George's addition about the

14 importance of parental care and behavior as endpoints. Sorry.

15 DR. MARTHA SANDY: Dr. Johnson and if you want

16 to speak for Dr. Portier as well.

17 DR. DALLAS JOHNSON: Okay. I have nothing to

18 add and I'm currently looking at what Dr. Portier -- reviewing

19 what he had said again, and why don't we pass by and I'll see if

20 I want to add anything. I don't want to waste your time.

21 DR. MARTHA SANDY: That's fine. Dr. Perkins.

22 DR. EDWARD PERKINS: The only thing I would

23 add is that it would be nice on the behavioral to have more --

24 to be able to examine more impact on learning behavior

25 especially on song, which isn't too easy to do in quail. I'm

26 referring back to the first one, maybe if some effects point
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1 towards some potential effects you might also investigate in an

2 alternate species.

3 DR. MARTHA SANDY: Dr. Bentley?

4 DR. GEORGE BENTLEY: I thoroughly agree with

5 that. I was going to comment on that more later but learning

6 and cognition are aspects of behavior that are probably more

7 easily tested in the passerine species than in Japanese quail.

8 DR. MARTHA SANDY: Dr. Dallas Johnson.

9 DR. DALLAS JOHNSON: Yes, Dr. Portier had put

10 some indications in this section dealing with power of some of

11 these tests and there is discussion in the reports about certain

12 types of 80 percent power for certain types of percentage

13 increases and he brought up the idea, I guess, as to whether

14 these percentage increases in the end variables are biologically

15 relevant or not and that there need to be some indication of

16 what the biological relevance is of some of the endpoints that

17 they might be looking for changes in. 

18 In other words is a 20 percent decrease in

19 body weight, is that biologically relevant or statistical

20 significance doesn't imply biological significance and vice

21 versa. I think the point he was trying to get at.

22 DR. MARTHA SANDY: Does anyone else have

23 anything to add on this Question 2? No? Okay I'll turn over to

24 EPA. Do you understand the answers to this charge question?

25 DR. LESLIE TOUART: Yes, I understand. I

26 appreciate, but just a couple of clarifications in the
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1 conversation in terms of the behavior and its importance in

2 trying to add that. I gather that's going to be something else

3 discussed in terms of some more specific recommendations later. 

4 Is that correct or?

5 DR. GEORGE BENTLEY: Yes, I am going to bring

6 it up later, but we can also discuss it now if you'd like.

7 DR. LESLIE TOUART: I think the main thing is

8 just to whatever specific recommendations or suggestions in

9 terms of how to add that in terms of the Japanese quail but also

10 if there are alternative, you know, suggestions to make those

11 prevalent as well.

12 But the other clarification point that I had

13 was the question about dose spacing that Dr. Scanes brought up. 

14 And I don't know if he recommends a specific dose separation

15 that we should use. Because right now within the guideline it's

16 left somewhat open and flexible, but I gather that the

17 suggestion is to have that a little bit more stringent to use a

18 specific geometric progression based upon whatever a high dose

19 might be identified as.

20 DR. COLIN SCANES: The view was that it should

21 be consistent. Because then if you're using four doses and

22 you're using log to the base 2 you got one spectrum and if

23 you're using log to the base 3 you got a whole different

24 spectrum. If you're using log to the base 5, different

25 spectrum. 

26 And so without -- we were in many ways looking
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1 for consistency, such that there could be ready comparison

2 between compounds of potential toxicants. 

3 DR. LESLIE TOUART: I guess my question then

4 is what would you recommend? Is there a specific dose spacing

5 that you think is best to maybe span two hours of magnitude.

6 DR. COLIN SCANES: We did not discuss this. 

7 For myself I tend to like a log to the base 3 because you spend

8 more concentrations. But that's a personal view. We have not

9 discussed this.

10 DR. MARTHA SANDY: Mary Manibusan.

11 MS. MARY MANIBUSAN: Just as an over arching

12 request from the Agency. Where you are seeking to provide the

13 Agency recommendations, if you can be as specific as possible,

14 so an example would be dose setting. An example also would be

15 how to establish that top dose so that we're not tripping into

16 overt toxicity for example. That is a resonating issue across

17 all these assays as well as the Tier 1 assays.

18 So if you can be please specific in your

19 recommendations in the final report and provide any references

20 that you believe the Agency would benefit from having that would

21 be greatly appreciated. Thank you very much.

22 DR. MARTHA SANDY: So I'll suggest to this

23 group addressing this question you can think about it and talk

24 about it later as the day goes on and tomorrow and I'll give you

25 a chance if you come to any decision and recommendation to make

26 it at a later point. Okay. Are we ready to move on to Question
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1 3? Let's do that.

2 DR. LESLIE TOUART: Reading the third question

3 and it's a two part question but we'll take the first part first

4 and the test protocol should be sufficiently detailed and should

5 include a description of what is measured and how it is

6 measured. 

7 The selection of endpoints within the assay

8 should be reflective of the biological processes of interest and

9 the endpoints should be intrinsically relevant and have

10 established sensitivity. The test protocol should demonstrate

11 the ability to measure the endpoints and provide adequate

12 performance criteria for evaluation. 

13 Please comment on the selection, optimization

14 and demonstration of the assay endpoints as outlined in Sections

15 4 and 5 of the ISR.

16 DR. COLIN SCANES: We will go through the

17 endpoints first then come to some suggestions for improvements. 

18 The first endpoints I'd like to pull together are egg

19 production, growth, embryonic development, sex reversal and

20 mortalities.

21 These endpoints meet the criteria for

22 reliability, for relevance. They are well demonstrated in the

23 protocol. One issue that we raise on these and that is the

24 issue of adding a positive control. And certainly the more

25 we've seen of inter-lab differences, the issue of a positive

26 control could make a lot of sense.
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1 Egg shell thickness is an obvious measure. It

2 meets the criteria of reliability and relevance but -- the but

3 is the lack of standardization. If one looks at the OECD report

4 it provides clearly a range, and to the best of my memory it's

5 from 0.18 to 0.21. We saw some reports in the appendices where,

6 when one was seeing values of over .4 and in one case over .9. 

7 There needs to be both training and standards

8 that could be used for inter-lab comparison and actually

9 intra-lab comparisons.

10 Consideration should be given to the addition

11 of epigenetic endpoints. Now this work was designed in the late

12 1990s to early 2000s and that is not a criticism, it's the

13 endpoints included were state of the art when they were first

14 envisioned, but now there is a case to look at for future

15 research.

16 The behavioral indices are labor intensive but

17 they add a novel and very important biological parameter. The

18 time to first mount, the number of successful copulations are

19 good measures, but they are restricted to male behavior; some

20 indices of female behavior would be nice to see.

21 Our overall recommendation on the behavioral

22 indices is that they should continue, they are important

23 endpoints, they meet the criteria of relevance. We do have a

24 recommendation and that is for the behavioral indices, there

25 needs to be an appropriate training program. Now that may be a

26 web based training program, but nonetheless it could reduce the
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1 coefficient of variation and make this an even more powerful

2 test. 

3 Another set of endpoints that make a lot of

4 sense are spermatozoa characteristics. However, spermatozoa

5 characteristics were only looked at in epididymal samples. And

6 even there there was tremendous variation. In one case the

7 number between the F0 generation and the F1 generation, the

8 number of spermatozoa declined by 94 percent. And that's in the

9 controls. 

10 Our recommendation is for spermatozoa to

11 continue to be an important endpoint, but consideration be given

12 to having a semen sample collected. Now semen samples are

13 collected in turkeys, they're collected in chickens, I'm told

14 they are collected in starlings and so that a method to collect

15 semen from quail is an area where it is not outside the bounds

16 of science to develop such a method. It would require some

17 research but I think it would provide a greater reliability were

18 there to be major changes.

19 Again, because of the variation we would

20 encourage the development of web based training for spermatozoa

21 characteristics. Or web based or other way of doing training,

22 we're not prescribing that. 

23 Cloacal area should in theory be a very good

24 index of androgenic or antiandrogenic activity. However, there

25 was little evidence provided in any of the studies suggesting

26 that cloacal area was a useful measure. To the best of my
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1 knowledge it was not looked at prior to photostimulation and so

2 the potential androgenic effects may well be missed.

3 So our recommendation is without supporting

4 any supporting data or without investigating the situation in a

5 short day length, non-reproductively active quail, this

6 parameter may or may not be useful. 

7 Hormone assays. Hormone assays are

8 appropriate, useful and clearly meet the criteria for

9 reliability and relevance.

10 That said; we consider the number of

11 replicates was very low and for the protocol if it's in revision

12 or further clarified to up the end -- and we would say probably

13 to an end of eight rather than an end of five. And particularly

14 where samples get lost where you're then getting down to ends of

15 three and four. And you could be pretty well guaranteed not to

16 get anything significant.

17 An area that requires further research is the

18 area of vitellogenin. The information provided was such that it

19 really was not done in a consistent way. It was not clear if it

20 was being measured by ELISA -- if it can be measured by ELISA. 

21 Certainly the ability to use real time BCR would make a lot of

22 sense.

23 Recommendation; there is a need for further

24 research to assess the validity of assays for plasma

25 vitellogenin in the Japanese quail and whether this is a good

26 endpoint for endocrine disruptors. 
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1 A further suggestion or recommendation for

2 research was the potential to develop in ovo or in vitro

3 vitellogenin production as almost like a screen one, so looking

4 at whether chick embryo hepatocytes respond to estrogen or to

5 endocrine disruptors; something that might be worth considering

6 by the Agency.

7 This concludes the summary report.

8 DR. MARTHA SANDY: So Dr. Bentley.

9 DR. GEORGE BENTLEY: Well I agree with

10 everything that Dr. Scanes has said. Let me just move this

11 over. I would just like to add that there are a couple of good

12 vitellogenin assays available to measure plasma vitellogenin in

13 birds and qPCR to measure messenger RNA for vitellogenin in the

14 liver. It works very well in several labs. So that should be

15 possible.

16 DR. MARTHA SANDY: Dr. McCarty.

17 DR. JOHN MCCARTY: Two points about selection

18 of endpoints. 

19 One is fairly straightforward and that has to

20 do with increased aggression, which in the current protocol is

21 treated as a husbandry issue. It's mentioned in Section 4.1.6

22 as a possible problem. But I guess I'm wondering why it can't be

23 an endpoint especially given that if you look in your summary of

24 other studies. For example, 5.3.1 it's found that one of the

25 effects of the treatment chemical was increased aggression. So

26 that seems fairly straight forward to keep track of that as an
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1 endpoint through the process.

2 The other more vague and harder to address

3 addition would be given its -- I'm a little concerned that

4 immunofunction is not given the attention it deserves given both

5 its links to endocrine disruption and then given the attention

6 we've heard about wanting to have these endpoints be relevant to

7 an ecological risk assessment. Immunofunction would certainly

8 be relevant in that context as well. And in my written comments

9 I'll try to be more specific about how you might do that, but

10 there are certainly lots of methods that are well established

11 for testing the immunofunction in birds.

12 DR. MARTHA SANDY: Dr. Johnson.

13 DR. DALLAS JOHNSON: I had no comments but I

14 would like read into the record what Dr. Portier had to say and

15 in case he wants to expand upon it in the written report. 

16 He indicated in Section 4 it provides detailed

17 discussion on 15 different classes of assay endpoints. This

18 discussion should be followed up with a section on how an

19 evaluator might choose one or more of these endpoints to be used

20 in combination with the information in Tables 11 to 13 in the --

21 I think it's Appendix C is it. He gives a reference to it but I

22 believe it's one that is known as Appendix C.

23 To guide and justify the highest or second

24 highest test concentration the information to create this

25 guidance is available in the two documents that just need to be

26 put together. The discussion in Section 5 provides another set
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1 of information to support guidance that matches detection

2 distances that give power to key assay endpoints in Section 5. 

3 Each chemical has a discussion of the levels used in the

4 endpoint the researchers found most important on which to

5 comment.

6 For example, the estradiol studies Section

7 523.2 neural systems and bursitides as key assay endpoints. If

8 one were to design a new JQTT for estradiol would we again use

9 0.550 over 500 milligrams per egg or would a sample size

10 analysis point to a very different sequence.

11 And then he also said I recommend Section 5.5

12 be expanded and integrated with the information from the power

13 analysis. 

14 DR. MARTHA SANDY: And Dr. Perkins.

15 DR. EDWARD PERKINS: I have a couple of

16 points. On the protocol in general I think it might -- and I

17 noticed this with most of the models that there seems to be a

18 lot more technique and execution problems with these life stages

19 between multiple laboratories, and it may do to have a library

20 of YouTube videos based to actually show people how to hold

21 things, how to do things, troubleshoot things. Sort of an

22 idiot's guide to toxicology, so that they have something to look

23 back to that they can easily look at.

24 In regards to positive controls and standards

25 and probably the supply may also apply the Question 3.2 actual

26 standard slides, histo slides that you can show that you know
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1 the results on that you can give people to see how their ability

2 to actually interpret what your interest in, the endpoints are

3 interest in. Some sample studies might help out quite a bit to

4 get a standardized analysis results and get people to practice

5 basically. 

6 As far as endpoints I think one thing and I

7 hate to add things to it, but I guess that is sort of our job. 

8 In a system sort of context, one of the things I sort of view

9 the Tier 2 as and it's actually stated in the EDSTAC,

10 characterizing the nature likelihood and dose response

11 relationship of types of endocrine disruption. One of the

12 things I don't really see is the animals being look at as a

13 system very well. 

14 We're looking at multiple endpoints. We're

15 not really relating one to another really tightly, I don't

16 really see analysis for that. 

17 So one of the things I wanted to recommend was

18 the possible inclusion of transcriptomics or metabolomics. One

19 advantage is transcriptomics may provide some underlying causes

20 that relate different signaling pathways together and can also

21 help provide weight-of-evidence for androgen and antiandrogen

22 effect.

23 That in combination with the apical effects

24 and the secondary sexual characteristics and so forth, these can

25 make really nice evidence that support each other towards a more

26 defined conclusion than independent things themselves. And
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1 through that you can also address some of the epigenetic effects

2 with methylation status and things like that. Oh and

3 metabolomics.

4 DR. MARTHA SANDY: Dr. Bentley. 

5 DR. GEORGE BENTLEY: Just to follow up from

6 Dr. Perkins' comment about the YouTube idiot's guide, I think

7 that would be extremely helpful but I also firmly believe that

8 there is nothing as powerful as hands on experience. So perhaps

9 before different labs embark on this test, maybe there can be a

10 workshop to cover some of these more problematic techniques. 

11 We see this theme coming up of inter-lab

12 variation throughout these tests so why not have a workshop and

13 then make videos during that time and then the people can refer

14 back to them.

15 DR. MARTHA SANDY: This is Dr. Martha Sandy. 

16 I have a couple of questions of my own. I wondered, Dr. Scanes,

17 in your reading of the group's response you mentioned that they

18 should consider measuring female behavior endpoints and I

19 wondered if you had any example endpoints to suggest or if you'd

20 like to put them in the written comments.

21 DR. COLIN SCANES: I think the female endpoint

22 would be probably much better done in zebra finch. Apparently

23 males do all the decision making in terms of sexual selection. 

24 And females do not have that same role. Where as in zebra finch

25 the females have a much stronger role, and this is providing

26 some support for the use of zebra finch because of the ability
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1 to measure some female behavioral parameters.

2 DR. MARTHA SANDY: Dr. Bentley.

3 DR. GEORGE BENTLEY: Again, I agree with that

4 and I would just like to add that, well reaffirm that female

5 quail don't seem to have much of a choice when it comes to

6 mating. Female zebra finches, their mate choice behavior has

7 been quite well documented. They seem to make decisions based

8 on symmetry of males. The perfection of the male song and this

9 gets back to the question about learning vocalizations in zebra

10 finches. 

11 Quail do not learn the vocalizations. They

12 don't have a neuro song control system. So I think if we're

13 going to look at aspects of female behavior, which are, in my

14 opinion, critical at a population level for most bird species

15 then you need to test it in the species where it is easily

16 measurable.

17 DR. MARTHA SANDY: And I also wondered, would

18 you want to expand on -- oh Dr. McCarty.

19 DR. JOHN MCCARTY: Just very quickly, one

20 female behavior that could be done in Japanese quail that's

21 ecologically relevant would be incubation behavior.

22 DR. MARTHA SANDY: Thank you. The other

23 question I had was in suggesting a positive control be included

24 in the experiment. Can you say a little more about how you

25 would chose a positive control?

26 DR. COLIN SCANES: Without having talked to
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1 the group first, I'm a little loathe to do this but I'm going to

2 do it anyway; and that would be if one is looking at an agent

3 that has androgenic activity to include something like

4 trenbolone. If something is a putative estrogen, to include

5 estradiol and if one gets no effect let's say with estradiol

6 when you're expecting one that tends to mean that you're kind of

7 surprised with that. So it's a similar agent to the agent that

8 you're testing.

9 DR. MARTHA SANDY: Dr. Delclos.

10 DR. BARRY DELCLOS: Would you then - I mean

11 this -- this is complications when you're doing a Tier 2 type of

12 test. You use one dose of the positive control or two doses. 

13 You're also going to have analytical chemistry costs involved

14 and so forth.

15 DR. COLIN SCANES: If there is substantial

16 data in the literature showing an effect, and certainly with the

17 trenbolone there is and at 20 parts per million the data that

18 Les so well showed yesterday, that would provide confidence that

19 the contract lab was performing the test in an adequate and

20 satisfactory manner.

21 DR. MARTHA SANDY: Dr. McCarty.

22 DR. JOHN MCCARTY: I was going to say this

23 later but I'll address it now. I am sympathetic with yes it is

24 going to add complications and it's going to add cost. On the

25 other hand I'm concerned that some combination of poor

26 contractor lab performance or low statistical power is going to
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1 result in production of nothing but negative results that we

2 don't know if they are false negatives or real negatives. 

3 And if those are accepted for regulatory

4 purposes and they're false negatives, then we've wasted all the

5 money. Yes, it's an added expense, it's an added effort, but

6 given the results we see here, I think it's a necessary addition

7 in my opinion. 

8 DR. MARTHA SANDY: Dr. Bentley.

9 DR. GEORGE BENTLEY: I entirely agree. I

10 think it would be a false economy not to include a positive

11 control of that sort. And I think a single dose at a level that

12 has been proven to work before in the literature would have the

13 predicted effect before in the literature would be entirely

14 appropriate.

15 DR. MARTHA SANDY: Other comments from the

16 panel on Question 3.1? And EPA any questions?

17 DR. LESLIE TOUART: Just for clarification on

18 the positive control. Do I understand correctly that you're

19 suggesting that a positive control be included with every test

20 or are you suggesting that it might be appropriate as maybe a

21 kind of a one time or periodic inclusion to demonstrate

22 proficiency at that particular laboratory?

23 DR. COLIN SCANES: We were suggesting that it

24 would be included in every test which would give, I think, a

25 degree of confidence in the results of that individual test with

26 those same personnel, that same lab.
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1 DR. MARTHA SANDY: I believe we're ready to

2 move on to Question 3.2.

3 DR. LESLIE TOUART: Okay. Reading Question

4 3.2. Pathological evaluation of histological tissue

5 preparations is an established, sensitive and integral endpoint

6 in the assessment of effects in long-term in vivo assays. The

7 tissues targeted for histopathological evaluation should be

8 shown to be sensitive to exposure and relevant to a mode of

9 action or pathway determination. Tissue samples from several

10 organs or glands for example kidney, liver, thyroid, gonads,

11 underwent histopathological examination in the Japanese Quail

12 Two-Generation Test. Inter-laboratory validation and a

13 discussion of histopathology as an endpoint is provided in

14 Section 4.1.13 of the ISR. 

15 Please comment on the value of

16 histopathological analyses in the Japanese quail test assay for

17 each of the tissue types examined and what, if any, critical

18 information is gained from their inclusion or would be lost if

19 histology were not examined.

20 DR. MARTHA SANDY: Dr. Scanes.

21 DR. COLIN SCANES: I'm going to answer this,

22 first of all, in the abstract. In principle histopathology

23 needs to be included. Abnormal histopathology provides very

24 strong evidence for endocrine disruption. However, the data

25 that is presented is insufficient to make definitive

26 prescriptive conclusions. There will be a series of tentative
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1 recommendations proposed.

2 Before I get to that I'd like go through --

3 I'll go through these recommendations and then I'll go through a

4 detailed analysis of what we did. For the multiple endpoints

5 employed in the Japanese quail Two-generation Test there was

6 predominantly a lack of consistent responses to the putative

7 endocrine disrupter. 

8 Histological data albeit limited was

9 consistent. So the first thing is we've got a consistency in

10 the data. There is a very strong case for epididymal and

11 gonadal histology.

12 The gonadal histology providing evidence of

13 sex reversal, the epididymal histology was consistent with a

14 lesion. There is a reasonable case for liver histology. In the

15 absence of testing a known goitrogen in any of the tests that we

16 saw, it was not possible to draw conclusions on whether or not

17 thyroid histology should be part of the test. 

18 So it's an area that needs to be researched on

19 whether there are changes in the thyroid and certainly

20 consistently you would expect them with a goitrogen, but we saw

21 no data on that area.

22 There is an overwhelming case for research to

23 develop quantitative morphometric indices for epididymal liver

24 and possibly thyroidal histology and one should add in gonadal. 

25 This would greatly facilitate statistical analysis. The number

26 of replicates must be -- should be sufficient to draw
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1 conclusions. In some of the cases we were down to ends of

2 three, four and at most five. Consideration should be given to

3 in situ or immunocytic chemistry. 

4 I want to go through some of the data and

5 first of all complement the contract labs for the quality of the

6 histology. The histology was very, very nice. 

7 Five replicates should have been used for each

8 treatment. We'll come and see that this wasn't always the case.

9 The pathologists used a grading or a severity score. And it

10 states in one of the references "pathologists for each study

11 laboratory conferred to harmonize diagnostic terms and severity

12 grading."

13 The pathologist used a severity grading. They

14 talked about Grade 1 for the testing for the gonads. Morphology

15 consistent with the male. Grade 2 was 80 percent containing

16 spermatogonia and less than 20 percent containing oogonia. 

17 Grade 3 gonad contained distinct foci of ovarian and testicular

18 tissue that encompassed about 50/50. 

19 We go onto Grade 4 and Grade 5

20 proportionately. When looking at the liver a system was used

21 for non response -- the liver and other tissues I should

22 clarify. A non response of less than 5 percent of the tissue

23 affected; minimal 5 to 30 percent; mild 30 to 60 percent;

24 moderate 60 to 80 percent; severe over 80 percent of the cells

25 affected. 

26 Now if you've got -- first of all if you've
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1 got a score, one could do statistical analysis on those scores. 

2 That was not done. But if you're talking about percentages, why

3 not actually go to those percentages and say that 5.7 percent of

4 the slide was showing abnormality or 5 percent of the cells. 

5 If one has a continuous measure, one can use

6 regular statistical analyses. It also means one can use images

7 and use automated systems. Dr. Proper yesterday mentioned some

8 of those techniques that can be used. So I actually went back

9 and just on the scores and I looked at the scores and will

10 include this in the appendix in the report, I converted the

11 scores into just initially doing a mean and standard on the

12 scores and then comparing them between labs. 

13 In comparing them between labs there was

14 remarkable consistency. It was not picked up in the summary

15 report or in the reports on the histology. It gave a lot of

16 reason to have confidence in the data. An example was looking

17 at the testes. Zero testes showed abnormalities. Now that's a

18 pretty powerful piece of evidence that you're getting no change.

19 On the other hand with the epididymal data, we

20 were seeing on average in the toxicant treated group scores of

21 twos and uniformly twos. Now we're dealing with here sometimes

22 ends get down to three, four, samples get lost. That's why we

23 are saying we want to have an end of at least eight. 

24 Now we also have a concern on the histology

25 and the histopathology; that the pathologists used a

26 representative section. Now we would much prefer either to have
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1 a randomized situation or to systematically go through the organ

2 and take every fifth slide or fifth section. The issue of

3 saying well this is the most representative brings in very much

4 a subjective viewpoint as opposed to using an objective

5 viewpoint, and we're trying throughout this discussion on the

6 histology to look for this to be as objective as possible and

7 amenable to statistics. 

8 So in summary in principle we view

9 histopathology as very important. The data was not strong

10 enough to make definitive conclusions but strong enough for us

11 to make recommendations that were tentative to say that we

12 should at least continue with testes, epididymis and liver. And

13 look to the development with research of morphometric analyses,

14 quantitative estimates.

15 DR. MARTHA SANDY: Thank you. Any additional

16 comments from Dr. Bentley?

17 DR. GEORGE BENTLEY: Thank you. Well I agree

18 with what Dr. Scanes had said. I would just like to add that it

19 wasn't really clear which individuals were actually selected and

20 sampled for analysis, because not all individuals in a group

21 were selected. So I would like information or I'd like to see

22 some standardization as to how individuals were selected,

23 whether they're preferably randomized. 

24 And also as described in Docket Article No. 69

25 describes the evaluation of the sections; and it says "after the

26 initial evaluation the study pathologist performed a
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1 reevaluation of all specimens." The single most representative

2 section was histologically evaluated from each tissue. 

3 So I would like to see in the absence of any

4 further systematic analysis, that Dr. Scanes described, how is

5 that most representative section decided upon and what are the

6 criteria for that. This might go away if you do a systematic

7 analysis that Dr. Scanes described. 

8 DR. MARTHA SANDY: Dr. McCarty.

9 DR. JOHN MCCARTY: My only addition would be

10 that to the extent the goal of this test is to identify the

11 mechanism then I see this as an extremely important set of

12 information to both try to establish endocrine disruption or to

13 evaluate the presence of toxicity in the birds.

14 DR. MARTHA SANDY: Dr. Johnson. 

15 DR. DALLAS JOHNSON: Yes, I don't have

16 anything to personally add. I did want to read what Dr. Portier

17 had written with response to this item.

18 He indicated that the national toxicology

19 program has extensive experience with histopathological analysis

20 of tissue from toxicology studies. That experience is primarily

21 with rats and mice, but the lesson learned over 30 years of this

22 kind of analysis are sure to be applicable for these analyses

23 performed on Japanese quail tissue.

24 My recollection -- well then this is the

25 second paragraph -- my recollection of the take home message for

26 statistician from these discussions is that if data from
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1 histopathological analysis is to be useful, pathologists have to

2 be extensively trained, continually tested and challenged with

3 samples from known conditions or possible multiple pathologists

4 should be used for key studies.

5 DR. MARTHA SANDY: Thank you and Dr. Perkins.

6 DR. EDWARD PERKINS: I have no further

7 comments to add.

8 DR. MARTHA SANDY: Do any other members of the

9 panel want to add anything to Question 3.2? No. All right I

10 think we're ready. EPA do you have questions of clarification?

11 MS. MARY MANIBUSAN: Just another general

12 request from the panel as we move forward and I request this as

13 part of your verbal recommendations, but certainly in the

14 written final report speaking to the specificity. 

15 As the Agency has been hearing some of the

16 verbal recommendations so far, we really appreciate that

17 distinction that you're making in terms of areas and additional

18 measurements or methods where we must have for the assays to be

19 reliable and relevant. And where there are areas as we

20 understand there are advancements of science and technology and

21 methods that are always coming on line, areas where we might

22 want to investigate further and those areas are more research

23 focused. If you can please make that distinction to the Agency

24 that would be greatly helpful.

25 And on top of that if you can provide just

26 justifications or rationales for those additional measurements
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1 and by not having them what are we missing. And then in the

2 research areas by having them what would we gained. That would

3 be again just very, very helpful to the Agency. Thank you.

4 DR. MARTHA SANDY: Okay. Thank you. Sorry. 

5 We will go on to Question 4 for now please.

6 DR. LESLIE TOUART: Reading Question 4. 

7 Demonstration of the test method performance should be based on

8 the testing of reference chemicals representative of the types

9 of substances for which the test method will be used. 

10 Test substances should adequately represent an

11 appropriate range of responses and physical/chemical properties

12 for which the test method is proposed to be appropriate. The

13 selection of the most appropriate statistical approaches depends

14 in part on the nature of the data and also on the design of the

15 validation study. Statistical and non-statistical methods used

16 to analyze should be described. 

17 Please comment on the selection of test

18 substances and methods, (analytical and statistical where

19 appropriate) chosen for the demonstration and validation of the

20 Japanese quail assay.

21 DR. MARTHA SANDY: Dr. Johnson is the lead

22 discussant on this.

23 DR. DALLAS JOHNSON: Are we ready to go for

24 the rest of the day. Hopefully that's a joke, but I'm not sure

25 whether it will be by the time I get finished or not. 

26 I'd like to break the charge down into three
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1 questions I guess. Asked to please comment on the selection of

2 test substances and I'm going to let my associate discussants

3 address that because I don't feel like I have any expertise to

4 address that particular issue and I didn't feel comfortable with

5 taking whatever they might provide to me and being sure that I

6 could get it out in a manner that would make sense. And so I'm

7 going to rely upon the associate discussants to address the

8 selection of the test substances.

9 A second part of the charge has to do with the

10 analytical methods that are used and whether they're appropriate

11 for demonstration and validation of the quail assay. And again

12 that's an area where I don't think I have the proper expertise

13 to judge and I'm going to again rely upon the associate

14 discussants to address that part of the charge.

15 The part of the charge which I feel completely

16 comfortable with is the part that has to do with statistical

17 methods. And I do have -- part of this -- maybe I'm going to

18 add something into the charge that is not there because

19 somewhere along the line I guess I was looking for something

20 that might be related to the protocol design and whether the

21 protocol design is appropriate or not and there really hasn't

22 been in any of the charges throughout the four studies that

23 hasn't been addressed. But I feel it's an important issue and I

24 want to take some time to discuss it.

25 I had mentioned yesterday, and the order that

26 I have these is probably not the best order for our report and
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1 hopefully when we do the report it will be in a better order. 

2 The order that I have them in is sort of the order that they

3 arose to me as I was reading the ISR. And so if you feel like

4 I'm jumping around a little bit it's because the order is fixed

5 by the ISR itself and I apologize for that.

6 One of the things that I did ask a question

7 about yesterday and I just say it again today just to make sure

8 that we can talk about it in the report is there is a statement

9 made in the report that said most of the variability in the

10 controls is due to strained differences. And I asked a question

11 about whether the different laboratories used different strains

12 or whether the different strains were interspersed across the

13 laboratories and I believe the answer to that was that the

14 strains used within the laboratory were pretty consistent but

15 they were different across laboratories. 

16 And so with that in mind then laboratory

17 effects and strain effects are completely confounded with one

18 another and there is no statistical way that we can separate

19 those. So any separation that you make with respect to those is

20 based on expert opinion and not on the statistical analysis. So

21 if you believe the differences are due to strains and not

22 differences due to laboratories then have at it. I don't have

23 any problem with that it's just not a statistical test that we

24 can make. 

25 The study designs and a lot of what I'm going

26 to say here today I think probably can relate to maybe each of
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1 the studies that we're evaluating. And I'm not sure that all

2 the points will need to be made every day. So hopefully the

3 next day and the next day and the next day when I get a chance

4 to talk I won't talk so long and just say refer back to what I

5 said about the Japanese quail study. Because a lot of the

6 issues I think are consistent across all of those studies.

7 And this study design as well as the others do

8 a good job I think of discussing factors that can be controlled

9 like the amount of chemical to use, the dose levels, the birds

10 used, the cages that they're done in. But they say very little

11 about how randomization is used and how it should be used.

12 I did find in a statement somewhere that they

13 were supposed to do something impartially and I didn't know

14 whether impartially was another acronym for randomization or

15 whether it meant something different. So I have a feeling that

16 it's not quite as much as randomization but you're not supposed

17 to bias your results. You're supposed to look at them

18 impartially and so I think that is important to replace that, I

19 think, with some mechanism to ensure that randomization is used.

20 Now just some questions about the quail study

21 that come to mind and I don't know the answers and I didn't try

22 to clarify these with the discussion yesterday, but they are

23 questions that arise and I think that if you're going to propose

24 a protocol for other studies to be done and laboratories to do

25 these things, I think it is important to get things a bit more

26 standardized and a bit more under control in terms of -- so the
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1 results are less dependent on the laboratory and more dependent

2 on the chemicals that are being studied.

3 So some of these things that came to mind with

4 respect to the pairs of birds that are placed in the same cage. 

5 We have in this study I think there is 12 caged pairs and one

6 male and one female, but then it doesn't say anything about what

7 are done with the cages. So the questions I have are the cages

8 in the same room, are they in different rooms? If they are all

9 in the same room where are they located in that room? Were

10 these locations selected randomly? How is randomization used to

11 select the pairs to be mated? And then eggs are taken from the

12 pairs that are put together and how are these eggs selected. 

13 Are they selected randomly?

14 In some sense you select all the eggs because

15 you count the eggs so you have them all there but then some

16 tests are done on some of those and other tests are done on

17 other ones. So which eggs are used for which tests and how are

18 they selected. I think just to make some comments I guess on

19 the process that should be done in some random manner.

20 The feed and water -- how is feed and water

21 placed in to the cages, are they going in the same order every

22 day, was there a different order used every day. If the order

23 randomized in the order in which the feed and water was placed

24 in the cage. And then are all the birds in a given treatment

25 group housed together. 

26 In other words if the control group is located
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1 in one portion of the room and the -- one of the treating groups

2 is in another portion of the room and another treated groups in

3 another potion of the room, then you got differences in

4 treatments that are confounded with the location in the room. 

5 And so those locations in the room that I think in which -- or

6 wherever they're at some thought and care should be taken to

7 make sure that those sorts of ideas are getting randomized and

8 so that the idea of randomization is to guarantee -- there are

9 certain things you know might bias the data and so you want to

10 randomize to avoid those things but there are other things that

11 are unknown that might also bias the data that you don't know

12 about but you want to guard against them. And so the idea of

13 randomization is to guard against all those things that might

14 bias the data whether they are known or unknown and to try to do

15 things in as random a process as is possible.

16 What wasn't clear, I guess I wasn't able to

17 tell whether in this study involving the Japanese quail so we

18 have 12 pairs under each treatment. Say there is maybe 24 cages

19 for the controls and 12 cages under each of the dose levels and

20 then it's not -- I couldn't really tell in the report whether it

21 was all done at the same time, did they all start at day one and

22 they all finished on the day the thing ended or was this

23 staggered out throughout the study period and again something

24 needs to be -- I think it's important that something needs to be

25 said about the order in which these things are done and how they

26 are done and where randomization is just taking place and where
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1 not.

2 And I guess in this I also threw in a question

3 about how are the initial birds chosen for each of the

4 laboratories. Later on in the report it stated that dose level

5 during the sixth and seventh post treatment weeks, if that dose

6 level does not produce at least 40 eggs then that level is

7 dropped from further analysis to the test.

8 And it wasn't clear to me whether that meant

9 40 eggs from a single pair, or 40 eggs from all the pairs that

10 are in a single dose level. This wasn't clear to me what that

11 was referring to. And so I think that can be clarified a bit. 

12 A statement was made at four weeks of age the

13 F1 control treatment group birds are weighed and then randomly

14 allocated to pens by pairs with male offspring from odd numbered

15 parental pairs being paired with females from even numbered

16 parental pairs. And even numbered pen males being compared with

17 odd numbered females; I guess that is randomization coming into

18 play there. 

19 But the question that arose for me is does

20 that guarantee that paired birds do not come from the same

21 lineage or does that matter?

22 Earlier it was important there somewhere that

23 the birds come from different lineages and then I think in a

24 follow up statement it said that the process should be a

25 stratified random process to assure to the extent possible that

26 each of the F0 pairs are contributing genetically to the F1
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1 breeding population in order to represent individuals that may

2 vary in sensitivity to chemical exposure.

3 And again it's just a little bit confusing to

4 me about what all that means. Maybe to the scientists in the

5 room that all makes sense, but I was not really able to follow

6 it through myself. 

7 Another statement that says the frequency of

8 diet renewal should not be more than once a day and not less

9 than once a week. That's a pretty big range. And so there is

10 another area where I think things could be tightened up a bit to

11 where you do it every other day or you do it every day. So at

12 least that one lab is not doing it once a day and another lab is

13 doing it once a week, I think that is an issue than can be

14 straightened up a bit or tightened up a bit and made less

15 variable.

16 While I'm thinking about it I don't have this

17 written down here but I hope I don't forget about it, is I know

18 when we do studies with animals back at Kansas State University

19 and anytime we're involving weight, it sort of became a standard

20 practice that you kept them from drinking water over night and

21 then you weighed them the first thing in the morning before they

22 had any chance to eat or drink or do anything so that you sort

23 of took away some of the variability that might occur in weight

24 due to the amount of water that they've ingested. 

25 So I'm wondering if things like that should be

26 incorporated into the weight of Japanese quail. If you're going
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1 to weigh these things, should you weigh them at some certain

2 time of the day when after they've been -- after they've been

3 removed from food and water for a few hours or so or overnight

4 so that maybe that can take some of the variability out of that

5 response and might make the assays and other kinds of analyses

6 more powerful in that way.

7 There is a question I had about incubators and

8 hatchers that said incubators and hatchers preferably with

9 automatic temperature and humidity controls and an egg turning

10 device are necessary. In addition suitable equipment is

11 required to maintain stored eggs within the temperature and

12 humidity ranges specified. And so I guess my question there is

13 there an incubator for each pair or are all the eggs placed in

14 the same incubator at the same time and if they're all placed in

15 the same incubator at the same time are their locations in the

16 incubator randomized?

17 I don't know how big these incubators are, are

18 they really huge and do you get a whole bunch of eggs in it or

19 are they real small so I can only put one egg in one at a time. 

20 And again a place where -- again the idea is to try to guard

21 against any affects that might bias the results whether you

22 think they might bias or whether they might not bias.

23 I guess my concern, I usually have a -- put it

24 in here that some of the things when I teach classes I try to

25 tell the students that when I - my concern is that when people

26 do things in a certain way because it's convenient to do it that
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1 way, certainly a lot of ways that are more convenient to do

2 things than it is randomization. And randomization is not easy

3 and it's not very convenient to do it. 

4 But I try to tell my students that convenience

5 is an enemy of good experimental design. If you do something

6 because it is convenient to do it that way there is probably a

7 better way. And so that's sort of my driving philosophy I guess

8 behind some of the comments and things that I'm making here.

9 There is a statement in the reports that says

10 chicks should be identified individually or by pen of origin. 

11 Maybe be housed in groups of approximately equal number by

12 treatment group. And I guess that sent up a red flag. 

13 Are we taking chicks that came from one pair

14 of adults and mixing them up with chicks that came from another

15 pair of adults so that we can get them in groups of

16 approximately equal numbers. So it's a little bit like Medaka

17 thing there yesterday where we're putting all the fish in a

18 basket and stirring up the basket and then selecting out to go

19 on to the next generation.

20 And so it's not clear to me in my mind as to

21 how that is being done here and but I am a bit concerned about

22 what effects it might have on the statistical analysis and about

23 mixing chicks from different pairs. And I think its -- maybe

24 it's not an issue if you're not trying to follow the adult pair

25 that you started with all the way through the process. If you

26 want to do that and you wanted to compare F2 to F1 then you
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1 probably need to follow that thing through.

2 If you just want to talk about F2 by itself

3 and F1 by itself, then perhaps this idea of mixing might be

4 okay. I haven't really made up my own mind on that yet. But

5 there again I realize this is a convenient way to do things but

6 again I wrote down that convenience is an enemy of good

7 experimental design.

8 There is a statement that made all F0 birds

9 used in a test should be from the same hatch. And I don't know

10 enough about Japanese quail to know what that means. I'm sure

11 it doesn't mean that they should all come from the same parents

12 so just for us non scientists, I guess, that can be clarified a

13 bit.

14 It's indicated that the eggs were stored in a

15 cold storage facility for a maximum of two weeks prior to

16 setting them in the incubator and again were the eggs placed in

17 that cold storage facility. I'm sure that some parts of the

18 facility are colder than other parts. And the temperature is

19 probably not constant in there. Are the eggs placed at random

20 locations in the cold storage facility or are eggs from the same

21 parents kept together. 

22 A statement was made that during the study one

23 egg per pen is collected from odd numbered pens -- in odd

24 numbered weeks and even numbered pens and even numbered weeks to

25 evaluate egg shell strength. I guess in some sense, going odd

26 and even is part of a random process and I can live with that,
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1 but is the egg selected for looking at egg shell thickness is

2 that done at random. And if so how is the randomization

3 assured. Here I can see that somebody trying to do something

4 impartially might not be the same as being randomized.

5 Aren't you glad you invited a statistician to

6 be a part of this panel? 

7 Fertility and embryo viability in all

8 incubated eggs are determined by cattling the eggs after eight

9 days of incubation, EDA and two days before hatching at

10 embryonic day 15. Eggs will be transferred to a hatcher. And

11 then is randomization involved in this process and if so how. A

12 statement was made that some sample sizes were too small for

13 statistical analyses and I don't know how small is too small?

14 Well there is again some statements that came

15 about comparing the data from the three labs that involved

16 strains again, but I think we've already determined that. 

17 Power determinations were carried out for each

18 endpoint based on the control group within run variance. I'm

19 not sure that I understand exactly what is meant by "within run

20 variance." I think I know what is meant but it would be helpful

21 to have that clarified.

22 In these power analyses and I think this also

23 relates to the co-efficience of variation that are talked about

24 several times throughout all of these reports is that we have

25 these different sources of variability. There is sometimes pen

26 to pen variability and sometimes there is variability within
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1 groups and variability between groups and when you look at a

2 co-efficient of variation you would look at the variance divided

3 by the mean. Standard deviation divided by the mean. 

4 Well then the question arises which standard

5 deviation are we using? And it's not always clear what standard

6 deviation is used in calculating that ratio. So is it the bird

7 to bird variation or fish to fish variation or in the Medaka

8 study is it the tank to tank variation. Just what type of

9 standard deviation is being used when the co-efficience of

10 variation are calculated.

11 In Table 14, this is I guess in Appendix C. 

12 It was also, I think, a table that was shown, it has the means

13 and standard errors in comparisons of avian vinclozolin

14 concentrations to control across laboratories and they're

15 reported as the mean in the standard error and in Lab 1 I'm just

16 looking at the adult body weight. 

17 This sort of issue occurs in all the variables

18 that you're looking at in Table 14 but the one I copied in and

19 put in here is adult body weight.

20 And in Lab 1 you have -- and this is in the

21 control group -- 190.6 standard error of 3.36. Lab 2 is 202.7,

22 standard error 4.357. Lab 3 is 113.4, standard error 1.8. But

23 then when you go to the average the average is 170.4 which is

24 kind of between -- somewhere around the 113, the 190 and the 202

25 so it looks like a reasonable average, but then instead of

26 standard error like 3.36 and 4.35 and 1.82 you've got a standard
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1 error of 29.5. 

2 And so there again it comes in as to what

3 variance is being used and what quantity is being incorporated. 

4 Now that could be that that is the right standard error report

5 because now you have to have lab to lab variation that goes into

6 that variance. But it looks a bit -- if you're comparing the

7 control group to the other groups, then you get a lot different

8 comparisons within labs than you might get across labs. And if

9 you're doing it across labs you ought to be able to compare

10 controls to treated groups within a lab, get that difference in

11 standard error and then take a weighted average of those short

12 of a metaanalysis to do a comparison across labs. 

13 So just taking the average and the standard

14 errors associated with that average and doing a comparison

15 between control and treating groups, certainly wouldn't be the

16 most powerful way to do it. And I'm not even sure it would be

17 an unbiased way to do it. 

18 I struggled a lot with Table 11. I think now

19 I'm getting to where I feel a little bit better about it. But

20 I'll probably have some comments about it in the report. 

21 This is a table where you give the control and

22 then you give the standard error the control, you give the

23 sample size per group and then you indicate the detection that

24 can be detected at a certain distance with 80 percent power. 

25 And then you have a lower limit and an upper limit, which appear

26 to be confidence limits on the control mean. 
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1 Then if you look at the detection distance and

2 the standard error, take the ratio of those two then that's

3 constant across all variables. So there is evidently some sort

4 of critical point in there. I'm trying to decide in my own mind

5 whether that critical point makes sense or not. It's about 4

6 point something and originally I thought it was way too big but

7 then I forgot about the fact that you might need to incorporate

8 a fact -- a standard -- a square root of two in that to account

9 for the fact that you're comparing a control group to a treated

10 group and then if you put that square root of two in there then

11 it seems to bring it back into line. 

12 So I'm mentioning it here just so if I want to

13 write about it in the report, I can. I at least brought it up

14 but I'm not sure that I'm going to have to write about it in the

15 report. I'll talk to Mary about that. 

16 There is a section that deals with statistical

17 analyses in -- there is some sort of bolded remarks there that I

18 don't have much of a problem with. Need to consider and allow

19 for nonlinear data analysis. Analysis with many endpoints may

20 dilute the detection of effects, primary responsive endpoints

21 must be identified for assessment, parametric tests preferred

22 but many of the data do not meet statistical assumptions. 

23 Depending on the measurement endpoint the statistical analysis

24 must be preplanned to accommodate a range of issues that are

25 inherent in the types of data to be collected. 

26 Assessment should include some acknowledgement
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1 of variability as an indicator of effect. And then it goes into

2 nonparametric tests may be appropriate for behavioral tests and

3 ordinal data and standardized methods should be prescribed so

4 that there is consistency in the analyses.

5 I want to address this, these non parametric

6 tests a bit. But before I do that I want to talk a little bit

7 about the transformations of data because transformations of

8 data for analysis have been suggested. They have been suggested

9 when the data are not normal and they have been suggested when

10 the data had unequal variances.

11 I guess I agree that transformations are

12 useful when nonnormal data can be transferred in a way that

13 makes the data more closely normally distributed, but

14 transformations that deal with unequal variances, it seems to me

15 at this day and age are no longer necessary. We have

16 statistical software that can deal with analyses of data with

17 heterogeneous variances.

18 And so I feel pretty good with transformations

19 to try to make the data more like the analysis. In other words,

20 a lot of our analysis methods are based on normally distributed

21 data and the good thing about those analyses is they're fairly

22 robust than non-normal data, more robust than what some might

23 think. 

24 So even though the data are not entirely

25 normally distributed they probably still do a pretty good job in

26 my opinion. It's the report and not just this report but some
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1 of the other reports suggest that you do a Shapiro-Wilk test and

2 test for normality and if it is rejected you try a

3 transformation, log, square root, arcsine whatever

4 transformation comes to mind and try to find a transformation

5 that is more normally distributed.

6 My experience has been with these normality

7 tests is that they are highly dependent on sample size. That if

8 you have a large sample size you'll reject any distribution you

9 try. If you have a small sample size you won't reject any. And

10 so I'm not sure these tests on normality are all that useful.

11 I think that whether the data are normal or

12 not probably is better based on your experience. These data

13 endpoints that you're looking at are endpoints that have been

14 used over and over and over again in many, many studies about

15 which people have lots and lots of experience, or at least I

16 would think they would, and the appropriate units for analysis

17 ought to be known. And that seems to me that those are the

18 appropriate units that should be used whether the data are

19 normal or not. 

20 For example in the classes I teach I think I

21 used -- I'm not a chemist so I never get it right. Anyway so pH

22 is the transformation of the negative ion concentration or

23 something like that. Is that right? Hydrogenised ion

24 concentration? Hydrogenised Ion concentration? Okay. We'll

25 get that right in the report.

26 But the idea is that there is a variable that
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1 was known to be non-normal and so it got transferred to pH and

2 so everybody does the analysis in terms of pH because that

3 variable tends to have a more normal distribution. And it seems

4 to me that many of these data that we have for endpoints here,

5 there ought to be able to make a good scientific guess on what

6 the appropriate distribution might be and if you know what the

7 appropriate distribution is then you perhaps know what the

8 appropriate transformation might be. Or there are

9 transformations that are made for lots of kinds of data. Like

10 if you're looking at proportions, proportions is often

11 transformed using an arcsine transformation or something similar

12 to that. 

13 And so it's sort of the way that the variables

14 come about and the way they're used it seems to me that you

15 ought to be able to decide what the appropriate unit of analysis

16 is or what the appropriate distribution is and then base

17 transformations on that. 

18 I guess I made a statement here that I have

19 some issues with letting the data drive the analysis. I think

20 in some sense the science should drive the analysis and there

21 maybe -- if you're looking at something completely new and you

22 never looked at it before and you don't know anything about it

23 then yes, maybe you should try to decide what the appropriate

24 unit for analysis is, but on a lot of these things that you're

25 looking at, time to first mount, time measurements often has an

26 exponential distribution or a log normal distribution or some
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1 Weibull distribution so there ought to be a distribution that

2 might be able to be used for that. Okay. Enough about

3 transformation.

4 The next thing I wanted to address is the

5 nonparametric stuff. It seems like this report in other words

6 talks about doing the analysis of variance on ranks as sort of a

7 way to treat the data nonparametrically and that was a process

8 that was introduced a fairly long time ago by a very good friend

9 of mine and is a major professor. 

10 And turns out it works pretty good under

11 certain circumstances but studies have been shown on later that

12 nonparametric analyses and these ANOVA on ranks don't work very

13 well under certain types of conditions. 

14 I sort of knew in my mind what these

15 conditions were, but I was too lazy to go find the books and get

16 them in so I Googled it; got Wikipedia and I'll just give you

17 some of this information that is there and I tend to believe

18 these things and I come across it several times in the past and

19 so on.

20 One of the places that these rank

21 transformations don't work at all is if you have interaction. 

22 So if you have a one-way treatment structure and you're just

23 doing analysis of a one-way experiment then the rank transforms

24 seems to be working pretty good and they give you almost as good

25 a result as you might get with normal, even if the data are

26 normal.
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1 But if you have interaction they don't work at

2 all. There is an increase in type one error resulting in a

3 complete failure of the statistics with as high as 100 percent

4 probability of making a false decision or type one error. 

5 And they also found -- I don't know that it's

6 appropriate or worried about it here -- but it's found that rank

7 transformation increasingly fails in the two dependent samples

8 as the correlation between the pre-test and post test scores

9 increase.

10 Transforming ranks and I think at least one of

11 the reports talks about transforming ranks by sort of looking at

12 the ranks and then transforming them into the quantiles of the

13 normal distribution or some other distribution so they are

14 sometimes called a quantile normalized data. The statement in

15 Wikipedia is that random normal scores and expected normal

16 scores transformation have been shown to greatly inflate type

17 one errors and severely reduced statistical power. And so that

18 sort of technique should not be done.

19 The reports seem to indicate that the ANOVA on

20 ranks might be appropriate when you have unequal variances. 

21 Again quoting from Wikipedia and this is, I think, what I've

22 known to be true in the past so I'm not just completely relying

23 on Wikipedia, but the ANOVA on ranks has never been recommended

24 when the underlying assumption that homogenous variances have

25 been violated either by itself or in conjunction with a

26 violation of the assumption of population normality. 
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1 In general rank based statistics become

2 nonrobust with respect to type one errors for departures from

3 homoscedasticity even more quickly than their parametric

4 counterparts that share the same assumption.

5 But there again the good news is that we now

6 have software that we can analyze with unequal variances very

7 easily and we can do it accurately and control type one/type two

8 error rates and so on. So we really don't need nonparametric

9 methods in the case when we have unequal variances. And as this

10 states here they should not be used. 

11 And then there is a series of references that

12 are given in that and their references that I sort of relied on

13 when I started doing my look into the Wikipedia stuff. I think

14 that's all I have for now.

15 DR. MARTHA SANDY: Thank you. So I think it's

16 a good time to take a break. It's 3:10. Why don't we come back

17 at 3:25. 

18 

19 (Whereupon a break was taken)

20 

21 DR. MARTHA SANDY: As we start up again we're

22 in the middle of the response to Question 1 and Dr. Johnson who

23 is the lead has suggested that we break this response up --

24 sorry four. I'm looking at four but yes. Thank you.

25 He's broken up the questions so he's addressed

26 the statistical methods and design study protocol issues that he
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1 had. I'd like to ask the other panel members that were listed

2 as discussants on Question 4 if they have anything to add on the

3 statistical issues and if not we'll turn to the whole panel. Is

4 that alright? Okay. I will let you yes, Mary Christman please

5 go ahead.

6 DR. MARY CHRISTMAN: Hi, I just wanted to

7 comment on Dallas' discussion. Very quickly I promise. 

8 Basically I wanted to reiterate what he said

9 earlier about trying to understand what the true underlying

10 probability distribution of your endpoint is. Use the

11 literature, use prior knowledge, use similar experiments just

12 like when you do power analyses. You use this prior knowledge. 

13 Take advantage of it. And in this day and age there is a lot of

14 statistical methodology out there that would allow you to use

15 the true distribution and there is methods for checking the

16 diagnostics of the true distribution. So you don't need to

17 always do a transformation. There are times when you want to

18 actually model it as a count variable, like poisson or something

19 or as my advisors used to call it a poisson. That would just

20 confuse the heck out of me.

21 The second thing I wanted to mention was

22 everybody seems to think that nonparametrics means no

23 assumptions. The reality is nonparametrics have assumptions on

24 them as well. They just don't tell you what the shape is, but

25 they demand that the shape be the same in every treatment and

26 two, that the variances be equal. So you weren't getting away
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1 from this requirement to meet assumptions, it just doesn't tell

2 you what that shape is. So keep that in mind when you're

3 thinking about doing nonparametrics.

4 And last but not least one more thing that

5 causes nonparametrics to breakdown are when you have a lot of

6 ties. So if you do have things like counts or small sample

7 sizes where you have the same value, that results in the ranking

8 order being ties and you can't use nonparametrics for when you

9 have many ties. Thank you.

10 DR. MARTHA SANDY: Okay. And a tie is

11 multiple -- two. 

12 DR. MARY CHRISTMAN: Ties, they're ties.

13 DR. MARTHA SANDY: Dr. Bentley.

14 DR. DALLAS JOHNSON: Just to add to that if I

15 might. In some of the cases where you're looking at these type

16 scales, you know one, two three four five and so you're ordering

17 the data according to some numerical value dealing with severity

18 and if you're using one, two, three, four, five or one, two,

19 three or one through seven or whatever you're using, you're

20 going to end up with a lot of ties just because there is not

21 enough information in those variables to distinguish between a

22 five and a five or a four and a four and so on.

23 DR. GEORGE BENTLEY: I don't have anything

24 further to add on the statistical analysis.

25 DR. MARTHA SANDY: And Dr. Scanes.

26 DR. COLIN SCANES: Ditto.
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1 DR. MARTHA SANDY: Dr. McCarty.

2 DR. JOHN MCCARTY: And nothing to add on the

3 stats.

4 DR. MARTHA SANDY: Dr. Perkins.

5 DR. EDWARD PERKINS: Nothing more to add.

6 DR. MARTHA SANDY: And Dr. Johnson did Dr.

7 Portier send in anything on this one?

8 DR. DALLAS JOHNSON: Surprising he said less

9 about the statistics than anything else. So not on the

10 statistics for the quail he didn't seem to add anything in that

11 we needed to worry about.

12 DR. MARTHA SANDY: Okay so any other panel

13 members want to comment on the statistical issues here. No. 

14 Okay. So among the other associate discussants on Question 4

15 shall we see who has any comments on the selection of test

16 substances, those issues related to that? And I'll start with

17 Dr. Bentley.

18 DR. GEORGE BENTLEY: Well first I should

19 mention that we really haven't discussed these subcomponents of

20 this question as a group so these are my own opinions and we

21 have briefly discussed them.

22 My main question about the selection of test

23 substances is something that came up yesterday I believe or the

24 day before. I don't know how long we've been here now.

25 And that is whether the Tier 1 testing is

26 wholly appropriate for birds given that the Tier 1 tests appear
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1 to be geared more towards mammalian systems and looking for

2 effects of endocrine disruptions via mammalian or potential

3 effects via mammalian receptors. There may well be subtle

4 differences in avian hormone receptors that would provide a

5 false positive in the Tier 1 testing. Now I had stressed --

6 DR. MARTHA SANDY: So excuse me are there any

7 avian tests in Tier 1? I don't think there are. No, so I'm not

8 clear what your commenting on when you're saying Tier 1 tests

9 and avian assays.

10 DR. GEORGE BENTLEY: Oh with the initial

11 screening of chemicals to go to a Tier 2 test. There are no --

12 right so.

13 DR. MARTHA SANDY: Okay so you're saying --

14 DR. GEORGE BENTLEY: Chemicals are selected

15 for Tier 2 tests as a result of Tier 1 test. Right?

16 DR. MARTHA SANDY: That is correct.

17 DR. GEORGE BENTLEY: And there are no avian

18 specific Tier 1 test?

19 DR. MARTHA SANDY: That is correct.

20 DR. GEORGE BENTLEY: So if a chemical is not

21 selected for a Tier 2 test that might not -- because it doesn't

22 have an effect in a mammalian system that doesn't mean it's not

23 going to have an effect in an avian system. 

24 DR. MARTHA SANDY: Okay so that's your point. 

25 Okay. Thank you. Dr. Scanes.

26 DR. COLIN SCANES: I've got a couple of
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1 points. One I do think that George Bentley's comment about the

2 absence of any avian Tier 1 test is very appropriate. A small

3 change in the receptor could change its affinity, could change

4 whether a compound binds and so that one could be either having

5 false positives in terms of using a mammalian test or a false

6 negative.

7 And so a compound that doesn't work in Tier 1

8 could still have, let's say, an estrogenic effect in Tier 2 and

9 so I hope I'm saying that correctly George. 

10 DR. GEORGE BENTLEY: You're paraphrasing me

11 quite well. You're explaining me much better than I can myself.

12 DR. COLIN SCANES: So that's a point of

13 agreement. Two points within the test though I think need to be

14 addressed in the protocol. 

15 One is the issue, and it's a very simple

16 thing, of measurement of spillage of food. It says in the

17 protocol spillage is noted. Well, you know, it could be

18 weighed. And I think that would be preferable.

19 Further issue, which could confound things,

20 particularly if cages are randomized, is the possibility of dust

21 or spilled food moving into a different bird's feeder; and so

22 that issue of either dust contamination or spilled food

23 contamination should be addressed because that could easily get

24 confounding effects particularly when we've got marked

25 differences in concentrations. 

26 And the final point that I'd like to make was
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1 in terms of the test material that was used I thought trenbolone

2 was a very good test subject. The other material was less

3 compelling.

4 DR. MARTHA SANDY: Okay. Dr. McCarty. 

5 DR. JOHN MCCARTY: Yes I think a lot of our

6 comments -- so regarding the choice of the test substance for

7 the inter-lab comparison and that's the most complete comparison

8 -- you know I think the comments yesterday the EPAs response to

9 the question of why vinclozolin was chosen that something along

10 the lines of it looks harder to defend in hindsight. You know,

11 I think a lot of our uncertainty about the tests goes from that.

12 And it certainly -- several of our questions would be clarified

13 if inter-lab tests had been run with something with more clear

14 cut expectations of what the results should be. 

15 Right now, you know if you look at the

16 inter-lab comparisons we don't know how much of it is birds

17 really don't respond to the chemical or the protocol wasn't good

18 or the labs weren't any good or the statistical power isn't

19 strong and because, you know, if a different substance had been

20 chosen it would be easier to evaluate the outcome of those

21 inter-lab comparisons.

22 DR. MARTHA SANDY: And Dr. Perkins.

23 DR. EDWARD PERKINS: I agree with what John

24 said. I think for the purposes of validating, doing round robin

25 inter-lab comparison a chemical with a more defined

26 characteristics or toxicological affects probably should have
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1 been used.

2 DR. MARTHA SANDY: Do other panel members have

3 any comments on selection of test substance? Then we'll go to

4 the third and final part of this question. Comments on the

5 analytical methods that were used. 

6 DR. GEORGE BENTLEY: I thought that in

7 addition to histology on the gonads for example and the brain,

8 methods such as qPCR could have been used and this could be done

9 without increasing the number of animals used. You could do a

10 qPCR on one testes for example and histology on the other

11 testes. And that way you could target a particular MRNA you

12 want to look at such as star protein, LH receptor, FSH receptor,

13 17-beta, et cetra. 

14 This could also be done in the brain. You

15 could do histology and qPCR on the same brain using alternative

16 brain section and therefore look at changes in the expression of

17 GnRH-1 in addition to doing immunocytic chemistry for the GnRH-1

18 peptide.

19 And I think that can give you a lot better

20 picture of what's going on in terms of these endpoints in

21 females; qPCR could be done on individual follicles, for

22 example, as long as they were standardized as to which follicle

23 you were performing it on.

24 And again this could be done without

25 increasing the number of animals used and it could be done quite

26 easily. 
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1 DR. MARTHA SANDY: Dr. Scanes.

2 DR. COLIN SCANES: No further comment.

3 DR. MARTHA SANDY: Dr. McCarty.

4 DR. JOHN MCCARTY: No. I think any of the

5 other comments on analytical methods that I have are going to be

6 captured in Question 5 and 6 and no need to cover them here. 

7 DR. MARTHA SANDY: Dr. Perkins.

8 DR. EDWARD PERKINS: I would just support what

9 George is saying that there is potential for supporting evidence

10 in terms of weight-of-evidence for looking at expression.

11 DR. MARTHA SANDY: Other members of the panel.

12 No questions or comments. And EPA staff do you have any

13 clarifying questions?

14 DR. LESLIE TOUART: Just a clarification in

15 terms of test substance and remarks made about vinclozolin and

16 that we needed to maybe have looked at a more defined compound

17 when -- vinclozolin we pretty much understand an androgen

18 antagonist. So we knew that -- that context we didn't know how

19 it was going to respond in bird specifically.

20 I'm not sure what more we would have gained,

21 again, given that this is a more kind of definitive, you know,

22 test so there is nothing to compare it to. I mean there is

23 nothing to say what we could have expected per se.

24 So the context in terms of what more might we

25 have determined given that we did have good correspondence in

26 terms of the endpoints that were looked at and measured. It was
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1 just those didn't have a particular response. 

2 So is the concern that, you know, compounds

3 should have had more responses across different endpoints to be

4 able to evaluate kind of reproducibility or would it be that the

5 question would be, you know, that we're trying to have more

6 definitive information on say vinclozolin per se as opposed to

7 the method as it is.

8 DR. JOHN MCCARTY: I guess what I'm referring

9 to are those cases where we don't have a response and so take

10 behavior for example, and given the chemical we don't have that

11 information on -- we don't have detailed information on the

12 transfer of this to different labs. Because the three labs used

13 a chemical that we're not really sure to expect and they found

14 negative results. Well is that a reflection on vinclozolin or

15 is that a reflection on the protocol. And that's all I was

16 referring to, that some of our other questions would be answered

17 if we had say inter-lab comparisons running this protocol with

18 something we know has strong effects on birds in the endpoints

19 of interest. 

20 DR. MARTHA SANDY: Les Touart.

21 DR. LESLIE TOUART: And I guess the question

22 then with Trenbolone that was used, and again, only two

23 laboratories in that context but there were measures comparable

24 in terms of the effects that we're seeing. I don't know if

25 that's considered.

26 But I guess I kind of understand what you're
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1 saying, I'm just not clear as to if additional tests are done

2 exactly what they might be able to inform us beyond what we've

3 kind of seen and expect in terms of what we can actually

4 measure.

5 DR. JOHN MCCARTY: First I'd reiterate what

6 Colin said. I think the Trenbolone tests were useful. On the

7 other hand we've been -- either Colin or George can correct me

8 on this but we've emphasized that we think behavior are

9 important set of characters and my understanding the sensitivity

10 of male behavior is very sensitive for example to estradiol. 

11 And so right now we don't know is the protocol at fault for not

12 finding behavioral responses in the males or is it the fact that

13 the chemical just doesn't affect behavioral development in

14 males. And that would be the added benefit there in my opinion.

15 DR. MARTHA SANDY: Dr. Scanes.

16 DR. COLIN SCANES: I think my reaction was

17 based upon all of the models. Not only the quail model that the

18 need for consistency across the contract labs. And that's why

19 our recommendation is to include a positive control. Because by

20 having the positive control where you will hopefully see an

21 effect of the positive control adds confidence to the data of

22 the negative control. Particularly if the test agent has no

23 effects, that is exactly what one wants to see from a regulatory

24 point of view. That is an agent that has no effects, the

25 positive control works and the negative control is the same as

26 the test agent.
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1 DR. MARTHA SANDY: All right are we ready to

2 move on to Question 5? 

3 DR. LESLIE TOUART. Reading the Charge

4 Question No. 5. Considering that variability inherent in

5 biological and chemical test methods, a test method needs to be

6 repeatable and reproducible. A test is robust and reliable if

7 the results are repeatable and reproducible within a laboratory

8 and between different laboratories, respectively. A test

9 protocol should provide sufficient guidance to ensure proper and

10 consistent performance across labs and chemicals. 

11 Please comment on the test method robustness

12 and reliability and the repeatability and reproducibility of the

13 results obtained with the Japanese quail assay.

14 DR. MARTHA SANDY: Dr. Bentley you're the lead

15 discussant on this.

16 DR. GEORGE BENTLEY: Okay thank you. Well a

17 few of these things have been touched upon by Dallas already and

18 his comments on statistical analysis, I'm not going to comment

19 on them from a statistical viewpoint but I will also try to keep

20 repetition to a minimum.

21 As you've heard a recurrent theme is that we

22 feel the assay would benefit from the use for positive control

23 where possible. This would allow us to evaluate how

24 reproducible and repeatable the test is. And we've talked about

25 the use of vinclozolin showing not much in the way of treatment

26 effects in any lab and as is already mentioned we can't tell if
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1 it's a result of any issue of the test reliability, lab rat

2 reliability or if it's a real result. And the use of a positive

3 control would greatly help with our interpretation of the data.

4 There were other potentially identifiable

5 sources of variation between labs and one of those has been

6 commented on, is a strain differences in quail and we have a

7 recommendation about that at the end of my spiel. 

8 It could also be differences resulting from

9 subtle differences in incubation parameters of the eggs. For

10 example, humidity, egg storage temperature. These can all

11 influence the hatchability of the eggs. 

12 One issue that doesn't appear to have been

13 accounted for is the light intensity the birds were exposed to. 

14 And this is not only a potential issue between labs but within a

15 lab and within a room. If quail are housed in racks then the

16 birds at the top of the rack will receive exposure to much

17 greater light intensity than birds who are on subsequently lower

18 racks.

19 And this can really greatly influence that

20 photoperiodic response when you transfer from short to long

21 days. And they'll interpret lower light intensities as shorter

22 days than they are actually housed upon once they're below a

23 certain threshold of light intensity.

24 The issue of the strained differences is

25 potentially a big one but we think this could be somewhat

26 addressed using a single supplier to standardize the quail
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1 source and then we have a recommendation on that that I'll get

2 to. And the other parameters that we'll discuss really could be

3 accounted for using a more standardized SOP and increasing

4 training. Overall it appears the SOP needs to be tightened and

5 adhered to to decrease variability in data. 

6 Another issue that wasn't apparently counted

7 for is that within a strain of quail there are so called

8 personality differences. There are often high stress and low

9 stress lines. And these different lines have very different

10 behavioral and physiological responses to a stressor. 

11 And as a result circulating glucocorticoids

12 might well change differently between these different lines and

13 that is going to influence the amount of circulating gonadal

14 steroids and that interaction between corticosterone binding

15 protein and the gonadal steroids. So that might well influence

16 the response of these quail to endocrine disrupting chemicals.

17 So we feel that circulating corticosterone

18 needs to be measured to ascertain the responsiveness of the HPA

19 axis to handling many of these animals used. So this is not

20 just measuring the baseline corticosterone which would be

21 measured by measuring fecal corticosterone metabolites, but we

22 would like to see how these animals respond to a stressor and

23 whether that is uniform across groups. More so we would like to

24 see the parameters of if CORT clearance could differ as a result

25 of treatment. 

26 We talked quite a bit about histology. We
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1 will refer back to our earlier comments on histology there. And

2 as we have already mentioned the data from behavioral assays are

3 quite highly variable and again just solidifying the SOPs,

4 increasing training, identifying where the variance is and why

5 it is there could help interpret the data.

6 Another comment is that several of the tests

7 have very high CVs and apparently low power to detect changes

8 and again we're not given enough detail to distinguish between

9 variability and introduced by the test protocol itself and/or

10 inherent biological variability. So if the variabilities are

11 function of different labs using slightly different procedures

12 then that could be corrected by correcting the SOPs or being

13 more specific in those SOPs. But if it reflects biological

14 variation then in certain circumstances the sample size may need

15 to be increased. 

16 So overall recommendations here are one;

17 consistent use of a positive control; the second one is to

18 standardize the use of the quail strain. Perhaps have a single

19 supplier. And one suggestion we had was maybe to build a

20 foundation of a single genetic stock that is then housed in more

21 than one location in case of disease or natural disaster wiping

22 out that stock. 

23 And then have appropriate genetic husbandry

24 performed between these locations periodically. And perhaps

25 they should be made internationally available.

26 The next recommendation is to have stricter
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1 more standardized SOPs. And housing light intensity should be

2 stated in the protocol and standardized across labs, across

3 rooms. And as Dallas mentioned earlier, the time of day of

4 sampling can be an important source of variabilities not just

5 for body mass but also for hormone measurements. So that should

6 be standardized.

7 Histological assays we've talked about

8 standardizing those and Colin provided some details as to how

9 that can be standardized. This should be at least one measure

10 of the CORT response to stress to ensure it is equivalent across

11 groups. And then behavioral assays which I think should be

12 expanded and standardized and adequate training should be

13 provided for the different labs to perform those assays. So for

14 example, appetitive male behavior could be measured in addition

15 to consummatory behavior. 

16 Ideally we would like to see less of a focus

17 on male specific behavior and incorporation of measurements of

18 female behavior and make choice and we've already talked a

19 little bit about this and how that might be difficult to do in

20 quail given that the females don't appear to have much of a

21 choice. And so this is where a passerine model such as zebra

22 finch might be useful.

23 And then finally possibly increase the sample

24 sizes if the use of those stricter SOPs don't reduce the

25 observed variability.

26 DR. MARTHA SANDY: And Dr. McCarty any
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1 additional thoughts?

2 DR. JOHN MCCARTY: No I think he's covered my

3 comments.

4 DR. MARTHA SANDY: Dr. Scanes.

5 DR. COLIN SCANES: He's covered my comments

6 but I want to point to the research in the appendices supports

7 the robustness of the Japanese quail three generation test.

8 DR. MARTHA SANDY: Dr. Johnson.

9 DR. DALLAS JOHNSON: I don't think I have

10 anything to add except that in the preamble to the charge it

11 indicates a test protocol should provide sufficient guidance to

12 ensure proper and consistent performance across labs and

13 chemicals. And I guess my feeling it from what I've seen so far

14 is that we're not at that stage yet.

15 DR. MARTHA SANDY: Did you have anything from

16 Dr. Portier? We could take that now. While you're looking,

17 I'll turn to Dr. Perkins.

18 DR. EDWARD PERKINS: I have nothing more to

19 add.

20 DR. DALLAS JOHNSON: Dr. Portier mentions an

21 issue that the document doesn't provide a conclusion regarding

22 test reliability. To the certain extent EPA is asking SAP to

23 make this assessment. The panel can do this if the biologically

24 important detection differences are first identified. 

25 Unfortunately EPA has not identified the key endpoints or

26 assigned biological important detection differences. Is this
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1 also something the panel must decide. 

2 He adds on EPA should consider creating a

3 database of test endpoint values from all control animals from

4 all test sites to continue to add to this database and continue

5 to add to this database as the test gains popularity in use. 

6 This will allow us to establish historical controls. We should

7 allow external assessment as to whether a specific study

8 implemented sufficient test condition controls at critical

9 endpoints from the control animals that are reasonable compared

10 to a historical control. Only in this way can we begin to

11 ensure reproducibility of results and comparability among

12 studies.

13 He also says the test protocol described makes

14 a valued attempt to identify many of the factors that impact

15 outcome variability and to standardize them. It remains to be

16 seen whether all of these factors can/will be controlled by

17 laboratories performing the test. There are a lot of

18 conditions. The test sites implement all controls or only some.

19 If some are the ones that they do chose to control sufficient

20 and that the resulting variability is controlled.

21 Bullet 2 in Section 5.5 gives good advice. 

22 Need consistency in test design, photo period, timing of

23 initiation of both photo period and dosing, dosing period,

24 timing of collections, measures and genetic strength. 

25 As mentioned previously I feel that dosing

26 amounts need to be tied to some definition of what represents a
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1 biologically significant detectable difference taking into

2 account the power of comparisons at specified sample size.

3 A discussion of reliability in draft versions

4 5.4 it's one of the -0008 Page 11 Section 3B provides a very

5 good summary of what EPA is attempting to examine when they talk

6 about test method reliability. Their concept of reliability

7 ties in exactly with what I have been saying about matching the

8 test sample size to power and the biologically important

9 detection difference.

10 The three types of variability identified

11 within tests, between tests in the same laboratory and between

12 laboratory variability are all important in assessing test

13 reliability. The issue I have with JQTT ISR is that it never

14 provides a conclusion regarding test reliability. To a certain

15 extent EPA is asking the SAP to make this assessment. The panel

16 can do this if biologically important detection differences are

17 first identified. Unfortunately -- maybe I read that already. 

18 I think I did. Sorry about that. That's all.

19 DR. MARTHA SANDY: Thank you. Other comments

20 from other panel members on Question 5? I see none. I'll turn

21 to the EPA do you have -- was that clear or any questions?

22 DR. LESLIE TOUART: I think I understand the

23 comments made, I welcome that. I look forward to any of the

24 additional suggestions the panel will make on that point.

25 DR. MARTHA SANDY: So maybe we'll turn to

26 Question 6. Would you like to read that for us please.
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1 DR. LESLIE TOUART: Reading Charge Question 6.

2 The test protocol should be descriptive enough to be fully

3 transferable to a competent laboratory. The protocol should

4 describe the methodology of the assay in a clear and concise

5 manner so that a laboratory could comprehend the objective,

6 conduct the assay, observe the measure prescribed endpoints,

7 compile and prepare data for statistical analyses, and report

8 the results. Section 6 of the ISR outlines the process of

9 challenges experienced in the inter-laboratory studies. 

10 Please comment on the transferability across

11 labs and provide any suggestions or recommendations for

12 improvement of the Japanese quail assay.

13 DR. MARTHA SANDY: And Dr. Bentley is the

14 lead.

15 DR. GEORGE BENTLEY: Okay. Well there is

16 going to be a little bit of repetition from the last question

17 but not too much I hope. Again the high variability in some

18 aspects of this assay makes it difficult to interpret

19 transferability across labs. For example, because of the strain

20 difference issue and because of the lack of use of positive

21 controls. So we have some suggestions and recommendations over

22 all and I'll just go through the list.

23 First off, there needs to be clear guidance on

24 which animals will be initially selected for histological and

25 subsequent and statistical analysis. And which tissue from

26 those animals and then within the tissue, which sections are
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1 present. This is vague and we have discussed that. 

2 The second one is to measure CORT

3 responsiveness within a strain or the strain to be used to

4 ensure normal distribution to the stress response. Keep the

5 strain as genetically uniform as possible, again using the

6 single supplier of single source. The egg storage and

7 incubation metrics should be standardized. Humidity and as I

8 said slight temperature effects can influence hatchability. Is

9 there a positive control for each endpoint that we think there

10 should be. There were some endpoints not measured that we

11 thought would be valuable to include. This is in addition to

12 the endpoints I mentioned in terms of running qPCR on the gonads

13 and brain tissue.

14 For example GnRH-2, Gonadotropin-releasing

15 Hormone 2 in the mid brain has quite a strong influence on

16 sexual behavior. And that could certainly be measured. It can

17 also influence the HPG access directly as evidence for that.

18 LH and FSH were not measured. It's a critical

19 component of the HPG axis that is impacted by steroid feedback. 

20 So they should be measured. Progesterone was not measured. 

21 Progesterone is critical for ovulation and for oviduct function

22 in birds.

23 So we feel it is highly appropriate to measure

24 all of these to investigate the potential for population level

25 effects of the chemicals tested. 

26 I talked about qPCR on gonadal and brain
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1 tissue. I talked about standardizing the time of day of

2 sampling, just reiterating it here. 

3 Just a comment really and I want to emphasize

4 that we think this test seems to be highly appropriate to test

5 potential endocrine disrupting chemicals in galliform species

6 with precocial chicks. Again, I want to mention it is not clear

7 how applicable it will be to avian species in general including

8 the passerine species. 

9 Something else to consider is that

10 longed-lived species such as albatross and gulls takes several

11 years to enter into puberty, not just a few weeks like a quail. 

12 And as a result they potentially experience a much longer

13 exposure to endocrine disrupting chemicals than quail. Not just

14 as a result at the time of exposure, but also because of

15 bioaccumulation, often higher up in the food chain.

16 And this delayed entry into puberty might make

17 them either more or less sensitive to the chemicals tested. 

18 We're not making a recommendation on that, but we wanted to make

19 sure everyone is aware of that. 

20 And we've talked about how passerine species

21 with altricial young might be more or less sensitive to

22 endocribe disrupting chemicals than quail. The altricial young

23 hatch in a highly undeveloped state in a way their external

24 embryos and they're often fed insects by the parents.

25 So again bioaccumulation of endocrine

26 disrupting chemicals combined with exposure during critical
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1 developmental stages might make them more sensible to endocrine

2 disrupting chemicals with precocial young. 

3 There was a mention of overall fitness

4 measures in the report and we felt that overall fitness might be

5 more easily measured in a song bird model with more pronounced

6 secondary sexual characteristics. Or other parameters that

7 indicate fitness might be more easily measured should I say. 

8 And these are infiltrated into sexual

9 selection such as symmetry of the males -- the chest bars on the

10 male zebra finch or accuracy of song, size of song, control

11 nuclei. 

12 And again parental care not just incubation of

13 eggs but feeding of hatchlings could also be measured more

14 easily. And as has already been mentioned, we felt that

15 behavioral measures in the adults could be standardized more.

16 We could measure female behavior in a

17 passerine species, we could actually measure female behavior in

18 quail and put the females in a choice chamber and see if there

19 is any preference for males that either have or have not been

20 treated with endocrine disrupting chemicals. And that way there

21 is no confound of the male trying to attack the female. 

22 You might want to emphasize that often in labs

23 studied behavior is often considered an easy option versus

24 biochemical assay. So the SOPs are not always as detailed as

25 they might be for an assay and RAA. 

26 But we feel that differences in behavior can
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1 be quite subtle and hard to measure to the inexperienced eye so

2 as detailed protocols as for RAA, for example, need to be

3 developed. 

4 And we want to emphasize that behavior is an

5 important endpoint in birds because there is a clear link

6 between endocrine disruption as a mechanism and its affects on

7 behavior which in turn provides a good link to the fitness

8 endpoints that are incorporated into an ecological risk

9 assessment.

10 And I mentioned potential measures of

11 appestive behavior as well as consummatory sexual behavior could

12 be used. And as John McCarty mentioned earlier a positive

13 control for behavior could be the use of estrogen as it is known

14 to influence male sexual behavior.

15 So to summarize we think this test clearly has

16 great potential as a standardized assay and it is justified and

17 we feel it will be well suited for its purpose. But at present

18 they need some refinement, particularly in terms of endpoints

19 measured, guidance for transferability across labs and data

20 analysis. We think the test has a great deal of merit in terms

21 of measurement, the effects of endocrine disrupting chemicals in

22 galliform birds. 

23 But it is not clear how broadly applicable the

24 findings will be to other orders of birds with very different

25 reproductive life histories. For example phallus size in ducks,

26 song control systems size in birds, behavioral differences,
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1 courtship behavior differences such as directed and undirected

2 singing in zebra finches, effects on female sexual behavior

3 could all be affected by endocrine disrupting chemicals and

4 would not be detected in this assay.

5 So again, we recommend consideration of adding

6 the use of a passerine species such as zebra finches as a

7 component to this test where necessary. These birds breed very

8 well in captivity. They develop very quickly, their behavior is

9 very stereotypical, very easy to quantify or relatively easy to

10 quantify and is well characterized. And as we mentioned the

11 zebra finch genome is available and we know a lot about their

12 physiology, endocrinology, female choice, parental care and

13 sexual differentiation.

14 And the vocal learning in passerine species as

15 we've mentioned is a unique aspect of these birds that may well

16 be affected by endocrine disrupting chemicals. And it's not

17 possible to test this within the quail test per say. And the

18 development of the song control system and the short time window

19 in which zebra finches are able to learn this song, makes this

20 aspect of their neurobiology and behavior potentially,

21 particularly prone to effects of endocrine disrupting chemicals.

22 Okay that's all I have to say.

23 Oh sorry. I do have one more thing to say on

24 the recommendations. In terms of the decreasing of variability

25 across labs and allowing this assay to be transferred across

26 labs easily I thought it might be an idea to have pre-approval
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1 of contract labs based on their proven ability to measure the

2 endpoints decided upon. And we thought there might be a

3 possible involvement of a consortia of public and private

4 partnerships. Okay that is all I have to say.

5 MARTHA SANDY: Thank you and now we'll turn to

6 the associates. Dr. McCarty.

7 DR. JOHN MCCARTY: The only thing I'd add is

8 that in order to be successfully transferred as we have

9 reiterated it's not just that the protocols be descriptive

10 enough but training. You know, like I think the fish

11 presentation yesterday made a really good job of emphasizing

12 that, you know, hands on training, web training. We've

13 mentioned it before would be a really good addition to ensuring

14 that a good protocol gets adopted correctly.

15 MARTHA SANDY: Dr. Scanes.

16 DR. COLIN SCANES: Not wishing to reiterate

17 what other members of the team have said but I want to go back

18 to the issue of training and even suggest the consideration of a

19 simple certification program. We as members of this panel have

20 to go through an ethics certification. Does it seem

21 unreasonable for people who are completing the studies to have

22 some form of certification program?

23 The second point to add in is the issue of

24 having standards, certainly for the biochemical measures,

25 certainly for shell, which can be distributed between the

26 different labs doing the testing to ensure that there is good
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1 inter- and intra-lab standardization. 

2 And third thing is to reiterate a comment that

3 George made. The need for a random bred genetic stock that is

4 available throughout the U.S. and internationally. If one is

5 thinking about that having it at three locations -- at least

6 three locations and checking the genetics. I think there is

7 really a concern that you could so easily get genetic drift in

8 one supplier and so there does need to be an oversight mechanism

9 to ensure that truly this is a random bred population.

10 MARTHA SANDY: Dr. Johnson.

11 DR. DALLAS JOHNSON: Perhaps I should

12 apologize I missed the first line of the thing here I guess as I

13 was reading through this and many of my comments that I put in

14 for Question 4 should probably more appropriately be placed here

15 and I'll try to make sure that we do that in the final report.

16 MARTHA SANDY: Dr. Perkins.

17 DR. EDWARD PERKINS: I have no further

18 comments to add.

19 MARTHA SANDY: And Dr. Johnson I'll ask you

20 did Dr. Portier submit anything on this one?

21 DR. DALLAS JOHNSON: He did not.

22 MARTHA SANDY: Other members of the panel any

23 comments on Question 6? No. Okay and EPA staff any

24 clarification questions?

25 DR. LESLIE TOUART: No I think I understood

26 fairly succinctly what the panel suggested and some of the
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1 recommendations so we can move to Question 7.

2 MARTHA SANDY: Thank you. 

3 DR. LESLIE TOUART: Charge Question 7. The

4 purpose of the validation process is to determine the readiness

5 of a test for inclusion in a testing program. A component of

6 readiness of a test is the evaluation of the usefulness and

7 limitations of the test, including the classes and types of test

8 substances that can and cannot be tested. 

9 Please comment on the strengths and/or

10 limitations of the Japanese quail assay, as described in Section

11 7 of the ISR.

12 DR. MARTHA SANDY: And the lead is Dr. Scanes.

13 DR. COLIN SCANES: Starting with an overview

14 that the Japanese quail multigenerational test assay exhibits

15 readiness. It's justified as ready with the caveats that have

16 been mentioned and will be mentioned. Looking at the strengths

17 and weaknesses the first is looking at the strengths of the

18 system and the OECD report of 2002 stated that there are

19 sufficient differences within many avian systems to warrant the

20 development of an avian specific test. We fully concur with

21 that statement.

22 There is a strong background research in

23 endocrine physiology of the Japanese quail, particularly on its

24 reproduction of thyroid function. We fully concur with that. 

25 Next is the OECD concerning two species

26 Japanese quail and Bobwhite quail and opted very reasonably in
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1 our opinion for the Japanese quail. Although it is not

2 indigenous to the U.S., it may have undergone extensive

3 domestication and therefore may be less representative of wild

4 species. The OECD recommended it here as the preferred test

5 species because of its small size, high fecundity, well

6 characterized reproductive biology and particularly its very

7 rapid incubation maturation.

8 While we concur with that, we also mention

9 that the zebra finch also exhibits many of these same

10 characteristics. The next in terms of strengths, the

11 multigeneration studies measured egg production growth, embryo

12 development, phenotypic sex, appear to be very strong

13 parameters. I think in particular something that Les showed

14 yesterday, which was the F2 data for trenbolone, that was a very

15 nice demonstration of an endocrine disruptor effect where one

16 was only treating the F0 generation, the F1 generation and in

17 the F2 one was seeing a holdover of effects.

18 It does suggest, by the way, that it might be

19 worth holding in the terms of the test that you keep the F2

20 birds and measure reproduction because that's the only time

21 you'll clearly get demonstration of an endocrine disruptor

22 effect versus a straight toxicological effect. 

23 Now I'll put in a caveat on that is it would

24 be nice to see the trenbolone study in a published paper form

25 rather than as an EPA report. 

26 Next the effects of the putative endocrine
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1 disruptors on eggshell thickness and on behavior appear to be

2 critical endpoints. So this is among the strengths of the

3 system. Weaknesses or limitations are the strain differences

4 that we talked about before. And this is exemplified by the

5 large differences in body weight between the different

6 laboratories.

7 Second weakness is it's not clear how

8 representative Japanese quail are of birds in general. Quail

9 are in the order Galliformes, the super order Galloanserae and

10 this is considered -- it is generally considered now as a side

11 group within the birds on an evolutionary basis. The precocial

12 development is interesting and very, very important. But it

13 ignores the possibility of an altricial development which is

14 seen in throughout for instance the passerine forms.

15 Weakness again, lack of epigenetic endpoints,

16 but one could equally argue metabolomic or transcriptomic

17 responses. Again we mentioned here the issue of a positive

18 control, lack of consistency of the egg shell thickness needing

19 standardization. 

20 Need to clarify terminology and that is the

21 issue that in some parts of the documentation it talks about the

22 P generation as opposed to the F0. And I think it's very

23 important that we have a consistent nomenclature and that

24 nomenclature fits with the one generation Bobwhite or mallard

25 duck nomenclature. Because there is this danger that one is

26 talking across systems and if you use different words for the
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1 same thing or the same word for different things it leads to

2 considerable conclusion. 

3 The conclusion of the OECD that the Japanese

4 quail has one of the most sensitive measures of impaired

5 reproduction is the age of sexual maturation didn't seem to be

6 borne out by the data presented. The utility of thyroid weight

7 was questioned. Whether that is a strong parameter except if

8 one is dealing with something which is truly a goitrogen. 

9 Although limitations -- and this is one for

10 what it's worth -- and that is in the OECD draft guideline it

11 talks about having 20 replicate pens to increase the likelihood

12 that 16 remain in each group before test termination and yet the

13 number used in the protocol was 12. OECD recommended

14 determination of sperm motility, morphology, fertilization

15 success. It also included circulating corticosterone, it also

16 included vitellogenin. Now these are either not done in the

17 protocols that we saw or done sparingly and no reason was given

18 for their not being done.

19 And I think that is something which EPA may

20 wish to consider. Particularly as OECD mentioned in their

21 report that there is an OECD validation management group,

22 ecotoxicology testing, which is responsible for supervising the

23 planning of and conduct of experimental work in fish, birds,

24 amphibians and invertebrates and that's quoting the OECD report.

25 So that's a limitation for what it's worth. 

26 Overall the readiness is justified with the caveats mentioned. 
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1 DR. MARTHA SANDY: Thank you. Associates

2 we'll start with Dr. Bentley.

3 DR. GEORGE BENTLEY: I have nothing further to

4 add.

5 DR. MARTHA SANDY: Dr. McCarty.

6 DR. JOHN MCCARTY: Nothing to add.

7 DR. MARTHA SANDY: Dr. Johnson.

8 DR. DALLAS JOHNSON: I don't have anything and

9 Dr. Portier had nothing on this question.

10 DR. MARTHA SANDY: And Dr. Perkins.

11 DR. EDWARD PERKINS: I just wanted to add that

12 the addition of transcriptomics might be a benefit in the sense

13 it can help extrapolate. One of the advantages of the species

14 is that they both have genomes characterized and you might be

15 able to extrapolate fundamental pathways across species to guide

16 more directed research or provide some weight-of-evidence for

17 impacts on passerine birds.

18 DR. MARTHA SANDY: Any comments from other

19 members of the panel? None. And any questions of clarification

20 from the EPA staff. 

21 DR. LESLIE TOUART: Not questions but just a

22 kind of a plea in terms of some of the recommendations in terms

23 of like for some of the methods mentioned, anything that could

24 be provided in terms of the actual written comments in terms of

25 references or suggestions would be welcome, you know, just to

26 help us in terms of trying to look at these and figure out how
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1 best to try to incorporate some of those into the methodology.

2 DR. MARTHA SANDY: We'll ask the panel members

3 to include those references. Shall we move on to the last

4 question for this group?

5 DR. LESLIE TOUART: And Charge Question No. 8.

6 There is sufficient evidence to indicate that endocrine

7 disrupting chemicals can disrupt normal development and

8 reproductive success, however the sensitivity of the F2

9 generation compared to the parental or F1 generations is less

10 clearly defined.

11 In the Japanese quail test, there is an

12 increase in endpoint CVs with each subsequent generation. An

13 example given is Table 6-4, which indicates decreased power of

14 discrimination. 

15 Please comment from a scientific and risk

16 assessment perspective on the value added of multiple

17 generations in the Japanese quail assay.

18 DR. MARTHA SANDY: Dr. McCarty. 

19 DR. JOHN MCCARTY: Well as a group I think our

20 first message is over all we didn't think there was definitive

21 support for or against the value of the F2 generation based on

22 the documents we reviewed. In principle the information of the

23 F2 generation where exposure is occurring only during that

24 embryonic development does play an important role. Certainly in

25 establishing endocrine disruption as a mechanism for any

26 potential adverse effects seen in the Tier 2 tests. The
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1 inter-lab tests with vinclozolin showed little or no evidence of

2 effects on those F2 birds. If this is a general outcome of

3 those tests, results demonstrating a lack of effects in F2 that

4 are seen in F1 are still potentially important for an ecological

5 risk assessment given work emerging from research lab suggesting

6 that these cross-generation effects are to be expected. So if

7 they're not demonstrated that is still informative. 

8 That said members of the group express concern

9 that the inter-lab tests presented may not characterize the

10 potential for adverse effects to be exhibited in the F2. If the

11 lack of a difference are driven by the low statistical power

12 that we've discussed, or protocol issues then the lack of

13 effects in the F2 may prove to be misleading for risk assessment

14 purposes.

15 The framing of the question implies that the

16 answer to this question about the value of the F2 is found

17 simply by looking at the patterns of CVs across generation where

18 it states up there about the sensitivity of the F2s compared to

19 the other generations is suspect because of the greater CVs. 

20 And it refers us to Table 6.4 to support that assertion. 

21 And in our discussions we didn't agree with

22 that premise. If the pattern of increased variation is actually

23 biological and not an artifact of the way the tests were

24 conducted, then this is a biological result of interest.

25 If within a test variability actually

26 increases in offspring relative to their parents that seems to
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1 actually confirm the need for a multiple generation test, that's

2 a biological change. In that case the limited statistical power

3 that's being referred to associated with those greater CVs

4 becomes a sample size issue and the protocol -- the response

5 should be to revise the protocol adding breeding pairs to ensure

6 that these potentially relevant differences can be detected

7 given the protocol. To us it doesn't automatically say well the

8 F2 isn't worthwhile because that sample size of 12 is not

9 written in stone.

10 Even if we do accept the premise though that

11 an increase in CV across generation indicates something the

12 information provided isn't even convincing us that the results

13 in, for example, that table support the conclusion, the pattern

14 of variation and the CVs doesn't seem straightforward. As

15 Dallas mentioned the methods of determining the CV were a little

16 unclear. We're unable to draw conclusions about the sensitivity

17 of the F2 generations from those results. And if you look at

18 Table 6.4 as the question direct us, the CVs are very test

19 specific. There is a calculation of total CV which the F2 is

20 indeed higher. But at least some of this is because the testing

21 on the F2s includes two variables with very high variation.

22 The biochemistry and behavior are included in

23 that total. But it doesn't include several variables with very

24 low CVs in the other generations, the reproductive effect. So

25 we're not even convinced that there is a very strong pattern of

26 an increase in CVs across generations.
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1 There may be valuable insights to be obtained

2 from analyses of variation but the summary report and our time

3 with it isn't sufficient to lead us to any conclusion on that. 

4 Results from the full test using a chemical

5 with a stronger endocrine disrupting effect would be very

6 informative for this. Again, going back to that positive

7 control, several members noted that the inability to detect the

8 effects in the F2 from the inter-lab tests with the vinclozolin

9 is more than offset by the results we saw from Trenbolone where

10 the effects on egg production were clearly demonstrated in the

11 F2. These are in the report in the appendices and were also

12 highlighted in slide 37 of EPAs presentation yesterday.

13 In addition, other results from that study

14 shown in slide 38 of the presentation yesterday indicate that

15 exposure of the F0 generation seemed to increase the sensitivity

16 of F1 ends to endocrine disrupting effects of this compound. 

17 This finding argues for the importance of performing dose

18 studies with F2 animals to determine if lower doses might impact

19 the reproductive function with longer intergenerational

20 exposure.

21 It seems prudent at this stage to conduct

22 additional studies of trenbolone and possibly other androgenic

23 compounds on reproductive functions of the F2 generation. The

24 endpoint of F2 generation reproduction is not in the current

25 protocol, however if effects on reproduction of F2s is a

26 standard response of exposure to endocrine disrupting chemicals
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1 or if exposure increases sensitivity of across generations then

2 inclusion of F2 endpoints and possibly expansion of those

3 endpoints seems to be critically important for feeding into

4 population models that the EPA has indicated will play an

5 important role in ecological risk assessment.

6 That said, if the trade off is to eliminate

7 the F2 part of the testing and increase sample sizes in the

8 earlier generations, as was done with some of the other models,

9 perhaps that tradeoff should be considered. We have a general

10 concern in this protocol that sample sizes are small making it

11 difficult to detect biologically and ecologically important

12 differences if they exist. 

13 The tradeoff between the F2 and greater sample

14 size is beyond the scope of what we were able to consider. If

15 such a decision were being considered, however, to eliminate F2s

16 and increase sample sizes in the F0 generation, it would be

17 necessary to revisit earlier discussions that the agency had

18 about appropriate dose strategies and the choice to continue

19 treatment through adulthood in the F1.

20 It would also be important to evaluate the

21 impacts on this choice on the goal of using these tests for

22 establishing any adverse effects were associated with the

23 mechanism of endocrine disruption.

24 DR. MARTHA SANDY: Thank you. Turning to the

25 discussants. Dr. Bentley.

26 DR. GEORGE BENTLEY: I have nothing further to
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1 add.

2 DR. MARTHA SANDY: Dr. Scanes.

3 DR. COLIN SCANES: The more I can sit through

4 this perhaps my position shifts a little bit. And sometimes

5 kind of listening -- and one of the things that I noticed and

6 that is the only test that definitively showed an endocrine

7 disruptor effect was the F2 reproduction effects of trenbolone. 

8 Otherwise the effects could all be viewed as direct toxic

9 effects.

10 But the results that you showed, Les,

11 yesterday, were very clear of an effect which could be mediated

12 through an embryonic development. And so I think there is a lot

13 of justification not only for F2 but to extend F2 to egg laying.

14 And that's a parameter which has a very tight co-efficient of

15 variation. And I think there are ways actually to make it even

16 tighter co-efficient of variation. 

17 Where if there is going to be sensitization

18 across generations, or a direct endocrine disruptor effect, and

19 then prove it's an endocrine disruptor effect, the only way to

20 do it is with the F2.

21 DR. MARTHA SANDY: Dr. Johnson.

22 DR. DALLAS JOHNSON: I have no comments, and

23 Dr. Portier had no comments.

24 DR. MARTHA SANDY: And Dr. Perkins.

25 DR. EDWARD PERKINS: I have nothing further to

26 add.
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1 DR. MARTHA SANDY: Other members of the panel

2 any additional comments on Question 8? I see none. Dr. Touart

3 any clarifications?

4 DR. LESLIE TOUART: I think I understood what

5 was being suggested and I don't think we have any further

6 clarifications in terms of suggestions or comments made.

7 DR. MARTHA SANDY: Mary Manibusan.

8 MS. MARY MANIBUSAN: I just want to add on top

9 of my previous general comments for distinction between

10 additional endpoints or measurements considered as must haves

11 within the assay compared to future research in an area that

12 might be helpful to answering some subsequent questions. If I

13 can add to that list is for those additional measurements or

14 endpoints or methods, if you can please comment within the must

15 have column on its readiness and transferability across contract

16 laboratories for regulatory use that would be very, very helpful

17 to the Agency. Thank you.

18 DR. MARTHA SANDY: So I'll ask the members

19 that commented on this question to do that. Thank you. 

20 So I think we'd like to get started with the

21 Medaka questions but it's been requested that we take a five

22 minute stretch break and then we'll come back and tackle the

23 fish.

24 (Whereupon a break was taken)

25 DR. SHARLENE MATTEN: With just an

26 announcement so everyone can return to their seats or standup
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1 but we're going to start back here. Yesterday Mary Christman on

2 the panel had asked for some clarification of some statistical

3 analysis related to the one way and two way ANOVA for the Medaka

4 multigeneration test and EPA has sent -- it's about a one page

5 or so email that has the answers to those questions. I have

6 made just about eight hard copies of the -- it's a two page Q&A.

7

8 And also Mary Christman had asked another

9 question related to block effects with references to statistical

10 analysis with the Mysids and EPA also provided a study that

11 wasn't included in the 20 that were given to the panel as

12 appendices but this one has a specific details on the testing

13 regime and explanation of block effects which I also sent to the

14 panel by email for their leisure. 

15 But I also made eight copies in case somebody

16 would really like to read it now which is fine. And the rest of

17 it I am going to put those into the docket. It's a PDF of the

18 email for the public way, way in the back and also the

19 additional study that has the explanation about the block

20 effects for the Mysid shrimp assay.

21 DR. MARTHA SANDY: Thank you Dr. Matten. So

22 now we are ready to turn to the set of questions related to the

23 Medaka Multigeneration test. So is it Dr. Johnson that will be

24 reading the charge questions? 

25 DR. LESLIE TOUART: I'll read the charge

26 questions.
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1 DR. MARTHA SANDY: Oh Les.

2 DR. LESLIE TOUART: And for the Charge

3 Question 1 for the Medaka Multigeneration test. A rationale for

4 the test method should be available, including a clear statement

5 of scientific basis and the regulatory purpose and need for the

6 test method.

7 The EDSTAC introduced the Tier 2 tests as

8 having the purpose to "characterize the nature, likelihood, and

9 dose-response relationship of endocrine disruption of estrogen,

10 androgen, and thyroid in humans and wildlife." Tier 2 tests

11 were designed to be definitive tests which generate sufficient

12 data to characterize the specific hazard of the substance and

13 provide sufficient information on dose-response and adverse

14 effects to permit risk decisions. 

15 Please comment on the rationale and purpose of

16 the assay as part of the Tier 2 testing in the EDSP, as

17 described in Sections 2.1 and 2.2 of the ISR. 

18 DR. MARTHA SANDY: Dr. Schlenk is the lead

19 discussant.

20 DR. DANIEL SCHLENK: Thank you. I'd like to

21 preference our comments just to say that myself and the

22 associate discussants for this question and the following

23 questions met last night, discussed many of these issues ad

24 nauseam and so my comments, at least in this initial question

25 represent that subset of the panel including Dr. Portier's

26 questions that he had emailed in to me personally. 
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1 And one thing I would note in terms of these

2 comments and the comments that will be coming after this, you'll

3 note that there will be fairly several redundancies and I think

4 that is sort of important for at least the Agency to sort of

5 hear that these several issues keep coming up over and over

6 again. So I just want to preface that before we go forward.

7 So with regard to rationale and purpose of the

8 MMT assay as per Item 1 of the ICCVAM validation criteria

9 scientific and regulatory rationale for the test method

10 including a clear statement of its proposed use should be

11 available.

12 Those of us that met largely agree that the

13 MMT and MRT assays provide a clear statement of rationale and

14 purpose. The one exception would probably be Dr. Portier who

15 actually indicated some other issues which we'll get to in a

16 minute.

17 We felt the Section 2.1 of the document meets

18 criteria for justification MMT assay including stated necessity

19 for multigenerational testing, full life cycle assessment and

20 appropriate dosing regime. Dr. Portier thought that Section 2

21 could be written in a more positive tone. He was looking for a

22 more clear bulleted list of advantages for the Medaka for the

23 benefits of using the Medaka. 

24 In fact he felt it was too long -- 20 pages

25 was too long before you could actually start seeing those

26 particular benefits such as ease of reproduction, relatively
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1 small genome, growth, et cetera, anal fin papillae, the

2 different sort of things that were listed in the document in

3 Section 2. 

4 In contrast a majority of the subpanel that

5 met felt that clear and concise statements are given regarding

6 the advantages of the Medaka model. Although more attention

7 should be given in fact that there is a well-defined mechanism

8 to determine both phenotypic gender and genetic gender in this

9 model.

10 This is an advantage for utilization of the

11 Medaka model that cannot be recapitulated in other small

12 aquarium fish models at least that we know of. Fathead minnow

13 there is some evidence that you can use some things there but it

14 seems to be a little bit more robustness with Medaka. 

15 For this reason greater advantage should be

16 utilized in either the MMT and/or the MRT assay for establishing

17 ratios based on phenotypic gender and genetic gender. In fact

18 one of the slides actually indicated this was not an endpoint

19 and we really feel this actually should be an endpoint and it is

20 actually one of the benefits of that particular model. 

21 As is the current MMT and MMRT doesn't exploit

22 this aspect, it seems that its sole purpose is to be on a

23 genetic gender for establishing breeding pairs and not really an

24 assessment of genotype or phenotype ratios within either F1 or

25 F2 populations.

26 Section 222 provides a comprehensive
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1 assessment of the conceptual basis for multigenerational test

2 paradigm. Again one panel member thought that more detail

3 should be provided for the less informed reader regarding

4 differences between activational and organizational effects

5 following exposure. In this regard this panel member felt lines

6 463 to 485 were somewhat convoluted, do not clearly state the

7 rationale for timing of adult embryonic exposure. In fact some

8 statements appear somewhat speculative, such as those addressing

9 chemical permeability across the egg corian. 

10 There was not consensus regarding the benefit

11 of model evaluating maternal phenyl transfer of EDCs. One panel

12 member indicated little information is available on most EDCs as

13 to maternal transfer from the F0 to F1 populations while

14 experimental design of the assay has been established as

15 safeguard for this event, this aspect of the assay remains

16 equivocal.

17 In contrast another panel member felt that

18 maternal transfer of many chemicals is a significant concern as

19 is exposure of various life stages that may vary in sensitivity.

20 The panel understands the rationale for using Medaka. Again a

21 long history of experimental use, et cetera, easy to maintain in

22 a lab, tolerant to range of environmental conditions and it can

23 be induced to spawn throughout the year. 

24 However one panel member felt these very

25 attributes most likely make Medaka less representative of native

26 species of concern than the majority of important and
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1 ecologically and economically native fishes.

2 Many native fishes will accumulate chemicals

3 in the eggs or ovary for months starting in the fall when they

4 begin to incorporate yolk until they spawn in the spring. The

5 young maybe exposed to chemicals in the sediment from maternal

6 sources and through the water. In addition mature fishes will

7 have been exposed for years, not a few months and these issues

8 in relevance need to be noted.

9 The summary report states that the recommended

10 fish Tier 2 test will characterize the nature likelihood and

11 dose response relationship of any adverse outcomes from

12 potential endocrine disruption via E, AEA and/or TT pathways. 

13 Again, since the fish used is not native nor

14 representative of many ecologically important species, one panel

15 member indicated the statement may overstate the utility of the

16 model. For instance there have been many studies showing

17 endocrine disrupting chemicals adversely affect disease

18 resistance and the immune response. There is no attempt to

19 address this adverse effect. Also no assessment of the thyroid

20 is included. Either thyroid histology nor plasma thyroid

21 hormones per measure. Hence both MMT and the MRT really focus

22 on reproductive endpoints, but not the other adverse outcomes

23 endocrine disrupters may cause, which can also have population

24 effects.

25 To specifically address the EDSTAC definition

26 of Tier 2 again characterizing nature likelihood and dose
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1 response relationship of endocrine disruption of EAT, the

2 following components were specifically addressed. 

3 With regards to nature, although non native

4 and given the weaknesses discussed above, the biological

5 advantages in Medaka as an international model for reproduction

6 should allow the characterization and nature of most EAT AOPs. 

7 Its rapid reproductive cycle, secondary sex characteristics,

8 genetic markers for gender, well study developmental and

9 reproductive life history and ability to evaluate maternal

10 female transport make Medaka an excellent model to characterize

11 the nature of EAT endpoints.

12 With regard to likelihood for the eight

13 compounds evaluated, there is strong likelihood that E or A

14 responses maybe detected. However, the likelihood of

15 characterizing specific thyroid effects or antagonism or

16 indirect effects on steroidogensis was less likely (i.e.

17 certain). Disruption of the HPT access may cause atypical

18 endpoints detected in this assay but the response will not

19 likely be specific for thyroid disruption unless compared with

20 other thyroid specific assays such as the amphibian assay.

21 Now with regard to dose response relationship,

22 the data provided significant uncertainty of how dose response

23 relationships were considered or the meaning of what will be

24 considered significant in relation to surrounding doses if a

25 dose is statistically different from the control but not from a

26 dose higher or a dose lower than itself.
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1 Given the variability of the inter-laboratory

2 calibration studies particularly those performed by laboratories

3 outside of MED and NIES obtaining significant concentration

4 response data outside a single LOEC determinations may be

5 limited in the MMT.

6 If a multiple dose concentration response

7 curve with more than two statistically significant treatment

8 points, that again are different from each other is desired,

9 than additional statistical power is required.

10 Potential reasons for inter-laboratory

11 variability were provided by the Agency and will be discussed in

12 the questions subsequent to this.

13 However, whatever the reason in most of the

14 data provided in the ISR primarily demonstrated single point

15 LOEC measurements with those concentration dependent trends in

16 the MMT, which limit the causal criteria associated with a Tier

17 2 paradigm.

18 If the Agency is only interested in obtaining

19 single point deterministic quantitative endpoints of LOEC or

20 atypical endpoints, which may be impaired by EAT AOPs then MMT

21 may be useful. E & A biomarker responses may also have LOEC

22 measurements but specific T, EAT antagonism or steroidogenesis

23 AOPs may not be as certain, which again we've already stated. 

24 In addition monotonic dose response is assumed

25 to be dominant with the evaluations. Overall the panel strongly

26 feels that caution should be used in evaluating LOECs and other
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1 statistical inferences given with this assumption since several

2 ADCs such as EPA and other compounds may not exhibit typical

3 sigmoidal concentration response relationships.

4 Enhanced statistical power for the MRT may

5 improve this relationship. However removal of F0 evaluations

6 for overt toxicity particularly in females and the MRT is of

7 concern which will be discussed later. We just have to get to

8 the MRT. And that's all that I have.

9 DR. MARTHA SANDY: Dr. Blazer.

10 DR. VICKI BLAZER: I think the only thing I

11 would add is something that came up earlier this morning and

12 that was how does Medaka compare to the fathead minnows, since

13 it's the fathead minnow that is used in Tier 1.

14 DR. MARTHA SANDY: Dr. Friedman.

15 DR. JENNIFER FREEMAN: I don't have any

16 additional comments.

17 DR. MARTHA SANDY: Dr. Kullman.

18 DR. SETH KULLMAN: Sure, I'd just like to

19 reiterate some of the comments based on genotype and phenotype

20 for gender determination. I think that the Medaka is a really

21 unique model for that and in the ISR we did not see any

22 relationships to gender ratios and gender numbers. 

23 And I think here that since genotype is fixed

24 and phenotype is possibly sensitive to environmental exposures

25 or compound exposures that you have a unique opportunity to not

26 only fix the idea with gender, use that as an internal control
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1 to look at your husbandry conditions to know that every

2 subsequent generation of animals is giving you a consistent

3 ratio of males to females, temperature being the variable that

4 might effect that and then also compare that to the phenotypic

5 characteristics such as the secondary sex characteristics, the

6 papillae, the split anal fin as well as gonadal sex to really

7 give you a nice indices of genotypic versus phenotypic or

8 phenotypic versus genotypic gender.

9 The second comment was or is based on the dose

10 response that Dan had suggested in our summary; is that it may

11 be prudent to include additional indices that we can derive from

12 full dose response curves such as the NOEL and the EC-50. I

13 think that was suggested in some of the public comments this

14 morning. And any other determinance that can be derived from

15 this. 

16 Also it may be again from an internal

17 perspective to use some of the transformation processes that we

18 have with dose response relationship such as a probit. Again to

19 start comparing a cross-trials of the MMT, to compare expected

20 versus observed effects across different compounds that you're

21 working with. So strong agonists may have a steeper slope to

22 the left versus weaker agonists and so forth, and that might

23 give you a nice serial analysis on the performance of the

24 assays. That's all.

25 DR. MARTHA SANDY: Dr. PatiQo.

26 Dr. PatiQo: no further comment.
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1 DR. MARTHA SANDY: Dr. Perkins.

2 DR. EDWARD PERKINS: No further comment.

3 DR. MARTHA SANDY: Dr. Johnson.

4 DR. DALLAS JOHNSON: I have nothing to add.

5 DR. MARTHA SANDY: Any comments from other

6 members of the panel on Question 1 from Medaka. None. Okay. 

7 And we'll go back to the EPA staff, do they have any questions

8 of clarification?

9 DR. RODNEY JOHNSON: I have no questions for

10 clarifications. I am clear on what the panel stated so far.

11 DR. MARTHA SANDY: Thank you very much. Let's

12 go to Question 2 please.

13 DR. LESLIE TOUART: Reading the Charge

14 Question 2 for the Medaka test. Test methods and their

15 associated endpoints should be scientifically relevant to the

16 biological processes of interest and should be demonstrated to

17 be responsive to the specific type of effect/toxicity of

18 interest. Each species presents unique characteristics from a

19 biological perspective and allows for specialized endpoints to

20 address a specific toxicological mode of action. 

21 Please comment on the biological and

22 toxicological relevance of the assay in regards to the stated

23 purpose of characterizing endocrine disruptors, as described in

24 Section 3 of the MMT ISR.

25 DR. MARTHA SANDY: And Dr. Freeman is the

26 lead.
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1 DR. JENNIFER FREEMAN: Okay. In terms of

2 biological and toxicological relevance, relevance was defined by

3 the Agency as whether a test is meaningful and useful for a

4 particular purpose. Given the unique characteristics of the

5 Medaka life history, which was mentioned already in the first

6 response, the biological and toxicological relevance of the

7 assay is theoretically adequate.

8 Specific responses throughout the rapid

9 lifecycle allow AOP responses including vitellogenin, secondary

10 sex characteristics, and gonadal histopathology to be evaluated

11 and linked to atypical endpoints. The availability of genetic

12 sex markers also allows the evaluation of husbandry conditions,

13 which can be refined to provide better reliability and

14 precision. 

15 Based on established AOPs that provide a

16 foundation of modes of actions for defined EDCs numerous

17 endpoints can be exploited to assess general toxicity including

18 growth, mortality and hatching success. 

19 In addition to defined HPG impacted endpoints

20 in Section 2.2 of the ISR these are described as either

21 activational or organizational endpoints. As such assessment of

22 secondary sex characteristics such as formation of papillae on

23 Medaka, anal fin can be informative of either androgenic or

24 antiandrogenic/estrogenic activity. Assessment of vitellogenin

25 mRNA in males is known as a gold standard of estrogenic effects

26 in ova paired species and is well characterized to be mediated
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1 by ligand dependent transactivation of the ER. Thus

2 vitellogenin is likely to be one the most sensitive assays

3 proposed for estrogenic effects.

4 The fact that Medaka can be induced to spawn

5 daily by controlling light and temperature is also highly

6 relevant to the assay as both fertility and fecundity can be

7 tightly monitored. This component of the assay must be assessed

8 with significant rigor as slight modifications in regulating

9 husbandry conditions may be significantly impacted on spawning

10 success of this species.

11 Additionally there is likely to be significant

12 variability between brooding pairs as was demonstrated in their

13 inter-laboratory comparison conducted during the validation

14 process. 

15 The fact that Medaka exhibit phenotypic sexual

16 dimorphic characteristics is an additional advantage relevant to

17 use of the species. However this aspect of the model is

18 currently underutilized in the proposed MMT and MRT assay. More

19 attention should be given to the fact that there is a well

20 defined mechanism to determine both phenotypic gender and

21 genetic gender in this model. And I think Dr. Kullman just

22 highlighted a lot of these responses but I'll just cover them

23 again to highlight how we felt the importance of them.

24 This is an advantage for utilization of Medaka

25 model for the EDC studies that cannot be recapitulated in other

26 small Aquarian fish models. For this reason greater advantage
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1 should be utilized in either the MMT or the MRT assay for

2 establishing genotypic versus phenotypic gender ratios. As is

3 the current MMT and MRT does not exploit this aspect of the

4 model with the sole focus on genetic gender for establishing

5 breeding pairs and no assessment of genotype to phenotype ratios

6 within the F1 or F2 populations.

7 It is also suggested that the Agency should

8 pursue the development of a population estimate model that is

9 similarly reported in Miller et al 2007 that has been

10 established for fathead minnows and we'll include the reference

11 for that particular paper in the write up for our report.

12 An additional concern particularly with the

13 MRT is that the assay may not differentiate endocrine responses

14 with agents that cause non-endocrine responses. For example,

15 severe hepatotoxicity to the reproductive females in the F0 may

16 alter the endocrine development of F1 offspring providing

17 endocrine response that may not be mediated directly through the

18 E, A or T pathway or an endocrine like response.

19 While there is adequate redundancy with regard

20 to endocrine endpoints, additional evaluation of hepatic

21 histopathology for severe morphological damage in whole animal

22 cross-sections would allow that particular variable to be

23 assessed for compounding of endocrine responses. Thus the basic

24 endpoint should be collected in the parental F0 to determine

25 parental toxicity.

26 The full range of endpoints is needed in the
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1 F1 while assessing reproductive function and the F1 can be fully

2 assessed by evaluating the F2 through hatch. It is also

3 suggested that additional steroidal endpoint measurements are

4 added to gain further knowledge on the impacts to the endocrine

5 system. 

6 Overall it is initially proposed that MMT

7 study has 29 weeks assessing adult parental F0. Full life of F1

8 and partial F2 assessment. Based on the findings of the initial

9 studies a reduced study design is proposed in the MRT ending the

10 test after the hatching of the F2 for a total of 19 weeks.

11 Based on the continuous exposure study design

12 a longer F2 assessment does not appear to be needed. If the

13 goal is no longer a multigenerational study; as such the new

14 study design is more representative of a full life cycle assay

15 with the focus on both general toxicity and EDC AOPs. That is

16 all.

17 DR. MARTHA SANDY: Thank you. And the

18 associate discussants are Dr. Blazer.

19 DR. VICKI BLAZER: I have nothing else to add.

20 DR. MARTHA SANDY: Dr. Kullman.

21 DR. SETH KULLMAN: Sure I'll add just a couple

22 of things. So bear with us. So it is certainly obvious that

23 the Medaka model is going to be proficient for E&A activity

24 maybe a little less proficient for anti-E&A. I also wonder if

25 thyroid measures could be added and that's not without precedent

26 and in the fish models. So just a couple of examples of that. 
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1 In the zebra fish model there has been

2 established (inaudible) hormone level analysis and it's been

3 done by something called the Immunofluorescence Quantitative

4 Disruption Test. And I can provide a referenced for that. 

5 That's (inaudible) in 2009. Okay. And I think it may be just

6 something worth looking into because it would increase the

7 utility of the model for E, A and T.

8 The second thing is it's also not without

9 president again in the zebra fish using gene expression and

10 transcriptional targets for thyroid hormone and other

11 transcriptional targets within the HPT. And these have been

12 validated, I guess validated or at least tested using

13 (inaudible) as well as PTU. And it's a 20 gene PCR rating in

14 zebra fish that may be adaptable to Medaka. I can also provide

15 reference for that. 

16 DR. MARTHA SANDY: Dr. PatiQo.

17 DR. REYNALDO PatiQo: No further comments.

18 DR. MARTHA SANDY: Dr. Perkins.

19 DR. EDWARD PERKINS: No further comments.

20 DR. DANIEL SCHLENK: Just wanted to reiterate

21 I really think it's important to at least do the steroid

22 analysis. If you're doing it in fathead minnow I think it's

23 doable with tissue aliquots and for whole tissue analysis and

24 those methods are available and I can provide references if

25 needed.

26 DR. MARTHA SANDY: And Dr. Johnson.
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1 DR. DALLAS JOHNSON: I have nothing to add.

2 DR. MARTHA SANDY: Other comments from members

3 of the panel. And EPA staff any questions?

4 DR. RODNEY JOHNSON: No questions I appreciate

5 the recommendations and as has been mentioned earlier I really

6 appreciate the idea that references or specific -- if there

7 aren't references available but you have personal knowledge

8 please share because it will be very helpful.

9 DR. MARTHA SANDY: I'm sure the discussants on

10 that question will do that. I think it's rather late and we'll

11 call it a day you think? Yes. And so I thank everyone for all

12 their hard work and attention and Dr. Matten do you have

13 something to say?

14 DR. SHARLENE MATTEN: I would just like to

15 thank everyone for their good work and coming on time. The

16 panel has been excellent and this microphone really works we can

17 call people out from the hallway. 

18 So we'll start again at 9 o'clock in the

19 morning and we'll continue with the Medaka multigeneration test

20 charge questions. Thank you very much.

21 (Whereupon a recess was taken for the day)

22

23

24

25

26
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1 DR. SHARLENE MATTEN: Good morning. My name

2 is Sharlene Matten. I am the designated federal official for

3 this FIFRA Scientific Advisory Panel meeting that is reviewing

4 providing advice to the EPA on the Endocrine Disruptor screening

5 Programs Proposed Tier 2 Ecotoxicity Test. 

6 Today is Thursday, June 27th. If you're

7 listening in the radio broadcast area, we are on our third day

8 of our discussions. We left off yesterday with the exciting

9 discussion on the Medaka Multigeneration Test on Question 3.

10 I am going to turn the microphone over to Dr.

11 Martha Sandy, who is the session Chair for this meeting.

12 DR. MARTHA SANDY: Thank you, Dr. Matten. 

13 We've been asked to go around the room quickly, one more time,

14 an identify ourselves and then we'll jump into Question 3. 

15 I'm Martha Sandy and I'm with the California

16 Environmental Protection Agency.

17 DR. BARRY DELCLOS: Barry Delclos from the

18 FDA's National Center for Toxicological Research.

19 DR. JAMES MCMANAMAN: Jim McManaman,

20 University of Colorado.

21 DR. DANIEL SCHLENK: Dan Schlenk, University

22 of California, Riverside.

23 DR. STEPHEN KLAINE: Steve Klaine, Clemson

24 University.

25 DR. MARION EHRICH: Marion Ehrich, Virginia

26 Tech.
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1 DR. GEOFFREY SCOTT: Geoff Scott, National

2 Oceanic and Atmospheric Administration.

3 DR. VICKI BLAZER: Vicki Blazer, U.S.

4 Geological Survey.

5 DR. REYNALDO PATINO: Reynaldo Patino, U.S.

6 Geological Survey, Texas Cooperative Fish and Wildlife Reserve

7 Unit.

8 DR. CATHERINE PROPPER: Cathy Propper,

9 Northern Arizona University.

10 DR. EDWARD PERKINS: Ed Perkins, U.S. Army

11 Corps of Engineers.

12 DR. ROBERT DENVER: Bob Denver, University of

13 Michigan.

14 DR. JOHN MCCARTY: John McCarty, University of

15 Nebraska, Omaha.

16 DR. DALLAS JOHNSON: Dallas Johnson, Kansas

17 State University.

18 DR. COLIN SCANES: Colin Scanes, University of

19 Wisconsin, Milwaukee.

20 DR. SETH KULLMAN: This is Seth Kullman, North

21 Caroline State University.

22 DR. SHEA TUBERTY: Shea Tuberty, Appalachian

23 State University.

24 DR. JENNIFER FREEMAN: Jennifer Freeman,

25 Perdue University.

26 DR. MARY CHRISTMAN: Mary Christman, MCC
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1 Statistical Consulting.

2 DR. GEORGE BENTLEY: George Bentley, U.C.,

3 Berkley. 

4 DR. MARTHA SANDY: Thank you. So when we left

5 off yesterday, we were in the middle of addressing questions on

6 the Medaka Multigeneration Test. We have with us, EPA staff; we

7 have Mary Manibusan, Dr. Les Touart, and Dr. Rodney Johnson.

8 Dr. Touart, would you be reading us Charge

9 Question Number 3?

10 DR. LESLIE TOUART: Les Touart reading Charge

11 Question Number 3 for the Medaka Multigeneration Test. 

12 The test protocol should be sufficiently

13 detailed and should include a description of what is measured

14 and how it is measured. The selection of endpoints within the

15 assay should be reflective of the biological processes of

16 interest and the endpoints should be intrinsically relevant and

17 have established sensitivity. 

18 The test protocol should demonstrate the

19 ability to measure the endpoints and provide adequate

20 performance criteria for evaluation.

21 Please comment on the selection, optimization

22 and demonstration of the assay endpoints as outlined Section 3

23 of the ISR.

24 DR. MARTHA SANDY: Dr. Freeman is the lead

25 discussant on this.

26 DR. JENNIFER FREEMAN: Selection on the
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1 MMT/MRT endpoints exhibit biological and toxicological relevance

2 with regards to EDC exposure and assessment. 

3 As demonstrated in Section 2.2 of the ISR,

4 small aquarium fish, specifically medaka, poses unique

5 biological feature that make them highly amenable to en vivo,

6 EDC toxicity testing. Based on established AOPs that provide a

7 foundation for modes of action for defined EDCs, numerous

8 endpoints can be exploited to assess general toxicity, including

9 growth, mortality hatching success in addition to defined

10 HPG-impacted endpoints. 

11 In addition, Section 2.2 of the ISR, these are

12 described as either activational or organizational endpoints. 

13 And we have some comment on the selection of some additional,

14 secondary sex characteristics to evaluate. 

15 Since gender alteration is a significant

16 endpoint related to endocrine disruption, additional redundancy

17 with secondary sex characteristics may be an option. This is

18 especially of interest, given the potential difficulties of DMY

19 measurements. 

20 In addition to the anal papillae endpoint,

21 Emma Watts, et al., 2003, and will include the reference to this

22 publication, has indicated that additional morphometric

23 characteristics can be recorded for each fish to generate a

24 genders score.

25 The morphometric characteristics include

26 degree of urogenital protuberance, number of anal fin ray nodes,
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1 number of anal fin rays, number of anal fin rays for papillary

2 processes, and number of dichotomously branched anal fin ray and

3 degree of ventral fin length. 

4 Total length can be determined by a

5 measurement from the most rostral point to the most caudal point

6 of each fish. Degree of urogenital protuberance can be

7 quantified on a scale of one to three, with 1) indicating almost

8 no visible protuberance; 2) indicating slight to moderate

9 urogenital protuberance development; and 3) indicating full

10 urogenital protuberance that resembled a half-balloon type

11 appearance. 

12 Number of anal fin ray nodes can be counted

13 down the third anal fin ray from the fin or origin. Numbers of

14 anal fin rays with papillary processes can be counted for each

15 ray with any visible papillary processes. 

16 Degree of ventral fin length can be quantified

17 on a scale of one to three, with 1) indicating a short ventral

18 fin not extending to the genital core region; 2) indicating a

19 ventral fin that extended to the genital core region; and 3)

20 indicating a ventral fin extending to or beyond the genital

21 core.

22 A gendered determination score can then be

23 generated for each fish in control and treatment groups. Score

24 can be generated by adding degree of urogenital protuberance

25 index, number of dichotomously branched anal fin rays, and

26 ventral fin length index for each fish. 
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1 Male fish have only one or two dichotomously

2 branched anal fin rays; whereas, female fish have six to eight. 

3 A greater gendered determination score might be attributed to an

4 individual fish exhibiting female morphometric characteristics

5 and a lower score for fish with male characteristics.

6 There are also automated programs that are

7 available for these types of morphometric analysis to help them

8 be more quickly attained. One specific software package out

9 there is the BIOQUANT image analysis software that's available.

10 I guess now I want to talk a little bit about

11 the optimization and demonstration of assay endpoints. 

12 Optimization of MMT endpoints appears to be critical to

13 establishing rigorous statistical inference. As such, MMT has

14 been modified, reflecting several changes, increase in

15 biological replication, repeatability and fecundity assessment

16 in the MRT assay, other endpoints such as secondary sex

17 characteristics that appear to be highly correlative to EDC AOPs

18 and mode of action and indicative of ADC function. 

19 Overall, a relatively small number of

20 compounds with differing EDC modes of action have been studied,

21 using the MMT or MRT assay and thus, validation of the assay

22 needs to be further substantiated. To ensure consistency in

23 evaluation of individual defined endpoints, further

24 standardization and possible optimization of methodology may be

25 required.

26 As is, Section 3 of the MMT provides a
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1 reasonable global description of the MMT/MRT assay, including

2 timelines, exposure of conditions, husbandry, and overall

3 measurement of defined AOPs and general toxicity endpoints. 

4 However, overall, the IRS and provided protocols lack sufficient

5 methodology to fully recapitulate the assay. This is

6 particularly true with regard to methodology and data analysis

7 for each defined endpoint of the assay. 

8 I'm going to walk through a list of certain

9 items that we think further details need to be added for

10 standardization of the protocols; the first are further

11 guidelines on water quality. While water quality conditions

12 were briefly mentioned, water quality is a major differential

13 factor among laboratories and more details should be provided in

14 the research protocol in water quality requirements. This

15 should also help to reduce variability among laboratories that

16 would be conducting these studies. Currently, limited

17 information is provided for additional water quality parameters.

18

19 Most fish laboratories, especially those

20 working with the small fish models, generally have an extensive

21 protocol developed for water quality guidelines. These type of

22 guidelines need to be shared and included in the ISR so that

23 they can be undertaken by the different laboratories that work

24 to perform these assays.

25 The second is additional standardization on

26 the assessment on the reproductive activity of the fish
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1 population before the assays have begun and detailed husbandry

2 guidelines. Guidelines should be added that indicate that the

3 size of the adults and reproductive capability of that

4 particular fish population should be assessed before starting

5 the assay. 

6 This is important, as these basic population

7 characteristics can widely vary among the different

8 laboratories. Adding standardization on minimal size and

9 reproductive function of the specific fish population to be used

10 in the studies should help to assist in removing some of the

11 variability among the different laboratories. 

12 In addition, most small fish laboratories have

13 extensive protocols developed for husbandry and care of the

14 fish, and similar guidelines should be included in the protocol

15 for guidance and optimization of the assay in all these

16 laboratories.

17 There is a few additional items that I'm

18 going to list for optimization. I'm just going to walk through

19 this list because most of these are going to be described in

20 more detail when we get to Question 6.

21 Items that I think need to be standardized for

22 optimization of the assay include standardization of

23 vitellogenin analysis, standardization of the analysis of the

24 anal fin papillae, standardization of the embryo collection,

25 more details on how embryo viability is determined, more

26 information on the determination of the genetic sex in regards
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1 to the Vtg analysis, and also further details about an inclusion

2 of the hepatotoxicity assay because these types of evaluation

3 and endpoints need to be clarified. It was unclear what types

4 of toxic responses are expected or how histological finding will

5 inform endocrine disrupting effects of test compounds.

6 It is important to establish normalization

7 procedures for histopathology assays to ensure that assay

8 reproduces the ability for the hatter toxicity assays. 

9 So overall, adding additional details and

10 parameters such as those that were just described, will assist

11 in optimizing the assay. More information, as I mentioned

12 before, will be mentioned when we get to Charge Question 6.

13 DR. MARTHA SANDY: Thank you. The associates,

14 Dr. Blazer.

15 DR. VICKI BLAZER: I have nothing additional

16 to add.

17 DR. MARTHA SANDY: Dr. Kullman.

18 DR. SETH KULLMAN: As Dr. Freeman suggested,

19 I'll provide more details in some of these comments in Question

20 6.

21 DR. MARTHA SANDY: Dr. Patino.

22 DR. REYNALDO PATINO: Nothing new to add.

23 DR. MARTHA SANDY: Dr. Perkins.

24 DR. EDWARD PERKINS: I have nothing further to

25 add.

26 DR. MARTHA SANDY: Dr. Schlenk.
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1 DR. DANIEL SCHLENK: My comments are

2 incorporated.

3 DR. MARTHA SANDY: And Dr. Johnson. 

4 DR. DALLAS JOHNSON: I have nothing to add. 

5 I'll look up and see if Dr. Portier had anything he wanted to

6 include. I kind of forgot about my job this morning.

7 DR. MARTHA SANDY: Okay. While you're looking

8 that up, I'll ask if any other members of the panel have

9 additional comments on Question 3.

10 (No response.)

11 It doesn't look like it. We'll let you take a

12 look, Dr. Johnson, at Dr. Portier's comments.

13 DR. DALLAS JOHNSON: His comments are okay. 

14 There is nothing unusual here that needs to be brought out, I

15 don't think.

16 DR. MARTHA SANDY: Okay. So I take that means

17 that it is along the same lines of what he mentioned about other

18 assays, perhaps.

19 DR. DALLAS JOHNSON: Okay. Thank you. So I

20 will turn to the EPA staff and ask if they have any questions of

21 clarification or any other issues that you don't think we've

22 answered the question adequately.

23 DR. RODNEY JOHNSON: I'd like to ask one

24 clarifying question. It has to do with the statement of

25 relating the genetic sex to the vitellogenin endpoint. Is that

26 what you said?
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1 I'm trying to understand. It's the point that

2 you made just before the conversation or the discussion on

3 hepatotoxicity. 

4 DR. JENNIFER FREEMAN: There was just that

5 more details needed to be added in regards to the methods if

6 we're determining genetic sex. Dr. Kullman will go into detail

7 about the specifics about adding those details when he gets to

8 Question 6.

9 DR. RODNEY JOHNSON: Thank you.

10 DR. MARTHA SANDY: And that was Dr. Freeman. 

11 Dr. Dallas Johnson. 

12 DR. DALLAS JOHNSON: Yes. Further looking at

13 what Dr. Portier had to say, I might indicate two things that he

14 mentioned. On Section 3.3.3, it says the protocol for deciding

15 which surviving larval fish are kept and which are culled at the

16 start of week 4 is not provided. With a maximum of 10 fish per

17 aquaria some surviving larval fish are discarded. How is cull

18 choice made? 

19 And then in Section 3.3.5: A caudal fin

20 sample is used to determine genotypic sex of individual which is

21 then used to establish female/male breeding pairs. For some

22 chemicals, some F1 subadults, XX fish have testes, female fish

23 with male SSC-androgen acting chemicals, and/or some XY fish

24 have decreased SSC, males with female phenotypic

25 characteristics, estrogen-acting chemicals. 

26 The wrong choice of male and female fish, at
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1 this point, can and has led to creating non-breeding pairs. Is

2 there a better protocol using other than genotype that could

3 reduce the likelihood of getting non-breeding pairs?

4 I assume that he'll expand on that in the

5 final report. 

6 DR. MARTHA SANDY: Okay. Thank you. I'll ask

7 EPA again, are there any questions of clarification on that?

8 DR. RODNEY JOHNSON: No questions. Thank you.

9 DR. MARTHA SANDY: I believe we're ready to

10 move on to Question 4.

11 DR. LESLIE TOUART: I'll read Question 4 as

12 soon as it advances on the screen. Demonstration of the test

13 method performance should be based on the testing of reference

14 chemicals representative of the types of substances for which

15 the test method will be used. 

16 Test substances should adequately represent an

17 appropriate range of responses and physical chemical properties

18 for which the test method is proposed to be appropriate.

19 The selection of the most appropriate

20 statistical approaches depends, in part, on the nature of the

21 data and also on the design of the validation study. 

22 Statistical and non-statistical methods used to analyze should

23 be described. 

24 Please comment on the selection test

25 substances and methods, analytic and statistic, where

26 appropriate, chosen for the demonstration and validation of the
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1 MMT and MRT assays.

2 DR. MARTHA SANDY: Dr. Dallas Johnson is the

3 lead on this. I presume you would like to also, as we did

4 before, just split this question and you will just address the

5 statistical issues at this point.

6 DR. DALLAS JOHNSON: That is correct. I have

7 emailed with the other discussants on this question and they

8 have all agreed to that approach.

9 This will be a lot shorter than yesterday. I

10 promise you. Many of the remarks that I made yesterday could

11 also be made here. In the discussion that I will do, I'm going

12 to refer to the aquaria as tanks in my remarks. It seems like

13 most terminologies were used in the report. 

14 The Table 3.1 shows the test conditions for

15 the Medaka Multigeneration Test protocol and then there is a

16 similar table for the revised protocol. The remarks that I have

17 are probably appropriate for both protocols. 

18 As mentioned yesterday, and similarly here,

19 Table 3.1 does a very good job of describing those conditions

20 that can be controlled by the researcher and/or the lab, but it

21 doesn't say anything about the randomizing process, randomizing

22 fish to these conditions.

23 Some of their questions that came to mind

24 initially, and I'll probably add some others as the final report

25 comes out, how many flow through systems are there. Is there

26 one test replicate chamber size in each flow through system or
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1 all test chambers placed in the same flow through system? 

2 I'm thinking that there's a single flow

3 through system for each tank, but I don't know whether that's

4 true or not. The report leaves a lot to be described, I guess,

5 in terms of how the data are actually collected and how things

6 are randomized -- how the fish are randomized and so on. That

7 just needs to be tightened up a bit more.

8 Similarly, one of the feeds placed in the tank

9 is just done in a random order or the feeds for one treated

10 group, if a pair of fish are in a tank, then I assume that each

11 tank gets its own feeding treatment. I don't know whether the

12 feed is in the flow through thing so they all share the same

13 feed or whether each tank has its own feed and where the tanks

14 are placed, and are their locations randomized. 

15 I'm thinking there must be a lot of other

16 things that could be said that would help some laboratory or

17 some researcher that would try to reproduce the data that are

18 collected here that they need to be made aware of and it should

19 be in the protocol or in a standard operating plan.

20 The types of analyses that were done here were

21 similar to the types of analyses that were done on the Japanese

22 quail data. A lot of the analyses were done automatically,

23 using the StatCHARRMS program. 

24 My thinking about the -- well, my comments

25 that I had yesterday about what is the appropriate probability

26 distribution for each endpoint variable. I think that letting
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1 the data drive the analysis, it seems that this StatCHARRMS

2 program is trying to automate things. 

3 Well, I know in my consulting activities in

4 the past, I've had a lot of researchers, what they look for is

5 some sort of PROC analysis. That they just bring their data and

6 dump it into a program and it will do everything for them. 

7 That's a little bit about what I'm seeing about this StatCHARRMS

8 program. I think that could be dangerous. 

9 I can see that some endpoint variables might

10 be analyzed one way by some labs or researchers and it might be

11 analyzed another way by other labs and researchers. So I think,

12 again, the science and what you know about the endpoint

13 variables that you measure should give indication at what the

14 underlying probability distribution might be and then that

15 appropriate distribution could help you do the best analysis for

16 the data that has been collected.

17 In the StatCHARRMS program, it appears that

18 those tests were for equal normality and tests for equal

19 variances that are performed and then the analysis just kind of

20 depends on whether those tests are passed or failed. Again, as

21 I mentioned yesterday, the analysis with unequal variances isn't

22 much of a problem anymore. Tests for normality aren't very

23 powerful or highly related to sample size. 

24 So again, I think that the right way to go

25 about that is to let the science drive the appropriate -- what

26 other people are doing and what we know about the variables that
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1 are being collected and what similar kinds of variables and

2 those should drive the statistical analysis that is going to be

3 performed.

4 A lot of the analyses appear to be doing the

5 ANOVA on ranks. And as I mentioned yesterday, the DOM

6 parametrics isn't free of assumptions, it's just free of the

7 normality assumption, equal variances and no interactions. You

8 have interaction or you have unequal variances in non-parametric

9 methods on ranks don't work very well -- work quite badly, in

10 fact.

11 There was a table, I guess, Table 4.3, which

12 described the only two parameters in adult control fish that

13 were measured the same in both MMTs, the one in the U.S. and the

14 one in Japan. The report says that the MMTs are compared to

15 showing similar data across the two labs. 

16 One of the variables that was looked at,

17 fecundate in the F1 generation in the U.S., it was 17, plus or

18 minus two. In Japan, it was 29, plus or minus five. So the

19 difference in the means there is 12, and it looks like there are

20 more than four standard deviations apart. And it would just

21 wonder why you would say that that data are similar. Maybe the

22 science or maybe the biological difference isn't relevant. I

23 don't know, but it just seems strange. And then a similar kind

24 of thing might be said about the variable SSC.

25 I will say that after receiving the response

26 about the correction that were made in the one way ANOVA, when
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1 the data were reanalyzed. I do believe that data now are being

2 analyzed correctly. Our confusion had to do with being told

3 that they were using a repeated measures analysis, with only a

4 single repeated measure. 

5 The response that we received yesterday

6 afternoon indicated that the repeated was a misnomer, as the

7 program actually used a mixed model analysis with tank as a

8 random affect, which is what we were proposing. And this is the

9 correct way to analyze the data, with unequal numbers of

10 observations per tank.

11 I probably don't need to say it, but as you

12 can probably tell from my previous comments regarding

13 statistical analyses, I'm not very charmed with the StatCHARRMS.

14

15 As I stated earlier, knowledge of the science

16 should drive the appropriate ways to analyze endpoint variables.

17 I don't think it's appropriate to let the data drive the

18 analysis unless you're dealing with an endpoint variable that

19 science knows nothing about. I don't think there are such

20 variables being proposed as endpoint variables here.

21 That's the close of my remarks.

22 DR. MARTHA SANDY: Thank you. So I'm going to

23 go around and ask the other discussants if they have additional

24 comments related to the statistical issues part of this

25 question. Dr. Patino.

26 DR. REYNALDO PATINO: Good morning. So I
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1 think what I'll do is I'll address the question, the parts that

2 remain are about the compounds that were used and then the

3 methods.

4 DR. MARTHA SANDY: Excuse me. I wanted to ask

5 anyone else if there were statistical issues, first.

6 DR. REYNALDO PATINO: Oh, I'm sorry.

7 DR. MARTHA SANDY: Let's go around quickly and

8 then we'll start with the other parts. Is that okay?

9 DR. REYNALDO PATINO: That's fine, and I don't

10 have any comments on that.

11 DR. MARTHA SANDY: Okay. Dr. Perkins.

12 DR. EDWARD PERKINS: No further comments.

13 DR. MARTHA SANDY: Dr. Schlenk.

14 DR. DANIEL SCHLENK: Actually, I guess this is

15 probably the question just to reemphasize the dose dependence

16 statistics. If we could actually get something that showed,

17 perhaps, what the ANOVA differences were between the actual

18 points in the figure that showed if there were differences

19 between the points in the dose response group. I think that

20 would be very helpful.

21 DR. MARTHA SANDY: Dr. Freeman.

22 DR. JENNIFER FREEMAN: I just want to add and

23 emphasize the importance, I think, of adding those additional

24 replicates in the MRT assay. I think that will help to account

25 for some of the variability that you get naturally from the

26 fish, with some of them breeding daily and some days they're not
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1 breeding. So I think that's a true positive to the assay.

2 DR. MARTHA SANDY: Dr. Kullman.

3 DR. SETH KULLMAN: I have nothing further to

4 add.

5 DR. MARTHA SANDY: Dr. Blazer.

6 DR. VICKI BLAZER: I have nothing further to

7 add.

8 DR. MARTHA SANDY: Dr. Johnson, did Dr.

9 Portier have anything?

10 DR. DALLAS JOHNSON: I think he does. I did

11 notice that I missed one thing in my report and it had to do

12 with the coefficient of variation again, the CVs and in fish

13 study, we have additional sources of variation. We've got

14 tank-to-tank variation; we have lab-to-lab variation; we have

15 fish-to-fish variation. 

16 So there are three places that variation could

17 be described and so there are probably three different ways that

18 one could calculate coefficient of variation and it's not clear

19 in the report as to which way is being used, whether it's the

20 standard deviation of all the fish divided by the mean or

21 whether it's the tank standard deviation divided by the mean, or

22 whether it's lab standard deviation divided by the mean. So it

23 would be nice for that to be clarified a bit. 

24 And then you want me to report on Dr.

25 Portier's comments?

26 I think Dr. Portier like statCHARRMS better
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1 than I do. He seems to be okay with the way the analyses are

2 being done. He believes that proper attention is paid to

3 important issues, such as analysis model selection, inclusion

4 and elimination of high order predicting factor interactions,

5 model assumptions and testing that the data adequately meet

6 model assumptions. The issue of accounting for experiment-wise

7 error and multiple comparisons is also discussed. 

8 The couple of issues that he does have,

9 Appendix G presents a somewhat statistically conservative,

10 systematic approach to data analysis. It classifies the

11 biological data generated by the MMT into one of three types: a

12 continuous response that is measured once per subject, a

13 continuous response that is measured multiple times per subject,

14 and ordinal data, such as histopathology findings that are

15 expressed as a severity score. This approach may be too

16 conservative for some data that could lead to missing important

17 findings. 

18 The StatCHARRMS tool uses a different general

19 linear model for different kinds of data, including using a

20 repeated measures model for the fecundity data. The general

21 approach assumes normal and homogeneous data or that an

22 appropriate transformation can be found that produces a response

23 variable that is normal homogeneous, or if everything else

24 fails, a normalized rank-order transform is used.

25 This analysis approach would seem to work

26 quite well for parameters such as adult bodyweight, length,
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1 vitellogenin levels, et cetera, but the approach may be more

2 problematic for some variables in subadults since in some cases

3 the levels tend to bunch up near zero. There's a good

4 discussion of how to handle outliers on page 10.

5 The fecundity data from the MMT can have

6 characteristics that make the use of the repeated measures GLM

7 questionable. Fecundity data are inherently counts. If the

8 counts are relatively consistent and not too variable,

9 transforming them by the natural log and using a GLM is not a

10 bad analysis strategy. And then he says see for example,

11 Appendix H. 

12 He talks a little bit about non-breeding pairs

13 fecundity data. These kinds of data can be seen in the box

14 plots presented for a number of the studies in Appendix HH, page

15 56, Figure 1, Reproduction for OPDDT; page 70, Figure 1,

16 Reproduction for CMP; and page 102, Figure 1, Reproduction for

17 Trenbolone.

18 Egg counts for breeding pairs will typically

19 be centered on a non-zero value. Egg counts for non-breeding

20 pairs will, by definition, be zero. It's difficult to imagine

21 or find any data transformation that will make these data nice. 

22 That is, give it a more normal distribution and adjust for

23 heterogeneity. It is not clear how the back-up plan, using of a

24 normalized rank order transform produces data that have better

25 properties for analysis. 

26 And then he says the same issues shows up in
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1 the analysis of anal fin papillae and the 4tOP subadults. See

2 Appendix H, page 46, Figure 2. Although, to be fair, in this

3 case, the variability is so broad that the dose response is not

4 significant. A couple of other things:

5 Note that the zeros for non-breeding pairs

6 really point to limitations in the study design protocol and not

7 a failure of reproduction between the two males and females. A

8 case can be made for legitimately excluding egg count data from

9 non-breeding pairs and then repeated measures analysis of

10 fecundity. This could be considered a weighted analysis, see

11 page 13, where the weight for the non-breeding pairs is set for

12 zero. 

13 At a minimum, the data analysis protocol needs

14 to alert researchers to the implications of non-breeding pairs

15 on fecundity data. The StatCHARRMS tool may need to be modified

16 to implement more appropriate analysis for these data.

17 And then he has a note to the other

18 statisticians on the panel. In your opinion, should we mention

19 here more complex models such as a Bernoulli-Poisson generalized

20 linear model or should we push to exclude zeros from the

21 analysis under the assumption that these zeros come from

22 non-breeding pairs?

23 If the zeros are excluded, the StatCHARRMS

24 model approach is likely not that bad. I assume that we

25 statisticians will have to discuss that a little bit in the

26 final report.
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1 He talks more about analysis of fertility. 

2 There is very little discussion about how fertility is analyzed.

3 Fecundity and fertility are shown on the same graphs as counts.

4 In this case, fertility would be analyzed in the same way as

5 fecundity, but now zero fertility in the presence of non-zero

6 fecundity is a true treatment effect on reproduction.

7 Fertility could also be analyzed as the

8 proportion of eggs that are viable. In this case, the arc-sin

9 square root transformation could be used as the response

10 variable in the StatCHARRMS tool. Note that for non-breeding

11 pairs, fertility is zero divided by zero and, hence, is

12 undefined.

13 Fertility as a proportion could become a

14 problem as the average fertility percent approaches zero or one.

15 There is a similar issue with analysis of hatching rate and

16 hatching survival.

17 The assumption of monotonic treatments

18 effects, he has a comment or two. StatCHARRMS uses a novel

19 approach termed "RSCABS" (Rao-Scott Cochran-Armitage by Slices)

20 developed by John Green and DuPont; Timothy Springer, Wildlife

21 International, Easton, MD), and Amy Saulnier, the Franklin

22 Company. 

23 RSCABS incorporates the replicate vessel

24 aquarium experimental design and the biological expectation that

25 severity of effect will tend to increase with increasing

26 concentrations or reduced. Clearly, making this assumption
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1 facilitates identifying an LOEC or NOEC. 

2 There does not seem to be general agreement

3 that severity of effect will always tend to increase with

4 increasing concentrations for EDCs. Appendix G discusses this

5 on page 8, deciding between pair-wise and trend-based

6 comparisons, but all of the citations for expecting and using a

7 test based on monotone dose response seems to precede those

8 arguing against such tests when there is evidence of a

9 non-monotonic response. It looks likes it can, though, in this

10 verbose here. 

11 The monotonic response pattern assumption is

12 appropriate for traditional toxins, but there has been much

13 discussion before the SAP and other toxicology panels over the

14 years as to whether EDC can produce U-shaped dose responses that

15 are not strictly monotonic. There are hints of non-monotonic

16 dose response patterns in the data reported in Appendix H. 

17 Although the data are definitive and the case

18 could be made that the response was flat for the first couple of

19 doses before the effect increase is observed. Examples include

20 page 6, F1 and F2 fecundity for E2; page 10, F1 subadult

21 vitellogenin -- well, you know what I mean. Page 24, F1

22 fecundity for 4tOP; and page 26, F1 adult SSC, anal fin papillae

23 for 4tOP

24 The points made on page 9 of Appendix G in

25 particular that Both formal tests and visual inspection are

26 recommended to determine whether there is significant departure
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1 from monotonicity needs greater visibility in the protocol

2 document. 

3 The discussion in Appendix G suggests that the

4 EPA rule is to assume monotonicity, with strong evidence needed

5 before one can depart from this rule. The StatCHARRMS tool does

6 provide safeguards, in that the program will not perform the

7 Jonckheere test, a test for monotonic trend, in situations where

8 a linear contrast of treatment trend is not significant but the

9 quadratic contrast is significant.

10 He has some remarks on power, the 

11 sensitivity of the power analysis. The power analysis assumes

12 that the rate of creating non-mating pairs is the same across

13 all three generations. There does not seem to be any data for

14 or against this assumption.

15 The power analysis assumed that average

16 fecundity is normally distributed with a mean of 21 and a

17 standard deviation of 7. While the likelihood of simulating a

18 negative mean is small, what does the program do if a simulated

19 mean is less than zero or equal to zero?

20 In the power analysis for SCC, are zeros,

21 actual measurements or simply coded to zero when the assessment

22 could not be scored. In the latter case, the zero implies

23 censored data suggesting a different estimation approach may be

24 needed. The sensitivity of the power calculation to the percent

25 censored should be examined.

26 The power analysis for VTG examines the raw
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1 data responses for each treatment group for normality. Since

2 the analysis found a significant study effect, the two-way ANOVA

3 model, two-factor GLM, should be fit and then the residuals

4 examined for normality. And that concludes his comments. 

5 DR. MARTHA SANDY: Thank you very much. So

6 I'm going to ask the statisticians here, Mary and Dallas, if you

7 have any thoughts now. We need some discussion in the meeting

8 if you're going to comment on the proposal that Dr. Portier has

9 put forward for alternative analysis when you have non-breeding

10 pairs. So either now or later. Would you like to discuss it?

11 DR. DALLAS JOHNSON: Yeah. We probably better

12 discuss it and talk about it later.

13 DR. MARTHA SANDY: Okay. So we'll come back

14 to that later. I'll ask all the Panel members, do you have

15 anything else to comment on for the statistical issues?

16 (No response.)

17 No. Okay. That was a lot of information. 

18 Maybe we'll go EPA staff and ask, regarding the statistical

19 issues that were just discussed, do you have any clarifying

20 questions?

21 DR. RODNEY JOHNSON: I have no clarifying -- I

22 do not need any answers or comments clarified. Thank you very

23 much. Why should that be so hard to say?

24 DR. MARTHA SANDY: So now, shall we take the

25 rest of this question all together, the test substances? The

26 selection of test substance and methods, analytical methods?
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1 DR. REYNALDO PATINO: I think so.

2 DR. MARTHA SANDY: That's fine. So I'll let

3 you, Dr. Patino, start off.

4 DR. REYNALDO PATINO: What we'll do is we'll

5 address the part of the question that has to do with the test

6 chemicals. The methods are probably the way we did it, they are

7 addressed partly in Question 5 and then probably in a little

8 more detail in Question 6, during the discussion on

9 transferability. So that's the way we organized this answer and

10 hopefully that will be okay with you.

11 So it would be primarily test chemicals and

12 it's going to be a very short presentation. For this assay

13 there were a number of compounds that were used that were tested

14 in the assay. It included four ER agonists, and that included a

15 strong compound. It's the same thing beta-estradiol, a medium

16 potency compound, DDT, and too weak -- well, maybe

17 differentially weak compounds of octylphenol and

18 4-Chloro-3-methlyphenol.

19 It also included in the assays, Tamoxifen,

20 which is an ER antagonist with some agonist activity. 

21 Trenbolone, which is a strong AR agonist; Prochloraz, which was

22 in the report that listed an aromatase inhibitor with AR

23 antagonist activity, but we will discuss more later when we deal

24 with the LAGDA assay, but there is evidence that it is also a

25 thyroid active compound. But we'll deal with that in the next

26 assay. 
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1 And then the last one that was used here was

2 Vinclozolin, which is listed in the report as an AR antagonist,

3 but there's also evidence for this compound that it may be a

4 thyroid active compound with possible thyroid -- potentially

5 with activity to inhibit thyroid peroxidase. So I'll deal with

6 that a little bit more here and that will probably be the end of

7 my comments. 

8 But the Vinclozolin was used in the MMT assay

9 on the basis of the presumptive mode of action that is initiated

10 by binding to the AR without activating anergy-dependent genes. 

11 But there is a recent study with rats, where evidence was

12 presented, suggesting that Vinclozolin is also a thyroid active

13 compound that affects TH production and/or metabolism. And I'll

14 provide that reference in the written report.

15 And there is also another study using human

16 recombinant TPO that show that or suggested that Vinclozolin is

17 able to suppress thyroid peroxidase activity.

18 Although there is no information concerning

19 the ability of Vinclozolin to act as a thyroid endocrine

20 disruption in fishes, the Agency or the Panel would encourage

21 the Agency to review the data for Vinclozolin that was presented

22 in the ISR, in the context of potential effects of both the

23 androgen and thyroid endocrine systems. 

24 Also, given the report of association between

25 -- and there is evidence for fish, as well as amphibians, but no

26 direct evidence for Medaka, but there is evidence for fish and
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1 amphibians that disruption of the thyroid endocrine system can

2 also skew sex ratios in populations.

3 Again, I'll provide some references for that. 

4 So given this, even though there is no direct evidence for

5 Medaka, it is notable that the Agency study with Vinclozolin

6 reported mismatches between the genotypic sex or the genetic sex

7 and the gonadal sex in some of their fish treated with

8 Vinclozolin, apparently not in their control fish.

9 Again, for two of the compounds that were

10 listed, using assay, Prochloraz and Vinclozolin, there may be

11 additional modes of action, if considered, may help explain, at

12 least conceptually, some of the results, the unexpected results

13 that were reported by the Agency.

14 We'll go a little bit more into this when we

15 are addressing Question 5 as well, but for now, those are my

16 comments on the test chemicals that were chosen. I should say

17 that probably the rationale, to add, the rationale for using

18 Vinclozolin was not very clear in the report. We had some

19 discussion about this when the Agency presented the report. 

20 Other than that, those are my comments. If anybody has any

21 input or change, please do. Thank you.

22 DR. MARTHA SANDY: So I will ask each of you

23 individually. Dr. Perkins.

24 DR. EDWARD PERKINS: I would just like to

25 reiterate about the choice of Vinclozolin, similar, I think we

26 talked about that before. It would be better for validation
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1 studies to have something that has a stronger effect for

2 interlaboratory effects. So you can look at assay performance

3 and endpoint perform and reproducibility in a more predicable

4 manner.

5 DR. MARTHA SANDY: Thank you. We have some

6 noise outside. It would be nice if people directly into the

7 microphone. I'll remind everyone again. Dr. Schlenk.

8 DR. DANIEL SCHLENK: Yeah, just a couple of

9 comments. I think as we met the other night, I think what came

10 across was that I think you've got the estrogen and androgen

11 side covered pretty well. Those seem to be fairly potent

12 compounds for those particular AOPs. 

13 The rationale for the other AOPs was fairly

14 weak. I don't think the documentation really supported why

15 those particular compounds were chosen. One would think you'd

16 want something a little stronger. I know MEDs don't work with

17 flutamide, so I was actually kind of shocked that that wasn't

18 used as the anti-androgen for the Medaka, since you have such

19 pretty good data and Fatheads on that. 

20 The thyroid, the quail studies use

21 perchlorate. That would be sort of an obvious sort of thyroid

22 or PBDE. Something like that that would sort of target thyroid

23 endocrine function. Something like would be a little stronger,

24 I guess I would recommend in that particular case. 

25 The steroidogenesis inhibitor was also

26 somewhat unclear why Prochloraz was selected. If you want to
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1 look at that, why not pick a stronger Ketoconazole, for example,

2 or some stronger azoles, aromatase inhibitor, I guess would be

3 another recommendation for that.

4 I guess this is my own personal thing here,

5 but I think a non-endocrine hepatoxicant would be a really good

6 sort of negative control in a way, just to rule out the

7 non-endocrine pathway. So something like ANIT or something like

8 that that actually targets the liver that doesn't necessarily

9 have a endocrine mode of action that you could base the assay

10 against, would be strong.

11 Overall, I think we're consistent with what

12 the Japanese quail folks were saying too. I think you really

13 think you need a reference toxicant. Any other assay that was

14 ever run, at least that I've ever ran, usually have a reference

15 toxicant that you run alongside it, periodically or every once

16 in awhile just to make sure that you're producing the results

17 you need to produce. 

18 So again, depending on what you AOP is, I

19 would say that probably one of these compounds would be a nice

20 reference toxicant that you would want to run alongside that;

21 maybe one or two doses, something like that. But at least you

22 know what you're getting.

23 As we all know, it's very difficult to publish

24 negative results and it's also very hard to prove a negative

25 result. It's harder to prove a negative result than is a

26 positive result. So again, the Prochloraz results, I think,
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1 were somewhat felt not be very strong. Even though there wasn't

2 any effect, it nice to actually have a compound that you do see

3 in effect, to make sure the assay is valid. So those are my

4 comments related to the compounds.

5 DR. MARTHA SANDY: Dr. Freeman.

6 DR. JENNIFER FREEMAN: Most of my comments

7 have already been covered, but I just wanted to add one other

8 thing that we also talked about was that if the MRT assay is

9 going to be implemented, we felt that needed to be assayed with

10 the fuller panel of chemicals to get the effects and more to be

11 understood from that assay.

12 DR. MARTHA SANDY: Dr. Kullman.

13 DR. SETH KULLMAN: The same. Most of the

14 comments have been covered. One thing that basically has not

15 been put out to the Panel or to the EPA then is, however. How

16 many compounds -- well, I'm put in a form of a question and in

17 essence. How many compounds do we need to test to unequivocally

18 determine expected versus observed results in any of the AOPs

19 tested? 

20 So at this point, you've got one --

21 DR. MARTHA SANDY: Can you speak into the

22 microphone?

23 DR. SETH KULLMAN: I'm sorry. You've got one

24 strong estrogen and a couple of weak estrogens or moderate, one

25 AR and anti-AR. So the question, essentially, is how many

26 compounds do we need to use in order to assess the
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1 reproducibility, liability validation of the assay?

2 DR. MARTHA SANDY: Dr. Blazer.

3 DR. VICKI BLAZER: All my comments have been

4 covered.

5 DR. MARTHA SANDY: And Dr. Johnson.

6 DR. DALLAS JOHNSON: I have nothing to add. 

7 DR. MARTHA SANDY: And nothing from Ken on

8 these?

9 DR. DALLAS JOHNSON: No.

10 DR. MARTHA SANDY: Thank you. Any other

11 comments from the Panel on any other issue related to Question

12 4, selection of test substances and analytic methods?

13 (No response.)

14 So seeing none from the Panel, I see Dr.

15 Bradbury has something to say.

16 DR. STEVEN BRADBURY: More of a clarification

17 as you put together the report. I'm trying to figure out how to

18 say this carefully because you can go lots of different

19 directions, in terms of how much is enough to know to go

20 forward. 

21 So I'm not asking to get into policy, but I am

22 asking if you can help clarify some of the degrees of

23 uncertainty and what kinds of uncertainty, in terms of

24 understanding how well a protocol could be used in

25 cross-laboratories. How much of that uncertainty could be

26 reduced by doing some of the things that you're all describing? 
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1

2 So it's not to push back on what you're

3 suggesting, but it's understanding, if you will, what will be

4 the return on investment, in terms of reduced uncertainty, as we

5 think about the cost and time, in terms of gathering information

6 to make informed risk assessments, and if needed, regulatory

7 decisions. 

8 So again, I'm not asking you to go into that,

9 but knowing what the uncertainties are, degrees to which the

10 uncertainty could be reduced, for example, by doing one chemical

11 more, two chemicals more, three chemicals. Each chemical may

12 reduce uncertainty. It may create more questions too, but it is

13 understanding where you see the biggest sources of uncertainty

14 that additional endpoints or whatever it may be, what's it

15 tackling, by how much so that we could sort of understand the

16 policy choices we have to make, which would be informed by the

17 science peer review you're providing us.

18 DR. MARTHA SANDY: Dr. Rodney Johnson, do you

19 have any areas where clarification is needed?

20 DR. RODNEY JOHNSON: Just one small question

21 with respect to the comments that Dr. Schlenk made. Towards the

22 end of your comments you mentioned the lack of response with

23 Prochloraz. I'm thinking that perhaps you meant Vinclozolin or

24 did you mean Prochloraz?

25 DR. DANIEL SCHLENK: I think it's Vinclozolin.

26 I think you're right. Yes. Sorry.
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1 DR. MARTHA SANDY: Okay. I believe we're

2 ready for Question 5 to be read into the record.

3 DR. LESLIE TOUART: Yes. I can read the

4 question as soon as it's advanced. This is Les Touart to read

5 Charge Question Number 5. Considering the variability inherent

6 in biological and chemical test methods, a test method needs to

7 be repeatable and reproducible. 

8 A test is robust and reliable if the results

9 are repeatable and reproducible within a laboratory and between

10 different laboratories, respectively. At-test protocol should

11 provide sufficient guidance to ensure proper and consistent

12 performance across labs and chemicals. 

13 Please comment on the test method robustness

14 and reliability and the repeatability and reproducibility of the

15 results obtained with the MMT and MRT assays.

16 DR. MARTHA SANDY: Dr. Patino, you're the lead

17 discussant on this. 

18 DR. REYNALDO PATINO: What I would like to do

19 first is define how we defined the terms on the questions that

20 you posed to the Panel so that you understand when we use the

21 terms here, what exactly do we mean.

22 Reliability, we define as, as was defined in

23 the ISR as the reproducibility of results from an assay within

24 and between laboratories. For robustness, there was no specific

25 definition that was provided in the ISR, so we looked on the

26 internet and found one that we liked. 
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1 We put the full citation here, but in general,

2 being robust, according to Investopedia, means that a system can

3 handle variability and remain effective under a variety of

4 different conditions. So you can just take it; took it this

5 way, took it the other way and if you get results that make

6 sense, then that's a robust system. So that's the context that

7 we'll be using in this answer.

8 For each of the reference chemicals used for

9 the validation, the assay validation was associated with one or

10 more endocrine-dependent modes of action that were summarized in

11 the integrated -- in the ISR. 

12 At one level, at a basic level, the general

13 reliability of the ISR can at least be initially evaluated by

14 comparing the effects of each test chemical with what's known in

15 the literature. What do all the people have found,

16 historically.

17 With the exception of Vinclozolin, the MMP

18 assay, generally performed as expected for the reference

19 materials. To provide a little more specific information, all

20 the ER agonists typically resulted in reduced fecundity. And in

21 males, also, decreased secondary sex characteristics as CC, OP

22 regulation of hepatic vitellogenin, messenger RNA (mRNA). So

23 that was a typical result when you use the ER agonist. 

24 Tamoxifen, which has both ER antagonist and

25 some agonist activity, also reduced fecundity. In females,

26 Tamoxifen reduced vitellogenin production or mRNA same as above.
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1 This result is expected in view of this ER antagonist activity.

2 In males, Tamoxifen reduced SSC and increased vitellogenin to

3 some degree, and that is expected in view of the compounds ER

4 agonist activity. So in this regard, the assay performed as

5 expected.

6 The androgen agonist resulted in decreased

7 fecundity, and in females, also caused development of

8 male-specific secondary sex strains and also reduced hepatic

9 mRNA. It probably would be a disruption of the HPG axis and

10 also cause sex reversal and masculinization of XY fish. So

11 that's expected.

12 Concerning Prochloraz, in fish, unlike the

13 situation in many others, aromatase inhibitors in Medaka did not

14 result in sex reversal of genetic males. I'm sorry, of genetic

15 females. But they did disrupt -- I'm sorry. 

16 In Medaka, the aromatase inhibitors do not

17 result in sex reversal of genetic females, but they did disrupt

18 their normal environment development, and therefore, it is still

19 expected to lead to exposure to aromatase inhibitor, it's still

20 expected to lead to reproductive impairments.

21 Indeed, in the MMT assay, the Prochloraz,

22 which is an aromatase inhibitor, caused a reduction in fecundity

23 and also inhibition of hepatic vitellogenin, especially in

24 female. And had no clear or consistent effects on SSC in males.

25 Again, these observations are generally as expected for Medaka,

26 given what is known about the effects of aromatase inhibitors. 
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1 For androgen antagonist, the ISR noted that

2 the effects of androgen antagonist have been inconsistent or a

3 little ambiguous in the literature, and therefore, no expected

4 result for this compound was clearly defined in the document;

5 therefore, an assessment of this performance cannot be fully

6 made.

7 So again, with the exception of Vinclozolin,

8 everything else kind of performed as expected, and therefore,

9 that's one measure of reproducibility, a general measure of

10 reproducibility. To more specifically address the question of

11 reproducibility in a little more in depth, two sets of

12 interlaboratory assays were discussed in the document in the

13 first set. The Agency, in a Japanese laboratory, independently

14 examined the effects of octylphenol which is a weak ER agonist,

15 using the full MMT, the full format. 

16 Although the test format was not identical

17 between the two labs, the Panel feels that the differences are

18 small enough that the results can be compared, validly compared.

19 In general, the results between the two labs

20 with the MMT, using octylphenol were consistent. Both reported

21 impaired reproduction, reduced fecundity, reduced SSC and

22 induction of hepatic vitellogenin in males, and sex, either sex

23 reversal or intersex condition in XY individuals.

24 For fecundity, SSC and hepatic vitellogenin,

25 the LOEC values were also fairly comparable between the two

26 laboratories. They were in good agreement. So overall, these
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1 observations suggest that when using a test chemical with fairly

2 defined, established AOP, in the hands of experienced

3 laboratories, with extensive experience in the test protocols,

4 then you can expect a high level of reproducibility from this

5 assay. 

6 Such was not the case for the second round of

7 ring test. In the second round, there were three contract

8 laboratories involved in that ring test. And this is actually

9 the formal ring test for the assay. This test used Vinclozolin

10 as test chemical and an abbreviated MMT format. It was 24 weeks

11 instead of 29. The results of this ring test were not very

12 encouraging. Several major inconsistencies or technical

13 difficulties were noted. I have a few here as an example to

14 highlight what the Panel means.

15 On the technical side, the measured

16 concentrations of Vinclozolin were apparently difficult to

17 achieve by some of the labs. There were inconsistencies, either

18 achieving or maintaining the concentrations. 

19 Second, another inconsistency was several of

20 reproductive endpoints in the F1, as well as endpoints in the

21 control F1 and F2 were significantly different between these

22 three laboratories. 

23 The ISR attributed this variability, or at

24 least part of it, to the unusually small size that was achieved

25 by fish in one of the labs. And in fact, correcting for size

26 generally eliminated were differences. So that may be part of
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1 the reason why there was a lot of variability between the

2 laboratories. But the fact still remains that there were

3 differences in size, major differences in performance of the

4 fish itself.

5 Third, because there were problems with the

6 measurement of SSC in one of the labs -- and this was an

7 important endpoint -- three-way comparisons for the F1 for this

8 particular endpoint were not possible. 

9 Although the ISR states that when data were

10 available for this endpoint, they were consistent, we actually

11 looked into the report and it appeared that only one lab showed

12 a change in the SSC in F1 males that the other lab actually did

13 not report any changes. 

14 So, you know, for that, even if you exclude

15 the lab where there was a problem measuring that, the results

16 were still inconsistent between laboratories, or at least that's

17 how the data appeared to us.

18 Another point is that the Agency study with

19 Vinclozolin, using the full MMT, as well as those of the

20 contract laboratories, which used the abbreviated MMT, had a

21 problem in that a number of their fish showed genetic sex that

22 did not match their gonadal sex. This problem was much more

23 serious for the contract laboratories, which reported high

24 levels of both types of mismatches in XX males and XY females

25 and also in their controls. 

26 The ISR ascribed this problem to potential
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1 artifacts of contamination during sample collection or

2 processing. And this is probably a correct assessment, at least

3 for the case of the contract laboratories, because as I said,

4 they also had high levels in their control animals.

5 I already said this, but I need to point out

6 that in the Agency study, their controls, apparently there was a

7 perfect match there. It was no problems with matching their

8 animals, genotype and phenotype in the controls. 

9 As we noted already, because Vinclozolin may

10 have alternative AOPs that were not considered in the IRS that

11 it could potentially cause sex reversal in fish. And by that, I

12 mean the thyroid axis. At least some of the mismatches, both in

13 the Agency study, as well as the contract labs, may potentially

14 could have been true biological responses to Vinclozolin.

15 Overall, the Panel feels that an evaluation of

16 group possibility from the ring test, using Vinclozolin is

17 difficult because, 1) there was relatively undefined AOP for

18 this compound, and therefore, all the expected outcomes; 2) the

19 technical problems that were encountered by the performing

20 laboratories, the contract laboratories, and also because of the

21 largely negative results that this compound had in the assay. 

22 So it's kind of difficult to assess reproducibility under these

23 conditions.

24 But to possibly shed some light on the reasons

25 for the relatively poor performance of the ring test, the Panel

26 recommends that the Agency considers, at least conceptually, the
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1 possibility, at least for some of the observations, of

2 alternative AOPs for this test compound.

3 Overall, while the interlaboratory study

4 between the agencies and the Japanese labs with octylphenol show

5 similar data responses between labs, and that indicated, at

6 least the assay itself, is reproducible when you competent or

7 experienced laboratories with a well-defined chemical. The

8 Vinclozolin results were very inconsistent or not very clear at

9 all.

10 The ring test with Vinclozolin that was

11 conducted by the contract laboratories is probably more relevant

12 to assess the reproducibility of this assay because it is

13 laboratories like those that will be doing the testing in the

14 future.

15 For practical purposes, the Panel feels that

16 the reliability and the robustness of the assay have not been

17 fully demonstrated for this assay. And again, I'm defining

18 robustness as the performance of the assay on the conditions of

19 variable levels of experience among the laboratories conducting

20 the assay. In that regard, robustness was poor because you

21 tweak it here, tweak it there, in terms of technical expertise,

22 and the assay kind of fell apart. 

23 So to improve both the reliability and

24 robustness of the assay, the Panel recommends that the Agency

25 develop more specific guidelines on how to conduct the assay and

26 in greater detail. More of this will come up in more detail
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1 under Question 6. 

2 For both the MMT and the MRT assays, greater

3 emphasis needs to be placed on the overall health of fish

4 populations being used for these studies, specifically the

5 problem with growth that was noted among the labs. 

6 In particular, procedures and conditions for

7 the adequate care in husbandry of the animals, and this includes

8 the specific ranges for pollution water quality variables such

9 as hardness and micronutrients also need to be better

10 highlighted in the guidance. So those are my comments. I'm

11 sure all those would have more things to add. Thank you.

12 DR. MARTHA SANDY: We'll turn now to Dr.

13 Kullman.

14 DR. SETH KULLMAN: I think everything has been

15 covered. Thank you.

16 DR. MARTHA SANDY: Dr. Perkins.

17 DR. EDWARD PERKINS: I think everything has

18 been covered for me.

19 DR. MARTHA SANDY: Dr. Schlenk. 

20 DR. DANIEL SCHLENK: Just one thing to add,

21 with Dr. Patino's comments about Vinclozolin. This probably

22 should've actually probably gone in Question 4, but in terms of

23 choice of compounds, again, I wouldn't select a compound that

24 degrades as readily as Vinclozolin. If you're going to pick a

25 compound to run through a ring test, it probably would be

26 something a little easier, analytically, to evaluate. 
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1 So I just wanted to add that for Question 4,

2 as well, but nothing further to add for Question 5.

3 DR. MARTHA SANDY: Dr. Blazer?

4 DR. VICKI BLAZER: Nothing further to add.

5 DR. MARTHA SANDY: Dr. Freeman.

6 DR. JENNIFER FREEMAN: I don't have anything

7 additional to add.

8 DR. MARTHA SANDY: And Dr. Dallas Johnson. 

9 And he can comment for Dr. Portier.

10 DR. DALLAS JOHNSON: Right. I don't have much

11 to say for myself, except some of the comments that I made in

12 Question 4 could also probably be put into here. Indicated, it

13 seems like some of the things experimenters have control over is

14 pretty well laid out in the protocol, but things that they may

15 not have control over, the randomization process, there is

16 nothing really about it in the protocol. 

17 So you're trying to develop a protocol that

18 might be reproducible and robust. There needs to be a lot more

19 said about how tanks are randomized, how fish are randomized to

20 tanks and so on and so on.

21 Dr. Portier also defined robustness. I don't

22 think it's contradictory to the way it's been defined here, but

23 the ability -- he defined it as the ability to produce results

24 with deviations from protocol. And reliability, he defined as

25 the ability of a procedure. I guess it's defined in the report,

26 the ability of a procedure to yield the same results on repeated
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1 trials, within and between laboratories. 

2 Repeatability: the ability of the protocol to

3 be repeated. And reproducibility: the ability of results from

4 one run of the protocol to be reproduced by others running under

5 the same protocol conditions. And then with respect to the test

6 method, robustness, he says I don't think we have sufficient

7 data to address this characteristic. 

8 There are four sources of variation that must

9 be accounted for to address reliability, repeatability and

10 reproductive ability. These are among individuals within

11 replicates, among replicates within studies, among studies

12 within laboratories, and among laboratories.

13 Section 6.1 controlled data correlation

14 endpoint variability of Vinclozolin interlaboratory report is

15 closest to what I was looking for in terms of having data

16 capable of estimating these sources of variation. At least for

17 Vinclozolin, there are sufficient data to estimate all these

18 sources of variation for the zero control animals.

19 Test method reliability, he says an assay will

20 -- well, I think the report says an assay will generally be

21 considered to be reliable by EPA if its overall variability is

22 low enough to give a level of sensitivity or power consistent

23 with the purpose of the assay as intended to serve; page 11,

24 lines 14 to 16. 

25 He says here, the variability among

26 individuals within replicates could be pooled across studies
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1 and/or laboratories as used in the power analysis to address

2 whether a reasonable number of replicates can be used to find

3 biologically significant differences in key endpoints. With

4 respect to test method repeatability, he indicates repeatability

5 can be assessed by examining control endpoint measurements from

6 study to study within a lab, both within generation and between

7 generations. 

8 For fecundity, the only endpoint I attempted

9 to examine, it looks like at least within one lab the control

10 endpoint measurements are repeatable. From what I conclude,

11 there is some degree of ability to repeat the protocol.

12 With respect to test method reproducibility,

13 it says there are clearly some unresolved issues with test

14 method repeatability that can be identified with various

15 interlaboratory reports, and refers then to the Vinclozolin

16 interlaboratory report, Section 6.1, control data correlation

17 and endpoint variability.

18 I believe the report says comparison in

19 control data from each of the laboratory studies summarized in

20 Table 3, and then he adds, this analysis is indicated that for

21 most endpoints, the control response was not consistent between

22 laboratories and two endpoints that showed greater consistency,

23 which were not statistically different between labs of F1 and

24 F2, anal fin processes and hepatic expression of VTG1; however,

25 it should be noted that successful measurement of anal fin

26 processes only occurred for two labs in both the F1 and F2
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1 generation, limiting interlab comparisons.

2 In Appendix H, MMT Summary Report, on page 2

3 of the report, he says again, there were some unexplained

4 differences between weekly egg outputs within treatments,

5 including the controls. And then Dr. Portier says for the DDT

6 data, the write-up says there are six replicates, but the

7 control for F0 displays 10 dots.

8 For the CMP study, fecundity means were 50

9 percent higher than all other chemical tests. No explanation is

10 given. This produced more variability across all treatments

11 making differences much more difficult to be significant. Did

12 something change in the test protocol? 

13 At the same time, it seems that the test fish

14 are about twice as heavy and much longer.

15 For the Prochloraz, several of the treatments

16 were lower than controls in the fifth week; however, controls

17 seemed to be decreasing as well, suggesting a general trend of

18 ever decreasing reproductive output as the reproductive

19 assessment progressed. The mean number of eggs spawned daily in

20 the 41-milligram leader group was 17.3 in the first week, and

21 this declined to just three eggs per day, with several of the

22 pairs not producing any eggs in the fifth week. 

23 These data suggest a strong trend of

24 decreasing reproduction in F0, however, because of the generally

25 low statistical power of the MMT, it was not detected by our

26 statistical tests until the last week. Since this MMT was
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1 completed, a power analysis has shown that the power to see a 30

2 percent reduction in fecundity is only about 0.5 and to see a 40

3 percent reduction is still only 0.7; therefore, it is not

4 surprising to see trends in the data that are not statistically

5 significant. 

6 Study protocol control/unexplained

7 variability: Looking at F0 fecundity, replicate ranges were

8 generally 18 to 25, but occasionally you would get a 28 to 48 in

9 the CMP study or a 6 to 22, Trenbolone, F1 bodyweight and

10 lengths for control group subadults ranged from an overall

11 average low of 130 milligrams, 23 milliliters (Tamoxifen) to a

12 240 milligrams, 27 milliliter (DDT). These findings suggest

13 that the level of control is less than good.

14 MMT ISR final report, Section 5.1.4. and

15 Figure 5.4., examining the subadult endpoints we see differences

16 in subadult bodyweight/length that could be simply one week

17 difference in when measured. This could indicate a weakness in

18 the protocol. 

19 And then note in Appendix H, all of the box

20 plot footnotes indicate that the box represents one standard

21 deviation and the whisker represents one standard error. He

22 says since in all cases, the whisker half-length is greater than

23 the box half-length it is not clear to what these two terms

24 actually refer. If both refer to the estimate of the mean, the

25 footnotes are all in error.

26 That concludes what Dr. Portier has to say.
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1 DR. MARTHA SANDY: Thank you very much. Are

2 there other comments from anyone else on the Panel on Question

3 5?

4 (No response.)

5 Seeing none, I'll turn to Dr. Rodney Johnson

6 with the EPA.

7 DR. RODNEY JOHNSON: I have no questions for

8 clarification.

9 DR. MARTHA SANDY: I believe we're scheduled

10 for a break. Let's take a 10-minute break and be back at what

11 this clock says would be 10:35. Thank you.

12 (Whereupon, a break was taken)

13 DR. MARTHA SANDY: Thank you. I believe we

14 are on Question 6. The slides have been advanced, so Dr.

15 Touart.

16 DR. LESLIE TOUART: The test protocol should

17 be descriptive enough to be fully transferable to a functional

18 laboratory. The protocol should describe the methodology of the

19 assay in a clear and concise manner so that a laboratory could

20 comprehend the objective, conduct the assay, observe and measure

21 prescribed endpoints, compile and prepare data for statistical

22 analyses and report the results. 

23 Section 4 of the MMT ISR outlines the process

24 of and challenges experienced in the interlaboratory studies,

25 and Section 5 presents the optimization and proposed Medaka

26 Reproduction Test protocol.
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1 Please comment on the transferability across

2 labs and provide any suggestions or recommendations for

3 improvement of the MMT and MRT assays.

4 DR. MARTHA SANDY: Dr. Kullman, you are the

5 lead discussant.

6 DR. SETH KULLMAN: Okay. I'll see if I can

7 talk clearly into this. So I think we formulated our response

8 to this question based on looking at some of the ICCVAM

9 requirements for validation criteria throughout the ISRs. So

10 the first ICCVAM definition was within tests, intralaboratory

11 and interlaboratory variability and how these parameters vary

12 with time should be evaluated. 

13 Here, we may substitute the idea of time with

14 individual trials. As Dr. Schlenk alluded to before, there's

15 going to be some redundancy in this answer to some of the other

16 answers because we felt it was appropriate.

17 So with this in mind, to evaluate

18 transferability, we felt it can be evaluated following the

19 criteria of the trials that were conducted either within the

20 lab, within the EPA lab or across the different labs that were

21 working on this. There were sort of three scenarios that we

22 were able to pull from the ISR based on that.

23 The data presented in the MMT ISR suggested

24 that there is some indication of moderate -- and I'm using these

25 as sort of soft terms -- moderate agreement between defined

26 assay parameters between labs. And the first instance of that
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1 was the comparison of the trial for 4-t-octylphenol in the U.S.

2 EPA 2007 trial and NIES Japan, where they reported similar

3 values for fecundity for the LOEC, and the same for other

4 components of the assay for the secondary sex characteristics. 

5 And that there was moderate agreement within those two trials

6 between those two labs. 

7 The Panel felt, basically that the strongest

8 agreement, however, for transferability would be made by

9 assessing the control parameters across each of the trials. 

10 If you look at Section 4.1.4, it illustrates

11 that comparisons between trials 4-t-octylphenol, that between

12 the U.S. EPA lab and the NIES Japan lab, there was this fair

13 amount of consistency and reproducibility for fecundity and SSC

14 in both F1 and F2 populations. And this section also continues

15 to state successful reproducibility by comparison of additional

16 endpoint LOEC data for 4-t-octylphenol. 

17 So we felt like that was a very good indicator

18 of agreement between the two different labs; however, let me

19 clarify this a little bit. I think the Panel felt that that was

20 more indicative of reproducibility rather than transferability. 

21 And that since the Japanese laboratory was highly trained in

22 medaka techniques and already had existing protocols that only

23 needed slight modifications to comply with the MMT assay, that

24 that might have played into the ability for these two labs to

25 have such close concordance with the ability to run the trial. 

26 And that maybe, as what we saw with some of the contract labs
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1 and Vinclozolin, that that may not necessarily be the case. 

2 So onto the interlab tests that were conducted

3 to assess basically the AOP of AR antagonism with the

4 Vinclozolin study, I think as addressed in Question 5, the Panel

5 really found that this ring test was highly variable and

6 suffered from a lack of consistency and assay performance. 

7 The ring test also suffered from a lack of,

8 essentially, the compound tested; Vinclozolin is a difficult

9 compound. The mechanism of Vinclozolin is somewhat

10 questionable, and certainly the fate the parent compound is

11 questionable as the M1 and M2 metabolites are active. And that

12 just makes comparison of that compound very difficult and I

13 think it was more detailed in Question 5, so I won't belabor

14 that.

15 So I guess in conclusion, the Panel felt that

16 based on the Vinclozolin study that the data at this time

17 suggests that the assay is not 100 percent transferable. 

18 Next, let's detail or go over another

19 component of the ICCVAM validation criteria. And that's the

20 necessity for a formal detailed protocol and that protocol must

21 be provided and be available in the public domain. And mostly,

22 the second part is that it should be sufficiently detailed to

23 enable the user to adhere to it and should include data analysis

24 and decision criteria. 

25 So for the first part of that is the detailed

26 protocol. I think in total, the Panel felt that the MMT and the
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1 MRT protocol should include refined methods for sections of all

2 analysis conducted. 

3 As present, we found that there is an

4 inadequate, essentially, description of the assays and that the

5 defined components of general experimental principles including

6 experimental design, biological treatments, technical

7 replicates, husbandry conditions, assay performance, test agents

8 used and concentration range. And definition of the primary and

9 secondary endpoints of the assay, such as water chemistry and

10 overall timing of multigenerational approach could be improved. 

11 Okay.

12 So we did identify noticeable components of

13 the MRT and the MMT assay that were not present in provided

14 documentation and that we felt that it would aid in reducing

15 data variability, the assay results, the overall concordance and

16 transferability of the assays. That's essentially what we're

17 assessing in this particular question, with both the inter and

18 intralaboratory trials.

19 We felt that it would be expected that most

20 laboratories running these trials would have some working

21 knowledge of Medaka biology and/or fish biology, but we felt

22 that further standardization of the protocol would really help

23 in the ideas of transferability. 

24 And this is where some redundancy will come in

25 from Question 3 and a little bit from Question 5. I'm going to

26 one of these in great detail and then the rest will just sort of
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1 be inferred for similar detail.

2 For example, vitellogenin: we noticed across

3 the two different labs that one lab was using ELISA and another

4 lab was using sort of an RT-PCR method to do it. So as an

5 example, if we simply chose an RT-PCR method to assess

6 vitellogenin, what are we looking at as far as all the

7 components that are associated simply to run a qPCR analysis and

8 how can we make this qPCR analysis consistent across all labs

9 that will conduct all the analysis?

10 So 1) we have to collect tissue. How is the

11 liver dissected? What washes are used? How is the liver

12 stored? For how long? Under what conditions? Are substances

13 like RNA later used, right, or is this tissue flash frozen. 

14 Okay. So these are the levels of detail that

15 I think are necessary to be defined in the protocol to really

16 ensure consistency across all these different laboratories that

17 would be doing it.

18 So let's stay with vitellogenin. Standardize

19 RNA extraction processes. You know, what reagents are used? 

20 What methods? What QA/QC is done on RNA extraction? Do you use

21 concentration and quality? Do you use a bioanalyzer to assess

22 that? Do you run a gel, you know, to look at 18S RNA? 

23 cDNA synthesis and amplification reagents and

24 methods: what reagents are used for cDNA synthesis? Is it

25 random priming or poly A priming? What concentration of RNA is

26 used in the reaction? What type of RT incubation time? What
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1 type of RT enzyme, et cetera? So all of these components.

2 qPCR primers: I think a standard set of qPCR

3 primers should be used and it should be clearly defined in what

4 those are, what their concentrations are, what their

5 purification is. 

6 Also, it's not even noted which VTG is

7 targeted. Is it VTG1 or VTG2; there are two forms in Medaka. 

8 What reference genes was evaluated for this? Is it 18S? Is it

9 tubulin acting as something else, as far as comparing?

10 All qPCR conditions need to be determined. 

11 What amplification conditions. What is your annealing

12 temperature? What's your association temperature? What's your

13 extension temperature? How many amplification runs are you

14 doing?

15 Concentrations of your buffer; concentration

16 of your dNTPs, technical replicates, so on and so forth. I know

17 I'm belaboring this, but I just want to put this in for the

18 record.

19 Also, qPCR analysis: what is your data

20 analysis? There are a number of different ways to crunch qPCR

21 data. What is your data analysis protocol

22 Reference standards; I think this will be

23 important. We can easily produce a reference standard for VTG,

24 especially when you're measuring copy number. Simply have a

25 vector with a VTG copy in it and have that expressed and you can

26 make VTG and know exactly what your copy number is.
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1 Overall guidelines should make sure that MIQE

2 (Minimum Information for Public Quality of real-time PCR

3 Experiments). There is a reference for this and it's Bustin, et

4 al., 2009. This is similar to what happened with the microarray

5 data and everything should be qualified and the standard should

6 be put into a database where it is publically available.

7 So with this in mind, I'm just going to

8 mention other things that could be added with this degree of

9 detail. I'm certainly not going to go with this degree of

10 detail into each of these things to make sure that we don't stay

11 here all day.

12 As was mentioned before, there are a number of

13 secondary sex characteristics that can be determined, and

14 particularly, the anal fin papillae, which is used now. But in

15 Question 3, we addressed some other issues and some other assays

16 that could be used or measurements, if you will, that could be

17 used.

18 Let's have sufficient detail on how to measure

19 those. I think it was suggested before, having YouTube videos

20 or some sort of web type of thing. It would be the Journal of

21 Observational Visualization (JOV), has detailed protocols where

22 there is sufficient webcasts associated with these that can be

23 done.

24 Embryo collection: there is no mention to how

25 embryo is collected. There are several methods to collect

26 embryos. Some labs use scissors to remove all the threads
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1 between the embryos. Other labs use sandpaper and they roll it.

2 Using that sandpaper method is very effective, but it also has

3 the disadvantage of possibly crushing embryos, since you could

4 lose embryo viability simply by doing that. 

5 With embryo viability, how is it determined? 

6 Are you looking at opaqueness of the embryos? Are you using

7 methylene blue? So if you're going to be counting survival of

8 embryos, we should have a standardized method in how we're

9 actually determining viability.

10 It was suggested before that a detailed

11 protocol be presented for genetic sex. Rodney, I'll say that

12 I'm very familiar with your publication from Padilla, et al.,

13 where you were a co-author in 2009, where you have a detailed

14 description on how to determine genetic sex. I'd imagine there

15 is some modification since then, but I think that's already

16 available. 

17 Water quality parameters were presented

18 earlier, so I won't reiterate those, but we definitely, as a

19 Panel, agree that strict adherence to defined water quality

20 parameters hardness solemnity, conductivity, pH, temperature, is

21 necessary to ensure generalized success of breeding and

22 reliability of results that will definitely translate towards

23 transferability.

24 Reference materials for the DMY, again, and

25 the VTG, I suggested this. We thought that maybe they should be

26 deposited with NIST. So again, readily available. And then
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1 certainly fish husbandry characteristics, not only for the

2 animals that are in the assay, but certainly fish husbandry

3 conditions for animals that are used as brood stock for the

4 assay. 

5 And within that, we noticed that there is no

6 assessment of fish health and quality within. Some general

7 histopathology, some other types of general assessment of fish

8 health that could be made prior to initiating each of the

9 experiments may provide really beneficial to ensure that there

10 is consistent quality of animals across all trials of these

11 assays. We do it regularly in our lab.

12 So it was generally decided that all these

13 guidelines should be as detailed as possible to communicate the

14 importance of proper QA/QC parameters, as needed for the assay. 

15 And that QA/QC parameters be incorporated into the assay and

16 that the labs that are going to be running these assays have a

17 mechanism to assess quality and assurance for running there.

18 Consequently, the Panel found that the

19 protocol should be reevaluated to determine if clarity could be

20 improved. And it is recommended that both the MMT and MRT

21 should incorporate reference toxicants and materials that can be

22 used for internal assessment of responses. Reference toxicants

23 include EAT, anti-E, anti-A, anti-T should be used if disruption

24 of these pathways are identified in Tier 1. 

25 It was also thought that a positive control be

26 run in each of these trials, or maybe part of the QA/QC, where
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1 every third trial, or something like that, a contract lab runs a

2 positive control to ensure that all the parameters of the assay

3 are performing correctly.

4 There was also some comment on whether

5 unresolved assays should really undergo any type of data

6 manipulation. I think that came about in the Vinclozolin ring

7 test. I think it was Lab 2, where the data was manipulated to

8 normalize for the lack of size. We really wondered if that's an

9 appropriate method to sort of qualify whether a trial has been

10 run appropriately or not. And it may be that, simply, if the

11 data does not look appropriate, that that trial should be

12 nullified and that no manipulation should occur to try and get

13 that within a reasonable stretch to sort of justify the data.

14 And then lastly, there was some mention that

15 there was no justification for a non-solvent control, and it

16 doesn't seem to be clear in the documentation. So I think in

17 conclusion, the Panel believes that the MMT and the MRT assay is

18 sound, but it has the potential to detect EDC toxicity and

19 inform MOA. 

20 But the incorporation of recommended

21 refinements and strict adherence to designated, MMT and MRT

22 assays protocols are likely to demonstrate a stronger

23 correlation between labs and demonstrate that at least under

24 most defined conditions to perform with a degree of

25 repeatability and reliability, which would relate to

26 transferability of the assay. 
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1 It was suggested that subsequent assessments

2 should then be made with test agents that are less definitive

3 AOPs and assess apparent sensitivity of assay conditions across

4 labs. So certainly, start this and I think this will be, in

5 summary, for Question 9, start the assay ring test with some

6 strong agonists, antagonists, for each of the AOPs and then

7 maybe once that's been validated, move to some compounds that

8 may test the sensitivity of the assays and give you, in essence,

9 of transferability across a range of sensitivities.

10 I'll stop there.

11 DR. MARTHA SANDY: Thank you. I'll turn to

12 the associates, Dr. Perkins.

13 DR. EDWARD PERKINS: I guess it's similar to

14 the quail. I would also recommend consideration of some

15 alternative methods for providing clear guidance on methods and

16 maintenance and other issues like that, such as alternative

17 media, YouTube videos, things like that, that might be able to

18 give a little clearer guidance for people on exactly you want

19 things performed.

20 DR. MARTHA SANDY: Dr. Freeman.

21 DR. JENNIFER FREEMAN: I don't have anything

22 additional to add.

23 DR. MARTHA SANDY: Dr. Patino.

24 DR. REYNALDO PATINO: Nothing new to add.

25 DR. MARTHA SANDY: Dr. Blazer.

26 DR. VICKI BLAZER: Nothing additional to add.
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1 DR. MARTHA SANDY: Dr. Schlenk.

2 DR. DANIEL SCHLENK: My comments are

3 incorporated in Dr. Kullman's. 

4 DR. MARTHA SANDY: And Dr. Dallas Johnson and

5 Ken Portier.

6 DR. DALLAS JOHNSON: Okay. During the break,

7 I mentioned something to one of the panelists, and they didn't

8 laugh me out of the room, so I'll bring it up here. I assume

9 that a small part of the total cost for these studies involving

10 Medaka is the cost of acquiring the Medaka and feeding the

11 Medaka. 

12 So given that this is true, would it be

13 possible and/or make sense to include several pairs of adult

14 Medaka within each tank? 

15 That is, currently, each tank at the beginning

16 contains one male and one female Medaka, and then at each stage,

17 there is again, only one male and one female Medaka that goes

18 into the tank. It seems to give a fair chance, then, of having

19 pairs that are not able to breed and not able to produce any

20 results. 

21 So I was just wondering, as a dumb

22 statistician, whether or not it would be possible to say,

23 include five pairs, five female and five male Medaka within each

24 tank at each of those stages. You wouldn't need to analyze all

25 of them or select all of them for analysis, but it might, at

26 least in terms of egg production and so on, it might make sure
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1 that you at least have a chance of getting some eggs that might

2 be analyzed. So I just throw that out there as something that

3 could be considered.

4 Ken -- Dr. Portier responded to this charge

5 and says I keep coming back to the quote in the summary report

6 of Task Order Number 32. Comparison of control data from each

7 of the laboratory studies provides a measure of the natural

8 variation for each of the variables and is summarized in Table

9 3. 

10 This analysis indicated that for most

11 endpoints, the control response was not consistent between

12 laboratories. I worry that inconsistency in control endpoint

13 responses are indicative of the protocol being not fully

14 transferable. Inconsistencies are different from variability. 

15 I recognize that some lab-to-lab variability

16 will occur in some parameters. For a fully transferable

17 protocol, I would expect lab-to-lab control animal variability

18 to be of a similar order of magnitude as study-to-study control

19 animal variability within one lab.

20 DR. MARTHA SANDY: Thank you. Did any of the

21 discussants want to respond to Dr. Johnson's suggestion of

22 adding more breeding pairs?

23 Yes. Dr. Kullman.

24 DR. SETH KULLMAN: Yes, Rodney. I think there

25 is a number of labs using Medaka when they do reproductive

26 studies. There is sort of a pretest period, where you take
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1 breeding pairs and you put them together and you assess

2 viability or reproductive capability for, say, a week and you

3 assess the number of embryos produced prior to treatment. That

4 might be a suggestion to make, along Dr. Johnson's

5 recommendations so you do identify breeding pairs that are

6 performing well that feed into the study.

7 DR. MARTHA SANDY: I'll open it up to other

8 members of the panel for any comments on this question. I'll

9 turn to the EPA, Dr. Rodney Johnson. Do you have questions of

10 clarification?

11 DR. RODNEY JOHNSON: I have no clarification

12 questions. 

13 DR. MARTHA SANDY: All right. Thank you. Dr.

14 Touart.

15 DR. LESLIE TOUART: Just some clarification. 

16 I mean, clearly, here we need to provide more detail that would

17 go into a test guideline, but also recognizing that an SOP is a

18 lot more specific than a test guideline where they might be some

19 flexibility. 

20 This is more of, I guess a plea to the Panel,

21 since I'd likely be one of those involved in revising a test

22 guideline, is the more you can identify of those elements, you

23 know, procedures or methodologies that need to be more

24 prescriptive than flexible, would be of help because with

25 guidelines, you know, we don't necessarily have to detail every

26 specific thing. 
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1 I also recognize and I think it's a good

2 suggestion that we have some auxiliary materials for guidance. 

3 You know, things like videos or other information, but I think

4 that when you identify some of the elements that need more

5 detail, how much of that needs to be very prescriptive, versus,

6 you know, just some general guidance and maybe additional

7 materials that would be associated with the test guideline but

8 maybe not part of the test guideline.

9 DR. MARTHA SANDY: So the discussants can add

10 the detail, such as specify that you have one set of primers

11 that are used to target one of the vitellogenin genes. Things

12 like that.

13 Are we ready for Question 7?

14 DR. LESLIE TOUART: I'll read Charge Question

15 7. The purpose of the validation process is to determine the

16 readiness of a test for inclusion in a testing program. A

17 component of readiness of a test is the evaluation of the

18 usefulness and limitations of the test, including the classes

19 and types of test substances that can and cannot be tested. 

20 Please comments on the strengths and/or

21 limitations of the MMT and MRT assays.

22 DR. MARTHA SANDY: Dr. Kullman.

23 DR. SETH KULLMAN: Essentially, we just have a

24 list. So this will not be too embellished. I'm just going to

25 basically read off a list and you guys can reference that in the

26 final summary report.
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1 For strengths, we kind of broke it up into two

2 different forms, model and assay. So let me speak first to the

3 advantage of the model. I think it was, again, described in

4 Question 1 and the initial sections of the ISR, but certainly,

5 the advantage of using this particular model, where the

6 husbandry models are well-established and there is a general

7 ease of manipulation of handling these animals. 

8 There is a rich history available with the

9 resources for endocrine toxicity. So certainly reference to

10 that can be made. They have a short generation time. It

11 decreases your assay time. There is routine external spawning

12 with daily spawn, which really makes this a nice animal for your

13 embryo collection.

14 And then certainly, external embryonic

15 development occurs, which I think a comment was made before that

16 if there is going to be any early life stage toxicity

17 assessment, it certainly makes that assessment much easier. And

18 then lastly, there is a well-sequenced genome, again, for things

19 like assay endpoints such as vitellogenin being able to readily

20 assess the genome or looking at primer sequences and such rated.

21 Also, from the EDC perspective, the ability to

22 assess EDC AOP diagnostics, certainly the capability for

23 determining genetic sex, as been mentioned before, is a real

24 strong, strong advantage of this model and then the ability to

25 make a comparison to phenotypic sex is also well-established in

26 this model and makes it very strong. So again, the comparison
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1 of genotypic and phenotypic sex is capable here, which is a real

2 strong advantage.

3 Well-established and readily available

4 biomarkers for the EDC effects, such as VTG and those applying

5 to the phenotypic sex, such as secondary sex characteristics and

6 then the ability to use a large number of animals and make this

7 a statistically robust assay really helps this assay.

8 And again, the same thing speaking to that

9 from the perspective of performing multiple generation assays. 

10 The assay itself, the MMT assay includes an assessment of an F0

11 through F2 to reproduction. This was seen as a full assessment

12 of reproduction through the second generation that allows the

13 ability to evaluate epigenetic effects that may occur as a

14 result of EAT disruption. This was seen as a strong component

15 of the assay, particularly for the MMT and not necessarily as

16 much for the MRT. 

17 The strengths and usefulness of this assay

18 include inclusion of chemical exposure in F0 to assess parental

19 transfer. I know there has been some, I'd say disagreement

20 among the Panel to how much parental transfer occurs or maternal

21 transfer occurs with different chemicals. That's to be

22 determined, but certainly the parental transfer and maternal

23 transfer of endogenous factors are also important. 

24 The MRT assay is much improved as the

25 replicate nature of the experimental design significantly

26 improves power for both the fecundity and fertility tests. And
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1 then the strengths, specifically to the MRT assay include

2 reduction in time in performing the assay, as well as sort of

3 the number of human hours necessary to perform that. I think

4 that was a big added bonus for the MRT.

5 Limitations; there are numerous limitations as

6 with any assay. The MMT has a lower power than the MRT assay

7 for statistical comparison. And this is important, given

8 limited causal dose concentration dependence in documented

9 studies.

10 The MMT assay has higher labor requirements,

11 as I just suggested, time, animal use, and overall cost than the

12 MRT assay. So the Panel was excited about the reduction of

13 those components for the MRT assay.

14 To date, there are a limited number of trials

15 with either assay, particularly the MRT and we still felt as if

16 there are a limited number of trials with the MMT with

17 representative camels -- camels (sic) -- representative

18 chemicals for each of the EDC AOPs. And as mentioned before, a

19 nice comparison to make would be with a strong reference of

20 hepatic toxicant such ANIT that does not act as an EDC. 

21 So maybe a really good inference for just

22 looking at general toxicity and how generalized toxicity may

23 impact some of the overall apical and/or biomarker endpoints of

24 the assay. Certainly, the strength lies in the strong agonists,

25 the E, the A, and possibly the steroidogenesis compounds, but

26 certainly the anti-E and anti-A was not as strong in this
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1 particular assay. 

2 And again, sort of the lack of standardized

3 protocols, as we've mentioned previously, we wonder if some of

4 the reproductive endpoints really may be sensitive to animal

5 husbandry conditions. 

6 So that really, again, drives some of the

7 aspects of Question 6 and really being able to define those

8 husbandry conditions very well because this organism has been

9 known to be sensitive to some husbandry conditions with

10 fecundity and fertility.

11 As stated for the quail assay, there was no

12 information on the strain used. So maybe there is a number of

13 Medaka strains that are there out there that the Japanese tend

14 to use, a northern versus southern, versus CAB. 

15 There are a number of different types of

16 strains that are available to run the assay. And it's known

17 that these strains differ in their polymorphisms, and thus,

18 could potentially differ in responses to the different AOPs. As

19 of yet, unproven interlaboratory reproducibility need for

20 greater replication MMT, thus, considered MRT. 

21 Let's see. I'm going to pass that one because

22 we've talked about it already, and the same with this, full

23 potential for phenotypic sex is not necessarily utilized in the

24 assay as is. We talked about the statistical power, the lack of

25 proper training in assays or guidance to other laboratories. 

26 So certainly not just the protocols, but the
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1 necessity to make sure that the people running these assays can

2 actually conduct them. We felt that that was not necessarily

3 stated. Animal health was stated. Then they felt as if the

4 assessments of steroid concentrations may also -- could be added

5 to the assay.

6 And then certainly last but not least is the

7 need to correlate and integrate possible transfer between Tier 1

8 Fathead Minnow studies and MMT and MRT assay.

9 DR. MARTHA SANDY: Could you repeat that last

10 phrase, last recommendation?

11 DR. SETH KULLMAN: Sure. So the last

12 recommendation was the need to correlate and/or integrate

13 between Tier 1 Fathead Minnow studies and the MMT and MRT.

14 DR. MARTHA SANDY: So I'll now turn to the

15 associate discussants. Dr. Perkins.

16 DR. EDWARD PERKINS: I have nothing further to

17 add.

18 DR. MARTHA SANDY: Dr. Freeman.

19 DR. JENNIFER FREEMAN: All of my comments have

20 been incorporated.

21 DR. MARTHA SANDY: Dr. Patino.

22 DR. REYNALDO PATINO: I'm good. Thank you.

23 DR. MARTHA SANDY: Dr. Blazer.

24 DR. VICKI BLAZER: I think the only one that

25 wasn't mentioned and maybe it was and I missed it was that we

26 discussed that in MRT, the loss of any endpoints for the F0 and
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1 F2 could result in some loss of essential data for AOP and

2 toxicity.

3 DR. MARTHA SANDY: Dr. Schlenk.

4 DR. DANIEL SCHLENK: Nothing to add.

5 DR. MARTHA SANDY: Dr. Dallas Johnson. 

6 DR. DALLAS JOHNSON: I have nothing to add and

7 Dr. Portier had nothing for this charge.

8 DR. MARTHA SANDY: Other Panel members, any

9 comment?

10 (No response.)

11 Seeing none, I'll turn to Dr. Rodney Johnson

12 of the EPA.

13 DR. RODNEY JOHNSON: I have no clarifying

14 questions.

15 DR. MARTHA SANDY: Thank you. Can we have the

16 next question, Question 8 read, please?

17 DR. LESLIE TOUART: Les Touart, and I'm

18 reading Charge Question 8. There is sufficient evidence to

19 indicate that endocrine disrupting chemicals can disrupt normal

20 development and reproduction success, however, the value added

21 if the F2 generation compared to the P0 or F1 is less clearly

22 defined. The EPA is proposing the Medaka Reproduction Test as

23 an EDSP Tier 2 assay. 

24 The MRT-test terminates after the F2 embryo

25 hatch, and a rationale for the proposed protocol is provided in

26 Section 5 of the MMT ISR. 
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1 Please comment on the Agency's rationale that

2 the value added by the F2 generation is not sufficient to

3 warrant its inclusion in the Tier 2 fish test protocol.

4 DR. MARTHA SANDY: Dr. Perkins, you're the

5 lead discussant.

6 DR. EDWARD PERKINS: The current structure of

7 the MMT assay appears to be a true multigeneration assay with

8 reproduction testing of F1 and F2 fish examined. Removal of the

9 F2 generation of the MRT then begs the question if the MRT is

10 truly a multigenerational test.

11 As designed, the P0 or F0 generation is solely

12 utilized for production of F1 animals putative maternal transfer

13 of test agent. While chemical exposure in the P0 or F0 may

14 likely have impact F1 embryos by maternal transfer modulation of

15 maternal factors, information regarding activation and some

16 organizational impacts of EDCs may be lost, such as effects on

17 mating behavior in the F2 or F1. This is particularly true for

18 inductive responses observed in F0 adults, such PTP expression.

19 However, comparison of data across MMT trials

20 suggest that the analysis of biomarker and histopathology data

21 for the F2 generation may be redundant, as no significant

22 difference in these endpoints is observed in between F1 and F2

23 generations. 

24 This hypothesis is illustrated in Tables 5.7

25 and 5.8 in the summary review that demonstrate concordance

26 between the F1 and F2 LOEC values or fecundity and biomarker
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1 data, respectively.

2 The authors provide a strong case for removing

3 the F2 reproduction portion of MRT to create the MMT to create

4 the MRT. That's assuming that EDSTAC doesn't really require a

5 multigeneration test.

6 So the rationale for these modifications are

7 to increase statistical power, reduce animal number, reduce

8 person hours to perform the assay, reduce overall assay time and

9 reduce possible variability of data, due to increased risk of

10 experimental incidence resulting in test failure. 

11 Overall, the generational rationale suggests

12 that additional generational data would not increase the quality

13 of risk assessment. Assessments of the F1 generations were

14 likely to formative for both activational organizational

15 responses and assessments of F1 reproductive capacity, will

16 likely reflect both, thus, satisfying these associations to EDC

17 toxicity in a single generation.

18 Exclusion of F2 generational effects thus

19 makes this test the single generation, or at most, one and a

20 half generation assay. One possible modification that could be

21 made is to allow F2 animals to reach sexual maturity about nine

22 weeks post fertilization so that additional data could be

23 derived regarding both phenotypic and genetic gender

24 development.

25 As currently described, neither the MMT assay

26 nor the MRT assay exploit the ability to establish genotypic or
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1 phenotypic gender ratios. Establishing this analysis with data

2 already derived from existing assay structure will further

3 substantiate modifications in phenotypic genders or genetic

4 state will change the exposure.

5 Given the current dataset determination of the

6 Medaka assay, FF2 hatch, appears warranted without significant

7 loss of sensitivity for endocrine points. Animal number and

8 labor costs will also be minimized; however, it is recommended

9 that not all F0 endpoints should be discarded. In particular,

10 endpoints that evaluate organ histopathology or overt toxicity

11 should not be removed due to potential confounding of

12 non-endocrine maternal effects.

13 It should be noted that the dataset that this

14 decision has been made, has been based upon as limited to fewer

15 than 10 compounds with only comparison of MMT with MRT using

16 octylphenol. While the eight compounds represent a range of

17 endocrine responses, there were still significant uncertainty

18 with AR antagonism and steroidogenesis AOPs. 

19 It is unclear, based upon this current data

20 whether more potent air antagonist aromatase inhibitor might

21 affect F2 in the chronology. With the continuous exposure

22 design in the MRT assay, it is expected that results will be

23 similar in F1 and F2, but we also suggest the addition of tissue

24 chemical analysis to aide in determining if bioaccumulation of

25 chemical in tissues is occurring. Although it's realized that

26 this would lead to an increase in the overall number of animals
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1 for the study.

2 DR. MARTHA SANDY: Thank you. And now turning

3 to the associates. Dr. Blazer.

4 DR. VICKI BLAZER: I have nothing additional

5 to add.

6 DR. MARTHA SANDY: Dr. Freeman.

7 DR. JENNIFER FREEMAN: All my comments have

8 already been addressed.

9 DR. MARTHA SANDY: Dr. Kullman.

10 DR. SETH KULLMAN: I'll just state one thing,

11 essentially that I talked about initially when we had the first

12 round of discussions is that sort of the loss of the F2 and the

13 new assays within the design of the endpoints in the F1 really

14 limits the ability to make comparisons between gonadal

15 histology, vitellogenin and reproduction.

16 I would argue for inclusion of histopath,

17 vitellogenin and reproductive measures at nine weeks and

18 termination of the F1.

19 DR. MARTHA SANDY: Dr. Patino.

20 DR. REYNALDO PATINO: Nothing further to add.

21 DR. MARTHA SANDY: Dr. Schlenk.

22 DR. DANIEL SCHLENK: Nothing to add.

23 DR. MARTHA SANDY: And Dr. Dallas Johnson. 

24 DR. DALLAS JOHNSON: I have nothing, but Dr.

25 Portier indicated that the answer to this question depends on a

26 clear statement of the key endpoints for each EDC study and
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1 this, on the hypothesized AOP. 

2 If the endpoint is bodyweight and the issue is

3 increasing bodyweight in subsequent generations, the EDC link to

4 obesity issue, it would not be beneficial to not take the F2

5 generation to adulthood to allow adult weights to be collected. 

6 I realize that the focus of this test is

7 reproduction and bodyweight other than as a measure of vigor and

8 readiness to reproduce is typically not considered a

9 reproductive endpoint of concern. I am not certain that there

10 are no other reproductive endpoints that may require assessment

11 through the F2 adult stages.

12 DR. MARTHA SANDY: I'll open it up to the

13 entire Panel for any additional thoughts.

14 (No response.)

15 Seeing none, I'll turn to Dr. Rodney Johnson

16 with the EPA.

17 DR. RODNEY JOHNSON: I once again have no

18 clarifying questions. I appreciate all the comments.

19 DR. MARTHA SANDY: Thank you. Shall we move

20 on to Question 9?

21 DR. LESLIE TOUART: This is Charge Question 9

22 for the Medaka. It is the Agency's opinion that the outcomes of

23 the various MMT trials had provided enough information to

24 recommend a Medaka Reproduction Test for use as a fish test in

25 Tier 2 of the EDSP. 

26 Two major changes from the MMT are proposed,
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1 an increase in the number of replicates per treatment for

2 evaluating effects on reproduction, and terminating the test

3 after the embryos hatch in F2. Other proposed changes include

4 minimizing the collection of endpoint data from the F0 and

5 evaluating pathology in only the F1 adults sampled after the

6 assessment of reproduction.

7 Overall, the authors conclude that both the

8 MMT and MRT are transferable methods and are capable of

9 adequately characterizing potential disruption of the endocrine

10 system by putative endocrine disrupting chemicals; however, the

11 MRT is recommended as a preferred EDSP Tier 2 test method for

12 fish because is it less resource intensive with improved

13 statistical power, appears to be as sensitive, and is better

14 able to ensure consistent findings when performed routinely by

15 testing laboratories. 

16 Please comment on the scientific rationale of

17 the Agency's proposed Tier 2 fish test, the MRT, with respect to

18 statistical power, sensitivity, and consistency in performance

19 across laboratories. 

20 In addition, please comment on the adequacy of

21 the MRT to characterize potential endocrine disruption

22 requirement for the Tier 2 of the EDSP.

23 DR. MARTHA SANDY: Dr. Blazer is the lead

24 discussant on this.

25 DR. VICKI BLAZER: So the majority of the ISR

26 focuses on the MMT with Section 5 providing a rationale for the
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1 MRT. As stated, the MRT differs from the MMT with two

2 fundamental modifications; 1) increased replication of breeding

3 pairs for evaluation of reproduction in the F1 generation, and

4 2) the termination of the test after hatching of F2 embryos with

5 no assessment of the F2 individuals. 

6 So based on statistical considerations,

7 evaluations of reproductive parameters will be much improved

8 with the increase of biological replication and are likely to

9 enhance the accuracy of acquiring precise LOEC data from

10 reproductive endpoints in the F1 population.

11 Detailed modeling of reproductive replicates

12 strongly supported the modification and trial experiments with

13 only one compound, being octylphenol, demonstrated increased

14 sensitivity, enabling detection of a lower reproductive LOEC,

15 which was 23 micrograms per liter compared to the MMT, which was

16 100, depending on the lab, but approximately 100 micrograms per

17 liter.

18 However, if you look at the MMT, the LOEC is

19 based on biomarker assays, many of which will not be completed

20 for more than one of the lifestages or generations in the MRT or

21 lower. So again, it gets back to what was discussed in Question

22 Number 8, as far as sensitivity of other endpoints.

23 The MRT assay has many less endpoints than the

24 MMT, and hence, there was some concern about the impact of these

25 modifications to assess multigenerational effects, as well as to

26 assess non-reproductive parameters. 
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1 In the MRT, no assessments are made on the F0

2 animals. Vitellogenin is only conducted in the subadults at

3 nine weeks and histopathology only in the F1 adults at week 15

4 after spawning. While the rationale for these modifications is

5 given, we do feel like exclusion of some of these endpoints may

6 reduce the overall assay performance. 

7 So for instance, pathology should be included

8 at nine weeks, as there is significant variability in growth and

9 development in Medaka at this stage. Given that vitellogenin

10 will be assessed at this stage only, some support of assessment

11 of gonad development and maturation should also be determined. 

12 A similar argument can be made for

13 determination of vitellogenin in the adult F1 generation, as

14 these animals are expected to have fully matured gonads. And

15 it's currently unknown if correlations can be demonstrated

16 against the vitellogenin MRNA levels, gonad morphology, degree

17 of intersex or other gonadal abnormalities and fecundity.

18 Hence, important data may be lost by only

19 having one of these endpoints in a particular generation. So

20 some effort should be made to coordinate the endpoints. 

21 Additionally, it was felt that the F0 animals

22 should be evaluated for histopathology to determine whether

23 non-endocrine effects or overt toxicity, particularly at the

24 maternal stage, are not confounding the assay and perhaps,

25 indicating endocrine AOPs in offspring when it may not be an

26 endocrine effect.
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1 So for instance, overt toxicity may diminish

2 blood flow to ovaries or cause liver damage which may lead to

3 responses in reproduction that are not mediated through the EAT

4 AOPs. Along these lines, as has already been stated, I think a

5 couple of times non-endocrine toxicants that may indirectly

6 effect reproduction should be evaluated during validation of the

7 MRT assay.

8 So while there have certainly been a

9 significant amount of work evaluating the MMT, additional

10 confirmatory studies, using the specific and more detailed

11 guidelines are necessary before the MRT can be adopted as a Tier

12 2 assay. Overall, it appears that global validation of both the

13 MMT and MRT is rather subjective.

14 So on a global scale, there is little

15 statistical indication of performance parameters needed to

16 establish assay reproducibility and reliability. Additional

17 validation tests would address this issue, and additional

18 interlaboratory studies are needed to demonstrate

19 transferability, reliability and reproducibility.

20 So in conclusion, it was felt that the MRT

21 clearly showed promise with regard to statistical power,

22 sensitivity, and consistency, and this was shown in the

23 performance of the octylphenol test. So the rationale for

24 modification is adequate; however, given the uncertainties

25 inherited in fish husbandry and endocrine variability with this

26 species, additional confirmation with other chemicals is
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1 necessary for validation.

2 It was observed that initial EDSTAC panels

3 recommended 50 to 100 compounds go through Tier 1, but it's

4 unclear how many should go through Tier 2. So we had discussion

5 about recommending what might be needed both for adoption of

6 this, as far as performance, but also ring testing. And we

7 recommend that representative compounds, both low and high

8 potency of at least seven endocrine AOPs. So in other words,

9 the estrogen, androgen, thyroid, both agonists, and antagonists,

10 and steroidogenesis inhibitors and a non-endocrine AOP are

11 evaluated for MRT assay performance prior to adoption of as a

12 Tier 2 assay. And then ring testing with, perhaps, one AOP. 

13 Using the MRT assays is also recommended. And that's all I

14 have.

15 DR. MARTHA SANDY: And now we turn to the

16 discussants. Dr. Schlenk.

17 DR. DANIEL SCHLENK: Nothing to add.

18 DR. MARTHA SANDY: Dr. Dallas Johnson.

19 DR. DALLAS JOHNSON: I have nothing to add and

20 I think Dr. Portier's comments have been covered.

21 DR. MARTHA SANDY: Dr. Freeman.

22 DR. JENNIFER FREEMAN: All of my comments have

23 been addressed.

24 DR. MARTHA SANDY: Dr. Kullman.

25 DR. SETH KULLMAN: Nothing to add.

26 DR. MARTHA SANDY: Dr. Patino.
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1 DR. REYNALDO PATINO: Nothing to add. Thank

2 you.

3 DR. MARTHA SANDY: And Dr. Perkins.

4 DR. EDWARD PERKINS: I have nothing to add.

5 DR. MARTHA SANDY: Do other members of the

6 Panel have any comments in response to this charge question?

7 (No response.)

8 Seeing none, I'll turn to the EPA staff. Ms.

9 Mary Manibusan.

10 MS. MARY MANIBUSAN: Just for clarification

11 from Dr. Blazer on your comment, it was indeed the 1999 SAB SAP

12 joint panel that made the recommendation to the Agency with

13 regard to the Tier 1 data, that after validation and recognition

14 that the validation process would only be limited to a few

15 laboratories for interlab testing that we gain additional

16 experience after having issued test orders for 50 to 100

17 chemicals to bring those back to give the Agency a reflection of

18 what the real world laboratory performance would be for the Tier

19 1 assays for review, and that review was had just last month.

20 Is that your recommendation for Tier 2 with

21 respect to the test methodologies that once validated to go out

22 and wait for 50 to 100 chemicals to come back to the Agency for

23 review? 

24 So just clarification on what the intention of

25 that comment was.

26 DR. VICKI BLAZER: No. It was just
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1 recognizing that 50 to 100 was what was recommended in the Tier

2 1 and that, as far as we know, there is no recommendation for

3 how many should be looked at in Tier 2. So then we came up with

4 that number, which would be more like 14 if you did a low and a

5 high potency of each of those AOPs. Is that what you're asking

6 or did I misunderstand?

7 MS. MARY MANIBUSAN: So the clarification

8 was that for the 50 to 100 Tier 1, that was post-validation. So

9 the clarification, I'm asking from you, again, just for

10 understanding, is that post-validation that you're asking for

11 the Agency to collect that experience to bring back to a panel

12 for review?

13 DR. VICKI BLAZER: I think what we were

14 suggesting is that those 14 should be used for validation.

15 MS. MARY MANIBUSAN: Yes. Okay. And then

16 following up to a previous comment made to the overall panel, as

17 we move forward on the Tier 2 validation effort, discussion of

18 what -- how many test chemicals would increase X number of

19 certainty or confidence as we move forward. So is it 14? Is it

20 15? What's the difference between 16? That clarification would

21 be very helpful to the Agency.

22 DR. MARTHA SANDY: Okay. Dr. Schlenk.

23 DR. DANIEL SCHLENK: Mary, I think we outlined

24 which particular AOP and how many chemicals we would recommend

25 for each particular AOP. So you should have, I think it's 14 or

26 something, is what we came up with. So justification for those
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1 high potency, low potency at each of those different AOPs is the

2 justification for that.

3 DR. MARTHA SANDY: Are there any other

4 comments from the Panel in response to that? Any questions of

5 clarification from Dr. Rodney Johnson?

6 DR. RODNEY JOHNSON: My clarification question

7 regards the issue of validating what would be the AOP pathways

8 as a discreet part of validation compared to what would be

9 reproduction. A few of the identified apical endpoints and

10 would like some sort of clarification of whether or not you

11 believe that the apical endpoints, perhaps, are validated. 

12 So the apical endpoints in this case being

13 reproduction, growth, and gonadal sex phenotype as it relates to

14 intersex conditions and sex reversal. I mean, those are

15 identified in the ISR by us as being the apical endpoints that

16 are part of either the MMT or the MRT and then we can discuss

17 the AOP or biomarker endpoints as indicative of, in fact, the

18 AOP. 

19 But I think of them as distinct as compared to

20 the apical endpoints for risk assessment, which I think if it is

21 being primarily reproduction growth and the sex reversal, sex

22 phenotype outcome. So sex reversal on intersex as distinct from

23 let's say, secondary sex of vitellogenin.

24 So all I'm getting at is clarification on that

25 would be helpful in the Panel's review.

26 DR. DANIEL SCHLENK: I, for one, think the
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1 apical endpoints are definitely justified for risk assessment. 

2 I would totally agree with that. I don't know what the rest of

3 the panel thinks, but I would agree those are definitely

4 justified in terms of endpoints you'd want to use in a risk

5 assessment paradigm, those apical endpoints.

6 DR. MARTHA SANDY: That was Dr. Schlenk. Dr.

7 Patino.

8 DR. REYNALDO PATINO: I probably repeat a

9 little bit here, but just to make sure it's clear, I agree with

10 that. I think the apical endpoints are okay. My take on why we

11 made those recommendations on the 14 chemicals is to demonstrate

12 that if you tweak those AOPs that you've identified as leading

13 to the apical endpoints, then you will actually get a change in

14 the -- it will validate the assay in the context of the AOPs. 

15 That's why I think we recommended that.

16 DR. MARTHA SANDY: Dr. Kullman.

17 DR. SETH KULLMAN: So, Rodney, one thing that

18 comes to mind about this is, you know, whether we're validating

19 the individual endpoints of the assay or we're validating the

20 assay as a whole. So certainly the fecundity growth survival,

21 those types of things can be validated as individual endpoints,

22 but as a whole, I believe the assay is supposed to be indicative

23 of an AOP. 

24 So I think in total, AOP combined with all the

25 components that are measured needs to be validated. The ring

26 test essentially says no impact on reproduction. I don't think
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1 that's a reasonable measure of whether reproduction is

2 functioning. I think the control values are a good measure of

3 that. I think some of the trials with octylphenol are a good

4 measure of that, but overall validation of the assay as is,

5 probably is not -- from a ring test perspective, has not been

6 substantiated.

7 So let's make sure that we talk about the

8 validation of the assay, versus the validation of the individual

9 components of the assay.

10 DR. RODNEY JOHNSON: Thank you.

11 DR. MARTHA SANDY: Dr. Bradbury.

12 DR. STEVEN BRADBURY: As you can tell, we're

13 asking for additional clarification on this and part of it is

14 that some of the endpoints like vitellogenin, measuring

15 different species and works through the Tier 1 and some of the

16 secondary sex characteristics in a different species through

17 Tier 1. 

18 Having said that, the Medaka is used in Japan

19 for a similar kind of program. So there is some historical

20 information. 

21 So I'm just kind of doing the math and 14

22 chemicals, maybe a half a year to do the assay, probably several

23 months to analyze X number of labs. We could be talking four

24 years, five years from now before you go through this, which

25 could be a path forward. But just sort of curious if the Panel

26 has any suggestions on how existing information or information
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1 that could be generated in different ways to help get at the

2 attributes of the different endpoints. 

3 I think I'm hearing from the Panel that the

4 apical endpoints, just thinking about the hundreds of

5 reproductive studies that go to Europe and Japan and the United

6 States and Canada to, I guess, your pesticides, maybe we know

7 more about those than we do than maybe some of the genome typing

8 of the fish going in. 

9 So if there is just some suggestions on how to

10 tackle the issue in as an efficient way as possible. Any

11 suggestions you have to balance time with the ability to start

12 answering questions to evaluate the chemicals properly.

13 DR. MARTHA SANDY: Okay. Any comments?

14 (No response.)

15 No? Okay. I believe we have gone through all

16 the charge questions in the Medaka test. Should we take a break

17 for lunch now and then come back and start the amphibian

18 questions?

19 I'd like us to come back from lunch at a

20 quarter to 1:00. We'll give ourselves an hour. Thank you.

21 (Whereupon, a lunch break was taken)

22 DR. SHARLENE MATTEN: Good afternoon. We're

23 back in action. It's about 10 minutes to 1:00. We're going to

24 start with the questions on the Larval Amphibian Growth and

25 Development Assay (LAGDA). 

26 So with that, I will turn the microphone back
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1 over to Dr. Martha Sandy.

2 DR. MARTHA SANDY: Thank you, Dr. Matten. So

3 I welcome everyone back to the meeting. If EPA, is it Dr.

4 Touart again, will be Charge Question 1 for the LAGDA assay.

5 DR. LESLIE TOUART: Yes. I need to get the

6 slide projected and it will be the first question for the LAGDA.

7 This is Charge Question Number 1 for the LAGDA. A rationale

8 for the test methods should be available, including a clear

9 statement of scientific basis and the regulatory purpose and

10 need for the test method.

11 The EDSTAC introduced the Tier 2 test as

12 having the purpose to characterize the nature, likelihood and

13 dose-response relationship of endocrine disruption of estrogen,

14 androgen, and thyroid in humans and wildlife.

15 Tier 2 tests were designed to be definitive

16 tests which generate sufficient data to characterize the

17 specific hazard of the substance and provide sufficient

18 information and dose response and adverse effects to permit risk

19 decisions.

20 Please comment on the rationale and purpose of

21 the assay as part of the Tier 2 testing in the EDSP as described

22 in Section 3 of the LAGDA ISR.

23 DR. MARTHA SANDY: Dr. Propper is the lead

24 discussant on this.

25 DR. CATHERINE PROPPER: So our group got

26 together and we've all formulated a fairly common opinion, but
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1 I'll encourage everybody if I miss something to please step in.

2 Amphibian development can be used as assays,

3 both for gonadal development-related issues associated with

4 androgen and estrogen disruption and for thyroid and hormone

5 disruption. The development of validated consistent techniques

6 is critical to being able to interpret individual laboratory

7 findings and understand where sources of variation may occur.

8 The rationale behind the testing was well

9 developed and built upon earlier EDSP protocol development. It

10 includes the ease of monitoring morphological and growth

11 endpoints in amphibians, the ease of exposure -- relative ease

12 of exposure -- and the background knowledge and availability of

13 sensitive endpoints for both the gonadal and thyroid hormone

14 with the gonadal endpoints being things like testicular oocytes

15 or overt pathologies. 

16 Gonadal duct, as well, seems to become a

17 fairly sensitive and probably one of the more consistent

18 endpoints, and then time to development is potentially

19 significant. Thyroid endpoint, especially when it's coupled

20 with the histopathology; however, we'll address some issues

21 between trying to identify direct endocrine disruption versus

22 overt toxicity as we move through our discussion.

23 Vitellogenin levels also, and possibly T4

24 levels, and we'll talk about that more. Furthermore, the

25 ability to genetically and phenotypically sex the animals does

26 make this a stronger model, as was seen with the Medaka assay. 
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1 So having the capacity to know whether or not

2 you have sex reversed your individuals by the treatments with

3 estrogenic or anti-estrogenic compounds is very valuable. So

4 these are important strengths of the assay. All of these

5 endpoints, although subject to overt toxicity, may be valuable

6 as endpoint measures that can address estrogen, androgen or

7 thyroid hormone disruption. And again, we'll discuss that a

8 little more.

9 The criteria used to develop the validation

10 methodology we found to be sound and it was based on ICCVAM and

11 OECD published criteria, so it wasn't starting from scratch. 

12 Amphibians were recognized by the EPA to have limited utility

13 for long term, multigenerational studies and this is because

14 they take so long to get to that developmental stage that the

15 cost of growing them out to do those studies becomes really

16 pretty prohibitive in time to an answer with the data under

17 those circumstances also becomes very long. So we appreciated

18 that and that was explained very clearly in the integrated

19 summary report. 

20 They also adequately addressed the issues of

21 the differences between the commonly two used amphibian species

22 for these kinds of ecotoxicity tests, Xenopus laevis and Xenopus

23 tropicalis, and we believed addressed why they went with laevis

24 rather than tropicalis.

25 We still have some concerns with understanding

26 negative results because of the risk of false negatives, just
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1 utilizing this assay alone because you don't have those really

2 strong apical endpoint outcomes.

3 As was shown on page 24, in Table 3-4 in the

4 ISR for the Medaka assay, there was a very nice contingency

5 table of expected outcomes for the different modes of action of

6 each of the compounds that they chose to use for the validation.

7 We really like that table and wish we had had one similar to

8 that in the amphibian ISR. 

9 But the functions of the assays to determine

10 -- I hit that point before so I'm going to move on. We do

11 overall feel that as an assay, whether or not the mode of action

12 is overt toxicity or endocrine disruption, the assay itself has

13 potential to evaluate at least surrogates for adverse outcomes,

14 if not real adverse outcomes. And that's going to depend on the

15 endpoints. 

16 DR. MARTHA SANDY: Discussants. We have Dr.

17 Denver.

18 DR. ROBERT DENVER: I have nothing to add. I

19 concur.

20 DR. MARTHA SANDY: Dr. Patino.

21 DR. REYNALDO PATINO: Nothing new to add.

22 DR. MARTHA SANDY: Dr. Blazer.

23 DR. VICKI BLAZER: Nothing more to add.

24 DR. MARTHA SANDY: Dr. Christman.

25 DR. MARY CHRISTMAN: I have nothing to add,

26 but Dr. Portier does. So Dr. Portier says that he felt that the
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1 rationale for the use of Xenopus laevis was a bit weak, since

2 most of the discussion was on why they wouldn't use tropicalis. 

3 So he thought there must be other things they could bring out to

4 explain why Xenopus was a better choice. I mean laevis was a

5 better choice. I apologize.

6 The other thing he wanted to comment on was in

7 a previous SAP, concern was expressed the Xenopus laevis might

8 be too robust. That is, might not be very sensitive to EDCs. 

9 Plus, laevis is not native to the U.S. (I may be wrong here). 

10 And it was thought that a native U.S. species such as Rana

11 should be recommended. What happened to this recommendation?

12 That was his comment.

13 DR. MARTHA SANDY: Does that prompt anyone

14 else to have any other comments? 

15 Yes, Dr. Propper.

16 DR. CATHERINE PROPPER: We'll be addressing

17 that a bit more later.

18 DR. MARTHA SANDY: So I'll ask other members

19 of the Panel. Anyone have anything to add?

20 (No response.)

21 No. Then we're turn to EPA staff, Dr. Degitz.

22 DR. SIGMUND DEGITZ: I have no clarifying

23 questions and appreciate the comments.

24 DR. MARTHA SANDY: All right. Thank you. 

25 Shall we move on to Charge 2?

26 DR. LESLIE TOUART: Les Touart reading Charge
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1 Question 2 of the LAGDA. Test methods and their associated

2 endpoints should be scientifically relevant to the biological

3 processes of interest and should be demonstrated to be

4 responsive to the specific type of effect toxicity of interest. 

5

6 Each species presents unique characteristics

7 from a biological perspective and allows for specialized

8 endpoints to address a specific toxicological mode of action. 

9 Please comment on the biological and

10 toxicological relevance of the assay in regards to the stated

11 purpose of characterizing endocrine disrupters as described in

12 Sections 3 and 4 of the LAGDA ISR.

13 DR. MARTHA SANDY: Dr. Denver is the lead

14 discussant.

15 DR. ROBERT DENVER: Good afternoon. The

16 purpose of the assay is to detect effects of chronic exposure to

17 potential endocrine disrupting chemicals identified in the Tier

18 1 assays on some aspects of the amphibian development. And

19 exposure begins during embryogenesis and continues throughout

20 metamorphosis, a period of postembryonic development, and then

21 into the juvenile stage. 

22 Now, if it can be executed in a reproducible

23 way the assay can have a high degree of biological and

24 toxicological relevance for understanding chemical impacts on

25 amphibian development that could also have relevance to other

26 animal taxa. 
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1 It may also suggest endocrine modes of action,

2 but it is uncertain whether the LADGA will be able to clearly

3 distinguish endocrine from non-endocrine modes of action, or

4 possibly toxicity as was recognized in the ISR. 

5 Whole organism developmental progression

6 expressed as the time to reach metamorphic climax is measured in

7 the LAGDA as a proxy for thyroid hormone production and/or

8 action. 

9 Concordance among measures of developmental

10 timing, thyroid histopathology and possibly plasma T4

11 concentration could be a powerful means to support that a

12 chemical has thyroid active properties; however, results of the

13 validation tests with Prochloraz suggests that an effect on

14 thyroid histopathology that was seen in three laboratories,

15 although the LOEC ranged from 6.7 micrograms per liter to 180

16 micrograms per liter, and in one lab was correlated with an

17 increase in T4 was not expressed as a change in development

18 rate. 

19 So while the potential exists for the LAGDA to

20 be a powerful diagnostic for thyroid disruption that can impact

21 animal development. No data from the interlaboratory analysis

22 support that it has been validated for this purpose. 

23 One might expect that compounds that are

24 identified as thyroid disrupters in the amphibian metamorphosis

25 Tier 1 assay will also be identified as such in the LAGDA. 

26 However, because chemical exposure begins during embryogenesis
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1 in the LAGDA, the effects on development could be manifest as a

2 failure of organogenesis, independent of thyroid hormone, and

3 any thyroid effects that are seen may be secondary to the

4 teratogenic effects. 

5 So distinguishing between an endocrine and a

6 non-endocrine mode of action, even if the chemical results in

7 altered thyroid gland development and function may actually be

8 quite difficult. This is also true for the measures of

9 reproductive development.

10 The LAGDA also monitors gonadal and urogenital

11 ductal morphology and plasma vitellogenin as evidence for

12 disruption of estrogen or androgen production and/or action. 

13 Altered reproductive system development is intended to be a

14 proxy for impacts on fertility and fecundity, which are not

15 directly measured in the LAGDA. 

16 The oviductal morphology is likely to be the

17 best diagnostic endpoint for predicting effects on reproduction 

18 if there is no oviduct the female cannot oviposit her eggs. It

19 will not be possible to definitively link alterations in

20 testicular morphology or elevated plasma vitellogenin with

21 impacts on fertility or fecundity. Vitellogenin is low or

22 nondetectable in 10-week old female frogs, and so while an

23 elevation in vitellogenin may be diagnostic of an estrogenic

24 action, this endpoint cannot detect an anti-estrogenic effect. 

25 However, anti-estrogenic effects may be

26 detected by comparing genetic and phenotypic sex. Frogs are a
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1 good model for this type of outcome since estradiol is necessary

2 for ovarian organogenesis. Male-biased sex ratios can support

3 an anti-estrogenic mode of action. 

4 Effects on growth may be due to endocrine or

5 non-endocrine mechanisms; that is, such effects should not be

6 dismissed as simply representing toxicity, unless they are seen

7 only at the highest concentrations and exhibit clear liver

8 histopathology. They could represent endocrine or non-endocrine

9 modes of action that may or may not have been represented in the

10 Tier 1 assay. 

11 While it may be difficult to distinguish overt

12 toxicity from endocrine disruption, it does not compromise the

13 ability to identify adverse outcomes at apical endpoints. It

14 does, however, compromise the ability to predict mode of action

15 with certainty. 

16 Overall, the biological and toxicological

17 relevance of the LAGDA for detecting developmental abnormalities

18 caused by chemical exposure is strong. Some endpoints may be

19 good measures of endocrine disruption; for example, thyroid

20 hypertrophy can support a goitrogenic action; elevated plasma

21 vitellogenin can signal an estrogenic action; failure in oviduct

22 formation can signal an androgenic action and sex reversal

23 compared to the control may indicate either steroidal or

24 anti-steroid action. But many effects may not be directly

25 related to endocrine disruption, per se, but instead due to a

26 failure in organogenesis. 
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1 So distinguishing a non-endocrine from an

2 endocrine mode of action will be difficult and assessing kidney

3 and liver histopathology will be important to help support an

4 endocrine mode of action versus toxicity.

5 DR. MARTHA SANDY: I'll turn to the associate

6 discussants. Dr. Patino.

7 DR. REYNALDO PATINO: Nothing need to add.

8 DR. MARTHA SANDY: Dr. Propper.

9 DR. CATHERINE PROPPER: Only one more thing to

10 add and that is just a reminder than nonmontonic dose responses

11 may be important and can't be completely ignored here as just

12 statistical anomalies because of the nature of the feedback

13 loops.

14 DR. MARTHA SANDY: Dr. Blazer.

15 DR. VICKI BLAZER: The only thing I'd like to

16 add is just that for the liver and the kidney histopathology, I

17 wonder if some of those can't also be induced by husbandry or

18 water quality and that might have to be differentiated between

19 toxic.

20 DR. MARTHA SANDY: And Dr. Christman.

21 DR. MARY CHRISTMAN: Dr. Christman has no

22 comments, but Dr. Portier does. Point of order, can I

23 paraphrase his statement or do you want me to read it,

24 specifically?

25 I can paraphrase it? Good. Because

26 otherwise... Essentially, his concern is that there are some
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1 pieces of information missing in the description for biological

2 and toxicological relevance because they are a little too, my

3 word: "squishy." 

4 For example, what is meant by adverse

5 consequences or interferers with the normal development. So he

6 feels that it is hard to justify relevance without having more

7 quantitative measures and basically he goes on to say he cannot

8 really interpret the tables that show some of the outcomes

9 because there was no power analysis to determine whether they

10 were statistically relevant or measurable, given the sample

11 sizes. 

12 So for example, he says is a 15 percent

13 difference in body weight under Prochloraz actually biologically

14 important, relevant measure of an effect. So he goes on to talk

15 about how he feels that from a statistical point of view, the

16 protocol is incomplete until we see a power analysis that would

17 help us identify biologically important differences for

18 endpoints. 

19 DR. MARTHA SANDY: Thank you. Any other

20 comments from Panel members?

21 (No response.)

22 Seeing none, I'll turn to EPA for questions of

23 clarification.

24 DR. SIGMUND DEGITZ: I have no clarifying

25 questions, and I appreciate the comments.

26 DR. MARTHA SANDY: Thank you. Dr. Touart,
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1 would you read Question 3, please.

2 DR. LESLIE TOUART: Les Touart on Charge

3 Question 3, and this is the first part, Question 3.1. The test

4 protocol should be sufficiently detailed and should include a

5 description of what is measured and how it is measured. 

6 The selection of endpoints within the assay

7 should be reflective of the biological processes of interest and

8 the endpoints should be intrinsically relevant and have

9 established sensitivity. The test protocol should demonstrate

10 the ability to measure the endpoints and provide adequate

11 performance criteria for evaluation.

12 Please comment on the selection, optimization

13 and demonstration of the assay endpoints, as outlined in

14 Sections 3 and 4 of the LAGDA ISR.

15 DR. MARTHA SANDY: Dr. Denver.

16 DR. ROBERT DENVER: I'm going to address each

17 of the point selection, optimization and demonstration

18 individually. So for selection, the assay endpoints are

19 appropriate for evaluating chemical effects on growth and

20 development of Xenopus larvae, and on gonad and thyroid gland,

21 and urogenital tract organogenesis that may reflect endocrine

22 disruption.

23 Alterations in growth of larvae combined with

24 clear liver and possibly also kidney histopathology may be

25 scored as toxicity. Effects on growth, particularly at lower

26 concentrations of test chemicals, and possibly without clear
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1 liver or kidney histopathology could indicate disruption of

2 specific cellular signaling pathways that could be either

3 endocrine or non-endocrine. 

4 A change in the timing of metamorphosis

5 combined with thyroid histopathology and possibly also changed

6 plasma T4 can signal chemicals that alter thyroid activity. 

7 Elevations in plasma vitellogenin in males or females, since

8 females are immature and have low or nondetectable circulating

9 vitellogenin can signal estrogenic activity as can the presence

10 of testicular oocytes. 

11 Abnormal oviduct development can indicate

12 androgenic activity. The oviduct morphology may be, as I said

13 earlier, the best diagnostic endpoint for potential effects on

14 reproduction. Comparison of phenotypic sex ratios with the

15 genotypic sex ratios is potentially a very powerful endpoint for

16 assessing effects compounds that may alter sex steroid

17 signaling. 

18 The assay endpoints can support hypotheses for

19 modes of action that involve disruption of the endocrine system,

20 but may not be able to distinguish endocrine from non-endocrine

21 modes of action, or disruption of specific cellular signaling

22 pathways from overt toxicity. 

23 Optimization: the variability in results among

24 laboratories that participated in the interlaboratory study

25 suggests that some or all of the methods are not optimized,

26 and/or that the guidelines for their execution can be improved.
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1 Assay endpoints appear to be very sensitive to

2 animal husbandry and rearing parameters. The husbandry

3 guidelines for these assays must be established by controlled

4 studies and clearly defined in the protocols. Deviations from

5 the husbandry protocols may disqualify the results, as we

6 discussed yesterday. 

7 A source of variation that can impact

8 optimization of the protocol is variation in the rearing water;

9 using local water sources without standardizing for hardness, or

10 iodine content, and there was wide range that was considered

11 allowable under the guidelines of iodine, may lead to variation

12 in the response to thyroid hormone disruption and growth

13 parameters. 

14 Furthermore, potential endocrine disruptors in

15 source water, if not removed by charcoal filtration, could lead

16 to variation across testing regimes even if other issues like

17 the feeding regime are standardized. Some of the

18 interlaboratory variation in the LOECs may be related to water

19 quality; for example, mineralization in kidney tubules seen

20 occasionally may be related to water quality. 

21 There was a range of animal densities used in

22 the different laboratories. This may not be an important issue,

23 since as the EPA indicated, the flow rate is a more important

24 variable than stocking density, but it would be good if the EPA

25 could provide data to support this conclusion. 

26 Impaired growth, combined with liver and
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1 kidney histopathology may be robust endpoints for detecting

2 toxicity; however, there are issues related to the

3 histopathology that were identified in the ISR. 

4 Gonadal histopathology, particularly testes

5 appears particularly robust. Ductal histopathology is a

6 particularly important endpoint for supporting that a chemical

7 can impact fertility and/or fecundity, since the potential for

8 population level effects will not be directly addressed in the

9 LAGDA. There are significant issues with the plasma

10 vitellogenin and T4 assays that are addressed in subparts 2 and

11 3 of this charge question. This is Part 3.1. 

12 Similar for the quail and Medaka assays, we

13 considered that the lack of positive controls is a significant

14 limitation to the LAGDA. Positive controls are important for

15 evaluating whether the assay is behaving the way that it should

16 be in the contract labs. However, there are many potential

17 modes of action represented in the Tier 1 assays, and therefore,

18 selecting an appropriate positive control may be challenging in

19 many cases. 

20 In some cases it may be straightforward; for

21 example, a compound that appears as a goitrogen in tier 1 can be

22 compared to a well-known goitrogen such as methimazole or

23 thiourea in the LAGDA. An apparent estrogenic compound can be

24 compared to estradiol. 

25 The positive control chosen will help

26 demonstrate that the assay is working for the predicted mode of
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1 action, but may not account for modes of action that are not

2 predicted in the Tier 1 assays. So the positive control may not

3 always be useful in the analysis.

4 Demonstration: there were four chemicals

5 analyzed in the interlaboratory validation studies. For some of

6 the endpoints there was consistency across labs, at least for

7 the presence of an effect, but the LOECs typically showed great

8 variation among labs. For each of the four chemicals analyzed

9 there were significant issues related to reliability and

10 reproducibility of assay endpoints. 

11 Prochloraz is predicted to inhibit aromatase

12 and act as an androgen receptor antagonist. There are recently

13 published data that support that it can impact thyroid function

14 in anuran amphibians. There was large variability among labs in

15 the LOEC for thyroid histopathology. With Prochloraz, the LOECs

16 ranged from 6.7 to 180 micrograms per liter. 

17 The effects on gonad and ductal morphology

18 were more consistent among the labs, at least for the males,

19 less so for the females. Plasma vitellogenin could not be used

20 since it was below the limits of detection of the assay. 

21 4-tert-octylphenol is predicted to be a weak

22 estrogen receptor agonist. Effects on thyroid endpoints were

23 seen in only one lab, Lab A. Lab C reported an effect opposite

24 to Lab A on time to metamorphic climax, possibly due to problems

25 with animal husbandry. 

26 Most labs found effects on measures of
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1 reproductive development and function, but these were variable

2 among labs; some found effects, some not, and the LOECs varied

3 among labs when an effect was found. For example, effects on

4 duct morphology in females varied from 6.25 to 50 micrograms per

5 liter. That is, each lab found a different LOEC. 

6 17B Trenbolone is predicted to be a strong

7 androgen receptor agonist. It was tested only in lab A and they

8 found some effects on the testes and male and female ducts at

9 the higher doses tested.

10 Benzophenone-C is predicted to be an estrogen

11 receptor agonist and inhibitor of thyroid peroxidase. It was

12 tested only in Lab E. They reported dramatic effects on thyroid

13 histology at the lowest dose, but increased time to metamorphic

14 climax only at the highest dose, which was 6 milligrams per

15 liter which exhibited significant toxicity. 

16 As for Prochloraz, these results question the

17 relationship of thyroid histopathology to thyroid hormone

18 production and the apical endpoint metamorphosis. There were

19 clear effects of the lowest dose of Benzophenone-C on

20 reproductive measures, supporting its predicted estrogenic

21 activity.

22 And I have some general comments on the

23 histopathology which were raised with the other assays. 

24 Automated morphometric analysis could allow for quantitation of

25 some, but perhaps not all histological features. It would also

26 provide for greater objectivity in the analysis provided that
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1 the choice of histological planes; that is, their position and

2 the number of representative sections in relation to the extent

3 of the organ is standardized. 

4 For example, the size of organs like the

5 thyroid, the testes, and the oviduct, and thyroid follicular

6 cell height are potentially amenable to morphometric analysis

7 that could be automated to some extent. 

8 Now, the interlaboratory analysis showed that

9 there could be divergent results with liver histopathology among

10 labs; this was seen in the Prochloraz trial. The basis for

11 these differences may be that two histopathologists conducted

12 the analyses, or that there were animal husbandry issues in the

13 lab with divergent results, that is Lab C. The ISR authors

14 suggested that this could be due to disease in Lab C animals. 

15 The two possible explanations illustrate that

16 a better method, or better guidelines are needed for

17 histopathological analyses, and that more comprehensive

18 guidelines are needed for animal husbandry. The interlaboratory

19 analysis intended to rely on one histopathologist to analyze

20 tissue sections from each of the participating labs. 

21 In validating a method that will be applied in

22 different settings it is important to demonstrate that it

23 performs similarly across laboratories. To assess the validity

24 of the assay, it is important that several histopathologists

25 score the same sets of slides and that the results be compared. 

26 So given the uncertainty in the reason for the anomalies, it is
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1 not currently possible to determine if the liver histopathology

2 is sufficiently robust and repeatable to be used in the Tier 2

3 assay.

4 DR. MARTHA SANDY: Thank you. The associates,

5 Dr. Patino.

6 DR. REYNALDO PATINO: Nothing needed to add.

7 DR. MARTHA SANDY: Dr. Propper.

8 DR. CATHERINE PROPPER: Nothing to add.

9 DR. MARTHA SANDY: Dr. Blazer.

10 DR. VICKI BLAZER: Nothing to add.

11 DR. MARTHA SANDY: Dr. Christman.

12 DR. MARY CHRISTMAN: I concur and just wanted

13 to point out that part of the problem with trying to identify

14 optimal endpoints is that we don't know if the lack of results

15 or the inconsistent results are that they're not important

16 endpoints or were simply underpowered. That was all I had to

17 say.

18 Now, Dr. Portier, his comments, he says

19 upfront that he assumes that optimization refers not to the

20 endpoints, but to whether the test concentrations were chosen

21 appropriately, assuming those were appropriate endpoints. 

22 So he discusses the fact that he is not

23 particularly sure that the choice of concentrations were, in

24 fact, the best choices for biologically important differences. 

25 That they should cover maximum concentrations that would be

26 expected to be seen under environmental contamination. That
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1 they should also elicit responses if there are responses to be

2 seen. And he does not think that the choice of equal distance

3 on a power or log scale is necessarily the best choice because

4 you would not necessarily be able to identify non-monotonic

5 responses.

6 DR. MARTHA SANDY: Are there any other

7 comments on Charge Question 3.1 from the Panel?

8 (No response.)

9 Seeing none, I'll turn it over to the EPA

10 staff. Dr. Degitz.

11 DR. SIGMUND DEGITZ: I have one clarifying

12 question from some of the comments that were made in the

13 previous charge questions. The point that reoccurs is this

14 overt toxicity versus specific endocrine. I'm asking would the

15 Panel provide us recommendations in later charge questions on

16 methods to discern overt toxicity from endocrine toxicity. I

17 guess I'm asking could they provide recommendations.

18 DR. ROBERT DENVER: Short answer: "No." I

19 mean, this point has come up various times throughout this Panel

20 and we've discussed it individually. I don't think there are

21 any good ways to distinguish that in these Tier 2 assays. 

22 As you pointed out yesterday, there is overlap

23 in what might be considered an endocrine disruption in toxicity

24 in many of the doses that are used for a lot to the chemicals

25 that are being tested. Sorry.

26 DR. MARTHA SANDY: That was Dr. Denver. I'll
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1 turn to Dr. Propper.

2 DR. CATHERINE PROPPER: We discussed this

3 quite a bit last night. Certainly if you see no liver

4 histopathology, no kidney histopathology and then no ovarian

5 ducts, it might be suggested of an endocrine mechanism, but the

6 problem is going to be when you probably will not often get that

7 kind of clean cut result. 

8 So again, it's more of a continuum kind of

9 question and I don't have a full recommendation for how to sort

10 that continuum out, but the other side of that, and I think

11 you're the one that brought it up yesterday, is if you're

12 looking at adverse outcome potential endpoints, it may be that

13 figuring out that mode of action from a regulatory standpoint

14 isn't as relevant as knowing that you have an adverse outcome

15 and that that's really what needs to be focused on, is the

16 potential for adverse outcome that has population level effects,

17 which is really what the Tier 2 assays are supposed to support.

18 So knowing exactly what the mode of action is,

19 isn't as relevant as knowing that you have a potential adverse

20 outcome.

21 DR. MARTHA SANDY: Dr. Denver.

22 DR. ROBERT DENVER: Could I just follow-up? I

23 see the Tier 2 assays as a means to evaluate potential endocrine

24 disruptive chemicals identified the Tier 1 assay for effects on

25 development, physiology and reproduction. Period. 

26 I don't see these Tier 2 assays as necessarily
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1 providing any clear information on mode of action of endocrine

2 disruption.

3 DR. SIGMUND DEGITZ: I appreciate your

4 comments and encourage you to -- in terms of this issue, any

5 guidance you could provide to the Agency would be helpful.

6 DR. MARTHA SANDY: That was Dr. Degitz. Are

7 we ready to move on to Question 3.2? I believe so.

8 DR. LESLIE TOUART: Les Touart reading the

9 second part of Question 3, 3.2. Three of the four labs in the

10 interlaboratory validation of the LAGDA were unsuccessful in

11 properly performing the thyroxine (T4) analyses, using

12 commercially available, enzyme-linked Immunosorbent assay

13 (ELISA) kits with antibodies specific for the human or canine

14 T4. 

15 Subsequently, the EPA developed an extraction

16 method, Section 5.3.2, and presents a revised ELISA method for

17 measuring T4 in amphibian samples in Appendix 7 of the LAGDA

18 ISR.

19 Please comment on the technical feasibility,

20 reproducibility, and accuracy of the revised ELISA T4

21 measurement method. Provide any recommendations regarding

22 additional guidance to ensure the reproducibility of T4

23 measurements.

24 DR. MARTHA SANDY: Dr. Denver. 

25 DR. ROBERT DENVER: The contract labs were

26 unable to accurately determine plasma T4 concentrations in
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1 metamorphic climax stage tadpoles using commercially available

2 ELISAs. 

3 The ARMY lab reported plasma T4 concentrations

4 in tadpole and juvenile frogs; however, the measures were highly

5 variable and the values, at least for the juveniles, are much

6 higher than plasma T4 concentrations reported in the literature

7 in which radioimmunoassay was used. 

8 The Fort Lab also measured plasma T4 in

9 tadpoles and juvenile frogs. The values were much lower than

10 those from the ARMY lab, but were closer to the range reported

11 in the literature for Xenopus laevis, I think. 

12 The Mid-Continent EPA lab in Duluth has

13 developed an ethanol extraction protocol to remove interfering

14 substances like plasma proteins that may enhance the ability to

15 measure plasma T4 in tadpoles using ELISA. However, this method

16 is still under development, as I understand, and therefore,

17 insufficient information is currently available to determine the

18 technical feasibility, reproducibility and accuracy of the

19 revised extraction and T4 ELISA method. So that's answering the

20 question, but I just want to add one other thing. 

21 Members of the SAP questioned the rationale,

22 and thus the need for determining plasma T4 in the LAGDA. 

23 Beyond the technical difficulties of obtaining sufficient blood

24 from small tadpoles and measuring plasma T4 by ELISA, published

25 findings show that there is often large inter-individual

26 variation in plasma and whole body thyroid hormone levels at
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1 metamorphic climax. Thus, with the small sample size it may be

2 difficult to detect treatment effects. 

3 More importantly, assessing plasma T4 at

4 metamorphic climax may not provide information on impacts on the

5 thyroid function. That the animals have reached metamorphic

6 climax; that is Nieuwkoop and Faber stage 62, generally

7 indicates that they have produced sufficient thyroid hormone to

8 support tissue transformations. They could have achieved this

9 stage of development through compensatory mechanisms that may be

10 evident in the thyroid histopathology, but maybe not in plasma

11 T4.

12 Hypertrophy of the thyroid may reflect

13 compensation such that sufficient thyroid hormone was made to

14 get to NF stage 62. It is also possible that a chemical will

15 alter tissue sensitivity to thyroid hormone. It could affect

16 thyroid hormone receptor levels or tissue monodeiodinases, as

17 has been shown for hormones in some chemicals without affecting

18 thyroid hormone production. 

19 Therefore, given the technical challenges in

20 measuring plasma T4 and the limited interpretative power of

21 measurements at the single stage of metamorphosis; that is, NF

22 stage 62, the SAP recommends that the EPA consider eliminating

23 plasma T4 measures from the LAGDA.

24 DR. MARTHA SANDY: Dr. Patino.

25 DR. REYNALDO PATINO: No further comments.

26 DR. MARTHA SANDY: Dr. Propper.
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1 DR. CATHERINE PROPPER: No further comments.

2 DR. MARTHA SANDY: Dr. Blazer.

3 DR. VICKI BLAZER: No further comments.

4 DR. MARTHA SANDY: Dr. Christman.

5 DR. MARY CHRISTMAN: No further comments from

6 myself or Dr. Portier.

7 DR. MARTHA SANDY: Any other comments? Dr.

8 Scott.

9 DR. GEOFFREY SCOTT: Just a general comment

10 about ELISAs and ethanol or solvent extractions. There is quite

11 a bit of literature on other ELISA-based assays for contaminant

12 measurements and other endpoints which have used ethanol or

13 other solvent extraction methods. I would only point that you

14 could look in the literature and see that that's not specific to

15 this one, but in general.

16 DR. MARTHA SANDY: Dr. Scanes.

17 DR. COLIN SCANES: A couple of very minor

18 points. The question has a problem within it. And that is, it

19 says, "Antibodies specific for canine or human T4." T4 has the

20 same structure in dogs, frogs, chickens, quail. It's really

21 antibodies specific for the measurement of T4 in human and

22 canine samples. I raise that just to be helpful to the EPA.

23 DR. MARTHA SANDY: Any other comments from

24 Panel members? I'll remind Panel members, if you've made a

25 comment, you'll need to send that in writing to the lead

26 discussant so he can capture that.
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1 Does the EPA have any questions of

2 clarification?

3 DR. SIGMUND DEGITZ: I have no further

4 questions for clarification, and appreciate the well-constructed

5 and thorough response.

6 DR. MARTHA SANDY: Thank you. Dr. Touart,

7 would you read Question 3.3 for us, please?

8 DR. LESLIE TOUART: Charge Question 3.3. 

9 Vitellogenin (VTG) is an established biomarker of estrogenic

10 exposure and is used as a key endpoint in endocrine disruptor

11 testing. There is currently not a standardized commercial

12 source of Xenopus laevis VTG antibodies for an ELISA. 

13 Please comment on the protocol for measuring

14 and reporting VTG levels. Provide any recommendations regarding

15 additional guidance to ensure the consistency, repeatability and

16 reproducibility of VTG measurements.

17 DR. MARTHA SANDY: Dr. Denver.

18 DR. ROBERT DENVER: Plasma vitellogenin or

19 liver vitellogenin messenger RNA are useful diagnostic tools for

20 disruption of sex steroid signaling, particularly estrogenic

21 signaling. 

22 There was large variation in the control

23 plasma vitellogenin concentration measurements among the

24 contract laboratories that conducted the assays. The reasons

25 for the variation are unknown. Most were unable to detect

26 vitellogenin, and this is likely due to the immaturity of the
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1 frogs at 10 weeks post-metamorphosis. 

2 So there are two issues to be addressed: 1)

3 the performance of the vitellogenin ELISA, and the other is the

4 inability to detect plasma vitellogenin in immature frogs. 

5 Now, regarding issue number two, the low

6 circulating plasma vitellogenin at 10 weeks post-metamorphosis

7 may allow for sensitive detection of estrogenic compounds

8 compared with mature females, but this needs to be demonstrated.

9

10 Regarding issue number one, the performance of

11 the vitellogenin ELISA, it's difficult to know why the contract

12 labs had trouble with the method, since assay performance

13 metrics, for example, data on the standard curves, quality

14 control samples were not provided; however, there are some

15 ambiguities in the protocol that could have contributed to the

16 problems, so I'll just list a few of those. 

17 The protocol says that at the end of the

18 sampling, tubes should be centrifuged to remove the blood cells

19 from the plasma. Guidance should be given on the g-force

20 generated during centrifugation to avoid hemolysis. It's

21 possible that some of the variance in the assays is due to

22 hemolysis caused by centrifugation of the blood sample.

23 The pre-absorption of the primary vitellogenin

24 antiserum with control male serum could have introduced a

25 variable that may have led to variation in the assay. It was

26 not clear if this was done at the EPA Duluth lab or in the
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1 contract labs.

2 Other potential sources of variability include

3 preparation of collection tubes with aprotinin which requires

4 lyophilization in a Speedvac. The normal goat serum used for

5 blocking; different lots may give different results.

6 Generating serial dilutions of the top

7 standard as opposed to providing sets of prediluted standards as

8 is typically done in commercial kits; inaccuracies caused by

9 dilution of the samples, particularly the one to 300 (1:300)

10 dilution that relied on pipetting only one microliter of plasma,

11 which is not very accurate.

12 The ABC Laboratories used a fortified

13 interassay quality control sample which was freshly prepared

14 each day of analysis by spiking pooled male Xenopus plasma with

15 vitellogenin from the same source as was used to prepare the

16 standards. And this was done to a nominal concentration of two

17 micrograms per deciliter. So such quality control samples could

18 be incorporated into the assay. 

19 However, this same lab accepted a coefficient

20 of variation of up to 20 percent between assays, the interassay

21 coefficient of variation, which is quite high. Most samples

22 analyzed by this lab were nondetectable, although they are

23 reported as half the minimum detectable value. And that is

24 presumably to provide a number for statistical analysis;

25 although, in fact, they are nondetectable.

26 The minimum and maximum detection limits on
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1 the different plates varied widely, and samples from a single

2 LAGDA trial were run on different plates on different days. 

3 Now, this could introduce an important confound if the samples

4 were detectable since there was wide variation in the general

5 parameters of the assay and just how it was set up. So I think

6 more guidelines on how the assay is actually set up, especially

7 in labs that don't have experience running these types of assays

8 is important.

9 However, members of the SAP agree that

10 measuring plasma vitellogenin is an important endpoint for

11 detecting estrogenic activity, but this requires a robust assay

12 method that laboratories contracted to conduct the LAGDA can

13 perform. 

14 Alternatively, a contract assay laboratory

15 should be identified with expertise to process samples from the

16 contract LAGDA labs, which might be preferable. The SAP

17 recommends that the EPA consider identifying a contract assay

18 laboratory to run the vitellogenin ELISA, or alternatively,

19 identify a company willing to develop and market a vitellogenin

20 ELISA kit with a standardized protocol. 

21 DR. MARTHA SANDY: Any additional comments

22 from the associates? Dr. Patino.

23 DR. REYNALDO PATINO: No additional comments.

24 DR. MARTHA SANDY: Dr. Propper.

25 DR. CATHERINE PROPPER: An alternative might

26 be real-time PCR for liver vitellogenin as well.
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1 DR. MARTHA SANDY: Dr. Blazer.

2 DR. VICKI BLAZER: I have no additional

3 comments.

4 DR. MARTHA SANDY: Dr. Christman. 

5 DR. MARY CHRISTMAN: I have no additional

6 comments. Dr. Portier does, though. I'm just going to read

7 what he says. VTG, at least as measured in Xenopus laevis,

8 seems to have nice statistical characteristics, making it easy,

9 within the size protocol used in these studies to demonstrate a

10 dose response. 

11 I did notice a slight increase in variances as

12 the mean increased, suggesting that the data may need to be

13 transformed or the transformation used was not adequate to

14 adjust for this pattern in the data.

15 DR. MARTHA SANDY: Dr. Propper.

16 DR. CATHERINE PROPPER: I thought of one more

17 thing. This assay in particular does have value as being on

18 that's probably a nontoxic and a direct endocrine measure. So

19 for that, I think this is one of those places where it will have

20 value. So it just needs to be gotten right.

21 DR. MARTHA SANDY: Dr. Christman.

22 DR. CHRISTMAN: Actually, that does make me

23 think of one more thing. If in fact this particular endpoint

24 has responses as sort of on a threshold level, then some serious

25 thought needs to be given to how to optimize the experiment to

26 capture exactly that effect. 
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1 DR. MARTHA SANDY: Any other comments from the

2 Panel?

3 No. Dr. Degitz, any questions?

4 DR. SIGMUND DEGITZ: Yes. I have one

5 clarifying question. You made mention of evaluating gene

6 expression. Does the Panel intend to provide more detailed

7 advice on that in your report?

8 DR. CATHERINE PROPPER: In regards to that, I

9 did look into the literature to see if there has been

10 well-documented development of vitellogenin real-time PCR assay

11 for Xenopus because these do have to be done fairly species

12 specifically, and there was not. So no, at the moment, I don't

13 have a recommendation for how to go about that, other than with

14 very standard mechanisms for developing those assays. But Bob

15 may know something I don't know.

16 DR. ROBERT DENVER: In fact, at a conference I

17 was at just last month, there was a lab that reported

18 vitellogenin real-time qPCR assay. We develop them in my lab

19 all the time, so I think it is fairly straightforward, as do

20 also.

21 DR. SIGMUND DEGITZ: I guess I should clarify.

22 Is the Committee going to recommend that we do that?

23 DR. CATHERINE PROPPER: So we haven't spoken

24 as a group about that as a recommendation. And maybe we can do

25 so later today and come back to you tomorrow, unless my opinion

26 is agreed upon here and now which is my recommendation is to
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1 choose one or the other and go for it and just do it well. And

2 which one, they should both give you an assay that's viable. It

3 would take a lot of validation to see if one was more sensitive

4 than the other. 

5 So at this point, I'm reluctant to make a

6 recommendation. If other folks would like to discuss this

7 later, we can, and then come back to you. 

8 DR. ROBERT DENVER: I agree. We'll discuss it

9 and get back to you because I understand it would require,

10 perhaps additional animals to collect the tissues, given what's

11 in the assay right now. 

12 DR. SIGMUND DEGITZ: Thank you.

13 DR. MARTHA SANDY: Dr. Touart, I believe we're

14 ready for Charge Question 4.

15 DR. LESLIE TOUART: Les Touart reading Charge

16 Question 4. Demonstration of the test method performance should

17 be based on the testing of reference chemicals representative of

18 the types of substances for which the test method will be used. 

19 Test substances should adequately represent an appropriate range

20 of responses and physical chemical properties for which the test

21 method is proposed to be appropriate.

22 The selection of the most appropriate

23 statistical approach depends, in part, on the nature of the data

24 and also on the design of the validation study. Statistical and

25 non-statistical methods used to analyze should be described. 

26 Please comment on the selection of test
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1 substances and methods, analytical and statistical where

2 appropriate, chosen for the demonstration and validation of the

3 LAGDA assay.

4 DR. MARTHA SANDY: Dr. Christman is the lead.

5 DR. MARY CHRISTMAN: Yes. Hello. As was done

6 for the other two assays, I will defer for the test substances

7 in the analytical methods to my esteemed colleagues. 

8 Secondly, I'm very visual. So I have a

9 PowerPoint. There are no pictures, though. I apologize; it's

10 all words, which is really boring, but it helps me to remember

11 things.

12 I would like to start by reiterating

13 everything that has been said to date about the statistical

14 methodologies as valid here as well. There are, of course,

15 minor differences. This is not a multigenerational study, so

16 some of those questions are irrelevant. 

17 I also have some additional points I want to

18 bring out, on top of what's already been discussed. And in some

19 cases, they just remind you of what's been discussed. So I'm

20 boring you, just take a nap for a minute and then come back.

21 I apologize. This is probably not as well

22 organized as it ought to be, but it was 11:00 last night when I

23 decided that I needed to have a PowerPoint, so bear with me.

24 I'm representing comments from me, discussions

25 with Dallas and Dr. Portier. So Dr. Johnson and Dr. Portier,

26 I'm simply doing a presentation. There are some things, though,
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1 as I said, that are very specific to LAGDA and there are some

2 things that are more generic for statistical methodology. 

3 Just as a reminder, there are three different

4 kinds of data types in this analysis, in this particular assay

5 rather. There are the truly continuous ones, like length or

6 weight, even though we may measure them on units, like

7 centimeters or something. Then there are truly discreet

8 variables as well, which are things like counts or the number

9 that are dead out of end observed. And then finally, there is

10 the histopathology ordinal categorical. 

11 Now, these should all be handled distinctly. 

12 The continuous variable should be analyzed according to

13 probability distributions that are continuous. The discreet

14 ones should be analyzed according to the distribution that

15 discreet random variables take on. 

16 And finally, ordinal categorical should be

17 handled by categorical techniques, such as the (inaudible) that

18 has been described by Dr. Green in the documentation provided,

19 or other models such as Cumulative Logit Models or what are also

20 known as Proportional-Odds Models.

21 Part of the problem is that the current

22 statistical methods, as you've heard repeatedly, have a tendency

23 to treat the data, any quantitative data as continuous and its

24 default is to look for transformations to force things to be

25 cookie-cutter normal homogeneous variance. Statistical

26 methodology has moved beyond that nowadays. We have the
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1 computing capability to handle data as it really should be

2 handled. That optimizing power. The same reason why you use a

3 normal distribution when things aren't normally distributed

4 because it optimizes power. 

5 The way it's set up, what happens is that when

6 normality fails, the next-test is to go to something like a

7 non-parametric, but the problem is that the decision tree that

8 is described by, say, StatCHARRMS, one differs among labs and

9 among assays. What some people do as the decision tree isn't

10 even the same. So you've got people who are using completely

11 different approaches. It's not unlike the one lab that decided

12 to feed their Xenopus twice as much as all the other labs. I

13 mean, it's the same kind of thing where you have variations in

14 the protocol.

15 This leads to loss of power. The other thing

16 is, you're not using your biological expert opinion and

17 knowledge and expertise to inform your ability to analyze the

18 statistics today. This is the same thing that Dallas Johnson

19 was saying that you should be taking advantage of the historical

20 knowledge of the distribution of these endpoints and using that

21 information in the methods. It's so much more powerful when you

22 know what you're doing. Sorry.

23 Part of the problem too is that, you know, we

24 all have our own terminology. Statisticians have a terminology

25 and non-statisticians have a terminology. The way the

26 statistics are described in the ISR, I'm sorry; in some cases
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1 they're just downright wrong. I think what it is, is that they

2 don't understand that this test does this as opposed to doing

3 that and it's just a matter of -- so if a statistician is

4 reading it, I'm sitting here saying, you can't possibly be doing

5 the right thing. 

6 So how can you understand whether these

7 endpoints or useful or not or whether the study has been

8 validated or not if you can't even understand what the

9 statistical methods are?

10 I've noticed this for the LAGDA. I've only

11 really read two of them, but in the case of the LAGDA, the ISR,

12 at least, I found confusing. I don't mean this in a negative

13 way. It's literally just a matter of my language versus

14 somebody else's language. It would be like me trying to speak

15 French; it definitely would not work. And I give an example

16 there.

17 I have a whole list of those sorts of things

18 in the write-up, so I'm not going to go into them. But the

19 other thing I want to point out that the way that the

20 methodologies are described varies among the various documents

21 that were provided, which makes it that much more difficult for

22 me to understand what's being done. And that's an example of

23 where we're using different decision trees.

24 You have one lab that decides that they'll go

25 non-parametric if at least half of the treatments are not

26 normally distributed, which is different than statCHARRMS's
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1 decision to go to non-parametrics. So this yields all sorts of

2 problems, I think. Basically, how do you compare results among

3 labs when one lab is using a really liberal test that tends to

4 reject more easily than the stated alpha and another lab is

5 using a real conservative test?

6 That might be one reason why you're seeing

7 differences in the LOECs among labs. One is like, oh, yeah,

8 sure. That's good. And the other one is going oh, no. We have

9 to have really strong evidence. So I find that it's difficult

10 to compare among labs as a result.

11 The other thing I wanted to point out is that,

12 you know, we think that by having an auto option in StatCHARRMS,

13 where you simply let it default to the decision tree is

14 objective. But it's not. It's completely data-driven, which

15 means that every dataset you get is going to be a whole new

16 route for the decision tree, which makes it extremely difficult

17 to determine what the true Type I error is for this particular

18 method. 

19 Type I error is based on the idea of repeated

20 long-term repetitions of the same experiment with new data each

21 time. That assumes you're using the exact same methodology as

22 well. If you start varying methodology, depending on what your

23 data happen to show you, one data point, all it needs is to be

24 over one thing and you're suddenly in the non-parametrics. You

25 don't know what your Type I error is anymore. That causes all

26 sorts of problems I think.
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1 This is one thing I find a little confusing

2 and I'm sure you guys can help me. There is no statistical

3 interlab comparisons done, except for the mysids. It's the only

4 I've seen this. All of the study to determine whether there is

5 reliability and robustness among laboratories is based on

6 comparing CVs, visually, descriptively, or looking at the means

7 or looking at whether they reject or accept and what direction

8 their rejection or acceptance is for the various tests.

9 From a statistician's point of view, that's

10 not evidence. Evidence is if I actually test whether the labs

11 are reproducing the same sorts of results. So it may not be

12 possible for you to do that with these particular procedures

13 because you don't necessarily have enough information to be able

14 to do it, but I urge that you think in future that if there were

15 going to be redesigns of these standardized techniques and

16 experimental designs that some thought be given to that ultimate

17 goal of actually testing whether the labs are showing

18 reproducibility.

19 Rather than simply describing the control

20 result and saying oh, look, all the labs stay within 20 percent

21 CV or their range of CVs is nice. I would instead argue that

22 you need something a little more statistically relevant.

23 You've heard this repeatedly. I think you

24 guys are underpowered with your sample sizes. That may not be

25 something that you can correct by just adding additional

26 animals. If there is no density dependence, then maybe you
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1 could add a few more into the tanks or something, but if there

2 is, then we have to think of other ways to increase our power. 

3 I've mentioned one already, which is to use the best test for

4 the data, which means you have to bring your expert knowledge to

5 identify what that best-test is.

6 The easy one is we'll do a power analysis and

7 just add some more replicates. And of course, that's the one

8 that you all cringe at because it's expensive to add replicates,

9 or you have to add lab facilities or whatever. So we want to

10 think about other ways we can increase power here. 

11 Well, it may be that you need to consider --

12 and this was brought up earlier -- that you need to consider all

13 the sources of variance that are being introduced and then the

14 experimental design, that has already been said, among

15 individuals. So that's biological variance, among tanks, tanks

16 within treatments among the labs. So you want to think of those

17 four sources. But there is additional variance that's

18 introduced by the lack of the statistical test -- all right. So

19 I did that already.

20 One of the things that I'm surprised is

21 everything is analyzed independently. If it's a gender-specific

22 endpoint, we analyze it for that gender. Female lengths are

23 analyzed separately from male lengths. One of the things you

24 learn in an experimental design class is this technique or this

25 idea of what's known as hidden replication. If you use a more

26 complete model, like include gender as an effect in the model,
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1 you actually gain power in some ways because what you have now

2 is if you have homogeneous variances, even if the means are

3 different. 

4 If you have homogeneous variances, your

5 estimate error of variance, the error, is increased because you

6 have more replication. Because you have both the males and the

7 girls in there as part of the leftover noise. You have more

8 tanks to get tank variability, so you increase your replication,

9 even though your testing the female length in Treatment 1

10 against the female length in Treatment 2. It's the denominator

11 of your test that you're getting more precision. 

12 So you might want to consider altering your

13 statistical methodologies to capture some of that. That's an

14 easy way to possibly -- I mean, I'm not saying that his has to

15 be done; I'm saying that this is something that if you have a

16 statistical intern sitting around, put them on this task of

17 taking the data and playing with it, basically, to see if you

18 can gain some power this way. 

19 Ultimately, what this is it's a plea to

20 rethink your statistics, basically. Other things you could do

21 is, as I said, identify the sources of uncertainty. I've

22 already talked about what we think of as the experimental design

23 sources of uncertainty, but there are additional sources. There

24 is the lack of comparability of statistical methodology among

25 the labs when they do it independently. That needs to be

26 corrected to reduce that uncertainty. You're introducing added
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1 uncertainty by doing that. 

2 Another thing is, as has been pointed out

3 repeatedly, the protocols are not always followed. Maybe you

4 need to send something emphasizing that you're not going to get

5 good reproducibility if you don't put more emphasis on the need

6 to follow the protocols more explicitly.

7 One of the things -- sorry. I don't know how

8 important this is because I know the tendency is to use things

9 like the having of the dose response and the choice of the

10 maximum dose is based on range-findings studies and things of

11 that nature, but if you have a priori belief of a monotonic,

12 linear increase in the dose response, then you should set your

13 choice of concentrations appropriately. 

14 If you believe that is it sigmoidal curve,

15 then you should set your concentrations accordingly. You could

16 probably save, if you could decrease the number of treatment

17 levels, you could probably put those replicates to one of the

18 other treatments and increase your power to see a difference. 

19 Of course, it relies on this a priori belief, which you need to

20 be fairly strong about, otherwise you've just wasted a lot of

21 time on your experiment. But it's something to think about. 

22 And this is actually a question I have, which I think might be a

23 source of variation. 

24 In our discussions with the mysids, someone

25 commented about how even just 24 hours can change the response

26 of the animal because they molt in that particular case. Well,
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1 in the case of the frogs, other than going from a tadpole to a

2 frog, I don't think there is any great -- I don't know. I'm not

3 a biologist. I'll let you guys figure it out. 

4 But the way the experiments are set up is that

5 you have things happening at different rates for the different

6 animals and is it possible that that's causing some of the

7 uncertainty and should the covariate be included in the models

8 to somehow capture that source of noise and adjust it out? Just

9 something to think about.

10 This has been mentioned repeatedly, I don't

11 completely understand the description of the protocol of how it

12 should be performed. If I had to follow that, I'd probably

13 screw it up. Let's put it that way. So I think it needs to be

14 made more clear. 

15 One of the problems, and you saw yesterday

16 when I asked about the experimental units is clearly, the

17 protocol wasn't very clear about what the experimental units

18 were, what the measurement units were. This is Ken's comment. 

19 What was the measurement being made on? 

20 Was it being made on an individual or a tank? 

21 What is your ultimate goal with respect to the ecological risk

22 assessment? What is your population unit that you want to

23 describe the behavior for? It's not a tank. It's usually an

24 individual animal. 

25 So you want to think in terms of what you're

26 measuring, what you're analyzing, and what is your replicate
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1 with respect to data analysis.

2 So we saw that that was part of the problem

3 with StatCHARRMS; the problem with going to the tank replicate

4 was because this wasn't explicitly stated or analyzed then

5 either. 

6 There are so many pages of methodology and

7 then the statistics that's half-a-page. Well, it's not an

8 afterthought, guys; it's a major part of the study. At least I

9 think it is. Being a statistician, I want to say that. 

10 As I said, there are some issues about the

11 methods being incorrectly applied. They're not accounting for

12 the nesting structure correctly to get some additional sources

13 of information.

14 Oh, this is one that hasn't been mentioned

15 yet, which I think is a problem. When you're doing the time to

16 event, in this particular case, they're looking at time to stage

17 NF 62, I think it is, the way the method is described is you do

18 a Kaplan-Meier product estimator, which you look for the

19 survival curve, essentially. 

20 In this case, "survival" means survival to

21 stage 62. It doesn't mean they died. And you use this curve,

22 you pull the median off of it and you then treat that as your

23 response variable and you analyze it. Well, that's like using

24 the tank mean without recognizing that that mean is itself,

25 variable because there is some -- it's based on a set of data.

26 So the appropriate test is actually the Wallis
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1 (phonetic) rank test; not pretending that that median is a

2 number that I can throw into an ANOVA. So that's an example of

3 where there's misuse of the analysis. 

4 Just to reiterate: randomization,

5 randomization, randomization. Very important. One of the

6 things is the power analyses that I've seen done, I didn't see

7 one for LAGDA, but the power analyses I've seen done have talked

8 about the percent difference detected with the given data. 

9 Instead, I would argue that you need to actually be trying to

10 determine what the appropriate sample size is and whether you

11 meet that condition or not.

12 And that if you do a power analysis, please do

13 the right one. The power analysis I saw assumes that all you're

14 doing is a two-sample T-test, but that's not what you're doing. 

15 You're doing a Jonckheere-Terpstra Trend Test or a Dunnett,

16 which is totally different. And as a consequence, it has a

17 difference source of error in the denominator. It has a

18 different-testing structure that's not being accounted for in

19 the power analysis. 

20 The power analysis is pretending and all

21 you're doing is comparing two means, which is not even remotely

22 what you're really doing. You're trying to get this overall

23 picture of behavior. And if you're trying to get an overall

24 picture of behavior, you have think in terms of all the tests

25 that go into that overall picture. 

26 So I would argue that if you are going to do a
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1 power analysis, do it for the actual testing structure that

2 you're planning on performing; base it on that. So as an

3 example, if I'm doing a t-test of two means, I use the typical

4 alpha .05, power of .8, I get an "n" of 17 in each of my two

5 groups. But if I'm doing a one-way ANOVA with three levels and

6 I want the same -- I want to know how many observations do I

7 have to have in order to see an difference of this amount, the

8 same amount as my two sample t-test, because I'm using -- is

9 there a pointer on this? Oh, wait; this might be the pointer.

10 You know that Dunnett's or Bonferroni-Holm

11 step-down procedure, or any of those, adjusts the alpha that you

12 are testing at. So even to adjust a simple thing like this,

13 adjust the alpha, I went from 17 to needing 20 to see a

14 difference of 10. And if I'm doing something like an LOEC,

15 based on this kind of simple test, to see a difference between

16 control and Dose 1, when they're apart by five units, I need 78

17 observations. And I'm not even getting into nesting structure

18 here. This is just the simplest. 

19 So my point here is that you need to be

20 calculating power based on your actual methodology, not on a

21 simple t-test that's a hand waiver. And that's it. That's

22 enough. I apologize for taking so long.

23 Oh, and just quickly, Ken's comments are

24 incorporated. His only other comment was "See everything I said

25 about the MMT."

26 DR. MARTHA SANDY: Thank you very much. 
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1 Rather than going all through the names, if anyone has any

2 additional comments on the statistical methods, raise your hand.

3 (No response.)

4 None seen. All right. Oh, yes? You're

5 welcome to comment.

6 DR. DALLAS JOHNSON: Thank you. I don't know

7 whether you are going to be able to put up a slide that I

8 prepared or not, but I'm not ready for it yet, anyway. 

9 I just had a few additional comments, I think.

10 Again, they're kind of in the order of the report. One thing I

11 wanted to mention is when I teach classes, in terms of designing

12 and experiments, there has been a lot of confusion between what

13 is the treatment structure and what is the way the

14 randomizations are taking care of. 

15 So the terminology here is an experimental

16 design is not my definition of experimental design. 

17 Experimental design is used in the document is really the

18 treatment design; how the treatments are going to be compared. 

19 What things we're going to look at and what the analysis units

20 are going to be and so on, and there is nothing about the design

21 itself. 

22 So I would like to encourage people to think

23 of design structure and treatment structure. And you can almost

24 use any treatment structure with almost any design structure. 

25 So they're really separate. I think if you try to separate

26 those and think of them in that way, what are we going to study,
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1 is treatment structure; how are we going to study is design

2 structure. So I would just encourage people to think about that

3 when you write up a plan or a standard operating procedure or a

4 guideline for people to follow.

5 I did notice that in this particular report,

6 there is a bit more attention paid to the type of data that is

7 being collected and the analysis that is going to be done. It

8 said that there were statements in there that indicated that

9 mortality data that is expressed as a percentage that died

10 should be arc-sine square root transformed. Time to

11 metamorphosis data should be treated as time to event data. And

12 then some other things, liver weight was taken as a proportion

13 of the total bodyweight. So there was some things in this ISR

14 that showed the science, it sort of gave a little bit of a lead

15 into the way the data should be analyzed. That's the kind of

16 thing that I've been trying to encourage when I was talking

17 yesterday about -- and what Mary and I has been talking about

18 today to think about how the data are collected and how it's

19 historically analyzed and try to pick the different analyses

20 that are similar so that the analysis is associated with the

21 type of data that is collected and not the data that's actually

22 collected in a particular study.

23 DR. MARY CHRISTMAN: Dallas, this Mary

24 Christman. Peer review.

25 DR. DALLAS JOHNSON: Oh, yeah. I guess Mary

26 and I talked about this over lunch is whether the methodology --
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1 my understanding is, at least, is that StatCHARRMS is sort of a

2 series of SAS Macro. So SAS Macro that you sort of run data

3 through and it actually uses SAS programs to do the analysis. 

4 So we had a question about whether that

5 process has been peer reviewed or not. And if it hasn't,

6 perhaps it should be peer reviewed. And particularly, if you're

7 going to create a new package in AR, that it would be important

8 to have that peer reviewed before you put too much weight in the

9 use of it. I wanted to see if you could put up my slide. 

10 DR. MARTHA SANDY: I think they have it up. 

11 We'll get it.

12 DR. DALLAS JOHNSON: Yesterday we talked about

13 the corrected analysis that was being done, the Medaka used a

14 tank as a random effect and did the analysis that way. And I

15 mentioned this morning that that analysis appears to be correct.

16 And in this case, they did an analysis on the tank means, but

17 they corrected it by doing weights. And it's the DEJ comments

18 -- well, that's the one that's there. 

19 I just wanted to say that the analysis, using

20 weights is not correct and I wanted to try to give an indication

21 of why. Is it going to open?

22 DR. MARTHA SANDY: I'm understanding that they

23 cannot open up a WordPerfect file. 

24 DR. DALLAS JOHNSON: It's not WordPerfect. It

25 should be Word. It's got some formulas in it, probably.

26 Well, let me describe it. If I describe it
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1 well enough, maybe it'll be in the document. 

2 DR. MARTHA SANDY: Excuse me, Dr. Johnson. If

3 you could give it to Sharlene and she'll try and convert it to

4 something else.

5 DR. DALLAS JOHNSON: It's in Word.

6 DR. SHARLENE MATTEN: Yeah, but I have to

7 convert it to .pdf.

8 DR. DALLAS JOHNSON: Pardon? I'll put it up

9 as a .pdf file.

10 DR. SHARLENE MATTEN: Well, if you want to

11 convert it, we can put it up as a .pdf or we can cut it and put

12 it in a PowerPoint. 

13 DR. DALLAS JOHNSON: We can come back to this

14 later if you want. During the break we can fix it up.

15 DR. SHARLENE MATTEN: Right. Well, this

16 particular computer was out of commission for a few months.

17 DR. DALLAS JOHNSON: I'll create a .pdf file

18 and we can come back to it later.

19 DR. MARTHA SANDY: So let's take a short break

20 for five minutes so that we can get this converted to a .pdf so

21 we can all see it.

22 (Whereupon, a break was taken.)

23 DR. MARTHA SANDY: Okay. Can we reconvene? 

24 The dozen or so Panel members that need to come back in the

25 room, please come and we will start. We have our quorum. So we

26 left off on Question 4 for the LAGDA assay. We have Dr. Dallas
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1 Johnson's calculations on the screen because we had to wait for

2 the Word updates and now we've converted it to .pdf.

3 DR. DALLAS JOHNSON: Thank you. I wanted to

4 put this up there so you could see what us statisticians really

5 like to do. We have our own little nerdy, geeky things. 

6 So I wanted to sort of describe, I guess, why

7 the weighted analysis is wrong. If we have a model for the

8 data, we have the response that has taken on an individual

9 animal, in this case a tadpole or a frog, and then it could be

10 modeled by some overall mean which changes, depending on the

11 dosage included. And then there is an error component given by

12 the tank that it has to be in. 

13 So delta IJ ($ij) represents the variability

14 in the tank and then we have variability with respect to the

15 animal's stealth that is denoted by the Eijk. So we have I

16 equal one, two up to the number of doses, J one, two up to the

17 number of tanks and then K is the sample size that's within a

18 tank. It goes from one, two up to NIJ. 

19 So delta IJ is a random tank effect. Eij is a

20 random error component associated with the animal. So if you

21 look at the tank mean, which I denoted as Yij dot bar and follow

22 this through, it's equal to the overall mean plus the delta IJ,

23 plus the average of the Eijs. K is the error. And if you look

24 at the variance of one of these, in the variance it's sigma

25 squared delta (32$), to branch the delta term, plus the variance

26 of the error divided by the sample size.
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1 So when you do a weighted analysis and you use

2 the sample size as a weight, then it only affects one of these

3 components of variance. It only effects the E sigma squared E

4 ([(32E)]E) part. So if sigma squared delta is zero [(32$)= 0],

5 then the weighted analysis is going to be correct. But if is

6 sigma squared delta (32$) is not zero, which it very seldom will

7 be, then the weighted analysis would be incorrect.

8 The analysis that was used in the Medaka -- I

9 can't even think of how you pronounce it anymore. In the little

10 fish study, the reanalysis there did take into account, used

11 this model up here by putting in the tanks, effects as random. 

12 So that analysis ended up being correct, but this analysis if

13 you use weights, will only be correct if sigma squared delta

14 (32$) is zero.

15 Now, will it change any of your conclusions? 

16 I don't think it probably does. So I'm guessing that the report

17 that was given and the conclusions that were made in it are

18 probably okay, but if you're going to rewrite or adjust the ISR

19 for this study, you ought to go ahead and reanalyze again, using

20 the appropriate model and the appropriate analysis. Thank you.

21 DR. MARTHA SANDY: Any additional comments on

22 the statistical analysis from the Panel?

23 (No response.)

24 No? Then I'll turn it over to the EPA for any

25 clarifying questions.

26 DR. SIGMUND DEGITZ: Clarifying question, huh?
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1 I have no clarifying questions. Look forward

2 to the advice that we get in the report.

3 DR. MARTHA SANDY: Okay.

4 DR. SIGMUND DEGITZ: I have no clarifying

5 questions. 

6 DR. MARTHA SANDY: That's great. Thank you. 

7 I will turn to the other Panel members then for comments on the

8 other parts of Question 4; is that correct, Mary?

9 Yes. Comments on the issues related to the

10 selection of test substances and the analytical methodology

11 chosen for this assay. Dr. Patino.

12 DR. REYNALDO PATINO: Like I did with the

13 Medaka assay discussion, I'm going to limit my comments to the

14 selection of the test compounds. The analytic methodology, some

15 of it has already been discussed and maybe in subsequent

16 questions some of it may come again. So we'll limit this, at

17 least in my discussion, to the selection of the test compounds. 

18 Four test compounds were used to validate this

19 assay; Prochloraz was one. This was listed in the ISRs and

20 aromatase inhibitor with AR inhibitory activity. This has

21 already been mentioned by Bob, but there is at least one

22 previous study that I'm aware of with amphibians, anuran

23 amphibians, a Rana, where they tested Prochloraz in this

24 organism and found that it disrupted the thyroid endocrine axis

25 and delayed metamorphosis. And I'll provide a reference for

26 that in the report.
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1 My suggestion is that this AOP, a thyroid

2 active dependent AOP should be added to the list of AOPs that

3 the report had originally associated with this compound. That

4 may help you. I guess we'll visit a little bit about this more

5 on Question 5, interpret some of the results that you had with

6 this compound. 

7 So this was one of the compounds. The second

8 compound on the list was octylphenol. That's a weak ER agonist.

9 Trenbolone was used as well. That's a strong AR agonist, then

10 Benzophenone-2 BB2 has characteristics of an ER agonist and also

11 thyroid peroxidase inhibitor.

12 The Panel did not have concerns about the

13 specific compounds on the list. The logic behind the choice of

14 these absences was clear and each of them can provide -- are

15 good for proof of concept outcomes for the assay. 

16 However, because the purpose of these various

17 studies is to validate the LAGDA assay and at least to our

18 understanding, also the AOPs in which they are based. And this

19 may be my misunderstanding, but it's not just the apical

20 endpoints but in the context of the AOPs, the Panel wondered why

21 it was not as strong and well-characterized thyroid active

22 compound not included on the list for the validation of

23 development endpoints.

24 A strong AR agonist was used. That was in the

25 validation that was Trenbolone, but there was no strong ER

26 agonist used. The issue of positive control is to validate the
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1 assay is important and it has already been pointed out for a

2 number of the other assays as well. Just a more general comment

3 was that it would've been nice, for comparison purposes, just to

4 compare on assay against the other in terms of which one is

5 better to do what. 

6 If there would've been more overlap between

7 the test compounds that were used for the validation assays, for

8 all the -- at least the vertebrate models. So that's all I have

9 as far as comments on the test compounds. If other members have

10 comments, they'll add them themselves. Thank you.

11 DR. MARTHA SANDY: Dr. Denver.

12 DR. ROBERT DENVER: I have no further

13 comments.

14 DR. MARTHA SANDY: Dr. Propper.

15 DR. CATHERINE PROPPER: I just have a couple

16 of things to add, and again, one is emphasizing that positive

17 control because I have seen studies before, especially when you

18 get no effect outcome without that positive control. It becomes

19 very difficult to interpret that negative outcome and it puts it

20 at risk as being potentially considered a false negative. 

21 Whereas, if you have the positive control in there, it does add

22 validity to the assay. 

23 And then second, and this is a bit of a

24 different issue, but this was just on establishing the highest

25 test concentration, I wasn't able to read through the protocol

26 and decide that I had confidence that I would be able to repeat
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1 that, and I read through it several times.

2 I wanted to know what were the endpoints that

3 were being used in the initial range-finding study. Is it LD50?

4 Is it a shift in weight? I wasn't altogether clear. What will

5 the labs be looking at when they do that test to determine that

6 high end of your dosing that you're then going to go down from

7 for the EDC testing?

8 It may well be that it's in the AMA protocol

9 and I just don't remember seeing it because it's been years

10 since I read that through from beginning to end, but if it is,

11 you can just reference those pages.

12 The protocol itself says that if 50 percent

13 dilution series would be used for at least three test

14 concentrations, and I want to know, does this start at 50

15 percent as the highest no effect level in your initial study, or

16 does it start at the no effect level of the initial study? 

17 And then again, there was kind of

18 inconsistency on using either 50 percent dilution or it went to

19 .33 percent dilution. So that kind of consistency needs to be

20 in there for the range-finding parts of the study. 

21 Overall, I just think a little more detail in

22 those protocols would be a real useful guideline.

23 DR. MARTHA SANDY: Dr. Blazer.

24 DR. VICKI BLAZER: I have no further comments.

25 DR. MARTHA SANDY: Any other members of the

26 Panel? No? Dallas.
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1 DR. DALLAS JOHNSON: I remembered a couple of

2 other things I wanted to say, if I may. In the ISR, there is a

3 statement made that the step down Jonckheere-Terpstra Trend Test

4 should be applied to the data as long as the monotonic response

5 was observed. 

6 I wonder if that shouldn't be changed to as

7 long as a monotonic response is expected. I think there's a

8 difference between what you observe and what you expect and

9 again, it's a situation where I don't think you want to let the

10 data drive the analysis. You should let the science drive the

11 analysis.

12 Another thing I noticed, in Table 1 was giving

13 a listing of when certain things should be collected. The

14 timing of apical endpoint measurements on the course of the

15 study. Two of the things that were in there were mortality and

16 clinical signs of disease and said that observations should be

17 done daily. 

18 I guess I wonder, I'm curious as to whether

19 daily means that you do it on Saturday and Sunday as well. And

20 then I think also the data is probably not descriptive enough. 

21 Maybe it should be at the end of the day or a certain time of

22 the day so that's it done consistently over time. That again

23 would perhaps remove some of the variability that might be in

24 the data that you're trying to get analyses on.

25 DR. MARTHA SANDY: Dr. Propper.

26 DR. CATHERINE PROPPER: One more point along
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1 there. It did say the animals should be checked daily for

2 mortality and abnormal behavior. While those of us that work

3 with Xenopus may be able to recognize abnormal behavior pretty

4 quickly, a contract lab just starting out with this might need

5 further guidelines for that as well.

6 DR. MARTHA SANDY: Okay. Last call for

7 comments from the Panel.

8 (No response.)

9 Seeing none, we'll go to the EPA.

10 DR. SIGMUND DEGITZ: I do not have any

11 clarifying questions. Thanks for the comments.

12 DR. MARTHA SANDY: Dr. Touart, if you would be

13 kind enough to read Charge Question 5. 

14 DR. LESLIE TOUART: Les Touart reading Charge

15 Question 5. Considering the variability inherent in biological

16 and chemical test methods, a test method needs to be repeatable

17 and reproducible. 

18 A test is robust and reliable if the results

19 are repeatable and reproducible within a laboratory and between

20 different laboratories, respectively. A test protocol should

21 provide sufficient guidance to ensure proper and consistent

22 performance across labs and chemicals. 

23 Please comment on the test method for

24 robustness and reliability and the repeatability and

25 reproducibility of the result obtained with the LAGDA assay.

26 DR. MARTHA SANDY: Dr. Patino is the lead
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1 discussant.

2 DR. REYNALDO PATINO: I also lead this

3 question for the Medaka and I refer to some definitions of the

4 terms in the question. I refer you to our answer to that

5 question for definitions to save time here.

6 Each of the reference chemicals used for the

7 assay validation was associated with one or more endocrine

8 dependent AOPs in the ISR. Again, at a basic level, the general

9 reliability of the assay can be initially at least evaluated by

10 comparing the effects of pain with each test chemical against

11 the expected endpoint results of their respective AOPs.

12 With the exception of Prochloraz, which had

13 some unexpected effects on the thyroid on the basis of the AOPs

14 originally described, the assay generally performed as

15 anticipated, for the reference materials. 

16 To go one-by-one, perhaps, specifically,

17 Prochloraz was listed as aromatase inhibitor and with AR

18 antagonist activity. We've already mentioned that there is an

19 additional AOP for this compound that has been reported in the

20 literature that could potentially explain the unexpected results

21 of thyroid hypertrophy and hyperplasia that were noted in three

22 of the four studies where this was measured in the ring study,

23 where they measured -- determined effects of Prochloraz. 

24 It's likely that that unexpected finding may

25 be explained by the additional AOP and makes that result more

26 consistent with what one would expect. And again, I said
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1 already, I'll provide the reference that I have for that in the

2 report. 

3 4-octylphenol listed as a weak ER agonist. 

4 This compound did not yield consistent observations with plasma

5 vitellogenin, but when the effects were reported, they were

6 positive and that is an anticipated outcome. 

7 Similarly, also, effects on duct development,

8 especially in females, were noted that are consistent with the

9 ER agonistic activity of the phenol. So there is concordance

10 there.

11 Trenbolone is a strong AR agonist, resulted in

12 reproductive effects, duct development in particular in both

13 males and females that were consistent with its AR agonist

14 activity. For Benzophenone-2, it strongly induced vitellogenin

15 in both males and females and had gonadal and duct pathologies

16 consistent with this ER agonist activity, including 100 percent

17 sex reversal of the highest concentration in genetic males. It

18 also delayed metamorphosis and effected thyroid endpoints in a

19 manner that was consistent with the TPO inhibitor activity. 

20 So generally speaking, this passes the initial

21 level of determined reliability. It kind of consistently

22 reproduced, based on the expected of a piece of what's known in

23 the literature that you have.

24 To more specifically address the question of

25 reproducibility, the report, the ISR presented the results of

26 two sets of ring tests; one with Prochloraz involving four
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1 different labs and another with octylphenol with three labs.

2 In a test with Prochloraz, there was one, if I

3 read the report correctly, only one individual endpoint that

4 showed a consistent positive response across the four

5 laboratories and that was a concentration-dependent delay in the

6 involution of the oviduct in juvenile males. 

7 Also, below X or across the labs were fairly

8 similar, so this one probably passed the test. Among the

9 consistent negative results with this compound, all laboratories

10 recorded no delays in time to NF stage 62. No effects on

11 oviduct development in females. And in all cases, the genotypic

12 sex matched the gonadal phenotype. So those are consistently

13 negative across labs.

14 Technical issues, however, concerning

15 histological sample processing and reading and also with the

16 thyroxine analysis, prevented a full assessment of the

17 interlaboratory variability for many of the apical endpoints

18 associated with endocrine axis. So in this context, it was kind

19 of a mixed result in that regard. Some individual endpoints

20 performed well. Others didn't. 

21 In the second set of assays using octylphenol,

22 observations of thyroid histopathology or metamorphoses yielded

23 inconsistent results among laboratories. T4 comparisons could

24 not be conducted because of technical problems, as has been

25 noted in some of the laboratories. 

26 Female duct development, there was one
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1 endpoint that was consistent among, laboratories and in a

2 concentration-dependent manner. But the LOEC, even thought it

3 was a consistent result in the same direction,

4 concentration-dependent, the LOEC was about a 10-fold difference

5 between the lowest value and the largest value that was reported

6 by the different labs. 

7 Among the consistent negative results that

8 genetic sex matched gonadal phenotype, made a result there, in

9 terms of sex reversal. 

10 So overall, the interlab test yielded mixed

11 results. There were a few endpoints that showed consistent

12 results, especially for the thyroid and (inaudible) endpoints. 

13 And among the reproductive endpoints, the oviduct, both in males

14 and females, generally, was the only endpoint that where a level

15 of consistency was demonstrated. 

16 The ISR noted that some of the variability end

17 results may have been due to technical difficulties with

18 procedures or in some cases by the lack of complete adherence to

19 the guidelines, but this could indicate a problem with the

20 transferability of the assay in its present form.

21 So overall, the Panel feels that the

22 reliability and robustness of the assay, for practical purposes,

23 has not been fully demonstrated for this assay. And again, the

24 same definition we used for the Medaka, robustness here is one

25 of the performance of the assay under the conditions of a

26 relatively inexperienced labs. Those are my comments. Thank
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1 you.

2 DR. MARTHA SANDY: Thank you. I'll go to the

3 associate discussants. Mary Christman.

4 DR. MARY CHRISTMAN: I concur with what Dr.

5 Patino said. I would add or emphasize that I am not sure that

6 reliability or robustness has been demonstrated yet. And Ken's

7 comment, if I may. Dr. Portier's comments are: nothing to add.

8 DR. MARTHA SANDY: Okay. Dr. Denver.

9 DR. ROBERT DENVER: I concur and have nothing

10 to add.

11 DR. MARTHA SANDY: Dr. Blazer.

12 DR. VICKI BLAZER: I have nothing to add.

13 DR. MARTHA SANDY: Dr. Propper.

14 DR. CATHERINE PROPPER: I concur and I only

15 have one small point to add, which is standing alone negative

16 data on gonadal or gonadal duct findings does not necessarily

17 mean there isn't some biochemical shift that's going on that

18 could affect long-term fitness outcomes in this assay because it

19 does not explicitly hit that adverse outcome potential unless

20 you have no gonadal duct. It makes it difficult to evaluate for

21 population level types of effects and will need to be coupled

22 with an assay that does make those outcomes -- or does evaluate

23 those outcomes.

24 DR. MARTHA SANDY: Any comments from other

25 members of the Panel?

26 (No response.)
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1 None. All right. Dr. Degitz from EPA.

2 DR. SIGMUND DEGITZ: I appreciate your

3 comments and I have no clarifying questions.

4 DR. MARTHA SANDY: Shall we read Question 6,

5 Dr. Touart.

6 DR. LESLIE TOUART: This is Charge Question 6.

7 The test protocol should be descriptive enough to be fully

8 transferrable to a functional laboratory. The protocol should

9 describe the methodology of the assay in a clear and concise

10 manner so that a laboratory could comprehend the objective,

11 conduct the assay, observe and measure prescribed endpoints,

12 compile and prepare data for statistical analyses and report the

13 results. Section 5 and 6 of the LAGDA ISR outline the process

14 of and challenges experience in the interlaboratory studies. 

15 Please comment on the transferability across

16 labs and provide any suggestions or recommendations for

17 improvement of the LAGDA assay.

18 DR. MARTHA SANDY: Dr. Propper.

19 DR. CATHERINE PROPPER: So we've covered a lot

20 of the issues associated with improvement of the assays, so I'll

21 try to hit on those briefly. The first one being there is a

22 need for positive controls. 

23 Again, this has been brought up across all the

24 different assays and we've brought it up several times. We did

25 see a lot of variability in the outcomes and some of that could

26 be taken care by very straightforward and directed protocols.
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1 Consistency or figuring out a way to have more

2 consistent sources of control water may be very useful in

3 limiting some of that type of variation because that's not

4 following the protocol kind of variation; consistency in iodine;

5 availability is important and density of the animals being

6 treated. 

7 One suggestion, and I don't know if the EPA is

8 planning on incorporating this into the weight-of-evidence SAP

9 that's coming, but figuring out how to incorporate all of the

10 data in a kind of weight-of-evidence approach may also

11 ultimately be useful, rather than evaluating all of these

12 different endpoints as their own individual outcome, might

13 provide some usefulness and actually reduce the potential for

14 variation across laboratories.

15 So really, most of our suggestions have now

16 been incorporated into a lot of our other responses to

17 questions. So I don't have much more to add beyond that. The

18 transferability, I think we've also pretty much addressed, in

19 that there are problems with transferability, but maybe these

20 can be addressed through these mechanisms we've already

21 mentioned.

22 DR. MARTHA SANDY: Dr. Propper, if you could

23 just clarify in your comments, I'll just reiterate, the

24 weight-of-evidence panel that's coming up is discussing

25 weight-of-evidence for the Tier 1 assays, and this is a Tier 2

26 assay. I believe I heard on another question, another assay, a
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1 recommendation to integrate the results from Tier 2. Do you

2 want to elaborate a bit on what you just said? 

3 DR. CATHERINE PROPPER: That's exactly what I

4 am suggesting is that find a way to integrate all of the

5 outcomes from the Tier 2 measures, either within one assay or

6 potentially across all assays to help support whether or not

7 there's evidence for the potential for adverse outcomes that can

8 have population level effects.

9 DR. MARTHA SANDY: Thank you. And now we'll

10 turn to the associates from Question 6. Dr. Denver.

11 DR. ROBERT DENVER: I agree with everything

12 that Dr. Propper said. I just want to reiterate that from

13 previous recommendations that modification of the histopathology

14 to provide for quantitative measures should be investigated. 

15 The plasma vitellogenin ELISA should be analyzed in a single

16 contract lab with appropriate expertise or a commercial kit

17 should be developed. These are just ways to improve the assay.

18 We did briefly mention measuring liver

19 vitellogenin mRNA as a possible sensitive measure and we'll

20 revisit that and provide some recommendations there. The

21 thyroxine assays, we recommend that they be dropped. And

22 lastly, that there be clear guidelines for statistical analysis

23 that should be established and adhered to.

24 DR. MARTHA SANDY: Dr. Patino.

25 DR. REYNALDO PATINO: Nothing needed to add. 

26 Thank you.
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1 DR. MARTHA SANDY: Dr. Blazer.

2 DR. VICKI BLAZER: I guess the only thing I'd

3 like to add is, as we mentioned in the Medaka, evaluating

4 general health in some way because a number of the frogs had

5 granulomatous inflammation and other inflammation which might

6 suggest infectious disease.

7 DR. MARTHA SANDY: Dr. Christman.

8 DR. MARY CHRISTMAN: I concur with Dr.

9 Propper, Dr. Denver, and Dr. Blazer's comments. Dr. Portier's

10 comments are: one gets the impression that the LAGDA assay has

11 higher likelihood of being transferable among different labs

12 than was observed for the JQTT or the MMT. 

13 This may be because of the robust nature of

14 Xenopus laevis, which means that even with lab-to-lab

15 variability and adherence to the protocol, this animal should

16 perform and respond consistently to similar test concentrations

17 if the protocol problems are worked out.

18 DR. MARTHA SANDY: Are there any other

19 comments from Panel members on Question 6?

20 (No response.) I see none. I'll turn to Dr.

21 Degitz.

22 DR. SIGMUND DEGITZ: I have no clarifying

23 questions.

24 DR. MARTHA SANDY: I think we'll move on to

25 Charge Question 7, please.

26 DR. LESLIE TOUART: This is Charge Question 7
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1 for the LAGDA. The purpose of the validation process is to

2 determine the readiness of a test or inclusion in a testing

3 program. A component of readiness of a test is the evaluation

4 of the usefulness and limitations of the test, including the

5 classes and types of test substances that can and cannot be

6 tested. 

7 Please comment on the strengths and/or

8 limitations of the LAGDA assay.

9 DR. MARTHA SANDY: Dr. Propper is the lead.

10 DR. CATHERINE PROPPER: I apologize, but this

11 also includes summary, so it may be a little long. Amphibians,

12 generally, and Xenopus laevis in particular have been strong

13 developmental models for vertebrates in general, especially with

14 relationship to thyroid hormone-related issues associated with

15 development. And these animals have been used as a model for

16 endocrine disruption, specifically because of the readiness,

17 both gonadal and thyroid hormone outcomes, which is a particular

18 strength of this assay. 

19 Although it may be difficult in this assay to

20 determine whether the endocrine endpoints are a result of direct

21 endocrine mode of action or toxicological endpoint,

22 nevertheless, thyroid and gonadal and gonadal duct in

23 particular, histopathologies will be indicative of adverse

24 endocrine action following exposure. 

25 The exact mode of action may not be

26 determinable, but nevertheless, the adverse outcome potential
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1 can be evaluated such that the assay is a Tier 2 measure of

2 outcome on growth development and reproduction as endpoints that

3 can be necessary towards an AOP evaluation.

4 Because this assay ends in the juvenile stage,

5 however, a significant limit of this assay includes the

6 inability to determine what the adult outcome of developmental

7 exposure will be. And that's the point I brought up in the last

8 question as well. So it needs to be coupled with some kind of

9 adverse outcome with respect to fitness and potential population

10 level effects.

11 Because of the inability to determine

12 fecundity and fertility issues, the lack of overt histological

13 changes in the gonads during development -- and I said this

14 earlier -- doesn't necessarily mean there won't be adult

15 outcomes. Careful comparisons between this, and in particular,

16 the Medaka assay will be needed to really evaluate those kinds

17 of outcomes.

18 The one problem with Xenopus as a model, and I

19 believe that Ken Portier was the one that first brought this up,

20 is that it is a rather unusual amphibian, as far as amphibians

21 goes because in the adult animal, it's still completely aquatic.

22 So physiologically, it is a bit different from other

23 amphibians. There also already been documented some sensitivity

24 between strains in response to endocrine disruptors. 

25 So extrapolating the data from Xenopus out to

26 all amphibians needs to be done very carefully and in the
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1 context of both the literature and in perhaps, having an error

2 -- a level of buffer when setting LOEC levels in the regulatory

3 context.

4 Ultimately, the EPA charge question document

5 specifically states that the purpose of the Tier 2 assays is to

6 characterize the nature of likelihood in dose response

7 relationship of endocrine disruption of the estrogen, androgen,

8 and thyroid in humans and wildlife. 

9 While we find the logic, development, and

10 implementation of this assay has attempted to meet the nature

11 and likelihood of endocrine disruption through these modes of

12 actions, and this can occur through the toxicity or endocrine

13 disruption, the demonstration of the assay can determine the

14 dose response relationships and ultimately, an LOEC for

15 regulatory purpose has not yet been demonstrated and that's

16 because of all of the variation across laboratories that we saw.

17

18 However, a few endpoints that are related to

19 AOPs were remarkably consistent -- and Reynaldo brought that up

20 during his evaluation -- and they do provide specifically useful

21 information in a Tier 2 approach. 

22 Many of the other endpoints, there was a lot

23 of variation among labs with some labs finding effects and

24 others not. This variation suggests there will be considerable

25 risk of either false negatives or positives if this assay is

26 implemented in a single laboratory kind of context. 
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1 So therefore, in discussing the readiness of

2 this test for inclusion in a testing program, we believe that

3 with further protocol development, the assay may perform at

4 least as a yes/no outcome for some of these endpoints, but that

5 the use for it with LOECs may still be difficult.

6 So as a tool for risk assessment, applicable

7 to other wildlife and/or humans, which more the mammalian assay

8 that was evaluated previously, again, the endpoint outcomes are

9 suggested of toxicity or endocrine disruption. They will

10 provide valuable information with regards to risk assessment and

11 potentially regulatory action, but the interassay variation,

12 especially in the LOECs, suggests that extrapolating out to

13 other species is going to be pretty complicated because if you

14 can't even get the same results with one species across

15 laboratories, how do you even begin to evaluate this across all

16 wildlife. Again, I suggest that if it is implemented that a

17 buffer needs to be put in there when making a regulatory

18 decision. 

19 In summary, once the protocols are clear and

20 the QA/QC parameters are stringently outlined and met by the

21 contract companies running the assays, the LAGDA assay will

22 function towards the stated purpose of a Tier 2 assay. It has

23 potential for understanding the risk of adverse outcomes,

24 however, the variation across laboratories shows that the

25 validation to date has not demonstrated the capacity for

26 consistent dose outcomes leading to clear LOECs at this time.
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1 DR. MARTHA SANDY: And now we turn to the

2 associates. Dr. Denver.

3 DR. ROBERT DENVER: No further comments.

4 DR. MARTHA SANDY: Dr. Patino.

5 DR. REYNALDO PATINO: No additional comments.

6 DR. MARTHA SANDY: Dr. Blazer.

7 DR. VICKI BLAZER: No additional comments.

8 DR. MARTHA SANDY: Dr. Christman.

9 DR. MARY CHRISTMAN: I concur, and Dr. Portier

10 requests a power analysis.

11 DR. MARTHA SANDY: Comments from other members

12 of the Panel on this last question on the LAGDA?

13 (No response.)

14 Seeing none, I turn to Dr. Degitz of EPA.

15 DR. SIGMUND DEGITZ: I have no clarifying

16 questions. Thank you.

17 DR. MARTHA SANDY: This is a good stopping

18 point, I think, to take another break. Let's convene back and

19 ten after 3:00.

20 (Whereupon, a break was taken)

21 DR. SHARLENE MATTEN: Hello. We are ready to

22 reconvene. We're now about to begin the charge question

23 discussion on the aquatic and vertebrate assays. I am going to

24 turn the microphone over to Dr. Martha Sandy.

25 DR. MARTHA SANDY: Thank you. We are going to

26 address Charge Questions 1 and 2, we hope, today. We'll start
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1 tomorrow with Question 3. That's the plan. So I'll ask Dr.

2 Touart to read Charge Question 1, please.

3 DR. LESLIE TOUART: Thank you. This will be

4 the first charge question for the Mysid Toxicity Test. A

5 rationale for the test method should be available, including a

6 clear statement of scientific basis and the regulatory purpose

7 and need for the test method. 

8 The EDSTAC introduced the Tier 2 test as

9 having the purpose to characterize the nature, likelihood and

10 dose response relationship of endocrine disruption of estrogen,

11 androgen, and thyroid in human and wildlife.

12 Although the hormones produced and used by

13 invertebrates are not directly analogous to those of

14 vertebrates, example: estrogen, androgen, and thyroid, growth,

15 reproduction, development and other aspects of invertebrate

16 physiology and life cycle are known to be under endocrine

17 control. 

18 EDSTAC went on to note that chemicals that

19 affect these vertebrate hormones may also effect invertebrate

20 hormones, resulting in altered reproduction development and

21 growth.

22 Tier 2 tests were designed to be definitive

23 tests which generate sufficient data to characterize the

24 specific hazard of the substance and provide sufficient

25 information on dose response and adverse effects to permit risk

26 decisions. 
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1 Please comment on the rationale and purpose of

2 the assay as part of the Tier 2 testing in the EDSP, as

3 described in Sections 2.1 and 2.2 of mysid ISR.

4 DR. MARTHA SANDY: And the lead discussant on

5 this is Dr. Scott.

6 DR. GEOFFREY SCOTT: Thank you. I'd like to

7 thank my colleagues. We have put together sort of a summary

8 statement and I will start by going through that statement and

9 then we will let individual Panel members come back. Let me

10 just go ahead and get started.

11 The rationale and purpose defined for

12 inclusion of invertebrate or endocrine disrupting chemical

13 effects is clearly defined in the ISR document. Invertebrates

14 are significant parts of global ecosystems and thus, they must

15 be protected from the adverse effects of chemicals that disrupt

16 their endocrine system. 

17 Invertebrate endocrine systems will respond

18 differently -- actually quite differently than vertebrate

19 endocrine systems, and thus, they need to be addressed in terms

20 of ecotoxicological risk assessment through and EDC assessment

21 process. Both the mysid and the harpacticoid copepod described

22 in the document are relevant assays for addressing endocrine

23 effects in invertebrates.

24 While the initial EDSTAC charge for developing

25 the Tier 2 assays was focused on characterizing the nature,

26 likelihood, and dose response relationships of endocrine
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1 disruption of either estrogen, androgen or thyroid

2 invertebrates, including humans and wildlife, EDSTAC clearly

3 defined the linkage between that endocrine disruption in

4 vertebrates and the importance of invertebrates by further

5 stating, as is stated in the question, assessing the effects of

6 chemicals that disrupt the ANT, invertebrates should be further

7 invertebrates in invertebrates for disruption of growth

8 development and reproduction.

9 The current mysid and copepod bioassays

10 described in the ISR are designed specifically to address

11 alterations in survival, growth, development, and reproduction

12 in a way, and when taken in toto, to define endocrine effects in

13 invertebrates.

14 What is missing in these current invertebrate

15 bioassay approaches, however, is a clearly defined EDC mechanism

16 of action test endpoint or the hormone systems in invertebrates

17 for either ecdysone or other important controlling hormones that

18 control growth development and reproduction.

19 And this would be similar to the E, A and T

20 approaches that we have invertebrate testing. What is unclear

21 in the initial EDSTAC charge and in the ISR is the linkage

22 between the disruption of E, A, and T in vertebrates within

23 vertebrate testing and the linkage with the disruption of

24 invertebrate hormones. This is particularly important in seeing

25 how disruption of invertebrate hormones not found in

26 invertebrates will work in accompanying adverse outcome pathway
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1 approaches unless parallel and diverging AOPs for invertebrate

2 endocrine disruption are developed.

3 If an invertebrate adverse outcome pathway

4 approaches develop that will compliment the current A, E, and T

5 of AOP approaches for vertebrates, it will be important for EPA

6 to consider the large variety of invertebrate EDC pathways that

7 must be protected, given that crustaceans are only one member,

8 in addition to mollusk and other classes of invertebrates that

9 we are trying to protect in this approach. 

10 This is particularly important, given the

11 ecological -- not only their ecological importance as

12 invertebrates in ecosystems, but also the large commercial and

13 recreational importance of many of these invertebrate species as

14 fishery species around the world.

15 As a result, it would be very important to

16 consider multiple invertebrate species for possible

17 consideration as EPA has done. The ISR clearly defines why

18 vertebrate protection from EDC effects is important, and that is

19 the significant ecological linkage between invertebrates and

20 vertebrates in terrestrial, aquatic, estuarine, and marine

21 ecosystems. 

22 The ISR indicated the importance of the

23 linkage of the mysid test, for example, to estuarine ecosystems

24 for environmental protection. For example, in estuarine

25 systems, most of the important estuarine-dependent and

26 commercially important fin fish species -- in fact, in the
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1 southeast, over 86 percent of the fin fish species begin their

2 earliest life history stage in the estuary, consuming copepods

3 and later consuming arthropods and mysid shrimp and grass

4 shrimp, and then eventually penaeid shrimp as they mature and

5 then move out to the ocean. 

6 This crustacean cascade is vital to the

7 functioning of estuaries as nursery grounds for the vertebrate

8 fin fish species that utilize those for protection and growth.

9 Since 53 percent of the U.S. population lives

10 in only 17 percent of the land area of coastal areas around the

11 U.S., this results in more than 5.2 times more people per square

12 mile and 5.87 times more gross domestic product per land area

13 and the attending pollution that goes with economic development

14 is thus much larger in these area. Thus, providing species that

15 afford protection, not only to all invertebrates, but to these

16 coastal areas, we think, from ANOVA perspective particularly,

17 here, I'll speak, is critical. 

18 As a result, there may be greater potential

19 for impact in these coastal ecosystems, as I've said, as a

20 result. The current battery of invertebrate Tier 2 tests are

21 crustaceans, and indeed based upon their ecological importance,

22 but also their toxicological sensitivity.

23 Crustaceans are generally more sensitive than

24 other invertebrates in terms of their conventional acute and

25 chronic toxicity, as this was brought out in an earlier SAP

26 Panel, i.e., harmonization of FIFRA and Clean Water Act
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1 protocols. The FIFRA 5 was good and the crustaceans drove that.

2

3 So we really think tying this invertebrate

4 protection to crustaceans is logical and is sound. By

5 extension, the EDC testing crustaceans have played a critical

6 and effective role already in protecting the environment for

7 known endocrine disrupting chemicals. By Office of Pesticides

8 has used these current assays with the mysid test to safely and

9 effectively regulate insect growth regulating pesticides with

10 known endocrine properties for invertebrate adverse outcome

11 pathways; compounds such as Methoprene, Dimilin and Fenoxycarb,

12 just to name a few. 

13 The conventional one-generation mysid test

14 played a vital key role and effectively defined a risk-based

15 approach for regulating these EDCs, establishing clear dose

16 response relationships as prescribed by the EDSTAC process. The

17 mysid test provides a clear assessment of the ecological effects

18 to aquatic organisms. We also have to be concerned about

19 settlement dwelling organisms. 

20 Having assessment endpoints that can assess

21 sediment toxicity as well may be very important for certain

22 classes of chemical in certain particular pesticides, since the

23 fate of many chemical contaminants in aquatic and marine and

24 estuarine ecosystems may be sediment exposure.

25 The addition of the harpacticoid copepod assay

26 provides an additional assay for the assay for the assessment of
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1 EDC effects and invertebrates, which addresses not only aquatic

2 effects through the current pathways, but provides the potential

3 to predict effects in benthic communities and also can be

4 modified, as we'll get into later, into a sediment assay. 

5 Although that's not under consideration, but I'll simply point

6 that out.

7 I will also point out, in addition, the

8 copepod assay was developed, was assessed in study that we did

9 for EPA and found to be one of the most sensitive test species

10 for superfund site testing in the southeast, U.S. So there's a

11 clear and established role of a sensitivity of that assay.

12 Invertebrates are a critical part of the

13 ecosystem, and as such, need to be included in an endocrine

14 disrupting screening protocol. It is unclear, however, that the

15 rationale for inclusion of this assay in Tier 2 is adequate. 

16 According to the EPA Tier 1 test should screen for the potential

17 to interact with estrogen, androgen, and thyroid systems.

18 Further, they should be shorter term and lower

19 cost test with minimal chances of false negatives. The Tier 2

20 test should determine interactions with the endocrine system and

21 quantify dose response relationships. Further, it is

22 anticipated that these would be longer term and capable of

23 detecting modes of action. We interpret that to mean modes of

24 actions for invertebrates. 

25 The Panel endorses the use of an invertebrate

26 assay, but as prescribed in the protocols. But feels that
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1 without some Tier 1 invertebrate assay that focuses on a

2 chemical's potential to interact with the hormone system in

3 invertebrates or other appropriate invertebrate hormone metrics,

4 either ecdysone or what-have-you, that this Tier 2 test, in some

5 ways, is out of place without having some way to define the

6 potential to effect endocrine systems; otherwise, we're left

7 with testing all compounds with A, E, and T to be tested for an

8 invertebrate endocrine effects.

9 The Panel has raised the issue of a lack of

10 the Tier 1 screen for invertebrates in the current EDSTAC

11 process and the importance in how EPA would address the

12 identification of E, A, and T active compounds in the current

13 vertebrate screenings for selection of testing in the Tier 2

14 invertebrate protocol. So that's really where we spent a lot of

15 time talking.

16 Public comments by the Endocrine Policy Forum

17 raised this same issue and obviously, we're not the only ones

18 asking that question. The process at this point is we've asked

19 some question and EPA has said there will be a process. 

20 I know we have a weight-of-evidence panel and

21 a lot more can become apparent after that, so I don't want to

22 put the cart before the horse, but in the absence of that today,

23 I would just say that we would ask the EPA to consider if any of

24 the current invertebrate Tier 2 screenings might be effectively

25 modified to develop into a possible Tier 1 or a pre-Tier 2

26 prescreen to help us in this process.
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1 For example, the copepod bioassay, given that

2 it is miniature and small, may be a minimal. Similarly, the

3 current mysid test that we do with the conventional mysid F1

4 generation, not multigeneration, could also be considered for

5 modification. And we'll have some more information on that as

6 we go through and discuss this. As possible, either Tier 1

7 screenings or Tier 2 prescreens. Okay.

8 In the ISR, it is stated that Tier 2 tests

9 were defined to be definitive tests which generate sufficient

10 data to characterize the specific hazard of the substance and

11 provide sufficient information on dose response and adverse

12 effects to permit risk decisions. 

13 Both bioassays address these tenets of Tier 2

14 test requirements, both the mysid and the copepod. But will be

15 dependent, presently on a weight-of-evidence approach in

16 differentiating between overt toxicity versus endocrine-specific

17 effects, at least as we see it right now, given the non-EDC

18 specific nature of many of the test endpoints for growth,

19 survival, development and reproduction.

20 We also in public comment had similar concerns

21 about this expressed in some of the comments by the Endocrine

22 Policy Forum. This lack of consistency in response between labs

23 and between F1 and F2 generations and interlab testing

24 undermines not only the complexity of these assays to be able to

25 maybe move a spore as a test-to-test in a multigenerational

26 aspect, but also on the ability to define dose response
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1 effectively.

2 Additionally, the consistency in response

3 among many of the labs, in some instances, was low for certain

4 aspects of this. We'll get into a lot more of that in the

5 subsequent questions. So many of these issues about specifics

6 will be addressed in the subsequent questions.

7 In conclusion, let me just say the Panel

8 strongly appreciates the importance of including invertebrates

9 in the battery of tests included in the EDSTAC testing

10 requirements and acknowledges that some compounds, generally

11 anti-steroidogenic compounds such Prochloraz, could have a broad

12 affect on both invertebrates and vertebrates. 

13 The use of the Tier 2 mysid test, without a

14 representative Tier 1 or Tier 2 prescreen to determine endocrine

15 disruptive dose response effects on estrogen, androgen, thyroid

16 controlling processes in human and wildlife has questionable

17 validity and is counterintuitive, considering the current EDSTAC

18 framework.

19 This is due both to the lack of guidance of

20 EPA decision-making as to how the Tier 2 will be required

21 following the Tier 1 battery of tests. Due to the differences

22 in the actual hormones and endocrine receptor controls of

23 reproduction and development invertebrates compared to

24 vertebrates, this really underlies a complexity of this issue.

25 Therefore, the SAP suggests that a goal of EPA

26 should be to gain inclusion of invertebrate or ecdysone or the
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1 other steroid hormone processes in invertebrates that should be

2 added to these requirements, along with the estrogen,

3 testosterone -- excuse me -- androgen or thyroid, in order to

4 help build consistency between Tier 1 and Tier 2. This would

5 also require possibly a Tier 1 or a Tier 2 prescreen as we sit. 

6

7 Let me just real quickly, potential to use the

8 copepods for Tier 1 as a Tier 1 screen or other Tier 2 assays,

9 or other Tier 1 assays such as daphnids, using ecdysone steroid

10 receptors, juvenile hormone receptors or other types of assays

11 would also provide and invertebrate mediated process assessment.

12 And finally, the Panel approves the rationale

13 and purpose of the mysid to generation test as a Tier 2 screen. 

14 It believes that it is a very sensitive test to measure the

15 possible environmental impacts of endocrine disrupting chemicals

16 on invertebrates, but we do need that linkage with the

17 invertebrate endocrine system.

18 With that, I'm going to stop and we'll

19 conclude my comments and see if any of the other panelist want

20 to jump in.

21 DR. SHARLENE MATTEN: I wanted to clarify the

22 objective of this particular meeting versus -- because you've

23 been to the previous meeting, you have a flow-through system of

24 knowledge that perhaps mixes the apples and the oranges.

25 What I'm trying to say is the response that

26 related to Tier 2 for these charge questions is independent of
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1 what, at least for this charge, is independent of what leads to

2 that decision to have a Tier 2. It's as if you were given tests

3 for assays and you're evaluating the assays in how they formed

4 and could be transferred to other laboratories. 

5 While it's very exciting and very interesting,

6 it gets a bit confusing since the information that was provided

7 as background doesn't include all or a portion of the Panel's

8 responses to that particular charge question. 

9 So at least from the role of the Panel to

10 focus on those points that led to the evaluation of the

11 rationale and choice of those assays. And the other things that

12 you mentioned, save them. They are useful to consider, but I'm

13 not sure how to package them into the report other than some

14 kind of appendix that might be useful as points to consider for

15 --

16 DR. GEOFFREY SCOTT: Maybe I misunderstood the

17 question. The question said "rationale and purpose." I think

18 that's what we focused on. So if that's not what we're supposed

19 to focus on, my apologies. But that was what we focused on is

20 whether there is a rationale for including these assays and

21 whether they have a purpose.

22 The other parts of what you're asking for are

23 more specifically spelled out, as far as evaluating the

24 individual assays in the subsequent questions. So we did not

25 want to mix apples and oranges in the charge on this question

26 about rationale and purpose. 
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1 So if we should've done that, we'll go back

2 and can do that, but at this point, all we were trying to do is

3 talk about the rationale of having an invertebrate assay that

4 essentially has no adverse outcome pathway that is being

5 identified through a screening process for vertebrate assays and

6 whether or not that makes sense.

7 Now, if that wasn't our charge, my apologies,

8 but that's what we did and I thought that's what the question

9 asked. 

10 DR. MARTHA SANDY: I appreciate what you said

11 that this is your interpretation of the charge question. I

12 think for now we'll leave it at that. I'd like to hear if there

13 are any comments from the associate discussants. Dr. Tuberty.

14 DR. SHEA TUBERTY: So many of my comments were

15 included in Dr. Scott's comments already. I think it's

16 problematic approaching invertebrates as if they are going to

17 have the same axis, reproductive axis, thyroid axis as

18 vertebrates. And we all, I think, can appreciate that they're

19 very different in many ways. We've already acknowledged that

20 they make up the great majority of biomass of animals on earth

21 and are often overlooked. 

22 As an invertebrate zoologist, I'm used to it. 

23 And I have to convince my students that they've chosen the right

24 class to take when they take invertebrate zoology and then I do

25 that. But choosing one invertebrate to represent one species of

26 invertebrate to represent 95 percent or more, it's probably more
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1 like 99 percent of animals on earth is already an

2 underrepresentation, but we appreciate that they were selected

3 for inclusion in the Tier 2 assays. We appreciate the

4 opportunity to make a chance to evaluate the appropriateness of

5 these assays. And I think they have a lot of value and there is

6 value added in adding them to Tier 2.

7 Having said that, linking them to the few

8 existing compounds that pass through the Tier 1 assay battery

9 creates problems with deciding mode of action, if that's even

10 important anymore. One of the reasons why our group has

11 struggled with moving forward quickly is because of our trouble

12 in grasping what approach to take this issue. 

13 Do we just assume that invertebrates are never

14 going to be added to Tier 1, even though they're a huge

15 representation in other groups that are involved in testing?

16 As an invertebrate zoologist, I feel it's my

17 duty to all those invertebrates out there that I say they really

18 do need to be on Tier 1 tests. I think the two assays that we

19 were given to evaluate are both very good. Either one can be

20 used. And I understand the problems with adopting the copepod

21 for some groups that have never adopted it before, however, I

22 don't think that's a reason why they shouldn't be adopted and we

23 never move forward just because we're not there already. It

24 doesn't make sense to me, but we'll come to those questions

25 later.

26 I understand Dr. Matten's issue with the
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1 direction we took, but I think it's one that we put a lot of

2 effort and time and thought into, and we thought it was worth

3 saying on the record.

4 Having said that, there are lots of

5 opportunities to using cutting edge techniques, all of which

6 have been represented in the other three assays to date. I

7 think it's been kind of a patent answer that non-invertebrate

8 folks, and even us invertebrate folks have had to say we don't

9 know enough to do anything about this yet, but I think we're

10 going to push forward a lot of evidence. 

11 Some of it is very, very recent, as recent as

12 just a month or two ago that screening assays have been

13 developed that would give us insight into mechanisms of actual

14 endocrine disruption effects in invertebrates. Measurable ones,

15 mode of action and POAs that are very similar to those for

16 vertebrates, but probably not going to be the same compounds

17 recognized through the E, A and T axis tests that are done now.

18 Having said that, it seems like the small

19 number that, Mary, you gave us that moved up from the 10,000

20 screening, which is good there's only that many, but it might be

21 a different group altogether if we had a Tier 1 assay with

22 invertebrates represented there. So it's problematic in trying

23 to show a cause and effect when we have thyroid compound or

24 anti-thyroid compound that somehow is being toxic to

25 invertebrate and gives us crazy result in the F0 and F1

26 generations. We can't make sense of it and it clouds our
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1 judgment and it flies in the face of the science we have

2 available to us to try to do this kind of thing. 

3 But again, as Les has mentioned, the apical

4 endpoints are still really critical and they're very important. 

5 So if we show effects on reproduction and development and

6 growth, for me, that's good enough at this point, just that it's

7 there. Thank you. 

8 DR. MARTHA SANDY: Dr. Perkins.

9 DR. EDWARD PERKINS: I just wanted to support

10 what everyone else has said and also reiterate that I thought

11 the rationale for the purpose of putting a chemical through the

12 EAT AOP Tier 2 screening and also looking for effect on

13 invertebrates, the rationale is very good for that.

14 However, given the caveats that have been

15 given, it's going to be very difficult to provide a rationale

16 for effects on endocrine systems of invertebrates just because

17 we don't have enough data on that. 

18 However, the endpoints that are being looked

19 at, they are very important for risk assessment. And the data

20 has already shown that the vertebrate endocrine disruptors have

21 effects on invertebrates. So it's clearly a useful assay. 

22 Whether or not when the assay need to be applied and how it

23 should be applied is a different beyond the scope of this Panel,

24 clearly.

25 However, we do think -- I think we're at a

26 consensus that it is a very useful assay and it has shown a lot
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1 of different effects for it.

2 DR. MARTHA SANDY: Dr. Klaine.

3 DR. STEPHEN KLAINE: Thanks. I'm not going to

4 go and reiterate what all my colleagues have said, but I would

5 like to say that we took this really seriously and irregardless

6 of the entire scheme, you asked us to look at this assay. So we

7 felt that this is a good opportunity to bring some really

8 current science to this assay. 

9 So yes, there is some connection issues and

10 yes, there are some other issues in terms of what triggers a

11 test, but that's not what we're focusing on. So some of those

12 things may have come out in the rationale, but we have focused

13 on this test. So in deference to Dr. Matten's comments, I think

14 we focused on what we needed to focus on. So I hope you will

15 agree when you hear everything.

16 DR. MARTHA SANDY: Dr. Scott.

17 DR. GEOFFREY SCOTT: And again, if you read

18 through the question, it really talks about the EDSTAC charge

19 and the EDSTAC charge for linking EAT effects with

20 invertebrates. That's what we did.

21 While we may have stepped beyond the

22 assessment of just the Tier 2 test, we have kept the Tier 2 test

23 as the basis of looking at the recommendations I've alluded to. 

24 And the specific comments we're going to make on the Tier 2

25 test, I think are pragmatic and are designed to stay within the

26 context of what this SAP is about. 
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1 We certainly are not cavalier enough to think

2 we can come in the room and change the rules. Rather, we're

3 trying to answer the question and use as much innovation as we

4 could and the cumulative knowledge around the table to do that. 

5 And I will apologize if we overstepped, out of bounds. We'll

6 back in bounds. Hopefully, we'll decide that as we go through

7 the rest of our questions and decide whether it was a charge or

8 a block and fail, one of the two, because that's always hard to

9 call.

10 Anyway, we'll move forward.

11 DR. MARTHA SANDY: I'll see if Dr. Christman

12 has anything.

13 DR. MARY CHRISTMAN: I have no additional

14 comments. 

15 DR. MARTHA SANDY: Any comments from anyone

16 else on the Panel? I'll turn over to EPA. Yes, Mary Manibusan.

17 MS. MARY MANIBUSAN: Dr. Scott, I do hope to

18 clarify my sensitivities as I've articulated them to Dr. Matten.

19 I was very concerned with the bleeding over of topics. You had

20 several SAPs very close in timing. So I wanted to be careful

21 that this meeting wasn't particularly focused on the performance

22 of the Tier 2 methods and their validation process. 

23 I do understand the interest and excitement

24 over rethinking the Tier 1 so that it matches with Tier 2, but

25 because of where we are as a program in honoring a lot of the

26 recommendations that have been made by previous panels and where
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1 we are today with our test order issuance and evaluating of the

2 initial list of chemicals for the 11 set of assays for Tier 1, I

3 want to make sure that we're holding that steady as we move

4 forward into Tier 2. But we do understand the interest in

5 trying to correlate the Tier 1 information with how we make

6 decisions on which Tier 2 to trigger, if you will, when we do

7 see an interaction.

8 I have been often very parochial, perhaps, in

9 this meeting in establishing that the clear line between Tier 1

10 and Tier 2 because of the issue of the difference in certainty

11 and the difference in questions that we're asking. So Tier 1,

12 we're always asking, does it even have the potential, not for

13 different species, not for all modes of action, but for E, A and

14 T, recognizing that we're looking across different biological

15 levels of organization and trying to present a good enough

16 screen, not the best. Certainly not the most perfect, but a

17 good enough screen to allow the Agency to determine that of the

18 10,341 chemicals in our combined universe of FIFRA and SDWA

19 chemicals, how do we begin to prioritize which chemicals to move

20 forward for Tier 2, the more expensive longer-term testing?

21 So that's a challenge that we're facing. If

22 you're hearing this punctuated sensitivity, it's because of

23 that. Just recognizing where we are in the SAP process. I

24 wanted to make sure they were walking through each of those

25 steps incrementally and not bleeding topics over so that we're

26 confusing this data for science. That's it. Thank you.
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1 DR. GEOFFREY SCOTT: Thank you for that. We

2 certainly will try to maintain our focus as we can. Again, I

3 just go back, the question was rationale and purpose - not

4 specifics on the assay. 

5 So we tried to get into the philosophy of the

6 assay. I read that to be philosophy of why we're doing the

7 assay. So if we over-stepped, my apologies. And I understand

8 the sensitivity because we don't want to put the cart before the

9 horse. There is a process you all are following to do that and

10 all the Panel recognizes that.

11 DR. SHARLENE MATTEN: I have talked to one of

12 our leaders in our office, deputy office director, and one of my

13 colleagues just to see a way to figure out a tactful way to

14 include all the scientific information, which is what I was

15 trying to explain, but I got a little crazy and sounded a little

16 too strident what I was trying to explain.

17 I have this problem of the over-exuberant and

18 enthusiastic panel in January that had a specific question, but

19 they wanted to answer a whole bunch of other things. So the way

20 the report was designed was answer directly the question related

21 to Tier 2 first and then everything else that you said, these

22 were the other consideration and then because if you interpret a

23 question, you interpret a question. 

24 I was only trying to say separate out into the

25 bits that are Tier 2 and then take the rest of your bits all

26 nicely organized: and these are the other considerations that we
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1 want to suggest. That's what I was trying to point out.

2 DR. GEOFFREY SCOTT: Sharlene, I think we can

3 certainly do that in our final written report. I've captured it

4 the way I've captured it today, and we certainly will work with

5 you to make sure it's in the right format.

6 DR. MARTHA SANDY: Dr. Touart, do you have a

7 clarifying question?

8 DR. LESLIE TOUART: Not a clarifying question,

9 just a comment just to say we appreciate that kind of

10 thoroughness in thinking that the Panel had considered here and

11 the opinions you all provided. I also appreciate some of the

12 comments you heard that really our primary objective is the

13 focus on the test method in terms of its ability to look at the

14 apical endpoints and that the invertebrate AOPs are a little bit

15 outside the scope of what was intended. And I think that's

16 understood. 

17 So I appreciate that and I look forward to the

18 comments and opinions on the other areas as they would apply,

19 especially in terms of some of the new science, in terms of

20 other recommendations that may be made. I think, as indicated,

21 as long as those are captured or caveated in the context of

22 those recommendations. I think that's appreciated. 

23 DR. MARTHA SANDY: I think we're ready to move

24 to Charge Question 2 for the Mysid Two-Generation Toxicity Test.

25 DR. LESLIE TOUART: This is Les Touart and

26 this is Charge Question 2 for the invertebrate test. Test
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1 methods and their associated endpoints should be scientifically

2 relevant to the biological processes of interest and should be

3 demonstrated to be responsive to the specific type of effect

4 toxicity of interest. Each species presents unique

5 characteristics from a biological perspective and allows for

6 specialized endpoints to address a specific toxicological mode

7 of action.

8 Please comment on the biological and

9 toxicological relevance of the assay in regards to the stated

10 purpose of characterizing endocrine disruptors as described in

11 Section 2 of the Mysid Two-Generation ISR.

12 DR. MARTHA SANDY: The lead is Dr. Scott.

13 DR. GEOFFREY SCOTT: Thank you. According to

14 the EPA guidelines in the presentation, relevance is defined as

15 whether a test is meaningful and useful for a particular

16 purpose. The Panel members felt that both the mysid and copepod

17 assays described in the ISR have high biological and

18 toxicological relevance for helping define EDC relevancy as

19 stated in the MTTT ISR. 

20 Both assays have multiple and interrelated

21 survival growth development and reproductive endpoints that are

22 useful in describing whether or not a compound identified in

23 Tier 1 screening is altering A, E, and T will affect

24 invertebrates.

25 Given that A, E, and T are lacking in a

26 vertebrate endocrine systems, there are several possible
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1 outcomes which can occur in Tier 2 screening in the invertebrate

2 assays, including 1) the compounds may affect some invertebrate

3 hormone mediated pathway; 2) the compound may cause overt

4 toxicities through a non-invertebrate endocrine mediated mode of

5 action, or 3) the compound may affect both an invertebrate

6 hormone mediated pathway and cause overt toxicity through some

7 non-invertebrate ECD-mediated pathway.

8 The mysid shrimp bioassay has been used in

9 regulatory testing for pesticides and other chemicals for more

10 than 30 years. The current mysid shrimp standard testing

11 protocol; that is, the conventional test, have been developed,

12 which assess a variety of toxicity, growth, development and

13 reproductive endpoints and provide an integrated evaluation of

14 chemicals by assessing these endpoints and relating them back to

15 the neuroendocrine control, at least under the current

16 guidelines for Tier 2 testing. 

17 Again, we'll just say, the Panel felt one

18 shortcoming of this approach for the chronic assessment is that

19 these combination endpoints of growth development and

20 reproduction may be affected by chemicals that have both EDC

21 effects and non-EDC effects.

22 And again, the Panel felt linking those back

23 to some juvenile hormone or invertebrate hormone-mediated

24 pathway is important. The current test guidelines underway do

25 measure growth, survival, and reproduction, but do not

26 specifically measure molting. In discussing this among the
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1 panelist, since molting directly controls the endocrine pathways

2 in the organisms, while our indirect measures of time and growth

3 and development reproduction try to capture that we may want to

4 go back and rethink a little bit about how we further could

5 assess molting. I think that was one feeling we had since there

6 is that direct linkage for invertebrate hormones.

7 The addition of the multigenerational assay to

8 the current mysid assay was designed to enhance the

9 effectiveness of the traditional use one-generation approach,

10 using this assay to be more predictive of endocrine effects by

11 including the potential for maternal transfer to offspring as an

12 additional and significant exposure route. I think that is a

13 very good thing to look at, given research that we see in many

14 places on this.

15 The conventional mysid test has been used

16 safely and effectively as we sat and answered a question for

17 Question 1 to evaluate a number of compounds and in the current

18 test has been also used to look at a number of additional

19 compounds.

20 We really believe that the program to expand

21 and look at this assay, this new assay in the current protocols

22 for Test 2 were warranted. And then what we have done under the

23 mysid test is list a number of what we feel are strengths and

24 weaknesses of the mysid assay. 

25 I'm not going to read every one those; they

26 will be in the written because I've said many of these already. 
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1 I'm just going to try to make a couple of points. We felt the

2 test endpoints are simple and easy to measure. Males and

3 females are distinguishable by conventional microscopy. These

4 are under "strengths." 

5 It is a standardized EPA protocol and we're

6 adding additional duration of exposure to it with some more

7 endpoints. And it is a test that looks at growth development

8 and reproduction. The test allows comparisons between F0 and F1

9 -- excuse me, F1 and F2, and we feel that is very important. 

10 Currently, the test is recommended as part of the OECD

11 guidelines as, I think Les said, a Level 5 test. And we think

12 that's consistent with this being part of our battery here in

13 the U.S.

14 Under "weaknesses," we just listed a couple. 

15 The lack of a specific measure of endocrine disruption in

16 vertebrate systems. The flow through nature of the test does

17 generate more waste; for example, the copepod test. 

18 The EDC effects can be confounded by possible

19 overt toxicity. I feel this way, I don't think we're able,

20 necessarily, to distinguish intersex and things like that,

21 currently, with the assay. Now, if I'm wrong, you all correct

22 me on that, but that was my impression.

23 Let's talk a little bit about the harpacticoid

24 copepod since that is also listed in the protocol. It is an

25 additional assay that has been developed to assess endocrine

26 effects, similar to the mysid test, has been used to address
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1 multigenerational effects. It's quite complimentary to the

2 mysid test and it is also currently included under the Phase 5

3 testing in OECD.

4 As was stated in discussions earlier in the

5 Panel this week, the copepod test has the ability to possibly,

6 in some ways, discern endocrine effects slightly differently

7 than the mysid test, since you are able to follow individuals

8 more directly by the nature of the design in a 96-well plate. 

9 The copepod species addresses aquatic risk,

10 but is a sediment-dwelling organism, but it has been modified

11 for this. The test can be run in plates, generates smaller

12 amounts of waste. The disadvantage is that it is not widely

13 used and that was another advantage of the mysid test was that

14 the mysid test has been widely used in pesticide testing for

15 over 30 years. 

16 The copepod test will require this additional

17 training. There were problems with test delivery of dose with

18 the small nature of the wells and there were some problems in

19 distinguishing between life history stages reported by

20 participants in the assay.

21 The Panel feels the Tier 2 test should

22 quantify, when we can, the interactions between the vertebrate

23 endocrine system to help better discern the measurements that we

24 have in the current mysid and copepod assay. I know we've

25 already said that, so I won't dwell on it as to why.

26 One other aspect I should mention is the
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1 ability to do population modeling with the copepod assay and I

2 think also with the mysid assay. I think we should really try

3 to look at how, particularly in some of our discussions, we were

4 looking at the mysid assay, the current mysid assay, to see if

5 we had actually applied modeling to outputs from that in a

6 predictive way. So we just simply had that comment.

7 I think we heard from the public response

8 also, from the endocrine policy forum, the need to look at where

9 we have existing mysid data and how we might use that. I think

10 the Panel also feels that way and I think adding modeling to

11 that would certainly seem to possibly be a way to help move the

12 process along, although that was not part of the protocol, but

13 we simply add that.

14 Finally, the last comment I'll make, and then

15 I'll leave it to my colleagues here, the Panel felt that the

16 current OECD approach of using a toolbox approach with the

17 testing at different levels, which includes both the mysid and

18 copepod assay, Level 5 as a wise and warranted approach, given

19 the large diversity of biological diversity in the invertebrate

20 pathway in trying to protect these with an assay. This issue

21 will be further addressed in subsequent charge questions. 

22 With that, I will stop, other than saying that

23 we felt that the addition of molecular endpoints, as we stated

24 in Question 1, that are endocrine-specific, may provide us

25 guidance on better interpreting data out of either the copepod

26 or mysid assay. Thank you.
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1 DR. MARTHA SANDY: Dr. Tuberty. 

2 DR. SHEA TUBERTY: As a test method being

3 pushed forward for the assay, I think it's very good. I have

4 some experience myself. It does test very well for growth

5 reproduction and developmental effects. As a Tier 2 test, of

6 course, it's not looking for the mechanisms and they're not

7 supposed to. 

8 So as far as the demonstrating responsiveness

9 to endocrine disruption compounds, it's hard to know that's

10 happening, for the reasons we've talked about already. But as

11 important endpoints for success of populations of invertebrates,

12 they're all critical. 

13 So I think that the assay is sensitive to

14 those measures and can be used for risk assessment and

15 population growth or loss, evaluations as they have been done

16 already with this assay. So I'm very supportive of this assay

17 pushing forward.

18 I think this might be an appropriate time to

19 mention some of the things that Dr. Scott has mentioned for me

20 and that is for some of the endpoints that could be added. Like

21 the other assay, I think these are going to be things that we

22 give to you as a list of resources to look into. Mary, you've

23 mentioned you want references and whatnot, and we'll be doing

24 that too. But the ecdysteroid and the retinoid X receptors in

25 the last two years and in the last two months in one case, for

26 mysids, both neomysis and America mysis have their ecdysteroid
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1 receptor and RXR receptors, the heterodimer, fully cloned and

2 made into a screening assay and it's even been screened with

3 some of the endogenous hormones to look for positive control. 

4 It's very reactive and also screened with a number of, actually,

5 almost the same compounds used for both mysids, so you've got

6 some crosstalk there. And comparability with some steroid

7 agenesis inhibitors, also a non-steroidal ecdysone inhibiter. 

8 I'm curious as to what the mechanism is there,

9 which does not bind at all to the receptor or with low affinity.

10 So it seems to have a very good applicability here. 

11 Considering the techniques that are being adopted in the other

12 assays, it might be something worth looking into that could

13 serve as just another endpoint to a Tier 2. It could be using

14 the material at the end of the test or once the material in the

15 animals not chosen for the F1 pairings would give you a nice

16 endpoint there. Thank you.

17 DR. MARTHA SANDY: Dr. Perkins.

18 DR. EDWARD PERKINS: I have nothing further to

19 add.

20 DR. MARTHA SANDY: Dr. Klaine.

21 DR. STEPHEN KLAINE: I have nothing further to

22 add.

23 DR. MARTHA SANDY: Dr. Christman.

24 DR. MARY CHRISTMAN: I have no additional

25 comments.

26 DR. MARTHA SANDY: Any other members of the
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1 panel have comments on Charge Question 2? Dr. Scott.

2 DR. GEOFFREY SCOTT: I would just add one

3 additional comment that we will be addressing in subsequent

4 questions and you've heard this with a number of other

5 discussions on the other test protocols and that's the use of

6 positive controls that would possibly be known invertebrate

7 endocrine disruptors and positive controls. You will be hearing

8 from us on that as a way to help augment the current

9 measurements of growth, development, and reproduction that we

10 look at, which in addition to having specific measures of

11 invertebrate hormone, may provide a combination of approaches to

12 help better define the assay, in terms of discerning endocrine

13 effects from non-endocrine effects. 

14 That's a subsequent question, but I just

15 thought I would mention that because I do think what we're after

16 is helping you assure that what we call an effect is an

17 endocrine effect. And I think that's what the four of us have

18 been focused on like a laser beam because I think that's

19 hopefully what you wanted us to focus on. So we'll do that in

20 the subsequent discussion.

21 DR. MARTHA SANDY: Any other comments? I'll

22 turn to EPA staff. Dr. Tuberty, do you have any questions of

23 clarification?

24 DR. LESLIE TOUART: This is Les Touart. Thank

25 you for the comments and also thank you for signaling some of

26 the comments to come that are going to be helpful as well. I
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1 think I can appreciate and look forward to those.

2 DR. MARTHA SANDY: And Mary Manibusan.

3 MS. MARY MANIBUSAN: I just want to thank

4 this subset of the Panel for their thoughtfulness in engaging

5 with the Agency to push forward and advance in this area of

6 technology in looking at invertebrates. This certainly is a new

7 area for us too. 

8 So Dr. Tuberty, any of the papers that you

9 have that you could share with the Agency, that would be greatly

10 appreciated. I'm also interested if you have any information --

11 and I'm going back to Dr. Scott, your thinking about a Tier 1 or

12 a prescreen -- and I'm saying it, not you -- but if you have

13 information that would help the Agency think about a prescreen

14 or a predictive tool or technology that would help us kind of

15 decide how to prioritize chemicals for this particular Tier 2

16 assay, that would be very much appreciated. Thank you.

17 DR. MARTHA SANDY: I'm getting tired. I think

18 we're going to stop here on this set of charge question. We'll

19 take up Charge Question 3 tomorrow morning. I wanted to circle

20 back with the first set of assays that we had started with and

21 turn to Dr. Scanes to see if you had some comments on the dose

22 selection for the Japanese quail.

23 DR. COLIN SCANES: The group got together last

24 night and came up with a consensus, and that was, first of all,

25 we were talking about dose response relationships LOEC to the

26 base 2, 2.5, 3, and also the top dose. We came back with the
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1 conclusion, which is that this needs to be a judgment call based

2 upon the chemical and the anticipated results. 

3 So I think we've been learning quite a bit

4 from our statisticians, in terms of not having this one size

5 fits all. So coming back with the recommendation to use

6 judgment call, top dose could be based upon the LOEC or the NOEC

7 dose, but there does need to be significant discussion and

8 looking at the individual chemical.

9 DR. MARTHA SANDY: I'll ask if any of the

10 other discussants on the Japanese quail assay had anything else

11 they wanted to say? Dr. McCarty?

12 DR. JOHN MCCARTY: I think the only other

13 addition to that that came out of our discussion was that

14 thoughtfulness should be reflected when its reported. I think a

15 lot of what caught our attention in the first place was no

16 explanation. 

17 DR. MARTHA SANDY: Dr. Touart, do you have any

18 questions?

19 DR. LESLIE TOUART: Well, a comment, I think,

20 more than a question. In terms of the dose, I appreciate the

21 comments. That's very helpful. But if you have something more

22 specific in terms of how to phrase that within the test

23 guideline would be helpful, and also in the context of with

24 judgment, you know, would there be a recommendation that

25 range-finding would be necessary or other alternative steps than

26 range-finding test may be useful in terms of the judgments to
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1 establish what doses would be appropriate for the chemical to be

2 used.

3 DR. COLIN SCANES: We will have discussions on

4 that to try and come up with a consensus recommendation. By

5 bringing it out now, it gives us a chance to put additional

6 thought into this and really come up with a very thoughtful and

7 consensus response.

8 DR. LESLIE TOUART: That's helpful. Thank

9 you.

10 DR. MARTHA SANDY: Any other comments from the

11 Panel? No? I see Dr. Scanes has something to say.

12 DR. COLIN SCANES: Based upon listening to the

13 other species, the quail group wanted to put something on the

14 table to enable them to have further discussions afterwards and

15 then look at a recommendation. 

16 I think summarizing review of the Japanese

17 quail to a three-generation test, conclusions were to recommend

18 validity, but with a series of caveats. That's not necessarily

19 the same approach as other groups and we stick to our

20 recommendation; however, one thought that might be helpful to

21 the Agency or to the industry or to the public, would be the

22 possibility of beta testing. 

23 Now, we have not discussed this and come to a

24 recommendation, but we wanted to put it on the table to allow us

25 to, perhaps have a series of emails back and forth between the

26 three of us on that issue of beta testing. 
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1 DR. SHARLENE MATTEN: Just from a process

2 standpoint, once the meeting is over tomorrow, you can't really

3 go about having a consensus. All you can do is say that beta

4 testing is, whatever, an option or an issue or a something, but

5 you can't have these ongoing discussions and develop additional

6 conclusions. That's just more of a Federal Advisory Committee

7 Act process that we need to follow.

8 DR. MARTHA SANDY: I'll say that we don't

9 require consensus. If you would like to put on the record that

10 at this point you are recommending that they consider something

11 -- Mary Manibusan would like to speak.

12 MS. MARY MANIBUSAN: Clarification question. 

13 It's unclear to the Agency what you mean by beta testing. What

14 does that look like? What type of test design are you proposing

15 for beta testing? And to whom should the beta testing be

16 available to? So it's a lot more specifics behind just beta

17 testing?

18 DR. COLIN SCANES: This is where we were

19 hoping we would have the opportunity to spend a little bit of

20 back and forth after the meeting, but if it's not going to be

21 possible, then we raise the issue of were the caveats

22 incorporated into the protocol, including the training programs,

23 including the positive control? 

24 In order to test whether a contract lab could

25 deliver reasonable results, at least one of the members of the

26 Panel believe that this is something which may be helpful to the
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1 Agency and helpful to the regulatory process.

2 DR. MARTHA SANDY: Dr. Bentley.

3 DR. GEORGE BENTLEY: I would like to put on

4 the record that I agree with Dr. Scanes on this issue. I think

5 it would be very helpful to establish any contract labs or any

6 labs that are contracted to perform this particular assay. It

7 can demonstrate that they can provide expected results from a

8 positive control in looking at different endpoints in the assay.

9 MS. MARY MANIBUSAN: Clarification. 

10 Sorry. I'm still confused about beta testing and how that

11 differs from interlab validation. I'm just still confused about

12 what beta testing looks like in reference to that. And then are

13 you speaking to demonstrating laboratory proficiency, which is

14 part of a certification-like program?

15 DR. GEORGE BENTLEY: Yes, that's my

16 understanding of it. Having small studies involving positive

17 control with a small number of animals, looking at specific

18 endpoints that are described in this assay, demonstrating that

19 different labs can come to a consensus on the finding they have

20 and that they can actually produce the expected results.

21 DR. MARTHA SANDY: And your use of the term

22 "beta testing," are you referring to with a more complete

23 protocol and study design parameters that the U.S. EPA would

24 provide to the performing labs? That you would see if that was

25 enough?

26 DR. COLIN SCANES: That's exactly what we're
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1 suggesting, is that if the protocol is refined, the training

2 programs brought forward, among the other recommendations was a

3 positive control, to beta test that in the contract labs exactly

4 in the way that Dr. Bentley mentioned, such that not necessarily

5 they got a consensus in terms of the results, but they got what

6 was expected. 

7 And if one found that a contract lab, for the

8 sake of argument, had no effect of were Trenbolone to be used as

9 a positive control and they saw no effects at a level where

10 other labs have seen them. That gets into an issue of lab

11 certification and it also may get into an issue of was the

12 protocol clarified sufficiently that contract labs can execute

13 the protocol in a satisfactory manner.

14 DR. MARTHA SANDY: Dr. Klaine.

15 DR. STEPHEN KLAINE: What I'm interpreting you

16 saying is that you're thinking of something akin to a reference

17 toxicant test in which a laboratory would perform a ref-tox

18 test. That's what you guys are familiar with, a ref-tox test. 

19 And you know what the expected results are going to be and that

20 way you can gauge the performance. Is that what you're

21 describing? 

22 DR. COLIN SCANES: Correct.

23 MS. MARY MANIBUSAN: Sorry. I apologize

24 again. Third time asking. Let me see if I can repeat back what

25 I heard to be part of this beta testing approach.

26 So the beta test would be after we've
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1 established the final study protocol, along with all the

2 supplemental training materials, as well as a much more refined

3 test guideline. We would allow laboratories, contract labs to

4 come in and participate in a beta testing using a reference

5 known compound that we know the expected results and see if they

6 can demonstrate proficiency in performing that particular study

7 protocol for that known chemical. And that would be a part of,

8 in my mind envisioning, is an extension of an interlab

9 combination of having a positive control as well as

10 demonstrating lab proficiency in a certification process. 

11 DR. COLIN SCANES: I would say the answer to

12 what you said is yes, with one little wrinkle, and that is, were

13 there to be found to be a lack of clarity in the revised

14 protocol, that gives EPA a chance to revisit the protocol or the

15 training program.

16 DR. MARTHA SANDY: Dr. Bradbury.

17 DR. STEVEN BRADBURY: I think we're getting

18 convergence. Well because there is sort of this spectrum of

19 ongoing validation to the one extreme of doing it again to see

20 if you get a consensus because the consensus is around whether

21 or not the endpoint itself is reproducible as opposed to moving

22 into did we explain how to do this clearly and getting some data

23 back and going, yep, it looks like we explained how to do this

24 clearly, which is a little different than continuing the

25 validation process, per se. It's more like did we explain it

26 clearly. 
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1 And then the other point, which I think came

2 up in all -- well, we'll see about the fourth assay -- but the

3 first three assays was a robustness. I think Dr. Portier's

4 thoughts about what robustness meant, is it some aspects of

5 running an assay. You don't have to go right at 55 miles an

6 hour. You could go 50 or you can go 65 and it's going to work

7 pretty good, but there is some components of these four assays

8 where you really need to go 55 miles an hour because if your car

9 won't run 55 miles an hour, i.e., you can't do the genotyping. 

10 You're going to have a heck of a time pulling this off. You get

11 the idea.

12 So to the extent there is some insight on what

13 attributes of pulling off the four assays, you really need to

14 demonstrate you can do that. So that when you go forward with

15 the assays, you're likely to execute them reasonably. That

16 would be really helpful. 

17 So what are the key parameters where that

18 capability is really important to document. How we would

19 actually implement that, there are all sorts of options in doing

20 that. But for sure from the Panel, for me at least, and my

21 other colleagues can tell me he's nuts, but to at least know,

22 like out of the Medaka assay, these three things are really

23 critical, whatever number you all think and what they are is

24 really critical to pull these off carefully. 

25 You need to document you've got the diet

26 right, the genotyping is right, whatever the keys ones are. 
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1 That would be really helpful to get some feedback a) on making

2 sure we explain this really, really clearly and b) it may be a

3 way to factor that into a lab, feeling like they can do it

4 right. And that could include a positive control or something

5 so that they can document it.

6 DR. COLIN SCANES: It seems very reasonable

7 what you just said.

8 DR. MARTHA SANDY: Dr. Ehrich.

9 DR. MARION EHRICH: I work in a toxicology

10 diagnostic lab and we do this all the time. We get a sample to

11 come out to test. It's a protocol that is sent from the FDA or

12 the USDA and we have to see if we fit within a certain range. 

13 So this would be no different than doing what we do for

14 diagnostic labs to make sure you're fitting standards.

15 DR. MARTHA SANDY: Dr. Propper.

16 DR. CATHERINE PROPPER: I want to be clear

17 because that is part of an internal kind of control within any

18 assay you run, is to have those diagnostic criteria there and be

19 met. 

20 Let's say tadpoles and the controls always

21 weigh one gram at stage 62, but what Dr. Scanes is saying is

22 even before the labs are allowed to run these assays, they go

23 through a protocol of certification demonstrating they can do it

24 as a first step and then they must still have those diagnostic

25 criteria in there. Am I understanding it correctly?

26 DR. COLIN SCANES: From my point of view, yes.
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1 DR. MARTHA SANDY: Dr. Bentley.

2 DR. GEORGE BENTLEY: I was just going to say

3 that that's also true from my point of view. I think it allows

4 for lab certification without using a large number of animals in

5 the first place where a lab might not be certified or the

6 protocol might not be clear enough. So it allows for a

7 reevaluation and retinkering of the protocol, per se, before a

8 full blow test is performed.

9 DR. MARTHA SANDY: Dr. Propper.

10 DR. CATHERINE PROPPER: So I'm very glad you

11 all brought this up because I almost brought it up during our

12 discussion and then I wasn't sure whether it was really within

13 the purview of the questions. So then I was quiet about it, but

14 I do just want to go on the record in saying that I do agree

15 with some kind of certification as a recommendation, but I

16 understand that that's not really within our questions.

17 DR. COLIN SCANES: I hope I wasn't jumping in

18 too fast. And that was to reiterate something that George

19 Bentley just said. This is testing not only the contract labs

20 and their ability to do the protocol, it is also testing with

21 the clarified protocol, is that doable? 

22 So I think it's a two-way test.

23 DR. MARTHA SANDY: Yes. So it's not just

24 certification. It's also allowing the EPA to see if the

25 protocol is clear enough or if they have to tinker with it. 

26 Mary, did you have thing?
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1 DR. MARY CHRISTMAN: I actually have a

2 question. I'm not 100 percent sure of what you guys mean by

3 positive control, but if you were to perform such an analysis, I

4 would recommend that you blind it to the expected result so that

5 they don't know what the outcome should be; otherwise, you

6 cannot guarantee objectivity. I just wanted to point that out.

7 DR. MARTHA SANDY: Dr. Scott.

8 DR. GEOFFREY SCOTT: As an analogous

9 comparison, your chemistry, you have to have certification in

10 chemistry, the same way like the border horizon. You have to go

11 through a certification process before, you know, you ran

12 samples. We had to go through NIST and our lab validation

13 before we started running samples. So to me, this is being

14 consistent with this approach, even though chemistry is only

15 part of it, but certainly this is consistent with other

16 performance-based metrics.

17 DR. MARTHA SANDY: Dr. McCarty.

18 DR. JOHN MCCARTY: I just wanted to comment on

19 the statement that this might not fall under our questions. In

20 my opinion, this falls directly under our Question Number 6,

21 talking about transferability of this to other labs and part of

22 what we're saying if this is going to be successfully

23 transferred, it has to be a back and forth. 

24 Maybe this is my bias as an educator, but part

25 of transferring is demonstrate that I've transferred it to you

26 through certification or passing the test. So it does fall
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1 under our questions.

2 DR. MARTHA SANDY: Dr. Bentley.

3 DR. GEORGE BENTLEY: I agree with that. And

4 we were also asked to comment on how to improve the assay and I

5 think that this is a good way to improve the assay.

6 DR. MARTHA SANDY: Dr. Ehrich.

7 DR. MARION EHRICH: I don't know if formal

8 certification is something that you really need. You send it

9 out and you don't know what's going to happen and you get it

10 back and then you know because you sent it out. 

11 I don't know that you need a formal, you know,

12 I've got a certificate I can hang on the wall for this

13 laboratory that I can do this test. I think that's going a

14 little farther than necessary. But that's my opinion.

15 DR. MARTHA SANDY: Dr. Bradbury.

16 DR. STEVEN BRADBURY: This was interesting. 

17 As the DFO said, you can write things down that you want to

18 write down. I would encourage you to think about the places

19 you're going to write some of this down because, frankly, we

20 don't certify laboratories to provide the registrant's data to

21 us. There are GOP labs that need to follow test guidelines. 

22 There are things we look at when a study is

23 submitted to ensure we consider that study acceptable. If each

24 test guideline, blah, blah, blah, say for the current Fish

25 Early-life Stage Test, it's been validated and we what a study

26 looks like if it's good enough to accept or we're going to
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1 reject it and make them do it over again. And we can look

2 across control data across many labs. We can look at control

3 data within that lab. Sometimes things happen and you make

4 adjustments. Like I said, at the first stage, but we don't

5 certify labs. 

6 Now, having said that, you also have talked

7 about within each, I'm assuming all four assays, there are going

8 to be certain attributes of those assays that you feel are

9 really critical to have a high likelihood that the overall assay

10 can be executed well. I think giving feedback on that is very

11 helpful. And that could be used in a variety of ways. 

12 So my careful request would be to focus on

13 what are the key parameters that you need to be performed really

14 well - really well. You know what I mean. Because if you can't

15 do that part right, you're unlikely to pull off the rest of the

16 assay and giving some advice about how to communicate clearly,

17 what that needs to be so that if somebody is skilled and they

18 read the instruction, there is a high likelihood they could at

19 least execute it the way we intended so we can go yeah, it looks

20 like that lab has got attributes that suggests that they can

21 pull the assay off well. And it can even be something that

22 needs to be periodically documented within a laboratory or

23 something like that. 

24 But I just wanted to let you know that some of

25 that policy outcomes -- I mean, you can say what you want to

26 say, but just wanted to be frank with you that there are certain
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1 things that we just don't do in this program.

2 Dr. Scott talked about certification and

3 enforcement. If you're an enforcement lab and the data coming

4 out of your lab may be used in an enforcement case, that's a

5 different place, but that's not what we are in the registration

6 program.

7 DR. COLIN SCANES: While it may not be

8 something that is done, listening to the last two days, it's

9 very clear that many contract labs are missing the boat on

10 certain parameters or it may be that the protocol is unclear,

11 not allowing the contract labs to come up with a reasonable

12 response. And that's why we suggested a beta test, which was an

13 opportunity for the lab to try to replicate reasonable data. 

14 Now, that progressed into a certification

15 program. You know, one can get into a situation of definitions,

16 but if a contract lab cannot, for the sake of argument,

17 determine whether it's a male quail or a female quail,

18 genetically, then we've got a problem with all of the results

19 that come out of that lab. And I think that might be true in

20 other situations. 

21 So that's why we're suggesting that the notion

22 of a beta test, beta testing the protocol one because there may

23 be clarity issues beta testing the training programs, but having

24 the labs beta tested to see if they are coming out -- that they

25 can detect; for instance, maleness, femaleness, genetically.

26 DR. MARTHA SANDY: I was going to suggest
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1 everyone remember what they said and capture it in the

2 appropriate charge question. I'm now turning to Dr. McCarty.

3 DR. JOHN MCCARTY: I guess I want to turn

4 around and ask a clarifying question to you guys because we seem

5 to be falling into this semantics morass, and I've got to help

6 write this section. You don't like certification. What do you

7 want us to call this in the report? If you understand what

8 Colin is saying, what do you want us to call it?

9 MS. MARY MANIBUSAN: I think as Dr. Bradbury

10 has suggested, we move away from terminologies like "lab

11 certification" or "beta testing" and focus on what we're really

12 after, which is demonstrated performance from proficiency within

13 the laboratory for the study protocol. So what do we absolutely

14 need to make clear? 

15 What do these labs have to demonstrate so that

16 we know that our protocol is clear and it's understandable. 

17 It's doable. I would just focus on the attributes rather than

18 give it a name or requirement or numbers or labs or any kind

19 naming clichI like that. Just move away from that and just

20 focus on concepts.

21 DR. MARTHA SANDY: That might be good. 

22 Instead of using a term that might be interpreted differently,

23 say more clearly what exactly you mean. And I think it's been

24 stated by the Panel members multiple times. Dr. Propper.

25 DR. CATHERINE PROPPER: So my understanding is

26 that the emphasis we placed as several or all of the groups did
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1 on positive controls always being run with each exposure may, in

2 fact, help with that kind of -- I won't call it beta testing or

3 certification -- but may at least give you that performance

4 criteria that's consistent and may allow for the kinds of

5 outcomes that Dr. Scanes is asking for. Does the Panel feel

6 that that would be something that would function or would you

7 want more than that?

8 DR. COLIN SCANES: I think it would be helpful

9 that the positive controls was one of the strong recommendations

10 from the quail panel. But also to see that the first time it's

11 run to see if it works in the lab where they can demonstrate,

12 using Mary's term, "demonstrate proficiency." Demonstrate the

13 clarity of the protocol and the effectiveness of the training

14 program.

15 DR. MARTHA SANDY: I think we've covered this

16 well enough. Before we close today, I would like to give the

17 folks, the few Panel members that will not be here tomorrow

18 morning and would like to give them a chance to make any other

19 comments that they would like to.

20 We typically go around and let everyone do

21 that at the close of the meeting. Dr. McCarty, I believe you're

22 one of the individuals.

23 DR. JOHN MCCARTY: I think I've said what I

24 needed to say.

25 DR. MARTHA SANDY: Dr. Bentley, am I correct

26 that you won't be here tomorrow morning?
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1 DR. GEORGE BENTLEY: I won't be here tomorrow,

2 but yeah, I've said enough. Thanks.

3 DR. MARTHA SANDY: Dr. Kullman.

4 DR. SETH KULLMAN: I had one point on Question

5 4 with the Medaka assay, and it's really just a technicality,

6 but one thing that wasn't said and I didn't catch it at the time

7 was looking back at the ICCVAM criteria for validation purposes,

8 Criteria 5 suggests that a test method performance must have

9 been demonstrated to use a series of reference chemicals,

10 preferably coded to exclude bias. 

11 I just want to put on the record that in any

12 of the assays that I saw today that none of these compounds have

13 be coded. So there may be inherent bias. I think that comment

14 was just made as well in this last round of discussion. 

15 DR. MARTHA SANDY: Is there anyone else that

16 won't be here tomorrow morning?

17 (No response.)

18 Okay. I thank you all for all your hard work,

19 to the Panel, and I thank the EPA staff and all the audience. 

20 We will be convening tomorrow at 9:00 a.m. I would like to ask

21 the Panel members to join us in the back room as soon as we're

22 adjourned. Dr. Matten, do you have anything else to say?

23 DR. SHARLENE MATTEN: Thank you for the lively

24 discussion. I'm sure that as the report comes together that the

25 organization will be an effective way to address the charge

26 questions and provide the other additional comments in a way
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1 that is very illustrative. 

2 Most everything that could have been loaded

3 into the docket has already been loaded. I'm just waiting for

4 the quality assurance process to be completed before it's

5 available to the public through the open portal, under

6 regulations.gov.

7 So I thank you again for your attentiveness

8 and appreciate the fact that everyone is leaving but me. I

9 know; my children just say the same thing. With that, I'll

10 close the meeting for today and I'll see everyone at 9:00

11 tomorrow morning.

12 (Whereupon a recess was taken for the day)

13

14

15

16

17

18

19

20

21

22

23

24

25

26
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1 DR. SHARLENE MATTEN: Good morning. Greetings

2 Panel. My name is Sharlene Matten. I am the designated federal

3 official working with the Panel and the Agency on the

4 Independent Peer Review of the Issues Concerning the Proposed

5 Endocrine Disruptor Screening Program, Tier 2 Ecotoxicity Tests.

6 This is June 28th. You are listening to a

7 live broadcast of the FIFRA Scientific Advisory Panel from

8 Arlington, Virginia. Thank you very much. I will turn the

9 microphone over to Dr. Martha Sandy.

10 DR. MARTHA SANDY: Good morning and welcome to

11 everyone. This is Martha Sandy. This is our fourth day and we

12 are discussing the Mysid Two-Generation Toxicity Test and a

13 Copepod Development and Reproduction Test. Before we get into

14 the charge questions for this set of assays, we still need to go

15 around the room and introduce ourselves for the folks that are

16 on the webcast. So again, my name is Martha Sandy and I am a

17 toxicologist. I work at the office of Environmental Health

18 Hazard Assessment, part of California EPA. 

19 DR. K. BARRY DELCLOS: Barry Delclos from the

20 FDA's National Center for Toxicological Research. 

21 DR. JAMES MCMANAMAN: Jim McManaman,

22 University of Colorado.

23 DR. DANIEL SCHLENK: Dan Schlenk, University

24 of California Riverside. 

25 DR. STEPHEN KLAINE: Steve Klaine, Clemson

26 University.
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1 DR. MARION EHRICH: Marion Ehrich,

2 toxicologist, Virginia Tech.

3 DR. GEOFFREY SCOTT: Geoff Scott, National

4 Oceanic and Atmospheric Administration. 

5 DR. VICKI BLAZER: Vicki Blazer, U.S.

6 Geological Survey. 

7 DR. REYNALDO PATINO: Reynaldo Patino, US

8 Geological Survey, Texas Cooperative Fish and Wildlife Research

9 Unit. 

10 DR. CATHERINE PROPPER: Cathy Propper,

11 Northern Arizona University. 

12 DR. EDWARD PERKINS: Ed Perkins, U.S. Army

13 Corps of Engineers. 

14 DR. ROBERT DENVER: Bob Denver, University of

15 Michigan. 

16 DR. DALLAS JOHNSON: Dallas Johnson, Kansas

17 State University, statistician. 

18 DR. COLIN SCANES: Colin Scanes, University of

19 Wisconsin, Milwaukee. 

20 DR. SHEA TUBERTY: Shea Tuberty, Appalachian

21 State University, ecotoxicologist. 

22 DR. JENNIFER FREEMAN: Jennifer Freeman,

23 Purdue University. 

24 DR. MARY CHRISTMAN: Mary Christman, MCC

25 Statistical Consulting. 

26 DR. MARTHA SANDY: We left off, we had
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1 finished responding to charge question two, and now we are ready

2 to move to charge question three. So if EPA staff, Dr. Touart,

3 wouldn't mind reading question three into the record please.

4 DR. LESLIE TOUART: Mysid Charge Question

5 number 3; the test protocol should be sufficiently detailed and

6 should include a description of what is measured and how it is

7 measured. The selection of endpoints within the assay should be

8 reflective of the biological processes of interest and the

9 endpoints should be intrinsically relevant and have established

10 sensitivity. The test protocol should demonstrate the ability

11 to measure the endpoints and provide adequate performance

12 criteria or evaluation. 

13 Please comment on the selection, optimization

14 and demonstration of the assay endpoints, as outlined in

15 Sections 3 and 4 of the mysid ISR.

16 DR. MARTHA SANDY: Dr. Perkins is the lead on

17 this. 

18 DR. EDWARD PERKINS: During the course of two

19 lifecycles, over 60 days, over 20 endpoints are collected with

20 optional biochemical measurements suggested. In general, we

21 felt the test protocol for the MTTT is described in sufficient

22 detail the requirements for test acceptability presented well.

23 However, additional details on animal

24 husbandry and maintenance should be provided as the mysid can be

25 easily disturbed during testing through lights and other

26 factors. The Panel will provide further details on animal
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1 husbandry that can be helpful if included in the protocol. 

2 However, the endpoints are also reflective of general toxicity

3 that might be caused by nonendocrine disruptors. 

4 The endpoints are not only reflective of

5 mysid, crustacean endocrine impact such as growth, molting,

6 reproduction, but they are also reflective of general toxicity

7 and might be caused by non-crustacean endocrine disruptors. 

8 Therefore, the selected endpoints are appropriate for detecting

9 the apical adverse effects. There is regulatory concern that

10 vertebrate EAT AOP act of chemicals may have on invertebrates,

11 regardless of whether or not they directly disrupt the

12 crustacean endocrine system. 

13 The Agency should consider inclusion of assays

14 for additional molecular endpoints such as activation of

15 ecdysteroid receptor, which have recently been developed as was

16 mentioned in an earlier response to question two. These

17 endpoints will provide more mechanistic information on effects

18 to be used in interpreting apical effects seen in the assay. 

19 In the absence of a direct measure of

20 invertebrate EDC effects, such as changes in endogenous

21 ecdysteroid or juvenile hormones, EcR/USP or RXR receptor ligand

22 complexing, inferences indirect based on these EDC-specific test

23 endpoints. In none of the optimization or demonstration tests

24 was it possible with a mysid or copepod assays discern specific

25 invertebrate endocrine effects per se in a mechanistic manner. 

26 A large number of endpoints results in many
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1 endpoints being interrelated, given that none individually are

2 definitive for endocrine disruption, the use of multiple

3 endpoints allows for a weight of evidence approach to draw

4 conclusions from the assay. The integration of these endpoints

5 is critical when attempting to understand cumulative effects of

6 a potential endocrine disruptor using these test endpoints. 

7 More importantly, inconsistencies among endpoints can be

8 examined for indication of other modes of action. 

9 In the ISR, the responses of the endpoints

10 were noted, but no attempt was made to characterize

11 interrelatedness or lack thereof of responses. This issue was

12 noted for endpoints measured in both generations during the

13 assay. 

14 One Panel member did question whether all the

15 endpoints are in fact needed for characterizing the effects of

16 endocrine disruptors on invertebrates. They suggested that it

17 would seem, for example, that weight and length measurements may

18 not be needed to be taken at three time points, rather two might

19 be sufficient and that the first brood size rather than the size

20 of the first two broods may be sufficient. 

21 The optimization demonstration of the

22 endpoints appears sufficient. However, the amount of

23 variability and general disagreement seen in inter-laboratory

24 testing of what endpoints are effected at which dose suggests

25 that either the protocols for carrying out the tests or

26 determining when an endpoint is impacted need further
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1 optimization and demonstration before the test can be reliably

2 reproduced or used in non-expert laboratories.

3 In the multigenerational testing approach,

4 some concern is placed on the loss of highly sensitive

5 subpopulations of males and females in the F0 population, which

6 may result in have an F1 population that is less sensitive to a

7 chemical, thereby resulting in a lower reproductive impact than

8 seen in the F0.

9 Given the known potential for many pesticides

10 that cause increased tolerance in development of pesticide

11 resistance, this is a valid concern. Public comments by the

12 Endocrine Policy Forum Presentation indicated concerns about the

13 higher NOEC in the F1 versus the F0 for the mysid test. In

14 additional studies using the copepod bioassay, development of

15 pesticide tolerance and resistance has been shown for some

16 pesticides such as chlorpyrifos. 

17 The Panel encourages discussions between the

18 EPA and Pesticide Registrants to use any known information on

19 development of pesticide resistance for a compound that may add

20 value to our conduct of these Tier 2 tests. 

21 DR. MARTHA SANDY: I will ask the discussants

22 on the question, the associate discussants if they have anything

23 to add. Dr. Tuberty? 

24 DR. SHEA TUBERTY: I have nothing further to

25 add.

26 DR. MARTHA SANDY: Dr. Scott?
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1 DR. GEOFFREY SCOTT: Just a reference of

2 Gardner et al. that will be added as the reference to what an

3 example of one of the molecular endpoints that was referred to.

4 DR. MARTHA SANDY: Dr. Klaine?

5 DR. STEPHEN KLAINE: My comments have been

6 incorporated. 

7 DR. MARTHA SANDY: Dr. Christman?

8 DR. MARY CHRISTMAN: At first, I took this

9 question to refer specifically just to the endpoints, in which

10 case Dr. Perkins' comments include anything I might have said,

11 but in thinking about this more, I thought if you are looking at

12 validation studies and you are interested in protocol, that

13 includes the statistical parts. What do you do once you have

14 obtained this information from these endpoints? And in that

15 sense, I do believe that the protocol is terribly incomplete. 

16 Let me give you an example. The test

17 guidelines document that was provided to us is supplementary

18 information says, page five, "The design of materials used for

19 the exposure system are optional. It is strongly recommended

20 that a statistician be involved in both the design and analysis

21 of the test results since the guideline allows for variation in

22 experimental design." It also goes on to say in the ISR, the

23 ISR recognizes that there are some problems here because it goes

24 on to say statistical guidance provided by the draft guidelines

25 is minimal, recommending that a laboratory include a

26 statistician in the process. 
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1 There is no guidance about appropriate

2 experimental design that should be used. The correct

3 comparisons are not addressed with respect to vehicle control or

4 regular controls, and the lack of guidance causes confusion over

5 the correct design, what types of tests should be performed, et

6 cetera, et cetera. So I would just concur with that statement

7 and make sure that it is clearly indicated that this protocol is

8 missing a lot of that information.

9 DR. MARTHA SANDY: Other comments from other

10 members of the Panel on this question? Dr. Klaine?

11 DR. STEPHEN KLAINE: I've got a question for

12 Mary. If you follow those guidelines and two different

13 laboratories ended up selecting statisticians to help them with

14 the design of the experiments, is it likely they'll come up with

15 the same design? 

16 DR. MARY CHRISTMAN: That is an interesting

17 question. I think it depends on how the problem is posed to

18 them, so how the laboratory sees the problem posed. 

19 DR. STEPHEN KLAINE: I was just curious --

20 that might explain some of the inter-laboratory variability.

21 DR. MARY CHRISTMAN: Well in this particular

22 case, in the mysid cases, I will mention later, Battelle did a

23 reanalysis of all of the data. They all used the same design as

24 it turned out. So it was not a problem, but more generally, it

25 could be an issue. That doesn't mean you can't compare across

26 labs even if they use different designs. You just have to make
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1 sure that the analyses were appropriate for the design that was

2 used. 

3 So I'm not claiming that it can't be done. I

4 mean, I like the statement about the involving a statistician,

5 of course. But the reality is that there needs to be more

6 guidelines as to what should be an optimal design for this

7 problem.

8 DR. MARTHA SANDY: Other comments from other

9 members of the Panel? Yes, Dr. Scott?

10 DR. GEOFFREY SCOTT: Just a followup to the

11 comment on the statistics for this assay, but also in light of

12 the presentation we saw yesterday on the statistics for really

13 looking at all the assays. I believe let's just don't pick on

14 this one, let's pick on them all. It would seem like we heard

15 yesterday the need for what I almost heard like a staff meeting,

16 not exactly, but something like that to guide something this

17 important. I don't know how the rest of the Panel feels about

18 that, but while we're picking on this particular assay on this,

19 I would bring up that broader issue across all the assays, more

20 specificity, like a statistics manual. I don't know how the

21 rest of the Panel -- if that's out of order, we can come back

22 and talk about it later. 

23 DR. MARTHA SANDY: Dr. Scanes? 

24 DR. COLIN SCANES: I fully agree with Geoff

25 Scott's comments. 

26 DR. MARTHA SANDY: Other comments? Dr.
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1 Touart, any things that need to be clarified?

2 DR. LESLIE TOUART: I guess some

3 clarifications in terms of some of the recommendations that are

4 heard, like on the weight measurements, to perhaps reduce those,

5 the more specific the recommendations could be made in terms for

6 our writing the guideline, if you could recommend which time

7 points may be better than other time points, like drop day zero

8 and only look at day seven or 14 or whichever you could do would

9 be helpful. 

10 With the statistical comment, I know there are

11 some other questions that deal with the statistics and

12 recognizing that designs that were used by the laboratory, say

13 in the inter-lab, and if those approaches were appropriate, then

14 those could form the basis for this additional, more

15 prescriptive guidance in terms of the statistical designs if

16 that is good. If not, then reconsider, so we would look for

17 some advice in terms of better guidance, recognizing that to

18 consult a statistician is a little weak in terms of specifics. 

19 But in terms of what more specific can be put into the guideline

20 in terms of the design and treatment level procedures would be

21 helpful for us. 

22 DR. MARY CHRISTMAN: I completely agree. I

23 don't know how specific it could be. It might not -- if we

24 wanted to do this across assays, it would probably not be too

25 specific. It wouldn't say, well clearly here is your major

26 source of variation. So you ought to put more animals per tank
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1 as opposed to number of tanks or something. But we can

2 certainly make recommendations about exploring that information

3 and then how you might use that information to optimize design. 

4

5 In other words, I can't get too specific just

6 because I don't have the raw data, but I can certainly, and same

7 with Dr. Johnson, I'm sure I'll let him comment separately. But

8 I do believe that we can provide guidance as to the approaches

9 that ought to be taken. 

10 DR. LESLIE TOUART: And that is fair and thank

11 you for that. I think the recommendation in terms of overall

12 for statistical workshop or discussions would be helpful to us. 

13 So we appreciate that recommendation. Thank you. 

14 DR. MARTHA SANDY: All right, I think we're

15 ready for question 4.1, if Dr. Touart would read that in.

16 DR. LESLIE TOUART: Reading the first part of

17 the question 4.1 -- demonstration of the test method performance

18 should be based on the testing of reference chemicals

19 representative of the types of substances for which the test

20 method will be used. Test substances should adequately

21 represent an appropriate range of responses and

22 physical/chemical properties for which the test method is

23 proposed to be appropriate. The selection of the most

24 appropriate statistical approaches depends in part on the nature

25 of the data and also on the design of the validation study. 

26 Statistical and non-statistical methods used to analyze should
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1 be described. 

2 Please comment on the selection of test

3 substances and methods (analytical and statistical where

4 appropriate) chosen for the demonstration and validation of the

5 mysid assay. 

6 Dr. MARTHA SANDY: Dr. Christman has the lead,

7 and I assume as we have done before, we'll split this question

8 into a couple parts, the statistical parts and the other.

9 DR. MARY CHRISTMAN: That is correct. So we

10 will start with the statistical procedures. I want to start

11 with what turned out to be a major question the other day

12 concerning blocking, if you recall. Dr. Touart in his

13 presentation had said that blocking was often ineffective, and

14 in the ISR itself, it says, "The design of the experiment should

15 be completely randomized, unless blocking is required. In that

16 case, blocks should be appropriately characterized by

17 environmental qualities." 

18 It then went on to say, "Blocking makes the

19 analysis of the data more difficult, especially if the

20 non-parametric approach is required, and so the preference would

21 be for a completely randomized design." In addition, the ISR

22 also said on page 30, and this is what motivated me to worry

23 about blocking, "Done its tests conducted after removing the

24 block effect indicates that there were significant differences

25 between treatment and controls," and then in parentheses says,

26 "the significance of these responses may have been over
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1 estimated due to removal of the block effect."

2 So there is a concern there from a statistical

3 point of view that you're not modeling correctly. So EPA

4 provided an additional document that goes back to something that

5 was reported in 2008 about determining whether blocks were a

6 problem or not. They had done a randomized block design, in

7 which they blocked -- at least as -- Dr. Johnson and I, when we

8 read it, believed that what they did was a true blocking design

9 and then went on to incorrectly analyze it to decide that there

10 was not block effect. 

11 So that was where the concern was, was

12 blocking being done and then being ignored as described by this

13 description in the ISR. We will just remove it, by the way,

14 here's a problem. If you go to the actual documents by the

15 individual labs, it turns out not one of them used a randomized

16 block design. They all used a completely randomized design, so

17 it's a nonissue now with respect to it, but I would recommend

18 the ISR be fixed because that is what generated the problem. I

19 don't know if you can fix a summary that is already written. 

20 Now with respect to other aspects, because of

21 the lack of detail about the statistical methodology that was

22 provided in the test guidelines, this particular assay was

23 probably the best analyzed of the bunch with respect to there's

24 still the issues of how to handle normality moving onto

25 heterogeneous variants, et cetera, et cetera, that we've seen

26 for all the other assays. So I'm not going to dwell on that. 
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1 But instead, I'd like to point out that what they did do was one

2 of the companies, Battelle, went ahead and reanalyzed all the

3 data using exactly the same methods. They did do them

4 independently, though. In other words, they treated each lab

5 separately, so the decisions about normality and non-normality

6 are still problematic. 

7 They also separated F1 and F2, or is it F0 and

8 F1? I'm not sure. I had problems keeping that straight all the

9 time. But the separated the two generations and analyze them

10 separately, et cetera, and I would still argue that this is

11 incorrect, that there are better ways to analyze these data to

12 increase your power to discern differences and take advantage of

13 some of the analyses instead of analyzing everything as its own

14 little data set. 

15 But they did do that. They did have one place

16 analyze it all, which was really nice because it allowed me to

17 see how it was done more clearly. They also very clearly

18 identified for every endpoint, they identified the experimental

19 unit that was used in the modeling, which was thank you, I

20 appreciate that. 

21 The other thing that was done here was EPA

22 requested Battelle to do what we've been recommending be done

23 all along, that there be an inter-laboratory comparison, which

24 they did. They also did a variance components analysis, tried

25 to identify where the sources of variation were, using the

26 controls among the labs, which was a very good thing to do, and
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1 I'm very glad to see that. I'm not 100 percent sure it was

2 completely done correctly, but it looks like it was. As far as

3 I can tell, it does look like it was done correctly. So it was

4 easy to follow. 

5 It was well written. They did do that

6 analysis, which was very nice to see. I'm not sure that they

7 followed up and actually drew conclusions out of it. They may

8 have simply provided results to allow EPA to draw conclusions

9 because I really didn't see any conclusions in there, but I just

10 thought I'd mention that. 

11 The other thing they did was a power analysis.

12 But once again, they still did the power analysis from, my

13 point of view, incorrectly because they used simple T test

14 comparisons as opposed to using the experimental design as part

15 of the analysis. So I have specific comments for particular

16 pages, but they all relate to these overview comments that I

17 just made that it was nice to see the inter-lab comparison. It

18 was nice to see the power analysis. It could have been done a

19 little better. It was nice to see the variance components, so I

20 was very pleased. I would have liked to have seen that kind of

21 thing done for all of the assays that I work on at least. So in

22 future, keep that in mind. 

23 Let me take a quick gander and see if there

24 was anything else. Yes, there was one more thing that came up

25 when Dr. Perkins just mentioned in question number three about

26 trying to synthesize the information about the endpoints. One
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1 of the things I would like to encourage thought would be to use

2 some sort of multivariant approach that does synthesize the

3 information from all of the endpoints and would allow you to

4 discern differences among treatments in a more holistic way. In

5 other words, in an animal level not at this endpoint level, but

6 to move onto that kind of more complete analysis in the future

7 and the recommendations that we heard for question three are

8 completely appropriate. 

9 So that was all I had to say about the

10 statistical methods. I will allow my esteemed colleagues to

11 discuss test substances and analytical methods. 

12 DR. MARTHA SANDY: Is there anyone else on the

13 panel that would like to comment on the selection of statistical

14 methods? Yes, Dr. Johnson?

15 DR. DALLAS JOHNSON: Perhaps we could have

16 said this several days ago, but it occurred to me last night and

17 I have been sitting here typing a little bit up about it. I

18 think it would be interesting or helpful to have it in the

19 report. 

20 A lot of the methods that are being used right

21 now are based just on statistical tests. I had a major

22 professor that always said confidence intervals had uniformly

23 more information in them than test statistics or hypothesis

24 tests. In this case, there is an interest in finding the NOECs

25 and the LOECs. I think it would be helpful to provide

26 confidence intervals for the mean differences between control
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1 and each of the dose levels. These confidence intervals should

2 provide a lot more information about the true differences

3 between controls and doses. These confidence intervals could be

4 based on Dunnett's procedure so that you can take into account

5 the multiple testing and the fact that you are testing

6 everything to control. If one provides confidences intervals

7 for the true mean differences, then it would seem that a power

8 analysis and coefficients of variations may not be as necessary

9 to consider. 

10 DR. MARY CHRISTMAN: I complete concur with

11 Dr. Johnson, and I just wanted to mention one other thing that I

12 forgot to mention earlier was when you do read the statistical

13 protocol aspect in most of the assays, there is quite a bit of

14 effort expended on identifying what is an outlier. But it

15 doesn't say what to do except we send that information to EPA

16 and they decide what to do. In most cases, if it truly is a

17 recording error, then of course it is appropriately removed. 

18 If it is not a recording error, I just want to

19 encourage thought about using statistical methods that are less

20 sensitive or more robust to outliers. If you have significant

21 outliers and you are not modeling the data according to a

22 probability distribution that recognizes that you sometimes have

23 extreme values in the tails. Examples include, a really simple

24 example is replacing the normal distribution with the T

25 distribution, which is a heavier tail distribution. So it

26 captures those more unusual observations at the two ends or
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1 moving to a statistical method that is robust to outliers. It

2 is specifically designed to handle outliers. So I just wanted

3 to bring up outliers as well. 

4 DR. MARTHA SANDY: Are we ready to move onto

5 the second part of this question 4.1 on the selection of test

6 substances and analytical methods? I'll turn to Dr. Tuberty to

7 lead off that discussion, 4.1. Maybe there is someone else that

8 has been designated or has comments ready?

9 DR. MARY CHRISTMAN: Well in our discussion

10 the other day, it was Dr. Tuberty was going to discuss the

11 appropriateness of the test substances and the analytic methods.

12 I don't remember who was going to take the lead, though.

13 DR. MARTHA SANDY: Would it be easier for Dr.

14 Scott to take the first crack at this?

15 DR. SHEA TUBERTY: Most of the comments came

16 from Dr. Scott. I have them here. I can read them if you'd

17 like. 

18 So the test substances used for the mysid test

19 include a large number of compounds for both the demonstration

20 and inter-lab testing. The compounds tested for demonstration

21 purposes, therefore Fenoxycarb, DCP and Fipronil include

22 compounds that were known invertebrate IGRs, especially

23 Fenoxycarb and they are very appropriate. 

24 With known impacts on crustacean hormone

25 mediated systems, I would include there that this mysid test was

26 in fact, developed specifically with many of these compounds in
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1 mind when designed. This was not the case for the optimization

2 with lindane, prochloraz, atrazine, which were estrogen,

3 androgen or weak estrogen androgen compounds. Perfluorodecanoic

4 acid, which is a thyroid disruptor, trials as these compounds

5 tested did not include known invertebrate and EDC effects, but

6 were in part based on EAT invertebrate effects and screening as

7 preliminary tests. This is way the Tier 1 screening will work

8 with future selection for Tier 2 testing as we understand it. 

9 Future testing should include the use of known

10 positive controls, which are known invertebrate endocrine

11 disruptors with new compounds as was described in more detail in

12 question two yesterday. That's all I have. 

13 DR. MARTHA SANDY: Additional thoughts, Dr.

14 Scott?

15 DR. GEOFFREY SCOTT: I think we had quite a

16 bit of discussion. I'll just add a couple. I think Shea

17 captured everything right there in our discussions.

18 The one comment I would make, and we will have

19 more discussion I think in the next question or two on positive

20 controls, I think it's very important with this particular

21 protocol lacking a screening endpoint specific to endocrine

22 disruption as we talked about yesterday, to include it's more

23 important probably for this assay than any of the others to have

24 positive controls. I think as you reform more assays with

25 positive controls and perhaps a range of positive controls, and

26 somebody said yesterday blinded positive controls, that really
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1 makes the labs have to perform. 

2 But I really believe you begin to develop

3 growth development and reproductive profiles that give you

4 specificity in deciding whether a compound that is an A, E or T

5 compound, perhaps most resembles an integrated growth

6 development and reproductive response that is most like an

7 ecdysone inhibitor or some other hormonal pathway. Now that to

8 me would be ideal in an ideal world. Whether in practice that

9 would actually occur, I'm not sure, but at least I think if you

10 include those, you do have that benchmark with which to say this

11 compound has 80 percent similarity, and I believe there are

12 similarity analysis that can be statistically run to identify

13 the degree of similarity. So at least in my mind, that's how I

14 would try to link up some of this with the positive controls. 

15 DR. MARTHA SANDY: Dr. Tuberty?

16 DR. SHEA TUBERTY: I guess I would like to add

17 that in the one example of Fenoxycarb as an IGR that was thrown

18 in here, the F1 generation was quite lacking and did not give us

19 a lot of statistical analysis to work with. I can't be sure,

20 but it seems like I know there are studies out there that would

21 have been better to use. I guess this is the demonstration

22 study that was reported with this particular group in mind, but

23 it seems like there would've been more applicable data available

24 out there. 

25 But that would have been the one time we could

26 have used the data to compare the other results to, as Dr. Scott
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1 mentions here, to determine if similar outcomes were found that

2 we could link them just by weight of evidence to endocrine

3 disrupting compound effect, not just generalized toxicity --

4 over toxicity. 

5 DR. GEOFFREY SCOTT: Just to make really clear

6 what I was trying to ramble and say here, I think the similarity

7 analysis leads you down the road toward helping with weight of

8 evidence. I think that is not intuitively obvious to everybody,

9 but let me just state that. 

10 DR. MARTHA SANDY: Dr. Perkins?

11 DR. EDWARD PERKINS: I have nothing further to

12 add.

13 DR. MARTHA SANDY: Dr. Klaine?

14 DR. STEPHEN KLAINE: Yeah, just to add onto

15 the previous discussion. In this particular assay, it seems

16 that we're really asking an organism to be very sensitive and be

17 consistent among all the different tests from lab to lab and

18 from test to test and from chemical to chemical. So the

19 incorporation of a positive control not only gives you the

20 ability to test how a lab is performing, but also allows you to

21 test the consistency in the culture. Because as I am sure you

22 are well aware, even when you do a simple acute aquatic

23 invertebrate bioassay, the sensitivity varies tremendously based

24 on the health of the culture, which isn't always apparent unless

25 you are doing something like a ref-tox test regularly. So I

26 think it's really critical here, even more critical than in the
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1 standard acute assays. 

2 DR. MARTHA SANDY: Are there any other

3 comments on the Panel? I'll turn to EPA and Dr. Touart, any

4 issues that need clarifying?

5 DR. LESLIE TOUART: I appreciate the

6 statistical comments. Those are helpful. But I do have a

7 question, when I hear the positive control, especially when I

8 hear controls -- plural -- and the context in terms of the

9 purpose of the positive control as I am understanding that you

10 are suggesting is that the positive control chemical that would

11 be chosen would be a known invertebrate-relevant compound that

12 effects ecdysteroid or some other feature, depending upon if you

13 can recommend a specific compound for that, but again the

14 purpose is in to evaluate or EAT active compound that is being

15 tested and the results seen and the pattern seen for that

16 compound correlates in some way in some way to the pattern seen

17 for the invertebrate-relevant endocrine-active compound. Is

18 that what's being suggested?

19 DR. GEOFFREY SCOTT: Les, that is what I would

20 suggest. I really think that's what I would -- if I was king

21 for a day, that's I would do. In fact, I think we heard a

22 comment it would be good to have blinded reference toxicants or

23 positive controls. I think if you are only going to end up

24 selecting one as opposed to going with multiple hormonal routes,

25 I think you probably could look at what you distinctly know

26 about known IGRs and pick the one pathway that appears to be
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1 most protective. 

2 That's an idea. That may not be right and I

3 don't know if the other panel members agree with that, but you

4 have to have some rationale to pick it if you're going to pick

5 one. I guess I would simply say that's consistent with the

6 logic in this assay in that we are using crustaceans because of

7 their, I believe in part because of their sensitivity in

8 protecting other invertebrates. 

9 So it would seem to me if you're using that

10 logic in the selection of which invertebrate, then you can also

11 use the logic of the pathway that protects the best to also be

12 perhaps used in the selection. We didn't really talk about

13 this, but I'll throw that out in my two cents.

14 DR. SHEA TUBERTY: I would just add that I

15 agree with Dr. Scott, but I think that the two things that need

16 to be weighed against one another is the benefit of knowing

17 whether or not the outcomes are in fact due to EDCs when we

18 don't have the Tier 1 screening first to identify that

19 initially. So if you want to separate the effect of just over

20 toxicity versus EDC-specific compounds in invertebrates, then

21 this is a way of doing it. 

22 So it would be the same assay with just

23 another cohort exposed to either before or after initially or

24 even just providing the data, so that when you look at the

25 results, you know that these are the types of endpoints that

26 should be affected. Not all of them, maybe just this one for
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1 this compound or this type of compound. In a few minutes, we

2 will talk about some of the data that were available to us for

3 assessment here. We put together some tables and tried to do

4 that thing given the minimal number that were run already. 

5 Actually, it wasn't that middle. It's about 10. 

6 But I feel the same concerns that this is not

7 giving us a whole lot of information about endocrine disruption.

8 That is what this Panel is here to talk about. We could talk

9 about toxicity effects and again, the apical endpoints are

10 really critical for population growth in these crustaceans and

11 very sensitive organisms that will in fact not just

12 invertebrates but protect vertebrates as well. I think that's

13 probably why they were put on here initially. 

14 DR. MARTHA SANDY: Any other thoughts on this?

15 So we move on to question 4.2 please.

16 DR. LESLIE TOUART: Yes, we can move on and

17 thanks for the comments and clarifications. That's helpful for

18 me. I understand now what you're suggesting. Reading the

19 second part of question 4, 4.2 -- the proposed mysid study

20 design uses three replicates per control and treatment group and

21 additional replicates require greater resources. 

22 Please comment on the proposed level of

23 replication and the subsequent statistical analysis of the data.

24

25 DR. MARY CHRISTMAN: The short answer is no

26 that three is not enough. Even Battelle's power analysis, the
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1 not-quite-correct power analysis indicates that three is

2 insufficient. There are several options. If it's just too

3 expensive to add much more replication is to reconsider the

4 treatment levels, the choices of treatment level. For example,

5 do you really need six levels if the behavior is in fact a

6 monotonic response? If it's highly nonlinear and you're not

7 sure what the form of that is, than you have a problem. You may

8 have to add replication.

9 But if you have some idea of a threshold

10 behavior or something, then you may not need the lowest dose. 

11 You may be able to combine that into the control, for example,

12 because this was the only study, the only assay of the four in

13 which the control had the same number of replicates as the

14 treatment levels. There is a reason why there were increased

15 controls. That aids in precision in the Dunnett's test, for

16 example. 

17 So trying to change treatment levels might be

18 an option. Running a different power analysis to identify how

19 much of a difference you actually can discern. Using the

20 correct statistical methods, I think, is an important aspect

21 that you also ought to consider. So those are the main

22 comments. I think you know you have too few. 

23 DR. MARTHA SANDY: Dr. Tuberty?

24 DR. SHEA TUBERTY: Outside the three block

25 replicate issues, there were some noticeable and understandable

26 issues with the design in regard to number of animals available
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1 for the F1 pairing and reproduction portions of the assay. The

2 tradeoff between mortality and loss of statistical power in the

3 F1 generation endpoints and measuring effects of the toxicant is

4 an enduring issue amongst EDC toxicologists. 

5 During the initial demonstration optimization

6 studies, there were many issues apparent with number of F1

7 available following reproduction in the F0, which is obviously

8 affected by the toxicity in chosen concentration ranges of the

9 toxicant. It seemed that for most toxicants, the highest

10 exposure concentration was chosen by taking anywhere from

11 one-third to one-one hundredth of the 21 day LC50. Sometimes,

12 that was a number chosen form a 96 hour LC-50, I believe. This

13 is a difficult decision to make. We all face it, and it must be

14 done cautiously because it will have significant impact,

15 obviously, on the outcome of the assay. 

16 Specifically in regard to the F1 generation

17 endpoints, due to the length of time for the exposure and the

18 number of things that can go wrong for those organisms along the

19 way, I would like to dis-acknowledge that in the comments by the

20 previous reviewers of the assays on page 43 of the mysid ISR, I

21 think hit it on the head when they state that higher

22 concentrations may be used due to lack of responsiveness and

23 this could be due to the chosen compounds as well. 

24 But higher concentrations might need to be

25 used, seeing that there was very low mortality throughout all

26 the studies. I think there was an average of about 88 percent
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1 survivorship, which is very good. So they suggested that maybe

2 an LC20 value would increase the likelihood of measuring an

3 effect while still preventing losses to the F1. That's all I

4 have to say.

5 DR. MARTHA SANDY: Dr. Scott?

6 DR. GEOFFREY SCOTT: I think Shea got most of

7 my thoughts and I agree with everything that he and Mary have

8 said. I think this question about toxicity in the first

9 generation is important if we assume the individuals that die

10 are the most sensitive. Because that then means what we're

11 looking at in the next generation may be more resistant, or

12 let's just say sensitive individuals for lack of a better term. 

13

14 So I really think one thing that would be

15 important in looking at the data we do have would be

16 potentially, and this is more a question for Mary and Dr.

17 Johnson, in looking at the results or those doses where there is

18 no mortality versus those doses where there are and comparing

19 those results in the two generations. While I realize you still

20 have the effect of dose that you do have in a lower number of

21 individuals contributing in the subsequent generation that could

22 be -- it may give you a way to hen at that question of concern

23 about sensitivity between generations. 

24 I think you obviously do not want to run a

25 test where you don't get a response. So I think this is one of

26 those fine juggling things you have to do in devising an
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1 exposure scheme that gives you the responses, then you want to

2 minimize and I would say with the survival you have -- what did

3 you say it was 88 percent I think Shea? To me, that's the range

4 you would really want to evaluate something like that at if you

5 lose 12 percent of your population. Remember, we don't have to

6 -- for nonhumans we do on population basis. It's a healthy and

7 well-balanced ecosystem, I believe is the term. That doesn't

8 mean every individual survives. 

9 So I guess my point it that at least allows

10 you to look at that question a little bit, maybe further with

11 some of the data you already have. 

12 DR. SHEA TUBERTY: Let me add one more thing

13 and that was a comment about the F1. I know Mary's hit on this

14 already. Mary has hit on the number of replicates, but the

15 target parings in F1 I believe was about seven. More often than

16 not, the comments at the bottom of the table said that the N was

17 three unless otherwise designated, and a few times it would be

18 between eight all the way to zero.

19 So it would seem like we were making a lot of

20 inferences based on an N of three, two, four, something like

21 that, which is awfully small. As Geoff has already mentioned,

22 that's calling out the most sensitive individuals, at least in

23 the parental population, assuming that the genetics would be

24 passed on. 

25 In the inter-laboratory statistical analysis

26 report that was generated from mysids, the CV that were produced
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1 for F0 versus F1 were quite different, further indicating, as we

2 might know already, that this is indicating these populations

3 are different. So 15 of the 25 F1 CVs exceeded the

4 corresponding the F0 CVs. What does that tell us? That there

5 is more variability in that population when exposed to the same

6 concentrations as the F0. 

7 It's quite telling. We'll talk about this

8 more later on, but the number of times the F0 population was as

9 sensitive or more sensitive than the F1 generation, if I was

10 someone that made decisions with the money and how it should be

11 spent, I might make the argument that maybe it's not necessary,

12 but as an ecologist that wants to be more conservative about our

13 effects of toxins the couple of times, and this was to say it

14 happened 20 percent of time out of 10, so two out of 10, if we

15 multiple it by 1000 compounds, then we are talking about a lot

16 more, were actually found to be more effective at lower

17 concentrations in that F1 generation. 

18 Regardless of our low statistical power, we

19 still were able to capture that. I just wanted to say we're

20 picking this assay apart, but having done it myself when you can

21 get that kind of number anyway, what does that tell us? If we

22 could be protective even 10 or 20 percent of the time in that

23 second generation, then I would argue that it is in fact

24 necessary. Again, when you're looking at just 10 compounds or

25 seven compounds and you only get one that hits on that second

26 one, that's fine but we're talking about 67 compounds, we're
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1 talking about six or seven, how many of them have to be out

2 there affecting our ecology before we start seeing loss of a

3 healthy environment. 

4 So I just want to make sure that we're not

5 just looking at 10 and 20 percent as insignificant numbers. 

6 We're talking about whole populations. Again, one assay

7 representing a million and one, 1.1 million invertebrate species

8 out there, we need to be cognisant that that number is not

9 insignificant. 

10 DR. MARTHA SANDY: Dr. Perkins?

11 DR. EDWARD PERKINS: The thing I want to add

12 sort of expands -- follows onto what Shea said is that in the

13 relating to the great variability seen in the inter-lab testing

14 that the low number of replicates also might play into some of

15 the variability seen between lab testing. As you've shown in

16 other assays where you increase the number of replicates, you

17 get much better results. And this also may come into a great

18 deal of play showing any consistency between labs. 

19 DR. MARTHA SANDY: Dr. Klaine?

20 DR. STEPHEN KLAINE: I have nothing further to

21 add. 

22 DR. MARTHA SANDY: Anything from other members

23 of the Panel? Yes, Dr. Johnson?

24 DR. DALLAS JOHNSON: I just wanted to add that

25 in this case where you have a small number of replicates, it

26 might be even more important to look at confidence intervals
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1 than in those cases where you have large sample sizes. So it

2 really -- a confidence interval will tell you what you know and

3 what you don't know. If the interval goes from minus 400 to

4 plus 675, you probably don't know much. Otherwise, if the

5 interval was a lot shorter than that, then you probably know a

6 lot. 

7 Thinking about this, I'm reminded about one of

8 my favorite statistical jokes about the scientist that came into

9 a friend of mine and said I got this data, I just don't know how

10 to analyze it. He says, well how much do you have? He says I

11 got lots. I got 80,000 observations. He said where did they

12 come from? Well they all came from this one cow. 

13 So you can have a lot of data and there's not

14 much information in it if you don't have very many replicates. 

15 I think also in reading this, it seemed -- and I don't know

16 about the science, so this could be completely off the wall, but

17 I noticed that in this case, each replicate could have, I think

18 30 to 40 invertebrates involved in that replicate. So the

19 question I have, I guess, is could I increase the number of

20 replicates if I decreased the number of invertebrates that are

21 in each replicate. And there might be some ways to think about

22 how one might do that. It probably depends on -- I know some

23 endpoints are measured on each invertebrate. Some endpoints are

24 measured on the tank. So anyway, it's something that I'll throw

25 out there that one can think about. 

26 DR. MARTHA SANDY: Any other comments from the
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1 Panel? Dr. Touart, any questions?

2 DR. LESLIE TOUART: I appreciate the comments

3 and just one clarifying question back with increasing

4 replicates. I think what I understood was that we first start

5 to look at controls, increasing the number of control replicate

6 tanks, that that would provide some advantage, but also consider

7 increasing the replicates and changing physical design and what

8 not to improve treatments and perhaps the value of replicates

9 may be more than number of treatments itself. Is that my

10 understanding?

11 DR. MARY CHRISTMAN: I'm sorry, could you

12 repeat your question?

13 DR. LESLIE TOUART: Again, just to kind of

14 cover what I think I heard you recommending was that in moving

15 to increase the replicates that we should first look at controls

16 to increase the number of controls as a first step as that

17 improves, but it would be nice to increase the actual replicates

18 across the test and in doing so, it might be worth tradeoff in

19 terms of increased replicates in a given level with fewer

20 levels. 

21 DR. MARY CHRISTMAN: Correct. There are many

22 ways you could proceed with this, but the two you just mentioned

23 would be one way to consider keeping the same number of tanks,

24 but varying the experimental setup itself. How many different

25 levels and things of that nature? The second thing is I don't

26 know enough about the actual physical lab setup. I don't know
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1 if your constraint is how many tanks you can fit in a water

2 table or how many tanks could be attached to a diluter system,

3 et cetera, et cetera. But if Dr. Johnson's recommendation about

4 maybe more tanks and few animals per tank or not too fewer but

5 some fewer, certainly the increasing in the breeding pairs, et

6 cetera, et cetera, things of that nature could also be

7 considered. 

8 DR. LESLIE TOUART: That's helpful. What also

9 would be helpful for those that are, I guess, experienced with

10 mysids and the test and the physical designs, any suggestions on

11 how one may go about that increase without pseudo-replication,

12 because that's the other concern that we have with the designs

13 that are practical within typical lab setups and diluter

14 systems. So, suggestions would be appreciated on that.

15 DR. MARY CHRISTMAN: Just to respond -- you

16 picked up a word that it a favorite word -- pseudo-replication. 

17 In fact, the lack of recognition of a tank effect leads to that

18 problem with pseudo-replication. So if the statistical model is

19 set up appropriately, that's not too much of an issue. The

20 problem is the lack of recognition of exactly that, which

21 doesn't seem to be too serious for the mysids, but some of the

22 other assays certainly. 

23 DR. MARTHA SANDY: Alright, I think we are

24 ready to move onto question five, Dr. Touart?

25 DR. LESLIE TOUART: This is the Charge

26 question 5. Considering the variability inherent in biological
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1 and chemical test methods, a test method needs to be repeatable

2 and reproducible. A test is robust and reliable if the results

3 are repeatable and reproducible within a laboratory and between

4 different laboratories respectively. A test protocol should

5 provide sufficient guidance to ensure proper and consistent

6 performance across labs and chemicals.

7 Please comment on the test method robustness

8 and reliability and the repeatability and reproducibility of the

9 results obtained with the mysid assay.

10 DR. MARTHA SANDY: The lead discussant is Dr.

11 Tuberty. 

12 DR. SHEA TUBERTY: So validation is a

13 scientific process by which the reliability and relevance of an

14 assay method are evaluated for purpose of supporting a specific

15 use. This is from the ICCVAM 1996 statement. Reliability is

16 the reproducibility of results from an assay within and between

17 laboratories and robustness has been defined by other Panelists,

18 addressing other species as a system that can handle variability

19 but maintain status quo and consistency. 

20 The pre-validation process included

21 demonstration of relevance or development of standard optimized

22 protocols and determination of readiness for validation. 

23 Validation in multiple laboratories and demonstration of

24 reliability across labs is the final process in this overall

25 process. Earlier inter-laboratory trials assessing

26 reproducibility of acute toxicity for certain aquatic test
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1 species demonstrated that LC50s may vary by a factor of four to

2 five among different labs conducting these acute toxicity tests

3 with the exact same species. You want to insert a reference to

4 Rayan and Petricelli (phonetic) from 1985. 

5 Our current testing of these more complex

6 multigenerational testing assays and protocols do not have

7 similar metrics to compare to, but appear to fall within a

8 similar range, more or less six, a factor of six based on the

9 Lindane metrics reported here by EPA despite the increased

10 exposure duration and increased complexities of the assays. So

11 I think the overall outcome there is that we feel that there is

12 fairly good reproducibility. Although the exact responses

13 between labs don't seem to match up very well, there are

14 responses between labs. 

15 We go on to another Panelist observed that one

16 laboratory was unable to provide sufficient data for the F1

17 generation and was therefore excluded from their analysis. 

18 Further, two laboratories that did provide data have results

19 that led to different conclusions. Overall, this indicates that

20 either one of the following occurred. The sample sizes are

21 simply too small for reproducible analyses. Can you guess who

22 put that in there? There are no treatment effects on the

23 measured endpoints and all significant results were type 1

24 errors. The appropriate concentrations of chemicals were not

25 used to show effects or the laboratories failed to observe the

26 protocols as directed. It is unclear whether the statistical
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1 methods are at fault since there is not sufficient information

2 to determine whether the tests for checking assumptions were

3 appropriate or adequate and how the decision for three

4 replicates was made. 

5 The description given for power determination

6 on page 43 of the EPA report is insufficient to determine how

7 the final sample size was decided since it does not discuss

8 whether the measures of time to first brood or length,

9 presumably body length, were for individual breeding pairs as

10 the measure of time or mysid shrimp length, or the average of

11 the tank from which they were taken. 

12 Since much of the analyses are for

13 experimental units as tank or basket within tank, the

14 determination of power at the individual animal level is

15 non-informative. 

16 As indicated in the ICR, an evolution of the

17 some of the endpoints of the protocol occurred over the course

18 of the studies, which allowed for more precise measurement, but

19 disallowed for direct comparisons between tests, the beginning

20 and the end of the demonstration process. For example on page

21 42 of the mysid ICR, "During the optimization study, total young

22 was evaluated as number per female, but the total was always

23 calculated in two ways. For the first calculation, reproduction

24 data for all females were treated equally. Therefore,

25 reproduction data for female that die before producing a second

26 brood or females no producing a second brood were being omitted.
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1 For the second, only the reproduction data for females that

2 produced a second brood were included. Both sets of

3 calculations were evaluated statistically and the results

4 occasionally differed. The guidelines should clarify which

5 approach meets the goals of the testing so that endpoint will be

6 evaluated consistently by various testing laboratories."

7 So this is one example that I picked up on,

8 and I understand why they wanted to exclude a zero from their

9 data. At the same time, that female that doesn't reproduce the

10 second brood when the controls are all reducing second broods is

11 a significant endpoint. Therefore, another endpoint was

12 generated, which was percent-reproducing females, which never

13 had a significant endpoint throughout the test. So they took

14 the data away from where it could've been helpful and put it

15 somewhere else or more informative and put it somewhere else

16 where it was less informative -- something to be considered.

17 In creating a simple table of reproductive

18 versus growth and development effects and survival, so three

19 different endpoints rather than 20 different ones that were

20 chosen for the mysid test, and looking over the nine or 10

21 different compounds tested, and then looking also which of the

22 generations, F0 or F1 were more sensitive. We've already talked

23 about the F0 being more sensitive a majority of the time, yet F1

24 still being important at least 20 percent of the time. This was

25 in attempt to look at the different known activities of some of

26 these A and T compounds on invertebrates. There is some
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1 consistency in the results in our tests, and that indicates that

2 100 percent of the time, we get growth and developmental effects

3 in the invertebrates in the mysid. In about 70 percent of the

4 time, we're getting effects in the reproduction. 

5 So again, these are really important apical

6 endpoints for risk assessment and they're both very responsive,

7 even to the E, A and T active compounds. So I don't want that

8 to be lost on EPA that this is still, and I'm sure you're aware

9 of this, this is still a relevant assay for looking at toxicity

10 whether or not it's specially an EDC compound. We know that

11 molting and growth, incremental growth in crustaceans and

12 arthropods specifically is under ecdysteroid control. That is

13 one of the most well understood hormonal pathways within

14 invertebrates. 

15 Looking at the time we talked about the

16 endpoints, day seven, day 14 or at the termination, in looking

17 at the lengths, they are not direct measures of hormonal

18 control, but they do indicate the incremental growth under the

19 control of ecdysteroids. So if you have a significant effect on

20 that growth rate, you're probably having an effect on the

21 ecdysteroid growth. Not to say nutrition and other stressors

22 can have an effect as well. But when we're looking at

23 population level effects, you never see mechanistic effects

24 inherited in those endpoints. We are not capable of doing that.

25

26 Dr. Denver made this point with some of the
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1 other assays. It's what we teach our undergraduates. You can't

2 do everything in one assay. So having that Tier 1 assay

3 available or some other type of a screening test available or

4 including an endpoint in that Tier 2 that is capable of doing

5 that would be the smoking gun, I think that some of this think

6 is important. That's all the comments I have ready. 

7 DR. MARTHA SANDY: Thank you. And the

8 associate discussants, Dr. Klaine?

9 DR. STEPHEN KLAINE: I have nothing to add. 

10 It is pretty well covered. 

11 DR. MARTHA SANDY: Dr. Perkins?

12 DR. EDWARD PERKINS: I've got nothing more to

13 add. 

14 DR. MARTHA SANDY: Dr. Scott?

15 DR. GEOFFREY SCOTT: Just one point -- Shea

16 really covered it all there. I think in looking at those

17 inter-lab calibration exercises of acute toxicity that we

18 referenced here, and in looking at the range, we saw in these

19 assays using the most sensitive endpoints. There was a question

20 raised early in this panel what constituted dose response. We

21 said LOEC may be that. I know some people were looking at that

22 and asking questions. Well, does that really define dose

23 response? 

24 I think if you look at those acute assays,

25 which were based on LC50s, you're in the middle of a dose

26 response group, most sensitive part. These assays were at the
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1 LOEC, at the bottom of the dose response curve. I think in some

2 ways the fact that it's pretty close tells me that my concerns

3 on dose response are somewhat better satisfied. So I don't

4 think anybody had pointed that out, but I would mention that

5 because I think that's pretty important. Anyway, I just wanted

6 to clarify that point. Thank you. 

7 DR. MARTHA SANDY: Dr. Christman?

8 DR. MARY CHRISTMAN: I concur with Dr.

9 Tuberty's comments. 

10 DR. MARTHA SANDY: Any additional comments

11 from the Panel? Then I will turn to Dr. Touart to see if

12 there're any issues that need clarification. 

13 DR. LESLIE TOUART: No clarification

14 questions, but I do appreciate the comments, especially yours

15 Dr. Scott. I think those are very helpful. I appreciate that. 

16

17 DR. MARTHA SANDY: I think we will take a

18 break now. It is 10:10 by this clock. Let's take a 15 minute

19 break and come back at 10:25. 

20 (Whereas a break was taken)

21 DR. MARTHA SANDY: I think the Panel members

22 have returned. Thank you. We're ready now if Dr. Touart, would

23 you please read charge question 6.1?

24 DR. LESLIE TOUART: The test protocol should

25 be descriptive enough to be fully transferable to a functional

26 laboratory. The protocol should describe the methodology of the
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1 assay in a clear and concise manner so that a laboratory could

2 comprehend the objective, conduct the assay, observe and measure

3 prescribed endpoints, compile and prepare data for statistical

4 analyses, and report the results. Section 5 of the mysid ISR

5 outlines the process of and challenges experience in the

6 inter-laboratory studies. 

7 Please comment on the transferability across

8 labs and provide any suggestions or recommendations or

9 improvement of the mysid assay. 

10 DR. MARTHA SANDY: Dr. Tuberty is the lead

11 discussant on this. 

12 DR. SHEA TUBERTY: Two major issues need to be

13 addressed, which will improve these current mysid bioassays. 

14 The first, use of positive controls, which we've talked about in

15 depth already, hit again on it here. Future inter-lab

16 calibration exercises should include positive controls with

17 known invertebrate EDC effects run at concentrations that should

18 always elicit these responses. Positive controls will add cost,

19 but are needed to assure the contract labs are adequately

20 performing the assays. 

21 Positive controls should be added, such as

22 methoprene or other known invertebrate EDCs, which would

23 initially run at multiple doses, but with increased confidence,

24 should be narrowed to a single dose with each new compound

25 tested. Some thoughts should be given to this idea of positive

26 controls, which may target different aspects of invertebrate
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1 endocrine pathways. As more and more data is generated for

2 specific invertebrate EDC pathways represented by these positive

3 controls, these QAQC data may become more important in

4 establishing other benchmark matrices or metrics for use and

5 comparisons on future EDC screening. 

6 This is in effect, doing QAQC with a purpose

7 as one is not only assuring the quality of these data, but also

8 enhancing our ability to better differentiate invertebrate EDC

9 effects with specific invertebrate mode of action effects. This

10 is particularly important in helping better distinguish what the

11 different combinations of test endpoint effects actually mean,

12 given the more indirect nature of discerning endocrine effects

13 with these Tier 2 assays. Since mysid tests have been conducted

14 on IGRs which have known AOPs for invertebrate-mediated

15 endocrine effects, it would be important for EPA to

16 statistically summarize those test endpoint responses for mode

17 of actions and then use those as a point of comparison with test

18 endpoints for future compounds tested. 

19 Using appropriate statistical comparisons,

20 similarity analyses for example, to given probability based

21 comparisons. This would provide a statistical-based approach to

22 the toolbox for future weight of evidence analysis. 

23 The second point is importance of defining

24 major or minor deviations in the labs performing the assay. The

25 EPA EDC screening program would greatly benefit from a hazard

26 analysis critical control point based approach used by other
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1 agencies for other regulatory purposes in reviewing the current

2 crustacean EDC bioassays. 

3 The EPA in writing the conformance criteria

4 should identify those test endpoints deviations, which represent

5 major and minor nonconformities. Identification of major and

6 minor nonconformities should focus on the conformance with

7 identified control test endpoint criteria in terms of the

8 survival, growth, development and reproduction endpoints. 

9 Defining these major and minor nonconformities can either be

10 statistically based, for example, plus or minus one standard

11 deviation of the mean or percentage based, less than 10 percent

12 of mean or minor, greater than 10 percent of mean for a given

13 test endpoint, which seems reasonable based upon best

14 professional judgement, including statistical analysis for the

15 endpoint. 

16 Major nonconformities should be significant

17 test deviations of the protocols that may affect bioassay

18 outcome or performance failures on positive and/or negative

19 controls requiring the assay to be rerun. Minor nonconformities

20 should be evaluated to determine if these deficiencies are

21 personnel, facility's equipment or test support, suppliers in

22 related issues. 

23 Review of these minor deficiencies should

24 address how these deficiencies will be corrected and should be

25 reported in subsequent bioassays to demonstrate that the

26 inadequacies have been addressed and corrected. 
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1 DR. MARTHA SANDY: Dr. Klaine?

2 DR. STEPHEN KLAINE: Nothing further to add. 

3 DR. MARTHA SANDY: Dr. Perkins? 

4 DR. EDWARD PERKINS: Nothing further to add. 

5 DR. MARTHA SANDY: Dr. Scott?

6 DR. GEOFFREY SCOTT: Nothing further to add. 

7 DR. MARTHA SANDY: Dr. Christman?

8 DR. MARY CHRISTMAN: I concur with everything

9 Dr. Tuberty said and anything specific to the statistical

10 approaches have been handled in questions three, four and five. 

11

12 DR. MARTHA SANDY: Thank you. Other comments

13 on this question from the Panel? We'll go to Dr. Touart of the

14 U.S. EPA.

15 DR. LESLIE TOUART: No clarifying question. 

16 DR. MARTHA SANDY: Would you please ready

17 charge question 6.2?

18 DR. LESLIE TOUART: This is the second part of

19 the charge question number six, 6.2. Based on the validation

20 results using the two different invertebrate species, the mysid

21 and the copepod, the EPA is proposing the mysid protocol as the

22 preferred Tier 2 assay. 

23 Please comment on the rationale to recommend

24 the mysid protocol as the preferred Tier 2 invertebrate assay,

25 as described in section 6.4 of the mysid ISR. 

26 DR. MARTHA SANDY: Dr. Tuberty, you are the
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1 lead discussant. 

2 DR. SHEA TUBERTY: Some Panelists felt the

3 inclusion of both the mysid and copepod bioassays was warranted

4 rather than selecting one bioassay at this time. Both assays

5 have their strengths and weaknesses and are generally equally

6 sensitive in comparisons presented by EPA in their presentation.

7 The use of both bioassays has been recommended by the OECD at

8 level five tests and the use of both bioassays provides enhanced

9 protection. 

10 In addition, the OECD has used a broader

11 toolbox approach for testing for invertebrates, which recognizes

12 the broad and diverse areas of biological species the

13 invertebrates represent, especially the crustacean, mollusk and

14 annelids in aquatic habitats.

15 The approach taken by EPA to use crustaceans

16 as their Tier 2 testing is wise given the overall greater

17 sensitivity generally seen in conventional, acute and chronic

18 toxicity testing of pesticides and other chemicals using

19 crustacean models. The choice of the mysid over copepod at this

20 point seems premature based on the results presented to date as

21 both assays have similar sensitivities and are responsive to

22 most chemical exposures. 

23 The major short comings of the copepod assays

24 seem to be more an issue of defining chemical exposures for

25 low-volume dosage, lack of stage specific morphological

26 character recognition and lack of familiarity with the testing
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1 protocol in general. Aside from determining the exposure

2 chamber chemical concentration issues, these are generally

3 issues than can be addressed through training and additional

4 experience with running the bioassay. Similarly, some of the

5 strengths of the mysid test are the familiarity and experience

6 of labs running the bioassay in large database EPA has developed

7 using the conventional mysid data mysid exposure testing. 

8 The present improvements to the mysid

9 multigenerational bioassay do not include taking advantage of

10 the current extensive mysid database and methods for exploiting

11 this database through population modeling and other uses of

12 existing data. 

13 If EPA is to select the mysid test over the

14 copepod test, the following additional inputs of data would help

15 the Panel in making this decision, including 1) Additional

16 testing of known invertebrate EDC compounds such as methoprene

17 and other known invertebrate hormone affecting chemicals in

18 mysids and copepods to better compare sensitivity and

19 effectiveness of multigenerational effects. These do not need

20 to be testing between multiple labs, but should be testing only

21 between the most competent with the goal of defining the

22 broadest and greatest sensitivity to full range of known

23 invertebrate EDC chemicals. 

24 2) EPA should select these chemicals for this

25 testing where existing mysid 1 generational data exists, and

26 these results should be modeled for multigenerational effect and
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1 compared with the bioassay results from the additional

2 multigenerational mysid and copepod testing. This will allow

3 two things to be defined, definitively determined, the value of

4 the mysid and copepod multigenerational test in assessing

5 invertebrate EDC effects, and secondly the value of the current

6 one-generational mysid test in predicting known invertebrate

7 endocrine disrupting effects. 

8 We are currently being asked to make a

9 decision with results from chemical exposures to a large variety

10 of chemicals, many of which have uncertain invertebrate EDC

11 effects and modes of action. The concern we have is that we are

12 not certain of which bioassays ability to currently discern

13 known invertebrate EDC effects across a broader spectrum of

14 invertebrate hormonal effects. Once this is established, we are

15 certain the assays can discern this and it will be easy to

16 decide which assay will be best.

17 Inclusion of the evaluation of the additional

18 analysis of the current one generation mysid bioassay results is

19 a clear indication of the Panel's recognition of the extensive

20 information that exists with the mysid bioassay, and its proven

21 performances in protecting the environment and regulating

22 current known invertebrate EDC chemicals or IGRs. 

23 As to the mysid protocol, the Panel has no

24 issues with the performance of the test. However, the large

25 number of endpoints seems to create duplication of effects. 

26 Therefore, consideration of a reduction in the number of
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1 endpoints assessed could streamline the protocol without losing

2 power to assess adverse impacts. 

3 In Dr. Touart's slide show a couple of days

4 ago, there is an acknowledgement that there were few endpoints

5 that had signal to noise ratios greater than two, and many

6 endpoints never demonstrated significant effects. Possibly an

7 assessment of more powerful endpoints could be made of the more

8 powerful endpoints and the resulting list of endpoints used as

9 guidelines to ensure inclusion of data to bolster these

10 endpoints when possible, even if this means reduction loss of

11 other endpoints. For example, the example I gave just a few

12 minutes ago regarding the second brood production in females in

13 the F1 generation.

14 There were two consistent comments from the

15 EPA Industry regarding negative issues with the harpacticoid

16 copepod assay that Dr. Scott's comments just a minute ago

17 identified, and that was the lack of ability to discern the

18 exposure concentrations and identify copepodite stages. The

19 Panel suggests that this issue is overcome simply by the

20 experience that will be gained once this test is required, and

21 that this shouldn't be the main reason why we don't move forward

22 with this second assay should it be accepted. 

23 DR. MARTHA SANDY: Dr. Klaine?

24 DR. STEPHEN KLAINE: I have nothing further to

25 add.

26 DR. MARTHA SANDY: Dr. Perkins?
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1 DR. EDWARD PERKINS: I just want to reiterate

2 that there was just a higher level of reproducibility seen in

3 the copepod assay despite all the difficulties and inexperience

4 of the labs in the assays. It was remarkably reproducible in

5 comparison to the mysid assays, which people supposedly had a

6 lot more experience doing, which supports suggesting that we

7 keep both assays despite the cost. 

8 DR. MARTHA SANDY: Dr. Scott?

9 DR. GEOFFREY SCOTT: As my fellow Panelists

10 have said, I think both assays have merit. We provided, I

11 think, some next steps and hopefully these are helpful next

12 steps and certainly feel optimizing for a wide range of known

13 invertebrate pathways may help us better define which assay may

14 be most effective. 

15 DR. MARTHA SANDY: Dr. Christman?

16 DR. MARY CHRISTMAN: I have no additional

17 comments. 

18 DR. SHEA TUBERTY: I'd like to follow up on

19 Dr. Perkins comment, and that is that an assessment of the mysid

20 studies looking through 25 inter-lab comparisons of just two

21 labs for Lindane, for example, really only one good

22 collaborative result came out of all of those 25 comparisons,

23 whereas if you look at the copepod assay, there were only given

24 maybe the same number of comparisons, only two that didn't

25 correlate as far as the direction and response, maybe the

26 concentration of responses didn't match up between labs, but a
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1 lot more consistent responses. That is just a cursory review. 

2 I think that development of that assay needs

3 to continue forward even given that the labs had less experience

4 with it, they still were able to create a lot more consistent

5 result. I think that's got to be one of the endpoints of

6 concern here. So although the mysid assay seems to be ready to

7 go right now and is definitely sensitive to the A, E and T

8 compounds, I think the harpacticoid copepods are going to be as

9 well. As far as getting false results, you're going to get a

10 lot few of them with the copepod test. 

11 DR. MARTHA SANDY: Other comments from the

12 Panel? Seeing none, Dr. Touart, any questions?

13 DR. LESLIE TOUART: No further clarifying

14 questions from me. 

15 DR. MARTHA SANDY: Shall we move onto charge

16 question seven?

17 DR. LESLIE TOUART: The purpose of the

18 validation process is to determine the readiness of a test for

19 inclusion in a testing program. A component of readiness of a

20 test is the evaluation of the usefulness and limitations of the

21 test, including the classes and types of test substances that

22 can and cannot be tested. 

23 Please comment on the strengths and/or

24 limitations of the assay, as described in section six of the

25 mysid ISR. 

26 DR. MARTHA SANDY: The lead for this is Dr.
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1 Klaine. 

2 DR. STEPHEN KLAINE: Okay. You've probably

3 heard some of these already. We'll go through -- the MTTT ISR

4 describes the perceived strengths and limitations of the assay,

5 pages 67 and 69, based on presentations, answers to questions

6 and a lot of Panel discussions, we believe the strengths of the

7 assay to be the follow.

8 Historical use of the organism in bioassays

9 and extensive experience of testing laboratories with animal

10 husbandry associated with this organism. Test endpoints are

11 generally simple and easy to measure. The assay includes

12 endpoints that are relevant to population level effects and the

13 results are amenable to common population modeling approaches. 

14 Males and females are distinguishable by conventional microscopy

15 based on anatomical differences, and this assay is recommended

16 as part of the OECD testing protocol at level five. 

17 We believe the limitations of that assay to be

18 the following. Large number of endpoints that are included make

19 the assay tedious and the apparent redundancy among the

20 endpoints suggest that they may not have been strategically

21 selected. Variability among laboratories in the ring test is

22 troublesome. This variability includes inability of some

23 laboratories to achieve control performance metrics suggesting

24 that testing laboratories are not proficient in conducting this

25 test, and/or that the test endpoints are highly variable. 

26 Earlier studies with mysids and other
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1 invertebrate species have indicated it is not possible to

2 predict life sensitivity in the F1 generation. Similarly in the

3 MTTT, it is not possible to predict generational sensitivity

4 among life stage endpoints. This variability also includes

5 variability in endpoints between replicates, treatments and

6 laboratories. Whether this variability is intrinsic, for

7 example animal husbandry, feeding, food quality, et cetera, or

8 as due to test design or lack of laboratory proficiency is

9 uncertain. However, it makes interpretation of the existing

10 data speculative at best. Further, given this variability, it

11 is uncertain if laboratories, if they were asked to repeat these

12 bioassays would even get the same results the second time. 

13 Given the variability discussed above, there

14 is insufficient sample size to adequately observe significant

15 differences among treatments. The addition of a second

16 generation significantly adds to the time and resources required

17 to perform the assay. Effects in the first generation may

18 result in an inability to complete the second generation due to

19 insufficient young. 

20 Some methods were poorly described and may

21 have led to poor test laboratory performance. There's concern

22 that toxic effects in the F0 generation will result in less

23 sensitive F1 organisms leading to less sensitivity in the F2

24 metrics. We base that on the fact that some pesticides are

25 known to cause pesticide resistance with increased generational

26 exposure in insects, although we're not suggesting resistance
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1 but certainly selection of a less sensitive part of the

2 population could occur. 

3 The flow-through nature of the test generates

4 large volume of waste. We estimate 6000 to 8000 gallons of

5 waste per test. It's a concern in terms of the test cost

6 because waste disposal of 120 to 160 55-gallon drums at $1000 a

7 drum -- so you can do the math -- substantial.

8 Finally, there is no obvious mechanism from

9 Tier 1 for trigging the need for this assay. Hence, in the

10 absence of data that allows for any discrimination is likely

11 that this assay would always be requested or alternatively,

12 never be requested. So either of these will be problematic. So

13 EPA in the document has proposed further refinements to the MTTT

14 assay in light of the limitations. These include better methods

15 for selecting doses based on results of a 21-day screening test,

16 more judicious selection of test chemicals for the validation

17 study based on known arthropod endocrine activity, and refining

18 endpoints to potentially reduce animal use or improve

19 statistical power. The Panel agrees with these additions. But

20 while they may provide improvement, it is not assured that they

21 would sufficient address all limitations. Hence, we recommend a

22 few additional changes in the protocol. 

23 First would be increasing the number of

24 organisms used to initiate the test. This would increase the

25 probability of survival and adequate reproduction in the later

26 generations, conduct a second validation study after allowing
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1 stakeholder comments on the test methods, to increase likelihood

2 that test laboratories understand the protocol and are confident

3 they can perform it under GOP. 

4 Finally, the addition as you have heard many

5 times, the addition of a positive control that is a known

6 invertebrate EDC -- for example methoprene -- with a known

7 invertebrate hormonal pathway effect for inclusion in future

8 testing, which will help establish growth, development and

9 reproductive fingerprints that may be useful in better

10 discerning invertebrate EDC effects in screening new compounds. 

11

12 DR. MARTHA SANDY: Thank you. And the

13 associates on this, Dr. Scott?

14 DR. GEOFFREY SCOTT: Nothing further to add. 

15 DR. MARTHA SANDY: Dr. Tuberty?

16 DR. SHEA TUBERTY: My comments are included. 

17 DR. MARTHA SANDY: Dr. Christman?

18 DR. MARY CHRISTMAN: No additional comments. 

19 DR. MARTHA SANDY: Dr. Perkins?

20 DR. EDWARD PERKINS: No further comments. 

21 DR. MARTHA SANDY: Comments from others on the

22 Panel? Mary Manibusan?

23 MS. MARY MANIBUSAN: Just a clarification to

24 Dr. Klaine's comment with regards to the Tier 1 and our

25 selection process of Tier 2. Just for the record, the Agency

26 has not declared its methodology for triggering Tier 2 assays
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1 and so there is no assumption that we will always require the

2 invertebrate nor the assumption that we will never require the

3 invertebrate assay. That's just for the record, thank you. 

4 DR. STEPHEN KLAINE: I appreciate that. And

5 we did too, we understand that. It was just a concern that was

6 raised. 

7 DR. MARTHA SANDY: Dr. Touart, any additional

8 questions?

9 DR. LESLIE TOUART: No additional questions. 

10 I think we understand the strengths and limitations that the

11 Panel outlined, thank you.

12 DR. MARTHA SANDY: I guess we are ready for

13 question eight. 

14 DR. LESLIE TOUART: There is sufficient

15 evidence to indicate that EDCs can disrupt normal development

16 and reproductive success. However, the sensitivity of the F1

17 generation compared to the F0 is less clearly defined.

18 Please comment from a scientific and risk

19 assessment perspective on the value added of multiple

20 generations in the mysid assay.

21 DR. MARTHA SANDY: Dr. Perkins is the lead. 

22 DR. EDWARD PERKINS: So the question at hand

23 is the utility of multiple generations. The use of these

24 multiple generations is intended to capture effects that may not

25 be apparent through exposure of a single generation for a number

26 that may occurred for a number of different reasons. Based on
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1 the tables that were provided by EPA in the presentation, slide

2 172 by Les, the additional multiple generations of mysid tests

3 provides information pertinent to risk assessment. 

4 In that comparison of mysid sensitivity by

5 generation, the LOEC for the F1 appears to be more sensitive

6 than LOEC observed in the F0 or number of chemicals. In that

7 comparison, I'm not showing here, is the 10 chemicals available

8 for comparison where there was both data in the F0 and F1

9 populations. The F0 mysids were more sensitive than F1 to two

10 chemicals. Mysids were more sensitive to three chemicals in the

11 F1 generation than the F0 generation, while the F0 and F1 were

12 equally sensitive to five chemicals.

13 We also went through and looked at the tables

14 ourselves in a cursory manner because we wanted to look at that

15 ourselves in a little bit more detail. So as looking at many of

16 the chemicals used, and this is dichlorophenol, if you look at

17 the F0 generation, it looks like the -- look at total offspring

18 per female. Some of the two most sensitive endpoints in that

19 assay, the mysids, the most sensitive generation was the F1

20 where it was at 7.21 and there was a direction going down,

21 whereas in the F0, was around 200. So it's quite a bit

22 different in the F1. And then time to molt, it was fairly

23 sensitive in the F0, but I would argue that total number of

24 offspring is more directly related to reproduction perhaps than

25 time to molt. So this may have a heavier weight in a weight of

26 evidence type of approach that we mentioned earlier and may be
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1 more biologically relevant to some of the mechanisms that you're

2 looking at. 

3 Fenoxycarb, which is a molt inhibitor or

4 juvenile hormone inhibitor I believe, 14 day length was the most

5 sensitive. However, that wasn't done for some reason in the F1

6 population, so we can't really compare it. Total offspring per

7 female was actually more sensitive in the F1. 

8 However, it went up, which would rephrase some

9 of the issues that the industry brought up that sometimes you

10 get confusing data. However, recent molecular efforts using

11 genomics to look at and examine these low-dose effects where it

12 might be a nonmonotonic response shows that some of the

13 inhibition pathways initiated by chemicals can cause changes in

14 lipids that cause shifts in signalling ecdysterone metabolites

15 and precursors that shift hormones in invertebrates. 

16 So you can get effects there that seem

17 apparently confusing, but they're actually low-level toxic

18 effects. So we can also suggest exploratory methods to

19 understand why these are happening to reduce the confusion on

20 that or reduce the uncertainty. 

21 In fipronil, the F0 was definitely more

22 sensitive. Time to molt, seven-day length, ketoconazole as was

23 pointed out, had to be redone, but the seven-day weight was most

24 sensitive in the F0, whereas the time to second brood or the

25 number in the second brood was much more sensitive and down in

26 the F1. It kind of goes on like that. 
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1 One of the big points I wanted to show out

2 that much as Les pointed out earlier, the F1 does have some

3 important information on several chemicals there. Just from

4 this cursory one, at least from my view point, it seems like a

5 lot of the endpoints being more sensitive in the F1 appears to

6 more be directly related to reproduction, which is more directly

7 related to the endocrine question that you're trying to look at.

8

9 So in my eyes, this really supports the case

10 that you do really want keep the F1. Then Shea Tuberty put

11 together a second chart, looking at different factors. He can

12 explain that one since he put that together late last night or

13 early today.

14 DR. SHEA TUBERTY: This table is a little

15 small, I apologize for that. I didn't know it was going to be

16 projected. So what I've done here is taken significant effects.

17 These were indicated by either Dunn's or Dunnett's tests from

18 the ISR, and broke things down into all things reproductive,

19 growth or development. So these would have been including

20 duration to reproduction or amount of time to reproduction or

21 full development, or survival, which on occasion had a hit. 

22 Then as Ed has indicated those compounds that had more sensitive

23 endpoints in the F0 versus the F1 or if they were equal. So

24 this is similar to what Les gave us a few days ago. 

25 These are organized alphabetically, the

26 compounds that were tested. I should have put them together by
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1 active ingredient. But if we look at just a couple of things

2 that how similar modes of action like ketoconazole here or

3 prochloraz, the prochloraz study had to be redone. So if we

4 take the one that wasn't problematic here and compare it to

5 ketoconazole, you see that the F1 generation for both of those

6 compounds actually, as Ed indicated, had reproductive effects

7 whereas only one of the compounds had reproductive effects in

8 the F0. 

9 Growth and development is always affected in

10 the F0 population, rarely so in the F1 generation. Survival

11 only for fipronil, survivorship in these adults -- we want to

12 have exposures where we're not getting lethalities. So that's

13 not to be expected for an EDC to begin with. Of course,

14 fipronil is not an EDC for endocrine compounds in invertebrates

15 related to reproduction and development, but rather a

16 neurotoxin. So I think this again, adds weight to inclusion of

17 the F1 especially because of the effects on reproduction.

18 DR. EDWARD PERKINS: So to summarize,

19 basically these different analyses really support the use and

20 maintenance in maintaining the second generation in the MTTT

21 test which clearly captures effects at lower concentrations or

22 certain chemicals more than just a one generation case. So a

23 significant amount up to 30 percent of the chemicals that were

24 looked at, so that is not a small amount. 

25 One other comment that would affect this and

26 was brought up a few other times is that sample size in the F1
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1 generation, especially at the termination point. It's often too

2 small to observe effects if they exist. Therefore, we again

3 recommend that you look at the sample sizes in the F1 generation

4 study so that there is enough samples in the F0 population so

5 you have enough samples in the F1 population to look at

6 subsequent effects. That was definitely a problem in some of

7 the studies. There weren't enough to see effects, the N of zero

8 and the N of one and two. That summarizes our comment. 

9 DR. MARTHA SANDY: Thank you. I will ask each

10 of the associates if they have anything to add, so Dr. Tuberty?

11 DR. SHEA TUBERTY: I've got nothing further. 

12 

13 DR. MARTHA SANDY: Dr. Scott?

14 DR. GEOFFREY SCOTT: Just a couple of points. 

15 I think the types of data that Shea and Ed have pointed out here

16 are kind of moving towards what I think what we were trying to

17 refer to as fingerprints or pattern recognition of effects. If

18 you take a look at that table with fenoxycarb, which we know is

19 an example of what we're talking about with positive controls, I

20 think you can begin to see certain compounds follow some of that

21 pattern recognition. If you take a look at a compound like

22 fipronil for example, using both assays we know in the copepod

23 assay it is male specific in its effect. 

24 So if you take a look in our long-term studies

25 of monitoring Ag runoff sites, we see reductions in males

26 because they don't have the lipid to be able to store the
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1 contaminant and they're more sensitive if you run tests between

2 males and females, particularly for that class of compound. 

3 So probably what you're doing if you look at

4 that fipronil where the F0 is most sensitive, you may be

5 removing the males and that's why that's critical. To me, that

6 kind of ability in a testing protocol to be able to have those

7 kinds of insight are going to be critical in my opinion, to be

8 able to do what the Agency wants to do. I think that's sort of

9 where we are with looking at these data and proving you our best

10 guidance as a panel here back to how we see you use it. Thank

11 you.

12 DR. MARTHA SANDY: Dr. Klaine?

13 DR. STEPHEN KLAINE: I concur with my

14 colleagues. 

15 DR. MARTHA SANDY: Dr. Christman? 

16 DR. MARY CHRISTMAN: I concur with my

17 colleagues as well. 

18 DR. MARTHA SANDY: Any additional comments

19 from the Panel? I will turn to EPA and Dr. Touart. 

20 DR. LES TOUART: No further clarifying

21 questions. 

22 DR. MARTHA SANDY: So that concludes the last

23 of the charge questions. I'd like to now go back and we need to

24 revisit a few questions on earlier assays. My notes say that in

25 the Medaka question four, I believe that Dr. Portier had made

26 some suggestions, and we were going to confer back with Dr.
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1 Johnson and Dr. Christman what they thought about that. 

2 DR. DALLAS JOHNSON: Yes, thank you. I think

3 that the way we've phrased our remarks so far about letting the

4 nature of the variables drive the analysis rather than letting

5 the data drive the analysis, I think that that is essentially

6 what Dr. Portier was also getting at. He was just suggesting

7 some particular routines that might be available in the

8 statistical software that might help do some of those analyses. 

9

10 In that regard, I might also mention that I

11 think it's our intention for Dr. Christman, Dr. Portier and

12 myself to suggest some additional analyses or programs that

13 writes some SAS code for you that might be included in the

14 statCHARRMS module or macro, in hopes that we can increase the

15 amount of charm that have with that macro. In other words,

16 eventually showing how I mentioned confidence intervals this

17 morning, we can show you the SAS code that would be used to get

18 the confidence intervals. We talked about analyzing data that

19 had unequal variances and we can show you the SAS code that

20 would be necessary to do that. So it's our intention, I think,

21 as well to perhaps put some of that kind of detail in the report

22 that we come up with. 

23 DR. MARTHA SANDY: Dr. Christman, did you have

24 anything to add? 

25 DR. MARY CHRISTMAN: No I do not. Dr. Johnson

26 covered it all. 
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1 DR. MARTHA SANDY: So before we leave Medaka,

2 were there any other issues that anyone wanted to discuss? 

3 Seeing none, thank you. The next thing I have on my list was

4 the amphibians. It was question 3.3 and had to do with the RT

5 qPCR assay. I see Dr. Denver.

6 DR. ROBERT DENVER: Yes, we discussed the

7 issues whether or not to include vitellogenin messenger RNA as a

8 means for detecting estrogenic compounds. The possibility is

9 that it could represent a more sensitive biomarker for

10 disruption of estrogenic signalling, but this would need to be

11 evaluated. To our knowledge, there is no current validated

12 assay for this. RT qPCR assay presented this vitellogenin

13 messenger RNA. There are technical challenges that are

14 different in character, but perhaps similar in complexity to the

15 vitellogenin ELISA. For example RNA isolation, evaluation of

16 RNA quality, reverse transcription, qPCR, they're all

17 technically challenging and they could be beyond the expertise

18 of some contract labs.

19 Also a panel of appropriate reference that is

20 housekeeping genes would need to be identified, assays developed

21 and then run in parallel with the vitellogenin assay.

22 Lastly, the concordance between plasma

23 vitellogenin and liver vitellogenin messenger RNA and the

24 relative sensitivities of those measures would need to be

25 evaluated. 

26 So at this point, we recommend that the EPA
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1 consider evaluating the feasibility of including it, but it's

2 something that would need to be investigated to determine each

3 of those issues that I laid out. 

4 DR. MARTHA SANDY: Dr. Degitz?

5 DR. SIGMUND DEGITZ: Thank you for your

6 comments.

7 DR. MARTHA SANDY: Thank you. Any other

8 comments on the LAGDA assay? Okay. So there have been a number

9 of crosscutting issues that have been brought up and common

10 themes across all four assays. I think allowing each assay

11 discussant group to hear the others has helped with that. Our

12 report may pull some of these common issues together if that's a

13 plan. 

14 Now, I'd like to give every Panel member a

15 chance as we go around to say any final comments, other issues

16 they'd like to discuss. We'll start here on my left with Dr.

17 Delclos. 

18 DR. K. BARRY DELCLOS: I'll maintain

19 consistency across days and pass on this.

20 DR. JAMES MCMANAMAN: After listening to all

21 of this, I really appreciate all the hard work that went into

22 trying to develop the assays by the EPA and to deal with the

23 complexities. But I'm beginning to think that we're in kind of

24 a Rumsfeldian dilemma in the sense that we have known unknowns

25 and unknown unknowns. The known unknowns include the problems

26 with experimental design, validation, experimental execution by
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1 some of the contract labs, which have suggest that maybe we're

2 not ready -- some of these assays are not quite ready for

3 primetime and there should be some further evaluations of the

4 assays. 

5 Regarding the unknown unknowns, the whole

6 invertebrate section seems like that there seems to be little

7 rationale for including it in a Tier 2 assay since we didn't

8 even consider these kinds or compounds or consider doing this in

9 the Tier 1 or in the pre-assay evaluations. Had we known that

10 we were going to consider that and had we known that, we might

11 have come up with some better suggestions or some better

12 observations help in trying to integrate the Tier 1 and the Tier

13 2. 

14 So these concerns have me a little leery that

15 we really not ready for Tier 2. There may be other factors that

16 are pushing us ahead, but I don't hear a lot of -- not really is

17 very sanguine about it at all. 

18 DR. DANIEL SCHLENK: I've been thinking a lot

19 about this over the week. It has been very interesting to try

20 to put an academic mind on a regulatory issue perspective, which

21 I think if I were the Agency, I think that's what you'd probably

22 unfortunately are getting is a severe academically challenged

23 Panel here that is real easy to criticize things, particularly

24 when not all the data is there, when not all the methods are

25 laid out clear and properly, and maybe you're missing a little

26 bit here, little bit there. It kind of reminds me of a grant
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1 review panel when a red flat goes up, the sharks just feast. It

2 may just be a little, you know like a cut on your leg kind of

3 thing, but everybody just kind of falls in on that. 

4 My heart is heavy because I know where the

5 Agency wants to move in this regard, but again I think there are

6 some issues, particularly I think on the positive control side

7 as you keep hearing over and over again. I think again, we just

8 want to be sure. Everybody wants to be sure. I know you guys

9 want to be sure too.

10 The good news is I think people want these

11 assays to move forward. I think that's definitely what I've

12 heard at least in conversations is that we do want them to move

13 forward, but we also want them to be correct in terms of linking

14 the responses to an endocrine-based response. At least the

15 consistent theme I hear, at least through most of the assays is

16 that it's just not there. I mean, we see the great apical

17 endpoints, but honestly, you already have those endpoints if the

18 compound is going through FIFRA or being used for some other

19 component, maybe not for the SDWA compounds, but at least for

20 FIFRA you've got those data. 

21 The point here is that you want to link it to

22 an endocrine response to an AOP and EAT particularly. I think

23 that's where, at least as I hear this, this is the common sort

24 of negative component that I hear throughout this whether it's

25 the inverts not even having a Tier 1 or even some of the bird

26 and amphibian things. It's really difficult to link those
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1 apical endpoints to that EAT AOP, and I think that's at least

2 the challenge that I see most people sort of presenting in this

3 particular Panel. 

4 So I'd like to say to we encourage the EPA to

5 keep moving forward because I think this is obviously something

6 that needs to be done. It's just a difficult challenge to do

7 and may take a bit. I can't define what a bit means, but I

8 think we're trying at least to provide you at least some of

9 that. That's all I have to say. 

10 DR. STEPHEN KLAINE: I want to thank the EPA

11 again for all the work as this was a lot of work to put

12 together. Also, I want to thank the public commenters because

13 it was also a lot of work to put together. I'm not going to go

14 and reiterate what Jim and Dan have said, but I think the length

15 of their comments reflects the work and discussions we've had

16 this week and how arduous this task has been. Knowing where you

17 want to go and our concern with getting there in a fashion that

18 allows you to have data that you have confidence in that you can

19 make conclusions and decisions upon has really driven a lot of

20 our comments. 

21 So yeah, we might have jumped on a few wounds,

22 but I think that we really tried to be objective in how to

23 improve these tests. We do want to see this move forward. It

24 just has to move forward in a fashion that everyone had

25 confidence in. So thanks again. 

26 DR. GEOFFREY SCOTT: Thank you for the
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1 opportunity to serve with my distinguished colleagues on this

2 Panel. I have learned from every one of you and thank you for

3 your collective wisdom in sharing in across the assays because I

4 think we all learned something along the way. I want to

5 particularly thank my colleagues on the invertebrate panel who

6 have come together to put our collective wisdom forward to

7 provide EPA with the best guidance we can. I really want to

8 thank EPA because I know all the hard work that has gone into

9 this. 

10 Dan's comment about the grant and the cut on

11 the leg and the sharks descended reminded me of something. I'll

12 share it as an anecdote to close my comments. My favorite movie

13 of all time is Jaws. Recently, it was just 38 years ago since

14 we had Jaws come out. The reason it's my favorite movie is

15 where industry, the fishing industry, academia and government

16 come together to solve a problem. You ever thought about that?

17 Now if you start at the beginning of the

18 movie, everybody's trying to do it their way and they get

19 nowhere. But you know when they finally come together and they

20 start picking up their legs and comparing scars, that's when

21 things started to happen. In the end, everybody got to try it

22 their way. The fisherman tried it his way. The academic

23 mission tried it his way. It didn't work and he got away. At

24 the end, government had to make the decision.

25 So I think the process -- that's why it's my

26 favorite movie because in many ways, we're not trying to kill a
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1 shark, but we are trying to regulate an issue. To me, this

2 Panel emulates in many ways what we try to do when we all come

3 together. Thank you for letting me be a part of it. 

4 DR. REYNALDO PATINO: I would like to thank

5 Sharlene for the opportunity to serve on this Panel. I also

6 enjoyed -- I was here a month ago and even though I'm supposed

7 to come here and share scientific advice, I feel that I learned

8 more than I shared. So I appreciate the opportunity for the

9 discussions and also the EPA for listening. 

10 As far as overall thoughts, I think that I'm a

11 little bit -- I don't know if confused is the right word, but I

12 heard a number of times what matters really, the outcome that

13 matters from the Tier 2 assays are the applicable endpoints of

14 growth, development and reproduction. So I'm wondering why are

15 we looking at AOPs when the concern is in regards the Tier 2

16 assays. If that really in some ways does not matter in terms of

17 the outcome that is going to be useful for the agency to make

18 risk assessment and regulation. 

19 So I guess I'm leaving here with a little bit

20 uncertainness to what the purpose is of many discussions that

21 we've had concerning the AOPs, there being no modes of action if

22 in some ways that may have been addressed already in the Tier 1

23 and it's not going to affect the purpose of the Tier 2. But

24 it's probably just me. I may have gotten things wrong, but I

25 just wanted to share that with you, my parting thought. Thank

26 you very much everybody. 
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1 DR. CATHERINE PROPPER: I also want to thank

2 the EPA for all their amazing hard work. The amount of data

3 presented through these different assays is stunning. I truly

4 appreciate the complexity of trying to evaluate both within

5 assay variation across laboratories and even within

6 laboratories, and certainly now trying to pull together what it

7 means across assays is going to be a huge task. 

8 So in fact, one of my questions is how are you

9 going to really do that, and is there a plan to move forward

10 once these data really do start coming in and a regulatory

11 mechanism for -- how are you going to make the decision --

12 what's the next step beyond just running the assays to get to

13 that regulatory standpoint? I'd be very curious, but I do

14 appreciate just how much work's even gone into getting this far.

15

16 One of the other issues I just wanted to

17 reiterate was for the statistics, I really appreciated Dr.

18 Christman's comment about using some multi-variant kinds of

19 analysis might be a way to try to pull in those data in a way

20 that can give you a broader picture. So that was just my last

21 kind of suggestion on the stat side of things. Then I just want

22 to say that also this is my second Panel and I learned so much

23 from being here that I take it as an opportunity to serve for my

24 own selfish gain, but also because part of my personality is I

25 want to fix things. This gives me an opportunity to try to give

26 input to help better the environment in a way that I find very
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1 personally satisfying. So I really appreciate the opportunity. 

2

3 DR. EDWARD PERKINS: I wanted to thank EPA and

4 my Panel members for all the hard work they've done in

5 presenting the tests and developing over so many years and try

6 to get something out of it after all that work. I was a little

7 bit overwhelmed by all the different panels or subpanels I was

8 on. There was a lot of commonality to them. So I was happy to

9 learn a lot and be able to contribute. 

10 One think probably might help and might have

11 helped would be little better overall conceptual presentation of

12 AOP context in relation to the Tier 2. AOP concept really only

13 took hold over the last two years throughout EPA. So I think it

14 was sort of slapped on to the Tier 2 after the fact, and it's

15 really unclear how AOPs will be used in risk assessment within

16 EPA and out. That's really an active area of research. 

17 Nevertheless, I think AOPs what they can in

18 the risk assessment context is help use a weight of evidence

19 approach and ensure that you're very sure about how and why it's

20 doing it. So ultimately, you can extrapolate to other species

21 and other things like that. It may not be directly the species

22 that you're doing the test on, but gives you more confidence in

23 extrapolation. 

24 It would help to have more of the molecular

25 endpoints to be able to do that, but I don't think it's

26 necessary to do a final assessment of whether or not this causes
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1 reproductive effects and extrapolate potential populational

2 effects. So that is going to be a big thing to struggle with is

3 how do you really integrate this AOP concept into a risk

4 assessment and use that to drive these assays, which were

5 developed and started some 10 or 15 years ago back when they

6 used to call it mode of action or mechanism of action. 

7 Anyway, I've enjoyed serving on the Panel and

8 anxious to see what you actually do with this. 

9 DR. ROBERT DENVER: I'm also appreciative of

10 the hard work of the EPA scientists and I hope that our comments

11 will help to move this program forward. I actually sat on the

12 SAP that evaluated the Tier 1 assays back in 2008. At the time,

13 the Tier 2 assays were just a concept that was being developed. 

14 So I'm glad to see that the Tier 2 assays have come along. I

15 remain uncertain still how the decisions will be made to move

16 from the Tier 1 to the Tier 2. So I hope that that can be

17 sorted out in the near future. 

18 DR. DALLAS JOHNSON: I also want to thank the

19 EPA for their hard work and for the opportunity to serve on the

20 Panel. I do appreciate the amount of statistical thinking that

21 went into these reports. While I think the stats can be

22 improved, I do know that they're being used to a much higher

23 degree than they were being used in some of the earlier SAPs and

24 human study review board panels that I've served on over the

25 last about eight years now that I've been serving on SAPs and

26 HSRVs. So the level of statistics has been going up. I'm glad
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1 to see that and hoping to see it rise to even more. I hope that

2 I've provided some insight that can maybe help the stats to be

3 done better. 

4 It's also good to get out of the house and

5 clear some of the cobwebs out of my mind. Being retired, I

6 don't get opportunities like this, so it's good to know I've got

7 something up there that I can use once in a while. Even though

8 I was trying to write some SAS code the other day and I had to

9 keep going to the help manual to figure out how to do it. Some

10 of the things that I used to just do automatically, now I have

11 to stop and think about and they don't come as automatically as

12 they used to. Again, thanks for the opportunity to serve. 

13 DR. COLIN SCANES: This is going to one of

14 those on the one hand, on the other, the yes but. First of all,

15 it's been a privilege to be on this Panel. I think great

16 colleagues, but also very well organized by Sharlene and her

17 staff, and very attentive listening from EPA. Obviously, you

18 are here to listen and to think and to show passion on

19 occasions. 

20 Now we're going to move onto some observations

21 and the buts. One of the nicest things coming out of this

22 meeting is that there are a series of common themes from

23 virtually every group. Positive control, statistics, number of

24 replicates and we'll come out with more of these. What I found

25 very useful was that in many ways the fish, the amphibians, the

26 invertebrates inform me on information that could be very useful
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1 on the quail act. I hope that this will be used by EPA that

2 some of the information coming from one species test or one test

3 be useful to inform you on different species. 

4 One of the things, we've all been on grant

5 review panels and the very good proposals are often easy to see.

6 The blood in the water is easy to see, but sometimes what is

7 not as easy to see is the fatal flaw. Sometimes, one can have

8 the first time you go through a proposal, you say, wow this is

9 from an outstanding group, outstanding proposal and then you go

10 back again and move from an excellent down to a poor because of

11 a fatal flaw. I think in some ways, despite looking at the

12 common features is something which is also looking for things

13 that need to be corrected, that must be corrected. I think that

14 that's something which having this final wrap up and looking at

15 common themes is extremely useful.

16 One minor suggestion or two minor suggestions

17 -- one is Les and his colleagues gave excellent presentations. 

18 I would have appreciated the chance to have those before the

19 meeting as the power point before the meeting because it would

20 give an impression of where EPA was thinking. A second area

21 that I think might be useful -- I said two, there's going to be

22 three, I can't count -- the second one is it might be useful to

23 EPA given that the questioning is in EPA language to have one

24 question at the end, and that is are there any concerns that

25 have not been addressed by these questions. 

26 Now I think basically as academics, we always
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1 give those anyway and we fit them in under other questions. But

2 if there was that, it could be helpful to EPA. It's not

3 addressing the question, but it could be points that could be

4 valuable to you.

5 The final area that I want to encourage EPA to

6 think about, and that is a lot of these supporting work was in

7 the form of EPA reports. If the work could be put in the form

8 of published work perhaps with appendices as EPA reports, it

9 would give a degree of confidence in the work that it's already

10 gone through some form of peer review. I say that because I

11 think obviously a regulatory agency wants to be on as firm a

12 footing as possible. 

13 I do want to go back to what Geoff said about

14 the example of Jaws. I'm not going to use another movie

15 example. But I was very encouraged by the process, not only of

16 the Panel but the process EPA is using and the quality of the

17 EPA staff. 

18 DR. SHEA TUBERTY: I also would like to parrot

19 my colleagues in saying thank you for inviting me to be part of

20 the Panel. It is really an honor. I thank my other subpanel

21 members for their input and guidance and levity at times. 

22 Geoff, good job. It was needed. 

23 In talking with Bob and others that had some

24 history with the EDSTAC program -- I teach it every year to my

25 toxicology class, a little bit of the history and how it got

26 started. I really do emphasize endocrine disruption in my class
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1 as what my students need to be worried about in their future. I

2 just want to applaud EPA's attention to endocrine disruption as

3 an issue that needs attention and continue pushing forward, even

4 though it seems to be in jumps and spurts given that Tier 1s

5 were in 2008 or 2009. I understand the amount of time that goes

6 into the EPA's development of these assays having been part of

7 it myself during my post doc. 

8 I sit here and watch you guys kind of cringe

9 and wince as we pick your assays apart. I think that's like Dan

10 put, you find one little think wrong with it and you make a big

11 deal out of it. It's really not. I think we appreciate the

12 amount of effort that goes into this work, both at the bench and

13 at the office. But I just want to mention again that this is

14 really important work. This toxicology is where we need to be

15 going. Having a screening program that will catch these

16 compounds before they're released in the environment is

17 critical. Whatever the amount of work it takes and how many

18 generations it takes to get the program finalized is well worth

19 it. I applaud your attention to it and I hope you continue to

20 move forward with it. 

21 Having said that, there seems to be no letter

22 of process in the EDSTAC program as it is to go back and change

23 things that were overlooked. I say overlooked because it's not

24 there and I wonder why and I can't get good answers from anyone

25 except for there's not enough public outcry for one thing or

26 another. Why is there no bird Tier 1? Maybe because there is
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1 just no good assay. I don't think Colin would agree with that. 

2 Why is there no invertebrate assay? I know there are good ones

3 out there and we use them all the time. 

4 So, it just seems like it was overlooked in

5 the first -- maybe it was all the way to the wingspread

6 conference. Maybe there wasn't good invertebrate

7 representation. It's hard to believe that that would be true. 

8 It's something I think we need to take a step back and correct. 

9 I just wanted to use the platform as an opportunity to say that

10 out loud and hope that it get corrected at some point in the

11 future and we don't just keep moving forward regardless of how

12 important we all know it is. So thanks once again. 

13 DR. JENNIFER FREEMAN: Well, what's left to

14 say? I think going near the end here, my other Panel members

15 have really highlighted the all last comments that I had jotted

16 down that I wanted to share. So I'll just keep it short and say

17 thank you to the EPA for giving me the opportunity to serve on

18 the panel, and that I really appreciate all the hard work that

19 has gone into developing these Tier 2 assays. Thank you.

20 DR. MARY CHRISTMAN: I'm just going to say

21 ditto. Thank you -- hard work -- can't believe you did all this

22 so quickly. That's a lot of information. 

23 The only comment that I wanted to make

24 separately was I would like to encourage EPA. There's an

25 enormous amount of data that's been collected and analyzed. I

26 would like to encourage the EPA to maybe take a step back and
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1 consider reanalyzing it in a more complete and correct manner

2 and allow you to sort of think in terms of where it is you want

3 to go with this information based on slightly different --

4 because I'm trying to take this step back to this idea of what

5 is the optimal experimental design for these problems and how

6 might they be approached. Taking a step back and thinking about

7 that more generally, and using data from different assays to get

8 to that to answer that question, I would encourage you to think

9 about it if you have the resources. Thank you once again. 

10 DR. MARTHA SANDY: We haven't been as vocal

11 during this panel until now. I apologize for that, but we do

12 all recognize all the hard work that EPA staff has put into

13 preparing the ISRs that we reviewed and all the immense amount

14 of documentation that supplemented that. I am very appreciative

15 of that. 

16 I found the ISRs to be written very clearly. 

17 It was very, very helpful. It was really worthwhile to have all

18 that other documentation so that the panel members could read

19 and look at that and give you detail, hopefully very helpful

20 informative comments on how to improve these assays. We are

21 very appreciative of that. 

22 I appreciate all the hard work that this panel

23 has done. Lots of thoughtful comments were made, a lot of work,

24 a lot of material read. Thank you very much. 

25 Now we'll turn it over to Mary Manibusan. 

26 MS. MARY MANIBUSAN: So thank you everybody. 
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1 I'm just going to take just a few short minutes to make my

2 appreciation known as well as giving you a brief glimpse into

3 the next SAP, which is our final SAP for 2013. 

4 I too want to acknowledge the work of the Tier

5 2 team that broadly includes experts from across the Agency, our

6 Office of Research and Development as well as our programatic

7 office.

8 Although there have been experts coming in and

9 out of that program in that project, there has been one mainstay

10 expert, and that is Dr. Les Touart, who I want to recognize

11 specifically for his leadership and expertise as well as his

12 eloquence and persistence in ensuring that we do this correctly

13 and we ensure sound science is maintained. That's why we're

14 here today. So again, I just wanted to acknowledge Dr. Les

15 Touart for all his work.

16 I want to acknowledge Dr. Bradbury, Dr. Dix

17 and Mr. Knott for their management support without which we

18 cannot move forward with the endocrine disruptor screening

19 program. 

20 Dr. Matten, who has been organizational leader

21 in ensuring that logistics happen and that we're here today and

22 we can have a timely discussion as well as the SAP Executive

23 Secretary, Laura Bailey and her staff, ensuring that all of your

24 travels are completed on time to make sure you're here and that

25 you have the necessary information loaded onto the public

26 docket, which seems to increase by the day. 
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1 I definitely want to acknowledge Dr. Sandy for

2 stepping up and serving as chair for this meeting, and Dr.

3 Schlenk for stepping down to serve as an expert on the Panel. 

4 We definitely appreciate everyone's participation because we too

5 want to ensure confidence as we move forward with these Tier 2

6 very expensive, very complex designs. 

7 We hear you all loud and clear in terms of

8 making sure that are test guidelines are specific and where we

9 are requiring certain parameters to be met that we state so. 

10 But not only that in terms of just making a test guideline

11 available, but ensure that we do the right thing and make sure

12 that laboratories demonstrate proficiency, they have the

13 necessary training within YouTube videos or different medias

14 that we have in the 21st century to deploy all those to ensure

15 that the message gets out, the training is available and that

16 we're here to provide consultation. It is no different than how

17 we pursued our other test guidelines. We will continue to do

18 that as we move the science forward. 

19 I just want to reiterate that this is the

20 close of the EDSTAC conceptual framework. We've completed our

21 Tier 1 test method validation. We've issues our test

22 requirements for the initial list. We'll be pursuing the second

23 list shortly. This is to validate the Tier 2 test methods. 

24 So that concept now becomes a reality. We

25 understand that the validation process includes several steps,

26 one of which is held here, the Scientific Peer Review. The next
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1 step of course, is a lot of the work in terms of getting the

2 test guidance regulatory accepted for use in our program as we

3 seek to address the mission that's stated before us, which is

4 quite large. We recognize that as well. 

5 So a couple of next steps will be finalizing

6 test guidelines. We look to your specific comments to help us

7 do so. We will be developing scientific evaluation procedures,

8 SEPs, as we've done for the Tier 1 assays. We've heard also

9 recommendations and we'll be taking into serious consideration

10 development of workshops specifically focused on statistical

11 methods as well as histopathology. So look forward for those

12 additional activities. 

13 Needless to say before the Agency deploys Tier

14 2 test orders and these methods, we will have to complete the

15 work of the Tier 1 weight of evidence evaluations. That is the

16 subject of the next SAP come the end of July. There, I just

17 want to calibrate expectations we will be bringing before the

18 Panel some specific case studies. Not all 52, not even all 21,

19 but specific examples that are quite challenging to how we

20 consider melding together the Tier 1 battery of information

21 along side with other science equally relevant information. 

22 That definitely will include the pesticide submission 158 data

23 as well as mode of action information where we have that

24 information, dose response to a setting, so toxicokinetics,

25 toxicodynamics, and we will be utilizing the AOP concept to kind

26 of use as a framework to fill out the information that we have
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1 before us to make that weight of evidence determination. 

2 Certainly weight of evidence approaches are

3 not new to the Agency. We do that every day, in fact, with the

4 158 information. What is unique in this circumstance is the

5 data and the decision that we'll be making post that evaluation.

6

7 So we look forward to seeing some of the

8 permanent members back in July, and we welcome some of the

9 remaining members who will be re-invited to that meeting. That

10 will close out our 2013 year of SAPs. Going back to the analogy

11 of building our house, we've built our first floor, we're here

12 to discuss a second floor and the last meeting will close out

13 our house. Thank you very much. 

14 DR. MARTHA SANDY: Thank you Ms. Manibusan. 

15 Thank you Dr. Bradbury and thank you Dr. Touart for all your

16 hard work during this last week and prior to that. 

17 I've been negligent. I need to thank Sharlene

18 Matten for all her hard work in planning this meeting and

19 organizing it so well and putting together such a great panel

20 and keeping my on track. And to the rest of the SAP staff,

21 thank you. 

22 DR. SHARLENE MATTEN: I have a few comments,

23 perhaps not quite as eloquent as Mary's, but I would like to

24 thank a number of individuals. I'll start with of course

25 thanking the Panel and Martha Sandy for sitting next to me and

26 indulging me in a few nudges and comments about keeping the
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1 discussions on track with the scientific issues. This meeting

2 was somewhat challenging in that regard and I'm hoping that in

3 the report, there is a clear distinction with the reference to

4 the charge questions and then other scientific comments. That's

5 always important to capture everything. It's just a challenge

6 sometimes to write it. 

7 So what you hear here might translate into 100

8 plus pages, which is great and hopefully the agency will find

9 that very informative. I am glad that everyone has enjoyed

10 their time here and have made their way through some of the

11 hiring process for the newest people. That was a bit more

12 challenging than usual. So I appreciate that and everyone's

13 travel here and being so gung-ho in doing the homework and

14 coming prepared to the meeting. I'm glad everyone appreciated

15 the fact that in organizing this meeting that I made sure that

16 everyone was the lead to write up something. That's always

17 important to me to be very inclusive. 

18 So in that theme, I wanted to thank the EPA

19 team of folks. Of course there are the folks that sit in the

20 table and of course, we have in this case two program lead

21 offices, Office of Science and Coordination Policy, which

22 encompasses the group and the Office of Pesticide Programs that

23 encompasses the group. There is also the Office of Research and

24 Development. I can't count either -- that's three offices and

25 we all are part of the big ship of the EPA, but you only see a

26 portion that give the presentations. So the only people
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1 remaining in the back are all important people. So Sig,

2 Patience, Pat, Amy, Ed, Catherine, they're from -- we have the

3 Environmental Fate and Effects Division. I was in that division

4 in 1993 to 1997. The Office of Research Development in Duluth,

5 they have a longer name but that's what I call them, Office of

6 Research and Development Duluth. I appreciate those folks for

7 coming down and being warmed up. 

8 So there is Pat and Sig and Rodney who

9 escaped, and Kevin, and there was a young man over here with the

10 glasses, John. Now in the Office of Science and Coordination

11 Policy, we had Patience. You know I could give the last names,

12 but this is a personal remark. And then Les because Les was in

13 the Environmental Fate and Effects Division. I'm never quite

14 sure how he responded and went back then, but he's been a very,

15 very, very important scientist in the development of this

16 program on the environmental side. He deserves the credit that

17 Mary has given him from 1997 to present. That's really

18 important that he's been here that long and has endured. 

19 Mary Manibusan -- she is the boomer. She is

20 one of the best question readers ever. Les had a hard task to

21 follow, but he came around. Then Steve Bradbury, who is always

22 very poignant, and I always appreciate learning various comments

23 on -- because I sat on that side of the table and I'm one of the

24 few that has been DFO and technical expert. So I go the whole

25 ball of wax. I know how people are thinking and so I try to

26 make sure that there is something for everyone for all
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1 audiences. 

2 There are other people back across the river,

3 as we say. There are other people that deserve some thanks,

4 Bill and Scott, so then I would also like to thank my

5 colleagues, Don Wood, Laura Bailey, Fred Jenkins, Joe Bailey,

6 Joyce, Shirley Percival, Carlos and Linda for all of their hard

7 work as well as the contractors. I got the same note that my

8 husband always gives me. 

9 With that, I'll wrap things up. I really

10 appreciate everyone's time for being here. I will meet with the

11 Panel in the back room. 

12 (Whereas the meeting was adjourned)

13 *****

14

15

16

17

18

19

20

21

22

23

24

25

26
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A

abbreviate 93:14 315:15

abbreviated 299:4 301:22 497:10 498:20

ABC 160:11 572:12

Aberdeen 43:9

ability 18:14 22:23 25:4,24 26:7,11 53:2 54:25

55:9 66:6 69:3 70:10 112:8 156:11 157:2 165:4

167:25 186:6 193:24 196:1,8 205:19 220:1 239:5

247:13 280:24 286:16 295:19 296:19 305:11

312:26 330:14 364:11 367:21 371:1 372:26 428:1

448:9 461:19 467:8 486:19 502:23,23,25,26

503:2,3,10 504:11 509:24,25 523:21,24 524:6,13

530:26 532:14 544:11 546:25 553:13,14 556:10

567:14 579:17 624:26 636:13 641:5 642:1 655:20

666:10 684:20 705:8 710:12 711:17 724:6

able 24:17 35:13 37:22 38:1,3 55:7 56:6,10,12

58:21,26 60:15 61:13 66:3,5 67:21 72:12,23

74:15,21 75:3 84:12 88:24 100:1 104:19 110:26

114:19 144:17 155:3 156:16 158:16 166:23 175:2

188:19 190:10 196:3 197:19 224:11 225:3,5

226:22 227:10 236:2 237:5,7 238:2 244:26 247:9

258:1 260:1,6 261:5 265:3 266:3,19 267:4 269:12

275:9,13 284:11 285:11 289:18 290:12 299:20

310:23 311:12 326:13 331:11 344:4,9 360:24

388:16 390:5 396:9 400:5,15 401:2 412:4 413:2

427:19 434:15 439:14 486:17 508:22 518:17

519:19,19 523:19 526:7 534:14 546:6 551:2

557:20 564:4 582:13 590:7 598:25,26 601:3

624:24 640:19 641:7 688:11 692:19 713:4 723:26

724:6,8 734:9,25

abnormal 376:23 557:11 601:2,3

abnormalities 125:17 162:6 175:14 379:17 536:17

553:17

abnormality 89:8 379:4

absence 154:5,25 203:3 333:26 358:3 377:15 381:3

408:2 623:22 667:19 716:10

absences 597:14

absolute 87:15 174:25

absolutely 113:26 124:3,11 140:5 298:24 660:13

abstract 376:22

academia 731:15

academic 61:22 728:20 731:22

academically 728:22

academics 310:22 737:26

acceleration 166:2,4

accept 28:21 194:23 196:1 265:12 437:10 582:7

657:26

acceptability 666:22

acceptable 22:2,8 86:9,12 87:7,24 88:3,11,12

99:14 195:6,6 200:12 312:11,12 657:23

acceptance 22:5,7 89:12 199:20 251:18 582:8

accepted 200:17 375:3 572:19 711:22 744:2

accepting 251:22

access 423:17 448:17

accommodate 397:24

accompanying 618:26

accomplish 39:10 154:8

account 132:20 138:4 161:3 182:24 291:14 318:15

397:8 421:2 476:24 560:1 595:10 680:4

accountability 193:12

accountable 193:6

accounted 154:5 415:13 416:3 503:9 588:18

accounting 478:6 587:11

accrues 84:12

accumulate 447:2

accuracy 425:10 535:9 566:20 567:18

accurate 276:24 572:11

accurately 403:7 566:26

accused 166:15

acetone 358:25,26

achievable 278:20

achieve 200:10 497:17 714:23

achieved 214:19 221:13 497:24 568:8

achieving 497:18

acid 84:18 209:23 220:22 237:10 682:4

acidic 19:10

acknowledge 10:11 29:16 153:19 742:4,14,16

743:1

acknowledged 301:16 628:19

acknowledgement 397:26 711:4

acknowledgements 10:23

acknowledges 625:10

acquiring 519:10 535:9

acronym 386:14

act 2:9 4:12 14:6,7 15:25 17:20 33:11 241:9 486:19

525:20 560:12 620:26 649:7 667:10 737:1

acting 8:22 83:9 134:17 283:21 469:23 513:9

action 15:5 21:15 23:7 38:5 68:16,18,19 81:18 83:9

83:10 91:18 93:4 133:8 135:5,6,10,12 148:4,9

168:7 278:9 283:6 286:17 293:24 296:20 297:22

298:25 299:20,23,24 300:3 302:5,6,26 303:2,5,8

303:13 304:6 305:12 307:14,15,18 308:9,18,21

314:9 324:16 325:1,16 328:9 329:13,17 333:22

334:17,22,25 336:11 357:18 376:9 452:20 462:7

464:18,20 486:9 487:11 489:9 494:10 544:23

548:5,11 550:8 551:1,3,8 552:6,12,24 553:3,9,14

553:20,21,22,24 554:2,4 557:19,21 559:17 560:1

560:1 565:13,18 566:1 611:21,24,25 614:11

618:16 622:23 629:9 630:15 634:13 637:7 638:5

668:8 705:9 710:11 722:2 732:21 735:6,6 744:23
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actions 2:22 295:21 453:16 613:12 622:24 705:17

activated 142:17

activating 486:10

activation 37:11 141:16,23 142:5,20 213:1 529:15

667:14

activational 141:7,9,13,21 142:1,12 213:4 446:4

453:21 462:12 530:14

active 18:25 37:22,24 60:6 207:22 208:4,5,7,12

212:18 214:4 217:23 230:13,16,23 246:16,16,18

259:17 367:5 485:25 486:4,12 510:11 551:12

597:2,21 623:12 685:14 701:7 722:1 734:16

actives 15:24

activities 38:4 61:10 63:14 81:26 208:6 230:21

252:7 260:5 473:3 700:25 744:12

activity 29:8 30:24 31:10 35:15 45:12 48:19 49:14

59:18 69:24 84:23,24 163:22 184:13 208:14,15

231:5 287:23 303:9 330:15 334:12 366:24 374:3

453:24 456:23 465:26 485:20,23 486:5,17 494:25

495:1,4 557:6,9,12 561:21 573:11 596:20 602:18

603:9,14,16,19 716:17

acts 134:11

actual 40:19 45:20 46:5 48:19 49:4,5,18 54:10 63:3

63:4 66:3 68:2 73:26 80:17 101:7 141:17 142:3

189:26 198:1 215:20 217:23 219:7 225:3,5

237:25 238:16,21 239:3 247:16 256:19 261:4

320:16 326:26 344:10 345:11 353:19 370:25

434:24 476:17 483:21 589:1,20 625:22 630:13

676:14 695:17,26

actualization 53:22

acute 125:2,18 208:23 341:19,20 620:24 684:22

685:1 697:26 698:2 702:17,24 708:17

ad 6:15,18 7:8,13,16 13:1 443:23

adapt 266:19 354:9

adaptable 457:14

adaptation 26:22 212:4

adaptations 267:6 268:25

adapted 266:24

adaptive 23:8

ADC 464:18

ADCs 450:2

add 118:4 186:14 211:14 228:5 237:9 238:19 247:8

261:7 265:13 291:1 307:6 312:17 313:20 317:16

323:14 344:15 353:3,6,9 356:13 360:5,6,18,20,23

361:23 362:2,9 365:17 368:11 371:7 373:4

374:24,24 377:24 380:18 381:16 382:7,9 384:18

404:2 405:14,24 406:2,5,10 419:10,19 420:4,5

428:7,23 429:18 434:4,6,11 440:1,26 441:8,13

450:11 452:4 456:19,21 458:1 467:16,22,25

468:4 471:24 476:22 477:4,7 487:17 490:7 491:6

501:11,20 502:1,2,4,7 518:22,24,26 522:9 527:17

528:4,6 532:5,20,22 538:17,19,25 539:1,4 548:18

548:21,23,25 549:19 554:7,10,16 563:6,8,10

567:20 583:1,7,8,9 598:10,16,21 606:5,7,10,12

606:15 608:17 609:25 610:3 642:13 644:19,22

645:2 669:19,23,25 682:16 683:16 684:12,14

686:14 688:3,8 691:12 693:11,21,24 702:9,13

704:18 707:2,4,6 711:25 717:14 723:10 725:24

added 39:20 73:18 78:4 89:22 107:19 137:19 159:4

171:4 260:7 266:20 280:11 294:7,18 321:22

328:12 338:13 375:5,5 413:14 435:16 456:4,25

465:9 466:2 469:5 514:8 525:4 527:4 528:20

529:2 584:26 597:2 626:2 629:6,14 643:20 670:2

704:21 718:19

adding 238:15 257:15 364:24 427:5 437:5 463:24

466:8 467:9 469:7 476:23 520:22 582:25 629:6

640:6 642:10

addition 3:8 9:7 160:1 244:4 248:3 282:14 301:11

360:13 365:10 369:3 375:6 381:9 391:10 410:7

410:17 418:14 423:11 428:13 434:12 438:13

447:6 449:24 453:19 462:9,11,20 466:12 531:23

534:20 619:8 621:25 622:7 639:7 642:23 645:10

647:13 675:21 708:10 715:15 717:4,5

additional 11:25 16:23,24 17:11 18:9 34:25 43:4

44:21 69:14 70:8 74:23,24,25 168:20 206:6

220:21 227:2 237:9 244:4,18,19 245:15 246:24

247:25 258:14 261:7 265:13 291:22 294:19

302:18 312:17,18,18,18 319:11 322:3,5,25 323:6

325:11 348:26 349:21 351:1 352:18,21 353:22

354:1 355:4 380:15 382:17,26 413:1 419:1

421:24 438:22 441:2,10,13 442:19 449:9 450:16

451:11 454:16 455:12,20 456:3 462:13,16,22

465:17,25 466:17 467:9,15 468:9 475:23 476:23

477:13 487:11 492:14 502:7 509:15 518:22,26

522:6 530:12,22 532:4 533:13 537:9,16,17,26

539:15 543:13 566:22 570:15 573:21,23 574:2,5

576:10 577:17 582:25 583:17 584:23 587:12

590:2,9 595:21 602:19,25 615:5,7 621:26 633:13

639:12,18 640:6,25 641:16 644:24 645:3 648:5

649:5 662:26 666:23 667:14 669:14 673:14 676:4

682:13 687:21 703:10 709:3,14,15 710:1,17

712:16 716:22 717:18 718:7,9 719:2 724:18

725:12 744:12

additionally 31:12 317:12 454:11 536:21 625:2

additions 348:5 716:19

additive 72:17

address 14:2 18:8 24:18 28:1 35:18 232:26 334:3

346:6 357:17 369:2 372:1 374:23 384:3,4,7,14

398:5 401:4 447:19,25 452:20 471:4 476:1 485:5

496:10 503:7,9 504:1 537:17 546:20 547:6 550:8

556:16 603:24 615:26 618:10 623:11 624:13
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637:6 640:26 662:25 706:24 716:21 744:3

addressed 292:14 303:23 310:16 352:5 384:23

403:25 408:14,23 415:26 448:2 485:7 510:4

514:15 532:8 538:23 547:20,23 559:8,10 571:2

608:18,20 617:19 625:6 642:21 671:3 704:13

706:26 709:3 732:22 737:25

addresses 9:19 135:14 622:1 641:9

addressing 363:23 446:8 461:5 487:15 549:16

617:22 645:3 697:18 738:3

adds 220:22 322:7 323:16 413:21 420:2 504:20

715:16 722:16

adequacy 275:25 534:20

adequate 103:23 219:13 293:16 364:11 374:19

418:12 453:7 455:19 461:19 501:7 537:24 556:10

574:13 622:15 666:11 699:3 716:25

adequately 35:5 81:4 99:17 103:16 280:18 282:26

295:12 296:13 297:7 310:14 313:24 314:6 320:15

360:10 383:10 468:22 470:16 478:5 534:9 547:20

576:19 674:20 704:19 715:14

adhere 173:1 510:23

adhered 175:1 416:5 609:23

adherence 515:19 517:21 605:18 610:15

adjourn 239:17

adjourned 240:5,7 662:22 748:12

adjust 154:2 479:22 574:14 586:8 589:12,13

595:18

adjustments 658:4

adjusts 589:11

administration 6:1 56:17 460:2 665:4

administrator 15:21

admit 340:9

adopted 42:16 251:20 252:26 428:14 537:11

629:21,22 644:11

adopting 629:20

adoption 41:26 538:5,11

adopts 252:17

adrenal 46:3 57:6

adult 53:7,17 57:26 64:14 76:21,26 77:1 91:5 95:6

95:17,21 97:13,15 147:1 165:14 191:15 392:24

395:16,19 446:7 456:7 474:12 478:26 482:22

519:13 533:5,11 536:13 612:6,14,21

adulthood 78:21 212:14 213:16,24 439:19 533:5

adults 41:12 78:18 86:6 88:13 108:5 124:22 213:3

223:3 224:18,22 261:18 289:4,21 339:4 340:10

340:12,20 341:2,3,8 392:14,15 425:15 466:3

529:18 534:5 536:3 722:11

advance 17:26 34:17 252:25 286:2 646:5

advanced 13:25 18:9 158:14 493:4 507:14

advancement 26:11 166:21 269:22

advancements 29:14 382:20

advances 352:15 470:12

advancing 16:22 167:12

advantage 58:16 61:19 77:5 145:14 187:25 226:10

228:14 371:19 404:13 445:10,15 454:16,24,26

523:3,5,24 524:2 579:19 641:13 677:12 695:6

709:9

advantages 56:15 206:17 210:18 222:23 224:10

266:14,17 267:5 350:1 351:4 352:9,13 434:13

444:22 445:6 448:5

advantages/limitations 39:19

adverse 21:7 22:24 25:24 32:18 35:14,16,18 38:2

39:23 40:2 41:8,17 49:24,26 50:9 52:8 53:10 58:1

58:26 70:12 136:2,5 155:12 185:14,17,18 186:4

188:7,10 208:7,18 212:16 218:3 222:8 232:23

233:1 260:3,11,25 261:21 262:12,14 287:20

298:17 306:22 308:3 327:7,9 328:22 329:12

330:1,3 347:13 349:17 350:25 352:4 435:26

436:10 439:22 443:13 447:11,19,22 545:18

548:13,14 553:13 555:4 565:12,14,16,19 606:19

609:7 611:23,26 612:9 614:23 616:25 617:15

618:26 619:3 621:10 624:11 628:4 667:9 711:2

adversely 76:7 112:13 208:16 447:17

advice 2:20 9:7,23 10:25 11:2 12:19 24:13 186:12

251:9 332:19 420:21 459:4 575:7 596:2 658:16

673:17 732:7

advisor 3:12 284:9

advisors 9:23 404:19

advisory 1:1 2:3,9,18,19 3:24 4:8,12,21 5:1 9:26

11:5,9 16:6 20:10 241:3,4,8,18,20 332:24 459:3

649:6 664:7

AEA 447:12

affairs 305:18

affect 26:25,26 59:1 63:10,11 68:21 111:9 135:25

196:24 207:20 293:24 304:7 413:13 447:17 475:8

531:21 568:15 606:18 616:19 625:12 637:23

638:2,5 706:17 722:25 732:23

affinity 408:3 644:9

afford 620:15

afraid 303:17

afternoon 105:6,9 345:25 475:6 544:22 550:15

afterthought 587:8

Ag 723:25

age 46:13 389:12 398:15 404:13 433:5

agencies 16:7,8 500:4 706:1

agency 1:13 2:8,20 3:4 9:17,24 11:2 13:4,6 14:26

15:16,26 16:11 19:18,23 24:5,14,16,20 30:11

31:2,7,17 34:14 35:3 49:25 57:12 199:15,24

200:10 296:9 327:17 328:2 329:25 331:8 351:1

358:4 363:12,13,20 368:6 382:15,23 383:3

439:17 441:17 444:4 449:11,18 453:3 455:7
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459:16 486:20,21 487:5,13,19 496:13 498:18

499:6,13,26 500:24 539:12,17,22 540:11,21

566:5 634:17 646:5,9,13 648:21 649:13 650:1

664:3 667:13 717:25 724:8 728:21 729:5 732:17

738:11 742:5 744:13 745:3 746:8

Agency's 3:1 4:9 20:5 529:1 533:22 534:17

agenda 4:3 148:8

agenesis 644:7

agent 125:20 358:24 374:2,7,7 413:22,24,26

529:13

agents 455:14 511:7 518:2

aggression 66:18 368:20,25

aggressive 66:19

agitated 121:4

ago 19:20 29:9 77:4 120:16 123:11 155:20 254:20

307:19 323:3 359:23 401:8 630:12 679:16 711:4

711:12,16 721:24 731:13 732:6 735:5

agonism 303:4

agonist 91:20 92:5 135:8,9 177:4 299:14 485:20,21

494:23,25 495:4,6 496:14 560:22 561:7,11 597:8

597:9,10,24,26 603:3,11,13,16

agonistic 603:9

agonists 302:22,23 451:21,22 485:14 494:20 518:6

525:24 538:9

agree 142:24 143:23 147:20 148:7,9,9 152:21

176:15 177:24 178:9 180:26 189:8 231:26 269:12

315:22 337:1,2,9 352:25 360:12 361:4 368:9

373:3 375:9 380:17 398:11 409:23 436:21 444:12

515:19 542:2,3,9 573:9 576:8 609:11 632:15

650:4 655:14 657:3 672:24 673:22 686:3,15

690:7 740:1

agreed 8:10 152:16 185:8 204:24 349:5 471:8

575:26

agreeing 356:8

agreement 160:7 193:21 408:13 482:2 496:26

508:25 509:5,8,18

agrees 716:19

ahead 105:11 168:7 331:11 404:5 595:19 617:10

677:2 728:16

aid 511:14

aide 531:24

aids 688:15

aims 280:8

air 121:4 531:20

airway 167:13

akin 651:16

al 165:1 181:17 306:6 455:9 462:21 514:4 515:12

670:2

albatross 424:10

albeit 377:8

alert 480:14

algae/TetraFin 173:7

aliquots 457:23

allocate 120:7

allocated 120:9 149:3 389:14

allow 68:17 76:13 78:3 88:24 103:17 112:18

139:25 141:10 196:6 212:21 234:7 315:3 397:18

404:14 414:23 420:6,7 448:6 453:9 455:22

530:21 533:5 561:24 571:7 634:17 648:24 652:3

661:4 678:8 679:3,10 710:2 741:2

allowable 558:11

allowed 168:9 324:16 654:22 677:16 699:18

allowing 233:13 234:17,21 427:25 655:24 659:11

716:26 727:10

allows 25:26 76:21 77:10 83:23 86:3 108:25

120:25 145:8 224:13 226:1 350:5 357:16 452:19

453:12 524:12 550:7 637:5 640:8 655:3,6 668:3

670:21 684:20 691:9 716:10 730:18

alluded 121:25 139:11 508:14 632:23

alongside 18:3,11 489:15,20

alpha 291:17 581:4 589:4,11,13

alphabetically 721:25

alright 404:4 696:23

ALT 137:4

alter 281:20,22 455:16 557:6,16 568:15

alteration 462:15

alterations 309:4 552:19 556:23 618:11

altered 27:1 552:7,13 616:20

altering 584:12 637:23

alternate 361:2

alternative 21:20 210:26 362:10 410:15 484:9

499:10 500:2 518:15,16 573:25 647:25

alternatively 573:14,18 716:11

altogether 233:24 599:4 630:21

altricial 57:8,13 351:8,11,16 352:5,8 355:8 424:21

424:22 432:13

altricials 354:4

AMA 26:10 171:19 599:8

amazing 733:2

ambiguities 571:15

ambiguous 280:1 496:3

amenable 178:13 226:4 269:9 380:7 462:5 562:6

714:13

Amendment 15:25

amendments 335:1

America 643:26

Americamysis 211:5

American 208:24

amount 9:13 14:19 44:22 54:14 67:10 100:16

110:15,26 111:15 139:18 156:3 168:19,20 225:15
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227:4 259:9,12 284:14 322:13 386:9 390:24

416:13 509:13 537:9 589:7,8 668:22 721:20

722:23,24 725:15 733:2 735:20 739:5,12,17

740:25 741:13

amounts 76:23 119:25 174:26 420:26 641:12

Amphiascus 208:25 248:18

amphibian 26:8,14 37:14 154:13 155:12,18 156:4

187:16 276:5 278:1,5 305:19,22 306:4,12,20

309:4 448:20 544:17,24 546:2 547:21 548:8

550:18,25 551:24 566:17 612:20 729:26

amphibians 37:5 155:18,22 305:25 306:8 311:22

313:9 314:3,4 433:24 486:25 487:1 546:11

547:12 560:14 596:22,23 611:11 612:20,23,26

726:4 736:25

amplification 512:23 513:11,13

Amy 481:21 747:2

anal 82:9,11,13 87:8 92:10 103:8 140:22 141:2

445:1 451:6 453:23 462:20,26 463:1,1,2,12,13,14

463:25 464:2 466:24 480:1 482:22 504:24,25

514:14

analog 318:19

analogous 156:12 245:1 318:6 616:13 656:8

analogy 244:21,25 745:10

analyses 53:24 71:23 179:23 180:7 200:18 214:21

268:14,23 269:5 275:3 276:16 288:17,17,26

289:19 297:2 360:8 376:16 379:6 380:13 381:22

391:5 394:13,22 397:17 398:4,16,21 401:12

404:12 422:7 438:2 472:20,21,22 474:4 475:13

478:1 507:22 562:12,17 566:11 588:6,7 591:19

600:24 607:12 672:1 677:13 698:21 699:12 704:4

705:20 722:19 725:8,12

analysis 20:5 24:4 33:18 83:24 90:7,13,14,18 91:3

91:6,8 97:22 110:6 131:20 138:10,12,18,19 139:1

139:2,4 144:5 147:4,10,17,17 158:17 161:20

162:15,20,20,22 166:16 174:25 180:5 183:7,8

192:6 233:21 245:13 268:18 269:10 272:25

274:24 275:2 293:17,26 295:6 325:4 332:3 337:6

370:10,13 371:4,16 377:4,25 379:1 380:20 381:4

381:7,19,22 382:1 385:20 389:7 392:22 397:19

397:19,23 398:8,19,20 399:16 400:2,15,19,20,24

401:6,23 405:24 414:18 422:25 426:20 442:3,10

451:23 457:2,22,23 464:7,9 465:6 466:23,23

467:1 473:1,5,15,19,21 474:2 475:3,7,18 478:3

478:10,25 479:10,25 480:1,9,10,13,16,21 481:1

481:15 483:11,11,15,20,26 484:2,9 504:1,20

506:1 510:23 511:2 512:7,8,9 513:19,20,21

519:25 520:10 529:20 531:1,24 551:21 555:9,16

560:3 561:24,26 562:6,8,19 572:14,24 578:4

583:6 587:1 588:3,12,13,19,20 589:1 590:19

591:7,20 592:3,13,14,15,16,19 594:7 595:1,5,7,8

595:12,12,20,22 600:10,11 604:16 609:22 615:10

656:3 670:20 675:19 677:24 678:6,11,12,15,18

679:6 680:8 683:12,19 684:7 687:23,26 688:1,18

691:25 698:17 705:22,26 706:14 710:18 725:4,5

733:19

analytic 470:25 491:12 596:14 681:11

analytical 304:12 322:10,12 344:6,7 374:13 383:18

384:10 410:5 411:5 484:26 577:1,7 596:10 675:3

679:11 681:6

analytically 501:26

analyze 264:25 291:10 293:19 383:16 403:6

470:22 475:9,16 519:24 543:23 562:19 576:25

579:17 583:22 587:23 674:26 676:9 677:9,11,16

694:10

analyzed 109:18 160:20 274:23 287:12,13,16,17

288:15,17,26 289:14 291:13 295:2 318:25 319:18

473:10,11 475:2 481:2,4,7 520:2 560:5,8 572:22

578:12,14 583:21,23 587:4 591:15,19 609:15

676:23 740:25

analyzing 265:17 586:26 677:13 725:18

anatomical 714:15

androgen 11:23 16:20 17:7 47:23 51:26 52:20

59:19,21 69:17 76:9 80:13 112:15 134:11 135:9

162:5 189:8 279:12 302:22 303:4 317:13 329:5

347:9 371:21 411:17 443:10 486:23 488:10 495:6

496:1,2 538:9 545:14 546:4 547:6 552:12 560:12

561:7 613:7 616:11,14 618:1 622:17 625:15

626:3 682:3,3

androgenic 49:15 366:24 367:2 374:3 438:22

453:23 553:22 557:12

androgens 208:5 230:15

androgen-active 69:25

and/or 143:22 260:3 279:2 417:9 430:9 445:16

447:12 469:23 471:20 486:13 504:1 511:21

519:13 522:20 525:23 527:12 551:7 552:12

557:26 559:7 611:7 614:7 706:18 713:23 714:25

anecdote 731:12

anergy-dependent 486:10

anesthetizing 80:21

animal 21:13 22:16 85:8 120:13 144:4 146:16,16

146:20 230:12 307:5 309:7 455:21 520:17,19

523:12 525:11 526:4 527:3 530:7 531:7 550:26

551:21 558:2,21 560:25 562:12,18 585:26 586:24

594:9,20 610:15 612:21 666:23,26 679:5 699:14

714:9 715:7 716:18

animals 22:17 23:2,25 31:12 51:17 61:15 62:12

73:8 80:12,20 81:14 82:7 85:9 86:13,19 88:14

89:16 120:12,22 131:15 142:6 143:11,14 144:13

144:15 145:12,12 155:24 156:23 157:1,4 158:26

159:18 165:12,19 167:12 171:13 182:25 183:1
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191:4 192:15 195:1 197:18 211:9,23,24 223:13

259:19,19 261:20 277:18 278:3,4,5,5,6,7,18

282:16 283:13 371:12 390:18 410:9,25 416:19,22

420:3,9 422:24,26 438:18 451:2 499:4,8 501:7

503:18 516:2,3,10 523:7 524:6 529:12 530:21

531:26 536:2,14,21 546:25 562:14 568:5 576:10

582:26 586:6 601:1 608:5 611:15 628:20 629:1

644:15 650:17 655:4 673:26 688:26 696:4

animal's 594:15

ANIT 489:7 525:20

annealing 513:11

annelids 708:14

annotated 83:26

announcement 283:3 441:26

anomalies 45:15 554:12 562:26

anomaly 246:1 262:22

ANOVA 89:15 90:10,12 137:14,26 138:5,6 153:18

161:4,7 183:23 219:18 401:12 402:19,23 442:3

474:5,26 476:17 484:2 588:2 589:5 620:16

answer 17:5 22:13 25:23 28:20 29:10,15 33:16

34:15 134:10 135:13 138:15 141:3 151:24 177:11

187:6 192:18,19 193:8,10 201:12 204:8 233:15

234:15,25 287:23 300:20 332:1 336:9 355:20,23

376:21 385:13 436:16 485:9 494:7 508:15 532:25

547:16 564:18 602:4 630:7 633:3 635:19,20

652:11 687:25 741:8

answered 130:8 234:1 276:6 280:3 355:19,21

412:16 468:22 639:16

answering 17:13 22:1,11,26 36:25 203:24 233:12

233:24 234:14 441:12 544:12 567:19

answers 69:23 134:8 151:26 153:16 361:24 386:21

442:5 484:22 508:16 714:5 739:24

ANT 618:6

antagonism 448:15 449:22 510:3 531:18

antagonist 52:20 59:19 279:12 301:18 411:18

485:20,23 486:2 494:24 495:1 496:1,2 531:20

560:12 602:18

antagonistic 162:5

antagonists 518:6 538:9

antennae 223:7

anthropogenic 317:7

antiandrogen 133:3,3 371:21

antiandrogenic 366:24

antiandrogenic/estrogenic 453:24

antibodies 566:13 569:19,21 570:12

antibody 309:24

anticipate 63:11 213:5 231:22 266:9 267:7 276:20

anticipated 46:15 602:15 603:6 622:22 647:2

anticipating 198:23

antiserum 571:24

anti-A 516:23 525:26

anti-androgen 60:6 226:19 303:9 488:18

anti-androgenic 162:7

anti-androgens 134:23

anti-AR 490:25

anti-aromatase 135:16

anti-E 516:23 525:26

anti-estrogen 303:9

anti-estrogenic 547:3 552:24,25 553:3

anti-estrogens 134:22

anti-E&A 456:24

anti-steroid 553:24

anti-steroidogenic 625:11

anti-T 516:23

anti-thyroid 630:24

anuran 560:14 596:22

anxious 308:7 735:8

anybody 114:14 157:20 335:11 487:20 703:4

anymore 106:6,19 107:24 134:4 181:11 236:15

473:22 581:25 595:9 629:10

anytime 93:21 124:17 390:19

anyway 166:24 201:9 374:2 399:21 590:8 633:10

692:21 694:24 703:5 735:7 738:1

AOP 21:6 36:25 80:4 81:24 82:1,1,2,7 90:19 91:3

119:9 133:20 135:5 136:4 233:17 273:11 453:9

489:18 497:2 499:17 510:3 523:22 528:1 533:1

538:10,12 540:24,25 541:7,17,18 542:23,24

597:1,2 602:19,25 612:3 619:5 631:12 667:10

729:22 730:1 734:12,12 735:3 744:25

AOPs 21:14 448:6 449:20,23 453:15 456:15 462:6

464:17 465:3 488:12,13 490:18 499:10 500:2

518:3,6 525:18 526:18 531:18 536:25 537:4

538:8 540:5 541:1 542:12,14 597:2,18,20 602:8

602:11,13 613:19 619:1 636:14 705:14 732:15,21

734:15,17

apart 146:10 249:15 360:6 474:20 500:22 589:16

692:20 739:9

apical 23:9 30:21 49:23 68:26 69:1,10 71:23 81:7

111:17 143:3 261:9 293:8 298:20,25 302:16

307:9 328:18,18 332:9 371:23 525:23 541:9,11

541:12,15,20 542:1,5,10,13 544:4 548:2 553:13

561:18 597:19 600:14 604:17 631:3 636:14 667:9

667:18 687:9 701:5 729:16 730:1

apically 259:26

apologies 627:19 628:7 635:7

apologize 198:25 227:20 343:20 385:5 429:12

549:5 577:9,21 589:22 611:10 633:5 651:23

721:15 741:11

Appalachian 7:11 243:8 460:22 665:20

apparatuses 114:8
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apparent 92:5 518:3 559:23 623:21 684:24 689:6

714:19 718:25

apparently 118:24 132:14 372:22 416:6 417:7

487:8 497:16 499:6 720:17

appear 58:24 71:6 139:13 280:18 396:25 406:26

415:12 418:20 431:12 432:1 446:8 456:12 464:17

474:4 558:1 698:7

appearance 463:11

appeared 120:23 139:12 244:16 327:13 498:11,17

appears 56:26 71:8 130:26 134:15,16 151:4 245:2

282:8 283:6 304:24 416:4 464:12 473:17 529:7

531:6 534:13 537:12 559:5,21 592:15 668:22

685:26 719:5 721:5

appendices 365:5 419:6 438:11 442:12 738:8

appendix 300:13 324:12 348:25 349:25 369:21,22

379:10 395:11 478:9 479:11,14 480:2 482:4,16

482:24 483:3 505:2 506:19 566:17 627:14

appendixes 93:25

appestive 426:11

appetitive 418:14

applaud 739:2,19

apples 626:24 627:25

apples-to-apples 320:14

applicability 644:10

applicable 57:1 116:17 381:22 424:7 426:23 614:6

683:23 732:13

application 352:16

applied 356:10 562:21 587:11 600:4 631:22,23

642:5

applies 177:26 283:24

apply 227:11 249:16 253:16 268:23 269:20,24

351:3 370:25 636:18

applying 524:4

appreciate 9:4 19:23 114:20 258:18 276:26 328:3

343:18 353:16 361:26 382:16 458:4,6 533:18

549:23 555:25 566:3 570:4 607:2 628:10,18

629:2,3 636:9,11,17 646:1 647:20 663:8 674:13

677:20 685:5 695:2 703:14,15 718:4 727:21

732:8 733:4,14 734:1 735:20 739:11 740:18

741:22 743:4 746:12 747:6,22 748:10

appreciated 271:13 363:21 547:17 636:22 646:10

646:16 696:14 733:17 737:18 746:14

appreciates 625:8

appreciation 742:2

appreciative 735:9 741:14,21

approach 17:19 34:20 166:11 181:16 190:14

253:14 265:25 319:8 333:22 349:11 356:26 471:8

478:10,15,21,25 479:1 480:24 481:19 483:23

511:10 576:23 608:10 613:21 619:9 621:15

624:15 629:12 638:18 639:9 642:16,16,18 648:19

651:25 656:14 668:3 669:3 675:20 679:2 700:5

705:21,26 708:11,15 719:26 734:19

approached 741:6

approaches 32:11 184:18 204:14,25 265:17 300:25

383:13 470:20 481:14 579:11 618:15,20 619:1,4

619:5 645:11 673:13 674:8,24 707:10 714:13

745:2

approaching 261:25 628:16

appropriate 3:3,4 15:5 42:24 43:24 54:19,20 59:23

87:16 94:20 180:7,9,12 183:23 184:19 196:2

203:12 205:12 209:11 214:9 236:17 258:10

261:10 265:23 269:20 286:10 291:6 292:17

295:14 300:4,10 304:1 308:17 315:24 319:24

323:4,4 343:22 365:25 367:8 375:14,20 383:11

383:12,13,19 384:10,21 398:2 399:16,18 400:6,7

400:8,15,16,23 402:6,20 406:26 408:2 417:23

423:23 424:4 439:18 444:20 470:17,18,19,26

471:17 472:25 473:15,25 475:16,17 478:22

480:16 482:12 508:16 517:9,11 556:19 559:18

563:21 576:19,21,22 577:2 587:26 588:10 595:20

595:20 609:16 623:3 643:18 648:1 660:2 667:8

671:1 672:1 673:13 674:21,23,24 675:4 679:8

681:23 698:24 699:3 705:19 726:19

appropriately 264:25 297:10 301:4 315:17 320:1

429:14 517:10 563:21 585:13 675:16 680:17

696:19

appropriateness 280:19 629:4 681:11

approved 251:21 349:2

approves 626:12

approximately 3:21 4:6,16 46:13 158:25 392:11

392:16 535:16

approximation 146:13

aprotinin 572:3

aquaria 76:22 469:17 471:12

aquarial 113:26

Aquarian 454:26

aquarium 121:3 445:12 462:4 481:24

aquatic 6:10 155:12 164:24 219:2,25 315:14 317:5

317:6,9,19 319:25 323:10,14,18 612:21 615:23

619:20 621:18,23 622:1 641:9 684:22 697:26

708:14

aqueous 132:13

AR 301:18 485:21,22 486:2,10 490:25 510:3

531:18 592:7 596:20 597:9,24 602:17 603:11,13

archaic 204:14

arching 363:11

archived 287:17

archiving 103:25

arcsine 399:3 400:11

arc-sin 481:8
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arc-sine 591:10

arduous 147:8 730:16

area 7:20 11:14 16:9 147:5,6 177:22,26 178:11,21

181:3 346:6 353:3 366:15,23,26 367:17,18

377:18,21 384:12 390:10 441:11 459:7 620:10,12

620:14 646:5,7 734:16 737:20 738:5

areas 28:10 49:11 66:24 70:1 162:17 180:16

278:13 351:1 358:8,9 359:15 382:17,19,21,22

383:2 492:19 620:10,16 636:18 708:12

arena 306:2 311:10

argue 432:16 532:16 582:21 588:9,26 677:10

692:23 719:23

argues 438:17

arguing 111:14 482:8

argument 172:16 256:24 536:12 651:8 659:16

692:11

arguments 348:25

arises 395:4

Arizona 6:13 242:21 460:9 665:11

Arlington 1:17 664:8

Army 6:17 25:14 43:8,14,14 49:7,9,16 51:4 60:26

62:21 160:6 242:22 460:10 567:3,10 665:12

Army's 66:1

aromatase 68:21 134:17,25 135:1,24,26 207:25

485:22 489:2 495:13,16,19,22,26 531:20 560:11

596:20 602:17

arose 385:3 389:19

arrangements 3:18

arrow 18:11 163:3,3 216:7,7

arrows 47:24 48:3 91:25 133:26 163:1

art 365:13

arthropod 716:17

arthropods 620:3 701:12

Article 380:24

articulated 352:12 633:18

artifact 164:19 281:12 436:23

artifacts 499:1

artificial 222:21

artificially 350:6

AR-agonist 92:6,15,18

ascertain 275:13 416:18

ascribed 498:26

Asian 77:18

Aside 197:15 709:1

asked 8:24 14:21 38:17 122:6 152:15 171:18 204:3

229:13 250:17,23 271:7 277:10,11,15 310:1

323:12 324:2 326:3 346:12 348:6,22 355:12,12

384:1 385:10 442:2,8 459:13 586:16 623:18

628:9 632:6 657:4 710:8 715:11

asking 27:24 61:6 126:14 131:17 188:17 198:19

202:16 297:18 310:16 324:15 325:23 336:17

342:9 355:20 357:5 419:22 421:15 491:21,22

492:8 540:5,9,10 543:13 564:14,17 623:18

627:22 634:11,12 651:24 661:5 684:16 702:22

aspect 33:8 66:11 77:2 206:6 263:2 427:15,20

445:22 446:15 454:17 455:3 624:26 641:26

680:13 688:20

aspects 65:9 80:17 139:12 189:17 203:5 247:10

361:6 373:13 422:18 526:7 550:18 616:15 625:4

653:4 676:20 704:26

assay 20:17 25:18 26:10 27:9,26 29:22,22 33:2

34:10 74:17 88:4 126:17 135:19 137:3 140:24

145:14 154:14 155:19 173:1 184:11 187:16,24

204:3 236:25 237:18,19 244:5,7 248:9,9 267:13

270:4,8 274:24 275:2,9 276:5 278:2 280:11,13,19

281:4 282:5 287:26 292:15 295:12,17 298:10

299:18,22 300:2 301:13,14 303:13,17,18 304:24

305:7,19,23 306:13,15 308:12 316:19,19 343:21

343:22 344:5,7 346:19 347:15 357:20 359:4

364:7,14 369:17 370:2,7 376:16 383:20 384:11

414:13,22 422:4,6,12,18 425:24,25 426:16 427:4

427:25 430:10,14 435:17 441:11 442:20 443:16

444:8,18 445:16 446:14,15 448:18,20 452:22

453:7 454:6,7,18 455:1,13 456:14 461:15,22

464:11,16,21,21 465:1,5,7 466:5,15,22 467:2,7

467:11 476:24 477:1 485:12,14,24,26 486:8

487:10 488:2 489:9,13 490:3,8,11 491:1 493:23

494:9,18 495:4,21 497:5,9 499:21 500:6,12,16,17

500:18,20,22,24,25 503:19,20,23 507:19,20

508:26 509:4,23 510:6,17 511:7,9,13,15 516:2,4

516:14,15 517:2,17,26 518:3,5 523:2,11,19 524:7

524:7,10,10,15,17,24 525:1,2,6,6,10,12,13,15,24

526:1,11,16,24 527:5,8 528:23 529:7,7 530:8,8

530:20,25,26 531:2,6,22 535:23 536:6,24 537:7

537:12,16 538:11,12 542:14,19,20,22 543:4,8,9

543:22 544:25 545:4,21 546:26 547:4 548:1,4,11

548:12 550:10,16,23 551:25 553:10 556:6,13,18

557:18 558:1 559:15,26 560:10,20 562:24 563:3

565:24 566:12 571:12,25 572:18 573:5,6,11,14

573:17 574:17 575:10,18 576:2,11 577:3 578:4

593:26 596:11,13,19 597:15,17 598:1,4,22

601:25 602:7,9,14 605:20,22,23,25 606:18,22

607:9,11,17 608:26,26 609:5,17 610:10 611:8,18

611:19 612:1,4,5,16 613:10,13,25 614:3,7,21,22

617:2 621:25,26,26 622:4,8,11,15,26 623:1 628:3

629:8 630:21 631:21,22,26 632:6,8 635:4,6,7

637:9 639:7,8,10,21,21,24 640:21,25 641:20,24

642:1,2,4,4,18,20,26 643:3,13,16,16,21 644:2

645:12 646:16 647:10 650:6,8,18 653:2,5,22

654:18 657:4,5 658:9,16,21 662:5 666:7,14
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667:18 668:4,13 672:11,18 675:5 676:22 682:23

684:15 686:6,22 688:12 689:1,15 692:20 693:6

697:9,14,16 701:9 702:2,2 704:1,2,9 705:24

706:19 707:22,24 710:16 711:16,22 712:3,13,23

713:2,6,24 714:4,7,11,15,17,19 715:17 716:9,11

716:14 718:3,20 719:19 723:23 726:5,12,12,21

727:8,10 728:7 733:5 740:1,2

assayed 490:9

assays 16:26 17:4,4,5 18:21,22 19:21 20:3,23 21:2

21:4,5,6 22:1,4,19,22 23:14 27:13 30:9 32:5 34:9

34:12,15 36:21,21 37:9,13,15 68:16,17 73:16

96:4 119:10 134:19 136:15,17,18 140:14 181:11

181:26 184:18 193:24 202:21 237:22 243:22

245:3 248:11 250:21 251:24 253:2,15 255:26

259:13,14,21 277:25 278:23 282:8 295:9 296:15

297:21 298:17 299:1 303:6 309:6 322:23 330:23

342:18 346:13 363:17,17 367:7,7,24 368:12

376:6 382:18 391:5 407:9 417:2 418:7,11,13

444:13 448:20 451:24 454:2 465:24 466:1 467:7

467:8 468:18 471:1 485:19 493:15 496:12 501:2

508:3 511:4,16 514:15 516:11,16 517:5,22 518:8

522:21 524:9 526:25 527:1 532:13 535:19 538:13

539:19 546:2 548:9 550:18 558:3 559:10,12,17

560:2 561:23 564:21 565:17,23,26 569:11 570:24

571:21 572:20 573:7 575:14 577:6 579:9 598:2,7

604:21 607:20,24 608:25 609:6,21 613:5 614:21

615:23 617:22,25 621:8 624:24 626:8,9,10 627:3

627:3,11,20,24 628:5 629:3,5,18 630:6,12 634:2

637:17,20 638:2 644:12 646:20 653:3,7,13,15

654:22 658:7,8 662:12 664:14 667:13,24 672:13

672:19 673:24 676:26 678:21 680:13 682:24

685:1 689:20 693:16 696:22 698:6,10 702:1,19

702:24,26 704:20 705:13 708:4,21,23 710:15

712:4,5,7,10 717:26 723:22 724:24 726:20

727:10,22 728:2,4 729:11,15 731:3 732:13,16

733:3,7,12 735:4,12,13,14 739:6,9 740:19 741:7

741:20 744:8

assertion 436:20

asserts 112:10

assess 41:5 184:25 275:9 297:7 324:17 367:24

453:17 462:8 490:26 499:22 500:12 510:3 512:5

512:21 516:17 518:3 521:1,3 523:20,22 524:18

535:25,26 562:23 621:20 638:12 639:5 640:25

711:2

assessed 80:2,3 273:17 319:10 454:7 455:23 456:2

466:4 504:5 536:10 622:8 711:1

assessing 58:14 275:8 302:19 421:12 456:1,7 509:9

511:17 554:2 557:16 568:3 618:5 638:14 697:25

710:4

assessment 5:5,6 10:9 12:1 15:9,10 17:16 18:3

21:22,22 23:10 24:7 26:20 30:16 32:7,8,11 33:17

34:22 35:1,23 49:25 69:11 74:7 79:24 95:26

130:20 159:20,21 184:21 186:8 199:22 212:20

230:16 231:15 234:11,16 239:6 242:3 257:3

263:1,4 274:18 280:9 286:15 287:25 293:10,24

294:18 295:19 296:18 297:14 298:19 302:17

304:8 305:10 306:3,23,25,25 307:3,6,7,11 308:6

308:8,13 309:6,12,14 313:1,6,7,14 314:17 321:8

323:14 324:21 325:12,13,13 326:5 327:24 328:13

328:17,19 332:8 333:23 334:7,18 350:16 351:18

351:21 352:1 355:6 356:3 357:1 369:7 376:6

397:21,26 419:23 420:7 421:15 426:9 435:16

436:5,13 439:5 444:19 445:24 446:1 447:19

453:21,24 455:5 456:8,12 462:2 464:15 465:26

483:21 496:5 499:2 505:19 516:6,7,22 523:17,17

524:10,11 530:13 533:10 534:6 535:5 536:10

541:20 542:1,5 586:22 604:16 614:6,10 617:20

617:20 621:17,20,26 626:11 631:19 632:22

638:18 643:14 664:18 687:3 701:6 711:7 712:19

718:19 719:3 732:18 734:15,18,26 735:4

assessments 12:6 33:9 256:19 261:22 348:19,21

349:18 350:19 352:19 492:6 518:1 527:4 530:13

530:15 536:1

assigned 62:6 160:24 347:21 419:26

assist 204:26 466:10 467:10

Assistant 7:16

assisting 70:8

associate 7:11 347:24 384:2,7,13 406:14 443:22

456:18 527:15 554:5 606:3 628:13 669:22 702:8

associated 1:4 2:5 8:19 39:17 59:21 67:6 71:6

144:17 145:9 196:26 228:1 241:5 298:5 344:14

357:12,25 396:14 437:3 439:22 449:16 452:15

494:9 512:7 514:22 522:7 546:3 550:1 591:20

594:20 597:3 602:7 604:18 607:20 611:14 637:1

714:10

associates 348:4 428:6 434:1 467:13 518:12 532:3

563:4 573:22 609:10 615:2 717:13 723:10

association 486:24 513:12

associations 530:16

assume 235:13 470:4 472:10 480:24 483:4 519:8

629:13 675:7 690:9

assumed 449:24 483:15

assumes 478:21 483:11 563:19 581:21 588:13

assuming 530:4 563:21 658:7 691:23

assumption 26:25 183:20 233:5 254:6 402:24,26

403:4 450:1 474:7 480:21 481:17,26 482:11

483:14 718:1,2

assumptions 397:22 404:23,23 405:1 474:6 478:5

478:6 699:2

assurance 28:15 203:7 241:11 278:8 516:17 663:4
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assure 15:10 389:25 645:16 704:19

assured 283:8 394:3 716:20

assuring 705:7

asterisk 249:22

ASTM 208:25

Atmospheric 6:1 460:2 665:4

atrazine 209:23 210:15 218:10 223:23 226:14

321:22 682:2

atresia 85:12

attached 220:13 696:2

attack 425:21

attained 464:8

attempt 19:2 20:10 131:10 152:12 233:11 420:14

447:18 668:10 700:25

attempted 66:4 166:9 348:2 355:15 504:8 613:10

attempting 421:5 668:5

attempts 54:14 170:5

attending 2:14 620:13

attention 29:24 63:12 73:4 166:25 171:11,18,20

175:16 279:3 336:26 369:4,5 445:6 454:19

458:12 478:2 591:6 647:15 739:2,3,19

attentive 736:17

attentiveness 663:7

attraction 222:10

attractiveness 350:11

attribute 80:9

attributed 53:6 464:3 497:23

attributes 11:26 446:25 544:2 653:13 658:8,20

660:17

atypical 448:17 449:20 453:11

At-test 493:10

audience 239:14 662:19

audiences 748:1

audio 8:2 243:18

augment 645:8

auspices 210:3

author 180:8

authority 193:7

authors 322:17 530:2 534:7 562:13

auto 581:12

automate 473:2

automated 379:7 464:6 561:24 562:7

automatic 34:18 391:9

automatically 18:6 105:24 181:2 437:7 472:22

736:10,11

auxiliary 354:11 522:2

availability 219:12 453:11 546:12 608:5

available 3:26 4:5,16 10:2 32:12 35:17 42:14 62:13

62:17 89:2 98:1 117:18 119:3 120:5 131:7 166:14

181:7 219:14 220:23 225:16 232:18 259:12

286:20 289:17 337:5 347:4 354:8 356:17 358:1

368:12 369:25 417:25 427:11 429:4 443:4 444:11

446:12 457:24 458:7 464:7,9 498:10 510:21

514:6 515:16,26 523:8 524:3 526:16 545:8

566:12 567:1,17 616:5 631:2 649:16 663:5

683:23 687:2 688:26 689:7 702:3,3 719:7 725:7

743:11,15

average 50:5 52:14 137:18 214:10 223:18 379:20

395:23,23,25 396:11,13,14 481:14 483:15 506:11

594:23 689:26 699:10

averaged 51:13 153:21

avian 6:25 7:5,25 25:1 39:7 40:22 42:13,18 43:22

45:2 57:1,11 58:22 63:18 64:3,7 132:25 267:22

270:4 280:12 287:7 289:11 291:20 293:26 294:10

347:21 348:9,23 349:6,19 395:13 407:4,7,9,17,23

408:2 424:7 430:19,20

avians 40:10

avoid 265:25 388:10 571:20

avoiding 220:15

aware 31:2 90:6 116:22 262:15 265:10 350:7

355:24 424:19 472:18 596:22 684:22 701:8

awfully 691:21

awhile 489:16

axes 163:26

axis 308:22,24 309:9,13 416:19 423:19 495:9

499:12 596:24 604:18 628:17,17,17 630:17

azoles 489:2

a.m 205:20 239:11 662:20

B

b 163:8 165:26 291:24 312:1 349:25 654:2

back 12:20 14:9,11 19:6 22:20 26:10 29:12 32:6

33:3 38:13,15 41:25 51:22 52:5 64:23 67:15,15

68:1 96:8 100:7 104:25 105:2 113:9 114:25 115:4

115:17 126:25 151:6,16 152:5,8 153:7 154:26

166:10,13 182:19 187:14 188:8,13 203:11 204:2

205:6 206:10 209:7 228:15 229:21,24 230:10

232:12 235:11 239:19 241:26 244:20 250:24

254:7 255:18 259:1,15 260:2 263:15,15 264:1

270:3 271:3 276:10 284:20 285:20 286:5 295:10

296:14 298:2 308:19 322:4 323:2 327:21 341:11

344:3 346:2 355:14,18,22 356:7 359:2,14,18,23

360:10,26 370:23 372:14 373:9 379:8 386:4

390:18 397:11 403:16 417:1 428:17 438:6 441:22

442:1,18 452:7 484:13 492:2 507:10 520:5

535:21 539:17,22 540:11 544:17,19,23,26 545:3

575:25 576:7,9 577:20 593:13,18,24 615:18

617:9 628:1 633:6 635:3 638:14,22 639:4 646:11

646:20,26 647:5 648:25 649:20 651:24 652:23

656:23 657:10 662:7,21 672:21 676:4 695:3

703:19 724:10,23,26 735:5,12 737:10 738:13
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739:22 740:8,26 741:4,6 745:8,10 747:1,14 748:2

748:11

backers 58:7

background 3:25 53:12 128:18 286:4,21 290:16

305:25 343:12 430:22 546:12 627:7

backup 11:4

back-up 479:23

bad 99:16 202:10 236:1 310:17 479:10 480:24

badly 474:9

bahia 208:24 211:5

Bailey 241:19 742:23 748:5,5

balance 22:19 221:3 236:23,23 544:11

balanced 192:2

ball 747:25

ballpark 235:22 249:3,10

ban 122:23

bar 70:22 349:18 354:20 594:21

bare 144:13

Barry 5:8,8 242:4,4 374:10 459:17,17 664:19,19

727:18

bars 71:9 425:9

base 63:22,22,23 269:23 358:18,18,19,20 362:22

362:23,24 363:7 400:16 489:9 589:2 646:26

715:24

based 19:2 24:4 25:3 33:10 35:16 43:22 53:5 60:11

65:13 67:3 69:15 74:13,20 75:4 114:9 125:2

138:19,21 139:1 146:6 158:15 167:4 169:24

185:25 199:16 215:2 219:16 227:7 232:16,16

246:2 248:21 254:26 255:12 275:14 287:5,8

291:3,12 293:15 309:5 310:2,5 312:13 362:18

365:26 366:20,21 370:20 373:7 383:7 385:20

394:18 398:20 399:12 403:1 413:17 428:1 435:21

445:17 450:19 451:9 453:15 456:8,11 462:6

470:13 482:7 508:8,22 510:16 531:14,19 535:6

535:19 547:10 576:17 581:19 582:5 585:10

587:25 589:15,20 597:18 603:22 620:21 647:1,6

648:12 667:22 674:18 679:21 680:4 682:6 684:23

691:20 698:8 702:25 705:20,26 706:10,11,13

707:19 708:20 714:5,15 716:15,17 718:26 741:3

baseline 24:10,11 78:8,8 416:20

bases 23:9 46:23 49:10 60:16 63:21

BASF 305:17

basic 19:11 77:14 206:9,17 455:23 466:6 494:12

602:8

basically 39:6 40:6 55:14 86:21 89:4,8,23 91:1,26

92:15 94:11 102:18,22 106:18 108:25 110:9,24

122:22 126:15 127:11 147:3 152:16 156:25 157:3

157:17 158:9 159:2,18 163:13 164:3 168:18

169:2,15 172:17 175:20 179:14 190:12 211:9

212:23 251:3 265:21 268:9 283:20 320:10 322:21

347:3 371:5 404:8 490:14 509:7 510:3 522:25

555:7 581:2 584:17,20 722:19 737:26

basis 34:21 39:7 40:11 51:20 147:22 173:12 181:8

194:3 209:6 275:26 286:25 347:5 348:7 352:11

432:11 443:5 446:1 486:9 545:9 562:10 602:13

616:6 632:23 673:14 691:6

basket 150:1,2,2,12,13 228:26 392:18,18 699:13

baskets 228:6 229:9

Battelle 160:10 671:22 677:2,22

Battelle's 687:26

battery 11:20 19:21 21:3 22:22 23:15 34:10 37:3,4

37:9,15 295:8 321:26 322:5 324:23 328:3 342:10

620:20 625:9,21 629:8 640:12 744:20

BB2 597:10

BCR 367:21

beam 645:18

bear 277:15 456:22 577:23

beating 85:15

becoming 129:6 342:3

beer 343:10

began 28:23,23 78:12 220:20

beginning 10:8 63:17 78:1,4 99:2 197:8 222:26

229:22 273:9 323:13 519:15 599:10 699:19

727:23 731:17

begins 46:13 211:16 212:6 273:7 550:19 551:26

begs 529:9

begun 466:1

behalf 8:25 277:17 283:12 284:4 296:4

behaves 135:10

behaving 559:15

behavior 45:20 54:7,8,11,15,18 55:5 56:1 65:8,9

65:10,13 66:2,8,14,15 141:18 360:14,24 361:6

362:1 365:19,20 372:18 373:6,13,20,21 412:10

413:8,10 418:14,15,17,18 423:16 425:16,17,23

425:26 426:4,7,11,11,13,14 427:1,2,8,20 432:1

437:22 529:17 586:23 588:23,24 601:2,3 688:5

688:10

behavioral 58:13 358:5 360:8,23 365:16,21,24

373:1 398:2 413:12,13 416:10 417:2 418:11

425:15 426:26

behaviors 351:17

belabor 510:13

belaboring 513:17

belief 585:11,19

believe 12:22 38:21 54:9 62:14,25 66:1 104:18

122:8 144:20 152:22 153:11 154:8 161:23 188:11

236:8 278:15 313:10 314:5 344:16 346:18 363:20

369:22 372:7 376:1 385:13,21 401:17 406:23

470:9 475:1 493:1 504:18 507:9,13 541:11

542:22 544:15 566:7 576:13 585:14 608:26
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612:19 614:2 639:20 649:26 661:21 670:15

672:13 674:8 683:2,11 686:7 689:12 691:7,15

714:6,17 720:4 724:25 740:7,21

believed 547:23 676:8

believes 478:2 517:17 626:14

belonging 228:26

bench 739:12

benchmark 683:10 705:4

benchmarks 188:17 189:6

beneficial 316:20 318:24 516:9 533:4

benefit 363:20 413:14 414:22 434:12 446:10

686:16 705:25

benefits 444:23,26 445:20

benthic 344:13 622:3

Bentley 7:22,23 65:6,7 66:13 180:13,14 204:1,2

243:14,14 347:21,26 352:21,22 355:10,11,23

359:19,20,21,25 360:4,6 361:3,4 362:5 368:8,9

372:4,5 373:2,3 375:8,9 380:16,17 405:13,23

406:17,18 407:10,14,17,20 408:10 410:6 414:14

414:16 422:13,15 434:2,3 439:25,26 461:2,2

650:2,3,15 651:4 655:1,2,19 657:2,3 661:25

662:1

Bentley's 408:1

bent-tail 171:14

Benzophenone-C 561:10,19

benzophenone-2 161:25 168:1 177:4 597:10

603:14

Berkeley 7:23 243:15

Berkley 461:3

Bernoulli-Poisson 480:19

best 10:2 69:18 123:13 125:8 144:2 184:23 190:16

194:10 205:18 222:5 265:25 283:25 289:25 331:3

342:4 363:5 365:4 366:26 384:26 435:1 473:15

552:17 557:13 563:24 564:3 583:3 634:16 676:23

686:11 706:13 710:16 715:10 724:9 731:7 747:20

best-test 583:5

beta 89:2 648:22,26 649:3,13,15,15,16 650:10,12

650:22 651:3,25,26 652:4 659:12,22,22,23,24

660:11 661:2

beta-estradiol 485:15

better 54:21,22 56:2 100:2 104:13 136:25 144:17

145:13 158:22 178:8 187:7 198:6 204:15 226:3

259:22 296:23 298:14 299:8 302:19 308:24 310:8

319:19 322:6 335:2 372:22 385:1 392:7 396:19

399:12 408:11 410:19 453:13 470:2 477:26

479:24 484:11 487:26 501:9 534:13 549:4,5

562:16,16 598:5 641:23 642:25 645:12 673:7,17

677:11 678:19 683:21 690:12 693:17 703:3 705:8

705:10 709:18 712:13 716:14 717:9 728:11,11

733:26 734:11 736:3

betting 264:21

beyond 16:19 139:23 198:26 199:10 280:12 413:2

439:14 463:20 567:23 578:26 608:17 631:23

632:21 726:17 733:12

bias 386:17 388:9,11,14 391:21,22,22 656:24

662:10,13

big 17:20 19:18 68:9 100:8 117:23 131:20 146:16

193:18 255:15 256:12,16 286:17 336:14 353:17

354:5 390:9 391:17 397:6 415:25 525:4 721:1

735:2 739:10 746:25

bigger 150:12,13

biggest 492:13

Bilateral 26:5,16

bill 178:23 748:4

billion 257:4,4

Bimolecular 6:2

binary 22:13

bind 644:9

binder 96:13

binding 37:11 38:8 416:14 486:10

binds 408:4

bioaccumulation 275:12,14,25 424:15,25 531:24

bioanalyzer 512:21

bioassay 82:4 618:15 624:1 638:8 669:14 684:23

706:17 708:4 709:4,6,9 710:1,18,20

bioassays 618:9 624:13 704:13 706:2,25 708:3,7,8

710:12 714:8 715:12

biochemical 64:16 137:2 267:25 288:17,17,26

289:19,24 298:24 425:24 428:24 606:17 666:20

biochemistry 437:22

biodegrade 303:26

biological 7:4 17:9 22:25 32:22 90:17,18 121:17

247:17 262:14,16 297:10 357:13,16,19,26 361:16

361:20 364:8 365:17 414:5 417:10,13 419:26

436:23,24 437:2 448:4 452:16,19,21 453:2,6

461:15 462:1,5 464:15 474:22 478:11 481:24

493:6 499:14 511:6 535:8 550:2,7,9,23 553:16

555:1 556:7 579:16 583:15 601:15 634:14 637:2

637:5,8,17 642:19 666:8 696:26 708:12

biologically 204:20 302:20 361:14,19 419:23 421:1

421:8,16 439:11 504:3 555:13,17 563:24 720:1

biologist 5:14 586:3

biology 6:21,24 7:9,11,24 77:14,20,20 113:6

225:20 313:19 431:6 511:21,21

biomarker 82:2,8 87:2 90:19 91:3 94:3,8,17 95:3

95:16 99:1 103:5 106:16 107:5,14 108:14 109:15

109:17 144:1,19 185:2,7,11,19 187:1,2 313:21

327:2,9 329:12,23 338:3 449:21 525:23 529:20

529:26 535:19 541:17 570:9 726:9

biomarkers 95:11 97:1,12 107:14 184:15 185:6
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307:1 308:11 312:9 338:2 524:4

biomass 628:20

BIOQUANT 464:9

bio-accumulate 275:19

bio-accumulating 275:10

bio-available 19:13

bio-chemical 185:11

bird 40:15 56:22 64:14 72:4 268:20,24 278:4

279:15 351:7 352:1 359:8 373:14 395:6,7 411:19

425:5 729:25 739:26

birds 25:8 39:21,25 40:1,12,13,14 46:18 47:1,2

57:8,9,12,13,22,23,26 60:7 62:19 65:14,21,22

66:10 72:22 189:24 231:13 269:25 286:19 287:20

289:5 348:12,20 350:25 351:2,23,23,25 352:18

356:24 358:2,13 359:6 368:13 369:11 381:13

386:9 387:4,24 389:3,13,20,23 393:8 406:26

409:16 412:18 415:13,16,17 423:22 426:5,22,24

426:26 427:7,15 431:20 432:8,11 433:23 434:17

436:2

bird's 408:21

Bishop 277:16 283:12 359:17

bit 8:3 15:22 21:1,23 32:6 39:9 43:2 47:7 52:3

55:11 56:3 63:19 64:5 69:5 76:18 79:14 87:5,11

87:18 88:21 97:8 98:26 100:20 105:4 109:1

111:16 126:10 140:11 141:8 151:19 186:9,16

187:15 189:19 192:24 193:11 196:8 206:7,10,12

206:18 210:20 211:8 212:26 213:25 215:21

217:15 218:26 222:24 224:24 225:11,19 226:13

229:15 234:26 238:21 245:25 247:12 248:16

249:19 251:2 253:13 254:20 260:1 262:14 266:23

275:11,23 296:21,23 302:25 303:12 305:24

306:13 307:19 309:20 323:22 328:26 330:18

343:5 345:6,13 355:18 358:9 362:17 371:3 385:4

386:25,26 389:11 390:3,10,14,14 392:16,21

393:13 396:6,19 398:6,6 416:26 418:19 422:16

440:4 445:14 464:10 472:7 473:7 477:23 479:12

480:25 486:6 487:14 509:19 511:25 542:9 549:1

549:17 565:3 569:11 591:6,14 597:4 598:23

609:2 612:22 627:6 636:14 639:4 640:23 647:3

649:19 679:17 680:13 682:16 691:10 719:15,21

728:26,26 730:7,7 732:11,19 734:7 738:25

746:11

bits 635:25,25

biweekly 173:11

blacks 218:21

blah 657:24,24,24

blank 162:17

blanking 227:19

Blazer 6:4,4 179:2,3,7,15,19 242:15,15 273:20,21

450:9,10 456:18,19 460:3,3 467:14,15 477:5,6

491:2,3 502:3,4 518:25,26 527:23,24 532:3,4

534:23,25 539:11,26 540:13 548:22,23 554:14,15

563:9,10 569:2,3 574:1,2 599:23,24 606:11,12

610:1,2 615:6,7 665:5,5

Blazer's 610:9

bleeding 633:19 634:25

blind 33:19 656:4

blinded 682:26 685:22

block 148:23 149:4,14,16,17,18 227:18,21 270:14

270:14 271:6,16 346:6 442:9,13,19 633:8 675:24

676:1,6,10,16 688:24

blocked 676:7

blocking 219:17,19 220:5,6 227:18,22 228:1,12,13

229:8 346:6 572:5 675:12,13,15,18,23 676:8,12

blocks 148:15,15,15,17,17,18,22 149:4,6 228:17

270:11,26 271:5 272:13 675:16 676:5

blood 119:16,17 190:18,25 288:20,24 289:1,8

337:20 537:2 567:23 571:18,22 737:6

bloods 119:25

blow 655:8

blowing 358:26

blue 51:24 70:22 515:7

blunt 233:24

board 121:14 735:24

boat 659:9

Bob 6:19 36:15 242:24 460:12 575:14 596:21

665:14 738:23

bobwhite 42:5,7 64:7 340:13 349:26 352:12

430:26 432:24

body 40:15 55:18,19 180:21 226:3 291:14 319:5

361:19 395:16,19 418:5 432:5 555:13 567:26

699:9

bodyweight 478:26 506:9 533:2,3,7 591:13

bodyweight/length 506:16

bolded 397:17

bolster 711:9

Bonferroni-Holm 589:10

bonus 525:4

bookends 37:17

books 26:23 246:10 401:15

boomer 747:19

booming 283:25

border 656:10

bordering 126:10

boring 577:10,20

borne 433:6

bottom 70:21 110:12 124:8 126:16 151:13 171:12

239:2 691:16 703:1

bound 96:1

bounds 366:15 633:5,6
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box 25:2 195:1 479:13 506:19,20,23

boy 148:23

BP-2 173:20,21

bracketing 312:8

Bradbury 8:16 10:19,21 12:21,26 14:3 19:19 32:4

33:26 34:4,21 194:11 195:26 491:15,16 543:11

543:12 652:16,17 657:15,16 660:9 742:16 745:15

747:21

brain 46:2 142:17 180:17,18,19 356:15 410:7,14

410:15,16 423:13,15,26

branch 5:5 594:25

branched 463:2,25 464:2

Brausch 314:22,23,24 337:8 341:16 343:24 345:1

Brausch's 338:13

bread 119:2

breading 109:21

break 38:12,14 104:24 105:4,5 153:6,9 205:5,8

276:7,8,9,11 345:24 383:26 403:16,19,23 441:22

441:24 507:10,10,12 519:6 544:16,21 593:14,19

593:22 615:18,20 703:18,19,20

breakdown 405:5

breaking 164:19

breakup 8:3

bred 429:3,9

breed 355:25 427:7 519:19

breeding 78:10 80:11,16 82:5,6,6 140:13 271:21

272:16,17,18,20 390:1 437:5 445:23 455:5

469:21 476:26 477:1 479:18 515:21 520:22 521:1

521:5 535:2 696:5 699:9

brief 10:22 39:12 43:18 211:2 284:21 742:2

briefed 3:6

briefing 251:2

briefly 14:15 39:14,22 406:21 465:12 607:21

609:18

brine 173:7 220:18,20,21,22 237:8

bring 10:14 13:25 14:18 34:22 56:15 164:2 171:18

189:17 204:17 362:5 397:11 473:5 519:8 539:17

540:11 549:3 577:18 583:4 632:7 672:19 681:3

bringing 19:21,26 20:5 276:20 648:5 744:17

brings 380:3

brisk 132:11

broad 102:18 313:1 480:3 625:11 708:12

broadcast 459:7 664:7

broader 672:19 708:10 710:13 733:20

broadest 709:22

broadly 11:6 57:1 156:17 426:23 742:5

broke 523:1 721:18

broken 403:25

brood 211:18,19 212:1,8,13 213:18,22 214:2,23,25

216:1 217:8,13,18 221:7,8,10,14,15 247:19,20

250:8 254:26 255:3,7,22 256:4,4,9 261:25,26

262:7 263:17 273:5 516:3 668:19 699:8,26,26

700:2,10 711:12 720:24,25

brooding 454:12

broods 214:12 215:7 247:14,19 668:20 700:10

brought 17:22 19:1,6,9,15,20 21:3 29:8 54:13

65:14,21,22 171:20 300:17 301:5 315:6 325:21

326:24 361:13 362:13 397:13 468:14 565:11

583:12 607:23,24 612:7,19 613:19 620:25 651:2

655:11,11 720:9 722:26 727:9

Brown 346:4

buds 37:5

buffer 513:15 613:2 614:17

build 12:2,3 18:2 27:19 28:11 157:13 295:4 417:19

626:4

building 1:15 9:22 12:3 14:3 189:3 229:24 745:11

builds 287:1

build-in 24:23

built 23:14 71:22 253:14 546:9 745:11

built-in 71:25

bullet 296:22 420:21

bulleted 444:22

bunch 30:5 391:18 479:3 635:19 676:23

bursa 46:4 48:15

bursitides 370:7

business 4:6 11:6,9 15:7

bust 174:18

Bustin 514:3

busy 54:24

buts 736:21

C

C 162:23 163:9,10 165:18,26 166:1 172:13,13,14

172:14 243:14 311:24 312:2 369:21,22 395:11

560:23 562:13,14

CAB 526:14

cage 387:4,24

caged 387:5

cages 289:5 386:10 387:7,7,21 388:18,19 408:20

calanoid 210:8

calculate 477:18

calculated 395:10 699:23

calculating 395:6 589:20

calculation 222:1 437:19 483:24 699:23

calculations 130:22 594:1 700:3

calculus 120:13

calendar 9:20

calendars 114:13

calibrate 744:17

calibration 151:7 449:2 702:17 704:16

California 5:7,17 242:3,9 459:15,22 664:18,24
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calipers 236:21

call 17:18 75:24 79:6 82:2 85:2,4 116:4 118:26

150:1 232:4 233:7 234:11,15 305:20 314:22

404:19 458:11,17 601:6 633:9 645:16 647:1,6

660:7,8 661:2 735:6 747:5

called 28:24 80:23 84:17 85:13 88:23 107:22

149:14 161:24 274:4 349:5 402:14 416:7 457:3

calling 78:14 79:26 111:19 116:4 691:22

calls 160:17

camels 525:17,17

camera 169:13

Canada 544:6

canaries 355:15

canary 354:4

Cancer 5:5

canine 566:13 569:19,22

can/will 420:16

capabilities 187:7

capability 58:4 140:15 260:1 268:22 466:3 521:2

523:22 579:1 653:18

capable 81:3 117:8 119:18 155:23,24 156:2 165:15

503:16 524:1 534:8 622:22 701:24 702:4

capacity 3:5 122:18 124:2 142:16 148:3,6 530:15

547:1 614:25

capitalized 77:21

captivity 350:4 355:25 427:8

capture 569:26 574:26 584:13 586:8 639:3 660:1

692:19 718:24 746:5

captured 221:17 411:6 636:3,4,21 682:17

captures 680:26 722:21

car 653:8

carbio 119:1

care 57:15 122:13 329:18 337:25 351:9,16 354:26

360:14 388:6 425:12 427:12 466:13 501:7 590:14

607:26

career 283:21,21

careful 72:6,13 120:11 132:6,8,10 148:21 278:21

279:3 329:11 330:7 331:4 332:10,12 342:20

612:15 633:20 658:12

carefully 14:18 118:10,11 129:21 351:1 491:18

612:26 653:24

Carlo 110:6 139:4

Carlos 748:6

Carolina 7:7 243:7 248:19

Caroline 460:21

carried 42:7 51:13 70:13 72:9 73:26 212:7 360:9

394:17

carries 263:1

carry 40:20 43:21 44:20 56:12 58:7 64:12 73:23

74:22 167:20 213:18,23 221:1,12 223:11 246:11

256:20 257:23 260:24 354:5

carrying 41:2 51:8 74:11 668:25

carryover 51:2

cart 623:22 635:8

cascade 620:6

case 20:5 24:8,8 41:22 50:3 72:22 73:3 76:6 79:25

81:8 82:9,17 83:5 84:11,18,20 85:4 87:7,10 89:14

93:18 94:9,14 95:11,12 96:8 97:8 98:18 106:17

112:6 116:9 117:14 140:25 145:1,13 149:6

161:18 162:1,2,23 163:23 167:24 168:14,26

169:2,9,17 170:10 173:20 174:10 179:8 187:26

214:17 215:4,22 216:1 226:18 231:20 245:18,19

249:24 250:13 254:1 256:2,18 257:8 258:2 262:2

264:3,5,14 278:26 279:21 327:13 330:9 331:6

349:6 365:6,14 366:6 369:15 377:10,14,22 378:8

403:9 417:21 437:2 442:15 480:3,8 481:4,8

482:17 483:22 488:24 497:6 499:3 510:1 530:2

541:12 580:11 585:26 586:1 587:16,20 592:16

594:9 643:25 659:4 670:10 671:22 675:16 679:24

682:1 693:25 694:17 721:9 722:22 744:18 746:20

cases 52:25 54:1 82:21,22 85:19,21 86:3,7,26 87:5

87:11 93:17 99:19,20 101:22 103:1 117:16,19

141:2 161:16,18 207:22 218:6,7 256:17 257:7

265:8 266:25 267:18,24 268:5 280:25 289:15

306:9 352:6 378:1 405:15 412:9 479:2 506:22

559:19,20 577:19 579:26 604:11 605:18 671:22

680:16 694:1

case-by-case 34:20

case-specific 32:26 33:18

cast 14:11

catch 154:20 662:6 739:15

categorical 578:10,16,17

category 53:25

Catherine 6:12 66:23 67:8,17 68:12 180:24 181:20

182:3,7,17 183:4,9,13,26 198:11,20,25 242:20

460:8 545:25 549:16 554:9 563:8 565:2 569:1

573:25 574:16 575:8,23 598:15 600:26 606:14

607:19 609:3 611:10 654:16 655:10 660:25

665:10 733:1 747:2

Cathy 6:12 242:20 460:8 665:10

cattling 394:8

caudal 463:5 469:19

caught 327:15,16 647:15

causal 329:26 330:8 449:16 525:8

causative 329:23

cause 25:24 92:6 230:8 236:4 282:2 328:22 447:23

448:17 455:14 495:10 499:11 537:2 630:23 638:3

638:6 669:10 715:25 720:13,14

caused 95:4 97:2,3,4 126:22 495:7,22 553:18

571:22 572:8 667:3,7
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causes 92:9 168:19 371:19 405:5 581:25 671:4

734:26

causing 298:25 586:6

caution 449:26

cautions 220:6

cautiously 689:14

cavalier 633:1

caveat 274:13 431:23

caveated 636:21

caveats 430:15 433:26 631:14 648:18 649:21

cavity 169:13,15

CC 494:21

cDNA 512:23,24

cell 85:13 92:13,21 274:16 562:6

cells 111:23,24 158:13,18 233:20 378:24 379:4

571:18

cellular 6:20 557:2,21

censored 483:23,25

center 1:14 5:9 6:1,5 151:15 160:6 242:5 459:18

664:20

centered 479:19

centimeters 578:7

centrifugation 571:20,22

centrifuged 571:18

cents 686:13

century 11:15 743:14

certain 58:24 63:14 80:13 120:10 122:10 123:20

129:20 146:9 159:16 172:4 194:21,23 202:2

247:10 298:24 341:22 361:11,12 388:9 391:1,26

396:24 401:11,13 415:23 417:14 419:22 421:14

448:17 449:23 465:8 533:9 600:13,21 621:21,22

625:3 654:12 658:8,26 659:10 697:26 710:12,15

722:22 723:20 743:9

certainly 13:9,23 19:12,17 26:20 32:8 78:9 86:7

87:16 118:8 121:19 124:16 133:4,20 134:26

136:11 140:4,17 187:3 231:25 259:3 305:22

335:26 336:6 364:24 367:21 369:7,10 374:16

377:19 382:13 392:1 396:15 409:12 423:16

428:24,25 435:24 456:22 510:10 514:9 516:1,2

518:4 523:4,9,14,17,22 524:22 525:24,26 526:26

527:6 537:8 542:20 565:3 633:1 634:16 635:2

636:3,4 642:11 646:6 656:15 674:2,6 696:5,22

712:12 716:1 733:6 745:2

certainty 23:1 540:19 553:15 634:10

certificate 657:12

certification 428:19,20,22 651:11 652:10 654:23

655:4,15,24 656:9,11,26 657:8 659:2,14 660:6,11

661:3

certification-like 650:14

certified 655:5

certify 657:20 658:5

cetera 37:5 272:21 348:5 445:1 446:21 479:1 513:1

671:6,6 676:25,25 677:10 696:3,3,6,6 715:7

cetra 410:13

chain 424:15

chair 2:11 5:3,16 12:20 205:26 459:11 743:2

chairing 241:25

chairperson 305:18

challenge 27:26 70:2 174:12 276:19 280:4 327:15

634:21 730:2,6 746:5

challenged 59:1 382:2 728:22

challenges 57:14 325:21 422:9 507:24 568:19

607:14 704:5 726:13

challenging 4:23 11:14 60:4 113:15 313:25 329:14

559:18 726:17 744:19 746:2,12

chamber 211:10,10,10 221:24,25 228:3,3 425:18

471:26 709:2

chambers 120:21 221:24 225:4 228:8 472:1

chance 70:5,6 134:6 267:12 270:6 303:18 309:19

333:5,12 363:25 386:3 390:22 519:18 520:1

629:4 648:5 652:14 661:18 727:15 737:18

chances 622:19

Chandler 248:18

change 30:4 55:3 79:3 93:25 102:12 115:24 118:14

118:17 163:2,26 182:15 185:22 187:8 235:24

247:1 262:16,16 298:22,24 379:18 408:3,3,3

416:12 437:2 487:21 498:12 505:12 531:4 542:13

551:17 557:4 585:25 595:15 633:2 688:17 739:22

changed 75:5 93:21,24,26 98:3 162:14 174:1

198:24 299:6 312:15 557:5 600:6

changes 24:7 94:12,12 99:3 101:7 110:22 161:14

164:12 197:19 204:6 237:14 262:14,15 274:14

292:8 335:1 361:17 366:18 377:19 410:16 417:7

464:14 498:13 533:26 534:3 594:10 612:13

667:20 716:22 720:13

changing 99:2 119:7 695:7

character 84:9 92:1 708:26 726:14

characteristic 120:26 318:18 503:7

characteristics 55:23 57:3 90:20 95:5 103:4

140:21 194:23 223:6 298:1 303:3 357:16 366:4,5

366:21 371:24 409:26 425:6 431:10 448:7 451:5

451:5 452:18 453:4,10,22 454:16 462:14,17,23

462:25 464:4,5,17 466:7 469:25 479:6 494:21

509:4 514:13 516:1 524:5 543:16 550:6 574:8

597:10 637:5

characterization 278:24 279:1 448:6

characterize 26:12 38:1 39:23 58:26 69:19 76:12

155:11 212:19 222:9 278:9 282:12 316:25 329:3

347:7,11 349:15 350:24 436:9 443:8,12 447:10

448:10 534:21 545:12,16 613:6 616:9,23 624:10
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668:10

characterized 44:7 56:19 77:15 118:18,20,20

217:1 427:10 431:6 434:14 453:26 675:16

characterizes 112:17

characterizing 352:4 357:21 371:10 447:26 448:15

452:23 534:9 550:11 617:25 637:10 668:15

characters 45:2 80:5 82:8 87:3 92:7,10 94:5 97:5

97:13,20 98:3 100:8 103:26 108:15 132:4 142:8

413:9

charcoal 558:15

charge 3:25 4:9 45:25 126:10 164:3 175:7 203:14

203:24 287:10 297:17 298:9 304:17 316:22

343:16 345:23 346:18 347:3 353:13 354:17

355:19 357:3,11 361:24 383:26 384:9,14,15,18

414:3 419:10 422:1 430:3 435:5 442:24,25 443:2

452:13 458:20 461:8,10 467:12 493:5 520:4

522:14 528:7,18 533:21 539:6 544:16 545:4,7

549:25,26 556:2 559:11 564:7,13,15 570:8

576:14,15 601:13,14 607:6 610:25,26 613:4

615:22,26 616:2,4 617:24 618:21 626:26 627:1,8

627:25 628:7,11 632:18,19 633:7 636:24,26

642:21 645:1 646:18,19 660:2 662:25 664:14

666:1,2,4 696:25 703:23 707:17,19 713:15

724:23 746:4

charged 285:15

charges 337:3 384:22

charm 725:15

charmed 475:13

chart 47:20 344:23 721:11

chase 201:3

cheaper 249:14

check 65:5 86:22 124:12 130:3 139:5 195:1 229:10

229:17 239:23 275:16,17 310:19

checked 601:1

checking 129:20 254:19 276:21 404:15 429:6

699:2

checkpoints 143:1

chemical 8:22 11:7,22,26 17:6,13,22 19:10,17 20:8

20:12 21:9 22:12 23:1 25:7,24 32:13 33:5 34:16

34:17 36:22 58:6 75:21 76:14,23 84:17 95:26

96:1,2,18 98:18 101:20 112:19 113:26 114:26

126:21 132:13,23 133:4,6,10,16 142:11 148:15

161:24,25 167:1,1 214:7 225:1 230:13 233:16

238:11 271:5 275:10,15,15 279:11 280:1,15,18

296:1 317:19 319:18 326:7,25 329:20 330:13

368:25 370:3 386:9 390:2 407:20 409:17,25

412:10,13 413:13 414:5 438:4 446:9 470:17

492:10,11 493:6 494:14 497:1,10 500:7 505:9

524:18 529:13 531:24,25 550:24 551:12,26 552:6

553:18 556:19 559:6 568:14 576:20 601:16

602:10 617:12 621:22,23 631:11 647:2,8 648:1

652:7 669:7 684:18,18 685:10 697:1 708:22,24

709:2 710:9

chemicals 11:17 12:3 14:7 15:5,6,7,11,19,24 16:1,4

16:19 17:7,21,24 18:5,8,14,24,25 19:22 22:5 24:5

26:25 29:2 41:19 59:23 72:23 93:19 100:21,22

113:18 123:20,22 155:22 159:15 161:21 163:17

197:19 217:22 234:21 275:19 279:23 283:5 293:2

299:3 313:8 348:14 351:12,14 356:2 383:8 387:2

407:11,14 414:10 416:16 419:13 423:25 424:5,13

424:17,22,26 425:2,20 426:21 427:3,16,21 435:7

438:26 446:18 447:2,5,17 469:22,23,25 470:14

485:6,11 487:16 490:10 492:11,11 493:12 494:8

524:21 525:18 528:19 534:10 537:26 539:17,22

540:18,24 542:11 543:22 544:12 550:17 556:26

557:6 560:4,8 564:24 565:24 568:17 576:17

601:22 602:6 616:18 617:15 618:6 621:7 626:15

634:2,18,19,19 638:9,14,20 646:15 662:9 667:10

674:18 697:6 698:24 708:18 709:17,23,24 710:10

710:22 716:16 719:6,7,10,10,12,16 720:13 721:3

722:22,23

chemical's 278:9 623:2

chemical-induced 175:14

chemist 399:21

chemistry 113:16 133:6 147:24 289:2 374:13

378:3 410:17 511:9 656:9,10,14

chest 425:9

chick 45:6 339:12 368:4

chickens 366:13 569:20

chicks 41:2 43:24 56:16 64:15 73:22 74:9,11

288:21 289:21 290:9,12 338:23 339:2,2,13

340:19 392:10,13,14,23 424:6

Chief 5:5

children 663:9

chitchat 343:14

chlorpyrifos 669:16

choice 42:12 143:25 349:23 352:11 373:5,6 409:6

418:18,21 425:18 427:12 439:18,21 469:18,26

487:25 501:23 549:4,5 562:1 563:23 564:2,3

585:9,13 597:13 627:11 708:19

choices 492:16 563:24 688:4

choose 253:15 292:15 325:6 369:19 576:1

choosing 324:24 325:2 349:26 628:25

chose 59:12 76:16 77:2 156:24 158:18 167:1

170:24 373:25 420:19 512:5 548:6

chosen 49:8 59:13 145:1 258:26 344:13 383:19

389:3 409:9,20 470:26 487:16 488:15 559:25

563:20 577:2 596:11 628:23 644:15 675:4 685:11

689:8,10,12,23 700:20

Christman 7:18,19 8:7 35:24,25 36:13 137:10,11
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Page 18

137:17 138:8,11,17,24 139:7 153:12,13,14 176:8

176:12,15,20,25 205:9 227:16,17 228:5,15,23

229:8,12,16,19 243:12,12 276:15,25 346:12

404:4,6 405:12 442:1,8 460:26,26 548:24,25

554:20,21,21 563:11,12 569:4,5 574:4,5,21,22

577:4,5 591:23,24 606:3,4 610:7,8 615:8,9

633:11,13 644:23,24 656:1 665:24,24 670:7,8

671:16,21 673:22 675:6,9 680:10 681:9 687:25

695:11,21 696:15 703:7,8 707:7,8 712:15,16

717:17,18 724:15,16 725:1,11,23,25 740:20

Christman's 733:18

Christmas 186:14

chronic 155:22 285:9 302:15 304:1 307:5 312:14

313:3 315:25 316:11 318:23,24 319:10 321:15,18

321:21 322:17,20 323:16 341:20,21,24 342:1

550:16 620:25 638:18 708:17

chronology 531:21

circle 646:19

circulated 204:5

circulating 297:26 416:11,13,17 433:15 557:8

571:6

circumspect 153:2

circumstance 312:12 745:4

circumstances 401:11 417:14 547:17

citation 494:1

citations 482:6

cited 301:1

claiming 672:3

clarification 30:2 35:26 59:8 61:6 63:19 70:18

114:24 140:11 152:25 179:4 198:19,20 199:13

201:18 202:21 218:10 229:18 245:7 249:22

253:19 261:24 267:10 270:7 276:2,15 300:16,18

301:3 334:1 337:18 353:14,16 357:6 358:10

362:12 375:17 382:10 411:14 429:24 434:19

442:2 452:8 468:21 470:7 491:16 492:19 507:8

521:10,11,15 539:10,24 540:7,9,20 541:5,6,10,24

543:13 555:23 570:2,4 645:23 649:12 650:9

703:12,13 717:23

clarifications 205:11,18 361:26 441:3,6 452:10

673:3 687:17

clarified 188:2 248:3 367:12 389:11 393:12 394:21

409:12 467:3 477:23 484:22 651:12 655:21 673:1

clarifies 234:25 273:19

clarify 65:8 141:8 148:18 205:21 233:11 237:23

238:12 271:12 337:2 344:24 354:17 378:22

386:22 432:20 491:22 509:19 575:21 608:23

626:21 633:18 700:4 703:6

clarifying 59:4 134:6 203:25,26 227:13 270:2,4

274:23 333:13 343:15 411:13 468:24 484:19,21

528:13 533:18 549:22 555:24 564:11 575:5

595:25,26 596:1,4 601:11 607:3 610:22 615:15

636:7,8 660:4 685:4 695:3 707:15 713:13 724:20

clarity 64:5 195:5 203:9 331:22 516:19 652:13

659:23 661:13

clarity's 255:25

clasping 223:7

class 116:11 313:3 318:4 583:24 628:24 724:2

738:25,26

classes 369:17 391:24 399:20 430:7 522:18 590:11

611:5 619:8 621:22 713:21

classical 116:14

classifies 478:10

clean 113:20 114:1 190:14 565:7 620:26

cleaning 113:21

clear 34:15 53:21 55:13,24 68:18 73:13 122:23

140:7 141:21 149:18 157:12 194:25 205:22

243:19 253:19 263:10 264:11 274:15,26 276:23

278:10,25 306:11 307:10 318:12 328:3 330:26

347:5 348:6,12 358:4,20,21 359:3 367:19 380:19

388:16 389:8,10 392:20 395:5 407:8 409:13

413:1 421:21 422:4,23 424:6 426:5,23 432:7

440:11 443:4 444:10,13,22 445:5 452:10 477:18

479:23 487:18 495:24 500:8 506:23 507:19

517:16 518:15 532:26 542:9 545:8 553:7 556:24

556:26 561:19 566:1 571:26 586:14,17 597:14

599:4 607:9 609:22 614:19,26 616:6 621:15,17

622:11 634:9 654:16 655:6,25 659:9 660:14,16

684:5 704:1 710:19 728:25 736:5 743:7 746:3

clearance 416:24

clearcut 173:21

cleared 236:5 309:26

clearer 518:18

clearly 11:13 38:18 53:15 68:20 69:9 103:22

118:10 194:15 209:19 229:6,15 231:1 280:6

284:26 288:5 315:20 346:26 350:1 352:12 357:26

365:4 367:8 426:15 431:21 435:10 438:10 446:6

481:26 496:4 504:13 508:7 513:3 521:16 528:21

537:21 547:18 551:2 558:4 586:16 617:13 618:2

618:15 619:17 631:21,24 652:22,24,26 654:2

658:16 660:23 671:7 673:25 677:17,17 718:17

722:21 741:16

clear-cut 69:23 122:9

Clemson 5:20 242:10 459:23 664:25

clichI 660:19

clicking 22:19

climax 551:6 560:24 561:14 567:1 568:1,4,6

clinical 600:16

clip 80:21,22

cloacal 45:19 46:4 366:23,26

clock 153:8 507:11 703:18
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Page 19

cloned 644:1

close 24:24 27:4 29:23 38:18 52:4,24 54:4 215:25

239:4 246:3 248:12 283:22 346:26 475:21 509:25

633:20 661:16,21 663:10 703:2 731:12 743:20

745:10,12

closed 8:9

closely 25:13 52:6 156:19 398:13

closer 8:1 316:19 567:10

closes 345:20

closest 503:15

closing 4:18 10:16 14:26 304:9 305:9

cloud 304:4

clouds 630:26

clustering 153:20

clutches 223:15,19

CMP 96:22 479:16 505:8 506:9

Coady 295:25,26 326:24

coastal 6:2 620:10,16,19

coauthor 130:17

cobwebs 736:5

Cochran-Armitage 481:19

code 129:6 154:6 183:20 725:13,17,19 736:8

coded 483:21 662:10,13

coefficient 218:18,20,22 235:5,13 236:1 237:18

366:1 477:12,18 572:19,21

coefficients 181:13 218:26 680:8

cognisant 693:8

cognition 361:6

cohort 686:23

cold 278:7 393:15,17,20

colder 393:18

Colin 7:3,3 36:15 37:1 38:7 63:15,16 64:2 145:19

146:4 147:2,15,21 177:15,18,25 178:10,14,19,24

203:15 204:19 205:2 243:4,4 263:23 264:5,16

267:15 268:3,15 269:7 338:12 339:5,14,18,22,25

340:3,7,21 347:22 352:25 354:13 356:7 357:24

359:20,24 360:2,13 362:20 363:6 364:16 372:21

373:26 374:15 375:23 376:21 405:26 407:26

408:12 411:2 413:6,7,16 418:8 419:5 428:16

430:13 440:3 460:18,18 569:17 646:23 648:3,12

649:18 650:26 651:22 652:11 654:6,26 655:17

659:7 660:8 661:8 665:18,18 672:24 736:13

740:1

Colin's 356:12

collaborate 25:13

collaborated 27:2

collaboration 26:4,15 43:12 161:24

collaborations 152:9 198:7

collaborative 9:24 712:22

collaborator 193:22

collaborators 168:8

colleague 359:17

colleagues 3:23 4:21 13:24 93:8 133:21 151:23

184:24 343:4,13 356:8 359:13 577:7 617:7 632:4

635:13 642:15 653:21 679:10 724:14,17 731:1,5

736:16 737:17 738:19 748:5

collect 58:21 62:12 109:5 268:12 288:20 359:7

366:14 512:10 514:25 540:11 576:10

collected 44:13,22 45:24 46:21,26 64:11 65:4,13

109:8 273:5 287:16 288:8,9,14,21,24 289:14

366:12,13,13,14 393:23 397:25 455:24 472:5,18

473:16 474:1 514:25 533:5 591:7,18,21,22

600:13 666:19 740:25

collecting 74:25 283:4 306:21

collection 267:23 283:5 332:23,25 340:14,15

466:24 499:1 514:24 523:13 534:4 572:3

collections 420:24

collective 731:3,6

colloid 178:21

color 51:25 193:18

Colorado 5:13 242:7 459:20 664:22

colors 50:15

column 149:26 160:24 272:2 441:15

columns 183:20

combination 21:13 22:18 262:4 369:20 371:23

374:25 638:19 645:11 652:9

combinations 705:11

combine 688:11

combined 99:22 424:26 542:24 556:23 557:5

558:26 634:18

come 11:21 24:14 29:11 33:4,14 36:18 38:13 54:7

104:25 105:2 150:10 151:25 153:3 154:16 162:15

171:10 187:14 193:13 194:19 204:14 254:23

257:4 264:1 276:4,10 277:1 285:20 286:5 305:15

306:6 307:11 311:3 312:26 322:19 324:4 332:1

333:12 350:20 356:7 358:7 359:13,23 363:25

364:17 378:8 386:21 389:20,23 393:11 400:14

401:18 403:16 441:22 480:21 484:13 500:26

511:24 539:22 544:17,19 564:19 575:25 576:7

577:20 593:13,18,24,25 596:16 617:9 629:24

632:12 633:2 645:26 648:4,6,23 650:19 652:4

654:11 659:11,19 671:14 672:21 693:17 694:12

703:19 725:22 728:11 731:6,14,16,19 732:2,7

735:14 736:11,24 744:16

comes 24:17 33:6 37:6 116:6 146:22 165:21 178:20

184:21 194:22 195:15 245:14 246:8,11 322:7

373:5 396:2 399:4 471:25 542:18 662:24

comfortable 329:9 384:4,16

coming 11:23 15:1 16:23 38:15 55:5 108:21 111:6

150:26 174:5,24 296:12 303:16 319:8 327:8
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359:2,17 372:11 382:21 389:17 444:2,5 458:15

520:5 608:9,24 647:5 659:3,24 733:10 736:21

737:2 742:8 746:14 747:7

comings 708:23

Command 43:9

comment 2:15 29:4 30:19 68:17 150:25 152:15,15

153:11 178:4 180:15 186:9 188:5 189:10 196:10

235:6,15 241:13 263:9 266:14 277:12 325:23,24

332:25 333:3,4 338:21 345:20 347:14 357:19

361:5 364:13 370:5 372:6 376:15 383:17 384:1

404:7 406:13 408:1 411:2 414:11,18 417:6

422:10 424:3 429:2 430:9 435:15 441:14 443:15

451:9,26 452:2,21 461:21 462:13 470:24 481:18

484:8,15 493:13 502:9 508:1 517:4 523:15 528:9

529:1 534:16,20 539:11,25 540:16 545:20 549:6

549:12 550:9 556:12 566:19 569:9,25 570:13

576:26 586:18 589:24 590:5 598:2 601:23 606:7

607:15 611:7 617:1 624:20 636:9 637:8 642:6,14

645:3 647:19 656:18 657:4 662:13 666:13 672:11

673:10 674:7 675:2 679:13 682:18 685:22 687:22

691:13 697:7 704:7 707:23 712:19 713:23 717:24

718:18 722:25 723:8 731:10 733:18 740:23

commented 259:4 415:6 441:19 585:25

commenters 3:19 284:4 289:11 295:23 345:20

730:12

commenting 285:26 305:18 407:8

comments 3:16,17,23 4:4,9 8:6,7,11 9:9 10:26

12:17 19:23 28:18 59:4,8 152:26 154:10 196:4

202:21 203:23 240:3,4 241:15,19 245:7 250:24

251:7 259:1 277:8,8,9,12,16,17,21,22,24 283:12

284:4 285:21 296:2 298:11 302:9 306:11 309:20

314:25 324:6 332:21 333:10,19 338:13 343:16

349:23 350:21 352:21 353:3,6,7,10,13,16 356:13

357:4 369:8,13 372:20 375:15 380:16 382:7

387:18 392:8 396:20 406:15 409:6,8 410:3,4

411:5,12 414:18 417:1 419:3,5 421:19,23 429:13

429:18,23 434:18,24 440:22,23 441:2,6,9 443:21

443:24 444:2,2 450:16,19 451:13 452:5 457:17

457:19 458:2 467:19 468:1,9,12,13 472:24

475:12,24 476:10,12 477:25 484:4,22 486:7

487:16,20 488:9 490:4,6,14 491:3,11 492:21,22

501:10,21 502:11 507:2 519:2 521:8 522:20

527:19 532:7 533:18 538:20,22 539:6 541:4

544:13 549:14,23 554:22 555:20,25 561:22

563:18 564:7,12 566:4 568:25 569:1,3,5,7,23

573:21,23 574:3,6 575:1 577:24 589:23 590:2,9

592:17 595:21 596:7,9,13 598:9,10,13 599:24

601:7,11 605:26 606:7,24 607:3 608:23 610:9,10

610:19 615:3,5,7,11 623:16 624:21 626:19

628:13,14,15 632:13,24 633:14,15 636:12,18

644:25 645:1,21,25,26 646:21 647:21 648:10

661:19 662:26 669:11 670:5,10 671:9 672:8,25

672:26 678:15,16 681:8,15 685:3,6 687:17

688:22 689:19 691:16 694:26 695:2 702:6 703:9

703:10,14 707:12 711:14,16 712:17 713:11 717:1

717:16,18,20,21 724:18 727:6,8,15 730:15,20

731:12 735:10 740:15 741:20,23 744:6 745:22,26

746:4 747:22

commercial 227:9 280:24 570:11 572:8 609:16

619:12

commercially 166:13 220:23 566:12 567:1 619:26

commission 593:16

committee 2:9,19 4:12 16:6,6,6,10 241:9 277:18

283:14 575:22 649:6

common 16:21 213:17 273:14,14 316:10 317:4,20

326:21 335:23 545:26 714:13 727:9,12 729:23

736:22 737:12,15

commonality 356:21 734:8

commonly 56:23 165:14 171:16 547:21

commonplace 267:8

communicate 193:23 516:13 658:16

communities 622:3

companies 614:21 677:2

company 160:8 296:2 481:22 573:19

comparability 420:11 584:24 644:6

comparable 63:7 94:20 97:24 100:18 101:3 174:23

174:24 215:3 226:24 412:23 496:25

comparably 101:24

Comparative 6:10,14

compare 52:5 71:15 93:17 96:4 123:11 170:6

202:9,10,13,26 250:1 265:21 292:2,10 295:4

300:26 301:14 321:20 345:1 347:26 392:26 396:9

411:22 450:12 451:4,19 581:2,10 598:4 671:25

683:26 698:7 709:18 720:6 722:4

compared 36:12 40:15 89:25 102:21 105:14

110:10,19 111:21 112:2 118:22 130:18 136:13

141:16 160:2 177:5 190:21 222:17 250:8 264:4

264:19 275:18,22 300:23 331:20 389:16 420:9

435:9 436:18 441:11 448:19 474:14 496:18,18

528:21 535:15 541:8,19 553:23 559:22,24 562:25

571:8 590:18 625:23 710:1 718:17

compares 118:13

comparing 47:22 60:17 81:13 85:21 105:14 140:22

141:4 217:3 239:3 244:11 250:9 254:11 264:26

265:1 320:11,12 344:23 356:18 379:12,13 394:15

396:6 397:9 451:19 494:14 513:9 552:26 582:6

588:21 602:10 690:18 731:20

comparison 21:26 42:2 55:12 60:2,23 116:25

140:23 200:16,25 214:17 224:2,2,9 248:8,24

263:16 264:18 279:17,22 281:6 289:18 294:25
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309:3 320:14 335:19 344:25 363:1 365:8 396:12

396:14 409:7,7,25 454:13 504:18 509:1,15

510:12 520:6 523:25,26 525:7,19 529:19 531:15

557:14 598:3 656:9 677:23 678:17 705:17 712:5

719:4,7,8

comparisons 63:5 64:22 80:7 100:22 104:22

106:16 151:12 170:9 174:18 206:15 215:4 222:5

248:22 264:3 279:8,9 281:19 287:14 293:1

321:24 365:9 395:13 396:8 409:16,21 412:17

421:2 478:7 482:6 498:7 505:1 509:11 532:14

582:3 604:23 612:15 640:8 671:3 678:14 699:19

705:5,19,21 708:6 712:20,22,24

compartments 220:11

compelling 409:3

compensation 257:11 568:13

compensatory 23:8 568:9

competence 142:23

competent 422:3 500:6 709:21

compile 181:4 422:7 507:21 607:12 704:3

compiled 85:18,19

complement 378:5

complete 20:23 35:1 78:17 158:9 160:15 201:8

226:22 237:15 402:3 409:7 583:26 605:18 650:22

679:6 680:10 715:18 741:1 744:14

completed 4:14 42:15 56:21 158:19 209:9 210:11

210:14,15 275:2 506:1 535:19 663:4 742:24

743:20

completely 57:22 148:8 155:2 169:23 220:4 266:7

337:8 384:15 385:17 400:21 402:22 554:11

579:10 581:14 586:11 612:21 673:22 675:15,21

676:16 678:2 679:8 694:16

completing 428:21

completion 19:26 158:5

complex 24:12 65:10 186:17 192:14 278:2 282:22

480:19 698:5 743:6

complexing 667:22

complexities 698:10 727:23

complexity 624:24 625:24 726:14 733:4

compliance 203:4

complicate 293:22

complicated 222:22 614:13

complication 258:20

complications 258:15 260:7 374:11,24

compliment 619:4

complimentary 641:1

comply 509:23

component 16:3 120:25 137:25 186:8 194:16

249:9 348:19 423:19 427:7 430:5 454:7 510:19

522:17 524:14 594:11,20 611:3 713:19 729:19,24

components 14:2 448:2 509:4 511:5,12 512:7

513:1 525:13 542:25 543:9 595:3 653:7 677:24

678:19

compound 39:25 43:16 47:13 49:17 50:10 59:1,12

60:4,16 208:7 209:5,23,25 223:16 230:24 238:24

260:19 294:24 295:7 305:6 320:3,15 321:10

338:18 408:4,7 411:16 438:16 450:25 485:15,16

485:25 486:3,4,13 490:2 496:4 499:18,21 500:2

501:23,25 510:8,9,10,12 535:13 559:21,23 597:3

597:6,8,22 602:19 603:4 604:9 630:23,24 637:22

638:3,5 652:5 669:19 683:4,5,11 684:3 685:11,13

685:14,16,17 687:1,1 701:10 704:24 723:21

724:2 729:18

compounding 455:23

compounds 27:26 40:16 73:19 134:15 135:14

207:20 208:4,11,11 209:16,19,21 212:18,22

217:23 224:25 226:12 231:11 244:13,23 246:14

246:19 247:4 248:12,20,25 249:10 258:3 286:20

292:26 294:22 303:24,25 310:4 317:7 318:2

320:2 321:17,19,22,26 322:2,4,13 336:12 337:12

356:10 363:2 412:2 438:23 448:13 450:2 451:20

464:20 467:5 476:2 485:13,17 487:9 488:12,15

489:19 490:4,16,17,26 495:3 501:23 518:7

525:25 531:15,16 538:3,7 547:3 548:6 551:23

557:16 571:7 596:14,17,18 597:7,13 598:7,9

621:11 623:7,12 625:10,11 629:8 630:16 638:2

639:17,19 643:9 644:5 662:12 681:19,20,22,26

682:3,4,11 686:20 689:23 692:15,24,25,26

700:21,26 701:7 705:18 709:16 713:8 717:10

721:22,26 722:6,7,14 723:20 726:8 728:8 729:19

739:16

comprehend 422:5 507:20 607:10 704:2

comprehensive 40:2 46:1 64:19 68:25 145:5,11

230:16 445:26 562:17

comprehensively 38:3 208:17

comprised 17:4 18:25

compromise 192:13 553:12,14

computational 253:7

computer 593:16

computing 579:1

concentration 35:22 36:2,7,8,10,11,11 39:23 48:24

50:17,22,23 51:9 52:11 61:1 70:12 74:20 75:5

80:13 96:7 97:20 105:16,22 113:18 162:25

165:11,13 167:14 168:22 170:12 185:21,22,23,24

216:2,3,25 218:11 224:5 238:1,16 255:23 262:11

275:4 298:23 306:21 312:25 320:26 338:6 344:10

345:7,8,9 369:24 399:22,24,24 449:3,6,15 450:3

511:8 512:21,25 513:15 525:8 551:11 570:23

572:16 598:25 603:17 689:8,10 709:2 712:26

concentrations 50:7,16,26 52:1 61:14,16 71:7

84:21 95:13 97:16 159:16 165:16 167:3,4,11
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168:17 169:18,19 177:8 211:25 214:9 215:17,20

216:4,8 217:12,20,23,26 218:9 219:6,8 220:1

223:21,22,25 224:5,7 225:3 226:3,16,24 231:12

237:25 250:14 302:17 304:1 307:22,25 312:10,17

363:8 395:14 408:25 481:26 482:4 497:16,18

513:4,15 527:4 553:7 556:26 563:20,23,25

566:26 567:3,6 585:13,15 599:14 610:16 689:22

689:24 692:6,17 698:24 704:17 711:18 722:21

concentration-dependent 604:5 605:2,4

concentration-response 280:7

concept 21:7 37:16 41:7 143:23 232:13 233:18

253:11 421:6 597:15 734:12 735:3,13 743:24

744:25

concepts 265:18 660:20

conceptual 14:11 17:3,16,17 19:26 446:1 734:11

743:20

conceptually 249:17 487:12 499:26

concern 54:3 66:8 74:6 75:4 134:18 185:25 230:15

231:9,23 234:10 245:10,10 246:26 257:6 261:9

262:25 304:18 319:17 321:5 328:26 336:14 344:8

344:12 350:23 351:25 352:1 359:11 379:24

391:23,25 412:2 429:7 436:8 439:10 446:18,26

450:7 455:12 533:9 535:24 549:7 554:26 667:9

669:4,11 676:2,11 690:22 696:12 710:11 713:6

715:21 716:5 718:5 730:17 732:15

concerned 35:22 125:16 152:20 171:3 182:20

236:7 252:10 257:21 260:10 271:15 275:24 283:2

340:22 352:23 358:25 359:5 369:3 374:25 392:21

621:18 633:19

concerning 139:10 152:18 322:26 430:25 486:18

495:12 604:14 664:4 675:12 732:21

concerns 222:16 313:8 349:10 350:22 359:16

547:25 597:12 624:20 669:12 687:6 703:2 728:14

737:24

concert 37:15

concise 422:4 445:5 507:19 607:9 704:1

conclude 282:7 504:10 534:7 626:19

concluded 50:7 158:15 172:25 201:22 269:17

306:8

concludes 368:7 484:4 506:26 724:22

conclusion 4:7,14 112:10 227:12 279:19 304:26

332:5 371:26 419:21 421:14 433:2,3 437:13

438:3 510:15 517:17 537:20 558:25 625:7 647:1

conclusions 76:1 232:8 286:3 295:10 314:5 315:3

330:13 376:26 377:16 378:1 380:10 437:16

595:15,17 648:17 649:6 668:4 678:7,8,9 698:19

730:19

concomitant 185:23 263:6

concordance 131:11 143:15 224:8 235:15 509:25

511:15 529:25 551:9 603:9 726:22

concordant 131:14 152:10

concur 430:20,24 431:8 548:19 563:12 606:4,9,14

610:8 615:9 671:6 680:10 703:8 707:8 724:13,16

concurrence 320:20 321:1

condition 65:22 85:3,6,8 86:25 234:8 420:8 496:23

588:11

conditional 341:22

conditions 81:14 82:20 92:15 117:10 234:5 355:26

382:3 401:13,15 420:18 446:22 451:1 453:12

454:9 465:2,11 471:14,19,22 494:4 499:23

500:18 501:6 503:5 511:7 512:12 513:10,11

516:3 517:24 518:3 526:5,8,9 541:14 605:25

conducive 268:25

conduct 49:18 175:2 224:12 280:24 341:17 422:6

433:23 438:21 500:25 507:20 512:9 527:2 573:12

607:11 669:20 704:2 716:26

conducted 157:16 160:5 276:16 286:23 302:2

304:15,20 307:20 315:26 320:1 342:1 344:3

436:24 454:13 500:11 508:19 510:2 511:2 536:2

562:11 570:24 604:24 675:23 705:13

conducting 20:18 80:17 143:9,11 194:3 208:26

465:16 500:19 698:2 714:24

conductivity 515:20

conductor 200:1

confer 724:26

conference 1:14 160:16 575:16 740:6

conferred 378:11

confidence 55:6 75:3 112:9 238:2 280:23,25

315:12 326:9,9 332:6 374:18 375:25 379:16

396:26 413:21 540:19 598:26 679:22,26 680:1,3

693:26 694:2 704:23 725:16,18 730:18,25 734:22

738:9 743:5

confidences 680:6

confident 158:17 261:8 717:2

confidential 3:8

confirm 96:16 181:25 437:1

confirmation 537:26

confirmatory 537:10

confirming 182:2

conflict 3:7

conflicting 8:24 70:2

conformance 706:3,6

confound 278:14 408:19 425:21 573:3

confounded 385:17 388:4 640:18

confounding 257:14 358:26 408:24 531:11 536:24

confounds 220:1

confuse 404:20

confused 270:10,13 271:3 650:10,11 732:11

confusing 71:5 343:7 390:3 580:12 582:1 627:6

634:26 720:10,17
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confusion 300:5,21 475:2 590:12 671:4 720:19

congress 11:10

congressionally 14:5 15:14

conjunction 172:22 402:25

connected 24:16

connecting 70:6 115:24 229:25

connection 24:22,24 231:20 263:20 287:21 632:9

consensus 29:5 358:5,21 446:10 631:26 646:24

648:4,7 649:3,9 650:19 651:5 652:20,20

consequence 35:14,16,18,20 41:9,17 49:26 58:8

102:24 111:5 185:14 186:5 231:9 232:23 260:3

588:16

consequences 79:13 136:2 155:12 185:17 555:5

Consequently 85:25 516:18

conservation 37:19

conservative 87:6 234:22 478:9,16 581:5 692:12

consider 2:4 13:6 31:17 39:7 73:4 81:7,16,17,20

84:5 86:8,11 99:14 104:3 141:15 144:12 204:6

205:21 225:8 232:15 241:5 251:19 253:11 265:6

275:25 286:10 290:18 294:3 308:19 309:3 338:2

353:21 356:9 367:10 372:18 397:18 420:2 424:9

433:20 439:14 568:22 573:17 583:11,12 584:12

619:6,16 623:23 627:12,14 649:10 657:23 667:13

680:9 688:21 695:6,23 727:1 728:8,8,10 741:1

744:20

considerable 433:2 613:24

considerably 156:9

consideration 9:10,18 14:19 17:23 19:9 41:22

58:16 59:19 70:7 230:11,12 257:25 258:5,11

266:10 277:5,23 336:18 346:6 365:10 366:11

378:2 427:5 428:18 518:14 619:17 622:5 635:22

710:26 744:9

considerations 213:1 232:11,17 256:20 264:24

335:26 535:6 635:26

considered 40:23 41:16 43:24 46:7 48:2 49:21

52:20 54:2 58:11 72:20 90:13 136:9 152:23

186:12,15 203:21 206:22 210:11,12,17,26 213:2

213:10 219:16 227:23 253:8 255:14 262:13

275:14 292:20 295:13 336:4 340:1,5 353:18

354:10 355:14 412:25 425:23 432:10,10 439:9,15

441:10 448:23,24 480:10 487:11 499:10 503:21

520:3 526:20 533:8 558:10 559:13 564:23 598:20

624:4 636:10 696:7 700:16

considering 17:9 36:18 42:13 191:7 226:11 263:1

354:2 368:5 414:4 493:5 601:15 625:17 644:11

696:26

considers 49:25 499:26

consist 90:9 148:14

consistencies 132:1 170:6

consistency 52:23 53:15,26 54:5 56:6 83:19 84:7

166:20 191:6 198:14 278:20 279:10 281:8 363:1

377:9 379:14 398:4 413:18 420:22 432:18 464:22

504:22 509:13 510:6 512:16 534:18 537:22 560:6

570:15 599:19 605:15 608:1,4 624:22 625:2

626:4 684:21 693:18 697:19 701:1 727:19

consistent 20:25 42:11 49:20 51:9 52:16,21 53:1

56:3,11 60:21,22 103:5,10 119:6,25 162:7 163:26

165:22 166:17,21 175:3 213:21 236:22 237:26

238:11 247:11 265:22 280:22 281:11 288:16

293:4 311:14 356:26 362:21 367:19 377:6,9,13

378:15 385:14 386:6 414:10 417:17 419:12

432:23 451:2 479:8 489:11 493:11 495:24 496:20

498:10 503:22 504:21 512:8 516:10 520:11

534:14 546:5,17 560:18 601:21 602:26 603:4,8

603:13,16,19 604:4,9 605:1,3,7,11 608:2 613:19

614:26 640:12 656:14,15 661:4 684:17 686:5

697:5 711:14 713:1,4 729:15

consistently 119:16 147:9 279:24 291:26 377:20

600:22 603:21 604:12 610:16 700:6

consisting 271:4

consists 81:8

consortia 428:3

consortium 284:22

constant 86:16 393:19 397:3

constituted 702:20

constraint 696:1

constructed 170:20

consult 673:18

consultant 7:19

consultation 3:12 24:15 33:11 743:16

consulted 171:10 251:15

consulting 160:8 243:13 337:4 461:1 473:3 665:25

consumatory 66:14

consumed 154:19

consuming 620:2,3

consummatory 418:15 426:11

consumption 291:14

contact 3:23 181:5 198:6

contain 297:7

contained 245:21 378:17

containers 220:14

containing 378:15,16

contains 40:6 519:16

contaminant 126:21 569:11 724:1

contaminants 621:23

contamination 408:22,23 499:1 563:26

content 220:22 558:10

contents 217:1

content-wise 13:11

context 35:12,20 37:9,19 39:21 40:18 41:9,18 43:3
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Page 24

44:10,24 46:6,18 47:18,24 48:6 49:12,15 52:14

52:26 53:21,25 55:8,10 56:1,15 57:11 59:23

60:11,19,20,22 61:2 63:1,8 64:18 65:25 66:12

68:7 69:11,22 70:8,12 72:23 81:20 105:15 107:8

109:18 131:17 135:2 136:1 141:14 142:23 148:8

185:16 199:8 202:1,4 204:18 207:5,8,11,16

208:13 210:18 211:1,2 212:10 215:2,4,9,19,26

216:18 217:10,21 218:1,17 219:3 221:8 223:3,9

224:15 227:5,24 228:2,4 229:3 230:20,25 231:3

232:21 235:20,25 236:14,16,18 238:20 239:5

245:9,21 246:4,12 247:15,22 249:5 252:12

253:13 255:16 256:23 257:13,13 259:25 260:10

260:21 261:3,26 262:6 263:19 264:8,24 265:2,23

268:19 269:13,19 271:18 311:17 353:20 369:8

371:8 411:18,24 412:23 486:22 494:6 542:14

597:20 604:18 613:1,3,26 632:26 636:21 647:23

685:8 734:12,18

contingency 548:4

continually 382:2

continuation 61:20 212:4

continue 44:16 73:22 74:2 186:7 341:24 365:22

366:11 380:12 420:4,4 439:18 458:19 713:3

739:3,19 743:17

continued 41:21 43:23 64:10

continues 509:14 550:19

continuing 145:19 652:24

continuous 42:20,23 89:5,15 108:16,23 211:7

226:1 379:5 456:11 478:12,13 531:21 578:5,12

578:13,23

continuum 142:21 565:8,10

contract 43:16 62:14 173:14,25 174:1,6,19 175:23

179:4,5 182:12 184:19 186:10 190:13 193:6,11

194:1,2 196:18 198:7 201:19,21 224:3 266:6

267:1 285:11 358:16 374:19 378:5 413:18 428:1

441:15 497:7 498:20,23 499:3,13,20 500:11

509:26 517:1 559:16 566:25 570:24 571:11 572:1

573:14,16,17 601:4 609:16 614:21 649:24 650:5

651:3,7,12 652:3 655:19 659:9,11,16 704:19

726:18 728:1

contracted 160:10 573:12 650:6

contractor 179:18 193:25 374:26

contractors 166:8 193:13 267:7 748:7

contracts 201:4

contradictory 48:14 326:20 502:22

contrary 317:14

contrast 445:4 446:17 483:8,9

contribute 734:9

contributed 571:15

contributing 175:2 389:26 690:21

control 36:3,6 40:11 44:2 45:18 51:1 53:22 55:1

66:25 67:11,21 75:21,21 84:20 85:18 86:10,13

87:21 88:5,11,11,13 89:25 96:13 98:24 99:19

102:19 110:7,10 111:20 118:13 127:25 131:16

148:16 149:20 160:25 164:15 168:13,15 169:12

169:26 173:16 175:26 180:16 191:23,26 192:6

193:25 201:9 202:6,24 203:8,12,18,20 211:13,14

211:26,26 215:12 216:15,24 221:8,16 237:4

241:11 249:26 250:8 259:9 263:25,26,26 264:4

264:10,10,19,19,26 265:1,5,8,13,21 271:5 280:23

295:4 300:12,17,18,19,20,24,24 301:1,6 309:23

321:3,4 331:20 364:24,26 373:12,23,25 374:12

375:11,18,19 386:26 387:26 389:13 394:18

395:14,21 396:7,15,21,22,26 397:9 403:7 413:19

413:20,21,22,25,25 414:22 415:3 417:17 420:3,9

420:10,19 423:9 425:10 426:13,26 427:18 432:18

438:7 448:25 450:26 463:23 474:12 487:8 489:6

497:21 499:4 502:13,15 503:18 504:5,9,16,19,21

505:7 506:10,13 509:9 516:25 517:2,15 520:6,11

520:12,17,18 543:2 553:23 559:18,25 560:2

570:22 571:14,24 572:13,17 582:19 589:16

597:26 598:17,18,21 608:2 616:17 618:18 638:15

644:3 649:23 650:8,17 651:3,9 652:9 654:4,17

656:3 658:2,2 671:3 679:26 680:6 684:19 685:7,9

685:10 687:20 688:11,13 695:5 701:12,18,19

705:26 706:7 714:23 717:5 729:6 736:23

controllable 171:21 172:5

controlled 25:8 36:12 84:3 99:10 172:6 175:3

254:11 259:8,11 386:8 420:16,20 471:20 503:13

558:3

controlling 454:5 618:17 625:16

controls 52:6 60:17 67:13 73:15,18 94:7 111:22

123:25 127:5,23 129:11,18,20 130:8 149:3,6

168:11 176:4 188:24 192:2,16 202:26 203:6,17

211:19 214:11,15 221:9 223:17 227:25 263:26

264:18,21 265:1,5 268:11 271:18 272:22,22

300:7 320:7,12 331:17,21 366:9 370:24 385:10

388:19 391:9 396:10 420:6,8,18 422:21 498:25

499:6,8 505:5,16,16 559:13,14 607:22 625:22

639:1 645:6,7 654:20 661:1,9 671:4 675:25

677:26 680:3 682:10,20,24,25,25,26 683:14

685:8,23 688:15 695:5,15,16 700:10 704:14,16

704:18,21,26 705:3 706:19 723:19

control/unexplained 506:6

convene 615:18

convenience 392:4 393:6

convenient 391:26 392:1,3,6 393:5

convening 662:20

conventional 244:5,6 248:5 620:24 621:13 624:3

638:11 639:15 640:3 708:17 709:7 714:14

conventions 104:3
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convergence 652:18

conversation 76:1 112:22 180:25 362:1 469:2

conversations 227:3 729:12

converse 133:20

conversing 130:15

convert 61:13 593:3,7,11

converted 269:23 379:10 593:20 594:2

convince 628:23

convinced 348:26 437:25

convincing 437:12

convoluted 446:6

cookie-cutter 578:25

cool 181:1

cooling 265:1

cooperated 77:21

cooperation 77:18

Cooperative 6:9 242:18 460:6 665:8

cooperators 193:20

coordinate 13:21 139:26 536:20

coordinated 143:12,19

coordination 3:11 8:15,21 10:3,4,10 13:20 746:21

747:10

coordinator 323:25

coordinators 251:18

copepod 206:5,13,16,26 208:25 210:6,8,21,23,25

211:4,21 222:10 223:5 224:9,10,26 225:9,9,25

226:6,9,12,13,14,19,23 227:1,7 237:19 238:8,14

238:24 239:4 243:22 248:8,18,26 249:9,11,14

251:5,25,25 252:8,16 253:9 259:15,21,26 260:3

315:16,24 322:12,23 343:22 344:1,5,7,12,13,14

344:18 617:21 618:9 621:25 622:8 624:1,14

629:20 637:16 640:17,24 641:5,9,16,24 642:1,18

642:25 664:13 667:24 669:14 707:21 708:3,19,23

709:14 710:2,4 711:16 712:3,23 713:10 723:22

copepodite 223:2 711:18

copepodites 224:18,22

copepods 208:20,26 210:2,2,11,17 222:6,22 224:12

225:11,20 226:5,26 238:3 258:25 620:2 626:8

709:18 713:8

copied 395:18

copies 152:14 442:6,15

copulation 54:14

copulations 365:18

copy 87:21 100:15 162:15 513:24,25,26

core 463:18,19,21

corian 446:9

corner 343:13

corollary 233:9

Corps 6:17 242:23 460:11 665:13

correct 33:25 34:4 36:6,9,13 37:7 61:8 66:17

124:15,25 129:18 130:3,22 137:26 229:3,4,7,10

232:5 235:17 266:18 272:9 339:25 355:26 362:4

407:16,19 413:7 471:6 475:9 499:2 582:25

592:15,20 595:5,12,13 596:8 640:21 651:22

661:25 671:2,5 675:9 688:20 695:21 729:13

740:8 741:1

corrected 137:14,15 179:24,26 180:1 331:8 417:12

584:26 592:13,17 706:24,26 737:13,13 740:10

correcting 179:22 417:12 497:25

correction 291:11 474:26

corrections 348:5

correctly 91:12 129:16 130:25 136:17 182:11

183:5 228:16 230:1 237:16 298:15 355:12 356:19

375:18 408:9 428:14 475:2 517:3 587:12 604:3

654:25 676:3 678:2,3 742:12

corrects 175:25

correlate 143:25,26 144:14 233:6 527:7,12 634:5

712:25

correlated 121:18 144:17 170:22 289:7 551:16

correlates 140:18 685:16

correlating 143:21 303:14

correlation 121:19 289:1 302:9 402:8 503:13

504:16 517:23

correlations 204:4 536:15

correlative 329:23 464:17

correspond 26:10

correspondence 411:25

corresponding 692:4

corresponds 159:6

CORT 416:24 418:10 423:2

corticosterone 416:14,17,20,21 433:15

cosmetic 14:6 168:2

cost 22:4 59:21 186:10 195:9 258:15 293:19

312:18 313:18 374:24 492:5 519:9,10 525:11

547:15 622:19 704:18 712:7 716:5

costly 298:6

costs 374:13 531:8

could've 700:14

Council 3:12

count 32:2 82:12 157:8 200:18 387:15 404:18

480:8 737:22 746:24

counted 44:11 65:4 82:12 416:6 463:12,14

counterintuitive 280:14 625:17

counterparts 403:4

counting 515:7

countries 77:23

country 251:21

counts 45:14 405:6 479:7,8,18,19 481:3 578:8

couple 10:22 49:8 63:16 70:17 115:16,16 139:10

140:10 184:7 202:20 207:23 210:16 216:13 233:4
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236:3 244:1,3,18 245:24 258:12 267:15 289:22

316:16 323:11 324:7 333:18 341:26 345:8 361:26

368:11 370:15 372:16 407:26 456:21,26 478:8

480:4 482:18 488:8 490:24 537:5 569:17 598:15

600:1 640:1,14 675:8 682:16 692:13 711:3 722:1

723:14 744:5

coupled 166:10 546:19 606:21 612:8

course 13:3 20:2 22:4 23:13 27:5 31:4 51:14 64:16

65:3 81:10 86:1 89:15 93:25 105:25 142:17

209:13 216:8 273:7 274:6 327:26 577:14 583:7

585:19 600:14 643:6 666:18 672:5 680:17 699:17

722:13 744:1 745:24 746:19,20

courtesy 7:19

courtship 427:1

covariance 154:2,4

covariant 138:13

covariate 586:7

cover 16:25 21:15,15 23:13 155:13,14 206:3,10

312:16,24 324:7,7 372:10 411:6 454:22 563:25

695:14

coverage 15:21 16:19 19:7,17 20:26 21:14,18 23:4

covered 40:4 181:18 419:2,5 488:11 490:7,14

491:4 501:15,18 538:20 607:19 661:15 702:10,16

725:26

covering 37:16

cow 694:12

co-author 515:13

co-authors 239:13

co-efficience 394:23 395:9

co-efficient 395:2 440:14,16

Co-operation 210:4

crack 681:14

crazy 630:25 635:15

create 369:24 492:12 530:3,3 592:7 593:17 710:25

713:4

creates 629:9

creating 420:2 470:1 483:12 700:17

credibility 2:26 358:4

credit 747:16

crew 62:16

cringe 583:8 739:8

criteria 58:21 89:12 156:4 174:13 194:21 195:8,21

195:24 197:26 199:17 202:7 203:21 214:6,19

223:15 232:19 234:5 247:14 250:19 320:7 328:5

353:21 364:12,21 365:2,23 367:8 381:6 444:8,18

449:16 461:20 508:9,19 510:19,24 547:9,11

556:11 654:18,25 661:4 662:7,8 666:12 706:3,7

critical 3:3 11:1 12:7 17:1 41:6 58:3 66:10 194:15

199:18 279:2 306:5 325:15 373:14 376:17 397:4

397:5 420:8 423:18,21 424:26 432:2 464:12

546:6 620:17 621:5 622:12 631:4 643:12 653:23

653:24 658:9 668:5 684:26,26 687:10 705:26

724:5,7 739:17

critically 282:9 439:3

criticism 365:12

criticize 728:23

critter 222:22

critters 236:16

crop 285:6

CROs 344:1

cross 158:3 230:21

crosscutting 727:9

crosstalk 644:6

cross-Agency 29:17

cross-generation 436:6

cross-laboratories 491:25

cross-sections 455:22

cross-talk 231:21

cross-trials 451:19

crucial 351:17

crunch 513:20

crushing 515:3

crustacea 260:13

crustacean 210:12 231:18 620:6 667:5,12 681:24

706:2 708:13,19

crustaceans 207:1,7,21 231:20 619:7 620:21,23

621:1,4,5 686:6 687:10 701:11 708:15

CRYSTAL 1:16

cuff 117:13

cull 469:17

culled 469:15

culling 258:22

culture 62:15 77:20 78:10 103:21 173:8 224:11

684:21,24

cultured 77:13 156:7

cultures 86:17,22,23

cumbersome 249:19

cumulative 40:8 222:4 578:19 633:4 668:5

cure 208:3

curious 30:11 34:8 52:6 59:10 115:20 116:25

117:25 122:3 123:9,26 142:25 143:11 152:10,12

153:1 184:10 204:4 236:26 237:1 253:26 273:21

543:25 600:18 644:8 671:19 733:13

current 5:16 31:15 62:24 63:18 64:3 188:24 189:1

250:20 265:20 288:13 304:25 306:3 315:18 335:2

338:1 340:12 342:15 344:1 349:2,20 368:20

438:24 445:21 455:3 529:6 531:5,19 578:21

618:9,14 619:4 620:20 621:8 622:2 623:10,12,24

624:3 625:17 632:8 638:10,15,24 639:8,17,21

641:24 642:4,16 645:8 657:24 698:5 704:13
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706:1 709:10 710:5,18,22 726:11

currently 57:11,12 89:1 155:15 156:5 175:8 212:6

252:4 267:1 288:24 295:15 315:26 322:12 323:5

340:17 341:22 344:1 349:12 360:18 454:18

465:16 519:15 530:25 536:15 563:1 567:17

570:11 640:10,21 641:2 710:8,12

cursory 713:1 719:14 721:4

curvature 171:15

curve 33:21 63:25 111:11,12 151:13 182:5 215:9

358:15,23 449:7 585:14 587:19,21 703:1

curves 30:26 31:1,3 33:12 451:12 571:13

cut 121:2 144:23 146:8 201:3 270:3 409:14 565:7

593:11 729:2 731:10

cutting 23:11 630:5

CV 54:4 68:2,9 437:11,15,19 582:21 691:26

CVs 53:24 54:1 58:22 190:9 202:11 219:2,26 236:3

236:10 237:25 238:1,8,8,10 325:5 417:7 435:12

436:17,19 437:3,14,18,24,26 477:12 582:6,21

692:3,4

cycle 307:24 444:19 448:7 456:14 616:16

cystis 158:12 170:3

C's 165:21

D

D 163:7 312:1 348:25

daily 114:13 159:19 181:7 454:5 476:26 505:19

523:12 600:17,19 601:1

Dallas 6:26,26 8:7 62:1 148:10,12 149:1,8,15,19,24

150:7,10,14,22 177:1 179:20,21,26 180:4,8

205:10 243:2,2 253:22,23 254:10 270:9,10,17,21

270:25 271:2,23 272:1,4,10,24 273:18 283:19

347:22 353:2,9 360:17 361:8,9 369:13 381:15

383:23 404:7 405:14 406:8 414:17 418:3 419:9

419:20 429:11,21 434:8 437:15 440:22 452:4

458:1 460:16,16 468:4,13,19 469:11,12 471:2,6

477:10 484:6,11 491:6,9 502:8,10 519:4,6 528:5

528:6 532:23,24 538:18,19 577:25 579:18 590:6

591:23,25 592:12,24 593:5,8,13,17,26 594:3

599:26 600:1 665:16,16 679:15 693:24 725:2

735:18

damage 455:21 537:2

damaged 84:21

Dan 5:15 242:8 451:10 459:21 664:23 730:14

739:9

danger 432:25

dangerous 473:8

DANIEL 5:15 30:3 31:16 33:22 34:1,5 115:15

116:21 117:25 118:12 119:8,14,20,26 120:7,18

121:6,9,21 122:21 123:1,4,9,26 124:5,12,19,24

125:1,9,14 126:1,12,14,24 127:6,10,15,26 128:3

128:5 242:8 443:20 457:20 459:21 468:1 476:14

488:8 492:25 501:20 519:2 528:4 532:22 538:17

540:23 541:26 664:23 728:18

Dan's 731:10

daphnid 341:18,19

daphnids 626:9

darn 197:14

dash 162:15,21

dashes 162:16,17

data 11:21 18:5 19:22 20:6,17 21:21 24:1 31:20,21

33:6 34:25 48:24 57:25 64:24 67:12 68:17 74:14

75:20,21 83:22,23,23 85:18 88:22 89:5,6,14 90:7

90:13,18,22,23,24,26,26 91:2,2,5,12,14 93:6,21

94:2,19 96:11 98:22,24 99:1,9,10,21 100:15,18

102:17,19,26 105:17 109:8,26 110:1,7 112:26

113:8 116:6,7,7 121:16 124:6 129:21,24 131:11

133:22,24 135:11 139:14 140:16 143:21 145:13

151:15 153:26 160:20,22 162:11,12 163:17

176:18 181:4,8 192:21 195:6,7,15 198:16 225:16

226:8,10 230:7 236:24 244:14 245:7 248:20

251:14 261:13 263:24 268:4,16 269:7,8 273:5,16

278:17 283:4 284:19 285:12 291:21 293:20

294:10 295:7 297:1 298:16,18 301:6,7 304:22

306:4,4 307:5 310:12,23 312:4 313:6 321:18

325:12 327:13 328:17 330:11,12 332:19 336:1,6

337:10 347:11 349:15 352:5 354:1 359:11 367:4

374:16,17 376:24 377:8,10,21 378:4 379:16,19

380:9 381:26 383:14 388:9,11,14 394:15 397:19

397:22,25 398:3,7,8,9,10,12,13,16,19,21,22,24

399:11,12,18 400:4,9,19 401:7,25 402:14 405:17

413:21 415:3 416:5 417:2,5 422:7 426:19 431:14

433:6 443:12 448:22 449:4,14 465:6 470:21

472:5,17,22 473:1,5,16 474:15,21 475:1,1,9,17

478:5,10,11,14,16,19,20,21 479:5,7,13,13,21,21

479:24 480:8,13,15,16 482:16,17 483:13,23

484:1 486:21 488:19 498:9,17 500:5 503:7,13,15

503:17 504:16,19 505:6,23 506:4 507:21 508:23

509:16 510:16,23 511:15 513:19,21,21 514:5

517:5,7,11,13 520:6 528:1 529:19,20 530:1,9,12

530:22 531:1,19 534:4 535:9 536:18 539:13

545:16 547:16 551:21 558:25 560:13 571:13

574:12,14 576:23 578:4,23,23 579:1 581:20,23

581:23 583:4 584:17 587:1,25 588:8 591:6,9,11

591:11,15,18,21,21 592:2 594:8 600:4,10,20,24

606:16 607:12 608:10 612:25 616:23 624:10

631:17,19 634:26 642:9,25 652:22 657:20 658:2

658:3 659:3,13 671:23 674:6,25 675:19 677:3,11

677:14 680:21 683:23,26 686:24 687:2,23 690:15

691:11 694:9,13 698:16,18 699:24,25 700:1,9,14

704:3 705:1,3,7 709:7,12,14,25 711:9 715:10

716:10 719:8 720:10 723:15 724:9 725:5,18
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728:24 729:20 730:18 733:2,10,19 740:25 741:7

744:22 745:5

database 420:3,4,5 514:6 709:6,10,11

dataset 24:5 135:10 161:20 261:13 262:23 295:4

300:9 304:25 531:5,13 581:15

datasets 268:24 269:25

data-driven 581:14

data-entry 161:13

date 252:19 304:20 325:4 525:14 577:13 614:25

630:6 708:20

dates 189:26

David 8:23

day 27:4 48:5,9,11,12,13,13,15,16 50:14 52:14,24

52:24 86:8 99:10 154:19 204:13 206:1 212:1

219:15 222:4 236:12,13 240:7 241:4 261:24

262:1 277:2 288:10 289:6 307:23,23,23 320:25

332:23 363:24 367:5 383:24 386:2,3,3,3 387:22

387:22 388:21,22 390:8,11,11,12 391:2 394:10

398:15 404:13 406:24 418:3 424:1 458:11,21

459:7 505:21 514:11 572:14 600:21,22 663:12

664:11 673:7,8 675:11 681:10 685:21 689:11

701:16,16 720:4 736:8 742:26 745:3

days 4:6,14,23 9:6 10:18 47:3 73:22 80:1 108:17

108:23 165:13 211:7,20 212:7 214:23,25 216:1

221:7,15 222:26 238:20 247:18 263:16 290:9

291:14 316:11,16 324:7 332:25 394:9,9 415:21

415:22 476:26 573:2 659:8 666:19 679:16 711:3

721:24 727:19

day-to-day 11:10

day-4 48:7

DCP 210:14 226:13 260:20 681:21

DDT 95:14,23 106:11 107:3,11 485:16 505:5

506:12

dead 85:15 578:9

deal 56:19 133:5 150:4,5 193:20 249:19 265:11,14

266:23 398:14,16 426:20 485:23,25 486:5 673:11

693:18 727:22 739:11

dealing 68:8 73:8 201:1 211:5,6 218:24 267:22

361:10 379:21 405:17 433:8 475:18

deals 397:16

dealt 193:17

death 8:5

debatable 257:20

debt 181:11

decade 18:20

decades 26:21

decent 184:15

decide 176:2 232:9 252:25 309:5 324:26 397:4

400:15,23 420:1 598:26 633:6,7 646:15 676:9

680:16 710:16

decided 157:23 177:21 210:22 293:18 381:5 428:2

516:12 577:23 579:11 699:7

decides 580:24

deciding 35:10 285:15 469:14 482:5 629:9 683:4

deciliter 572:17

decision 144:2 194:25 232:6 234:3,6 292:11 300:8

311:13 363:25 372:23 402:4 439:15 510:24

531:14 579:7,9 580:23 581:1,13,16 614:18 627:2

689:13 699:3 709:15 710:9 731:24 733:11 745:5

decisions 2:25 3:1 113:9 185:9 256:19 257:25

315:11 347:13 349:20 351:3 373:7 443:14 492:7

545:19 616:26 624:12 634:6 677:5 692:10 730:19

735:15

decision-making 255:26 256:11 625:20

declared 34:16 717:26

declined 366:8 505:21

declines 215:16 238:19

decrease 47:25 48:1 91:21 97:3 103:7 124:9

161:18 216:8 262:13 263:6 338:23 339:7 361:18

416:5 585:16

decreased 48:15 103:3 279:21 435:13 469:24

494:21 495:6 694:20

decreases 85:12 97:5,14 224:23 298:7 523:11

decreasing 50:24 427:24 505:17,18,24

dedicated 113:2

deem 200:15 202:5

deemed 17:8 25:18 218:15 269:5 356:1

deeply 351:23

default 232:13 234:7 332:11 578:24 581:13

defecating 289:6

defend 409:10

defer 355:8 577:6

deference 632:13

deficiencies 28:10 706:20,23,24

define 33:22 39:23 40:24 145:22 325:15 493:19,22

526:7 618:12 623:5 624:26 637:18 645:12 702:22

712:13 730:7

defined 27:12 33:25 87:14 121:17 274:19 280:6

291:6 311:17 313:22 371:26 409:25 411:16

435:10 453:2,16,19 454:20 462:7,9 464:23 465:3

465:7 493:19,22 496:4 497:2 502:21,22,23,24,25

508:25 511:5 512:15 513:3 515:19 517:24 528:22

558:4 617:11,13 618:3,15 621:14 624:9 637:14

697:17 710:3 718:17

definement 56:1

defines 619:17

defining 30:11 92:1 203:7 500:17 705:23 706:9

708:24 709:21

definite 211:23 212:6

definitely 10:22 92:9,20 317:19 322:26 334:26
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515:18,22 542:1,3 580:15 713:7 720:21 723:6

729:11 743:1,4 744:22

definition 27:7 227:18 229:13 317:2 420:26 447:25

479:20 493:25 508:10 511:8 590:16 605:24

definitions 602:3,5 659:15

definitive 123:15 212:22 253:8,10 274:20 278:12

287:22 298:26 306:21 320:19,22 347:10 349:15

350:14 352:3 354:18,20 356:3 376:25 380:10

411:21 412:6 435:20 443:11 482:17 518:2 545:15

616:22 624:9 668:2

definitively 69:24 274:17 278:9 282:12 286:16

295:20 296:20 298:17 305:11 440:6 552:19 710:3

degas 117:18

degassed 117:20

Degitz 26:17 29:22 154:12,15 176:11,14,17,22,26

177:7,16,24 178:5,12,15,22,26 179:6,10,16,25

180:3,6,11,22 181:15,21 182:6,9,26 183:6,11,18

184:17 185:7 187:5,17,19,22 189:1,7 190:7,23

191:1,5,10,16,21,25 192:23 193:4,8 196:15 197:9

198:3,22 199:3,15 200:19,22 201:2,7,10 204:8,24

205:4 276:4 549:21,22 555:24 564:10,11 566:3,6

570:3 575:3,4,21 576:12 595:26 596:4 601:10

607:1,2 610:21,22 615:14,15 727:4,5

degradence 132:12,14,15,17,18

degrades 501:24

degrading 132:13

degree 167:6 199:4 288:6 319:4 323:1 375:25

462:26 463:3,6,16,24 495:3 504:11 514:8,9

517:24 536:16 550:23 683:13 735:23 738:9

degrees 491:22 492:9

DEJ 592:17

delay 166:3,5 307:9 309:13 604:5

delayed 424:16 596:25 603:18

delays 170:16 604:10

Delclos 5:8,8 242:4,4 374:9,10 459:17,17 664:19

664:19 727:17,18

deliberated 16:10

delineate 30:26

deliver 303:20,25 649:25

delivered 121:3

delivering 237:22

delivery 114:4,5 213:26 214:7 223:16 301:12

641:17

delta 594:13,19,22,25,25 595:4,6,13

delve 32:6

demand 404:25

demonstrate 27:23 30:8 60:26 61:24 122:13 200:1

214:12 230:21 280:18,22 318:21 364:10 375:21

461:18 517:22,23 529:25 537:18 542:11 556:9

559:26 562:22 574:9 650:7 652:6 653:14 656:25

660:15 661:11,12,12 666:10 706:25 743:12

demonstrated 26:7 28:6,19 42:6 72:11 122:18

230:13 258:13 281:19 283:1 313:11 357:14

364:22 436:7 438:10 449:14 452:16 454:12 462:3

500:17 535:13 536:15 550:3 571:8 605:15,23

606:6 613:15 614:25 637:3 660:12 662:9 698:1

711:6

demonstrates 111:25 185:21 216:6

demonstrating 39:10 43:7 283:18 436:3 643:8

650:13,18 652:10 654:23

demonstration 39:18 209:14 297:14 298:11

364:14 383:7,19 384:11 431:15,21 461:22 464:11

470:12,26 556:13,17 560:4 576:16 577:2 613:13

666:14 667:23 668:21 669:1 674:17 675:4 681:19

681:20 683:21 689:5 697:21,23 699:20

denominator 254:14 584:10 588:17

denoted 594:15,21

densities 558:21

density 558:24 582:26 608:5

Denver 6:19,20 34:8 35:7 68:13,14 184:5 190:3,4

190:17,26 191:3,9,14,17,22 192:20 193:2,5

242:24,24 460:12,12 548:17,18 550:13,15 556:15

556:16 564:18,26 565:21,22 566:24,25 570:17,18

575:16 576:8 598:11,12 606:8,9 609:10,11 610:9

615:2,3 665:14,14 701:26 726:5,6 735:9

Denver's 36:16

denying 144:20

depart 483:5

departments 7:24

departure 35:21 74:6 185:25 199:22 482:26

departures 403:2

depend 263:4 548:14

dependence 31:26 476:15 525:8 582:26

dependency 129:8

dependent 73:9 118:25 173:3,3 247:17 351:8

387:1,1 399:7 402:7 449:15 454:1 597:2 602:8

624:15

depending 66:19 87:26 89:9 211:8 212:9 254:17

257:5 262:1 265:22 397:23 489:18 535:16 581:22

594:10 685:12

depends 199:3 383:13 470:20 473:20 532:25

576:23 671:17 674:24 694:22

depiction 17:17

deplete 220:12

deploy 743:14

deploys 744:13

deposited 58:7 515:26

deposition 47:11

depth 496:11 704:15

deputy 3:10 8:14,20 635:12
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derivative 118:26

derive 79:21 344:24 451:11

derived 78:16,21 79:3 91:15 107:26 110:7 133:22

345:15 451:14 530:23 531:2

descended 731:11

describe 194:14 422:4 507:18 586:23 592:26,26

594:6 607:9 703:26

described 113:16 283:7 347:6,15 356:5 357:21

380:24 381:4,7 383:16 420:13 430:10 443:17

452:23 453:20 462:12 466:19 467:10 470:23

472:4 474:12 477:17 523:3 530:25 545:21 550:11

576:25 578:18 579:8,26 580:20 587:17 602:14

617:3,21 618:10 637:10,17 650:18 666:21 675:1

676:12 682:11 707:25 713:24 715:20

describes 380:25 714:4

describing 75:20 149:14 275:12 471:19 491:26

582:19 637:22 651:21

description 138:26 222:24 324:13 346:5 364:5

461:13 465:1 511:4 515:14 555:1 556:5 586:11

666:6 676:13 699:5

descriptive 422:2 428:9 507:17 600:20 607:7

703:25

descriptively 582:6

deserve 748:3

deserves 369:4 747:16

design 17:11 24:16 39:13 109:2,23 110:4,17

111:19,20 120:11 140:7 148:7,14,22 149:9

153:25 168:10 191:23 211:9 219:17 227:19 271:4

286:7 287:11,15 290:1,25 292:7,23 293:5 294:4

297:6,11,15 299:8,11,13 302:1,4 304:10 370:8

383:14 384:20,21 386:7 392:5 393:7 403:26

420:22 446:14 456:9,11,14 470:21 480:6 481:24

511:6 524:25 531:22 532:13 576:24 583:14,24

584:22 590:16,16,17,18,20,23,24 591:1 641:8

649:14 650:23 670:18,20,22 671:2,5,14,15,23

672:1,6 673:20 674:3,25 675:14,21 676:6,8,16,16

678:14 687:20 688:26 695:7 715:8 727:26 741:5

designated 2:2 3:2 9:11 241:2 459:2 517:21 664:2

681:8 691:17

designed 25:7 26:12 40:25 47:10 68:25 267:3

297:9 347:10 349:14 365:11 443:11 529:11

545:15 616:22 618:10 632:25 635:20 639:8 681:2

682:1

designing 354:19 590:11

designs 32:10 220:4 290:4 293:1 294:1,23 385:25

582:16 671:26 673:12,15 696:10,12 743:6

desired 449:8

despite 290:14 698:9 712:3,7 737:11

destroy 113:26

detail 20:2 21:2,24 25:15 148:4 173:10 179:13

187:3 309:20 348:25 417:8 446:2 466:20 469:6

485:8 500:26,26 510:18 511:26 512:1,14 514:9

514:10,18 521:16,25 522:5,10 599:21 666:22

676:21 682:11 719:15 725:21 741:19

detailed 27:20 42:13 209:8 210:10 318:4 349:24

364:4 369:16 377:4 412:11 425:24 426:2 461:13

466:1 510:13,20,22,25 514:21 515:10,13 516:13

535:11 537:10 556:4 575:6 666:5

details 89:17 154:24 158:23 196:26 346:11 349:20

418:8 442:12 465:9,13 466:25 467:1,9,19 469:5,7

666:23,26

detect 25:7 37:23 55:7 56:6 67:20 110:15,26

111:16 112:8 136:2 148:2 190:24 214:13 220:2

221:4 247:13 281:13 293:11 302:22 417:7 438:7

439:11 517:18 550:16 552:24 568:2 570:25 571:4

659:25

detectable 129:10 421:1 572:23 573:4

detected 338:18 396:24 427:4 437:6 448:14,18

505:25 552:26 588:8

detectible 190:22

detecting 54:26 110:18 111:2 280:20 553:17 559:1

573:11 622:23 667:8 726:8

detection 176:1,21 191:12,13 370:1 396:23 397:1

397:20 419:24,26 421:9,16 535:14 560:20 571:7

572:26

detections 291:18

determinable 611:26

determinance 451:14

determinates 275:12

determination 20:14,18 35:16 103:23 128:8

139:17 140:16 160:2,3 169:24 199:21 231:22

232:20 253:17 348:15 376:9 433:14 450:20

463:22 464:3 466:26 531:5 536:13 697:22 699:5

699:14 745:1

determinations 35:3 141:11 195:16 263:3 394:17

449:4

determinative 308:18

determine 20:8 25:9 26:1 28:4 37:22,25 40:25

42:23 55:9 58:8 64:25 66:3 72:16 73:1 76:6 80:20

112:12 125:2 128:1 130:21 185:13,18,24 195:24

219:7 232:24 236:2 275:2,4 289:2 298:17 300:6

300:11 317:11 319:8,24 322:3 324:2,10 326:7,23

329:26 358:22 430:4 438:18 445:8 454:20 455:24

469:20 482:26 490:18 515:14 516:19 522:15

536:22 548:9 555:9 563:1 566:26 567:17 581:17

582:4 588:10 599:5 611:2,20 612:6,11 613:13

622:20 625:14 634:17 659:17 684:1 699:2,6

706:20 713:18 727:2

determined 37:26 48:23 58:23 80:19 81:12 282:3

287:22 307:14 326:13 394:8,16 411:25 463:4
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466:25 513:10 514:13 515:5 524:22 536:11

602:23 603:21 710:3

determining 49:24 80:24 103:12 156:2 165:3

293:23 304:5 437:15 469:6 515:9 523:23 531:24

567:22 668:26 676:5 709:1

deterministic 449:19

detoxifying 40:17

develop 44:7 56:24 57:13 77:22 84:7 100:1 112:24

116:3,17 156:3,16 189:18 213:19 223:1 366:16

368:2 377:23 427:8 500:25 502:17 547:9 573:19

575:18 619:4 623:25 649:5 683:2 727:22

developed 78:1,7,9 83:13,14 88:24 89:1,20 92:24

136:24 142:7 155:19 156:13,25 166:14 172:2

174:14 175:13 207:13 213:3 232:26 251:5 266:6

266:15,24 268:26 344:2 426:3 465:21 466:13

481:20 546:9 566:15 567:13 609:17 619:2 622:8

630:13 638:11 640:25 667:15 681:26 709:6

726:20 735:5,13

developing 85:24 104:12 165:15 174:15 223:2

268:24 290:18 575:14 617:24 734:5 740:19 744:7

development 10:13 13:24 26:13 27:1,20 29:19

40:24 41:3,14 42:9,11 55:8 78:11,20 89:2 141:17

142:2,15,15,23 154:14 155:13,25 158:1,9,13,20

159:7,9 164:5,6 167:13 170:23 172:9 175:20

186:24 194:12 195:11 206:5 210:5,5 211:8

213:11 222:12,25 223:4 225:23 247:9 251:10,24

266:1 269:22 278:2,21,22 280:7 297:12 298:19

305:19,23 307:9 309:5,13 312:9 330:19 335:22

335:23 340:23 342:17 350:5 351:8 352:8 356:23

364:19 366:20 380:13 413:13 427:18 430:20

431:12 432:12,13 435:7,24 440:12 455:8,16

463:9 495:7,18 523:15 528:20 530:24 536:9,11

544:25 546:2,5,9,18 550:18,20,25 551:17,21

552:1,7,9,13 555:5 556:20 557:11 561:1 565:25

567:16 568:9 575:10 597:23 603:7,12 604:11,26

611:15 612:2,13 613:9 614:3 616:15,20 618:8,11

618:18 620:13 624:19 625:23 631:5 637:21

638:12,19 639:3 640:7 645:9 664:13 669:10,14

669:19 683:3,6 697:21 700:18 706:8 713:2 717:8

718:15 721:19,21 722:9,15 732:14 739:6 742:6

744:10 746:24 747:4,6,15

developmental 6:20 7:9 45:17 85:7 125:17 141:14

141:26 142:26 158:7 212:9,17 213:8,9 226:8

354:10 356:23 425:1 448:8 547:14 551:5,9

553:17 611:13 612:6 643:5 701:2

development-related 546:3

deviate 122:13,15

deviation 253:26 254:6 395:3,5,6,9 477:20,21,22

483:17 506:21 706:11

deviations 52:10 122:7 254:2,15 474:20 502:24

558:4 705:24 706:4,17

device 391:10

devil 196:26

devising 690:26

de-gased 117:16

DFO 2:7 9:1 10:23 13:7 657:17 747:24

diagnose 261:5

diagnoses 68:18 83:24 84:5 85:2

diagnostic 69:6 84:8 85:1,16 174:13 187:7 212:22

260:1 273:11 299:21 302:25 378:11 551:20

552:17,23 557:13 570:19 654:10,14,18,24

diagnostics 404:16 523:22

diagrammatic 46:10

diagrammatically 46:11

diagrams 108:9

diameter 178:18

dichloroacetic 84:17

dichlorophenol 209:18 719:16

dichotomously 463:2,25 464:1

dictated 171:8

die 690:9 699:25

died 587:21 591:9

diet 47:2 170:21 171:24 172:1,2,6,7,9,22 173:6

196:22,24 237:8 390:8 653:25

dietary 48:25 56:17 61:14

diets 172:5

differ 57:8 207:17 416:24 526:17,18

differed 321:6 700:4

difference 52:17 54:26 55:3 63:8,9 67:9 70:19

71:26 73:6 86:1 93:3 100:5,6 101:18 110:18

111:25 127:25 156:20 161:17 185:3 200:6 216:15

216:19 217:4,13 218:8 226:7 249:2,25 250:12

254:12 255:15 257:3,3,7 264:9 265:4,6 275:22

288:4 312:3 320:22 321:7 345:4 351:22 396:10

421:1,9 422:20 436:11 474:19,22 506:17 529:22

540:20 555:13 585:18 588:8,17 589:7,14,15

600:8 605:4 634:10,11 688:19

differences 37:2,4 38:9 42:20 48:13 50:4,6 51:11

53:19 55:10,13,22 56:4 57:18 62:2,25 63:3,10

71:17 89:26 98:26 108:2 117:23 128:18 132:19

141:11 217:16 223:21 248:11 249:23 254:2

264:10 276:18,20 281:19 300:7 301:9 319:16

321:3 348:16 351:15,26 364:25 385:10,21,22

388:3 407:4 408:25 415:6,8,9,24 416:8 419:24,26

421:16 425:26 426:26 427:1 430:19 432:3,5

437:6 439:12 446:4 476:17,18 496:17 497:26

498:3,3 504:3 505:4,11 506:15 547:21 555:17

562:11 563:24 577:15 581:7 625:21 675:24

677:12 679:4,26 680:2,7 714:15 715:15

different 15:25 22:1,1,2,25,25 23:14 31:10,23,24
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32:5,10,18,21,22,22 33:24 34:11 36:3,5,6,26,26

40:13 46:12 50:15,26 52:13,26 53:13 55:16,21

56:10 59:15 60:8 61:10,22 62:4,6,18,24 63:2,7,21

64:22 65:1,3,20 72:19 83:19 85:22 89:10,11 94:7

95:10 100:14,24 107:12,25 109:3 110:9,11

111:26 116:3 127:2,3,5,8,13,23 141:3 146:23

147:18 161:24 166:16 174:2,22 179:24 182:22

186:1 190:24 195:25 196:22 197:18 198:21 199:1

201:24 203:19 210:8 215:21 216:20,20 217:4,11

227:26 228:22 233:13,13 234:5 238:21 248:19

250:15 253:5,12,13 255:1 257:16,25 258:13

262:17 263:25 264:1 265:9 279:22 281:22,25

282:4 290:4 291:14,15 296:26 297:3 298:21

299:3 300:19 301:8 303:16 305:25 306:15 307:8

312:4 317:24 318:8,10 320:1,2 331:26 341:17

345:13 348:13 355:1,24 358:15 362:23,24 369:17

370:10 371:20 372:9 385:11,11,12,15 386:15

387:8,22 389:23 392:23 394:25 396:7 408:21

409:19 412:3,12 414:8 416:9,9,12 417:11,11

418:13 426:24 428:26 432:5,26 433:1 445:2

448:25 449:8 451:20 465:23 466:7,11 477:17

478:18,19 483:23 491:18 493:10 494:4 497:21

504:23 508:20 509:18 512:3,16 513:20 520:14

523:2 524:21 526:15,18 541:1 543:15,16 544:1,2

548:5 558:22 561:5 562:22 572:5,5 573:1,2,2

578:3 579:11 580:23,26 584:3 586:5,5 588:16

591:19 598:24 601:20 604:1 605:6 607:24 608:12

610:11 612:22 628:19 630:21 631:23 632:1

634:13,14 642:17 650:8,19 652:24 654:13 659:5

671:12,26 684:17 688:18 692:1,3 695:24 697:4

698:2,19 700:19,19,21,25 704:26 705:11 707:20

718:26 719:22 721:11 722:19 726:14 733:3 734:7

737:3 741:3,7 743:13,16

differential 465:12

differentially 485:17

differentiate 67:21 455:13 705:8

differentiated 158:16 554:18

differentiating 25:8 624:16

differentiation 40:11 159:10 309:5 427:13

differently 148:17,20 207:19 254:9 351:13 416:12

617:18,18 641:6 660:22

different-testing 588:18

differing 264:24 464:20

differs 40:12 73:1 235:23 535:1 579:8 650:11

difficult 50:13 68:24 73:19 119:15,17 147:8 193:20

193:26 194:10 218:16 219:24,25,26 222:16,18

236:11 238:23 239:6 262:20 266:2,23 274:13,17

288:20 303:23 304:5 311:9,18 312:23 330:16

418:19 422:18 439:11 479:20 489:23 497:16

499:17,22 505:11 510:8,12 552:8 553:11 554:2

568:2 571:11 580:21 581:9,16 598:19 606:20

611:19 614:5 631:15 675:19 689:13 729:26 730:6

difficulties 190:17 195:26 219:5 224:17 227:2

249:16,18 266:20 317:11 354:5 462:18 497:13

567:23 605:17 712:3

difficulty 144:26 236:4 237:5 238:24

diffuse 274:7

dig 175:24

dilation 290:8

dilemma 334:2,3 727:24

diligence 114:18

diligent 239:16

dilute 397:20

diluter 114:5 267:3 696:2,13

diluters 266:16

dilution 114:5 182:4 264:9 265:1,9,21 300:24

312:13 572:9,10 599:13,18,19

dilutions 572:6

Dimilin 621:11

diminish 537:1

dimorphic 76:25 454:16

dimorphically 76:26

dimorphism 45:17 223:5

direct 5:20 23:23 201:12 220:15 259:17 260:6,26

263:12 351:19 437:18 440:8,18 486:26 487:4

546:21 574:18 611:20 639:6 667:19 699:19

701:17

directed 427:1 434:16 607:26 698:26

direction 117:3 266:8 270:20,22,23 582:7 605:3

630:1 712:25 719:20

directionality 163:2

directions 62:10 197:5 491:19

directly 8:2 143:26 144:14 173:25 194:15 234:18

243:17 260:23 308:26 323:12 423:17 455:17

488:6 552:15 553:24 559:8 616:13 635:20 639:1

641:8 656:20 667:11 719:24 721:6,6 734:21

director 5:26 6:24 8:14,16,20,23 10:9 635:12

disadvantage 515:3 641:12

disagree 143:24

disagreement 524:19 668:23

disagreements 348:5

disallowed 699:19

disappointed 330:3

disappointing 333:6

disaster 417:21

discarded 469:17 531:9

discern 32:9 56:10 66:6 74:15 281:10 564:16 641:6

641:23 667:24 677:12 679:4 688:19 710:12,15

711:17

discerning 645:12 705:12 717:10
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disclosure 3:9

disconnect 289:26 290:1

discount 350:10

discovered 160:21 161:1 165:3

discovery 291:12

discreet 541:8 578:7,13,15

discrete 20:11 222:25

discretion 122:11

discriminate 99:21

discrimination 177:23 435:14 716:10

discuss 39:17 76:16 78:14 82:24 105:13 152:18

187:3 296:21 348:1 362:6 363:6 384:24 416:2

480:25 484:10,12 485:23 541:16 547:7 576:6,8

624:6 679:11 681:10 699:7 726:2 727:16 745:12

discussant 347:19 383:22 414:15 443:19 461:25

493:17 508:5 529:5 534:24 545:24 550:14 569:26

602:1 617:4 697:10 704:11 708:1 727:11

discussants 347:24 357:2 384:2,7,14 404:2 406:14

439:25 443:22 456:18 458:9 471:7 475:23 520:21

522:9 527:15 538:16 548:16 554:6 606:3 628:13

647:10 669:21,22 702:8

discussed 93:13 187:4 252:22 265:18 278:26

291:12 294:4 327:16 357:4 362:3 363:9 406:19

406:21 423:1 436:12 443:23 448:4 449:11 450:7

478:7 484:19 496:12 527:26 535:21 558:6 564:20

565:2 577:18,19 596:15 648:23 715:13 726:6

discusses 91:9 482:4 563:22

discussing 91:8 93:26 188:3 386:8 608:24 614:1

638:26 664:12

discussion 47:7 155:20 270:3 327:14 337:19

352:23 353:11 361:11 369:17,18,26 370:3 376:13

380:5 386:22 404:7 421:3 459:9 469:2 471:11

479:4 481:2 482:13 483:3 484:7 485:8 487:19

538:4 540:17 546:22 549:2 596:13,17 615:23

645:20 647:7,13 655:12 662:14,24 681:7,9

682:16,19 684:15 742:22

discussions 9:6 10:18 12:19 252:10 349:4 381:26

436:21 439:17 459:8 532:12 577:24 585:24 641:4

642:3 645:5 648:3,14 649:5 669:17 674:12

682:17 714:6 730:15 732:9,20 746:1

disease 159:20 172:21 282:2 417:21 447:17 562:14

600:16 610:6

diseased 191:20

diseases 88:16

disease-resistant 77:14

dismissed 553:6

displays 505:7

disposal 716:6

disqualify 558:5

disrupt 435:7 495:15,17 528:19 617:15 618:6

667:11 718:15

disrupted 143:4 596:24

disrupter 377:7

disrupters 9:20 13:14,19 14:10 41:26 447:23

550:11 551:24

disrupting 29:1 39:25 135:15 185:3 261:1 348:14

351:12,13 416:16 424:5,13,22,26 425:2,20

426:21 427:3,16,21 435:7 438:5,16,26 447:17

467:5 528:19 534:10 550:17 617:12 621:7 622:14

626:15 684:3 710:7

disruption 25:7 41:7 45:12 46:7 56:9 163:26 178:3

207:8 252:11 258:26 259:3 297:19 316:26 317:12

329:5,11 333:24 347:8 351:19 369:5 371:11

376:24 381:12 426:6 435:25 439:23 443:9 447:12

448:1,17,19 457:4 462:16 486:20 487:1 495:9

516:23 524:14 534:9,21 545:13 546:4,5,21 547:7

548:12 551:20 552:12 553:12,19,25 556:22 557:1

557:19,21 558:12 564:23 566:2 570:20 611:16

613:7,11,13 614:9 616:10 618:1,3,7,22,23,25

619:2 630:14 640:15 643:9 668:2 682:22 687:7

726:10 738:26 739:2

disruptions 407:2

disruptive 565:24 625:15

disruptor 1:5 11:3 96:23,24 116:19 241:6 278:10

282:6 338:18 431:15,21 440:7,18,19 459:4

570:10 664:5 668:6 682:4 742:18

disruptors 2:5 10:5 159:15 163:21 244:8 280:14

280:20 281:13 282:13 335:15 357:21 367:26

368:5 432:1 452:23 558:14 612:24 631:20 637:10

645:7 667:3,7 668:16 682:11

dissected 512:11

dissolve 358:24

distance 396:24 397:1 564:2

distances 370:2

distantly 348:17

distinct 83:6 351:24 378:17 541:19,22

distinction 23:26 186:19 187:1 233:11 382:17,23

441:9 746:3

distinctions 141:22

distinctly 578:11 685:25

distinguish 333:23 405:21 417:8 551:3 553:11

557:20 564:21 640:20 705:10

distinguishable 640:3 714:14

distinguished 36:23 731:1

distinguishing 552:5 554:1 641:19

distribute 109:6

distributed 273:15 346:9 398:13,20,25 399:5

428:25 483:16 579:3 580:26

distribution 147:19 399:8 400:3,6,7,16,26,26

401:1,1 402:13,13 404:10,15,16 423:4 472:26
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Page 34

473:14,15 479:22 578:14 579:3,20 680:22,24,25

680:25

distributions 578:13

disturbances 261:5

disturbed 666:25

dis-acknowledge 689:19

dis-balance 191:23

ditto 405:26 740:21

divergent 562:9,13

diverging 619:1

diverse 708:12

diversity 287:19 642:19,19

divide 254:16

divided 159:12 395:2,3 477:20,21,22 481:11

594:26

division 10:10,12 75:13 747:3,3,13

Dix 8:23 742:16

DMY 80:23,25,26 122:22 123:11,13,19,22 128:8

128:25 129:3,4 156:13 462:18 515:24

dNTPs 513:16

doable 113:13 192:17 457:23 655:21 660:17

doc 739:7

docket 1:8,10 4:2 239:26 241:10 277:23 346:15

380:24 442:17 663:3 742:26

doctor 8:19

document 31:3 61:24 116:24 166:18 265:16

300:25 301:1 348:24 419:21 444:17 445:2 483:2

496:4,12 590:17 593:1 613:4 617:13,22 653:18

653:25 654:5 670:17 676:4 716:13

documentation 31:20 120:23 122:23 432:21

488:14 511:14 517:16 578:18 741:14,18

documented 373:7 525:8 612:23 658:22

documenting 195:12

documents 3:26 118:6 136:23,24 343:12 348:1,11

348:24 369:25 435:22 580:20 676:14

dogs 569:20

doing 14:24 24:19,20 33:18 57:14 61:15 74:17

80:22 81:3 83:13,21 104:7 113:9,15,22 124:25

125:3,7 127:20 136:25 137:22 140:14 142:14,22

143:17,18 145:6 147:18,22 151:19 157:12 194:5

195:1,9 203:19 210:24 212:11 236:22 238:17

244:11 264:17 268:22 273:22 284:24 296:14

299:20 309:3 315:12 321:7 330:10,14 331:3

334:7,9 366:21 374:11 379:11 390:12,13 396:9

396:14 401:6,23 403:13 405:3 409:24 410:17

428:26 457:22 473:26 474:4 491:26 492:10

500:13 512:17 513:14 515:4 543:21 577:26

579:22 580:2,4 585:1 587:15 588:14,14,15,21,22

589:3,5,14 592:17 635:6 643:23 652:19 653:19

654:13 684:25 686:21 695:18 701:24 702:4 705:6

712:6 724:3 728:8 734:20,22 746:13

dollars 285:8

dolphins 188:12,14

DOM 474:5

domain 19:7,17 199:14 510:21

domestic 620:12

domesticated 350:3

domestication 431:3

dominant 449:25

Don 748:5

door 15:20

dosage 594:11 708:25

dose 17:14 22:17 23:4 30:8,11,22,23 31:8,9,12,14

31:18,19,20,22,23,24,26 32:2,19 33:2,15,15,20

71:19 102:7,8 105:23 106:3,5 112:17 113:19

127:21,22 149:1,3,21,22 182:2,4 192:3 212:19

219:5 222:9 237:18,22 272:8,11,12,14,18,19,21

274:23 275:2,4,5 278:10 290:6 292:24 302:18

312:6,7 316:25 329:4 345:9,11,14,15 347:8,13

349:17 358:14,21,22,22 362:13,14,18 363:4,14

363:15 371:10 374:12 375:11 386:9 388:19 389:4

389:5,10 438:17 439:18 447:11,26 448:21,22,25

448:26,26 449:6,24 451:9,12,18 476:15,19 480:3

482:7,14,16 525:8 545:18 554:10 561:13,14,19

574:10 585:9,10,12 589:16 613:6,14 614:26

616:10,25 617:26 621:15 622:21 624:11,26

625:15 641:17 646:21,25,26 647:6,7,20 668:24

680:1 688:10 690:20 702:20,22,25 703:1,3

704:24 744:24

doses 22:13,16 23:3 25:25 31:11,24 32:8,22 33:6

33:24 47:17 71:17 92:4 192:1 312:22 362:21

374:12 438:18 448:24 482:19 489:21 561:9

564:24 594:16 648:1 680:3 690:17,18 704:23

716:15

dose-dependence 33:23,25 128:1

dose-dependents 127:16

dose-related 49:21

dose-response 11:26 17:14 30:13,14,23,26 31:1,3

31:18 32:1,20 33:12,21 35:26 48:4 63:21,25

76:12 127:19 443:9,13 545:13

dosing 42:18 43:22,24 203:4 420:23,23,25 444:20

599:6

dot 594:21

dots 505:7

double 48:3

doubling 171:7 202:26

Dow 174:25 296:1

downright 580:1

dozen 247:5 593:24

Dr 2:1,10 4:25 5:2,8,12,15,19,23,26 6:4,7,12,16,19
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Page 35

6:23,26 7:3,6,10,14,18,22,26 8:4,5,13,14,15,18

8:23 9:10 10:19,21 12:21,21,25 13:7,11 14:3

19:19 25:14 26:17 27:4 29:17,20,21,22,23,26

30:3 31:16 32:4 33:22,26 34:1,4,5,6,8,21 35:7,12

35:24,24,25 36:9,13,14,14,15,21 37:1,8 38:7,10

38:15,21,23 59:6,9,10,14 61:3,3,4,8,26,26 62:1,9

63:15,15,16,26 64:2,6 65:6,6,7,11 66:13,17,22,22

66:23 67:2,8,14,17,19 68:12,13,13,14,23 70:15

70:15,16,24,26 71:2,12,24 73:12,17 74:13,19

75:7,9,10 104:23,23,26 105:2,6,10,12 114:22,22

115:8,10,12,13,13,15,26 116:21 117:4,25 118:8

118:12,19 119:8,12,14,15,20,24,26 120:2,7,9,18

121:1,6,8,9,15,21 122:6,21,26 123:1,3,4,7,9,15

123:26 124:3,5,11,12,16,19,21,24,26 125:1,5,9

125:11,14,25 126:1,7,9,9,12,13,14,20,24 127:4,6

127:8,10,12,15,17,26 128:2,3,4,5,6,6,7,11,12,14

128:16,21 129:15,19,26 130:6,7,11,13,14,14,16

130:19,23,24 131:2,3,6 132:22 133:1,11,15,17,19

134:3,4,10,12,14,24 135:13,17,19,21,23 136:6,10

136:14,19 137:2,5,6,8,9,9,11,16,17,21 138:8,9,11

138:15,17,21,24 139:5,7,8,8,9 140:2,6,25 141:6

141:12 142:24 143:2,5,23 144:21,25 145:15,15

145:18,19,24 146:4,7 147:2,7,15,20,21,25 148:10

148:10,11,12,19 149:1,2,8,12,15,17,19,22,24

150:6,7,9,10,13,14,19,22,23,23,24 151:21 152:1

152:3,4,14,25 153:2,4,5,10,12,13 154:7,12,15

176:7,8,11,12,14,15,17,20,22,25,26 177:1,3,7,13

177:13,15,16,18,24,25 178:5,10,12,14,15,19,22

178:24,26 179:1,3,6,7,10,15,16,19,20,21,25,26

180:3,4,6,8,11,13,13,14,15,22,23,23,24 181:15

181:20,21 182:3,6,7,9,17,26 183:4,6,9,11,13,18

183:26 184:4,5,5,7,17 185:1,7,10 186:18 187:5

187:12,12,13,17,18,19,20,22 188:1 189:1,5,7,9

189:11,12,13,14 190:1,3,3,4,7,17,23,26 191:1,3,5

191:9,10,14,16,17,21,22,25 192:20,23 193:2,4,5

193:8,10 194:11 195:17,17,18,26 196:9,9,10,15

197:7,9,24 198:3,9,10,10,10,11,18,20,22,25

199:3,12,14,17 200:13,13,14,19,21,22,24 201:2,6

201:7,8,10,14,17,17,18 202:15,19,19,20 203:13

203:13,14,15,15,16,22 204:1,2,8,19,24 205:2,3,4

205:9,23,23,24,26 227:15,16,17,22 228:5,7,15,20

228:23 229:3,8,10,12,14,16,17,19,20,20,21 230:9

231:25 232:6 233:2,4 234:4 235:1,2,17 236:19

237:3,17,24 238:5,7,10,13 239:8,8,16,18 241:1

241:22,24 242:4,6,8,10,12,14,15,17,20,22,24,26

243:2,4,6,8,10,12,14,16,23,25,25 245:8 248:2,15

249:21,24 250:2,4,5,6,9,11,16,25 253:1,4,18,22

253:23 254:5,10,21,21,22 255:5,24 256:15

258:18 259:24 261:11,16,17,23 263:5,14,22,22

263:23 264:2,5,8,16,23 266:12,12,13,18 267:9,12

267:14,15,20 268:3,7,15,19 269:7,11 270:1,5,7

270:10,16,17,19,21,24,25 271:1,2,14,23,25 272:1

272:3,4,9,10,15,24 273:3,18,20,20,21,25 274:21

274:21,22,25 275:1,3,6,11 276:1,3,4,12,15,15,25

277:6 283:16 284:2,2,7 285:22,25 295:22,22,26

296:23 303:23 305:14 314:21,23 315:6 323:19,20

323:21 333:8,9,10,15,16 334:4 335:13 336:16

337:8,14,15,15,16,24 338:11,11,12,20 339:5,10

339:14,15,18,20,22,23,25,26 340:3,4,7,8,21,25

341:10,10,11,16 342:5,8,11,13,14,19,22,24,26

343:1,2,20,24 344:21,21,22 345:1,17,18,25 346:2

346:3,17,20,21,25 347:1,18,18,20 352:20,20,22

352:22,24,24,25 353:1,1,2,4,4,5,7,9,12,15 354:12

354:12,13,15 355:10,10,11,17,23 356:6,6,7,11,11

356:12 357:2,5,7,9,11,23,23,24 359:19,20,21,24

359:25,26,26 360:2,3,4,6,11,11,12,15,15,16,17

360:18,21,21,22 361:3,3,4,8,8,9,9,22,25 362:5,7

362:13,20 363:3,6,10,22 364:2,16 368:8,8,9,10

368:16,16,17 369:12,12,13,14 370:14,14,15

372:4,4,5,6,15,15,16,21 373:2,2,3,17,18,19,22,26

374:9,9,10,15,21,21,22 375:8,8,9,15,17,23 376:1

376:3,20,20,21 379:7 380:15,16,17,18 381:4,7,8

381:8,9,14,14,15,16 382:5,5,6,8 383:4,6,21,21,23

403:15,21,22 404:6 405:10,12,13,13,14,23,25,25

405:26 406:1,1,2,4,4,5,6,6,6,8,12,17,18 407:6,10

407:13,14,16,17,19,20,24,25,26 408:10,12 409:4

409:4,5,22,22,23 410:2,6 411:1,1,2,3,3,4,7,7,8,11

411:14 412:8,20,21 413:5,15,15,16 414:1,3,14,14

414:16 418:26,26 419:2,4,4,5,8,8,9,15,16,17,18

419:20,20 421:19,22,25 422:1,13,13,15 428:6,7

428:15,16 429:10,11,16,17,19,20,21,25 430:3,12

430:12,13 434:1,2,3,5,5,6,7,7,8,9,10,10,11,18,21

435:2,5,18,18,19 439:24,25,26 440:2,2,3,21,21

440:22,23,24,24,25 441:1,2,4,7,18,25 442:21,21

442:23,25 443:1,2,18,18,20,25 444:14,20 450:9,9

450:10,14,14,15,17,17,18 451:25,25,26 452:1,1,2

452:3,3,4,5,9,11,13,25,25 453:1 454:21 456:17

456:18,19,20,20,21 457:16,16,17,18,18,19,20,26

457:26 458:1,2,4,9,12,14 459:1,10,12,12,17,19

459:21,23,25 460:1,3,5,8,10,12,14,16,18,20,22

460:24,26 461:2,4,7,7,8,10,24,24,26 467:13,14

467:15,17,17,18,18,21,21,22,23,23,24,26,26

468:1,3,3,4,5,7,12,12,13,16,19,23 469:4,6,9,10

469:10,11,12,13 470:6,8,9,11 471:2,2,6 475:22

475:25,26 476:4,6,7,9,11,11,12,13,13,14,21,21

476:22 477:2,2,3,5,5,6,8,8,8,10,24,26 484:5,8,11

484:13,21,24 485:1,2,3,4 487:22,23,24 488:5,7,8

490:5,5,6,12,12,13,21,23 491:2,2,3,5,5,6,7,9,10

491:14,16 492:18,18,20,21,25 493:1,3,16,16,18

501:12,12,14,16,16,17,19,19,20,21 502:3,3,4,5,5
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502:6,8,8,9,10,21 505:5 506:26 507:1,5,7,9,13,14

507:16 508:4,4,6,14 518:11,12,13,20,20,21,23,23

518:24,25,25,26 519:1,1,2,3,4,4,6 520:4,20,21,23

520:24 521:4,7,9,11,13,13,15 522:9,14,22,22,23

527:9,11,14,15,16,18,18,19,21,21,22,23,23,24

528:3,3,4,5,5,6,7,8,11,13,15,17 529:4,4,6 532:2,3

532:4,6,6,7,9,9,10,19,19,20,21,21,22,23,23,24,24

533:12,15,17,19,21 534:23,23,25 538:15,16,17

538:18,18,19,20,21,21,22,24,24,25,26,26 539:1,3

539:3,4,5,11,26 540:13,22,22,23 541:3,5,6,26

542:6,6,6,8,16,16,17 543:10,11,11,12 544:13,22

545:1,2,2,3,5,23,23,25 548:16,16,18,20,20,21,22

548:22,23,24,24,25,26,26 549:13,15,16,18,21,22

549:24,26 550:13,13,15 554:5,6,7,8,8,9,14,14,15

554:20,20,21,21,22 555:19,24,26,26 556:2,15,15

556:16 563:4,5,6,7,7,8,9,9,10,11,11,12,18 564:6

564:10,11,18,26,26 565:1,2,21,21,22 566:3,6,6,8

566:24,24,25 568:24,24,25,26,26 569:1,2,2,3,4,4

569:5,6,7,7,9,16,16,17,23 570:3,6,6,8,17,17,18

573:21,22,23,24,24,25 574:1,1,2,4,4,5,6,15,15,16

574:21,21,22 575:1,3,4,8,16,21,23 576:8,12,13

576:13,15 577:4,4,5,25,25,25 578:18 589:26

590:6 591:23,25 592:10,12,22,24 593:2,2,5,6,8

593:10,13,15,17,19,23,26 594:3 595:21,26 596:3

596:4,6,11,12 598:11,11,12,14,14,15 599:23,23

599:24,25 600:1,25,25,26 601:6,10,12,12,14,26

601:26 602:2 606:2,4,4,7,8,8,9,11,11,12,13,13,14

606:24 607:1,2,4,5,6,18,18,19 608:22,22 609:3,9

609:10,11,12,24,24,25 610:1,1,2,7,7,8,8,9,9,9,18

610:20,22,24,26 611:9,9,10 615:1,2,3,4,4,5,6,6,7

615:8,8,9,9,11,14,15,17,21,24,25 616:1,3 617:4,5

617:6 626:21 627:16 628:10,13,14,15 629:26

631:8,8,9 632:2,2,3,13,16,16,17 633:11,11,13,15

633:17,18 635:1,11 636:2,6,6,8,23,25 637:12,12

637:13 643:1,1,2,19 644:17,17,18,20,20,21,23,23

644:24,26 645:1,2,21,22,24 646:2,8,11,17,21,23

647:9,11,12,17,17,19 648:3,8,10,11,12 649:1,8

649:18 650:2,2,3,4,15,21,26 651:4,14,14,15,22

652:11,16,16,17 653:3 654:6,8,8,9,15,15,16,21

654:26 655:1,1,2,9,9,10,17,23 656:1,7,7,8,17,17

656:18 657:2,2,3,6,6,7,15,15,16 659:2,7,26 660:2

660:3,9,21,24,25 661:5,8,15,21,23,25,25 662:1,3

662:3,4,15,22,23 664:1,9,10,19,21,23,25 665:1,3

665:5,7,10,12,14,16,18,20,22,24,26 666:2,4,16

666:16,18 669:21,23,24,26,26 670:1,4,4,5,7,7,8

670:10 671:9,10,11,16,19,21 672:8,9,10,23,23,24

672:26,26 673:2,22 674:7,10,14,15,16 675:6,6,9

675:12 676:7 678:25 679:12,14,15 680:10,11

681:4,6,9,10,13,13,15,16 682:13,13,15 683:15,15

683:16,26 684:5,10,10,11,13,13,14 685:2,3,5,19

686:14,15 687:14,16,25 688:23,23,24 690:5,5,6

690:16 691:12 693:10,10,11,19,19,20,22,23,24

694:26 695:1,2,11,13,21 696:3,8,15,23,24,25

697:10,10,12 701:26 702:7,8,9,11,11,12,14,14,15

703:7,7,8,8,10,11,13,15,17,21,22,24 704:10,10

704:12 707:1,1,2,3,3,4,5,5,6,7,7,8,9,12,13,15,16

707:18,26,26 708:2 711:3,16,23,23,24,26,26

712:1,8,8,9,15,15,16,18,19 713:11,12,13,15,17

713:26,26 714:2 717:12,13,14,15,15,16,17,17,18

717:19,19,20,21,24 718:4,7,7,9,12,14,21,21,22

721:14 722:18 723:9,10,11,13,13,14 724:12,12

724:13,15,15,16,18,19,20,22,25,26 725:1,2,6,11

725:11,23,23,25,25 726:1,5,6 727:4,4,5,7,16,18

727:20 728:18 730:10,26 732:4 733:1,17 734:3

735:9,18 736:13 738:18 740:13,20 741:10 742:10

742:14,16,16,20 743:1,2 745:14,15,15,22

draft 209:6 325:24 331:14 421:3 433:10 670:24

dramatic 164:17 167:10 169:10 249:2 561:12

dramatically 110:22 282:4 344:20

draw 244:20 377:16,26 437:16 668:3 678:8

draws 232:8

drew 678:7

drift 429:7

drink 390:22

drinking 14:6 15:25 16:1 17:20 342:25 390:20

drive 1:16 400:19,20 473:1,25 474:2 475:16,17

600:10,10 725:4,5 735:4

driven 436:11 730:19

driver 61:11 186:2 231:14

drives 526:6

driving 392:7

drop 236:17 264:21 673:7

dropped 61:5,19 101:6 214:20 237:12 389:7

609:21

dropping 350:22

drops 344:20

drove 621:1

Drs 347:26

Drug 14:6

drum 716:7

drums 716:6

dry 214:1 220:14 221:2 236:9,10,14,17 261:18

262:8

duck 64:8 286:21 340:13 432:25

ducks 338:16 426:25

duct 164:10,12 167:17,18,19,24 169:19 177:22

546:16 561:4 603:7,12,15 604:26 606:16,20

611:22

ductal 552:11 559:5 560:17

ducts 166:20 170:18 175:15,16,17,19 561:8 565:5
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Page 37

due 41:4 54:9 72:1 81:23 82:1 84:21 88:15 93:21

157:2 164:19 224:25 275:24 282:24 301:8 308:3

322:13 385:10,21,22 390:24 530:9 531:11 553:4

553:25 560:24 562:14 570:26 571:21 605:17

625:19,21 676:1 686:17 689:17,22,23 715:8,18

Duluth 93:8 94:24,26 95:25 100:10,24 106:1

155:19 156:13 265:26 334:26 567:12 571:26

747:4,6

dumb 519:21

dump 473:6

Dunnett 127:7 137:26 291:9 300:5 588:15

Dunnett's 127:10 589:10 680:4 688:15 721:17

Dunn's 721:17

duo/trio 239:22

duplication 710:25

DuPont 481:20

duration 79:12 108:4 155:7 157:2 211:7,16 217:12

217:14 221:6,19 222:12 235:8 247:20 255:8

262:5 278:2 301:24 640:6 698:10 721:20

durations 214:3

dust 408:20,22

duty 629:17

dwell 641:25 676:26

dwelling 621:19

dynamics 33:1 132:10 133:6

D.C 154:16,18

E

E 20:8,16 36:23 233:17 300:13 315:8 316:26 337:3

447:12 448:13 449:21 455:18 457:7 525:25

561:12 595:3,3,4 618:19,22 619:4 623:7,12

630:17 634:13 637:23,25 683:4 701:7 713:7

earlier 21:7 29:13 35:26 82:26 85:20 93:11 171:4

176:10 201:11 204:4 220:8 332:21 351:8 389:22

404:9 417:1 418:3 426:12 439:8,17 450:11 458:5

475:15 515:18 546:9 557:13 583:12 612:14

620:25 641:4 667:16 680:12 697:25 714:26

719:26 721:2 724:24 735:23

earliest 620:2

early 24:14 37:6 61:9 83:2,2 85:7 88:10 99:25

100:25 122:17 142:15 159:9 171:3 185:19 189:25

233:18 321:12 365:12 523:16 702:20 721:13

Early-life 657:25

earnest 46:15

earth 628:20 629:1

ease 444:26 523:7 546:10,11,11

easier 25:17 204:22 226:5 236:15 249:14 257:26

305:6 409:20 501:26 523:17 681:13

easily 60:13 76:26 77:13 180:19 191:19 235:19

288:23 289:9 313:21 361:7 370:23 373:15 403:7

408:23 410:26 425:5,7,14 427:26 429:7 513:23

581:4 666:25

Easton 481:21

easy 13:10 48:23 69:23 173:8 199:6 210:19 274:8

350:3,17,17 355:25,25 360:25 392:2 425:23

427:9,9 446:21 574:8 583:6 584:14 640:2 678:4

710:15 714:11 728:23 737:5,6,7

eat 390:22 448:1,6,11 449:20,22 516:23 524:14

537:3 631:12 632:19 667:10 682:6 685:14 729:22

730:1

ecdysal 259:20

ecdysis 259:10

ecdysone 318:1,2 618:17 623:4 625:26 626:9 644:7

683:7

ecdysteroid 207:24,26 230:24 231:2 259:22,25

643:24,26 667:15,21 685:12 701:12,21

ecdysteroids 207:13 259:9,17 261:6 318:1 701:19

ecdysterone 720:14

ECD-mediated 638:7

echo 10:23

eco 186:2 337:25

ecological 7:21 12:1 18:14 20:1 21:22 24:25 26:20

33:8 185:26 212:20 225:25 234:14 298:2 306:22

306:24,25 307:3 308:13 309:13 313:7,13,16

314:17 325:13 348:21 350:15 351:18,26 352:19

355:6 369:7 426:8 436:4 439:5 586:21 619:11,11

619:19 620:21 621:17

ecologically 318:13 373:21 439:11 447:1,14

ecologist 6:25 692:12

ecology 75:13 693:2

economic 210:4 620:13

economically 447:1

economizing 46:1

economy 375:10

ecosystem 206:24 622:13 691:7

ecosystems 16:17 207:3 617:14 619:12,21,23

620:19 621:24

ecotox 329:10 334:14

ecotoxicity 1:5 2:6 9:21 241:7 251:13 265:17

277:25 283:8 300:25 324:3 459:5 547:22 664:5

ecotoxicological 617:20

ecotoxicologist 6:3 296:1 665:21

ecotoxicology 7:13 433:22

EcR/USP 667:21

EC-50 451:12

Ed 6:16 48:4 242:22 347:22 460:10 665:12 721:22

722:6 723:15 747:2

EDA 394:9

EDC 41:21 85:2 96:21 207:18 341:12 454:25

456:15 462:2,6 464:17,20 482:14 517:18 523:21

523:22 524:4 525:18,20 530:16 532:26 533:3
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599:7 617:20 618:15 619:6,18 621:5 622:1

637:18 638:20 640:18 667:20 682:5 689:4 701:10

704:17 705:2,5,8,25 706:2 709:16,23 710:5,10,13

710:22 717:6,10 722:13,14

EDCs 40:25 47:11 56:26 58:1 77:22 206:25 207:5

342:16 446:11,12 453:16 462:7 482:4 529:16

549:8 621:15 686:17 704:22 718:15

EDC-specific 667:22 686:20

edge 630:5

EDR 134:15

EDSP 1:5 9:22 10:14 15:17 206:11 306:14,17

307:6 315:13 316:8 317:21 322:7 323:5,15

347:15 443:16 528:23 533:25 534:11,22 545:21

546:9 617:2

EDSTAC 16:6,6,21 20:25 42:1 230:10 232:12

234:7 329:1 337:3 347:6 371:9 443:7 447:25

530:4 538:2 545:11 616:8,18 617:24 618:2,21

621:16 623:10 625:9,17 632:18,19 738:24 739:22

743:20

EDSTAC's 316:24

EDSTAC-defined 112:11

educator 656:24

Edward 6:16 196:10 197:7,24 242:22 344:22

345:17 353:5 356:12 360:22 370:15 382:6 406:5

409:23 411:8 419:18 429:17 434:11 440:25 452:2

457:19 460:10 467:24 476:12 487:24 501:17

518:13 527:16 529:6 539:4 631:9 644:18 665:12

666:18 684:11 693:11 702:12 707:4 712:1 717:20

718:22 722:18 734:3

effect 35:21 36:10,11 38:2 40:9 43:6 44:18 47:13

48:21 49:24 51:9,10 53:4 55:7 66:3 67:20,20

70:12,14,25 71:11,14,18 72:2,7,13,15 73:6 74:4

74:14,21 75:4 76:14 83:8 91:23 92:2,3,5 96:7,18

103:6,10 105:16 110:18 118:9 132:3,3,6 135:2,2

135:3,15 136:7,8 137:20 141:14,15,24,26 142:12

153:19,22 162:8 163:23 165:18,25 167:9 169:10

171:16 177:11 182:14 183:14 184:16 185:22

186:5,24 197:20 208:8,16 212:18 216:4 217:8,16

217:18,23 221:20 223:25 224:5,7 226:23 231:4

238:2 250:6 255:23 260:26 262:8 264:6 265:7

290:14,15 298:17,25 319:6,8 320:17,18 327:9

328:23 329:12 338:18,26 339:4,6,7 340:23 341:5

341:7 344:9 345:8 371:22 374:5,16 375:13 398:1

407:22,23 408:8 413:21 431:15,22,22 437:24

438:5 440:7,11,18,19 447:19 451:4 481:6,25

482:3,19 484:2 488:1 490:2,3 536:26 537:6 550:4

551:13 552:24 555:14 560:7,23 561:3 574:26

583:26 592:14 594:19 598:18 599:15,16 616:19

623:6 630:23 631:12 637:3 645:16,17 651:8

675:24 676:1,10 684:3 686:19 690:3,20 696:17

701:19,20,22 705:6 709:26 717:7 723:23

effected 196:25 207:7 603:18 668:24

effective 494:3 515:2 621:6 662:25 692:16 712:14

effectively 88:25 621:9,14 623:24 625:1 639:16

effectiveness 134:19,22 186:11 639:9 661:13

709:19

effects 15:6,8,9,12,19 19:13 23:9 25:10 31:13

33:13 37:11 39:23 40:1,2,4,24,25 41:4,5,19 44:17

47:5 48:8,11 49:19,20 50:9 51:5 52:8,18 53:10,21

55:8 56:7 58:1,15,26 60:14,19 61:23 71:5 72:7,20

72:24,26 73:15,23 74:15 76:8 78:3,5 81:20,21

84:13 88:25 92:20 96:20,21 97:1,17 98:10,20

101:26 102:5 103:1 111:16 112:8,14,19 125:16

126:4,15,17 131:20 132:2 136:5,12 141:8,13

142:5,15 143:15 151:3 155:22 163:13,18,24

164:17 165:16 166:22 167:7,9 168:23,25 169:1,3

169:10,18,19,20 170:11,12,13,16,18 182:21,24

185:3,12 186:3,20 208:4,18 212:17,26 213:1,3

214:22,23 215:11,12,14,23,24 216:9,11,14,26

217:3 218:3,12 219:7,8,21,23 220:2 222:9 224:21

225:7 226:15 233:1 239:6 247:21 257:24 259:17

259:25 260:11,24 261:21 262:5 265:10 269:18

273:10,11 274:2 279:10,14,20 280:16 281:5,7,8

281:11 282:24 286:14,19 287:20,21 288:5,16

290:13,25,26 291:25 293:11 295:18 302:20

306:22,26 312:2,2 314:12 316:7 319:24 321:11

325:14 338:22 345:7,7 346:6,7 347:13 349:17

350:25 351:12 352:4 358:1 360:26 361:1 367:2

368:25 371:23 372:1 376:6 385:17,17 392:22

397:20 407:2,3 408:24 412:18,24 413:23,24

414:26 423:8,25 426:21 427:2,21 431:17,26

435:26 436:2,3,6,10,13 438:8,10,16,25 439:22

440:7,8,9 442:9,13,20 443:14 446:4 447:24

448:15,16 451:20 453:25 454:3 467:5 481:18

486:22 488:2 490:10 494:14 495:24,26 496:2,14

524:4,13 529:16 530:18 531:12 534:2 535:25

536:23 545:18 550:16 552:1,3,4,17,25 553:4,5,24

556:19,25 557:13,16 559:8 560:17,22,26 561:2,3

561:8,12,19 565:16,24 568:2 595:3,11 602:10,13

602:23 603:5,7,12 604:10 606:21 609:8 612:10

613:23 616:25 617:13,15,23 618:5,12 619:18

621:17 622:1,2,3 623:8 624:12,17 625:15 630:14

631:5,16,21 632:1,19 638:21,21 639:10 640:18

640:26 641:1,6 643:5 645:13,13 651:9 667:9,17

667:18,20,25 668:5,15 682:5,6 685:12 687:9

689:3 692:13 698:22,25 700:18 701:2,4,23,23

704:17 705:9,9,11,12,15 709:19 710:5,7,11,13,14

710:25 711:6 714:12 715:17,22 717:10 718:24

720:11,16,18 721:16 722:6,7,17,21 723:2,6,7,17

735:1,2 747:3,13
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effect/toxicity 357:15 452:17

efficient 83:24 544:10

effort 12:15 20:23 26:3,15,18 27:3 28:5 29:18

37:20 38:24 39:9,15 41:25 74:25 144:12 166:8

178:7 201:21 210:3,23 227:2,4 245:15 296:9,12

299:7,18 302:3 375:5 536:20 540:17 630:2

680:14 739:12

efforts 9:4,24 10:11,17 12:16 25:12 28:19 210:2

245:16 720:10

egg 39:14 40:17 42:26 45:3 46:14,24,25 47:8 48:23

48:25,26 50:3 51:14,16 52:21,23,24 55:19,19

58:7 59:17 61:4,9,19 63:6 64:9,14 67:4 70:19

71:14 79:25 85:12 120:21 288:8,9 340:14,15

364:18 365:1 370:9 391:9,19 393:23,25 394:1,1

415:10 423:6 431:11 432:18 438:10 440:13 446:9

479:18,19 480:8 505:4 519:26

eggs 40:15 41:1 44:4,10,13 46:18,22,25,26 47:11

50:5,14 51:19 52:1,14,24 53:2 64:11 65:4 67:5,7

67:10,12,18 81:9,10 86:8 99:10,11 109:5 149:25

150:1,3 157:24,24 288:7,8,9,10 339:14,15,23

340:1,4,6,6,16 341:2,3,4,7,8 350:6 387:11,12,14

387:15,17 389:6,9,9 391:11,13,18 393:14,16,19

393:20 394:8,8,10 415:9,11 425:13 447:3 481:8

505:19,21,22 520:1 552:18

eggshell 45:3 358:3 432:1

egg-laying 42:12 44:12 46:20 64:11 74:14

Ehrich 5:23,23 61:3,4 242:12,12 337:15,16 459:25

459:25 654:8,9 657:6,7 665:1,1

eight 85:23 157:24 159:4,5 168:11,13,15 176:23

191:23 214:11 223:18 247:14 256:8 347:24 359:6

367:13 379:23 394:8 442:6,15 448:12 464:2

531:16 691:18 718:13 735:25

Eij 594:19

Eijk 594:15

Eijs 594:23

either 50:6 77:12 92:16 131:1,5 132:6 153:3

162:19 177:20 267:18 269:8 340:22 351:11

379:26 402:25 408:4,22 413:7 424:17 425:19

433:16 445:16,24 447:20 453:20,23 455:1 462:12

484:10 496:22 497:17 508:19 525:15 541:16

553:23 557:2 587:5 599:18 609:5 613:25 618:1

618:17 623:4 624:6 629:19 642:25 668:25 686:23

698:20 706:9 716:12 721:17 746:24

elaborate 609:2

electing 196:19

element 16:16 66:10

elements 49:26 521:22 522:4

elevated 169:8 552:20 553:20

elevation 552:23

Elevations 557:7

elicit 564:1 704:18

eliminate 74:17 124:14 439:6,15

eliminated 497:26

eliminates 105:23

eliminating 314:13 568:22

elimination 478:4

ELISA 100:16 190:5 367:20,20 512:3 566:13,16

566:20 567:15,19,24 570:12 571:3,11 573:18,20

609:15 726:15

ELISAs 309:23 567:2 569:10

ELISA-based 569:11

Ellen 284:7,8 323:21,24 333:9,20 334:4 337:24

340:8 342:8,13,19,24,26 343:1

eloquence 742:12

eloquent 151:7 745:23

else's 172:18 580:14

elucidate 56:25 337:12

elucidated 136:4 296:23

email 239:22,23 442:5,14,18

emailed 443:26 471:7

emails 648:25

embark 372:9

embarked 157:9

embellished 522:24

embracing 245:4

embryo 40:18,19 43:5 44:11 45:5 47:13,14 48:5,7

48:9,11,12,13,13,15,16,19,20 64:14 73:8 142:15

213:9 288:25 340:23 368:4 394:7 431:11 466:24

466:25 514:24,25 515:4,5 523:13 528:24

embryogenesis 318:3 550:19 551:26

embryonic 41:15 58:1 364:19 394:10 435:24

440:12 446:7 523:14

embryonically 41:13

embryos 78:19 81:11 87:23 108:10,11 141:19

213:18 222:26 271:21 273:8 288:22,23 289:17,22

424:24 514:26 515:1,3,6,8 521:3 529:14 534:3

535:4

emerging 436:5

Emeritus 7:1 243:3

Emma 462:21

emphasis 202:25 203:8 501:3 585:5 660:26

emphasize 79:24 113:4 114:16 349:4 350:21

354:19 424:3 425:22 426:4 476:23 606:5 738:26

emphasized 122:17 413:8

emphasizing 428:11 585:4 598:16

emphatic 122:9

employ 300:10

employed 78:9 220:8 229:5 300:11 377:5

emulate 57:22

emulates 732:2
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en 462:5

enable 510:23 648:14

enables 25:9,22

enabling 535:14

encapsulated 274:7

encompassed 378:18

encompasses 746:22,23

encompassing 155:24

encountered 499:19

encourage 33:4 88:2 366:20 486:20 546:1 566:4

590:22 591:2,16 657:18 679:1 680:19 730:4

738:5 740:24,26 741:8

encouraged 738:15

encourages 669:17

encouraging 356:9 497:12

endangered 33:11 186:6

ended 137:21 287:14 309:24 388:22 595:12 671:13

endgame 304:7 334:10

endocribe 424:22

endocrine 1:5 2:5 3:20 9:19 10:5 11:2,13 12:4

13:14,18 14:10 15:6,8 16:9,24 17:26 18:15,20

19:12,13 20:13 21:10 22:12 27:6 29:1 30:24 31:9

35:15 37:22 39:24 40:1,3 41:6,26 45:10,12 48:19

56:9 58:12,23 61:10 66:9 68:15,18 69:4,13 81:18

81:26 82:1 83:6,10 84:22,24 93:3 96:22,23

116:19 125:20 126:2 141:17 159:15 163:18,21,22

178:3 185:2 207:8,12,16,21 208:14,15 212:17,26

230:13 232:22 241:6,15 244:8 246:16 252:6,9,11

253:6 258:26 259:3 260:4,5,19 261:1,4,5 277:13

278:10 279:3 280:13,20 281:13 282:6,12 284:5,9

284:21 286:16 287:23 296:4,20 297:19,22 298:8

299:23,26 303:9 305:12,17 307:12 316:26 317:10

317:12 318:19 319:1 321:11 322:24 323:25 329:4

329:11 330:15 332:11 333:24 334:12 335:8,15

338:18,22 347:8 348:13,26 351:12,13,19 357:21

367:26 368:5 369:5 371:11 376:24 377:7 381:12

407:2 416:16 424:5,13,25 425:1,20 426:6,21

427:3,16,21 430:23 431:15,21,26 435:6,25 438:5

438:16,26 439:23 440:6,18,19 443:9 447:12,17

447:23 448:1 452:23 455:13,16,17,18,20,23

456:4 459:4 462:16 467:5 486:19,23 487:1

488:23 489:9 523:9 528:19 531:7,17 534:9,10,21

536:25,26 537:25 538:8 545:13 546:21 548:12

550:11,17 551:1,3 552:5 553:4,8,12,19,25 554:2

554:4 556:21 557:3,19,20 558:14 564:14,16,23

565:5,23 566:1 570:10 574:18 596:24 602:7

604:18 611:16,20,21,24 612:24 613:7,11,12

614:9 616:10,16 617:12,16,17,19,22,26 618:3,12

619:2 621:7,10 622:13,20 623:6,8,16 624:21

625:14,22 626:15,17 630:14 631:16,20 637:10,26

638:4 639:1,10 640:15,25 641:6,23 642:8 643:9

645:7,12,17 664:5 667:5,7,12,25 668:2,6,16

669:12 682:10,21 684:2 687:7 705:1,12,15 710:7

716:17 721:7 722:14 729:22 738:26 739:2 742:18

endocrine-active 43:5 132:14 247:26 260:24

282:11 288:2 322:2 685:17

endocrine-based 729:14

endocrine-dependent 494:10

endocrine-mediated 76:8 112:13 259:23 286:14

288:5 291:25 293:11 295:17,20 338:26

endocrine-related 287:21

endocrine-relevant 69:6

endocrine-sensitive 278:23

endocrine-specific 291:22 293:23 304:6 624:16

642:24

Endocrinologist 6:11,14,14,22 7:25

endocrinologists 355:9

endocrinology 7:5,9 354:23 427:12

endogenous 76:14 112:19 524:23 644:3 667:20

endorses 622:25

endosulfan 43:10 49:6,8,9,13 50:8 62:22

endpoint 30:21 54:9 55:26 71:23 81:16,17 83:6

85:11,16 94:3,8 111:17 115:19 143:26 144:1,19

145:7 152:26 164:6 177:19 185:8,13 215:3 218:5

221:18 255:6,12,14,19 257:9 262:19,26 263:19

274:20 290:3 291:8,10,23 292:3 293:3,14 297:20

298:15,26 303:10 304:7 309:14 314:11 320:24

322:26 325:6 329:12 339:11,16,17 340:1,2

345:15 358:3 366:11 367:26 368:23 369:1 370:4

372:21 376:5,13 394:18 397:23 404:10 420:3

423:9 426:5 435:12 438:24 445:18,19 455:24

456:3 462:16,20 465:7 468:25 472:26 473:9,12

475:16,18,20 488:3 498:7,8,10 503:14 504:5,8,10

504:17 509:16 520:12 533:2,9 534:4 546:19

547:6 548:2 552:17,24 557:13,15 559:6 561:18

570:10 573:10 574:23 583:22 600:14 602:11

604:3 605:1,14 611:21 614:8 618:16 644:13,16

652:21 668:26 677:18 679:5 682:21 700:5,11,11

700:13 702:4 705:11,16 706:7,13,15

endpoints 23:9 39:13 44:25 45:1,2,9,10 49:20,23

50:1 53:10 54:3 58:24 59:2 63:4 68:26 69:5,9,11

69:20 70:3 78:8,8 79:8,22 80:4,18 81:5,7 82:3,8

82:24,25 83:1,11,12 84:26 85:1 87:2 90:19 91:4

95:3,16 99:7 103:23 107:1,9 108:4,7,13,14

109:16,18,24 124:1 130:20,22 140:1 152:5

157:10 158:26 159:12,24 161:8 163:23 164:1

166:19 167:8 169:21 175:6,8,18 184:20 186:13

196:13 197:3 199:5 204:20 212:21,23 214:1,26

216:9,16 217:1,5 218:22,25 219:16,20 222:11,12

225:23 226:6,24 235:8,20,26 236:6 247:20 255:1
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255:16 256:22 258:25 259:2,4 260:11 261:8,10

262:13 267:17,21 278:24,25 279:2,4 280:11,20

282:10 286:13,15,25 287:12,15,18,19,22,26

288:1,3,7,11,11,12,12,14 289:10,13,21 290:11,19

290:21 291:16,22,24,26 292:3 293:9,9 294:26

295:17,19 296:16 297:18 298:10,18,20 299:21,26

302:16,25 305:10 307:2,9,10,12 308:7,12,26

309:10,18 311:16 313:12,12 314:18 316:12,17,18

318:22,22,25 319:1,1,3,9 320:17,18,19,19 321:23

323:17 324:22 325:2,5 327:1,2 328:8,14,18,18,19

328:22 329:9,16,23 332:4,9 334:14 337:19

341:15 344:17 345:6,7,12,12 349:1 357:12,17,25

360:14 361:16 364:7,9,11,14,17,18,21 365:11,13

365:23 366:3 368:18 369:6,17,19 370:2,7 371:2,6

371:14 372:18,19 377:4 397:19,20 399:13,13

400:4 410:20 411:26 412:3,18 419:25 420:9

422:6 423:10,12 426:8,18 428:2 432:2,15 439:2,3

441:10,14 447:22 448:11,18 449:19,20 452:15,19

453:11,17,19,21 455:20,26 461:14,16,19,22

462:1,8,10,12 464:11,12,16,23 465:3 467:3

492:14 497:20,20 504:3,21,22 506:15 507:21

511:9 520:11 523:19 525:23 526:4 527:26 529:22

531:9,10 532:13,26 533:10 535:10,22,23 536:5

536:19,20 541:9,11,12,15,17,20 542:1,4,5,10,13

542:19,21 543:14 544:2,4 546:11,13,14,18 547:5

548:15 550:2,8 553:13,18 555:18 556:6,8,10,13

556:18 557:18 558:1 559:1 560:6,10,22 563:14

563:16,20,21 565:12 569:12 579:20 580:7 597:20

597:23 599:2 603:18 604:17,19 605:11,12,13

607:11 608:12 611:20 612:2 613:18,22 614:4

621:20 624:18 631:4,18 636:14 637:1,6,21

638:13,14,19 640:2,7 642:23 643:11,20 650:8,18

666:7,9,11,14,19 667:2,4,8,14,17,23,26 668:1,3,4

668:6,7,9,12,15,22,24 670:3,9,14 678:26 679:3

686:25 687:9 689:3,17 694:23,23 698:23 699:17

700:19 701:6,16,24 702:19 704:3 705:18 706:4,8

710:25 711:1,4,6,7,8,8,10,11 713:5 714:10,12,18

714:20,25 715:4,5 716:18 719:18 721:5,23

729:17,17 730:1 732:13 734:25

ends 178:17 197:16 267:22,23 312:24 367:14

378:1 379:22 438:16 612:4 680:26

endured 747:18

enduring 689:4

enemy 392:5 393:6

energetically 298:6

energy 260:22 262:17 298:3,5 309:7

enforcement 659:3,3,4

engaged 83:18

engagement 8:24

engaging 13:3,23 646:4

Engineers 6:17 242:23 460:11 665:13

English 205:10

enhance 286:18 315:13 316:2,6,8 535:9 567:14

639:8

enhanced 72:14 297:11 450:4 708:8

enhances 2:25 318:16

enhancing 705:8

enjoyed 335:18 732:6 735:7 746:9

enlighten 151:19

enormous 113:10 740:25

enriched 220:20

enriching 220:21

enrichment 220:23 237:9

ensulfan 60:12

ensure 3:4,13 41:8 44:1 174:9 219:13 237:26 265:4

358:11,14 386:19 414:9 418:10 419:12 420:11

423:4 428:26 429:9 437:5 464:22 467:7 493:11

512:16 515:21 516:9 517:2 534:14 566:22 570:15

601:21 657:23 697:5 711:9 734:19 742:13 743:5

743:11,14

ensuring 2:8 10:2 18:13 40:3 214:8 350:8 428:13

742:12,21,23

enter 253:12 424:11

entered 43:15 84:13

entering 4:1

entertained 61:11

enthusiastic 635:18

entire 2:15 295:8 307:21 316:5 321:26 341:3

533:13 632:6

entirely 82:3 90:19 107:25 375:9,13 398:24

entry 424:16

environment 2:22 16:18 29:2 160:9 167:21,25

326:8 329:20 495:18 621:6 693:3 710:21 733:26

739:16

environmental 1:12 5:6,16,21 6:2,14,24 7:7,21

16:8 26:5 76:19 160:6,12 231:23 234:13 241:6

242:2 304:8,12 306:1 307:10 308:6 313:4,5

329:15 358:2 446:22 450:24 459:16 563:26

619:24 626:15 664:17 675:17 747:3,13,16

environmentally 307:26

environments 31:15

envision 316:24

envisioned 365:14

envisioning 652:8

envy 39:1

enzyme 513:1

enzymes 135:25 136:1 204:7

enzyme-linked 566:12

eosin 147:23

EPA 2:15,24 4:19 5:7 18:19 27:4 75:13 93:8 94:24
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95:4,25 97:25 100:24,24 101:11,14,20 102:2,6

103:6,11 106:2,8,13 107:11,13 112:10 115:2,5

116:3 130:20 145:25 150:25 153:7 154:9 160:20

193:5 210:22 224:3 239:13 241:11,13,18 242:3

243:21 256:12 261:20 265:26 280:3 281:3 282:1

283:3 306:2 309:3,24 312:12 324:22 334:26

335:14 353:14 355:20 356:9 357:5 361:24 375:16

382:10 411:12 419:22,25 420:2 421:5,15,21

429:23 431:25 433:19 434:20 439:4 442:4,10

450:2 452:7 458:3 459:4 461:6 468:20 470:7

483:4 484:18 490:15 503:21 507:6 508:20 509:2

509:12 521:9 528:12,22 533:16 539:8 545:3

547:12 549:21 555:22 558:23,24 564:9 566:15

567:12 568:22 569:22 570:1 571:26 573:17

595:24 601:9 607:1 608:7 613:4 615:14 619:5,17

622:9,16 623:11,19,23 625:20,25 633:16 637:14

640:5 645:22 650:23 652:14 655:24 662:19

664:18 666:2 669:18 676:3 677:21 678:8 680:15

685:3 698:9 699:6 701:8 705:15,25 706:3 707:14

707:21 708:6,15 709:6,13,24 711:15 716:13

719:1 724:19 726:26 727:22 730:4,10 731:7,8

732:9 733:2 734:3,13,16 735:10,19 736:17 737:1

737:20,23,23 738:2,5,7,8,16,17 740:17,24,26

741:12 746:18,25

EPAs 351:20 409:8 438:12

EPA's 4:4 8:21 739:2,6

EPA-HQ-OPP-2013-0182 1:8 4:2

epididymal 366:5 377:10,13,23 379:19

epididymides 53:7 60:21

epididymis 269:17 290:3,6,8 339:1 380:12

epigenetic 365:11 372:1 432:15 524:13

epithelium 164:19

equal 106:26 107:12,16 232:13 256:1,8 392:11,16

404:26 473:18,18 474:7 483:19 564:2 594:16,22

721:23

equally 22:7 178:20 203:21 306:8 432:16 699:24

708:5 719:12 744:21

equipment 166:12 391:10 706:21

equivalent 115:21,22 216:2 226:20 418:10

equivocal 446:16

ER 19:6,16 91:20 92:5 454:1 485:14,20 494:20,23

494:24 495:1,3 496:14 597:8,10,25 603:3,9,16

era 161:9

eradicate 207:9

errata 90:6 276:17

erroneous 90:16 280:26

erroneously 219:18

error 90:11,11,21 160:21 161:13 253:25 254:12

278:14 281:1 395:15,21,22,22,26 396:1,4,11,22

397:2 402:2,4 403:8 478:7 506:21,25 581:17,19

581:25 584:5,5 588:17 594:11,20,23,26 613:1

680:17,18

errors 219:18 254:4,14 331:7,14 395:13 396:14

402:17 403:2 698:24

ERTA 233:19

ER-binding 233:19

ESA 33:11

escaped 747:9

especially 12:10 13:7 26:13 45:16 57:23 63:5 66:9

66:25 72:7 77:15 85:16 99:7 131:15 133:5 144:11

198:15 208:22 222:20 236:12 269:14 275:23

289:16 292:22,25 303:19 323:1 334:14 344:8

354:5 360:25 368:23 462:18 465:19 495:23

513:24 546:19 573:6 598:17 603:8 605:12 611:13

614:12 636:19 675:19 681:22 685:7 703:14

708:13 722:17 723:1

essence 141:10 168:4 490:17 518:8

essential 98:22 104:18 113:24 280:9 528:1

essentially 101:25 103:10 122:5 123:6 124:14

144:18 148:21 490:25 510:8 511:4,16 522:23

532:11 542:26 554:26 587:19 628:4 725:5

establish 44:8 47:17 61:15 139:24 274:13 326:26

356:20 363:15 381:12 420:6 467:6 469:21 530:26

537:16 648:1 650:5 717:8

established 18:19 40:22 63:1 188:15 201:4 202:2,7

209:2 294:19 330:8 345:13 364:10 369:10 376:5

446:14 453:15 455:10 457:2 461:17 462:6 497:2

556:9 558:3 570:9 609:23 622:11 652:1 666:9

710:14

establishing 48:26 435:25 439:22 445:16,23 455:2

455:4 464:13 531:1 598:24 621:15 634:9 705:4

esteemed 577:7 679:10

estimate 115:23 176:18 455:8 503:17 506:24 584:5

716:4

estimated 676:1

estimates 90:16,17 380:14

estimating 503:16

estimation 483:23

estimator 587:18

estradiol 42:19 47:23 48:8 51:26 68:20 94:3,15,22

99:10 105:18 106:5,17 107:10 177:5,9 370:6,8

374:5,5 413:10 553:1 559:24

estrogen 11:23 17:7 19:3 47:23 59:20 69:17 76:9

80:13 96:1,13 112:15 135:8 164:5,7 173:23 177:4

189:7 230:23 299:14 302:22 303:2 317:13 329:5

347:8 368:4 374:4 426:13 443:9 488:10 490:24

538:9 545:13 546:4 547:6 552:12 560:22 561:10

613:7 616:10,14 618:1 622:17 625:15 626:2

682:2,3

estrogenic 16:19 49:14 166:22 168:3 186:20 408:8
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453:25 454:3 547:3 552:23 553:21 557:9 559:23

561:20 570:9,20 571:7 573:11 726:8,10

estrogenic/androgenic 37:24

estrogens 15:20 208:4 230:14 490:24

estrogen-acting 469:25

estrogen/androgen 38:4 231:17

estuaries 206:24,25 620:7

estuarine 206:26 225:13 619:20,23,24 621:24

estuarine-dependent 619:25

estuary 252:4 620:2

et 37:5 165:1 181:17 272:21 306:6 348:5 410:13

445:1 446:21 455:9 462:21 479:1 513:1 514:3

515:12 670:2 671:5,6 676:25,25 677:10 696:3,3,5

696:6 715:7

ethanol 166:14 174:23 182:10,13 190:5,15,16

567:13 569:10,12

ethic 3:4

Ethical 277:18 283:13

ethics 3:6,7,10,12,13 241:9 428:20

ET&C 306:10

Europe 544:5

European 252:3

evaluate 35:20 40:2 41:23 42:19 44:21 47:4 49:16

61:22 63:1 72:16,20,23 74:9 80:18 81:22 86:3,18

93:19 104:6 192:21 208:17 209:11 210:1 212:16

216:14 219:22 222:8 223:10 232:25 245:16 252:5

258:12 260:3 262:21 265:4 269:12,15,21 274:1,3

274:8,15 285:11 325:7 331:26 332:18 354:4

381:13 393:25 409:20 412:4 414:23 439:20 448:9

462:14 501:26 508:17 524:13 531:10 544:12

548:13 565:23 606:20,22 612:16 614:15 629:4,19

639:17 685:14 691:4 733:4

evaluated 27:9 42:25 53:11,17 58:11 65:20 66:2

69:21 72:26 73:13,20 79:26 89:7 109:22 206:7

209:5 219:10 235:26 274:4 291:23 299:4 302:5

381:2 448:13 453:10 494:13 508:12,18 513:8

536:22 537:6 538:11 602:9 612:1 614:8 697:14

699:22 700:3,6 706:20 726:11,25 735:12

evaluating 35:9 47:13 54:12 60:12 69:14 88:22

134:19 155:22 175:5 212:23,26 219:5 225:21

231:11 269:2 386:1 446:11 449:26 456:2 534:2,5

537:9 556:19 559:15 575:5 608:11 610:3 627:3

627:23 634:1 727:1

evaluation 19:21 20:13 25:2 35:8 65:5 67:25 83:19

98:2 103:26 156:25 181:13 200:3 202:14 209:4

209:24 212:22 274:14 278:22 282:18 299:21

327:13,20 364:12 376:4,7 380:25,26 430:6

453:12 455:20 461:20 464:23 467:2 499:15

522:17 535:3 556:11 611:3 612:3 613:20 627:10

638:13 666:12 710:17 713:20 726:15 744:7 745:5

evaluations 42:1 66:4 199:25 269:25 449:25 450:5

535:7 643:15 728:3,9 744:15

evaluator 369:19

evening 243:20 346:15

event 69:16 209:4 233:18,19 446:15 587:16 591:11

events 21:8 37:10 142:2 214:13 259:10,16,20,22

303:19

eventually 620:4 725:16

everybody 12:25 105:1,12 117:5 122:3 192:1

247:2 296:6 316:5 336:13 400:2 404:22 546:1

684:8 729:3,8 731:21 732:26 741:26

everybody's 334:11 731:18

everyone's 743:4 746:12 748:10

evidence 17:9,10 18:3 20:5,18 34:2 136:11 187:11

225:16 319:7 328:24 330:11 333:21 336:4,7

337:6 351:10 355:3 358:1 366:25 371:25 376:24

377:12 379:18 411:9 423:17 435:6 436:1 445:13

482:8 483:4 485:24 486:3,11,25,26,26 487:4

528:18 552:11 581:9 582:10,10 609:7 630:10

668:3 684:2,8 705:22 718:15 719:26 734:18

744:15 745:1,2

evident 16:25 39:20 50:16 176:13 568:10

evidently 397:3

evolution 195:11 699:16

evolutionary 351:26 432:11

exacerbation 136:12

exact 316:17 581:21 611:25 698:3,12

exactly 46:11 77:12 79:2 87:26 141:8 146:17

232:18 264:11 310:20 334:17 394:19 413:2,23

421:7 493:21 513:26 518:18 565:18 574:26 609:3

650:26 651:3 660:23 672:16 677:3 696:20

examination 376:11

examine 360:24 421:5 504:9 720:11

examined 301:26 376:17,19 483:25 484:4 496:14

529:8 668:8

examines 483:26

examining 504:5 506:15

example 31:20 48:18 68:19 84:22 96:12 125:19,22

139:16 164:14 169:26 170:1 180:20 188:8,9

206:15 217:16 234:10 238:25 317:26 318:17

335:16 338:5 351:4,15 354:26 358:11,12,14

363:14,14,16 368:24 370:6 372:19 376:10 379:16

399:20 410:7,10,22 412:10 413:10 415:10 418:14

422:19 423:14 426:2,25 435:13 437:13 455:14

479:10 489:1 492:10 497:13 512:2,5 553:19

555:4,12 558:19 559:21 561:3 562:4 571:13

580:15,22 588:2 589:3 616:14 619:23,24 624:1

640:17 668:17 670:3,16 680:24 683:17 688:4,11

688:16 699:20 700:7 705:20 706:10 711:11,11

712:21 715:7 717:6 723:19,22 726:15 738:14,15
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examples 83:25 103:20 207:23 208:2 216:18

456:26 482:19 680:23 744:19

exceed 257:6

exceeded 692:3

excellent 203:23 333:17 448:10 458:16 737:10,17

exception 444:14 494:17 496:7 602:12

excess 120:12

excessive 197:20,22

exchange 119:3 121:4

excited 525:12

excitement 343:18 633:23

exciting 343:17 459:8 627:5

exclude 480:20 498:14 662:10 700:8

excluded 19:14 236:26 480:23 698:17

excluding 480:8

exclusion 237:2 530:18 536:5

exclusive 144:22

excuse 104:23 272:5 342:26 407:6 476:4 593:2

626:3 640:9

execute 651:12 653:15 658:19

executed 550:22 658:10

execution 103:17 370:18 557:26 727:26

Executive 742:22

exemplified 432:4

exercise 44:20 52:19 61:21 73:25 194:1 202:2

248:26 280:4

exercises 151:7 196:6 702:17 704:16

exert 41:19

exhausted 345:19

exhibit 450:2 454:15 462:1 553:7

exhibited 227:1 436:10 561:15

exhibiting 464:4

exhibits 430:14 431:9

exist 55:21 64:17 208:25 231:21 257:21 278:13

439:12 723:2

existed 157:14

existing 26:22 40:21 45:2 64:6 212:4 213:21 227:9

245:20 246:9 247:10,18 306:2 509:22 531:2

543:26 629:8 642:9 709:12,25 715:9

exists 289:2 551:19 709:25 710:20

exit 253:12

expand 15:21 16:14 19:16 369:15 373:18 470:4

639:20

expanded 16:18 19:7 370:12 418:12

expands 693:12

expansion 439:2

expect 71:19 83:25 89:2 92:19 104:19 113:8 114:1

151:11,24 164:6 194:20 232:4 258:22 275:20

291:3 344:10 377:20 412:13 413:3 497:4 520:17

551:23 600:8 602:26

expectation 17:12 124:6 239:24 301:17 481:24

expectations 91:14 135:12 150:26 151:11 195:14

409:14 744:17

expected 41:16 46:19 133:18 134:25 135:1 198:14

214:8 280:5 304:23 305:7 402:15 411:23 436:6

451:19 467:4 490:18 494:18 495:1,3,5,11,19,20

495:25 496:3,8 499:18 511:19 531:22 536:14

548:5 563:26 600:7 602:11 603:22 650:7,20

651:6,19 652:5 656:4 722:13

expecting 202:16 374:6 482:6

expedience 75:12

expended 680:14

expense 262:18 375:5

expensive 24:19 313:15 583:8 634:20 688:3 743:6

experience 28:12 77:19 83:2 91:17 103:18 104:18

118:2 151:22 179:12 188:18 219:1 225:11 236:6

263:10 284:25 293:25 294:11 303:17 311:1 314:2

314:3 372:8 381:19,20 399:6,12,15 424:12 497:3

500:19 539:16 540:11 573:7 607:14 643:4 704:5

709:4,5 711:20 712:6 713:3 714:9

experienced 104:8 197:12 200:8 422:9 497:2 500:7

507:24 696:9

experiences 225:10

experiment 373:24 401:23 574:25 581:20 585:21

675:14

experimental 109:2 110:4 111:19,19 148:14,22

149:9 153:18,22 154:1 168:10 227:25 228:10

271:4 392:5 393:7 433:23 446:14,21 481:24

511:5,6 524:25 530:10 582:16 583:14,24 584:22

586:16,17 590:15,16,17 670:22 671:2 677:18

678:14 695:24 699:13 727:26,26 741:5

experimenters 502:13

experiments 281:15 404:11 514:3 516:9 535:12

586:4 590:12 671:14

experiment-wise 478:6

expert 11:16 19:4,7,16 250:26 251:8,15 385:20

579:16 583:4 742:10 743:3 747:24

expertise 7:5,8,12,20 189:23 282:23 285:10 293:25

304:12,13 353:3 384:3,12 500:21 573:15 579:17

609:16 726:17 742:11

experts 16:8 20:3 742:5,8

explain 37:8 71:24 137:14 166:1 185:10 230:9

264:7 286:5 487:11 549:4 602:20 635:15,16

652:22,25 654:2 671:20 721:12

explained 74:16 229:14 282:2 348:24 547:18

602:25 652:23

explaining 93:22 408:11

explanation 133:20 145:10 442:13,19 505:9

647:16

explanations 562:15
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explicit 104:14 352:17

explicitly 134:1 140:3 273:26,26 274:1,4 585:6

587:4 606:19

exploit 66:11 445:21 455:3 530:26

exploited 207:6 453:17 462:8

exploiting 140:15 709:10

exploratory 720:18

explored 158:6 225:24

exploring 258:3 674:2

exponential 400:26

exponentially 72:19

expose 114:4 340:13 349:1

exposed 16:2 41:13 43:5 54:13 64:9 76:21 78:18

79:9 84:17,19 231:9 307:22,25 320:8,13 341:2,3

341:8 415:13 447:5,7 686:23 692:5

exposes 42:21,22

exposing 120:22 340:12 344:14

exposure 10:9 16:3 23:23 25:10 29:3 31:15 34:24

34:24 40:7,26 41:1,12,15,21 42:20,21,24,25 44:6

48:6,9,25 52:5 53:14 58:1,5 73:9,9 76:10,13

79:18 85:6 108:3,9 112:15 113:16,16,17,20

125:15 132:7,11 168:9 192:14 211:16 213:5

214:8 221:6 225:3 226:2 231:23 232:16 234:9,13

235:8 238:1,8,11,16,21,23 239:1,7 261:15,18,19

273:6 292:2 303:20,22 310:6 318:14,15 376:8

390:2 415:16 424:13,14,26 435:23 438:15,20,26

439:1 446:5,7,19 456:11 462:2 465:2 495:19

524:18 529:13 531:4,21 546:11,12 550:16,19

551:26 553:18 570:10 611:24 612:7 621:24

639:12 640:6 661:1 670:19 689:10,17 691:1

698:10 709:1,7 711:18 715:26 718:25

exposures 57:25 84:22 112:18 126:22 155:12

213:8 225:1 226:2 257:5 450:24,25 708:22,24

710:9 722:12

express 41:16 129:4 436:8

expressed 76:26 77:7 126:22 128:25 129:5 141:1,2

176:10 207:19 238:22 478:15 513:25 549:7 551:6

551:17 591:9 624:21

expression 96:10 123:19,21 133:14 176:13 184:26

185:4,5 204:7 410:16 411:10 457:9 504:24

529:18 575:6

extend 356:13 440:13

extended 21:20 23:21 24:3,23 42:8 47:4 233:7

234:12 335:6 463:19

extending 463:18,20

extends 139:23 230:20

extension 227:8 356:24 513:13 621:5 652:8

extensive 77:19 118:21 294:11 324:6 351:9 381:19

431:2 465:20 466:13 497:3 709:10 710:19 714:9

extensively 133:3 191:2 382:2

extent 31:10 39:24 65:19 66:10 87:14 96:13 142:19

148:7 165:8 182:15 196:25 215:1 226:3 265:26

274:14,18 328:20 381:10 389:25 419:22 421:15

562:2,7 653:12

extenuating 312:12

external 167:20,25 420:7 424:23 523:11,14

extra 171:4 197:14 260:7 297:3 316:16 358:10

extracted 139:19 346:8

extraction 166:14 174:23 190:5 512:19,20 566:15

567:13,19 569:13

extractions 569:10

extrapolate 350:18 434:13,15 734:20 735:1

extrapolating 612:25 614:12

extrapolation 734:23

extreme 55:6 169:16 266:25 652:19 680:23

extremely 283:2 372:7 381:11 581:16 737:15

eye 146:21,26 426:1

eyes 721:9

E&A 456:23

e-docket 4:1,6

E2 482:20

F

F 244:17 286:24

Faber 568:6

FACA 14:9

face 631:1 689:13

faces 284:11

facilitate 352:14 377:25

facilitates 482:1

facilities 583:9

facility 393:15,17,18,20

facility's 706:21

facing 634:21

fact 13:20 76:18 79:6 98:22 106:20 117:10 119:3

123:16 136:21 137:23 144:3,7 152:23 153:20

154:2 176:3,4 180:26 256:21 259:5,11 263:6

271:7 273:22 280:13 282:3 292:9 301:12 333:4

397:7,8,9 413:12 444:24 445:7,17 446:7 454:4,15

454:19 474:10 497:25 498:2 541:17 563:22,24

572:25 574:23 575:16 619:26 661:2 663:8 668:15

680:5 681:26 685:21 686:17 687:11 688:5 692:23

696:17 703:2 715:24 733:8 734:14 745:3 746:15

factor 55:22 63:11 151:4 226:14 312:13 358:26

465:13 478:4 654:3 698:1,8

factors 40:20 48:20 76:15 112:20 126:21 196:2

202:5,6 260:19 263:4 282:18 350:11 386:8

420:14,16 524:23 529:15 666:26 721:11 728:15

fail 258:16 633:8

failed 233:11 473:20 698:25

fails 89:10 202:4,8 402:7 478:24 579:6
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failure 105:21,23 175:20 201:9 281:10 402:3 480:7

530:10 552:2 553:21,26

failures 706:18

fair 53:26 153:4 224:8 480:2 509:12 519:18 674:10

fairly 52:21 54:16,24 60:13 67:26 68:5 76:26 77:13

77:14,16 87:1 110:26 111:5 117:8 123:4 158:14

159:25 165:7,9 166:11 168:21 170:12 172:4,22

173:1 191:13 196:26 197:13 227:4 231:12 236:4

238:25 267:24 286:8 293:12,26 302:21,23 303:6

305:26 324:5 356:4 368:19,26 398:21 401:8

429:26 444:3 488:11,13 496:25 497:1 545:26

546:17 575:11,19 585:20 604:7 698:12 719:22

fall 52:5 189:26 202:14 234:21 252:24 253:9 447:3

656:19,26 698:7

falling 660:5

falls 199:14 203:4 656:20 729:3

false 22:6,7 234:18 291:11,18 375:2,4,10 402:4

407:5 408:5,5 547:26 598:20 613:25 622:19

713:9

familiar 222:15 225:22 227:10 253:20 515:12

651:18

familiarity 708:26 709:5

family 8:6

far 2:12 116:19 229:4 263:11 304:14 316:21

352:23 353:11 371:6 382:16 419:13 452:10 512:6

513:9 535:22 538:6 540:2 598:9 612:20 627:23

643:8 678:2 712:25 713:9 725:3 732:10 733:14

fared 260:18

farther 657:14

fashion 89:19 278:10 730:17,24

fast 154:12 310:11 655:18

faster 305:21

fatal 737:7,11

fate 510:10 621:23 747:3,13

fathead 115:22 116:16 119:9 142:4 157:6 302:10

445:12 450:12,13 455:10 457:22 527:8,13

Fatheads 488:19

fatty 220:22 237:10

fault 413:11 699:1

favorite 694:8 696:16 731:12,14,26

FDA 654:11

FDA's 5:9 242:5 459:18 664:20

feasibility 566:19 567:18 727:1

feast 729:1

feather 45:17

feature 462:5 685:12

features 561:25 737:12

fecal 288:15,16,18,21,23 289:1,4,8 337:20 416:21

fecal/urate 45:21

fecundate 474:17

fecundity 79:25,26 81:7,8,9,26 84:18 86:6 88:1

91:5,9 92:5,22 93:1,2 94:14,21,26 95:15 96:8,26

97:7,11,18,23,26 98:19 99:7,7,10,12 101:3,22,25

105:19 106:14 108:7,17 110:9,11,21,26 111:2

112:3 115:2,5 116:7,10 121:18 125:23 135:4

138:12 143:26 151:2 157:3 263:10,12 308:25

431:5 454:6 464:15 478:20 479:5,7,13 480:10,15

481:3,5,6 482:20,22 483:16 494:20,25 495:7,22

496:21,24 504:8 505:8 506:2,7 509:3,13 524:26

526:10 529:26 536:17 542:20 552:14,21 559:7

612:12

fed 171:2 172:14,24 424:24

federal 2:2,9,19 3:2,6 4:12 9:11 14:6 16:5,7 193:21

241:2,8 329:2 459:2 649:6 664:2

feed 74:7 172:24 173:7 197:4,14,17 219:13,14

387:20,20,23 472:12,13,13 521:6 579:12

feedback 11:24 12:7,8,13 315:9 423:19 554:12

654:1 658:10

feeder 408:21

feeding 88:2 99:4 121:26 122:1 165:19 170:21,25

171:8 172:12 173:1 196:19,20 220:18 281:19

282:16 301:9 425:13 439:3 472:11 519:10 558:17

715:7

feeds 189:3 472:8,9

feel 13:15 54:18 55:2 56:11 81:3 83:16 87:6,16,23

87:24,26 88:5 103:16 104:2 110:24 112:25 113:1

113:2 119:22 144:17 194:8 202:2 206:19 221:6

253:12 256:12 258:19 284:15 315:17 321:9,14

322:6 323:8 324:9 329:8 343:21 360:8 384:3,4,15

384:23 385:3 396:19 398:18 414:22 416:17

420:25 423:23 425:26 426:17 445:19 536:5

548:11 629:16 639:23 640:9,19 658:8 661:5

687:6 698:11 712:12 732:7

feeling 148:16 386:15 419:13 639:5 654:3

feels 294:20 449:26 496:17 499:15 500:15 555:6,15

605:21 622:26 641:21 642:10 672:17

fell 120:15 155:26 249:15 500:22

fellow 333:11 712:9

felt 58:3 60:1,7,9,12,14 61:9 64:18 74:20 75:2 96:1

119:17 120:14 144:11 210:24 221:17 222:3 425:4

425:14 444:17,24 445:5 446:5,17,24 454:23

490:1,9 508:16,18 509:7,17,19 510:15,26 511:14

511:19,21 525:15 527:2,3 536:21 537:20 548:26

632:7 637:16 638:17,22 640:1 642:15,23 666:21

708:2

female 47:12 65:15,18 66:16,18 81:9 87:20 92:8,9

92:11,25 96:9 97:4,13 99:23 109:15 123:17,19,21

124:10 128:9 129:7,17 130:1 142:9 156:14,15

158:4 163:13 165:22 167:7,21,22 214:3,5,11

222:4 223:6 268:1 319:5,5,7 365:20 372:18,21
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373:1,4,6,13,20 387:6 418:18 425:16,17,21 427:2

427:12 448:10 464:2,4 469:22,24,26 495:24

519:16,17,23 552:18,22 561:8 583:22 584:9,10

604:26 659:17 699:22,25 700:9 719:18 720:7

femaleness 659:25

females 80:26 87:13,18 88:7,8 91:25 92:1,2 94:18

95:17,18,21 97:15 98:8 99:22 109:13,20 128:19

128:23 129:1,7 130:10,12 133:14 140:17 146:2

163:7 164:4,10,13 166:20 167:18 168:24,25

169:2,21 173:16 214:15 263:11 273:13,14 289:7

303:3 348:16 372:24,25 389:15,17 410:21 418:20

425:18 450:6 451:3 455:15 480:7 494:25 495:7

495:15,17 498:24 557:7,8 560:19 561:4 571:8

603:8,13,15 604:11 605:14 640:3 669:5 699:24

699:26 700:1,12 711:12 714:14 724:2

female/male 469:21

fenoxycarb 209:4 321:12 621:11 681:21,23 683:17

720:3 723:18

fertile 81:10

Fertilities 86:10

fertility 44:11 45:3 67:5 79:26 81:8,10 91:6 92:5

93:1 96:8 99:11,20 101:8,25 108:7,17 138:13

151:2 394:7 454:6 481:1,2,3,4,5,7,11,13,14

524:26 526:10 552:14,21 559:7 612:12

fertilization 167:26 433:14 530:22

fertilized 157:24

fewer 72:8 145:12 222:11 531:14 695:19 696:4,5

FFDCA 15:14 17:21

FF2 531:6

field 342:8

fields 16:8

FIFRA 1:1,9 2:3,17,18,25 3:24 4:21 11:8 241:3,19

459:3 620:26 621:1 634:18 664:7 729:18,20

fifteen 276:8

fifth 380:2,2 505:16,22

fighting 172:21

figure 61:13 118:3 158:22,23 232:2 358:21 434:26

476:18 479:15,15,16 480:2 491:17 506:15 586:3

635:13 736:9

figures 127:1

figuring 565:13 608:1,9

file 592:23 593:9,17

filed 3:8

files 270:12

fill 225:6 285:20 744:26

filled 164:20

filter 168:2 234:22

filtration 558:15

fin 80:21,21 82:9,11,13 87:8 92:10 103:8 140:22

141:3 445:1 451:6 453:23 462:26 463:1,1,2,3,12

463:13,13,14,16,18,19,20,25,26 464:2 466:24

469:19 480:1 482:22 504:24,25 514:14 619:26

620:1,8

final 13:5 16:11 28:13 73:12 244:3 259:19 276:21

284:20 313:17 363:19 382:14 408:26 410:4

429:15 470:5 471:24 480:26 506:14 522:26 636:3

652:1 697:24 699:7 727:15 734:26 737:14 738:5

742:3

finalized 210:12 309:26 331:12 332:15,17 739:18

finalizing 744:5

finally 29:22 57:21 109:12 138:17 147:21 310:19

314:5,16 418:23 578:9,16 626:12 642:14 716:8

717:4 731:19

financial 3:9 307:4

finch 352:7,13 354:3,3,10 356:9,19 372:22,24,26

418:22 425:10 427:11 431:9

finches 352:6 354:24 355:15,24 373:6,10 427:2,6

427:19

find 16:1 31:13 62:20 88:12 115:25 129:13,16

151:10 154:4 199:1 229:13,16 270:16,26 327:18

342:17 345:11 386:12 399:4 401:15 479:21 504:2

581:9 582:1 609:4 613:9 733:26 739:10 746:8

finding 48:26 133:13 165:21 230:6 279:25 413:12

438:17 467:4 602:24 613:23 650:19 679:24

findings 168:16 173:24 174:4 278:15 281:23

282:15 426:24 456:8 478:14,17 506:12 534:14

546:7 567:25 606:16

fine 104:26 105:1 178:3 179:13 360:21 442:16

476:9 485:2 690:26 692:26

finer 177:22

fingerprints 717:9 723:17

finished 59:3 243:22 333:7 360:1 383:25 388:22

666:1

finite 298:3

fipronil 209:19 321:22 681:21 720:21 722:11,14

723:22 724:4

firm 276:23 738:11

firmly 372:7

first 19:9,15 20:12 24:2 27:17 30:6 36:7 37:5 49:6

54:13 63:17,26 64:2 65:23 75:16,18 76:3,17

78:26 84:8 90:24 99:12,26 100:2 106:17 112:3

114:24 124:22 146:22 149:25 150:4 152:11

154:15 162:20,22 171:1 183:7,18 190:15 191:26

197:15 214:12 215:10 217:8 220:4 227:16 245:19

253:6,22 255:7 256:2 258:22 261:25 267:16

271:14 272:2 273:3 285:23 290:10 295:24 296:22

309:10 311:20 316:13 323:9 337:6,12 345:22

347:3 348:6 354:22 360:4,26 364:3,3,17,18

365:13,18 374:1 376:22 377:9 378:5,26 390:21

400:25 406:18 413:5 419:24 421:17 422:23
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429:12 430:17 435:20 453:5 465:10 476:5 482:18

493:19 496:13 505:20 508:10,26 510:25 523:2

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646:20,24 647:15 653:3 654:24 655:5 658:4

661:10 668:19,20 670:8 674:16 681:14 686:18

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716:23 736:14 737:8 740:5 745:11

first-generation 319:22

first-hand 9:2

fish 6:6,9 23:12 25:16 26:1 37:13,18 75:8,14,23

76:4,17,19 77:1,6,11,13,16,18,19 78:2 80:10,13

80:14,19,21,22,25,26 82:9,15 84:19 85:20 86:17

87:3 88:3 91:16 94:4,9 99:16,23,25,26 100:2,4,8

100:10 102:20,23,26 103:8,22,22 109:10,12,15

117:10 118:25 119:3,12 120:10 128:8 129:6

131:5,24 133:2,24 136:21 142:10 144:9,9 145:3

149:13 165:14 167:2 169:8 173:7 174:14 188:3

189:24 202:22,23 233:8,20,21,26 242:18 268:22

268:26 273:22 274:20 278:3 280:21,26 281:4

282:2 297:23,24,25 298:12 302:12,21 306:4,7,9

309:4 310:25,26 311:5 392:17 395:7,7 428:10

433:23 441:23 445:12 447:10,13 454:26 456:26

457:1,9,14 460:6 462:4,23 463:6,23,26 464:1,2,4

464:5 465:19,20,26 466:4,9,12,14 469:15,16,17

469:22,22,23,26 471:22 472:6,10 474:12 476:26

477:12,20 486:25,26 487:7,8 495:10,12 497:25

498:4,21 499:11 501:3 502:19 505:13 511:21

516:1,2,6,7 529:3,8 533:24 534:12,17 537:25

544:8 595:10 619:26 620:1,8 657:24 665:8

736:25

fisherman 731:22

fishery 619:14

fishes 128:24 129:3 217:26 231:13 274:6 447:1,2,6

486:20

fishing 731:15

fish's 95:5

fish-to-fish 477:15

fit 21:21 25:6 27:10 232:2 278:8 306:13,17 317:2

324:11 484:3 654:12 696:1 738:1

fitness 40:26 45:1 58:23 425:3,4,7 426:7 606:18

612:9

fits 25:5 68:18 353:23 432:24 647:5

fitted 36:24

fitting 206:12 654:14

five 3:17,19 4:6 17:4 21:4 58:20 80:1 86:23 146:26

148:15 149:20 151:9 165:13 171:5 205:5 211:12

211:25 223:2 224:5 234:6 239:19 253:5,7,10

267:18 268:4 271:5 291:17 310:15 332:26 367:13

378:2,7 405:16,18,22,22 441:21 474:18 519:23

519:23,23 543:24 589:16 593:20 670:18 696:24

698:2 707:10 708:8 714:16 719:12

five-minute 205:5

five-step 27:16

five-week 79:24

fix 327:18 331:11,15 450:26 593:14 676:19 733:25

fixed 385:4 450:23 676:18

flag 81:25 392:12

flagged 292:3

Flagstaff 6:13

flash 18:16 512:13

flat 482:18 729:1

flaw 737:7,11

flawed 131:23

flexibility 24:1 196:8 521:19

flexible 362:16 521:24

flies 631:1

flip 177:15 263:23

floor 745:11,12

Florida 7:20

flow 132:10 171:8 211:7 226:2 471:25,26 472:1,2

472:12 537:2 558:23 640:16

flow-rate 173:2

flow-through 211:6 266:16 626:23 716:3

flow-thru 113:17 120:19,22,25,25 121:7

flutamide 209:22 488:17

Flynn 130:17,17

foam 45:19

foci 378:17

focus 9:18 15:18 19:5 26:13 35:2 66:13,15 203:2

247:16 278:22 315:3,23 348:21 351:20 353:19

355:6 418:16 447:21 455:4 456:15 533:6 627:10

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focused 9:21 19:4 20:4 203:8 230:14 260:5 335:7

382:23 565:15 617:25 627:18,19 632:12,14

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focuses 534:26 623:1

focusing 14:10 15:17 203:12 632:11

fold 15:9 87:21

folks 89:23 174:12 184:2 188:24 198:6 283:17

489:12 576:6 630:8,8 661:17 664:15 746:19,19

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follicle 410:22

follicles 274:12 410:21

follicular 274:15 562:5

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392:24 393:1 585:6 586:12 591:4 594:21 641:7

649:7 657:21 671:12 678:4 712:18 714:7 723:20

747:21
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followed 14:16 27:17 43:10 122:2 154:16 369:18

585:3 678:7

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286:22 292:1 331:19 443:22 446:5 448:2 508:18

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709:14 714:18

follows 110:6 693:12

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follow-on 185:11

follow-up 35:8 36:15 128:7 130:19 147:2 177:3

180:15 189:14 195:19 203:15 253:1 266:13 275:7

565:22

food 14:6 19:4 171:5 173:5,12 219:11,12 259:14

291:14 391:3 408:16,21,22 424:15 715:7

football 178:16,17

footing 738:12

footnotes 506:20,25

force 578:24

forced 156:24

forefront 13:11

foremost 194:3

foresee 32:13

forget 238:6 390:17

forgot 197:9 397:7 468:6 680:12

form 46:23,26 83:15 89:2 113:11 205:15 220:5

228:9 428:22 431:24 490:16 605:20 673:14 688:7

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formal 117:14 482:25 497:9 510:20 657:7,11

formally 239:26

format 496:15,16 497:10 636:5

formation 17:2 85:12 158:13 170:3 453:22 553:22

formative 530:14

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formulas 592:25

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formulation 170:21 171:2 172:15,25 196:22

Fort 160:12 567:8

forth 186:25 246:12 270:3 355:18,22 371:24

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fortified 572:12

fortifying 237:9

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13:4,26 14:11 17:26 18:6,23 19:15,22,25,26 20:5

24:1,15,25 25:22 28:2 31:2 32:25 34:18,18 41:2

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256:11 267:4 269:20 285:2 302:14 305:13 310:4

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220:9,18,25 221:4,21 228:13,26 249:16 335:19

348:8 359:6,7 368:24 370:4 402:5,6 412:13

436:16 478:22 484:2 493:26 494:15 510:5 511:3

516:18 547:10 560:26 561:2,3,5,8 580:12 596:24

618:25 622:9 651:7 652:13 684:1 692:16 736:24

741:16

foundation 12:2 417:20 453:16 462:7

founding 349:2

four 4:23 20:11 21:22 41:5 80:1 86:16 100:26

107:16 109:13,13 146:9 151:9,9 161:21 162:2

163:25 164:9 168:12,12,13,14,15 170:13,14

191:18,24 192:11,21 199:6 284:17 285:19,19,24

310:20,23,26 311:11 312:7 323:24 324:3,5

329:10 362:21 367:15 378:2 379:22 384:22

389:12 403:24,24 405:16,18,22,22 474:20 485:14

503:8 543:23 560:4,8 566:9 583:17 596:18

602:22 603:26 604:4 645:17 653:7,13 658:7

688:12 691:20 698:1 707:10 724:25 727:10

fourth 56:12 205:24 653:2 664:11

four-day 2:4

framework 14:12 17:3,16,18 19:26 193:11 234:16

253:5,16 625:18 743:20 744:26

framing 436:15

frank 658:26

Franklin 481:21

frankly 131:22 200:7 657:19

fraught 164:18

Fred 748:5

Fredericks 285:23,25,26 295:22 338:15,20 339:10

339:15,20,23,26 340:4,25

free 266:2,7 267:5 474:6,6

Freeman 7:14,14 243:10,10 274:21,22 275:1,6

450:15 452:25 453:1 460:24,24 461:24,26 467:18

469:4,10 476:21,22 490:5,6 502:5,6 518:20,21

527:18,19 532:6,7 538:21,22 665:22,22 740:13

French 580:15
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frequencies 172:4

frequency 171:14 390:7

freshly 157:23 572:13

Friday 80:2 310:3

Friedman 450:14

friend 401:8 694:9

friends 331:25

frog 23:12 174:15 178:16 202:22 233:8 308:20,25

310:5,21,25,26 586:2 594:9

frogs 155:16 189:16,24 190:20 197:12 217:26

268:22 306:7 312:18 552:22,26 567:4,9 569:20

571:1,4 586:1 610:4

front 331:15 347:2 357:5

frontal 145:2

fronts 11:13

frozen 512:13

FSH 410:12 423:18

FSTRA 308:14

fulfilling 342:16

fulfills 112:11

full 20:18 32:20 41:13 78:1,2 79:10,18 93:12,15

99:26 108:26 299:4 334:13 346:10 359:11 438:4

444:19 451:12 455:26 456:7,14 463:9 494:1

496:15,15 498:19 524:11 526:22 565:9 604:16

655:8 709:22 721:21

fuller 490:10

fully 9:17 140:15 213:19 219:22 230:25 326:11

350:24 352:4,25 355:21 422:2 430:20,24 456:1

465:5 496:5 500:17 507:17 520:13,16 536:14

605:23 607:7 644:1 672:24 703:25

function 124:2 142:16 322:24 337:13 356:21

417:11 423:21 430:24 438:19 456:1 464:18 466:9

488:23 552:7 560:13 561:1 568:5 614:22 661:6

functional 507:17 607:8 703:25

functionally 318:8

functioning 543:2 620:7

functions 141:17 438:23 548:9

fundamental 207:18 356:15 434:15 535:2

fundamentally 348:13

funded 160:9

funeral 8:6

funnel 17:20

further 28:2 44:20 53:24 100:1 152:18 186:9

214:20 229:18 269:22 303:6 316:2 317:15 318:23

343:3 353:5 367:12,17,23 368:1 381:4 382:6,22

389:7 405:24 408:19 411:2 429:17 434:3 439:26

440:25 441:5 451:26 452:2 456:4 457:17,19

464:22,23 465:9,10 467:1,24 469:12 476:12

477:3,6 502:2,4 511:22 527:16 531:2 532:20

568:25 569:1,3,5 570:3 598:12 599:24 601:5

614:3 615:3 618:4,6 622:18,21 639:4 642:21

644:18,21 648:14 666:26 668:26 669:24 684:11

691:10 692:1 693:20 698:18 707:2,4,6 711:24

713:13 715:10 716:13 717:14,20 723:11 724:20

728:3

furthermore 317:6 546:24 558:14

future 150:18,21 204:10,13 205:19 266:9,11

352:14,16 354:11 365:14 441:11 500:14 582:14

678:22 679:6 682:8,9 704:15 705:5,18,22 717:7

735:17 739:1 740:11

fuzzy 141:22

F0 41:11 51:14 71:16,17 78:18 108:9 109:3,5

124:15,20,26 125:8,13 126:3,4,15,18,22 149:13

216:16 217:4,10,14,17 218:5,6 219:21 244:12

245:19 254:25 256:6 257:1,9 261:19 264:20

271:20 272:2 286:24 294:13 338:17 339:19,20,22

340:1,6,20 341:1,8,8 366:7 389:26 393:8 431:16

432:22 438:15 439:16 446:13 450:5 455:15,24

456:7 505:7,24 506:7 524:10,18 527:26 529:11

529:13,18 531:9 534:4 536:1,21 630:25 640:8

669:5,8,13 677:7 689:7 692:1,4,6,8 700:22,23

715:22 718:17 719:6,8,9,11,11,17,21,23 720:21

720:24 721:23 722:8,10 723:4 724:4

F0s 217:18 218:19 250:7,13 290:10

F1 23:21,21,23,25 44:8 46:23 47:6 50:19,19,21

51:14,25 52:2,15 53:7 58:3 71:7,16,17,26 75:5

78:19,20 79:6 93:18 94:16 95:11,20 97:4,11 98:5

98:19 104:22 105:14,16,20,23 106:2,4,10,11,14

106:18,21,22,24 107:2,4,9,14,15 108:5,5,6,10,10

108:13,14,14 109:3,26 112:3 115:8 125:12,12,15

125:15,24,25 126:23,23 136:7,13 143:9,13,18

216:16 217:4,12,13,19 218:5,7 219:19,22 244:12

254:25 255:3 256:5,9,13 257:9,11 264:6 271:26

273:3,8,9 275:10,18,22 286:24 290:25,26 291:3

294:13,14,15 304:19 338:24 339:2,16,24 340:1,1

340:5,6,6 341:4,6,6 366:7 389:13,26 392:26

393:3 431:16 435:9 436:4 438:16 439:19 445:24

446:13 455:6,16 456:1,1,7 469:22 474:17 482:20

482:20,21,22 497:20,21 498:7,12 504:23,26

506:9 509:14 528:21 529:8,12,14,17,22,26

530:13,15 531:23 532:13,18 534:5 535:3,10

536:3,13 624:3,23 630:25 640:8,9 644:15 669:6

669:13 677:7,8 683:18 689:1,3,6,16 690:3 691:13

691:15 692:1,3,9,17 698:16 700:22,23 711:13

715:2,23 718:16 719:5,8,9,11,11,19,22 720:5,7

720:26 721:2,5,10,23 722:5,10,17,26 723:3,5

F1s 41:13 51:6 54:5 58:4 74:5,21 115:2 126:2,18

218:14,19 290:10

F1/F2 107:3,8

F2 23:25 44:15 46:26 47:1 51:10,15,25 58:13 70:19
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70:23,24 71:3 73:13,14,21 74:14,16,17 75:5

78:21 79:6 93:15,17 94:18 95:1,5,6,12,15,20,21

97:4,5,19 98:20 104:22 105:14,16,21,22,24 106:3

106:10,12,15,22,22,24 107:2,5,10,10,14,15,19

108:3,11,22 109:3,22 110:1 112:4 115:9,11

134:25 136:8,12 152:26 275:8,19,22,23 290:25

291:3 294:13,15,16 304:18,19,21,23,24 338:24

339:2,6,11,13,16 340:5 392:26 393:2 431:14,17

431:19 435:8,21,23 436:2,3,10,13,16 437:8,17,19

438:8,11,18,23,24 439:2,7,13 440:7,13,13,20

445:25 455:6 456:2,8,10,12 482:20 497:21

504:24,26 509:14 524:11 528:1,21,24 529:2,8,9

529:17,21,22,26 530:3,18,21 531:21,23 532:12

533:4,11 534:3 535:4,5 624:23 640:9 677:7

715:23

F2s 41:14 50:25 51:8,12 52:4 70:21 71:10 73:20,26

74:8,9 125:14 134:21 436:18 437:21 438:25

439:15

F2/F1 95:1

G

G 478:9 482:4,24 483:3

gain 39:15 104:18 456:4 539:15 584:1,18 625:26

733:24

gained 376:18 383:2 411:20 711:20

gaining 192:9

gains 192:25 420:5

galliform 424:5 426:22

Galliformes 432:9

Galloanserae 432:9

gallons 716:4

game 239:20

gametogenesis 76:13 112:18

gander 678:23

gap 352:2

Gardner 670:2

garner 262:25

gather 285:22 336:1 362:2,16

gathering 492:5

gauge 651:20

gazillion 130:18

geared 407:1

geeky 594:5

gel 122:22,24 512:22

gen 335:21

gender 77:5,7 118:13,14,17 128:25 131:21 140:12

140:26 141:2 144:13 224:18 445:8,8,17,17,23

448:8 450:20,22,22,26 451:8 454:20,21 455:2,4

462:15 530:23 531:1 583:22,26

gendered 463:22 464:3

genders 462:24 531:3

gender-specific 583:21

gene 77:3 80:23,24 129:3,5 165:3 184:26 457:9,13

575:5

general 3:12 5:10 37:16 39:13 48:22 75:2 81:16

139:11 141:12,25 148:2 156:10 161:14 225:17

319:22 346:23 348:8 349:22 356:26 370:16

382:11 403:1 424:7 432:8 436:2 439:9 441:9

453:17 456:15 462:8 465:3 478:18,20 482:2

494:1,12 496:9,19 505:17 511:5 516:6,7 522:6

523:6 525:22 561:22 569:9,15 573:4 598:2 602:8

610:4 611:13 666:20 667:2,6 668:23 709:1

generalization 81:18

generalized 148:1 303:11 480:19 515:21 525:22

684:3

generally 45:18 49:25 52:24 57:26 76:20 128:17

141:14 149:11 213:8 224:11 225:17 226:22

246:19 269:1 291:6 293:3 311:14 312:11 317:24

432:10 465:20 494:18 495:25 497:26 503:20

505:24 506:8 516:12 568:6 602:14 603:20 605:14

611:12 620:23 625:10 671:24 708:5,17 709:2

714:11 741:7

generate 278:16 443:11 462:23 545:16 616:23

624:9 640:17

generated 161:3 307:6 313:6 463:23,24 478:11

544:1 571:20 676:18 691:26 700:12 705:1

generates 347:11 349:15 641:11 716:3

generating 160:26 572:6

generation 23:21,22 24:1,2,6 41:12 42:21 44:1,9

46:24,26 47:6 50:16,19,19,21 51:6,24,25 53:7

54:4 58:4,14 64:13 67:24 70:19,23 72:9,10,14,15

72:17 73:1,2,14 75:6 78:18,19,20,21 79:14,15,24

80:4,6 93:15 94:16,19 95:1,2,5,6,12,12,15,20,21

97:11,19 98:2,5,20 104:5 107:19 143:9,13 206:15

211:20 212:14 213:23 215:10,11 217:4,11,13

218:6,7 219:20 222:7 245:11,15,19,23,26 246:12

247:6,24 250:3 256:2,5,6,10,13,16 257:13,22,23

258:1,6,16,21,23 260:25 261:19 264:6,13,20

273:6,8,9 277:26 286:24,24 290:9,10,17 291:1

293:21 294:7,17 295:1 316:14,15 319:12 320:8

320:10,25 323:9,9 325:10 339:6 340:6,15 341:1,5

341:6,7 344:25 350:4 352:10 366:7,7 392:19

419:7 431:16,16 432:22,24 435:9,12,21,23

436:17 437:1,11 438:15,23,24 439:16 451:2

474:17 504:6 505:1 523:10 524:9,12 528:21

529:2,9,11,21 530:17,19,20 533:5 535:3 536:13

536:19 624:4 626:13 683:18 689:3,16 690:9,11

690:21 692:9,17,23 698:17 710:18 711:13 715:2

715:16,17,18,22 718:17,25 719:5,11,11,17,19

722:5,10,20,22 723:1,3

generational 72:20,23,26 73:6 217:8 530:11,12,18
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709:25 715:3,25

generations 41:20 42:8 43:25 46:12 50:7,14 52:16

54:6 71:7 76:15 78:5,6,17,17 79:9,9,11,19,23

104:4,22 107:24 108:24 109:3 217:3,6,16 226:10

249:8 250:10 254:24 291:15 294:19 318:26

322:25 332:2 435:9,17 436:19 437:17,24,26

439:1,8 440:18 483:13 504:7 529:23 530:13

533:3 535:20 624:23 630:26 668:12 677:9 690:19

690:23 700:22 716:26 718:20,23,24 719:2 739:18

generic 578:2

generically 194:18

genes 486:10 513:8 522:11 726:20

genetic 26:1 77:5 80:10,18,20,25,26 81:2,13 102:9

103:12,13,23 104:17 113:3 122:18 128:8 130:18

131:10,15 140:12,18,22 156:2 160:2 165:4

169:24 417:20,23 420:24 429:3,7 445:8,17,23

448:8 453:11 454:21 455:4 466:26 468:25 469:6

487:6 495:14,14,17 498:21 515:11,14 523:23

530:23 531:3 552:26 603:17 605:8

genetically 26:11 109:12 123:17 155:24 156:12

389:26 423:5 546:25 659:18,25

genetics 429:6 691:23

genital 463:18,19,20

genome 352:14 359:11 427:11 445:1 523:18,20

544:7

genomes 434:14

genomic 304:12

genomics 11:16 356:14,17 720:11

genotype 80:15 86:14,15 130:21 141:5 445:24

450:19,23 455:5 470:2 499:8

genotype/phenotype 129:12

genotypic 123:18 281:2 301:10 451:7,8 455:2

469:20 487:6 524:1 530:26 557:15 604:11

genotypically 130:26

genotypic-phenotypic 302:7

genotyping 298:12 653:9,26

Geoff 5:26 242:14 243:26 334:4 460:1 665:3

672:24 691:21 738:13,22

GEOFFREY 5:26 150:24 152:1,4,25 153:4 187:13

187:18,20 188:1 189:5,9 190:1 202:20 235:2

236:19 237:17 238:5,10 242:14 243:25 248:2

249:21 250:2,5,9,16 253:1,18 266:13 333:16

335:13 336:16 337:14 460:1 569:9 617:6 627:16

632:17 635:1 636:2 637:13 645:2 656:8 665:3

670:1 672:10 682:15 684:5 685:19 690:6 702:15

707:6 712:9 717:14 723:14 730:26

Geological 6:5,8 242:16,18 460:4,6 665:6,8

geometric 362:18

George 7:22,23 65:7 66:13 180:14 204:2 243:14,14

347:21 352:22 355:11,23 359:17,19,20,21,25

360:6 361:4 362:5 368:9 372:5 373:3 375:9

380:17 405:23 406:18 407:10,14,17,20 408:1,9

408:10 410:6 411:9 413:7 414:16 422:15 429:3

434:3 439:26 461:2,2 650:3,15 655:2,18 657:3

662:1

George's 360:13

germinal 164:19

germs 158:18

getting 11:20 12:7 79:20 85:8 88:1 94:1 113:8

119:18 126:10 131:18 136:16 181:8,22 195:3

219:13 250:23 271:3 304:15 306:5 336:18 346:2

367:14 379:18 388:7 396:19 404:26 470:3 489:22

520:1 541:24 584:11 589:17 646:17 652:17,22

701:4 713:9 722:12 725:6 728:22 730:17 733:14

744:1

ginning 166:15

girls 584:7

give 11:21 13:15 21:11 32:20 39:12 47:21 55:6

64:4 75:16 105:3 119:17 125:10 135:11 141:3

144:18 148:3 154:17,18 161:8,26 164:16 173:14

177:5 185:19 187:5 194:7 199:21 207:23 250:21

251:2,4 257:15 259:8 267:11 270:6 276:9 286:4

287:22 301:22 318:17 319:19 333:5,12 343:22

363:24 370:2 371:1 375:24 396:21,22,22 401:16

401:24 410:19 451:7,23 473:13 479:22 503:22

518:8,18 519:18 539:17 544:20 572:5 576:2

580:15 592:20 593:3 630:13 643:22 644:15

660:18 661:3,16,18 670:16 683:3,18 690:22

727:14 733:20,25 737:20 738:1,9 741:19 746:26

747:11

given 42:10 59:21 62:10 68:8 70:3 89:11 104:24

121:25 136:3,16 155:15 156:3,23 157:14,25

175:10,19 190:17 191:19 223:18 235:23 237:21

239:9 241:13 246:24 258:2 259:15 325:13 333:4

347:14 353:11,20 355:2 365:10 366:11 368:23

369:3,4,4,5 375:6 378:2 387:24 403:12 406:26

411:21,25 412:10 417:8 418:20 433:17 435:13

436:5 437:7 442:11 445:5,7 448:4 449:1 450:1

453:4 454:19 462:18 486:24 487:4 495:26 505:10

519:12 525:7 531:5 536:5,9 537:24 555:10

562:26 568:19 571:19 574:25 576:10 582:16

588:8 594:11 595:17 619:7,10 624:1,17 627:2

629:19 631:14,15 637:25 639:13 642:18 668:1

669:9 687:4 695:19 699:5 704:25 705:12,20

706:12 708:16 712:23 713:3 715:10,13 737:23

739:4 747:17

gives 32:21 60:18 84:8 158:22 176:19 236:14

247:20 369:21 420:21 630:25 648:5 652:14

684:19 691:1 733:25 734:22 748:8

giving 11:2 22:20 33:16 43:18 257:13 451:2 600:12
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658:10,16 687:7 740:17 742:2

glad 188:1 248:3 394:5 655:10 678:1 735:14,26

746:9,14

gland 45:19 46:4,4 167:9 169:11 178:1 180:20

552:7 556:20

glands 45:14 46:3 169:12,15 207:21 376:10

glandular 163:25 187:8

glass 174:5 179:11 189:3

glasses 747:10

glean 27:19 48:18 49:2 245:23

glimpse 742:2

GLM 479:6,9 484:3

global 135:15 136:8 465:1 537:12,14 617:14

globally 77:23

glucocorticoids 416:11

GnRH-1 410:17,17

GnRH-2 423:14

go 11:13 12:11,13 14:14 18:6 23:15 28:8 32:5,25

39:8 60:5 61:16 64:23 67:14,15 68:1 70:1 75:19

76:1 84:20 85:17 86:4 89:17 90:7 92:1 93:14 96:8

99:8,11 102:11 105:11,25 110:20 112:22 113:20

114:24,24 115:4 116:8 137:18 140:13 142:13

143:6 148:10 150:7 151:6 153:14 154:26 155:9

157:8,22,26 162:11,23 163:2,3 168:7 181:11

183:23 184:1 188:10,13 189:17 194:17 196:19

203:11 206:10 222:2 227:16 229:21 232:12

235:11,14 241:22,26 244:20 247:2,7 251:17

254:7,13 255:18 256:11 260:2,14 263:15 267:16

271:20,25 276:9 283:20 284:18 285:14 296:21

301:6 307:8 309:19 322:4 325:26 327:18 331:3

333:17,21 336:22 337:2,22 343:2 345:21 346:21

346:23 353:21 364:16 377:2,3,3 378:4,19 379:3

380:1 381:6 383:5,23 392:18 395:23 401:15

404:5 407:11 410:3 422:22 428:17,20 444:6

452:7,12 459:13 469:6 473:24 475:23 476:7

484:18 487:14 491:18,19 492:8 510:18 514:9

521:17 538:3,4 539:21 543:24 544:5 575:13

576:1 579:6 580:18,24 581:1 588:25 595:19

599:6 601:9 602:16 606:2 617:10 624:6 628:1

632:4 633:6 635:3 639:4 649:3 653:5,6,6,8,14

654:22 655:14 656:10,12 658:19 661:20 664:14

676:14 689:18 696:11 698:15 707:13 713:7 714:3

724:23 727:15 730:13,17 737:8,9 738:13 739:22

741:3 747:24

goal 200:4 287:25 290:12 348:18 349:21 352:3

381:10 439:21 456:13 582:17 586:21 625:25

709:21

goals 20:14 308:16 323:10 700:5

goat 572:4

goes 9:13 41:25 50:19 78:20 83:7 89:9,23 96:20

106:19 110:22 111:6 211:19 222:25 230:10

247:11 251:11 259:1 268:13 298:2 305:13 312:7

312:13 326:17 331:5 340:9,18 342:9 343:13

348:25 363:24 396:5 398:1 409:11 519:17 555:7

555:14 594:18 612:21 620:13 670:22,23 676:4

694:3 720:26 729:1 739:5,12

going 3:26 12:6,11 13:16 22:6,14 23:8,15 24:1

30:12 32:15 33:18,19,19 35:10 37:2,26 38:21

47:3,19 52:7 62:16 66:7 67:15 69:2 70:2 71:21

75:19,22 76:4 78:14 79:18,19 81:5 88:20 89:16

93:5 99:11,12 104:21 105:7 107:7 110:5 115:17

118:6 124:6,19,24 125:3,21,23 129:12,13 140:8

143:20 146:22 151:15,16 152:8 153:6 154:8,22

155:6 158:21 161:6 162:9,26 164:8 170:5 173:17

173:25 174:10,11 178:1,2 182:21,22 183:15,17

184:20,25 188:7,16,19 194:12 197:21 199:18

201:10,25 204:2,24 205:17,21 206:4 208:16

218:16 223:1,14 230:2 231:16 235:10 236:2

238:22 239:23 241:13,21,26 244:20 248:13

251:26 254:16 256:3,9,13,13 260:14 267:4,5,23

268:14 270:19,21 271:8 272:7,18,26 274:9 284:1

284:12,19 285:2,2 286:3,4 288:8,9 290:13 295:4

295:11 296:6 297:1,25 298:13 302:11 303:15,18

304:11 307:4,8 308:16,18 310:6,12 312:8,15,21

312:23 313:15 315:2,5,10,11,13,15,22 316:6

319:14,15 322:1 324:4,20 325:18,26 326:7,7

327:1,7,23,24 328:4,20 329:8,14,18,19,20 330:10

330:15 333:21 334:21,24 336:16 338:7 342:16

346:19 352:19 354:16 355:2,4 357:11,24 360:7

361:5 362:2,5 373:13 374:1,13,22,24,24,26

376:21 384:2,7,13,17 385:25 386:23 387:21

390:26 393:25 397:14 405:20 407:23 410:20

411:5,19 414:18 416:13 422:16 438:6 440:17

442:1,17 456:23 459:10 465:8 466:18,18,19

471:11 474:2 475:22 484:6,8 485:12 490:9

501:24 508:15 511:25 514:7,9 515:7 516:16

522:24 523:16 526:21 544:8,23 548:10,14 556:16

565:6 574:6 575:22 580:18 581:8,15 582:15

585:4 586:1 587:3 588:26 590:1,7,18,19,20,26

591:1,7 592:7,21 595:5,18 596:13 599:6 606:17

614:13 615:23,25 617:8 626:18 628:16 629:14

630:10,16 631:15 632:3,24 639:25 640:1 643:21

645:26 646:11,18 649:20 651:19 652:23 653:6,10

655:2 656:22 657:9,13,19,26 658:7 659:26

676:26 681:10,12 685:23,24 686:4 713:8,9

719:20 721:15 724:7,26 728:10 729:18 730:13

732:17,23 733:7,9,11 735:2,26 736:9,13,20

737:21 738:14 739:15 740:14,20 742:1 745:10

goitrogen 377:15,20 433:8 559:21,22

goitrogenic 553:20
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gold 453:25

gonad 82:18 92:3,24 123:21 141:1,4 142:16,16

143:16,22 145:4,8 146:17 158:4 159:10 160:1

164:8 167:16 170:18 309:4 378:17 536:11,16

556:20 560:17

gonadal 78:11 82:16,17,19 131:1,5,13 142:7

377:11,12,24 416:13,15 423:26 451:6 453:10

487:7 498:22 532:14 536:17 541:13 546:3,13,14

546:16 552:10 559:4 603:15 604:12 605:8 606:16

606:16,20 611:17,22,22

Gonadotropin-releasing 423:14

gonads 91:23 145:21 158:2,16 169:19 175:12,14

273:26 376:10 378:14 410:7 423:12 536:14

612:13

good 2:1 5:3,12 6:19 7:6,22 10:21 12:24 16:17

30:16 32:4 38:23 54:2 55:9 78:9,11,12 86:20 88:1

88:8,16 92:18 96:12,14 99:16,17 105:6,9 114:4,6

121:16 123:7 133:20 134:13 135:7,8 136:21

147:14,14,26 164:2 165:25 172:8 175:11,13

179:11 188:16 189:6 190:1 197:14 202:11,12

203:6 229:13,22 235:14 238:16 241:1,24 243:26

243:26 245:3,13,18 259:13 274:10,17 277:2

284:1,7 304:9 306:16 311:15,18 313:23 314:23

321:1 325:16 328:19 331:24,25 332:2,13 334:25

334:26 335:14 336:23 337:12 343:26 344:18

345:25 349:25 365:19 366:23 367:25 368:11

386:8 392:5 393:6 398:18,21,25 400:5 401:8,10

401:24,24 403:5,16 409:2,17,18 411:25 420:21

421:5 426:7 428:11,13,14,26 458:15 459:1

471:19 475:26 479:3 488:19 489:5 496:26 506:13

509:17 522:1 525:21 527:22 543:2,3 544:22

550:15 553:1,19 554:25 558:24 564:21 581:8

585:5 597:15 615:17 621:1 629:19 630:20 631:6

631:13 632:7 634:15,17 639:13 643:3 644:10

653:7 657:5,26 660:21 664:1,10 673:16 677:26

685:22 690:1 698:12 712:21 729:10 736:4,6

737:5 738:22 739:24 740:1,2,6

goodness 155:2

Googled 401:16

GOP 310:23 657:21 717:3

gotten 574:20 732:24

government 3:8 201:20 731:15,24

Grade 378:14,15,17,19,19

grading 378:9,12,13

grain 129:24

gram 654:21

grant 728:26 731:10 737:4

Granted 166:16

granulomatous 610:5

graphic 51:5

graphs 481:3

grasping 629:12

grass 244:22 620:3

gravid 224:11

great 56:19 129:22 134:8 140:13 148:4 154:5

219:21 235:3 244:2 265:26 304:21 309:16 322:3

343:14,17 348:25 426:16,20 511:26 560:7 586:2

596:6 628:20 693:13,17 729:16 736:15 745:19

746:8

greater 71:19 86:9 87:7,24 88:7,8,12 97:8,26

101:23 102:7 105:20 106:7,12 214:11,14,14

216:10 218:12,21,23,24 226:3 235:21,22 264:12

286:22 311:26 312:3 318:25 349:1 359:4 366:17

415:17 436:19 437:3 439:13 445:15 454:26 464:3

483:1 500:26 501:2 504:22 506:22 526:20 561:26

620:18 687:21 706:12 708:16 711:5

greatest 352:1 709:22

greatly 363:21 377:25 382:24 402:16 415:3,19

646:9 705:25

green 51:25 55:2 89:20 163:3 166:5 481:20 578:18

Greetings 664:1

gross 45:15 55:7 620:12

grounds 620:7

group 28:25 44:2 45:9 116:3 125:7 148:24 149:1,3

174:14 197:1 211:12 241:15 250:26 251:13

252:23 263:7,7 284:3 285:5 347:20 349:10,19

350:7 363:23 374:1 379:20 380:20 387:25,26

389:13 392:12 394:18 395:21 396:7,23 397:9,10

406:20 432:11 433:12,21 435:4,19 436:8 472:10

476:19 484:1 499:16 505:20 506:10 545:25

575:24 629:10 630:21 646:23 648:13 683:22

687:20 702:26 727:11 736:23 737:9 746:22,23

groups 50:5 63:10 66:25 67:11 148:25 251:8,12,15

264:4 277:9,22 321:4,5 351:2 352:18 388:1,2

392:11,15 395:1,1 396:7,10,15 416:23 418:11

463:23 589:5 629:15,21 648:19 660:26

group's 372:17

grow 46:24 80:12 109:11 212:14 213:16 262:17

growing 88:3 547:15

grown 220:21

growth 26:13 27:1 42:9 49:25 55:9 69:10 78:10

81:15,20,24 82:26 83:8 84:18 87:25,26 90:20

91:23 92:25 99:20 100:4 101:10,11,26 102:1

103:21 108:15,20 109:25 110:1 113:25 116:7,10

116:20 124:14,17,19 125:3,5,8 132:2 154:13

159:21,22 161:9 163:6,8 165:17,21 168:24

170:10,22 172:8,22 186:24 196:13,24 197:18,20

197:20,22 209:5 211:10 212:23 221:23,25 226:7

228:8,18,20 236:7 255:7,16 259:2,6,9,10,18

260:11,13 261:9,12,20 262:13 263:2,8 278:1
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313:5 316:13 318:3 325:14 329:16 334:15 338:8

342:18 364:19 431:11 445:1 453:18 462:9 501:5

536:8 541:13,21 542:20 544:24 546:10 553:4

556:19,23,25 558:12,26 612:2 616:14,21 618:7

618:11,18 620:8 621:9 624:18 631:6 637:21

638:12,19,25 639:2 640:7 643:4,15 645:9 667:5

683:3,5 687:10 700:18 701:2,11,11,18,20,21

706:8 717:8 721:19 722:9 732:14

guarantee 388:8 389:20 656:6

guaranteed 367:15

guard 388:12,13 391:20

guess 31:16 32:3 57:21 69:5 96:21 111:14 112:6

114:16 117:13 122:3 125:15 126:1,6,7 130:7

131:3 134:24 139:22 151:6,10 162:4 164:2,26

166:19 175:6 196:14,15 197:4,10 198:3 216:10

228:10,18 231:7 232:1 244:14 245:6,25 248:13

251:15 253:26 254:10,17 255:19 261:18 270:13

271:2 335:24 336:12 337:2 342:3,6 361:13 363:3

368:22 371:7 384:1,19 387:18 388:16 389:2,17

391:12,23 392:7,12 393:12,25 395:11 398:11

400:5,18 412:8,21,26 419:13 429:12 457:12

464:10 472:4 474:11 476:14 488:24 489:2,4

502:25 510:15 518:13 521:20 544:6 564:17

575:21 591:25 594:6 597:4 600:18 610:2 660:3

673:2 683:16,21 686:5 691:9 694:19 696:9

698:21 718:12 732:19

guessing 595:16

guidance 20:19,20 83:13,14,17,21 84:13,14 112:26

114:10 118:6 119:22 136:23,24 139:24 166:18

173:13 175:25 194:6 246:24 265:16 286:7 287:4

295:11 298:14 300:25,26 301:1,25 315:7 352:17

369:25 370:1 414:9 419:11 422:23 426:19 466:15

493:11 501:10 518:15,18 522:2,6 526:25 566:5

566:22 570:15 571:19 601:21 625:19 642:25

670:24 671:1,4 673:15,17 674:8 697:5 724:10

731:7 738:21 744:2

guidances 189:18

guide 15:16 33:20 154:25 369:23 370:22 372:6

434:15 672:16

guideline 28:13 61:20 73:21 122:15 168:11 189:2

199:26 200:10,12 209:7 247:25 251:9,19,20,22

252:4,18,26 274:4 286:23 288:13 294:12 306:2

325:19 330:18,22 331:1,4,9 338:1 353:19,23

362:15 433:10 521:17,18,22 522:7,8 591:4

599:22 647:23 652:3 657:24 670:21 673:6,19

743:10

guidelines 24:26 34:13 40:23 104:14 121:22,24

122:2 140:3 175:1 188:24 194:7,18,20 195:19,21

202:8 251:14,17 313:17 314:1 325:19,22,24,25

330:20,24 331:7,19,19 332:15 342:2 465:11,21

465:22 466:2,2,14 500:25 514:1 516:13 521:25

537:11 557:26 558:3,11 562:16,18 573:6 601:5

605:19 609:22 637:14 638:16,24 640:11 657:21

670:17,24 671:12 672:6 676:22 700:4 711:9

743:8,17 744:6

gulls 424:10

gun 702:5

gung-ho 746:13

guys 116:24 123:12 125:4 139:22 166:23 193:8

201:12 336:25 522:25 582:2,24 586:3 587:8

651:18 656:2 660:4 729:8 739:8

g-force 571:19

H

h 333:22 479:11 480:2 482:16 505:2 506:19

habitats 708:14

half 144:23,23,24 156:22 172:17 310:9 311:11

530:20 543:22 572:23 580:25

half-a-page 587:7

half-balloon 463:10

half-length 506:22,23

half-life 238:26

hallmark 88:16 274:18

hallmarks 158:1

hallway 458:17

hammer 193:18

hand 147:15 184:24 275:21 374:25 379:19 413:7

589:21 590:2 718:22 736:14

handle 25:18 83:22 176:23 226:5 479:4 494:3

579:1 676:24 681:2 697:18

handled 65:21 578:11,17 579:2 707:10

handling 160:22 165:16 224:19 416:19 523:7

hands 172:10 179:1 184:5 190:23,24 198:5,10

372:8 428:12 497:2

handwritten 205:15

hang 657:12

hanging 239:13

happen 13:9 96:17 173:22 207:8 246:5 257:22

314:3 326:17,18 327:17 337:1 581:23 657:9

658:3 731:21 742:21

happened 26:4 122:5 126:3,4 142:9 161:5 183:24

259:20 286:11 289:23 335:4 355:16 514:4 549:11

692:14

happening 32:21 190:8 197:16 586:5 643:10

720:19

happens 24:17 84:17 86:13,14 250:26 340:5 579:5

happenstance 258:17

happy 227:14 734:8

hard 2:16 10:25 29:16 86:18 117:21 181:25 186:26

204:16 216:12 227:20 301:14 303:25 307:26
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745:16,18 747:20 748:6

harder 117:15 303:13 369:2 409:10 489:25

hardness 501:9 515:20 558:9

hardy 77:14

hard-hitting 168:21

harmonization 250:18 620:26

harmonize 378:11

harmonized 77:22

harpacticoid 210:8 617:21 621:25 640:23 711:15

713:8

harping 174:10

harvest 224:11

hatch 78:20 87:22 108:3,11,19,22 109:8,24,26

110:1 222:26 224:12 304:24 351:8 393:9 424:23

456:2 528:25 531:6 534:3

hatchability 45:5 415:11 423:8

hatched 44:6,14 46:23 47:1 108:13 109:26 288:22

hatcher 394:10

hatchers 391:8,8

hatching 45:5 74:10 82:25 275:18,22 394:9 453:18

456:10 462:9 481:15,16 535:4

hatchlings 53:18 57:15 64:12,13 109:9 150:11,12

425:13

hate 178:22 371:7

hatter 467:8

haves 441:10

hazard 5:6 34:23 242:3 347:12 349:16 443:12

545:17 616:24 624:10 664:18 705:25

Hazardous 5:5

HCDRT 315:16

head 133:20 689:21

headed 13:16

heads 120:4

health 2:22 5:6 6:2 7:15 15:4,11 16:15,17 18:12

21:21 24:21 29:3 43:9 45:7 46:9 160:7 186:1,1

234:11 242:2 501:3 516:6,8 527:3 610:4 664:17

684:24

healthy 213:6 224:12 691:6 693:3

hear 11:1 25:11 26:16 27:3 29:20 38:21 75:9

126:19 186:18 189:11 274:25 305:20 322:11

331:24 444:5 628:12 632:15 685:7,8 727:11

728:16 729:15,23,24 743:7 746:7

heard 36:4 155:20 243:21 283:19,19,19,20 298:11

299:6 311:4,5 316:9 328:2 348:15 369:6 414:21

578:22 582:23 608:26 636:12 642:7 645:4 651:25

672:14,15 673:4 679:7 685:21 695:14 714:3

717:4 729:12 732:12 744:8

hearing 12:17 38:19 233:5 284:12 323:24 337:21

382:15 544:3 634:22 645:7 729:7

heart 729:4

heavier 680:25 719:25

heavily 92:8

heavy 505:14 729:4

heck 404:20 653:10

Hederaltrophic 113:25

heed 332:19

height 562:6

held 167:14 743:26

Hello 7:18 75:10 305:16 577:5 615:21

help 11:17 13:5 14:26 17:15 19:16 20:20 21:8,21

33:14,20 34:24 114:14 126:26 147:10 188:16

199:26 271:12 276:22 313:8 325:2 328:7,20

338:3 356:2 371:3,21 415:3 417:5 434:13,26

464:7 465:15 466:10 472:16 473:15 476:24

487:11 491:22 511:22 521:24 544:1 554:3 555:17

559:25 582:2 597:4 609:6 623:26 626:4 641:23

642:11 645:8,12 646:13,14 660:5 661:2 671:13

709:14 712:13 717:8 725:8 728:12 733:26 734:10

734:18,24 735:11 736:2,9 744:6

helped 16:11 727:11 734:11

helpful 39:2 49:3 114:14,14 244:15 253:20 295:1

335:25 338:6 352:16 357:8 358:10 372:7 382:24

383:3 394:20 441:12,16 458:8 476:20 540:21

541:25 566:5 569:22 645:26 647:21,23 648:8,20

649:26 650:1,5 653:16 654:1 658:11 661:8 667:1

673:9,21 674:12 679:18,25 685:6 687:17 696:8,9

700:14 703:15 712:11 738:2 741:17,19

helping 200:10 637:18 645:16 684:7 705:10

helps 10:24 15:16 328:24 524:7 577:10

hematoxylin 147:22

hemolysis 571:20,22

hen 52:14 67:4,7 288:10 690:22

hepatic 125:19 455:20 494:22 495:8,23 496:22,24

504:24 525:20

hepatocytes 368:4

hepatotoxicity 455:15 467:2 469:3

hepatotoxin 299:24

hepatoxicant 489:5

heterodimer 644:1

heterogametic 348:16

heterogeneity 479:23

heterogeneous 398:17 676:25

he'll 470:4

HH 479:14

Hi 5:19 30:3 153:13 274:22 404:6

hidden 583:25

high 18:26 19:16 22:8,15,16 54:8 66:5 85:6,26 92:4
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164:14 165:16 167:14 168:18,23 169:7 170:12

171:14 172:4 177:8 192:8 219:18,26 222:17

229:2 236:10 237:18,25 238:2 278:15 282:23

284:18 349:18 354:20 362:18 402:3 416:8 417:7

422:17 431:5 437:21 478:4 497:4 498:23 499:4

538:7 540:5 541:1 550:23 572:21 599:6 637:17

658:9,18

higher 17:12,25 20:15 60:9 84:20 86:7,11,11 87:18

98:13 102:7 105:23,24,26 127:24 185:22,23

215:17 216:3,25 217:12 218:26 224:7 234:23

257:10 335:20 424:15 437:20 448:26 505:9

525:10 561:9 567:6 610:11 669:13 689:21,24

712:2 735:22

higher-tier 306:20

highest 31:22 36:11 50:17 96:7 101:22,23 102:4,7

102:8,13 105:22 106:5 110:17 112:5 216:5

218:11 369:23,24 553:7 561:14 598:24 599:15

603:17 689:9

highlight 18:12 93:22 162:13 263:25 454:23

497:14

highlighted 28:10 55:2 90:2,2 93:20 162:21 218:4

261:13 294:20 346:8 438:12 454:22 501:10

740:15

highlighting 93:26

highlights 18:17 39:4 216:17

highly 54:9,16 146:12 289:20 358:2 399:7 417:3

423:23 424:4,23 454:5 462:5 464:17 473:23

509:21 510:5 567:4 669:4 688:6 714:25

hindsight 59:14 409:10

hints 319:11 482:15

hiring 746:11

histo 144:23 178:23 181:8 189:19 370:26

histological 144:26 158:17 159:6 289:15,18 376:4

377:8 418:7 422:24 467:4 561:25 562:1 604:15

612:12

histologically 81:12 381:2

histology 45:14 83:11,26 139:25 159:1,23 376:19

377:11,12,13,14,17,24 378:6,6 379:15,24 380:6

410:7,10,15 416:26 417:1 447:20 532:15 561:13

histopath 46:5 124:25,26 125:4 136:16,18 143:21

145:20 147:22 179:18 184:26 268:4,13,16 269:8

289:23 290:2 310:21,26 313:19,20 314:2,3 359:4

532:16

histopathological 289:10 290:14 293:25 304:13

339:4 376:7,11,16 381:19 382:1 562:17

histopathologies 611:23

histopathologist 562:19

histopathologists 562:11,24

histopathology 45:22 53:5,9 64:15 69:8 83:14

125:26 136:20 143:10,12,13,18,20 144:4,10,12

145:6,11 146:2 159:26 160:1 161:11 175:10,22

177:19 180:16 188:22 189:15 204:3 267:26

273:22 295:2 308:5 309:11 337:20 359:14 376:13

376:22,23 379:25 380:9 453:10 455:21 467:7

478:14 516:7 529:20 531:10 536:3,22 546:20

551:10,14 553:8 554:3,16 556:24 557:1,5 559:1,3

559:4,5 560:15 561:17,23 562:9 563:1 565:4,4

568:10 578:10 604:22 609:13 744:11

histopathology/pathology 196:12

historical 151:18 252:1 266:10 420:6,10 543:19

579:19 714:8

historically 494:16 591:19

histories 426:25

history 39:9 41:25 79:10 206:12 209:3 222:16

322:10 446:21 448:9 453:5 523:8 620:2 641:19

738:24,25

histotec 83:22

Histotech 45:24

histo-effect 60:21

hit 30:5 548:10 606:19 607:21 689:21 691:13,14

704:15 721:21

hits 34:9 312:20 692:25

hoc 6:15,18 7:8,13,16 13:1

hold 79:14 193:5,14 203:14 283:22 370:20 734:13

holders 284:23

holding 431:19 634:3

holdover 431:17

holds 305:1

holistic 679:4

Holmes 305:15,16,17 336:11 341:1

home 131:23 173:7 381:25

homework 746:13

homogametic 348:15

homogeneity 89:8

homogeneous 478:21,23 578:25 584:2,4

homogenous 402:24

homoscedasticity 403:3

honest 119:18 174:5 176:2

honestly 117:23 122:12 133:21,25 152:19 729:17

honor 738:20

honoring 633:25

hope 14:22 28:8 29:11 104:21 155:1 234:25 315:24

323:3 390:17 408:9 422:17 615:26 632:14 633:17

655:17 735:10,16 736:1 737:1 739:19 740:10

hoped 8:23

hopefully 33:6 39:1 70:7 174:15 196:3 270:11

307:13 309:19 329:24 383:24 385:1 386:2 413:20

485:10 633:6 645:19 712:11 741:19 746:8

hopes 725:14
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horizon 656:10

hormonal 29:8 38:5 40:11 45:18 46:5 52:10 268:4

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hormonalcomedic 40:13

hormonally 25:8

hormone 17:7 45:20 51:23 69:7 157:5 159:9

161:12 168:5 184:10,12,13 185:6 207:6,13

268:14 317:23 318:20 348:17 367:7,7 407:4

418:5 423:15 457:2,10 546:4,13 547:7 551:7

552:2 558:12 561:17 567:26 568:7,13,15,16,18

611:17 618:16 623:2,3 626:1,10 638:3,6,23

645:11 681:24 709:17 720:4

hormones 26:26,26 45:21 207:12,20 261:6 297:26

317:25 318:7,9 322:1 359:17 447:21 568:17

616:12,19,20 618:17,24,25 625:22 639:6 644:3

667:21 720:15

hormone-mediated 638:23

hormone-related 611:14

horse 85:15 623:22 635:9

hotel 154:26

hour 145:17 239:9 276:22 345:22 544:20 653:6,8,9

689:12

hours 173:9 211:17,18,26 307:23 363:5 391:3

525:3 530:8 585:25

house 14:3 229:24 344:2 736:4 745:11,13

housed 13:19 355:26 387:25 392:11 415:15,22

417:20

housekeeping 726:20

housing 418:1

HPA 416:18

HPG 308:22,24 314:12 423:17,19 453:19 495:9

HPG-impacted 462:10

HPT 309:9,13 448:17 457:11

HSRVs 735:26

http 1:9 4:1

huge 67:10 100:5 190:9 284:14 391:18 629:14

733:7

huh 595:26

human 2:22 15:20 16:2,15 18:12 21:21 29:3 181:5

186:1 234:11 486:15 525:3 566:13 569:19,21

616:11 625:16 735:24

Humane 277:19 283:14 332:22

humans 16:18 188:6 317:1 329:5 347:9 443:10

545:14 613:8 614:7 618:2

humidity 391:9,12 415:10 423:7

hundred 33:19 87:21 106:6

hundreds 544:4

hundredth 689:11

husband 283:22 748:8

husbandry 157:3 313:12 314:6 343:26 344:2

368:21 417:23 451:1 453:12 454:9 465:2 466:1

466:13 501:7 511:7 516:1,2 523:6 526:5,8,9

537:25 554:17 558:2,2,5 560:25 562:12,18

666:24 667:1 714:10 715:7

Hydrogenised 399:23,24

hydrolytic 238:26

hyperplasia 92:13,22 169:16 602:21

hyperplasic 191:4

hypertrophy 85:13 92:13,22 274:16 553:20 568:12

602:21

hypotheses 557:18

hypothesis 280:16 356:26 529:24 679:23

hypothesized 533:1

hypothetical 41:21

I

ICCVAM 27:7 444:8 508:8,10 510:19 547:10

662:7 697:15

ICP-MS 174:22 191:2,13

ICR 310:3 312:21 699:16,21

idea 81:22 93:2 138:19 141:25 160:8 192:10 230:7

310:17 324:26 343:26 351:10 361:13 388:8,12

391:20 393:3 399:26 427:26 450:26 458:6 508:13

581:19 583:25 653:11 686:2 688:9 704:25 741:4

ideal 156:21 683:8,8

ideally 155:23 156:1 418:16

ideas 388:7 511:23

identical 79:2,8 245:20 496:16

identifiable 415:4

identification 278:23 623:12 706:5

identified 21:16 35:14 37:10 44:12 45:8 53:13 61:1

65:17 66:24 73:23 77:3,4 156:26 158:19 208:6,13

218:14 228:25 232:22 255:6 268:21 362:19

392:10 397:21 419:24,25 421:10,17 504:14

516:24 541:9,15 542:12 550:17 551:24,25 559:3

565:24 573:15 628:5 637:22 677:18,18 706:7

711:17 726:20

identify 30:21 35:14 37:25 38:3 45:15 54:20 70:10

74:5,22 80:14,23 84:24 85:11 91:12 93:3 140:12

186:4 202:12 219:9 223:21 228:12 334:12 381:10

420:14 459:14 511:12 521:5,22 522:4 546:21

553:13 555:17 563:13 564:4 573:19 583:5 584:21

677:25 683:12 686:18 688:18 706:4 711:18

identifying 15:7 54:10 67:16 77:5 135:7 279:10

329:13 417:4 482:1 573:17 680:14

idiot's 370:22 372:6

ignored 554:11 676:12

ignores 432:13

IGR 683:17

IGRs 244:8 681:22 685:26 705:14 710:22
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illustrate 20:20 562:15

illustrated 529:24

illustrates 509:10

illustrating 318:24

illustration 143:7

illustrative 663:1

image 147:10,17 464:9

images 83:26 158:3 171:16 178:25 179:12,17

181:2 379:6

imagine 143:13,19 191:3 307:26 479:20 515:14

imaging 180:26

immature 557:8 571:4

immaturity 570:26

immense 741:13

immunasotic 147:23

immune 57:5 447:18

immunocytic 378:3 410:17

Immunofluorescence 457:3

immunofunction 369:4,7,11

Immunosorbent 566:12

impact 2:21 22:9 65:24 81:23 142:11 161:12,20

163:7,8 165:22 166:20 186:24 325:11 356:22,23

358:4 360:24 420:14 438:18 525:23 529:14

535:24 542:26 551:20 558:7 559:7 560:13 620:19

667:5 669:7 689:14

impacted 161:9 327:3,4 334:24 423:19 453:19

454:9 668:26

impacting 186:23

impacts 18:14 41:20 139:20 170:16 319:20 356:21

434:17 439:21 456:4 529:16 550:24 552:14,21

568:4 626:15 681:24 711:2

impaired 433:4 449:20 496:21 558:26

impairment 81:24

impairments 495:20

impartially 386:13,14,18 394:4

impinged 220:13

impingement 220:10,17

implement 11:2 204:17 350:9 420:18 480:16

653:19

implementation 76:2 112:22 174:11 205:1 613:10

implemented 90:12 315:10 420:8 490:9 613:26

614:16

implications 480:14

implicitly 274:3

implies 335:20 436:15 483:22

imply 141:22 361:20

importance 11:11 225:25 259:13 260:26 350:7,10

351:16 360:14 362:1 438:17 454:23 476:23

516:14 618:4 619:11,13,22 620:21 623:11 625:8

705:23

important 2:13 9:9,22 10:17 11:5 12:2,12 13:4

26:20 28:14 30:9,25 48:20 50:2 55:20 58:11

69:10 73:10 77:2 81:2,16,19,20 83:1 88:1 92:14

96:6 112:2 113:1 119:24 132:9 134:5 144:20

156:20 159:13 175:19 193:24 200:5,9 206:22,24

207:2,14 219:12 225:26 248:6 259:3 274:13

278:19 286:5,9 287:24 293:7 297:13 298:15

306:26 312:7 315:4 316:11 318:4,7 321:25 326:3

326:6 327:6 328:15 334:6 335:9 338:9 348:3,21

358:8 360:9 365:17,22 366:11 370:4 380:9

381:11 384:23 386:18,25 388:24 389:22 413:9

418:4 419:24,26 421:8,12,16 426:5 432:12,23

435:24 436:4 439:3,5,11,20 444:4 446:26 447:14

457:21 466:6 467:6 478:3,16 498:7 513:23

524:23 525:7 536:18 547:4 554:3,11 555:14,17

558:22,23 559:6,14 562:22,24 563:15,24 573:3,8

573:10 585:8 588:5 592:7 598:1 608:5 618:17,24

619:5,10,15,18,25,26 621:21 629:10 631:4,19

638:24 640:9 643:11 653:18 672:17 682:20,23

688:20 690:9,15 693:26 700:24 701:5 702:6

703:5 705:3,10,15 721:3 739:14 740:12 746:5,17

747:1,15,18

importantly 15:5 30:22 87:19 94:13 283:7 568:3

668:7

imposing 265:10

impractical 42:7 157:22

impression 121:23 610:10 640:22 737:20

improve 28:2 54:22 79:3 237:10 247:10 450:5

500:23 609:17 657:4,5 695:8 704:13 716:18

730:23 741:20

improved 112:8,26 113:1 358:9 511:10 516:20

524:24 534:12 535:7 557:26 735:22

improvement 227:5 246:26 248:4 296:8 305:1

349:8 422:12 508:3 607:17,20 704:9 716:20

improvements 247:22 335:2 364:17 709:8

improves 524:26 695:17

inability 157:4 281:13 438:7 571:4 612:6,11

714:22 715:18

inaccuracies 572:8

inadequacies 706:26

inadequacy 103:20

inadequate 172:26 511:4

inappropriate 183:1

inaudible 65:14 71:8 117:10 123:11 188:6 230:12

341:23 457:2,5,13 578:17 605:12

incentive 195:4

incidence 130:10 530:10

incident 24:17

include 15:21 16:14 29:8 67:5 76:10,13 82:21,22
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375:10 379:10 397:26 413:19 423:11 435:3

437:23 451:11 455:10 461:13 462:21,25 466:22

468:6 482:19 510:23 511:1 516:23 519:13,23

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681:25 682:5,9,22 683:10 704:16 709:9 716:14

726:7 727:25 744:22

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45:20 53:6 58:11 99:3 210:7 214:1 263:17 279:4

288:1,12 294:1 347:21 352:17 355:13 358:14

365:13 373:23 375:19,24 376:23 433:15,16

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485:19 536:7 586:7 594:11 597:22 622:13 625:9

628:15 641:2 667:1 697:20 700:2 714:18 717:16

725:13

includes 21:4 23:23 78:17 82:8 112:15,18 222:11

226:5 286:14 293:8 295:18 297:12 301:25 302:15

302:16 308:25 437:21 501:7 524:10 546:10

611:11 612:5 642:17 670:13 714:11,22 715:4

742:5 743:25

including 58:13 68:26 73:15 218:2 259:11 304:21

305:8 333:1 347:4 352:10,13 358:3 424:7 430:7

443:4,25 444:10,18 453:9,17 462:8 465:1 478:19

505:5 511:5 522:18 545:8 603:16 611:4 616:5

618:2 625:8 627:20 638:2 639:11 649:22,23

702:4 706:14 709:15 713:21 721:19 727:1 728:7

inclusion 25:2 107:20 214:20 371:18 375:21

376:18 430:5 439:2 467:1 478:3 522:16 524:18

529:3 532:16 611:2 614:2 617:12 622:15 625:26

629:3 667:13 708:3 710:17 711:9 713:19 717:7

722:16

inclusive 16:19 19:8 31:11 746:17

incomplete 555:16 670:15

inconsistencies 173:24 174:3,16 282:14 332:1

497:12,17 520:14 668:7

inconsistency 331:18 497:19 520:12 599:18

inconsistent 175:22 190:8 314:6 325:8 326:20

496:2 498:16 500:8 563:15 604:23

incorporate 58:12 297:1 397:7 435:1 447:4 516:21

608:9

incorporated 27:6 184:11 245:21 353:11 390:26

396:3 426:8 468:2 516:15 519:3 527:20 572:18

589:24 608:16 649:22 670:6

incorporates 137:25 323:17 481:23

incorporating 352:5 608:8

incorporation 418:17 517:20 684:19

incorrect 130:26 183:8 595:7 677:11

incorrectly 587:11 676:9 678:13

increase 31:13 47:25,26 51:26 52:2 91:21 92:6

94:10 95:17,18,19,21 96:9 97:2 98:8 108:6

161:17 169:18 170:25 171:6 173:12 192:24

197:17 220:26 262:3 264:19 294:12,13 402:2,9

418:23 433:11 435:12 437:11,26 438:15 439:7,16

457:6 464:14 481:25 482:3,19 530:7,12 531:26

534:1 535:8 540:18 551:17 574:11 583:2,10

584:8 585:12,18 677:12 690:2 693:16 694:19

695:15,16,17 696:11 716:24 717:1 725:14 742:26

increased 94:18 98:16 101:5 106:9 171:5 172:12

196:20 224:19 229:5 258:21 261:19,20 262:5

263:7,8 299:13 300:2 301:25 302:26 368:20,25

417:15 436:22 495:2 530:9 535:2,13 561:13

574:12 584:5 669:10 688:14 695:19 698:9,10

704:23 715:25

increases 48:16 85:11 97:13 111:9 361:13,14

436:26 439:1

increasing 50:15 52:1 112:7 262:11 410:9,25 416:3

417:4 481:25 482:4 533:3 695:3,5,7 696:5 716:23

increasingly 84:21 402:7

incredibly 157:11 175:19 190:8

incremental 12:2,6,13 14:19 701:11,18

incrementally 14:25 634:25

incubated 273:8 350:6 394:8

incubation 271:26 373:21 394:9 415:9 423:7

425:12 431:7 512:26

incubator 121:1 391:13,14,15,16 393:16

incubators 109:7,22 150:8 391:7,8,17

indentified 281:18

independent 2:20 82:1 123:19,21 141:3 371:26

552:2 626:26 627:1 664:4

independently 496:13 583:21 584:25 677:4

index 159:23 366:24 463:25,26

indicate 47:25 54:5 57:26 63:7 83:5 91:26 170:7

235:25 262:24 278:12 282:18 304:22 396:23

402:19 425:7 435:6 438:14 466:2 469:13 506:17

506:20 528:19 553:23 557:1,11 605:19 701:18

718:15

indicated 13:18 14:3 19:19 29:17 34:21 47:5 59:17

62:1 95:26 199:15,18 234:4 246:5 271:6 300:23

369:16 381:18 393:14 439:4 444:15 445:18

446:12 447:15 462:22 475:6 500:5 502:12 504:20

520:10 532:25 558:23 591:8 619:22 636:20

669:12 671:7 699:16 715:1 721:17,22 722:6

indicates 24:2 419:11 435:13 437:11 504:4 568:7

675:24 688:1 698:19 701:1

indicating 303:24 319:23 463:7,8,9,17,18,20

536:25 692:1,2

indication 47:21 52:6,7 60:18 65:16 84:16 164:16
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indications 41:4 44:17 52:9 69:16 262:9 361:10

indicative 45:11 46:7 55:25 56:8 185:17 464:18

509:20 520:13 541:17 542:22 611:23

indicator 16:17 135:4 398:1 509:17

indicators 46:8

indices 308:5 358:5 365:16,20,22,24 377:23 451:7

451:11

indigenous 431:2

indirect 125:18 275:17 448:16 639:2 667:22

705:12

indirectly 194:17 537:5

individual 72:3 90:14 153:19,20 156:15 181:3

182:25 183:1 193:15 211:16 213:6 221:9,10,23

227:25 260:18 261:2 268:11 283:17 290:7,7

311:9 337:20,26 375:25 410:21 464:4,23 469:20

508:14 542:19,21 543:8 546:6 586:20,24 594:8

604:3,19 608:12 617:9 627:24 647:8 676:15

691:8 699:9,14

individually 226:25,25 228:4 392:10 487:23

556:18 564:20 668:1

individuals 50:19 51:8 57:4 58:9 63:3 72:9,10

74:26 154:3 161:3 173:3,5 176:23 183:22 197:2

207:2,14 208:6 212:7 219:22 220:16,19 221:12

221:17,22 222:2 223:10 227:4 228:9,9,21,25

236:11,13 257:11 261:24 262:7 268:9 277:9

309:23 320:9 380:19,20,22 390:1 496:23 503:10

503:26 535:5 547:2 583:15 641:7 661:22 690:9

690:12,21 691:22 745:24

individual's 81:2

individual-based 224:13

induce 96:20

induced 446:23 454:4 554:17 603:14

induction 170:17 173:21 176:1 308:14 496:22

inductive 529:18

indulging 745:26

industry 285:6 310:1,21 319:18 336:5,15 648:21

711:15 720:9 731:15,15

industry's 335:26

ineffective 219:17 675:13

inert 15:24 18:26 19:5,8

inexperience 712:3

inexperienced 200:8 278:16 426:1 605:26

infectious 88:16 610:6

inference 464:13 525:21

inferences 450:1 667:22 691:20

inferior 174:7

inferred 512:1

infiltrate 164:20

infiltrated 425:8

inflammation 610:5,5

inflate 402:16

influence 33:2 68:9 230:17,23 252:22 415:11,19

416:13,15 423:8,15,17 426:14

influenced 288:3 298:20 319:2

influences 228:11

inform 12:1,5 14:26 16:11 17:15 22:24 24:20

30:20 31:7 34:24 307:7 308:12,16 309:11,12

311:13 314:8,9 413:2 467:5 517:19 579:17

736:26 737:3

information 11:1 12:1,5 16:24 17:10 18:4 20:6,7

22:21 23:16 28:14 32:7,12,16,16,17,18,24 33:5

33:14,20 34:11,23,26 35:10,17 39:3,17 43:4

44:21,22 48:18,26 49:2 56:19 58:21 59:17 62:21

63:13 64:4 65:2,12 69:15 70:2 74:24,26 75:3,11

75:15,26 77:21 78:10,11 79:3 84:12 86:2 91:15

95:7 96:14 97:6 98:1,10 103:17 104:15 105:13

106:6 109:16,17 112:24 114:7 118:1 119:19,19

120:14 128:17 137:13 138:4 155:1 194:26 195:3

200:9 218:2 232:8,17 236:15 245:22 248:22

250:19 251:9 257:1,14 269:2 283:5 284:15

301:23 306:21 308:8 310:2 315:11 317:15 319:11

322:16 325:3,11 327:25 330:12 332:23,24 336:2

336:8,19 337:5,9 346:22 347:12 348:20 349:17

350:15 351:2 352:18 353:22 354:1 355:2,4

358:13 367:18 369:20,24 370:1,12 376:18 380:21

381:12 401:17 405:21 412:6,11,11 435:22 437:12

443:13 446:12 465:17 466:26 467:11 484:17

486:18 492:5 494:19 514:2 522:3 526:12 529:15

533:23 543:20,26,26 545:18 555:1 566:1 567:17

568:4 579:21 582:13 587:13 613:21 614:10

616:25 624:5,11 627:6 634:5 635:14 646:10,13

667:17 669:18 670:14,18 671:8 674:2,3 678:26

679:3,23 680:2,15 687:7 694:14 699:1 710:20

719:3 721:3 736:26 737:2 740:22 741:3 742:25

744:20,21,23,24,26 745:4

informative 36:24 436:7 438:6 453:23 700:15,16

741:20 746:9

informed 24:5 33:6 250:22 251:6 446:3 492:6,16

informs 14:7 32:8

infrastructure 79:14 108:25 113:11 152:20,24

ingested 390:24

ingredient 722:1

ingredients 15:24 18:25,26 19:5,8

inherent 152:2 397:25 414:4 417:10 493:5 601:15

662:13 696:26

inherently 16:16 479:7

inherited 178:7 537:25 701:24
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inhibiter 644:7

inhibition 495:23 720:13

inhibitor 134:16,17,21,26 135:1,24 168:4 207:25

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561:11 596:20 597:11 602:17 603:19 683:7 720:3

720:4

inhibitors 85:14 92:20 303:10 495:13,16,26 538:10

644:7

inhibitory 596:20

inhibits 260:20

initial 18:23 42:1 51:16 67:24 155:14 209:4,6

210:7 220:25 284:10 317:2 380:26 389:3 407:10

443:24 456:8 523:4 538:2 599:3,15,16 603:20

617:24 618:21 634:2 689:5 743:22

initially 221:2 251:26 317:15 379:11 422:24 456:6

471:24 494:13 532:11 602:9 686:19,23 687:13

704:23

initiate 212:12,13 213:15,23 716:24

initiated 43:26 158:24 209:9 210:3 283:3 486:9

720:13

initiating 20:16 21:8 24:14 37:10 69:16 233:18

516:8

initiation 420:23

initiatives 57:13

injected 48:24

injection 39:14 42:26 47:8 48:24,26 59:17 61:5,9

61:13,19

injections 157:5

injectors 267:4

inland-based 222:20

inner 292:20

innovation 633:3

input 10:25 12:8 160:22 262:17 332:16 333:5

347:23 350:24 354:16 487:21 733:26 738:21

inputs 709:14

insect 207:6 621:9

insecticides 207:4 335:22

insects 424:24 715:26

insert 698:3

inset 261:13

insight 630:13 653:12 724:7 736:2

insights 11:20,22,25 39:15 438:1

insignificant 693:5,9

insistence 172:15

insofar 77:5

inspection 482:25

instance 79:5 81:26 82:20 83:6 84:2,3 94:2 104:2

142:4 146:9 147:5 167:23 187:2,2 203:1 223:7

299:24 302:26 432:14 447:16 508:26 536:7 537:1

659:25

instances 202:26 235:7,8 237:22 244:16,18 245:5

245:24 246:25 248:10 258:5 268:2 279:23 625:3

Instant 118:4

instill 174:12

Institute 5:21

instruction 658:18

insufficient 220:19 289:16 324:9 376:25 567:17

688:2 699:6 715:14,19

insurance 192:10

integral 376:5

integrate 33:12 329:17 527:7,12 609:1,4 728:12

735:3

integrated 7:24 37:15 38:25 47:9 93:23 154:23

160:15 161:16 165:8 172:20 300:15,22 301:2,17

346:24 370:12 494:11 547:18 638:13 683:5

integration 668:4

integrity 2:26 45:4 46:9 58:23

intelligence 28:12

intend 575:6

intended 21:11 22:5 39:8 44:4 58:25 74:8 75:17

76:12 78:15 79:7,12,21 83:5 99:6 234:25 306:20

503:23 552:13 562:19 636:15 658:19 718:24

intending 113:19

intensities 415:21

intensity 415:13,17,23 418:1

intensive 365:16 534:12

intent 38:2 155:13,14 157:7 231:17

intention 27:11 80:6 84:11 119:4 539:24 725:11,20

inter 129:23 181:13 249:15 429:1 511:17

interact 11:22 12:4 17:6,8,13,25 18:15 19:12 21:9

38:4 207:6 233:16 234:2 622:17 623:2

interacted 38:6

interacting 20:16

interaction 22:22 233:23 401:21 402:1 416:14

474:8 634:7

interactions 37:26 474:7 478:4 622:20 641:22

interacts 20:8 22:12 23:1 36:22

interagency 160:7 193:21

interassay 572:13,20 614:11

interest 3:7 49:9 57:9 66:16 68:15 133:7 252:2,8

252:13,15 285:1 357:13,15 358:1 364:8 371:2,3

412:19 436:24 452:16,18 461:16 462:18 550:3,4

556:7 633:23 634:4 637:2,4 666:8 679:24

interested 18:12 40:3 104:15 130:1 159:14 251:7

252:4,8 257:18 261:4 449:18 646:10 670:12

interesting 4:23 96:2 118:14,16 140:20 161:25

191:11 215:7,13 236:20 259:24 299:17 311:19

333:18 335:19 432:12 627:5 657:16 671:16

679:18 728:19
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interfere 207:25

interfered 136:3

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interferes 207:24

interfering 567:13

intergenerational 438:19

interim 18:7

interlab 505:1 510:2 539:15 582:3 605:10 624:23

650:11 652:8

interlaboratory 488:2 496:12 500:3 503:14 504:15

504:16 507:24 508:11 526:19 537:18 551:21

557:24 558:18 560:5 562:8,18 566:10 604:17

607:14

interlabs 99:15 102:19

intermitted 226:2

intern 584:16

internal 140:2 450:26 451:16 516:22 654:17

international 9:25 210:3 250:18 251:17 252:15

448:5 481:21

internationally 417:25 429:4

internet 493:26

interpret 44:16 68:22 69:3,22 70:5 193:16 198:16

198:21 236:5 292:7 371:2 415:21 417:5 422:18

546:6 555:8 597:5 598:19 622:23 635:22,23

interpretation 33:15,21 70:3,9 71:14,22 73:5 74:4

133:13 141:13 199:2,4 278:14 279:16 281:21

282:17 289:13 291:2 293:23 304:5 305:13 354:1

415:3 628:11 715:9

interpretations 72:6 186:16

interpretative 568:20

interpreted 230:1 272:26 660:22

interpreting 70:13 232:3 642:25 651:15 667:18

interrelated 319:6 637:20 668:1

interrelatedness 288:6 319:4 668:11

interrupt 355:17

intersects 169:22

intersex 81:14 82:20 85:3,4,8 86:25 91:23 92:15

95:6 169:25 170:1 496:23 536:17 541:14,22

640:20

interspersed 385:12

interval 694:2,3,5

intervals 679:22,26 680:1,3,6 693:26 725:16,18

intervention 157:5 185:15 351:6

inter-brood 214:2 255:8 262:5

inter-generational 80:6

inter-individual 567:25

inter-lab 28:5,7 52:12 53:23 55:12 67:25 75:21

93:9 101:21 102:2,3,7,17 103:11,12,13,16 132:17

151:7,12,18,22 200:7 215:6 224:1 279:22 281:6

281:18 364:25 365:8 372:11 409:7,13,16,21,25

412:17 436:1,9 438:8 673:13 678:17 681:20

693:13 702:17 704:15 712:20

inter-laboratory 26:15 39:18 43:15 52:19 59:11

130:9 132:24 155:8,9 156:25 160:5,20 162:1

170:8 174:18 175:16 197:26 199:2,25 200:16,20

202:1 209:24 210:23 226:23 279:7,9,11,17,19

280:9 282:25 298:12 319:26 324:16 376:12 422:9

449:1,10 454:13 668:23 671:20 677:23 691:25

697:25 704:6

intimal 51:15

intralaboratory 508:10 511:18

intra-assay 181:13

intra-lab 365:9 429:1

intra-laboratory 27:22 292:21

intrinsic 715:6

intrinsically 364:9 461:16 556:8 666:9

introduce 4:26 13:14 75:18,22 241:23 242:1 284:5

296:1 573:3 664:15

introduced 281:14,17 282:15 401:8 417:9 443:7

545:11 571:24 583:13,18 616:8

introduces 281:1

introducing 584:26

introduction 141:6 164:23 278:13 284:21

intuitively 173:4 684:8

invalid 182:23

invalidate 265:11

invertebrate 7:12 26:26,26 72:25 206:11,23 208:3

208:7,8 209:8,10,12 212:18 230:18 231:6,14

232:1,4 250:26 251:24 252:11 259:7 314:25

315:7 317:4,6,9,9,16 318:1,10 335:15 336:23

341:14,16 342:12 346:5,12 616:15,19 617:12,17

618:14,20,24,25 619:1,3,6,13,16 620:20 621:3,10

622:25 623:1,3,8,14,24 625:26 626:11,17 628:3

628:22,24,25,26 629:16 630:8,25 636:14,26

638:1,2,5,23 639:6 642:19 645:6,11 667:20,25

681:22 682:5,6,10 684:23 686:10 693:7 694:23

704:17,22,26 705:2,8,9 707:20,24 709:16,17,23

710:5,6,10,13,14,22 712:13 715:1 717:6,7,10

718:2,3 728:6 731:5 740:2,6

invertebrates 23:13 207:11,17,18 208:10,16

225:14 230:11 231:8,19,24 315:9,14 317:4,10,13

317:18,19,23,25 318:3,19 319:25 321:11,25

323:10,14,18 337:4 433:24 616:13 617:13,23

618:2,4,6,7,7,13,16,26 619:8,12,19 620:15,24

622:1,12,24 623:3,10 625:8,12,23 626:1,16

628:16 629:13,17 630:14,22 631:13,16,21 632:20

637:24 643:11 646:6 667:10 668:16 686:8,20

687:12 694:18,20 700:26 701:3,14 708:11,13

720:15 722:14 736:26
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invertebrate-active 258:4

invertebrate-mediated 705:14

invertebrate-relevant 685:11,17

inverts 337:13 729:25

investigate 64:25 210:9 361:1 382:22 423:24

investigated 321:20 609:14 727:2

investigating 367:4

investigation 11:18

investment 492:4

Investopedia 494:2

invited 394:5

inviting 738:19

involution 604:6

involve 557:19

involved 38:24 43:16 81:17 82:23 90:19 136:23

164:7 201:23 217:5 231:3 254:13 327:20 374:13

394:11,15 497:8 521:21 629:15 670:20 694:18

involvement 428:3

involvements 142:17

involves 27:17 181:4

involving 388:17 390:19 519:9 603:26 650:16

672:4

in-depth 12:18

in-house 62:15

in-life 56:21

in-stock 62:16

in-vitro 253:7

iodine 558:10,11 608:4

ion 166:10 399:22,23,24

irregardless 632:5

irrelevant 577:16

irreversible 213:10

IRS 465:4 499:10

isolate 40:7

isolated 82:18

isolation 32:11 726:15

ISR 25:19 93:23,24 141:7 152:15 229:15 287:3,6

288:15 291:5,12 293:6 306:19 311:8 315:20

318:17 319:10 323:12 325:25 347:17 357:22

364:15 376:14 385:3,5 421:13 422:8 430:11

443:17 449:14 450:21 452:24 453:20 461:23

462:3,11 465:22 486:22 493:23,25 494:11,13

496:1 497:23 498:9,26 506:14 507:23 508:22,23

523:4 528:26 534:25 541:15 545:22 548:4,8

550:12 551:4 556:14 559:3 562:13 566:18 579:26

580:11 591:13 595:18 600:2 602:8 603:25 605:16

607:13 617:3,13 618:10,21 619:17,22 624:8

637:11,17,19 666:15 668:9 670:22,23 675:14,21

676:13,18 689:20 704:4 707:25 713:25 714:3

721:18

ISRs 28:9,16,17 316:6 330:4,22 508:9 596:19

741:13,16

issuance 18:24 20:17 634:1

issue 24:18 29:1 116:23 118:17 121:26 171:13

175:25 182:11 203:16 267:16 289:12 300:18

301:3 312:6 320:6 327:26 331:16 350:20 359:1

363:16 364:23,24,25 368:21 380:2 384:4,23

390:13 392:24 395:17 408:15,19,22 415:1,12,14

415:24 416:6 419:21 421:13 422:20 428:18,23

432:17,21 437:4 478:6 481:15 491:11 533:2,4

537:17 541:7 544:10 558:22 566:4 571:5,10

597:26 598:24 623:9,17 625:24 629:12,26 634:10

642:20 648:26 649:4,21 650:4 651:10,11 668:11

671:25 672:19 689:4 696:19 708:24 711:19

728:20 732:1 739:3

issued 15:26 16:10 20:19 276:17 539:16

issues 1:4 2:5,21 4:10 10:4 28:1 30:26 103:21

118:5 163:11 165:19 172:21 241:5 279:8 280:22

289:22 301:12,15 308:24 309:17 313:8 321:2

343:4 348:1 386:6 397:24 400:19 403:26 404:3

406:13,16 436:12 443:23 444:5,15 447:7 468:21

471:5 475:24 476:5 478:3,8 479:26 484:15,19

504:13 514:15 518:16 546:3,20 547:20 558:16

559:2,9 560:9 562:12 571:2 587:10 596:9 604:14

607:20 611:14 612:12 625:5 632:9,10 659:23

664:4 676:24 685:4 688:25,26 689:6 703:12

704:12 706:22 709:2,3 710:24 711:15 720:9

726:2,7 727:3,9,12,15 729:6 733:16 743:21 746:1

issuing 23:17 283:5

item 353:2 381:17 444:8

items 465:9 466:17,21

iterations 49:6 78:25

iterative 116:8

it'll 593:1

i.e 448:16 620:26 653:9

J

J 594:16

JAMES 5:12 70:16,26 71:12 73:12 74:13 134:10

134:14 135:13,19 136:6,14 137:2,6 242:6 459:19

664:21 727:20

January 11:14 17:22 635:18

Japan 26:4 91:3 94:25 95:2,14 96:26 101:2,4,17

105:19 106:11,26 107:11,12 114:18 116:25,26

119:3,3 128:24 160:8 474:14,18 509:2,12 543:18

544:5

Japanese 25:3 26:5,16 29:21 39:3 42:5,10 43:20

45:19 46:17 51:18 53:1,26 54:11,19 55:15 56:15

56:16,18 57:7,10,17,21 58:18 59:7 62:2 65:12

66:11 77:4,15,18,19,26,26 78:7 79:1,15 90:23,26

91:2 93:8,13 94:2,21 95:9 97:22 99:21,24,25
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100:15 115:23 117:22 118:16,22 130:4 145:26

152:10 160:9 161:24 168:8,14 177:9 193:22

277:26 285:26 286:17 287:3 293:13 295:18

347:16 349:24,26 350:1,11,13,16,23 351:5,22,26

352:3,9,12 353:17 355:26 356:18 359:10,13

361:7 362:9 367:25 373:20 376:11,16 377:5

381:23 383:20 386:5 388:17 390:26 393:10

414:13 419:7 422:12 430:10,14,23,26 431:1

432:8 433:3 435:11,17 472:21 489:12 496:13

500:4 509:21 526:13 646:22 647:10 648:16

Jaws 731:13,14 738:14

Jenkins 748:5

Jennifer 7:14,14 243:10,10 274:21,22 275:1,6

450:15 453:1 460:24,24 461:26 469:4 476:22

490:6 502:6 518:21 527:19 532:7 538:22 665:22

665:22 740:13

Jim 5:12 134:8 242:6 459:19 664:21 730:14

jive 131:8

job 9:15 13:13 135:7,8 147:14 241:7 308:24 309:16

335:2 349:25 371:7 386:8 398:25 428:11 468:6

471:19 738:22

jobs 136:21

Joe 748:5

John 6:23,23 242:26,26 314:22,23,24 335:16,17

336:21,26 337:8 338:13 341:11,16 343:24 344:23

345:1 347:20 352:26 354:14,15 360:12 368:17

373:19 374:22 381:9 406:2 409:5,23 411:4 412:8

413:5 419:2 426:12 428:7 434:6 435:19 460:14

460:14 481:20 647:12 656:18 660:3 661:23

747:10

Johnson 6:26,26 8:7 29:21 61:26 62:1 75:9,10,11

104:23,26 105:10,12 114:23 115:8,12,26 117:4

118:8,19 119:12,15,24 120:2,9 121:1,8,15 122:6

122:26 123:3,7,15 124:3,11,16,21,26 125:5,11,25

126:7,20 127:4,8,12,17 128:2,4,11,14,21 129:19

130:6,11,14,16,23 131:2,6 133:1,19 134:12,24

135:17,21 136:10,19 137:5,8,16,21 138:9,15,21

139:5 140:2,25 141:12 143:2,23 144:25 145:16

145:24 146:7 147:7,20,25 148:11,12,19 149:1,2,8

149:12,15,17,19,22,24 150:6,7,9,10,13,14,19,22

151:21 152:3,14 153:2 177:1 179:20,21,26 180:4

180:8 205:10 243:2,2 253:22,23 254:10 270:7,9

270:10,16,17,19,21,25 271:1,2,14,23,25 272:1,3

272:4,9,10,15,24 273:3,18,25 274:25 275:3,11

276:3,15 296:23 303:23 347:22,26 353:1,2,9

360:15,17 361:8,9 369:12,13 381:14,15 383:21

383:23 403:22 405:14 406:6,8 419:8,9,20 429:10

429:11,19,21 434:7,8 440:21,22 442:23 452:3,4,9

457:26 458:1,4 460:16,16 461:7 468:3,4,12,13,19

468:23 469:9,11,12 470:8 471:2,6 477:8,10

484:11,21 491:5,6,9 492:18,20 502:8,10 507:5,7

519:4,6 521:9,11 528:5,6,11,13 532:23,24 533:15

533:17 538:18,19 541:5,6 543:10 577:25 579:18

590:6 591:25 592:12,24 593:2,5,8,13,17 594:3

600:1 665:16,16 674:7 676:7 679:14,15 680:11

690:17 693:23,24 725:1,2,25 735:18

Johnson's 520:21 521:4 594:1 696:3

join 662:21

joint 539:12

joke 383:24

jokes 694:8

Jonckheere 483:7

Jonckheere-Terpstra 126:25 300:6 588:15 600:3

jotted 740:15

Journal 514:20

JOV 514:21

Joyce 748:6

JQTT 279:10 280:10 370:8 421:13 610:12

JT 127:1,9 139:3 291:9

judge 198:4 199:19 261:21 262:18 384:13

judged 34:20 199:16

judgement 706:14

judgment 146:6,7 257:17 262:20 269:13 631:1

647:1,6,24

judgments 269:2 647:26

judicious 716:16

juggling 690:26

July 28:25 744:16 745:8

jump 155:3 184:2 193:1 459:14 626:20

jumped 730:21

jumping 385:4 655:17

jumps 358:19 739:4

June 1:18 459:6 664:6

justifiable 121:17

justification 349:21 350:14 440:13 444:18 517:15

540:26 541:2

justifications 382:26

justified 293:6 309:6 310:8 317:19 358:6 426:16

430:15 433:26 542:1,4

justify 293:20 317:21 318:14 369:23 517:13 555:6

juvenile 163:7 168:21 190:20 191:15 213:9,19

318:20 550:21 567:4,9 604:6 612:4 626:10

638:23 667:21 720:4

juveniles 172:17,23 190:25 567:5

K

K 594:17,23 664:19 727:18

Kansas 7:1 243:3 390:18 460:16 665:16

Kaplan-Meier 587:18

Katie 295:25,26 305:15,16,16 326:24,24 336:11

341:1
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keep 79:19 113:18 119:4 120:13 123:24 134:7

145:15 167:11 265:21 270:2 299:14 303:26

305:12 322:14 327:8 328:23 329:22 330:6,17

347:2 368:26 405:2 414:19 423:4 431:19 444:5

520:5 678:22 712:7 721:10 729:7 730:5 736:9

740:11,16

keeping 77:10 132:10 677:8 695:23 745:20,26

keeps 174:10

Ken 8:5,9 239:22 347:22 353:10 491:7 519:5 520:4

612:19

Ken's 586:18 589:23 606:6

kept 65:23 172:13 220:11 390:20 393:21 469:15

632:22

ketoconazole 209:22 217:17 255:9,21 489:1

720:22 722:2,5

Kevin 130:16,17,17 140:4 747:9

key 10:1 16:13 27:25 49:26 52:22 54:3 194:15

195:13 198:6 204:24 206:3 208:3 233:18 236:6

247:19 306:24 370:2,7 382:4 419:25 504:3

532:26 570:10 621:14 653:17 658:13

keyed 237:10

keys 653:26

kick 105:10

kick-start 78:12

kick-up 215:16

kidney 82:18 145:4 159:23 163:13 165:22 167:7

169:1,3,7 170:11 376:10 554:2,16 556:24 557:1

558:19 559:1 565:4

kidneys 46:3 169:4 273:26

kill 278:18 731:26

killed 167:12

kind 11:16,20 12:5 32:5,9 37:18 39:20 43:18 45:10

45:12 46:10 47:20,21 48:14 51:25 57:14 60:16

63:1 68:22,24 70:10 81:23 87:25 105:17 121:23

122:3 124:6 131:20 132:21 134:26 141:23 152:12

154:20 171:20 173:4 185:8 189:13 194:9 206:6

207:19 209:25 216:17,22 223:8,22 232:12 235:22

236:19 244:20,25 249:15 250:21 251:13 252:1

253:14 260:15 316:4,22,23 320:24 322:4 323:2

330:21 336:6,8 374:6 375:21 381:22 395:24

411:21 412:4,26 413:3 434:22 440:5 468:6

473:19 474:23 488:17 496:8 499:22 500:22 523:1

543:19,21 565:7,8 579:13 589:15 590:10 591:15

599:17,19 601:13 603:21 604:18 608:4,10 612:8

613:26 627:14 630:7 631:2 636:9 646:14 654:17

655:15 660:18 661:2 678:20 679:6 692:21 695:13

720:26 723:16 724:6 725:21 727:23 728:26 729:2

729:3 733:21 739:8 744:25

kinds 32:10 44:25 48:18 61:25 63:14 72:26 73:2

92:26 104:8 113:5 129:1 142:1 208:1 224:19

246:14 257:1 258:3 265:18 267:6 269:4 299:3

338:8 391:5 400:9 474:1 478:19 479:13 491:23

547:22 578:4 612:16 661:4 724:7 728:8 733:18

kinetics 22:15

king 685:20

kit 190:13 573:20 609:16

kits 166:14 566:13 572:8

Klaine 5:19,19 198:10 200:13,14,21,24 201:6,8,14

202:15 229:20,21 231:25 233:2 235:1 242:10,10

315:6 459:23,23 632:2,3 644:20,21 651:14,15

664:25,25 670:4,5 671:10,11,19 684:13,14

693:19,20 702:8,9 707:1,2 711:23,24 714:1,2

718:4 724:12,13 730:10

Klaine's 717:24

Kloas 165:1

knew 59:18 60:3 188:2 401:14 411:18

Knott 8:14,18,19,20 12:22,25 13:18 19:19 742:17

know 9:2 11:8 21:16 30:17 32:26 33:3,5 38:26

40:10 46:24 56:15 65:1 69:1 71:4,6,25 73:5 80:12

80:15 81:18 90:2 92:20 96:13,14,15 99:20 102:22

115:24,26 117:8,24 118:2,6,15,26 119:14 121:18

123:13 130:4 133:22 134:24 135:17 136:10 138:2

138:16 139:16,18 140:8 144:15,22 146:22 147:13

148:23 154:2 165:7,25,26 171:15,21,21 172:21

173:2 174:24 175:18 176:23,24 177:8 178:6,7,12

179:13 184:11,24 190:23 191:19 192:3,7,11,12

192:15 195:20 196:4,23 197:3,4,11 198:4 199:4,9

199:10 200:24 202:17 203:6 204:11,12 206:1,1

206:11 207:26 209:4,18 210:12 215:16 223:21

226:17 230:22 234:26 235:11 236:21 237:9

245:10,16,19,25 246:2,5,12,19,24,26 247:1,4,5,7

247:13,25 248:4 249:2,11,14 250:7,8,23 251:15

251:16 252:6,26 256:10,20,25 257:1,12,19 258:8

258:9 260:17,20 262:7 263:9 264:11,12,14,25

265:3,22,23,26 266:21 267:23 268:8,11 269:12

269:17,24 270:15 274:6 284:9 285:7,14 303:24

310:12 317:20,20 322:1 324:2,13,21,22,22 325:5

327:1,4,11,12,14,22 328:1,12,13 329:19,19,25

332:6 333:2,2,23,25 334:8,8,9,13,16,17,20 335:6

335:10,22,25,26 336:5 340:10,19 342:11 343:17

346:4,19,23 353:25 356:17 359:12,17,21 362:10

362:14 370:26 375:2 386:13,21 388:9,11 390:17

391:17 393:9,10 394:13,20 400:6,7,22 402:5

405:16 406:24 408:17 409:8,10,15,16,19 411:18

411:21 412:2,5,18,24 413:11 427:11 428:10,12

434:25 445:12 451:1 472:3,11 473:3,12,26

474:23 482:21 488:16 489:22,23 491:19 498:14

512:19,22 513:16,26 521:23,25 522:3,6 524:19

540:2 542:2,18 544:6 547:1 563:14 571:11

575:15,15 579:22,23 581:12,25 585:7,8 586:2
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641:24 643:9 647:24 651:19 652:5 653:21 656:5

656:11 657:7,9,10,11,11 658:14,24 659:15

660:16 663:9 672:17,20 673:10,23 676:19 683:20

685:25 686:3,25 688:22 691:13 692:2 694:2,3,4,5

694:9,15,22 695:26,26 701:10 702:21 721:15

723:18,22 729:2,4,8 731:8,19 732:11 735:22

736:6 740:2,12 747:11,25

knowing 251:7 257:24 492:9 565:14,18,19 686:16

730:16

knowledge 172:10 307:18 367:1 404:11,12 456:4

458:7 475:15 511:21 546:12 579:17,20 583:4

626:24 633:4 726:11

known 37:21,23 68:19 162:5 168:1 244:7 299:19

299:23 321:11 335:15 351:18 354:23 369:22

377:15 382:3 388:14 399:17 400:1 402:22 426:13

453:25 494:14 495:26 526:9,16 578:20 583:25

603:22 616:16 621:7,10 645:6 652:5,7 669:9,18

681:22,24 682:5,9,10 685:11,26 700:25 704:17

704:22 705:14 709:16,17,22 710:6,13,22 712:12

715:25 716:17 717:5,6 727:24,25 728:9,10 742:2

knows 154:11 160:19 310:21 475:19

Kullman 7:6,7 137:10 139:8,9 140:6 141:6 142:24

143:5 144:21 184:5,6,7 185:1 186:18 243:6,6

450:17,18 454:21 456:20,21 460:20,20 467:17,18

469:6 477:2,3 490:12,13,23 501:13,14 508:4,6

520:23,24 522:22,23 527:11 532:9,10 538:24,25

542:16,17 662:3,4

Kullman's 519:3

L

lab 93:8 94:26 95:25 100:24 102:6,8,8,23 106:1,2

121:11,12 122:4 150:26 152:21 156:8,13 162:3

162:23 163:7,8,9,9,10 164:12,12 165:18,21,25,26

165:26 166:1 172:13,13,14,14,24 181:23 190:6

197:13 199:6,8,9 203:8 214:19,24 215:8,8,14

222:13 236:26 281:23 282:3 290:6 292:11 293:2

299:13 303:16 311:24,24 312:1,1,2,5 320:10,11

330:25 331:2 343:25 350:17 351:6 352:8 359:22

374:19,26 375:26 390:12,12 395:15,20,21,22

396:5,5,10 414:26 415:1,15 436:5 446:22 471:20

477:22 498:11,12,15 504:6,9 508:20,20 509:12

509:12 512:3,4 516:11 517:1,7 520:19 535:16

551:16 560:23,23,23,24 561:5,7,12 562:13,13,14

567:3,8,10,12 571:26 572:19,22 575:17,18

579:11 580:24 581:3,4 583:9 601:4 609:16

649:24 651:7,10 652:10 654:3,10 655:4,5 656:12

658:3,20 659:3,4,13,16,19 660:10 661:11 677:4

684:17,17,20 693:15 695:26 696:13

labeling 159:15

labor 365:16 525:10 531:8

laboratories 27:13 35:5 43:16 44:20 52:13,15,22

53:8,25 56:5,12 58:20 62:4,4,6,10,12,14,26 65:2

65:20 66:4 67:25 83:20 87:6 102:16 103:5,13

113:5,7,11 116:23 130:25 139:26 147:9 160:11

160:12,14 163:12 164:10,11 165:23 170:7,7,17

170:19 174:6 175:2,4,23 196:18 198:8 200:9,11

202:14 214:18,19 215:23,26 216:20,21 217:5,9

222:20 223:23,24 224:4,7,8,10,17 226:22 227:9

249:16 263:18 266:6 278:16 292:1 299:12 304:11

304:13 310:20 370:19 385:11,13,15,22 386:24

389:4 395:14 412:23 414:8 420:17 432:6 441:16

449:2 465:13,15,19,23 466:8,11,12,16 493:10,24

496:26 497:3,8,22 498:2,16,20,23 499:3,20,20

500:7,11,13,19 503:1,12,12 504:1,22 511:20

512:16 520:12 526:25 534:15,19 539:15 551:14

557:24 558:22 562:23 570:24 572:12 573:12

582:5 601:20 604:5,9,23,25 605:1 608:14 613:16

614:15,24 627:4 652:3 657:20 669:2 671:13

697:4,17,23 698:18,25 700:6 714:9,21,23,24

715:6,11 717:2 733:5,6 743:12

laboratory 25:4 56:23 81:3 94:24,25 102:20

117:20 129:23 131:7 156:2,10 157:3,4,15 166:26

170:16,24 171:15 172:11,20,21 174:7,21 186:10

196:6,20,21 197:22 200:6,15,26 209:26 214:24

216:10 217:10 225:10 227:3,24 237:5,12 258:17

263:21 265:26 375:22 378:11 385:14,16 387:1

414:7 421:11,12 422:3,5 472:16 493:9 496:13

504:19 507:18,19 509:21 520:7 539:18 546:6

573:14,18 601:19 607:8,10 613:26 650:13 651:17

657:13 658:22 660:13 670:25 671:18 673:12

697:3 698:16 703:26 704:1 715:8,21

labs 28:6 62:18 67:11 86:22 102:21 103:2,18,21,24

104:7,13,15,18 114:5,8,17,19 117:26 121:24

136:17,20 151:3 152:22 162:2 163:25 164:12

170:11,13,14 173:14 174:19 179:4,5 182:12

190:13 193:6,15 194:1,2,13 196:11 197:11,12

199:6,7,23 200:8 203:2,19 209:25 223:23 225:12

237:10 249:15 267:1 279:10,20,21,24 280:22,24

281:3,6,8,10,18,25 282:14,25 285:11 290:5 292:2

292:3,4,8,13 293:22 301:3,8 310:15,24,26 313:11

313:24 314:7 320:1 321:1,3 322:26 326:15

358:16,16 368:14 372:9 378:5 379:12,13 394:15

396:8,8,9,12 409:18 412:12,12 413:18 414:10

415:5,14 417:11 418:2,13 419:12 422:11,19

425:22 426:19 427:25,26 428:1,26 473:10,11

474:15 493:12 496:17,19 497:17,25 498:6 499:13

500:4,5 501:5 504:23,26 508:2,20,26 509:6,18,24

509:26 512:3,8 514:26 515:1 516:16 517:23

518:4 520:25 543:23 559:16 560:6,8,14,18,26

561:2,3 562:10,20 566:9,25 571:12 572:1 573:7
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573:16 579:8,12 581:3,7,10 582:10,17,20 583:16

584:25 599:5 601:22 604:1,1,7,13 605:6,26

607:16 610:11 613:23,23 624:22 625:3 650:5,6

650:19,24 651:3,10,12 652:3 654:14,22 655:19

656:21 657:21 658:2,5 659:9,11,24 660:15,18

671:26 676:15 677:26 683:1 693:18 697:6,24

698:2,13,14 704:8,19 705:24 709:6,20 712:4,21

712:26 713:3 726:18 728:1

lab-to-lab 477:14 520:15,17 610:14

lack 156:23 157:2 268:17 279:9 282:24 314:2

344:7 365:3 377:6 422:20 432:15,18 436:3,11,12

465:4 492:22 510:6,7 517:8 526:2,24 559:13

563:14 583:18 584:24 605:18 612:12 623:9

624:22 625:19 640:15 652:13 668:11 671:4

676:21 689:22 690:12 696:17,20 708:25,26

711:17 715:8

lacking 29:9 637:25 682:21 683:18

lactation 23:24

LADGA 551:2

laevis 26:9 156:5,9,9,22,24 157:1,19 171:22,23,25

172:1 191:11 547:22,23 549:1,4,7,9 567:11

570:12 574:7 610:14 611:12

LAGDA 29:22 170:23 172:9 279:22 281:16 305:21

306:19 308:26 309:10 313:2 314:6,13 485:24

544:25 545:4,6,7,22 550:1,12 551:7,19,25 552:1

552:10,15 553:17 556:14 559:9,14,23 566:10,17

567:22 568:23 573:2,12,16 577:3 578:1 580:10

580:11 588:7 593:26 597:17 601:25 607:13,17

610:10 611:1,8 614:21 615:12 727:8

lagging 308:10

laid 14:25 20:12 44:11 50:14 52:14 67:4,5,7,10,12

67:18 81:9,10 157:25 288:9,10 341:4,7 502:14

727:3 728:25

Lake 116:24 117:5,17

land 283:21 620:10,12

landmarks 45:17 146:19,19

language 15:15 17:18 327:7 580:13,14 737:23

large 19:11 22:23 36:22 40:14 65:7 68:4 70:3 92:7

135:9 139:20 152:21 157:13 219:1 225:15 236:4

267:24 278:3 288:6 292:20 293:14 319:4 321:7

322:25 323:1 350:5,9 399:8 432:5 524:6 560:14

567:25 570:22 619:6,12 642:19 655:4 667:26

681:19 694:1 709:6 710:9,24 714:18 716:4 744:4

largely 224:24 444:12 499:21

larger 20:26 21:14 31:11 55:17 72:1 87:11 99:24

100:12 136:8 292:17 620:14

largest 263:11,11 605:5

larvae 171:7 244:22 245:2 556:20,23

larval 26:14 154:13 163:6 168:19,20 213:9,11

278:1 305:19,22 469:15,17 544:24

larynx 167:13

laser 645:18

lastly 14:21 20:4 26:19 91:11 517:14 523:18

609:22 726:22

late 104:24 239:9 276:19 365:11 458:10 721:12

latent 213:12

lateral 146:25

laugh 519:8

laughter 283:26

Laura 241:19 742:23 748:5

laws 3:7

laxed 196:5

lay 194:20,20

laying 14:4,24 46:15,24,25 51:17 64:9 440:13

lays 27:21 350:1

lazy 401:15

LC-50 96:17,19 151:14 689:12

LC-50s 151:8

LC20 690:2

LC50 689:11

LC50s 698:1 702:25

LD50 599:3

lead 10:5 125:23 141:17 142:2 279:15 280:23

315:5 317:11 329:13 347:18,23 348:4 383:21

403:23 414:14 422:14 430:12 438:3 443:18

452:26 461:24 471:3 478:16 493:16 495:19,20

508:5 529:5 531:26 534:23 537:2 545:23 550:13

558:11,15 569:25 577:4 591:14 601:26 602:2

611:9 617:4 637:12 666:16 675:6 681:7,12

697:10 704:10 708:1 713:26 718:21 746:16,20

leader 505:20 742:20

leaders 635:12

leadership 10:4 13:12 742:11

leading 280:5 542:12 614:26 715:23

leads 132:22 138:11 227:8 295:11 433:1 579:15

627:1 684:7 696:17

learn 15:9 28:1,7 155:10 373:11 427:19 583:24

734:9

learned 28:11 78:26 99:25 103:15 112:23 155:9

167:10 173:15 199:25 203:17 206:16 220:3 237:7

381:21 731:2,4 732:7 733:22

learning 195:11 326:17 356:16 360:24 361:5 373:9

427:14 647:3 747:22

leave 323:11 628:12 642:15 726:1

leaves 42:22 145:3 472:4

leaving 663:8 732:19

led 103:22 237:2 470:1 571:25 627:10 698:19

715:21

leery 728:14

Leetown 6:5
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left 2:10 18:11 22:3 46:5 62:11 82:7 105:10 149:10

149:12 158:4,12,24 192:4 210:16 221:17 243:20

271:20 362:16 451:22 459:8 461:4 593:26 623:6

665:26 727:16 740:13

leftover 584:7

left-hand 272:16

leg 729:2 731:11

legally 2:19

legislative 15:13

legitimate 127:18 269:18

legitimately 480:8

legs 731:20

leisure 442:14

lend 126:5

length 74:25 161:10 214:23,24 215:3,24 216:18

228:21 236:15,16 255:20,20 260:14,15 263:16,19

316:15 319:5 320:26 328:21 367:5 463:3,4,16,26

478:26 578:5 584:9,10 668:17 689:17 699:8,9,10

720:4,22 730:14

lengths 259:19 506:10 583:22,23 701:17

lengthy 282:22

Les 161:23 194:11 235:2 243:26 244:21 249:22

253:21 254:22 263:5 266:14 339:12 345:2 346:4

346:8,12 374:18 412:20 431:13 440:10 443:1

461:7,10 493:4 528:17 549:26 556:2 566:8

576:15 601:14 631:3 636:25 640:11 645:24

685:19 719:2 721:2,24 724:20 737:17 742:10,14

747:12,12,20

lesion 377:14

lesions 53:5,6,12,13,16 103:15

Leslie 35:12 36:9 37:8 38:23 59:14 61:8 62:9 63:26

64:6 65:11 66:17 67:2,14,19 68:23 70:24 71:2,24

73:17 74:19 135:23 185:10 189:13 193:10 195:18

201:18 205:26 227:22 228:7,20 229:3,10,14,17

230:9 232:6 235:17 237:3,24 238:7,13 245:8

248:15 249:24 250:4,6,11,25 253:4 254:5 255:5

256:15 259:24 261:16,23 263:14 264:2,8,23

266:18 267:20 268:7,19 269:11 270:24 344:25

346:21 347:1 353:15 357:7,11 361:25 362:7

363:3 364:2 375:17 376:3 383:6 411:14 412:21

414:3 421:22 422:1 429:25 430:3 434:21 435:5

441:4 442:25 443:2 452:13 461:10 470:11 493:3

507:16 521:15 522:14 528:17 533:21 545:5

549:26 556:2 566:8 570:8 576:15 601:14 607:6

610:26 616:3 636:8,25 645:24 647:19 648:8

666:4 673:2 674:10,16 685:5 687:16 695:2,13

696:8,25 703:13,24 707:15,18 713:13,17 718:9

718:14

lesson 381:21

lessons 155:8 173:15 203:17 206:15 220:3 237:7

lethalities 722:12

letter 739:21

letting 284:8 400:19 472:26 725:3,4 732:3

let's 33:4 85:6 86:4 91:13 143:5 148:24 181:5

249:13 271:16,17,18 272:7,17,21 276:9,9,10

298:23 299:25 306:19 331:15 341:12 364:1 374:5

408:8 452:11 476:7 507:10 510:18 512:18 514:18

526:21 541:23 543:7 586:13 593:19 615:18

640:23 654:20 672:13,14 690:12 703:18

level 1:14 17:15 22:9 36:5 38:8 42:25 44:5,8 49:15

50:20,21 52:1 60:26 61:14,22 67:20 68:26 74:4,5

75:4 82:14 86:19 89:24 96:11 97:12 98:1,3,6,8

101:17,18,22,23 102:4,12,14 110:17 116:4 117:2

149:21,22 164:18 173:10,13 185:24,25,26 186:2

201:21 211:25 213:7 214:9 219:1 234:24 246:26

253:6,7,9 257:6 272:18,19 282:23 286:15,19

290:13,15 292:17 293:10 304:1 308:4 340:18

351:20 373:14 375:11 389:4,6,6,10 423:24 457:2

494:12,12 497:4 503:22 506:13 559:8 565:16

574:24 599:15,16 602:8 603:21 605:14 606:21

609:8 612:10 613:2 640:11 642:18 651:9 673:20

679:5,5 687:22 688:4 695:19 699:14 701:23

708:8 712:2 714:12,16 735:26

levels 22:2,25 32:22 36:4 47:5 51:5,23 91:26 101:6

110:9 160:4 169:7,8 175:26 220:12 233:13

237:18 253:5 289:8,8 297:26 311:26 370:3 386:9

388:19 479:1,3 498:24 499:4 500:19 512:14

536:16 546:23,24 567:26 568:16 570:14 585:17

589:5 613:2 634:15 642:17 680:1 688:4,5,14,17

695:20,25

levity 738:21

LH 410:12 423:18

liability 491:1

liaison 2:7

liberal 581:3

library 370:19

lies 525:24

lieu 306:4

life 43:6 79:10 112:16 204:9,10,22 222:26 245:4

246:6 302:15 307:24 330:5 349:2 359:7 370:18

426:25 444:19 446:19 448:9 453:5 456:7,14

523:16 616:16 620:2 641:19 715:2,4

lifecycle 40:6 244:4,19,21 245:20 246:10 247:10,18

248:21 249:9 252:19 307:21 312:9 341:20 453:9

lifecycles 307:21,24 666:19

lifelong 41:13

lifestages 535:20

life-cycle 26:23 42:8 77:9,10 78:2,2 208:23 211:4

212:5,5,12,13 213:21,22 223:9 224:14 227:9

life-cycles 77:11
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life-stage 38:5 58:10 83:3 88:10

life-stages 40:8 41:6,8,16 64:17 76:10

ligand 454:1 667:21

light 70:22 173:13 275:6 415:13,17,21,23 418:1

454:5 499:24 672:11 716:14

lighting 233:8,19 234:19

lightly 24:12 244:5

lights 666:25

liked 493:26 678:20

likelihood 230:17 329:4 347:7 371:10 433:11

443:8 447:10,26 448:12,13,14 470:3 483:17

545:12 610:11 613:6,11 616:9 617:26 658:9,18

690:2 717:1

likes 482:9

likewise 169:20 351:25

limit 3:17 176:21 396:25,25 449:16 596:13,16

612:5

limitation 237:19 299:1 303:21 304:11,15 433:25

559:14

limitations 57:7 201:22 206:18 219:4,11 224:16

227:7 293:13 303:8,14 313:10 322:8,22 430:7,10

432:3 433:9 480:6 522:18,21 525:5,5 611:4,8

713:20,24 714:4,17 716:14,21 718:10

limited 22:13 49:1 84:24 103:18 156:4 168:9 170:9

201:20 286:13,15 289:13 293:20,26 294:7 295:17

295:19 296:19 305:11 313:7 314:16 322:23 377:8

437:2 449:5 465:16 525:8,14,16 531:14 539:14

547:12 568:20

limiter 288:18

limiting 505:1 608:3

limits 176:1 191:12,13 396:26 532:14 560:20

572:26

Linda 748:6

lindane 209:25 210:15,24 214:17 216:21 223:23

224:2,3 226:17 237:13 249:1,5 261:14,16,18

263:15 320:4,16 321:6 682:2 698:9 712:21

line 32:4 52:3 110:10,12,20,25 124:8 126:16

162:15 186:9 239:2 273:7 382:21 384:19 397:11

429:12 634:9

lineage 351:24 389:21

lineages 389:23

linear 72:17 100:4 478:19 480:20 483:8 585:12

lined 320:21

lines 66:23 110:8 193:19 198:11 203:24 217:2

409:10 416:9,9,12 446:5 468:17 503:24 537:4

lingering 210:17

link 230:4,6,7 260:1 263:10 286:16 295:20 296:20

298:26 305:11 329:10 332:10 351:20 426:5,7

533:3 552:19 683:14 684:2 729:21,26

linkage 618:3,21,23 619:19,23 626:16 639:6

linkages 308:3

linked 156:14 262:6 291:25 453:11

linking 629:7 632:19 638:22 729:13

links 329:26 330:8 369:5

lipid 723:26

lipids 720:14

list 17:22,24 18:21,23 45:26 254:23 283:4 284:23

285:5 291:23 321:19 422:22 441:13 444:22 465:8

466:18,19 522:24,25 571:16 580:17 597:2,8,13

597:22 634:2 639:23 643:22 711:8 726:3 743:22

743:23

listed 97:16,22 195:22,25 248:12 259:5 287:26

305:2 330:4 404:1 445:2 485:22 486:2 487:10

596:19 602:17 603:3 640:14,24

listen 736:18

listening 205:14 276:13 440:5 459:7 648:12 659:8

664:6 727:20 732:9 736:17

listing 600:13

liter 94:4,9,15,22,26 95:1,4,6,8,10,11,15,16,23

96:11,26 97:1,7,12,19,19,23 98:1,12,19,21

101:11,13,17,18 102:6,6 105:20,21 106:3,4,7,10

106:12,13,14,21,22,23,24 107:1,6 115:3,4,6

168:18 177:9 217:19,25 535:15,17 551:15,16

560:16 561:5,15

literally 83:22 100:18 116:7,9 142:14 181:21

580:13

literature 27:18 49:13 91:15 94:19 95:13,22 96:12

97:9,16,23 98:20 115:20 136:11 184:8,14 230:20

248:17 275:12 297:24 374:16 375:12,13 404:11

494:15 496:3 567:6,11 569:11,14 575:9 602:20

603:23 613:1

little 8:3 15:22 21:1,23 28:24 39:9 43:2 47:7 50:8

50:13 52:3,18 55:11 56:3 63:19 64:5 69:5 88:21

100:20 105:4 109:1 120:19 126:10 134:7 140:11

141:8 150:4,5 151:19 157:18 165:16 181:4

182:20 186:9,16 187:10,15,24 189:19 193:11,26

194:9 196:8 206:7,10,12,18 210:20 211:8 212:25

213:25 215:21 216:12 217:15 218:26 220:11

222:24 224:24 225:10,19 226:13 229:15 234:26

238:21 245:25 247:12 248:15,16 249:4,19 251:2

253:13 254:20 259:26 262:14 266:23 279:13

281:7 283:26 296:21,23 302:25 303:12 305:24

306:1,13 307:19 309:20 314:10 315:7,23 323:22

328:26 329:8 333:6 335:7 342:20 343:14 351:6

355:18 358:9 362:17 366:25 369:3 373:24 374:1

385:4 386:10 390:3 392:16 396:19 398:6 418:19

422:16 436:1 437:15 440:4 445:14 446:12 456:24

464:10 473:7 479:12 480:25 481:2 485:7 486:6

487:14 488:16,23 494:19 496:3,11 501:26 509:19

511:25 518:18 537:14 542:9 547:8 555:2 582:1
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582:22 591:14 594:5 595:9 597:4 599:21 611:11

635:15,15 636:14 639:4 640:23 649:19 652:12,24

657:14 673:18 677:14 678:19 679:17 691:10

719:15 721:14 728:6,14,25,26 729:2 732:11,19

734:6,11 738:25 739:10

live 16:18 246:9 393:26 664:7

lively 662:23

liver 46:3 53:12 82:14,21 83:4,4,7 84:15,17,21,25

85:15 88:13,15,15 92:26 96:10 106:20 107:1,4,6

125:20 139:19,19 143:22 144:7,7,23 145:8,21

146:23 147:6 159:22,23 163:13 167:7 168:26

169:1 170:11 204:7 274:1 281:24 308:5 368:14

376:10 377:14,23 378:20,21 380:12 489:8 512:11

512:11 537:2 553:7 554:3,16 556:24 557:1

558:26 562:9 563:1 565:3 570:19 573:26 591:12

609:18 726:23

livers 172:19

lives 620:9

loaded 241:10 663:2,3 742:25

loading 183:20

loathe 374:1

LOBBY 1:14

local 558:9

located 4:1 387:9,26

location 388:4 417:21

locations 2:16 227:26 387:10 388:5 391:15 393:20

417:24 429:5,6 472:14

LOEC 23:5 30:13 31:21 32:2 36:1,2 93:22 94:3,8

94:14,17,26 95:10,14 96:26 97:10,18 98:19 101:4

101:5,8,10,21 102:1 105:19,24 106:2,4,6,10,11

106:14,18,19,20,21,24 107:4,5,10,11,14 112:3,5

138:20,25 151:13 161:17 164:11 215:2 216:2

218:12 239:3 304:19 311:24,26 345:13 449:4,15

449:19,21 482:1 496:25 509:3,16 529:26 535:9

535:14,18 551:15 560:15 561:5 589:14 605:2,4

613:2,14 646:25 647:6 702:21 703:1 719:5,6

LOECs 94:14 101:24,25,25 106:26 107:8 161:15

198:15,16,21 199:1 200:4 215:25 304:23 312:26

319:15 449:26 558:18 560:7,15 561:2 581:7

614:5,12,26 679:25

LOEL 23:5

log 358:18,18,18,20 362:22,23,24 363:7 399:3

400:26 479:9 564:3

logged 63:22,23

logic 337:7 597:13 613:9 686:6,10,11

logical 621:4

logically 13:10 73:19

logistical 227:5

logistically 210:19 222:14 350:8

logistics 742:21

Logit 578:19

log-base 63:24

log-based 64:22

log-bases 65:4

long 51:13 98:21 108:16 123:10 145:16 156:8

178:1 206:1 225:21 278:2 293:8 303:17 312:5,21

316:10 322:9 343:25 354:5 355:22 386:4 401:8

406:24 410:22 415:20 444:24,25 446:21 512:12

547:13,14,17 589:22 600:4,7 611:11 636:21

747:18

longed-lived 424:10

longer 20:24 23:3 25:9 73:26 77:11 94:6 165:12

211:8 262:7 303:18 398:15 424:12 438:19 456:12

456:13 505:14 622:22 747:5

longer-term 634:20

long-lived 57:23

long-term 40:25 41:19 57:14 64:7 308:16 376:6

581:20 606:18 723:24

long-winded 114:21

look 10:17 14:12 20:9,11 21:17 24:24 27:23 28:18

32:8,16,19 36:16,18 37:10,20 39:1 40:10 50:14

51:3 53:24 54:25 61:10 63:20 64:24 71:14 77:6

78:5 85:15 86:2 89:25 96:17 100:20 101:10,26

105:15 115:4 118:10 122:22 127:21,22 134:21

142:11 143:8,10 147:5 148:1 149:24 151:2 152:8

153:16 157:8 159:19,19,22 160:3 162:23 166:23

168:6 170:5 177:7,26 178:20 184:8 188:13

189:19 195:23 196:3,23 204:16 209:9,25 210:25

217:6 230:26 235:10,11 239:2 244:6 250:18

255:18 256:21,22 258:10,26 260:19 273:23,26,26

274:1 285:12 295:5 296:14 297:2,10 308:21

309:19 311:9,11 312:1 316:12,14 317:15 320:16

321:5 322:19 324:21,25 325:4,4,13 327:2,25

328:10 329:10 331:11,21 334:18,20 337:10 340:2

349:13 356:14 365:14 368:23 370:22,23 371:12

373:13 380:6,13 386:17 395:1,2 397:1 403:13

409:15 410:12,16 421:23 434:26 437:17 451:1

468:5,11,12 473:4 488:2 489:1 509:10 512:22

517:11 535:18 569:14 575:9 578:24 582:20

587:18 590:19 594:21,23 596:1 632:6 636:13,17

639:13,18,21 642:3,8 643:22 644:3 645:10 646:1

648:15 649:14 657:22 658:1,2 673:8,16 678:3

685:25 686:24 691:10 693:26 695:5,15 700:25

702:24 712:23 719:14,16,17 720:11 721:7 722:1

723:3,5,18,21,24 724:3 741:19 744:6,11 745:7

looked 37:23 45:23 59:20,25 64:19 128:24 132:2

163:17 192:7 217:2 224:25 255:13 290:5,7

309:10 316:12 321:17 345:2,10 354:3 366:5

367:1 379:9 400:22 411:16,26 474:16 493:25

498:11 540:3 631:18 719:13 722:24
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looking 4:22 9:5 11:19 12:18 21:7 22:13,18 23:6

24:8 27:18,24 28:4,11 32:15,23 34:23,23 35:13

37:18 46:2,6 49:22 50:4 52:13 63:2 71:9 74:10,16

82:19 94:13 98:23 104:6 129:12 131:19 133:7

140:20 142:12,14,22,25 143:3 145:21 146:17

151:8,12,17 152:5,8 163:19 165:24 167:8 178:3

182:4,15 184:12 189:24 192:13 194:26 201:12

204:6,10,13 214:10,14 216:14 217:17 232:7

233:22,23 235:5,6 244:14,26 250:12 252:16

253:2 254:23 255:2,25 258:24 259:16,18 261:17

269:7 275:18 285:12,16 291:18 297:12 300:9

302:11 304:19 306:14 307:1,2 308:22,24 309:16

310:3 311:21 312:4,8 316:2,18,19 319:14 326:2

330:11 332:26 334:13 336:7 337:3,3,18 338:21

343:4,15 344:17 345:6 356:20 359:15,16 360:18

361:17 362:26 368:3 371:14 374:2 378:20 379:16

384:19 394:1 395:16,18 399:13 400:10,21,25

402:11 403:24 405:15 407:1 411:10 419:16

430:16,17 436:17 444:21 457:6 468:7 469:12

503:15 506:7 508:8 512:6 515:6 523:20 525:22

565:12 582:6,7 587:16 599:5 631:12 632:23

634:14 642:4 643:6 644:12 646:6 647:8 650:8,17

662:7 670:11 672:13 690:11,15,17 692:24 693:5

700:20,21 701:9,15,16,22 702:16,18,21 712:20

719:15 720:2 721:11 724:9 732:15 737:11,12,14

looks 16:12 68:2 70:20,22 71:16,18,20 85:26 89:24

115:1,5 189:8,8 224:1 253:25 276:5 306:6 308:23

339:5 365:3 395:25 396:6 409:10 474:19 482:9

504:9 640:7 650:12 652:23 657:26 658:19 678:2

719:17

loops 554:13

lose 111:10,11,13,16 191:19,26,26 192:3,4 312:25

515:4 691:5

losing 182:24 711:1

loss 111:5,9 182:20 183:12 258:20 304:18,21 359:5

527:26 528:1 531:7 532:12 579:15 643:15 669:4

689:2 693:2 711:10

losses 690:3

lost 162:24 172:16 192:5 220:14,16 221:22,24

222:2 268:5 349:8 367:14 376:18 379:22 529:16

536:18 701:8

lot 20:2 22:6,17 23:1,1,2 25:15 27:17 31:21 32:1

33:20 38:25,26 55:6 57:15 59:22 66:24 69:1,26

86:19 91:16 99:25 100:21,22 102:26 110:18

112:23 114:18 151:6,7 154:23 163:16 171:26

185:3 190:9,14,15,21 192:9 195:1 196:13,13

202:25 204:22 220:9 248:17,19,19 249:6,11

254:2,15 267:24 268:16 276:26 285:9,10 286:20

287:12 288:7,11,12,25 289:5,15,17 290:16

292:25 293:15 295:3 297:23 298:20 301:23

302:14 305:21 306:15,16 309:7 310:24 311:1

315:10 316:17,20 320:17 323:16 326:14,17 327:6

334:10 344:3 345:6 354:23 359:7 364:26 366:3

367:21 370:18 379:15 385:25 386:5 392:1 396:7

396:18 398:20 400:24 404:13 405:5,20 409:5,11

410:19 420:17 427:11 440:12 454:22 471:9 472:4

472:15,22 473:4 474:4 484:17 498:1 502:18

521:18 564:24 576:3 585:20 590:12 607:19,25

608:16 613:22 623:14,21 625:4 629:5 630:1,10

631:26 633:25 647:15 649:16 671:8 679:20 680:2

683:19 687:7 691:19 692:15 694:5,6,13 712:6

713:1,4,10 714:6 721:5 728:16,18 730:11,13,19

734:8,9 738:6 740:22 741:23,24 744:1

lotions 168:2

lots 49:10 79:22,22 82:23 102:25 114:10 148:2

369:10 399:15,15 400:9 491:18 572:5 630:4

694:11 741:23

loud 740:10 743:7

low 86:15 88:13 100:10 102:25 119:21 120:13

121:11 128:21,26 129:13 177:9 191:13 217:24

231:13 295:6 303:24 367:11 374:26 416:8 417:7

436:11 437:24 503:22 505:25 506:11 538:7 540:4

541:1 552:21 557:8 571:5 625:3 644:9 689:25

692:18 693:14

lower 22:4,6 31:9 51:16 52:10 70:22 87:5 99:14

101:14 102:2 105:25 106:2,22,24 107:10,11,14

107:15 165:13 169:14 185:23,26 215:11 216:3,25

217:20,25 231:13 246:13 268:5 272:21 275:23,23

297:26 298:1,1,23 304:22 319:23 396:25 415:17

415:21 438:18 448:26 464:5 505:16 525:6 535:14

535:21 556:25 567:9 622:18 669:7 690:20 692:16

722:21

lowered 257:23

lowest 36:2,5,10 75:4 99:13 105:15 106:19,20

127:22 217:20,26 219:8 224:5 226:23 255:23

320:26 561:13,19 605:5 688:10

low-dose 720:11

low-level 720:17

low-observed 70:11

low-volume 708:25

LSE 215:4

LSI 161:10 162:24 163:8 165:18 168:25

luckily 327:15

lumen 170:2

lumped 44:26 45:10

lunch 104:24 343:10 345:21,24 544:17,19,21

591:26

luxury 157:6

lyophilization 572:4

M
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macro 592:2,2 725:14,15

macrophages 164:20

magnified 136:8

magnitude 71:18,26 87:19 177:10 216:11 226:18

237:21 256:21 363:5 520:18

main 48:17 122:1 166:19 235:25 278:21 290:12

317:24 362:7 406:22 688:21 711:21

mainstay 742:9

maintain 120:25 195:7 222:19 226:2 241:7 391:11

446:21 635:2 697:19 727:18

maintained 62:15 358:5 742:13

maintaining 58:16 214:8 223:17 237:25 497:18

722:20

maintains 22:23 62:15

maintenance 518:16 666:24 722:20

major 72:7 117:9 129:24 275:21 288:18 294:21

296:6 321:3,14 343:24 344:8 358:7 366:18 401:9

465:12 497:12 498:3 533:26 587:8 673:25 675:11

679:21 704:12 705:24 706:5,5,9,16 708:23

majority 129:22 155:7 351:7,23,25 445:4 446:26

534:25 628:20 700:23

making 13:9 24:6 38:20 125:21,22 140:15 186:26

199:4 234:5 268:13 269:2 296:6,19 311:13

334:23 372:23 382:17 392:8 402:4 424:18 439:10

481:26 505:11 574:8 614:17 654:1 691:19 709:15

743:8,10 745:5

male 40:13 53:7 65:15,17 66:15,16,19,20 82:10,11

87:8,20 92:25 95:5 97:3 99:23 103:8 109:14

123:16,19,21 124:9 132:4 142:9 158:11,11

163:13 167:7,16 169:18,26 170:1 221:24,25

267:26 270:23 303:3 365:19 373:8 378:15 387:6

389:14 413:10 418:14,17 425:10,21 426:14 464:1

464:5 469:23,26 519:16,17,23 561:8 571:24

572:14 583:23 659:17 723:23

maleness 659:25

males 68:20 80:26 87:13,17 88:5,7 91:20 97:4,14

98:5 99:21 109:13,20 124:8 128:11,19 129:6,8,9

129:14 140:17,22 146:2 163:7 164:4,10,11

167:17 168:24 173:16 223:6 273:13,14 289:7

290:8 303:1 339:3 348:15 372:23 373:8 389:16

413:12,14 425:9,19 451:3 453:25 469:24 480:7

494:21 495:2,14,24 496:22 498:12,24 557:7

560:18 584:6 603:13,15,17 604:6 605:13 640:2

669:5 714:14 723:25 724:2,5

males/females 130:21

male's 92:6

Male-biased 553:2

male-specific 495:8

male/females 53:17

mallard 64:8 340:13 432:24

mammalian 21:18,19 23:19 24:13 33:3 37:12

294:2,4,6 329:7 407:1,2,3,22 408:5 614:7

mammals 40:12 137:3 162:6 348:13

man 747:9

management 241:11 251:12,13 252:23 433:21

742:17

managers 351:6

mandate 14:5 324:14

mandated 2:19 15:14

mandates 15:14

Manibusan 10:9 12:23,24 30:1,18 34:14 36:20

199:12,13 201:15 205:16 233:3 239:21 276:14

277:3 283:19,25 346:4 363:10,11 382:11 441:7,8

461:7 539:9,10 540:7,15 633:16,17 646:2,3

649:11,12 650:9 651:23 660:9 717:22,23 741:25

741:26 745:14 747:19

manifest 40:4 41:9 43:6 48:11 53:9 61:23 125:16

126:15 213:12 552:1

manifestations 69:4

manifests 73:11

manipulate 196:24

manipulated 517:7

manipulation 517:6,12 523:7

manner 269:16 281:11 356:26 374:20 384:6

387:19 422:5 488:4 507:19 603:19 605:2 607:10

651:13 667:25 704:1 719:14 741:1

manual 672:20 736:9

margin 146:21

marine 6:3 222:19,20 619:20 621:23

Marion 5:23,23 61:4 242:12,12 337:16 459:25,25

654:9 657:7 665:1,1

Marion's 338:21

mark 38:9 218:13

marked 50:6 264:6 339:7 408:24

marker 156:14 186:20

markers 123:17 448:8 453:12

market 55:19 573:19

marks 218:15

Martha 2:10 4:26 5:2,4 7:26 8:13 12:21 29:26 34:6

35:24 36:14 38:10,15 59:6 61:3,26 63:15 65:6

66:22 68:13 70:15 75:7 104:23 105:2,6,9 114:22

115:10,13 126:9,13 128:6 130:14 133:15 134:4

137:9 139:8 145:15 148:10 150:23 153:5,10

154:7 176:7 177:3,13 179:1,20 180:13,23 184:4

187:12 189:11 190:3 195:17 196:9 198:9,18

199:12 200:13 201:17 202:19 203:13,22 205:3,23

227:15 229:20 239:8 241:22,24,24 242:1 243:16

253:22 254:21 263:22 266:12 267:9 270:1 273:20

274:21 276:1,12 277:6 283:19 284:1,2 285:22

295:22 305:14 314:21 323:19 333:8,10 337:15
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338:11 341:10 344:21 345:18,25 346:17,25

347:18 352:20,24 353:1,4,7,12 354:12 355:10,17

356:6,11 357:2,9,23 359:19,26 360:3,11,15,21

361:3,8,22 363:10,22 368:8,16 369:12 370:14

372:4,15,15 373:2,17,22 374:9,21 375:8,15 376:1

376:20 380:15 381:8,14 382:5,8 383:4,21 403:15

403:21 405:10,13,25 406:1,4,6,12 407:6,13,16,19

407:24 409:4,22 410:2 411:1,3,7,11 412:20

413:15 414:1,14 418:26 419:4,8,15 421:19,25

422:13 428:5,15 429:10,16,19,22 430:2,12 434:1

434:5,7,10,18 435:2,18 439:24 440:2,21,24 441:1

441:7,18 442:21 443:1,18 450:9,14,17 451:25

452:1,3,5,11,25 456:17,20 457:16,18,26 458:2,9

459:11,12,15 461:4,24 467:13,17,21,23,26 468:3

468:7,16 469:10 470:6,9 471:2 475:22 476:4,7,11

476:13,21 477:2,5,8 484:5,13,24 485:2 487:22

488:5 490:5,12,21 491:2,5,7,10 492:18 493:1,16

501:12,16,19 502:3,5,8 507:1,9,13 508:4 518:11

518:20,23,25 519:1,4 520:20 521:7,13 522:9,22

527:9,14,18,21,23 528:3,5,8,15 529:4 532:2,6,9

532:19,21,23 533:12,19 534:23 538:15,18,21,24

538:26 539:3,5 540:22 541:3 542:6,16 543:11

544:13 545:1,2,23 548:16,20,22,24 549:13,18,24

550:13 554:5,8,14,20 555:19,26 556:15 563:4,7,9

563:11 564:6,26 565:21 566:6,24 568:24,26

569:2,4,7,16,23 570:6,17 573:21,24 574:1,4,15

574:21 575:1 576:13 577:4 589:26 592:10,22

593:2,19,23 595:21 596:3,6 598:11,14 599:23,25

600:25 601:6,12,26 606:2,8,11,13,24 607:4,18

608:22 609:9,24 610:1,7,18,24 611:9 615:1,4,6,8

615:11,17,24,25 617:4 628:10 631:8 632:2,16

633:11,15 636:6,23 637:12 643:1 644:17,20,23

644:26 645:21 646:2,17 647:9,17 648:10 649:8

650:2,21 651:14 652:16 654:8,15 655:1,9,23

656:7,17 657:2,6,15 659:26 660:21 661:15,25

662:3,15 664:9,10,11,16 665:26 666:16 669:21

669:26 670:4,7 671:9 672:8,23,26 674:14 675:6

679:12 681:4,13 682:13 683:15 684:10,13 685:2

687:14 688:23 690:5 693:10,19,22 694:26 696:23

697:10 702:7,11,14 703:7,10,17,21 704:10 707:1

707:3,5,7,12,16,26 711:23,26 712:8,15 713:11,15

713:26 717:12,15,17,19,21 718:7,12,21 723:9,13

724:12,15,18,22 725:23 726:1 727:4,7 741:10

745:14,25

Mary 7:18,18 8:7 10:9,11 12:23,24 30:3,18 32:20

34:14 35:25 36:13,20 137:11,17 138:8,11,17,24

139:7 153:13,13 176:7,8,12,15,20,25 199:12,13

201:15 205:9,16 227:17 228:5,15,23 229:8,12,16

229:19 233:3 239:21 243:12,12 276:12,14,25,25

277:3,6 283:19,25 346:4,12 363:10,11 382:11

397:15 404:4,6 405:12 441:7,8 442:1,8 460:26,26

461:7 484:6 539:9,10 540:7,15,23 548:25 554:21

563:12 569:5 574:5 577:5 591:17,23,23,25 596:8

606:3,4 610:8 615:9 630:19 633:13,16,17 643:22

644:24 646:2,3 649:11,12 650:9 651:23 655:26

656:1 660:9 665:24,24 670:8 671:12,16,21

673:22 675:9 680:10 681:9 687:25 690:7,16

691:14 695:11,21 696:15 703:8 707:8 712:16

717:18,22,23 724:16 725:25 740:20 741:25,26

747:17,19

Maryland 43:10

Mary's 229:25 661:12 691:13 745:23

masculinization 495:10

masked 281:15

mass 48:15 55:18 166:11 418:5

match 48:14 133:18 249:10 300:14 302:7 498:22

499:7 698:13 712:26

matched 248:26 604:12 605:8

matches 128:10 370:1 633:24

matching 129:12 421:7 499:7

mate 373:6

mated 387:11

material 38:26 40:17,19 43:5 70:14 73:9 208:14

209:17 231:23 238:15 246:15 260:4,24 266:3

299:18 301:12 409:1,2 644:14,14 741:24

materials 37:22 69:26 207:23 219:26 220:23

230:22 248:1 266:3,22 300:13 358:17 494:19

515:24 516:21 522:2,7 602:15 652:2 670:18

maternal 25:10 40:20 47:10 58:5 72:1 125:10,16

126:4,15,21,21 446:11,13,18 447:5 448:9 524:20

524:22 529:12,14,15 531:12 536:24 639:11

math 310:10 543:21 716:7

mating 23:24 54:7,8,11,15 55:5 65:8,12,13,22 66:1

66:14 373:6 529:17

matrices 705:4

matrix 182:11,14

Matten 2:1,2 8:4 9:10 13:8,11 29:17 205:9,23

239:16,18 241:1,2 283:16 284:2 342:26 343:2

346:2,3 441:25 442:21 458:12,14 459:1,2,12

544:22 545:2 593:6,10,15 615:21 626:21 633:18

635:11 649:1 662:22,23 664:1,2 742:20 745:18

745:22

Matten's 629:26 632:13

matter 196:7 389:21 580:3,13 732:16

mattered 239:5

matters 343:6,6 732:12,13

maturation 41:3,12 42:10 44:16 45:19 47:4,6

55:23 73:23,26 74:12 155:13 157:23 159:11

214:2 431:7 433:5 536:11

mature 58:9 84:14 213:16 447:6 571:8 620:4
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matured 536:14

matures 25:5 56:16 293:7

maturing 64:14

maturity 156:3,21 530:21

maximize 143:21

maximum 393:15 469:16 563:25 572:26 585:10

MCC 243:12 460:26 665:24

McCarty 6:23,23 242:26,26 347:18,20 352:22

354:15 360:11,12 368:16,17 373:18,19 374:21,22

381:8,9 406:1,2 409:4,5 411:3,4 412:8 413:5

418:26 419:2 426:12 428:6,7 434:5,6 435:18,19

460:14,14 647:11,12 656:17,18 660:2,3 661:21

661:23

McManaman 5:12,13 70:15,16,26 71:12 73:12

74:13 134:10,14 135:13,19 136:6,14 137:2,6

242:6,6 459:19,19 664:21,21 727:20

MD 481:21

mean 36:4,10,17 61:9 68:21 71:20 81:18 82:23

102:18 117:13,26 118:5 119:5,18 122:5,10,23

123:5,13,15 124:17 127:1,2 131:9 132:7,19 137:6

137:17 138:2 140:6 141:9,22 143:2,24 144:22

148:1,6,21,21 160:26 171:7,11 172:16,16,18

176:20 183:2,17 184:23 185:11 187:8 190:20

191:11 192:18,18,20 201:19 204:10 218:24

230:19 232:6,7,11,12 238:19 245:3 246:9,10

247:4 252:19 253:3 254:11 266:16 267:22 269:11

270:3 312:16 325:2,6,16,24 326:6 328:4,21,24

329:9 334:5,7,25 336:13 350:22 374:6,10 379:11

393:11 395:3,3,15 396:26 407:22 411:22 477:20

477:21,22 482:21 483:16,18,19 492:24 493:21

499:12 505:19 506:24 521:16 541:14 549:4

564:19 574:12 579:13 580:12 584:14 587:21,24

587:24 594:10,21,22 606:17 612:14 622:23

649:13 656:2 658:14,25 660:23 671:25 672:4

679:26 680:7 691:8 705:11 706:11,12,12 729:16

meaning 34:20 58:20 65:13 68:6 69:6 87:4 219:20

228:3 298:20 448:23

meaningful 27:14 204:20 334:10 453:3 637:15

means 23:2 48:1,3 90:18 127:13 137:19 153:25

161:3,6 162:14,21 163:25 166:5 183:21 254:11

311:23 312:13 320:25 324:11,13 326:11 327:9

329:12 379:6 390:4 393:10 395:12 404:22 468:16

474:19 494:2 497:14 505:8 551:11 565:23 581:15

582:6 583:4 584:2 587:20 588:21 589:3 592:16

600:19 610:14 690:10 711:10 726:8 730:7 733:7

meant 162:19 233:11,15 234:23,23 386:15 389:8

394:19,20 492:23 555:4 653:4

measurable 102:8 173:16 176:1,4 373:16 555:10

630:14

measure 54:17,22 56:2 63:4 66:5,15 67:3,4 82:14

84:19 87:9 108:19,22 124:17 126:26 132:16

157:3 174:25 180:17,18,19 181:3 188:12 190:5,7

190:19 191:6 204:21 221:5 222:4 228:21 236:11

236:15,18 259:17 260:6,14 273:10,11 288:7,8,9

364:11 365:1 366:26 368:12,13 373:1 379:5

413:4 418:9 422:6 423:2,23 425:16,17 426:1

428:1 431:20 447:21 461:19 473:13 475:4 496:9

496:9 507:20 514:18 520:7 533:7 543:1,2,4

555:14 556:10 567:15 574:18 578:6 607:11

609:19 612:1 626:14 638:25,26 640:2,15 666:11

667:19 699:10 704:2 714:11

measured 45:4 64:16 65:9,15 79:8 80:5 81:1 86:1

87:10,12 89:6,15 96:18 105:16 108:4,5,13,21

120:20 124:20 132:18 158:26 187:16 188:6

219:26 254:26 255:1 260:9 275:5 279:5 289:21

291:15 308:14 310:14 339:17 364:5,6 367:20,20

411:26 416:18,21 418:14 423:10,16,18,20,20

425:5,7,13 426:19 431:11 461:13,14 474:13

478:12,13 497:15 506:17 542:25 551:6 552:15

556:5,5 567:8 574:7 602:22,23 666:6,7 668:12

694:23,24 698:23

measurement 55:25,26 119:11 137:24 180:16

187:8,26 236:22 286:13 295:16 314:9 397:23

408:16 426:21 463:5 465:3 498:6 504:25 566:21

569:21 586:18,19 699:18

measurements 30:14 80:1 119:10 124:15 137:24

159:1,26 161:11,11,12 174:21 175:9 188:13

226:7 308:17 313:21,25 314:8 382:18,26 400:25

418:5,17 441:10,13 449:15,22 456:3 462:19

483:21 504:5,10 514:16 566:23 568:21 569:12

570:16,23 600:14 641:23 645:9 666:20 668:17

673:4

measures 45:20 46:5 56:8,12 58:12 66:20 67:5

69:7 79:22 88:1 89:14 137:23 138:3,12,22,25

153:25 157:8 159:13 173:18 177:21 186:11 221:2

226:4 236:7 260:6 308:25 313:4 338:17 342:17

365:19 412:23 420:24 425:4,15 426:10 428:24

433:4 456:25 475:3 478:20 479:6 480:9 532:17

547:6 551:9 552:8 553:19 555:7 560:26 561:20

567:4 568:23 609:5,14 639:2 643:14 645:10

699:8 701:17 726:24

measuring 65:9 100:15 108:16 142:5,14 154:3

166:9 174:20 184:18 203:5 204:21 259:22 308:19

372:18 416:20,21 498:15 513:24 543:14 566:17

567:24 568:20 570:13 573:10 586:26 609:18

689:3 690:2

meat-market 55:17

mechanical 303:19

mechanism 33:1 69:13 286:16 295:20 348:14

381:11 386:19 426:6 429:8 435:25 439:23 445:7
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454:20 510:9 516:17 565:5 618:15 644:8 716:8

733:11 735:6

mechanisms 23:8 37:23,24 40:3,4 56:25 261:4

293:23 553:5 568:9 575:14 608:20 630:13 643:6

720:1

mechanistic 11:25 32:17 225:23 313:6 328:18,19

328:22 667:17,25 701:23

MED 100:10 116:25 449:3

medaka 25:16,20,21 29:22 75:18,20,24,25 76:16

77:2,3 78:2 80:24 84:3 94:21 103:19,21 107:17

107:22 115:23 116:18 117:8 128:20 151:17

156:13 157:6 270:8 277:26 296:3,3,7,9,10,15,16

296:24 297:6,15 299:8 302:10,13,21 304:26

305:2 308:23,25 314:12 392:16 395:7 441:21

442:3,23 443:3 444:22,23 445:6,11,14 446:20,25

448:5,10 450:12,20 452:6,14 453:5,23 454:4,15

454:24 456:23 457:14 458:19 459:9 461:6,11

462:4 471:15 486:26 487:5 488:18 495:13,16,25

507:25 509:22 511:21 513:7 519:10,10,11,14,16

519:17,23 520:25 526:13 528:22 531:6 533:22,24

536:9 543:18 544:16 546:26 548:4 559:12 592:13

595:8 596:13 602:3 605:24 610:3 612:16 653:22

662:5 724:25 726:1

Medakas 192:15

media 220:21 225:5 238:18 518:17

medial 146:21

median 54:4 68:3,3,5,10 146:25 211:19 221:7,13

221:15 587:22 588:1

medias 743:13

mediated 81:24 159:9 184:10 304:3 338:23 440:11

453:26 455:17 537:3 626:11 638:3,4,6 681:25

mediator 186:21

Medicine 277:19 283:14

medium 485:15

MEDs 488:16

meet 16:3 58:21 122:14 202:4 239:18 327:20

328:14 342:2 364:21 365:23 367:8 397:22 405:1

478:5 588:11 613:10 748:10

meeting 1:2 2:3,4,7,11,13,14,17 3:3,16,26 4:2,5,7,7

4:10,13,15,15,17,20,22,23 5:3 8:9,11 9:1,9,11,14

9:16 11:14,19,24 12:15 13:2,3,5,9,9 29:11 89:12

240:7 241:3,4,26 251:1 252:21,23,24 311:25

348:11 459:3,11 484:7 545:3 626:22,23 633:21

634:9 649:2,20 661:21 663:10 672:15 736:22

737:19,19 743:2 745:9,12,18 746:1,14,15 748:12

meetings 9:3,12 10:24

meets 20:14 122:12 195:8 199:20 365:2 444:17

700:5

melding 744:20

melted 20:6

member 5:9,14,18,21,25 6:15,18 7:8,13,16 251:21

446:2,5,12,17,24 447:15 619:7 668:14 727:14

members 2:12 3:5,15 4:19,19 5:1 8:1 12:10 38:16

38:20 105:7 239:15,19 243:17 270:2 345:26

349:19 353:8 354:16 382:8 404:1 406:13 410:2

411:11 421:20 428:17,19 429:22 434:19 435:2

436:8 438:7 441:1,18 452:6 458:2 468:8 484:14

521:8 528:8 539:5 549:18 555:20 567:21 569:24

569:24 573:9 593:24 596:7 598:9 599:25 606:25

610:19 615:11 617:9 637:16 644:26 649:25

660:24 661:17 662:21 671:10 672:9 686:3 693:22

703:21 734:4 738:21 740:14 741:18 745:8,9

memory 365:4

mention 44:19 80:8 92:14 98:25,25 131:26 145:4

188:18 206:25 310:6 318:5 404:21 406:19 424:6

425:3 431:8 480:18 514:8,24 517:14 575:5

590:11 609:18 641:26 643:19 645:15 671:22

678:10 680:11,12 703:4 725:10 739:13

mentioned 61:4 63:17 65:7 73:25 82:26 84:15

85:20 87:3 90:9 93:11 99:2,6 100:10 113:14

120:18 133:23 152:9 159:25 161:23 189:2 227:18

236:25 237:17,19 264:2 286:6 289:11 290:3

298:6 307:11 321:15 336:26 343:11,21 368:21

372:17 379:7 384:25 414:26 417:2 418:3 420:25

423:12 425:14 426:10,12 427:10,15 428:13

430:16,16 432:17 433:20,26 434:23 437:15 453:5

458:5 465:12 467:11,12 468:17 469:14 471:18

473:21 474:5 492:22 514:12 519:7 523:23 525:18

526:3 527:25 583:3 586:10 587:14 592:15 596:21

602:18 608:21 610:3 627:12 631:3 643:19,23

651:4 667:16 678:25 691:21 695:22 719:26

725:16

mentioning 71:3 397:12

mentions 419:20 684:1

mere 23:22 26:22

merit 426:20 712:10

message 28:8 311:5 349:8 381:25 435:20 743:15

messages 170:4

messenger 184:9 368:13 494:22 570:19 726:7,13

726:23

met 2:9 3:14 89:12 194:21 237:1 241:9 320:7

341:23,24 347:26 349:21 443:23 444:12 445:5

488:9 614:20 654:19 743:9

metaanalysis 396:12

metabolic 60:5,9

metabolically 318:8

metabolism 60:8 260:22 486:13

metabolites 60:5 416:21 510:11 720:14

metabolomic 432:16

metabolomics 371:18 372:3
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metamorphic 551:6 560:24 561:13 567:1 568:1,4

568:5

metamorphosed 213:19

metamorphoses 604:22

metamorphosis 155:19 158:5,8,8 159:5,11 166:4

167:14 175:9 191:12 314:13 550:20 551:24 557:4

561:18 568:21 591:11 596:25 603:18

methimazole 559:22

method 19:2 25:1,15,16,22 26:7,8,22,23,24 27:9,19

39:26 40:1 44:26 54:10,20 55:26 60:18,24 87:14

122:22,24,25,26 123:5 136:2 145:1,2 148:2

155:17 166:18 191:2 195:11,12,13 211:4 222:7

223:12 225:24 227:10 249:17 252:5,25 260:6

347:4,6 348:23,24 366:14,16 383:7,9,12 412:7

414:5,11 421:6 443:4,6 444:9 470:13,15,18 493:6

493:13 503:6,19 504:4,12,14 512:4,5 515:2,8

517:9 534:11 545:10 562:16,21 566:16,16,21

567:15,19 571:12 573:12 576:16,18,21 581:18

587:17 601:16,23 616:5,7 636:13 643:2 662:8

674:17,20,22 681:1 697:1,7,14 743:21

methodologies 42:14 194:7 209:10 211:3 219:6

222:18 232:16 266:1 269:3 521:23 539:21 577:14

580:20 584:13

methodology 11:24 14:16 29:14 39:5,11 41:23

42:3 49:4 54:19 187:24 210:6,21 252:9 353:19

404:14 422:4 435:1 464:24 465:5,6 507:18

547:10 578:2,26 581:21,22 584:24 587:6 589:20

591:26 596:10,14 607:9 676:21 703:26 717:26

methods 11:17,21,21 15:18 16:23 20:1 21:18 23:20

24:9 25:20 27:5 28:21 29:10,12 78:9 113:3,13,22

113:23 114:3 147:7,11 166:16 174:26 188:15,16

195:20 208:26 209:1 282:19 290:21 291:5 300:4

344:2 354:8 357:12,25 369:10 382:18,21 383:15

383:18 384:10,17 398:3,20 403:9,26 404:15

410:5,8 411:5 414:5 434:23 437:15 441:14

452:14 457:24 469:5 470:22,25 474:9 476:3

484:26,26 485:6 491:12 493:6 511:1 512:20,24

514:25 518:15,15 534:8 545:8 550:1 557:25

564:16 569:13 576:25 577:1,7 578:22 579:21

580:9 587:11 590:2 601:16 633:22 637:1 674:26

675:3 677:3 679:10,11,14,20 680:19 681:6,11

688:20 697:1 699:1 709:10 715:20 716:14 717:1

720:18 728:24 743:23 744:11,14

method's 281:12

methoprene 318:18,18 335:16 621:11 704:22

709:16 717:6

methoxychlor 42:5

methylation 372:2

methylene 515:7

metric 115:19 125:1 188:6 203:12 236:23 239:7

metrics 202:24 203:5,9 235:5 237:1 423:7 571:13

623:3 656:16 698:7,9 705:4 714:23 715:24

mg 169:14

mic 10:19

mice 381:21

Michigan 6:21 242:25 460:13 665:15

microarray 514:4

microgram 95:11,23 96:10 97:26 98:8 221:4

micrograms 94:26 95:1,4,6,8,10,15,16 96:26 97:1

97:6,18,19,23 98:19,21 101:11,13,17,18 102:5,6

106:3,4,7,10,12,13,23 115:3,3,6 217:19,25 221:3

224:6,6 535:15,16 551:15,16 560:16 561:4

572:17

microliter 223:13 572:10

micronnutrients 117:2

microns 146:10,24

micronutrients 501:9

microphone 4:25 8:2 38:18 241:21 243:18 272:5

283:18,22,23 346:26,26 347:2 458:16 459:10

488:7 490:22 544:26 615:24 664:9

microphones 8:1

Microplate 224:13

microscopy 640:3 714:14

microwell 211:22 225:2 238:14

mid 151:14 423:15

middle 101:2 192:3 243:23 403:22 461:5 687:5

702:25

midline 146:24

midst 173:26

Mid-Continent 75:13 567:12

Mihaich 284:7,8 323:20,21,24 333:9 334:4 337:24

340:8 342:8,13,19,24 343:1

mild 378:23

mile 620:12

miles 653:5,8,9

milestone 9:22

Miller 115:22 116:2 455:9

milligrams 76:20,20 88:5 98:12 106:24 107:1

168:18 177:9 370:9 506:11,12 561:14

milliliter 506:12

milliliters 506:11

million 52:9 310:10 374:17 693:7,7

millions 285:8,8

Milwaukee 7:5 243:5 460:19 665:19

mind 14:1 15:2 232:1,3 267:13 271:13 299:15

305:13 322:14 328:23 329:22 330:17 342:9

385:16 386:21 387:3 392:20 393:4 397:4 399:4

401:14 405:2 471:23 508:17 514:7 542:18 652:8

666:3 678:22 682:1 683:13,22 728:20 736:5

minds 156:5
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mine 401:9 694:9

mineralization 558:19

miniature 624:2

minimal 44:23 57:18 119:22 322:13 378:23 466:8

622:19 624:2 670:25 687:4

minimally 68:7

minimize 691:2

minimized 531:8

minimizing 534:4

minimum 44:1,4,8 87:25 122:12,14 144:13 359:5

414:20 480:13 514:2 572:23,26

Ministry 26:5 160:9

minnow 119:9 142:4 302:10 445:12 450:13 457:22

527:8,13

minnows 115:22 116:16 157:6 450:12 455:10

minor 161:14 569:17 577:15 705:24 706:5,6,9,12

706:19,23 737:16,16

minus 474:18,18 694:3 706:10

minute 162:9 441:22 444:16 577:20 703:18 711:16

minutes 3:17,21 4:13 10:7 13:14 18:22 24:10 205:5

239:19 276:8,10 285:24 323:3 544:23 593:20

687:1 711:12 742:1

MIQE 514:1

misidentification 224:22

misidentifications 224:19

misidentifying 224:18

mislead 183:7

misleading 436:13

mismatches 128:18 129:17 130:1 487:6 498:24

499:12

mismatching 281:2

misnomer 475:6

misreading 115:6

missed 294:21 321:15 339:8 367:2 429:12 477:11

527:25

missing 105:8 289:15 383:1 478:16 555:1 618:14

659:9 671:8 728:25

mission 14:4 15:3 29:13 731:23 744:3

mistake 133:9

mistaken 65:26

misunderstand 540:6

misunderstanding 160:22 597:19

misunderstood 627:16

misuse 588:3

mitochondria 260:21

mix 150:14 627:25

mixed 226:13 271:11 475:7 604:19 605:10

mixes 626:24

mixing 150:3 392:14,23 393:3

mix-sexed 170:3

MMP 494:17

MMRT 445:21

MMT 78:15,15,22 82:17 85:2,18 90:18 91:17 97:9

97:24 99:26 100:21 103:18 104:22 107:25 108:1

110:8 114:25 116:7 129:11 134:19 135:5 142:13

142:21 279:17 281:5 297:6 299:4,4 300:15

301:22,22,23 304:10 305:9 333:1 444:8,13,18

445:16,21 447:21 449:5,16,20 451:19 452:24

454:18 455:1,3 456:6 464:12,13,21,26 471:1

478:11 479:5 486:8 493:15 495:21 496:15,20

497:10 498:19,20 501:2 505:2,25,26 506:14

507:23 508:3,23 509:23 510:26 511:13 516:20

517:17,21 522:21 524:10,15 525:6,10,16 526:20

527:8,13 528:26 529:7,19 530:3,25 531:15

533:23,26 534:8,26 535:1,15,18,24 537:9,13

541:16 589:25 610:12

MMTs 85:19 93:15 304:20 474:13,14

MMT/MRT 462:1 465:1

MOA 517:19

mode 68:18 83:9,10 133:8 135:4 148:4,8 296:20

297:22 298:25 299:19,22,24 300:3 302:5 303:2

304:6 305:12 307:14,14,18 308:8,18,21 314:8

325:1,16 328:9 329:13 333:22 334:17,22,24

336:11 357:17 376:8 452:20 464:18 486:9 489:9

548:11 550:8 552:6 553:3,14 554:2,4 559:26

565:13,18 566:1 611:21,25 629:9 630:15 637:6

638:4 705:9,16 735:6 744:23

model 33:13 51:26 56:16 57:22 59:20 75:1 115:18

115:21,22,22 116:10,11 164:24 257:15 352:8

359:12 404:18 413:17 418:21 425:5 445:6,9,11

445:20 446:11 447:16 448:5,10 450:21 454:17,21

454:25 455:4,8 456:23 457:1,7 475:7 478:3,5,6

478:19,20 480:20,24 484:3 523:2,3,5,24,26

546:26 553:1 583:26,26 594:7 595:11,20 611:15

612:18 696:18

modeled 594:10 709:26

modeling 226:9 245:12 322:16,18 535:11 642:1,5

642:10 676:3 677:19 680:21 709:11 714:13

models 116:2,4,5,8,9,14,15,17 359:10 370:17

413:17 439:4,8 445:12 454:26 456:26 465:20

480:19 523:6 578:19,19,20 586:7 598:8 611:13

708:19

moderate 177:5 378:24 463:8 490:24 508:24,25

509:5

moderately 56:26

modern 113:6

modes 21:15 23:7 68:15,19 91:18 93:4 135:6,10,12

168:7 302:6,26 303:5,8,13 324:16 329:17 453:16

462:7 464:20 487:11 494:10 548:5 551:1,3 553:9

557:19,21 559:17 560:1 613:11 622:23,23 634:13
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668:8 710:11 722:2 732:21

modification 168:10 356:5 515:15 530:20 535:12

537:24 609:13 624:5

modifications 141:10 349:5 454:8 509:23 530:6

531:3 535:2,25 536:4

modified 58:6 72:18 464:14 480:15 622:4 623:25

641:10

modify 152:11,12 194:6 237:7

modulated 136:3

modulation 307:13 529:14

modulator 318:19

module 89:5,13 90:10,12 137:22,23,25 138:1,3,22

161:7 725:14

modules 89:4 90:10

molecular 6:18,20 7:8,9 21:8 113:3,6 233:18

642:23 667:14 670:3 720:10 734:24

mollusk 619:8 708:13

molt 260:15,15 585:26 719:22,25 720:3,22

molting 207:13 259:8,10,16 260:8 638:26 639:1,5

667:5 701:11

molt-inhibiting 261:6

moment 184:1 295:26 575:12

Monday 251:1

money 224:15 309:7 336:19 375:5 692:10

monitor 225:5 287:20

monitored 454:7

monitoring 51:23 546:10 723:25

monitors 552:10

monodeiodinases 568:16

monopolize 180:25

monotone 482:7

monotonic 30:12 31:1,5 127:19 300:9 449:24

481:17 482:11,15 483:7 585:11 600:4,7 688:6

monotonicity 483:1,4

Monte 110:6 139:4

month 12:12 15:1 19:20 284:10,24 325:20 539:19

575:17 630:12 732:6

months 12:11 56:21 118:3 447:3,7 543:23 593:16

643:25

morass 660:5

morning 2:1 5:3,12 6:19 7:6,22 10:22 12:24 13:13

38:23 154:10 205:19 239:10,11 241:1,24 243:26

243:26 284:7 314:23 347:26 390:21 450:11

451:14 458:19 459:1 468:6 475:26 592:15 646:19

661:18,26 662:16 663:11 664:1,10 725:17

morphological 455:21 546:10 708:25

morphology 45:14 143:16 378:14 433:14 536:16

552:11,16,20 557:12 560:17 561:4

morphology/histopathology 45:13

morphometric 147:4,7,16 377:23 380:13 462:22

462:25 464:4,7 561:24 562:6

mortalities 159:19 364:20

mortality 168:19,20 171:3 307:9 309:23 453:18

462:9 591:9 600:15 601:2 689:2,25 690:18

motility 433:14

motivated 354:18 675:22

motivation 355:8

mount 65:17 146:10 365:18 400:25

mounts 65:15,16

mouth 283:23

move 10:2,7,14,26 12:8,14 13:4 18:4 24:15,18,25

25:22 28:2,9 33:17 34:18 35:11 75:8,8 88:20

91:13 104:21 145:17 153:6 154:11 156:24 157:1

157:7 174:16 182:13 251:23 263:14 277:12

283:23,23 285:2 302:14 311:6 315:21,22 329:24

342:2 363:26 368:10 376:2 382:12 414:2 430:1

435:3 470:10 518:7 533:19 540:17,19 546:22

548:10 549:25 566:7 610:24 620:5 624:25 629:23

633:10 634:3,19 636:23 642:11 660:10,19 666:2

679:6 681:4 687:15,16 696:24 711:21 713:15

729:5,11,12 730:23,24 733:9 735:11,15 736:20

737:10 739:20 742:18 743:5,18

moved 18:16,23 24:3 93:12 98:6 157:19 349:11

578:26 630:19

movement 117:2 228:24 266:8

moves 20:12

movie 731:12,14,18,26 738:14

moving 19:25 33:10 40:17 47:7 57:19 119:8

152:11 161:1 195:4 250:24 253:7 269:20 308:19

310:4 335:1 344:8 347:2 408:21 629:11 652:21

676:24 681:1 695:14 723:16 730:5 740:11

mRNA 82:15 87:12,17,20 96:10 100:14 185:4,4

410:11 453:25 494:22,26 495:9 536:16 609:19

MRT 25:21 98:17 107:24 112:11 116:6 124:13

126:17 129:11 134:19 135:5 141:10 142:13,21

143:7 144:11 275:8 297:9 299:11 300:3,14

301:24 302:8 305:9 333:1 444:13 445:16 447:21

450:4,6,8 454:18 455:1,3,13 456:9 464:16,21

471:1 476:24 490:8 493:15 501:2 508:3 511:1,13

516:20 517:17,21 522:21 524:16,24 525:1,4,6,12

525:13,15 526:20 527:8,13,26 529:9,9 530:3,4,26

531:15,22 534:8,11,17,21 535:1,1,20,23 536:1

537:7,11,13,20 538:11,13 541:16

MRT-test 528:24

MSMS 174:25

MTT 273:21 323:14

MTTT 315:15 316:1,18 637:19 666:21 714:3

715:3 716:13 722:20

muddied 234:26

muddled 359:22
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muddy 325:8

multidisciplinary 28:26

multigen 245:1 296:8,16 297:6

multigeneration 75:18,20,24 104:9 107:18,23

209:6 278:1 296:3 305:2 318:14 338:14 431:11

442:4,23 443:3 458:19 459:9 461:6,11 471:15

529:7 530:5 624:4

multigenerational 18:9 430:14 444:19 446:1

456:13 511:10 529:10 535:25 547:13 577:15

624:25 639:7 641:1 669:3 698:6 709:9,19,26

710:2,4

multigenerations 136:7

multiple 20:24 23:12 41:20 43:25 59:22 70:1 89:16

100:23 107:23 150:26 153:26 167:2 192:15 199:5

199:23 212:21 219:14 245:5 251:19 287:13 291:7

293:1 298:11 307:24 331:23 332:2 370:19 371:14

377:4 382:3 405:11 435:16 437:1 449:6 478:7,13

524:9 619:16 637:20 660:24 668:2 680:5 685:24

692:15 697:23 704:23 709:20 718:19,23,24 719:2

multiples 148:25,26

multiple-generation 318:15

multivariant 679:2

multi-gen 93:6

multi-generation 23:22 25:20 31:6 39:21 40:5

41:10 43:21 47:18 56:20 57:25 58:10 75:14 81:6

multi-generational 58:17

multi-variant 733:18

mutations 129:4

mysid 26:19,19 29:23 205:25 206:5,13,14,20,26

209:1,6 210:24 211:3,5 212:3,5,5 213:17,21

214:18 218:11 219:4 220:3 222:8,11,15 225:8,16

226:1,5,9,9,12,15,18,19,22 227:24 230:8 235:5

236:25 237:18 239:4 243:21 244:5,7 245:18

246:9 248:9,20,21,26 249:13 250:24 251:5,25

252:5,14,15,19 253:8 259:26 260:3 266:15

315:16,21,23,25 316:9,11 318:23,24,26 319:10

321:15,17,21,23 322:6,9,11,15,17,20,23 323:3,16

336:25 337:5 341:14,18,24 342:1 343:21 442:20

616:4 617:3,21 618:9 619:23 620:3 621:8,13,17

624:3,3,14 625:13 626:13 636:24 637:11,16

638:8,10 639:8,15,23,24 640:26 641:2,7,13,14,24

642:2,4,4,9,17,26 664:12 666:4,15,24 667:5,24

669:13 671:22 675:5 681:18,25 687:19 689:20

697:9 699:10,21 700:20 701:3 704:4,9,13 705:13

707:20,21,24,25 708:3,19 709:5,7,7,8,10,13,25

710:2,4,6,18,20,23 712:5,19 713:6,25 718:20

719:2,4

mysids 208:20,22,24,24 209:3 210:11,18,26 217:17

217:19,24 218:1 219:12 220:10,10,13,26 225:9

225:11,17,20,20,25 226:4,25 227:8 228:4 231:10

244:22 246:17,18 247:4,14 248:22 249:5,8 255:1

258:25 259:5,18 260:13 261:26 263:13 266:21

318:12 322:1 341:20 343:25 344:25 442:10 582:3

585:24 643:26 644:5 691:26 696:10,21 709:18

714:26 719:9,10,19

mysis 643:26

M1 510:11

M2 510:11

N

n 254:17 589:4 691:16,20 723:7,8

nail 199:18,26 303:13

nailed 181:22

naive 54:12,13 141:24

name 2:1 5:4,15 6:26 8:20 133:15 213:17 241:1

305:16 314:24 459:1 621:12 660:18 664:2,16

747:5

names 590:1 747:11

name's 284:8

naming 104:3 660:19

nanograms 94:4,9,15,22 97:10,12 105:20,20

106:14,21,21 107:6

nap 577:20

narrate 158:23

narration 154:25

narrow 68:11

narrowed 704:24

NAS 31:3 33:11

national 5:9 6:1 242:5 251:18 381:18 459:18 460:1

664:20 665:3

nations 251:19

native 446:25 447:1,2,13 448:3 549:9,10

natural 417:21 479:9 520:7

naturally 15:19 234:11 476:25

nature 94:12 168:3 215:23 258:3 269:3,16 307:20

329:3 347:7 350:2 371:10 383:14 443:8 447:10

447:26 448:3,6,11 470:20 524:25 545:12 554:12

576:23 585:11 610:13 613:6,10 616:9 617:25

624:18 640:16 641:8,18 674:24 695:25 696:6

705:12 716:3 725:4

nauplii 223:1 224:12

nauplius 223:1

nauseam 443:24

NBP-2 162:3

near 2:12 96:17 156:8 205:19 479:3 735:17 740:14

nearly 111:6,9 181:7

Nebraska 6:25 243:1 460:15

necessarily 30:23 55:25 64:19 71:25 81:17 85:26

92:7 100:17 169:4 173:3 175:7 179:8 221:12

298:8 313:13 325:25 328:21 330:8,16 331:2,9

336:14 489:8 510:1 521:25 524:15 526:23 527:2
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564:3,4 565:26 582:13 606:16 612:14 640:20

648:18 651:4

necessary 12:5 57:16 75:1 114:7 269:5 282:19

294:6 307:5 323:17,18 375:6 391:10 398:15

427:7 439:17 512:15 515:21 525:3 537:11 538:1

553:1 612:3 647:25 657:14 680:8 692:11,24

725:20 734:26 742:25 743:13

necessity 293:21 294:16 444:18 510:20 527:1

necropsy 83:22

need 16:3,25 22:8 23:1,16 24:2 34:24,26 35:17,22

36:6,18 41:23 57:18 72:19 122:9 175:11,24

176:24 177:1 194:5,14 196:5 197:25 199:22

200:2 202:25 203:2 205:5 208:17 211:14 219:9

220:26 222:3 232:1 234:3,15 243:17 244:11

258:7 283:18 296:14 297:1 298:14 302:6 303:6

304:11 305:5,5,8 309:8,18,25 310:11 311:3,12,15

312:21 313:23,26 314:13,14 315:10,11 323:7,7,8

326:5,8,9 327:5,8,22,24 328:12 329:11 331:4,22

332:1,2,5,6,12,12,14,15,16,18 334:2,9,16,17,22

335:11 337:17,22 338:13 342:20 344:2,3 347:6

348:22,23,26 355:4,17 360:9 361:15 367:23

369:25 373:15 386:2 393:1 397:7,18 403:8

404:16 408:13 411:6 413:18 417:14 420:22,26

426:2,18 429:3,8 432:20 437:1 443:5 447:8 465:9

465:22 466:21 467:3 472:18 475:11 480:15 484:7

484:22 489:13,17 490:17,26 499:5 501:9 513:10

519:24 521:16,23 522:4 526:19 527:7,12 545:5

545:10 554:7 567:22 569:25 574:12 582:22

583:11,12 585:4,5,19 588:9 589:16,19 593:24

601:4 606:21 607:22 616:7 617:19 622:13 626:16

629:18 631:22 642:8 647:7 649:7 653:8,13,25

657:8,11,21 658:13 660:14 664:14 668:26 672:15

673:1 685:4 686:15 688:5,10 689:24 693:8

703:12 704:12 709:19 716:9 724:23 726:10,20,24

727:2 737:13 739:1,14 740:8 745:17

needed 35:19 49:10 120:14 201:24 302:8 315:8

322:15 331:8 349:20 351:2 352:18 406:11 411:16

455:26 456:12 457:25 469:5 483:4,24 490:9

492:6,19 509:23 516:14 537:15,18 538:5 562:16

562:18 563:6 577:23 609:25 612:16 632:14

661:24 668:15,18 704:19 738:22

needing 185:14 309:24 432:18 589:13

Needless 744:13

needs 34:17 36:7 113:2 144:22 164:23 203:18

287:4 291:11,24 292:14 300:15,18 301:3 309:26

313:22 314:7 326:12 343:26 350:13 358:19,21

365:7,25 376:23 377:18 388:24,24 414:5 416:4

416:18 422:23 464:22 468:14 472:7 480:13 483:1

493:6 501:3 502:18 522:5 542:25 565:15 571:8

574:20,25 581:23 584:25 586:13 599:19 601:16

612:8,26 614:17 647:1 658:17,22 672:5 697:1

713:2 730:6 739:3

negative 96:12 261:21 292:15 300:20,24 326:25

331:17,21 375:1 399:22 408:6 412:14 413:22,25

483:18 489:6,24,24,25 499:21 547:26 580:12

598:19,20 604:9,13 605:7 606:15 706:18 711:15

729:24

negatives 22:6 317:8 375:2,2,4 547:26 613:25

622:19

negligent 745:17

negligible 322:24

neighborhood 219:2

neither 183:4 282:20 315:17 530:25

neomysis 643:26

nerdy 594:5

nesting 137:20 587:12 589:17

neural 370:7

neuro 373:12

neurobiology 427:20

neuroendocrine 638:15

Neuroscience 7:24

neurotoxin 722:16

never 117:22 120:2,3 154:17,18 155:9 166:23

171:23 227:18 282:2 345:2 359:7 399:21 400:22

402:23 421:13 629:13,21,23 700:12 701:23 711:6

716:12 718:2 747:13

nevertheless 611:22,26 734:17

new 143:7 151:25 152:9 180:26 197:13 201:25

213:15 236:13 279:4 282:22 284:11 292:22

293:26 296:11 305:26 340:18 344:2 356:25 370:8

400:21 456:13 467:22 518:24 532:13 548:21

581:15,20 592:7 636:19 639:21 646:6 682:11

704:24 717:10 745:3

newest 746:11

newly 165:3 301:24

news 403:5 729:10

next-test 579:6

NF 159:26 568:14,21 587:17 604:10

NF62 169:10

nice 30:3 143:15 179:13 181:8 197:15 203:11

270:14 294:23 360:23 365:20 371:25 378:6

431:15,24 451:7,23 477:23 479:21 488:6 489:19

490:2 523:12 525:19 548:4 574:8 582:21 598:3

644:15 677:16 678:6,17,18,19 695:17

nicely 635:26

nicest 736:21

NIES 94:25 101:12 106:3 115:3 449:3 509:2,12

NIES/Japan 101:14

Nieuwkoop 568:6

night 290:5 390:20 443:23 488:9 565:3 577:22
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646:24 679:16 721:12

NIJ 594:18

nine 18:26 85:24 87:10 97:19 100:4 108:14 109:12

109:12 155:19 301:26 530:21 532:17 536:3,8

700:20

nine-week 143:9 144:8

NIST 515:26 656:12

NOAA 27:2 242:14 243:26

nodes 462:26 463:12

NOEC 23:5 60:24,26 74:22 219:9 246:26 311:21

311:22 319:16,22,23 335:19 344:23,24 345:2,5

482:1 647:6 669:13

NOECs 304:22 312:26 319:15 321:6 335:21

345:15 679:24

NOEC/LOEC 262:25

NOEL 23:5 451:12

noise 119:21 219:17 488:6 584:7 586:8 711:5

noisy 229:1

nomenclature 432:23,24,25

nominal 223:22 238:23 572:16

non 378:21,22 393:12 398:5 448:3

nonconformities 706:5,6,9,16,19

nondestructively 80:20

nondetectable 552:22 557:8 572:22,25

nonendocrine 667:3

nonhumans 691:6

nonissue 676:17

nonlinear 397:19 688:6

nonmonotonic 720:12

nonmontonic 554:10

nonnormal 398:12

nonofficial 342:3

nonparametric 89:22 398:2 401:5,12 403:8

nonparametrically 401:7

nonparametrics 404:22,23 405:3,5,8

nonproductive 68:7

nonrobust 403:2

nonspecific 303:12

nonstatistical 269:3

nontoxic 574:18

non-breeding 470:1,3 479:12,19 480:5,9,11,14,22

481:10 484:9

non-crustacean 667:7

non-definitive 321:23

non-EDC 624:17 638:21

non-endocrine 81:23 125:2 304:3 326:25 455:14

489:5,7 531:12 536:23 537:5 538:10 551:3 552:6

553:5,8 554:1 557:3,20 645:13

non-endocrine-active 299:17

non-endocrine-mediated 293:22

non-expert 669:2

non-informative 699:15

non-invertebrate 630:7 638:4,7

non-linear 48:4

non-mating 483:12

non-monatomic 161:19

non-monotonic 30:26 31:3,6 482:9,15 564:4

non-normal 398:22 400:1

non-normality 677:5

non-parametric 474:8 579:7 580:25 675:20

non-parametrics 581:1,24

non-pesticide 19:8

non-reproductive 535:26

non-reproductively 367:5

non-solvent 517:15

non-statistical 383:15 470:22 576:25 674:26

non-statisticians 579:25

non-steroidal 644:7

non-zero 479:19 481:5

noon 276:22

normal 26:13 83:26 84:2 147:19 398:9 399:11,19

400:3,26 401:25,26 402:13,15,15 423:4 435:7

478:21,23 479:22 495:18 528:19 555:5 572:4

578:25 579:3 680:24 718:15

normality 399:2,6,10 402:26 473:18,22 474:7

484:1,4 579:6 676:24 677:5

normalization 467:6

normalize 121:12 517:8

normalized 402:14 478:24 479:24

normalizing 121:16

normally 398:13,20,25 399:5 483:16 579:3 580:26

North 7:7 243:6 460:20

northern 6:13 242:21 460:9 526:14 665:11

notable 487:5

note 8:4 51:12 87:13 444:1,3 480:5,17 481:10

506:19 616:18 748:7

noted 28:10 254:19 255:6 279:8 280:3 281:3,5,21

290:4 408:17 438:7 447:8 496:1 497:13 499:9

501:5 504:25 513:6 531:13 602:21 603:8 604:25

605:16 668:10,12

notes 724:24

noteworthy 164:26

notice 246:17 247:15 311:21 329:2 331:13 477:11

574:11 591:5

noticeable 511:12 688:25

noticed 92:8 116:23 235:7 370:17 440:5 512:2

516:5 580:10 600:12 694:17

noticing 123:10

notion 659:21

notions 147:12
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not-quite-correct 688:1

nourished 105:13

novel 365:17 481:18

nowadays 578:26

no-observed 36:11 70:11

nuclei 425:11

nudges 745:26

nullified 517:12

number 1:8 4:2,3 26:21 27:25 45:22 50:5 52:14

54:14 65:16 67:4,10,18 69:20 70:3 81:10 85:19

86:20 87:8,14,21 91:9 98:4 100:15 102:20 108:26

110:16,22 111:20,23 145:20 146:1 147:5,6

160:16 163:10 170:7 171:9 176:19 181:9 184:9

202:26 208:21 209:14 214:3,3 215:7,17 222:4

223:14 224:25 235:23 244:12 247:3 254:16,17

259:20 267:17,17,18 271:11 289:13 291:13 295:6

317:8 318:6 319:18 322:8,22 334:18 339:13,14

339:15 359:1,2,3 365:18 366:7,8 367:10 377:25

392:11 410:9,25 433:13 461:9,11 462:26 463:1,1

463:2,12,25 464:19 479:14 485:13 493:5 498:21

504:2 505:19 513:20,24,26 514:12 520:6,25

521:3 524:6 525:3,14,16 526:12,15 530:7 531:7

531:26 534:1 535:22 540:4,18 543:23 545:7

562:2 571:5,10 572:24 578:8 585:16 588:2

594:16,17 598:2 610:4 630:19 639:17,18,23

644:4 645:4 650:17 653:23 655:4 656:20 666:5

667:26 674:1 678:25 681:19 687:4 688:13,26

689:6,12,18 690:20 691:14 692:8,21 693:8,14,16

693:25 694:19,20 695:5,9,16,23 699:22 707:19

710:25,26 712:24 714:18 716:23 718:25,26 719:6

719:23 720:25 727:8 732:12 736:23 745:24

numbered 389:14,15,16,17 393:23,24,24,24

numbers 74:1 87:4,12 100:11 120:10 129:1 140:16

140:17 157:9 159:2 218:18 220:16 221:1 237:6

237:11 256:1,22 257:2 278:3,6 312:14 319:16

350:6 392:16 450:22 463:13 475:9 660:18 693:5

numerical 405:17

numerically 264:12

numerous 278:13 318:22 453:16 462:7 525:5

nursery 620:7

nutrient 117:1

nutrition 220:19 259:14 701:21

nutritive 259:12

nuts 653:21

O

obesity 533:4

objective 380:4,6 422:5 507:20 581:14 607:10

626:22 636:12 704:2 730:22

objectives 280:10

objectivity 561:26 656:6

obligated 312:15

obscure 224:20 225:7

observation 44:15 66:2 74:12 89:26 90:1 98:7

229:7 245:9 336:10

Observational 514:21

observations 74:20 138:14 153:20 475:10 495:25

497:1 500:1 589:6,17 600:16 603:4 604:22

680:26 694:11 728:12 736:20

observe 55:4 56:7 58:4 261:8 344:16 422:6 507:20

600:8 607:11 698:25 704:2 715:14 723:2

observed 36:10 46:21 47:3 48:4 53:4,9 54:8,11

60:25 65:18 75:4 95:21 103:4,6 105:15 159:18

161:17,18 163:5 164:7 167:6,8 170:6 226:23

269:17 281:8 316:21 318:23 345:9,16 418:25

451:20 482:19 490:18 529:18,22 538:2 578:9

600:5 610:12 698:15 719:6

observing 153:26 269:6

obtainable 173:8

obtained 226:8 319:11 414:13 438:1 493:15

601:25 670:14 697:9

obtaining 26:1 190:18 449:3,18 567:23

obvious 104:10,11 279:8 319:12 351:15 354:26

355:1 365:1 456:22 488:21 684:8 716:8

obviously 32:9 81:15 120:21 132:7 140:4 148:3

168:23 196:11 280:26 309:18 623:17 689:7,15

690:24 730:5 736:17 738:11

occasion 145:26 154:17 721:21

occasionally 506:8 558:20 700:4

occasions 246:13 359:2 736:19

occur 41:3 44:18 56:7 58:15 72:21,24 74:3 80:14

83:1 182:16 207:15 213:2,4,8 223:4 228:1 233:1

257:7 303:19 390:23 517:12 520:16 524:13 546:7

613:12 638:1 683:9 716:2

occurred 73:7 85:22 158:1 216:3 280:26 504:26

679:16 698:20 699:17 718:26

occurring 15:19 40:9 46:8 66:3 69:14 108:20

142:2 159:16,17 163:19 215:10 216:8 247:21

264:13 304:4 435:23 531:25

occurs 176:1 395:17 523:15 524:20,21

ocean 118:4 620:5

Oceanic 6:1 460:2 665:4

OCSPP 10:12 40:23 212:4

October 252:24

octylphenol 94:23,23 100:10,23 101:15 106:1,8,23

106:26 107:11,12,13 111:26 112:4 157:17 161:22

162:2 164:22,23 165:7,9 170:9,15,26 172:17

173:18 209:26 215:22 217:24 237:13 257:8

261:14 320:5,6 485:17 496:14,20 500:4 531:16

535:13 537:23 543:3 597:8 604:1,21

odd 199:6,8 389:14,17 393:23,23,25
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OEC 253:19

OECD 136:24 210:4,13 222:7 250:20,21,26 251:8

251:20 252:26 253:4 265:17 300:24 312:11

331:19 348:24 349:25 365:3 430:18,25 431:4

433:3,10,13,20,21,24 547:11 640:10 641:3

642:16 708:7,10 714:16

offer 210:18

offered 3:15

offering 2:14

office 3:11,12 5:6 8:15,16,20,21,22 10:3,13,19 11:6

11:7 13:19,21,22,23,24 29:19 204:26 242:2

265:20 621:7 635:12,12 664:17 739:13 742:6,7

746:21,22,23 747:4,5,10

Officer 3:10

offices 9:24 13:21 746:21,24

official 2:2 3:2 9:11 241:2 459:2 664:3

officials 3:4

offset 258:14 438:9

offspring 23:23,25 40:26 42:22 44:6,15 45:6 58:2

212:12 213:22 214:3,23 215:7,17,25 247:12

250:7 255:11 263:17,20 319:5,6 389:14 436:26

455:16 536:25 639:11 719:17,24 720:6

offsprings 41:14 211:17 213:16 222:4 223:15

off-the 117:13

oftentimes 22:15 72:6 92:24

oh 130:16 183:9 234:18 291:7 330:6 346:14 353:13

372:2 373:18 407:10 427:23 443:1 476:6 581:7,8

582:20 587:14 589:9,23 590:4 591:25

okay 8:18 33:22 34:5 37:25 67:8 68:12 71:1,13

74:13 115:13,15 116:21 119:8 120:18 121:6,9,21

123:9 124:5,12 126:13,24 128:5 129:15 133:11

137:16 138:11,17 139:7,9 140:10 141:6 143:5,7

149:8,13,15,19,24 150:15,22 153:10 162:9,18

166:25 176:2,9,15 179:19 180:22 181:20 182:3,7

182:17 183:18,26 187:20 188:1 189:11 192:12

193:4 198:9,20,22 199:5 201:6 203:14,26 204:9

205:2,26 229:17,19 230:15 231:25 233:2 234:3

243:16 245:22 246:2 250:5,16 261:17 271:1,14

271:23,25 272:3,10,15 273:3 276:13 277:24

285:26 290:11 297:17 300:5 326:2 327:5 328:24

329:14 330:2,20 334:9,21 335:12 336:25 339:20

340:11,14,19 341:9,16 342:5,13,19 345:17,18

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361:23 363:26 376:3 383:4 393:4 399:24 401:2

404:4 405:10 406:12,14 407:13,24,25 409:4

414:16 422:15 427:22 428:4 429:23 452:6 453:1

457:5 468:7,13,16,19 470:6 476:8,11 478:1

484:13,17 485:10 493:1 508:6 511:11 512:14

519:6 540:15,22 542:10 544:13,15 593:23 595:18

596:3 601:6 606:8 624:7 662:18 714:2 727:8

old 87:4 181:22 212:7 552:22

older 87:9

Olmstead 157:10 167:5 181:17

Omaha 6:25 243:1 460:15

omitted 699:26

once 34:16 85:6 87:25 88:10,12,15 89:6 92:12,16

92:26 95:3,7,12 97:14,15,22 98:17 100:13 101:16

102:1,11 106:4,18 114:6 129:10 168:7 195:4

213:20 221:8,14 234:1 314:25 318:23 319:9

321:2 390:8,9,12,13 415:22 478:12 489:15 518:7

533:17 539:21 614:19 644:14 649:2 670:13

678:12 710:14 711:20 733:10 736:7 740:12 741:9

ones 45:11 72:11 99:3,3 194:2 209:19 220:1 234:4

235:11 255:6 280:12 285:2 320:23 324:4,26

325:1 328:10 387:17 420:19 578:5,14 623:17

630:14 653:26 700:19 740:2

one's 248:25 268:13

one-by-one 602:16

one-gen 288:13 292:26 294:4,10 335:6,6 337:5

344:23

one-generation 21:20 24:4,23 234:12 286:19,23

291:4,20 292:23 294:12,23,25,26 320:13 338:19

339:17 621:13 639:9

one-generational 710:6

one-gen/two-gen 294:8

one-one 689:11

one-third 689:11

one-tier 339:9

one-way 90:10,12 137:14 153:18 401:22,23 589:5

one/type 403:7

ongoing 649:5 652:19

online 16:23 83:15,16 84:12

onset 54:12 64:9

oocyte 85:11 157:8 167:20,25

oocytes 84:4 85:5 147:5 158:10 169:6 170:2 546:14

557:10

oogonia 378:16

OP 165:1 494:21

opaqueness 515:6

OPDDT 479:15

open 1:2 4:11 11:12 29:24 89:5 270:12 295:24

362:16 521:7 533:12 592:21,23 663:5

opening 12:26

opens 15:20

operate 15:13 32:11

operating 69:16 472:19 591:3

operationally 18:17

operations 11:10

opinion 174:6 329:15 333:22 334:16 342:7,15

356:1 373:14 375:7 385:20 398:26 413:14 431:1
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opportunity 3:16 83:19 168:6 181:23 294:21

321:14 322:4 327:21 328:7 331:10 332:3,16

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732:5,8 733:23,25 734:1 735:19 736:12 740:9,17

opposed 131:5 147:23 151:14 166:3 228:26 380:4

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opt 267:2

opted 430:26

optimal 88:3 175:5 321:10 563:14 672:6 741:5

optimally 68:8

optimization 247:9 278:6 298:10 364:13 461:21

464:11,12,24 466:15,18,22 507:25 556:12,17

557:23 558:8 563:19 666:13 667:23 668:21 669:1

682:1 689:5 699:21

optimize 27:24 78:10 99:6 166:8 574:25 674:3

optimized 296:11 299:7 305:4 557:25 697:21

optimizes 579:4

optimizing 467:11 579:2 712:12

option 44:16 73:22 74:11 90:23 180:1 425:23

462:17 581:12 649:4 688:18

optional 666:20 670:19

options 653:19 688:2

oral 3:16

oranges 626:24 627:25

ORD 75:13 204:10,25

order 18:24 19:18 20:17 29:10 34:26 113:18

132:14 199:19 284:23 351:2 384:25,26 385:1,2,2

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ordering 405:16

orders 23:17 87:19 177:10 326:13 426:24 539:16

744:14

ordinal 89:19 398:3 478:14 578:10,16

organ 274:7,8 380:1 531:10 562:3

organigenesis 171:4

organism 46:8,9 76:7 77:8 112:13 158:2 305:26

307:22,22 312:9 352:8 526:8 551:5 596:24

641:10 684:16 714:8,10

organismal 170:22

organisms 165:9,15 171:5,9 186:1 207:2 305:26

310:7,11 313:3 621:18,19 639:2 687:11 689:18

715:23 716:24

organization 11:11 22:25 32:23 142:20 210:4

634:15 662:25

organizational 141:7,9,13,15,23 142:1,26 143:4

213:7,10 446:4 453:21 462:12 529:16 530:14

742:20

organize 83:23 184:2

organized 81:6 485:9 577:22 635:26 721:25

736:16

organizing 9:14 745:19 746:15

organogenesis 165:10 552:2 553:2,26 556:21

organs 45:15,16 274:1,2 376:10 562:4

organ/gland 45:13

oriented 162:26

origin 392:10 463:13

original 72:10 78:22 161:15 252:7

originally 222:7 397:6 597:3 602:14

OSCP 29:18 151:23

Oshima 359:13

ostensibly 143:3

ought 87:15 396:9 399:17 400:5,15 401:1 577:22

595:19 673:26 674:9 688:21

outcome 21:7 22:24 25:24 32:18 37:25 41:24 42:6

42:23 45:6 70:6 84:7 90:3 91:2,2 92:4 96:5,25

101:3 104:6 133:18 141:4,18 143:3 185:18 188:7

188:10 198:15 252:21 327:7 330:1,4 409:20

420:15 436:2 541:22 553:1 565:12,14,16,20

598:18,19 603:6 606:19 608:12 611:26 612:2,6,9

614:4 618:26 619:3 621:10 628:4 656:5 689:15

698:11 706:18 732:12,17

outcomes 47:14 58:5 61:25 63:12 69:15 75:21,22

81:1 89:9 90:25 91:17 94:23 103:10 107:8,17

117:23 148:2 210:16 251:8 254:26 255:3 308:3

447:11,22 499:18 533:22 548:2,5,13,14 553:13

555:8 597:15 606:18,22,23 607:25 609:5,7

611:17 612:15,17 614:8,23,26 638:1 658:25

661:5 684:1 686:17

outcry 739:25

outlier 680:14

outliers 68:6 479:4 680:20,21 681:1,2,3

outline 39:6 42:16 155:5 206:9 351:1 354:24

607:13

outlined 252:1 302:14 348:26 364:14 461:22

540:23 556:13 614:20 666:14 718:11

outlines 422:8 507:23 704:5

output 86:6 105:14 139:20 140:20 143:21 144:15

144:16 505:18

outputs 505:4 642:5

outside 62:16 193:19 353:3 355:5 366:15 449:3,4

488:6 636:15 688:24
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outstanding 9:15 737:9,9

out-bread 119:5

ova 453:26

ovarian 378:17 553:2 565:4

ovaries 537:2

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overall 16:17 46:9 55:8 161:20 171:11 173:15

198:15 223:24 304:6 306:18 336:15 337:21 349:5

352:11 365:21 416:4 417:16 425:3,4 433:26

449:25 456:6 464:19 465:2,4 467:9 489:11

496:26 499:15 500:3 501:3 503:21 506:10 511:10

511:15 514:1 525:11,23 530:8,11 531:26 534:7

536:6 537:12 540:16 543:4 548:11 553:16 588:22

588:23,25 594:10,22 599:21 605:10,21 658:9

674:11 697:24 698:11,19 708:16 732:10 734:11

overarching 285:20 323:23

overcome 227:3 711:19

overflow 220:16

overlaid 81:25

overlap 34:11 79:17 108:24 220:1 564:22 598:6

overlapped 79:11

overlook 104:9

overlooked 628:21 739:23,23 740:4

overly 45:26 331:6

overnight 267:13 391:3

oversight 429:8

overstate 447:15

overstepped 633:5

overt 159:14,17 163:17 169:4 287:23 288:3,4

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531:10 536:23 537:1 546:15,22 547:5 548:12

553:11 557:22 564:14,16 612:12 624:16 638:3,6

640:19

overtly 163:22

overview 10:8 14:1 75:17 110:4 149:10 155:7

206:10,21 211:2 213:26 223:12 357:25 430:13

678:16

overwhelmed 734:7

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over-exuberant 635:17

over-feeding 171:15

over-stepped 635:7

oviduct 167:19 177:18 178:11,12 423:21 552:18

553:21 557:11,12 562:5 604:6,11 605:13

oviductal 552:16

Oviparity 40:14

oviparous 73:8

oviposit 552:18

oviposition 47:11

ovo 25:10 40:26 368:2

ovulation 423:21

oxidative 260:20

oxymoron 119:5

o'clock 38:12 458:18

P

P 432:22

package 205:13 464:8 592:7 627:13

Padilla 515:12

page 88:19 288:15 421:4 442:4,6 479:4,14,15,16

480:2,11 482:5,20,20,21,22,24 503:23 505:2

548:3 670:18 675:22 689:20 699:6,20

pages 324:6 346:8 444:24 587:6 599:11 678:16

714:5 746:8

paid 175:15 201:4 279:3 478:2 591:6

pain 602:10

pair 44:5 51:19 68:6,7 81:10 86:8 89:16 138:14

144:15 154:3 236:21 389:9 391:13 392:13,15,24

472:10

paired 109:20 228:4 389:15,20 453:26

pairing 689:1

pairings 644:15

pairs 44:1,3,8 46:19 54:12,13 68:8 80:11,16 82:5,6

82:6 91:10 109:5,6,21 110:2,3,5,16,19,19,22,23

110:23 111:1,8,15,22,23 112:1,1,4 140:13 149:13

221:22 267:23 268:11 271:7,10,10,11,21,22

272:4,6,6,11,12,16,17,19,20 273:1,1,15,16

358:12 387:4,5,11,12 388:18 389:9,14,15,16,26

392:23 437:5 445:23 454:12 455:5 469:21 470:1

470:3 479:12,18,20 480:5,9,11,14,22 481:11

483:12 484:10 505:22 519:13,19,23 520:22 521:1

521:5 535:3 696:5 699:9

pair-wise 138:18 482:5

pale 179:8

panel 1:1 2:3,8,12,18 3:5,24 4:8,19,21 5:1,10,14,18

5:22,25 6:15,18 7:8,17 8:1,5,24,26 9:7,16,26

10:25 11:5,9 12:9,19,26 13:1 19:20 21:2 30:1

34:7 38:11,16,19,24 45:26 54:21 59:5,8 70:21

84:16 105:7 114:23 134:5 153:10 186:13 188:8

195:23 203:23 204:1 239:15,19 241:3,4,18,20,22

243:17 246:24 251:4,6 267:10,11 270:1,6 283:24

295:24 332:20,24 333:12,26 343:3,7 345:26

346:9 353:8 375:16 382:9,12 394:6 404:1,3

406:12 410:2 411:11 419:23 420:1 421:15,20,24

428:19 429:22,26 434:19 435:2 441:1 442:2,11

442:14 443:25 446:2,5,11,17,20,24 447:14

449:25 452:6,10 458:3,16 459:3 468:8 480:18

484:14 486:20 490:10,15 491:11,14 493:20

496:17 497:14 499:15,25 500:15,24 507:2 509:7

509:19 510:4,15,26 515:19 516:18 517:17 521:8

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panels 20:10 482:13 538:2 633:26 734:7 735:24

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papers 322:19 646:8

papillae 82:11 87:8 92:10 103:8 106:18,25 107:6

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482:22 514:14

papillary 82:10 463:1,14,15

papolary 98:5

paradigm 15:9 43:22 58:17 268:26 446:2 449:17

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paradigms 76:19

paragraph 381:25

parallel 182:5 250:20 619:1 726:21

parallelism 181:12

parameter 365:17 367:6 433:7 440:14

parameters 116:26 163:6 165:21,26 168:24 298:8

341:23 373:1 415:9 416:2,24 425:6 431:13

465:17 467:10 474:12 478:26 508:11,26 509:9

515:17,20 516:14,15 517:2 520:16 535:7,26

537:15 558:2,13 573:5 614:20 650:23 653:17

658:13 659:10 743:9

parametric 397:21 398:5 403:3

parametrics 474:6

paraphrase 554:23,25

paraphrasing 408:10

parasagittal 274:12

Pardon 593:8

parent 132:11,15,17 211:18 510:10

parental 41:12 42:21 44:1 50:16 51:6,24 52:15

53:7 54:4 57:15 71:7 76:14 112:19 351:9,16

354:26 360:14 389:15,16 425:12 427:12 435:9

455:24,25 456:7 524:18,20,22 691:23

parentheses 47:26 48:1,3 675:25

parenthesis 91:26

parents 41:1 351:6 393:11,21 424:24 436:26

parings 691:15

parochial 634:8

parrot 738:18

parsed 148:22

part 4:11 5:7 39:10,15,16 40:22 43:25 44:19 49:4

51:15 61:20 67:25 80:8 81:4 98:16 111:11,12

113:1,17 119:11 120:12,12 130:8 132:7 136:21

137:22 138:5 142:6 144:7 146:17,24 163:6

180:21 181:25 186:8 189:22 193:26 196:5 201:20

206:11 210:23 225:4 228:19 241:8 252:7 257:4,4

264:14 271:24 276:17 292:6 330:21 333:3 334:11

347:15 349:7 350:26 364:3,3 377:17 382:13

383:14 384:9,14,15,16,17 393:26 394:6 410:4

439:7 443:16 470:20 475:24 485:5 497:24,26

510:22,25 516:26 519:9 522:8 541:8,16 543:13

545:21 556:3 559:11 563:13 566:9 576:23 578:21

579:23 584:7 587:2,8 595:4 617:2 622:12 640:10

640:12 642:12 650:14 651:25 652:7 654:17

656:15,21,24 658:15 664:18 674:16,24 678:14

681:5 682:6 686:7 687:19 702:26 707:18 714:16

716:1 732:3 733:24 738:19 739:6 746:25

partial 64:13 456:8

partially 135:13

participant 3:8

participants 641:20

participate 2:13 9:3,17 160:16 284:14 652:4

participated 4:20 170:8,25 214:18 215:23 557:24

participating 9:8 162:2 282:25 562:20

participation 743:4

particular 14:17 15:15,18 18:8 23:7 25:13,18,19

25:25 26:2,3,6,9,21,24 27:3,15,26 29:4 30:13

33:23 35:2,18,19 38:5 39:5 43:4 46:8 48:7,10

49:17 50:1 52:18 56:19 59:13 60:24 61:18 62:5

62:11,19 81:1 83:9 87:20 89:24 90:3 98:7 110:20

117:7 119:10 121:24 123:12 124:1 137:25 138:1

147:26 148:4 184:13 186:4 208:14 209:15 210:6

221:11 223:10 232:20 237:24 246:6 251:22

252:17 253:16,24 254:8,19 255:12 256:21 262:1

262:26 263:21 264:3,15 265:24 268:12 271:17

274:8 297:15 311:19 324:26 325:15 328:9 349:13

349:22 355:5 375:22 384:4 410:11 412:1 431:13

444:26 445:20 453:4 455:11,22 466:4 482:25

488:12,15,24 498:8 501:6 511:17 523:5 526:1
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646:15 650:6 652:6 671:21 672:18 676:22 678:15

682:20 683:22 684:15 725:7 730:3

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287:24 304:17 313:5 335:22 336:26 367:13

408:20,24 413:22 426:18 427:21 430:23 431:6

433:20 449:2 450:6 455:12 465:6 514:14 524:15

525:15 529:17 536:23 556:25 559:4,5,6 563:23

570:20 572:9 592:6 618:24 619:10 620:16 633:21

642:3 705:10 724:2 728:23 729:6,22 731:5

parting 732:25

partly 99:1 352:4 485:7

partner 193:21

partners 9:25

partnerships 428:4

parts 32:18 113:15 118:4 131:22 136:26 152:16,17

152:18 266:21 374:17 393:17,18 432:21 476:1,8

596:8 599:20 617:14 627:22 670:13 675:8,8

pass 30:18 36:21 158:21 167:25 360:19 526:21

629:8 727:19

passed 473:20 604:8 691:24

passerine 352:7 355:12 356:2,23 361:7 418:21

424:8,20 425:17 427:6,14 432:14 434:17

passerines 351:7 355:7

passes 89:10 603:20

passing 656:26

passion 736:18

Pat 747:2,8

patent 630:7

path 11:9 12:10 182:22,23 543:25

pathological 185:12 376:4

pathologies 164:9,9,10 167:7 168:26 169:8,20

172:19 281:24 546:15 603:15

pathologist 45:25 83:17 179:9 189:20 378:13

380:26

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pathology 6:6 46:6 80:3 82:16,17,21 83:4,4,7,19

83:20,23,25 84:7,15 88:13,14,15 89:18 91:6

92:26,26 108:5,19 109:23,26 110:3 112:26 125:7

125:7 135:2 136:15,25 137:1 143:25,26 144:3,16

144:18 159:23 163:24,24 167:9,16,17,18 169:11

170:11,11 173:24 174:1 175:9 179:5 268:20

281:21 282:17 534:5 536:7

pathway 18:8 21:7 22:24 32:18 36:23 69:17,17

135:25 136:4 188:8,10 230:23,24 233:21 234:13

330:4 332:11 376:9 455:18 489:7 618:26 619:3

628:4 638:3,6,7,24 642:20 683:7 685:26 686:11

717:7

pathways 15:21 16:20,21,24 17:7 34:24 37:2 76:8

112:14 116:19,20 185:18 231:3,18 232:23 233:17

233:17 288:2 315:8 317:1 327:7 330:1 337:4

348:17 354:24 356:15 371:20 434:15 447:12

516:24 541:7 557:2,22 619:6 621:11 622:2 639:1

701:13 705:1,2 712:13 720:13

Patience 346:4 747:2,11

Patino 6:7,7 59:9,10 128:6,7,12,16 129:15,26

130:7,13,19,24 131:3 132:22 133:11,17 134:3

242:17,17 460:5,5 467:21,22 475:25,26 476:6,9

485:1,3,4 493:16,18 518:23,24 527:21,22 532:19

532:20 538:26 539:1 542:7,8 548:20,21 554:6,7

563:5,6 568:24,25 573:22,23 596:11,12 601:26

602:2 606:5 609:24,25 615:4,5 665:7,7 732:4

Patino's 501:21

PatiQo 451:25,26 457:16,17

patotoxicity 137:3

Patricia 277:16 283:12

pattern 57:8 69:21 123:21 215:15 234:9 436:22

437:13,25 482:11 574:14 685:15,16 723:17,21

patterns 162:11 234:9 436:17 482:16

pay 63:12 73:4 178:22 201:9,25

paying 336:26

PBDE 488:22

PCR 80:22 82:16 87:14 136:17 139:16 457:13

514:2 573:26 575:10

pdf 442:17 593:7,9,11,17,20 594:2

peek 187:25

peer 2:20,25 10:1 11:12 492:17 591:24 592:5,6,8

664:4 738:10 743:26

peers 237:21

pen 389:16 392:10 393:23 394:25,26

penaeid 620:4

pens 290:7 389:14 393:23,24 433:11

people 112:24 113:23 114:10 134:1 136:18 145:16

184:5 198:1,5 206:1 270:3 277:17 283:13 284:23

310:16 311:24 345:21 360:4 370:20 371:1,4

372:13 391:25 399:15 428:21 458:17 473:26

488:6 494:15 518:18 527:1 579:9,10 590:22

591:2,4 620:11 702:21 712:5 729:10 730:2

746:11,26 747:1,25 748:2,3

peptide 410:18

perceived 262:12 714:4

perceiving 202:12

percent 54:1,2,4,5,26 55:3 67:21,22,23 68:2,3,4

86:11,11,16,17,24 87:2,24,25 88:11 110:11,12,13

110:21,25 111:2,3 123:25 170:25 171:6,6 196:20

197:15 214:4,14,15 219:2 230:11 235:7,12,21

238:1 258:8,9,9 264:19 284:22 291:17 307:23
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317:5 361:12,18 366:8 378:15,16,22,23,23,24,24

379:3,4 396:24 402:3 481:14 483:24 505:9 506:2

506:3 510:17 555:12 572:20 582:20 588:8 599:12

599:15,18,19 603:16 620:1,9,10 628:26 629:1

656:2 678:1 683:11 689:26 691:3,5 692:14,22

693:5 700:24 701:2,3 706:11,12 722:23

percentage 86:18 361:12,14 591:9 706:11

percentages 379:2,3

percent-reproducing 700:12

perception 182:10

perchlorate 488:21

Percival 748:6

Perdue 460:25

perfect 499:7 634:16

perfection 373:8

perfluorodecanoic 209:23 682:3

perform 26:7 81:4 104:19 282:23 313:16,25

418:13 465:24 483:6 488:3 517:24 525:3 530:8

573:13 610:16 614:3 650:6 651:17 656:3 683:1

715:17 717:3

performance 19:21 58:21 175:3 193:12 194:21

195:21 197:13 199:16 200:2,6 202:2,7 214:19

237:14 245:17 247:13,17 281:3 286:12 295:16

320:7 364:12 374:26 383:7 414:10 419:12 451:23

461:20 470:13 488:2 493:12 496:5 498:3 499:25

500:18 510:6 511:7 534:18 536:6 537:15,23

538:6,11 539:18 556:11 571:3,10,12 576:16

601:22 605:25 633:21 651:20 660:12 661:3 662:8

666:11 674:17 697:6 706:18 710:24 714:23

715:21

performances 710:21

performance-based 656:16

performed 35:4 58:20 60:13 89:11 95:14 201:21

223:20 380:26 381:23 417:24 449:2 473:19 474:3

494:18 495:4 496:8 518:19 534:14 586:12 602:14

604:20 655:8 658:13 671:5

performing 104:15 297:13 301:4 374:19 410:23

420:17 438:17 499:19 517:3 521:6 524:9 525:2

566:11 589:2 650:24 652:6 684:20 704:20 705:24

performs 172:26 562:23

perifollicular 85:13 92:13,21

period 42:17 43:13 44:23 46:14,21 65:15,17

155:25 159:8,10 197:1 198:26 210:13 212:2

259:21 345:21 388:23 420:22,23,23 520:26

550:20 565:25

periodic 375:21

periodically 120:20 417:24 489:15 658:22

Perkins 6:16,16 196:9,10 197:7,24 242:22,22

344:21,22 345:17 347:22 353:4,5 356:11,12

360:21,22 370:14,15 372:6 382:5,6 406:4,5

409:22,23 411:7,8 419:17,18 429:16,17 434:10

434:11 440:24,25 452:1,2 457:18,19 460:10,10

467:23,24 476:11,12 487:23,24 501:16,17 518:12

518:13 527:15,16 529:4,6 539:3,4 631:8,9 644:17

644:18 665:12,12 666:16,18 670:10 678:25

684:10,11 693:10,11 702:11,12 707:3,4 711:26

712:1,19 717:19,20 718:21,22 722:18 734:3

permanent 5:10,14,18,22,25 12:9,26 745:8

permeability 446:9

permit 347:13 443:14 545:18 616:25 624:12

peroxidase 168:4 486:5,17 561:11 597:11

persistence 742:12

person 277:9 348:4 359:22 530:8

personal 118:2 258:19 303:17 363:8 458:7 489:4

747:12

personality 416:8 733:24

personally 144:22 257:19 381:16 443:26 734:1

personnel 227:3 375:26 706:21

perspective 161:15 248:13 252:1 337:25 357:16

435:16 451:17 452:19 523:21 524:9 543:5 550:7

620:16 637:5 718:19 728:20

pertinent 719:3

perturbation 158:17

perturbations 26:12 58:12,13 181:25 190:14

pesticide 2:21 8:16 10:20 11:6 13:22,22 15:24

18:24,25 225:13 265:20 279:6 285:7 294:10

315:26 641:14 669:10,15,18,19 715:25 744:22

746:22

pesticides 2:21 286:24 337:10 342:24 544:6 621:7

621:9,22 638:9 669:9,16 708:18 715:24

PETA 310:7 332:22

Petricelli 698:4

pH 399:21 400:1,2 515:20

phallus 426:25

Pharmacology 5:24

phase 42:9 100:4 159:9 168:19,20,21 210:15 641:2

phases 40:6,7 41:3 210:14

phenol 603:9

phenomena 335:23

phenomenon 171:21 172:5

phenotype 76:25 77:7 82:10 123:19 129:4 131:21

141:1,2,4 156:14 445:24 450:19,24 455:5 499:8

541:13,22 604:12 605:8

phenotypes 82:19

phenotypic 123:17 131:12 140:18 160:3 281:2

431:12 445:8,17 451:4,7,8 454:15,20 455:2

469:24 523:25 524:1,5 526:23 530:23 531:1,3

552:26 557:14

phenotypically 546:25

phenyl 446:11
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philosophy 392:7 635:5,6

phonetic 306:6 588:1 698:4

phosphorylation 260:20

photo 420:22,23

photoperiodic 415:20

photoperiodicity 57:2

photostimulation 367:1

phrase 274:26 527:10 647:22

phrased 725:3

phylogenies 351:23

physical 275:15 470:17 576:20 695:7,26 696:10

physically 277:14

physical/chemical 383:11 674:22

Physicians 277:18 283:13

physiological 185:11 416:10

physiologically 612:22

physiologist 259:8

physiology 7:13 427:12 430:23 565:25 616:16

pick 126:17 132:25 133:6 135:20 136:5 197:19

239:10 253:15 334:24 338:25 489:1 501:24

591:19 672:13,14 685:26 686:4,4 739:9

picked 26:2 200:7 303:5 339:1,6,11 340:18 379:14

696:16 700:7

picking 324:23 335:3 672:18 692:20 731:20

picture 61:6 410:20 588:23,24,25 733:20

pictures 270:15 577:9

piece 22:20 166:12 379:18 603:22

pieces 555:1

piggy-back 188:19

pin 330:16

pineal 359:6,8

pineals 359:7

pipetting 572:10

pituitary 145:8

place 9:6,14 58:6,13 62:7 90:24 118:5 124:22

133:9 146:9 169:5 174:20 201:11 258:7 351:24

388:26 391:20 623:5 647:15 655:5 659:5 677:15

placed 227:26 387:4,21,23 391:13,14 393:16,19

429:14 472:1,8,14 501:3 660:26 669:4

places 93:21 122:10 198:13 401:20 477:16 574:19

639:14 657:18

plain 205:10

plan 162:10 239:20 329:24 472:19 479:23 591:3

616:1 727:13 733:9

planes 562:1

planning 73:15 150:17 189:15 433:23 589:2 608:8

745:18

plans 35:3 136:17 189:25

plasma 45:21 68:20 166:11 367:24 368:12 447:20

551:10 552:11,20 553:20 557:6,7 559:9 560:19

566:26 567:3,6,8,14,15,22,24,26 568:3,10,20,23

570:18,23 571:4,6,19 572:10,14 573:10 603:4

609:15 726:22

plate 211:23 223:10,18 225:4 344:15 641:8

plates 211:22 223:9,14 225:2 238:14 573:1,2

641:11

platform 740:9

plausible 208:18

play 36:19 37:6 58:24 60:3 194:19 245:11 257:5

389:18 435:24 439:4 693:14,18

played 509:24 621:5,14

playing 584:17

plays 263:3

plea 434:22 521:20 584:19

please 3:16,23 105:11 130:2 153:7 182:22 205:6

241:19 284:6 314:22 329:22 345:26 347:14

357:19 363:18 364:13 376:15 382:23 383:5,17

384:1 404:4 414:11 421:26 422:10 430:9 435:15

441:14 443:15 452:12,21 458:8 461:21 470:24

487:21 493:13 508:1 522:20 528:16 529:1 534:16

534:20 545:20 546:1 550:9 556:1,12 566:19

570:7,13 576:26 588:12 593:25 601:23 607:15

610:25 611:7 616:2 617:1 637:8 666:3,13 675:2

687:15,22 697:7 703:23 704:7 707:16,23 713:23

718:18

pleased 75:11 678:20

plenty 190:20

plot 506:20

plots 479:14

plural 685:8

plus 100:2 187:24 211:20 230:11 474:17,18 549:9

594:22,23,25 694:4 706:10 746:8

POAs 630:15

poignant 747:22

point 10:15 14:9 15:15 30:14 33:3,23 35:21,25

48:7,10,17 52:22 55:1 57:5 65:3 74:3,6,10 78:4

84:6 91:16 97:7 101:23 102:14 105:25 108:22

110:14 126:2 127:2,2 138:8,9 146:13,15 147:16

151:14,15 156:18 157:7 160:13 161:6 162:4

164:25 165:6 169:3 172:3 173:10 181:8 183:6

189:4,14 192:26 194:5 199:21 203:6 204:17

205:3 216:13 218:10 235:18 245:23 253:12

254:10 259:15 261:11 266:7 285:4 290:23 296:10

296:19,22,25 297:19 299:2,5,10,16 300:1,13

302:24 307:16 310:17 315:23,25 319:14 323:6,15

326:3 338:20 343:6 344:4 345:5,13 353:15

360:26 361:21 362:12 363:26 370:10 397:4,5,6

407:24 408:12,26 413:24 419:6 421:24 428:23

449:14,19 463:5,5 469:1 470:1 471:5 480:6

490:20 498:18 499:5 548:10 554:22 555:15
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556:17 563:13 564:13,19 569:13 576:5 580:19

581:11,23 582:9 589:19 600:26 606:15 612:7

615:18 622:5,7 623:18 628:2 631:6 636:1 649:10

653:1 654:26 655:3 656:6 662:4 676:3 677:1

678:13 691:9 701:26 702:15 703:6 705:17,23,26

708:20 721:4 723:1 726:26 729:21 737:19 740:10

pointed 195:26 289:19 335:16 346:9 564:22 585:2

598:1 703:4 720:23 721:2 723:15

pointer 589:9,9

points 8:9 82:26 153:26 194:25 206:3 216:22 233:4

275:9 284:19 287:13 296:6 315:4 333:18 336:23

337:21 348:3 352:26 358:7 368:17 370:16 386:2

408:1,13 449:8 476:18,19 482:24 531:7 569:18

577:17 627:10,14 640:1 668:18 673:7,7 721:1

723:14 738:3

point-of-departure 70:11

poisson 404:18,19

policy 2:24 3:1,11,20 8:15,21 10:3,10 13:20 35:3

192:11 241:16 277:13 284:5,9,22 296:4 305:18

323:25 331:21 343:6 491:21 492:16 623:16

624:22 642:8 658:25 669:12 746:21 747:11

pollution 8:22 11:7 13:22 501:8 620:13

poly 512:25

polymer 19:11

polymorphism 156:14

polymorphisms 526:17

pool 331:20

pooled 109:14,20 271:22 331:17 503:26 572:14

pooling 236:13 265:13,13

poor 172:22 278:16 286:12 293:21 295:16 374:25

499:25 500:20 715:21 737:10

poorly 92:24 317:10 715:20

popping 161:19

popularity 420:5

populate 108:10 219:22

population 15:20 16:2 33:13 54:12 75:1 115:19,23

115:24 116:4,8,10,14 128:15,19 131:19 186:2,5

226:9 245:12 257:15 286:14,18 290:13,15 292:17

293:10 295:18 308:4 322:16,18 337:22 338:10

351:20 373:14 390:1 402:26 423:24 429:9 439:4

447:23 455:8 466:1,4,6,9 535:10 559:8 565:16

586:22 606:21 609:8 612:9 620:9 642:1 643:15

669:5,6 687:10 691:5,6,23 692:5,8 701:23 709:11

714:12,13 716:2 720:6 722:10 723:4,5

populational 735:1

populations 41:20 57:3 157:15 337:26 350:18

445:25 446:13 455:6 487:2 501:4 509:14 643:11

692:2 693:6 719:9

population-definitive 337:19

population-level 296:17 298:18 305:10 306:26

316:7 325:14 329:16 338:22

population-relevant 334:14

portal 663:5

Portier 8:5 239:23 347:22 353:10 360:16,18 361:9

369:14 381:16 406:7 419:16,20 429:20 434:9

440:23 444:14,20 468:5 469:13 477:9,26 484:8

502:9,21 505:5 506:26 519:5 520:4 528:7 532:25

548:26,26 554:22 563:18 569:6 574:6 577:25,25

612:19 615:9 724:25 725:6,11

Portier's 443:25 468:12 477:25 538:20 606:7

610:9 653:3

portion 41:10 56:21 388:1,2 530:3 627:7 746:26

portions 689:1

pose 57:14 74:21 231:24 354:4

posed 49:11 280:3 493:20 671:17,18

poses 462:4

posing 234:16

position 440:4 562:1

positive 22:7 73:15,18 188:24 203:18,20 232:5,14

233:6,6 234:18 261:21 292:14 364:24,25 370:24

373:23,25 374:12 375:10,18,19 407:5 413:19,20

413:21,25 414:22 415:2 417:17 422:20 423:9

426:12 432:17 438:6 444:21 477:1 489:26 516:25

517:2 559:13,14,18,25 560:2 597:26 598:16,18

598:21 603:6 604:4 607:22 644:3 645:6,7 649:23

650:8,16 651:3,9 652:9 654:4 656:3 661:1,9

682:10,19,24,25,25,26 683:14 684:19 685:7,9,10

685:23 704:14,16,18,21,25 705:2 706:18 717:5

723:19 729:6 736:23

positively 283:9

positives 317:3 408:5 613:25

possibilities 145:6

possibility 158:6 316:14 340:22 408:20 432:13

499:16 500:1 648:22 726:8

possible 9:12 39:24 47:19 56:22 58:14 72:3 74:11

79:7 117:15,16 120:11 131:25 147:9,10 155:15

156:16 264:23 277:4 322:10 348:21 359:12

363:13 368:15,22 371:18 377:16 380:6 382:3

388:15 389:25 414:23 423:5 427:17 428:3 464:24

486:4 498:8 516:13 519:13,22 527:7 530:9,20

544:10 552:19 562:15 563:1 568:14 571:21

582:12 586:6 609:19 619:16 623:25 624:6 626:15

637:26 640:18 649:21 667:24 711:10 715:1,3

738:12

possibly 186:23 191:4 334:26 336:4 377:24 418:23

438:22 439:2 450:24 499:24 515:3 525:25 546:23

551:4,10 556:24,26 557:5 560:24 580:4 584:14

626:5 641:5 642:11 645:6 711:6

post 23:23 44:22 158:7,8,18 159:5,11 191:12 221:7

389:5 402:8 530:22 739:7 745:5
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postembryonic 550:20

post-developmental 213:2

post-hatch 46:14 47:14

post-larvae 244:23 245:2

post-metamorphosis 571:1,6

post-release 211:18 212:1

post-stage 175:12

post-validation 540:8,10

potency 485:16 538:8 540:5 541:1,1

potent 488:11 531:20

potential 11:22 12:4 17:6,8,25 34:16,17 35:15 38:3

39:24 41:17,23 44:16 49:11 53:5 56:24 58:26

61:21 63:11 74:15 76:10,13 112:16,18 143:15

171:3 178:3 208:14 212:26 218:3 219:22 225:22

230:25 231:21 232:22 233:16,25 234:2 258:15

278:16 290:14 293:11 294:8 302:22 314:8 317:12

339:3 361:1 363:2 367:2 368:2 407:2 411:9

415:14 423:24 424:5 426:10,16 435:26 436:10

447:12 449:10 462:18 486:22 498:26 517:18

526:23 531:11 534:9,21 548:13 550:17 551:19

557:13 558:14 559:7,16 565:12,16,19,23 572:2

606:19 608:13 609:7 611:26 612:9 614:23 620:18

622:2,16 623:2,6 626:7 634:12 639:11 668:6

669:9 735:1

potentially 47:4,16 59:18 60:3 61:12 96:22 170:2

231:4 246:15 256:17 299:19 303:5,22 304:18

313:7 317:16 319:21 343:7 354:7 356:14 415:4

415:25 424:12 427:20 436:4 437:6 486:4 499:11

499:13 526:18 546:18 557:15 562:6 598:20

602:20 609:6 614:11 690:16 716:18

potentials 74:3

potion 388:3

POTOMAC 1:15

pouch 211:18 213:18

power 22:17 31:13 54:25,26 55:3 56:6 58:22 67:19

67:21 91:8 101:7 110:6,18,22 111:5,10,16 131:21

138:18,19 139:1,2 182:20,24,26 183:2,7,12,12

192:5,6,21 214:13 234:24 265:13 272:25 361:10

361:12 370:2,12 374:26 394:17,22 396:24 402:17

404:12 409:18 417:7 421:2,8 435:13 436:11

437:2 449:9 450:4 483:10,11,11,15,20,24,26

503:22 504:1 505:25 506:1,1 524:26 525:6

526:24 530:7 534:13,18 537:21 555:9,16 564:3

568:20 579:2,4,15 583:2,6,10 584:1,18 585:18

588:6,7,12,13,19,20 589:1,4,20 615:10 677:12

678:11,12,18 680:7 687:26 688:1,18 689:2

692:18 699:5,14 711:2 716:19 737:19

powerful 148:2 366:1 372:8 379:18 391:6 396:16

473:23 551:11,20 557:15 579:21 711:7,8

PowerPoint 577:9,23 593:12

ppm 50:17,20,21 51:1,5,6,7,8 70:20,25 71:10,12,15

71:17,18

practical 42:12 43:21 57:23 186:10 222:14 350:2,8

350:11 351:4 352:9 500:15 605:22 696:13

practice 44:2 265:20 311:3 313:26 331:25 371:4

390:20 683:8

pragmatic 632:25

preamble 346:22 419:10

precede 482:7

precedent 456:25

preceding 127:2

precipitating 190:9

precipitation 182:13

precise 535:9 699:18

precision 453:14 584:11 688:15

precocial 56:16 57:8,12 293:6 350:5 351:5,11

424:6 425:2 432:11

precursors 720:15

predicable 488:3

predicament 343:5

predict 204:12,12 244:21,26 245:2 258:1 553:14

622:3 715:2,3

predictable 274:9

predicted 68:16,19 375:13 559:26 560:2,11,21

561:6,10,20

predicting 478:4 552:17 710:6

predictive 639:10 642:6 646:14

prediluted 572:7

predominantly 36:17 37:4 54:1 285:5,6 377:6

preface 444:6

prefer 86:22 147:12 206:19 220:4 227:8 379:26

preferable 408:18 573:16

preferably 380:23 391:8 662:10

preference 425:19 443:21 675:20

preferred 25:22 397:21 431:4 534:11 707:22,24

preliminary 682:7

premature 283:6 708:20

premise 436:22 437:10

preparation 9:1 13:2 27:20 572:3

preparations 4:22 376:5

prepare 4:8 9:2 10:24 12:16 13:5 82:5 422:7

507:21 572:15 607:12 704:3

prepared 157:26 238:18 572:13 590:8 746:14

preparing 2:16 4:20 12:15 741:13

preplanned 397:24

preregistered 3:22

prescreen 623:26 625:14 626:5 646:12,13

prescreens 624:7

prescribed 146:1 356:25 398:3 422:6 507:21

607:11 621:16 622:26 704:3
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prescribing 366:22

prescriptive 331:7 376:26 521:24 522:5 673:15

presence 80:23 303:3 381:13 481:5 557:9 560:7

present 45:2 64:21 75:11,26 76:1 90:26 98:18

139:13 164:4,5 261:7 292:25 293:3,4 295:24

298:16 319:19 336:25 339:8 423:1 426:17 511:3

511:13 605:20 634:15 709:8 747:17

presentation 12:23 28:23 59:4 68:15 70:17 75:17

154:17,19 163:10 189:2 203:10 205:4,6,24 206:9

227:13,16 235:3 243:21 244:10 305:15 309:15

316:5 333:17 335:18 428:11 438:12,14 485:12

577:26 637:14 669:12 672:12 675:13 708:6 719:1

734:11

presentations 4:4 10:8 27:4 29:20 38:20 153:7

154:9,16 239:15 240:1 241:12 244:2 285:24

324:5 347:25 348:2 714:5 737:17 746:26

presented 50:12 64:24 91:3 195:25 202:22 245:10

253:25 287:3,6,18 326:21 339:12 340:26 344:26

345:2 376:25 433:6 436:9 479:14 486:12,21

487:19 508:23 515:11,17 603:25 666:22 708:6,20

726:12 733:3

presenter 3:21 179:22 285:23

presenters 239:13 277:14 284:17 333:11 345:19

presenting 34:26 75:14 91:19 115:10 314:24

319:16 730:2 734:5

presently 624:15

presents 31:21 357:15 452:18 478:9 507:25 550:6

566:16 637:4

preserved 45:24

president 457:9

press 306:9

pressure 327:12

presumably 572:24 699:9

presume 35:9 471:3

presumptive 486:9

pretending 588:1,20

pretest 520:26

pretty 48:14 54:2,8 57:6 65:19 86:18 96:14 120:10

132:6 145:3 163:14 177:8 184:15 185:2 188:11

191:2,19 192:8 197:14 199:6 235:14 260:15

266:2 285:1 286:25 288:3 303:4 315:20 332:22

349:18 355:25 367:15 379:18 385:14 390:9

398:18,25 401:10,24 411:17 488:11,19 502:14

547:16 601:3 608:18 614:13 653:7 702:10 703:2

703:5

prevalence 29:2 86:15,23 88:14 89:26 128:14,22

128:26 129:9

prevalent 86:26 231:18 362:11

prevented 604:16

preventing 129:5 168:5 690:3

Prevention 8:22 11:7 13:22

previous 9:23 26:3 31:19,23 68:1 77:26 110:8

115:7 149:14 158:22 171:19 179:22 180:8 255:22

271:3 295:2 328:11 441:9 475:12 540:16 549:7

564:13 596:22 609:13 626:23 633:26 684:15

689:20

previously 163:16 167:2 420:25 526:3 614:8

pre-absorption 571:23

pre-approval 427:26

pre-assay 728:9

pre-egglaying 43:23

pre-test 402:8

pre-Tier 623:25

pre-validating 290:20

pre-validation 27:22 286:11 287:9 290:18 292:20

295:13 305:3 697:20

pre-vitellogenic 158:9

primarily 40:21 46:2 54:11 67:6 82:9,19 83:2,5

91:15 101:6 102:9 108:3,5,24 122:1 134:17

141:16,20 142:5 225:12 237:4 258:4 267:22,25

299:23 315:23 381:20 449:14 485:11 541:21

primary 6:6 56:8 82:17 85:2 133:1 140:7 147:25

280:8 397:20 511:8 571:23 636:12

primer 523:20

primers 139:19 513:2,3 522:10

primetime 728:3

priming 512:25,25

principal 274:5

principle 298:2 348:8 349:11 376:22 380:8 435:22

principles 19:3 511:5

prior 3:17 47:11 64:9 65:24 161:6 167:13 175:16

310:16 367:1 393:15 404:11,12 516:8 521:3

538:11 745:16

priori 122:19 300:9 585:11,19

prioritization 17:23 20:14 32:14

prioritize 11:17 634:19 646:15

prioritized 17:24

prioritizing 230:3

private 7:19 428:3

privilege 736:15

probability 17:25 20:15 110:15 111:1,8 116:11

402:4 404:10 472:25 473:14 578:13 680:22

705:20 716:25

probably 34:2 49:3 52:4 69:18 87:16,19 90:4,6

94:13 99:14 117:10 119:1 120:4,5 123:13 124:23

131:15,25 148:5,20 152:24 164:18,24 173:7

174:7 178:13 181:7 184:23 197:12,21 233:10

235:13 255:20 277:1 288:1,18 292:14 293:17

323:3,5 343:24 346:15 350:20 353:2 361:6

367:12 370:25 372:22 384:26 385:26 392:6 393:1
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393:19 396:20 398:25 399:12 409:26 429:14

444:14 471:17,24 475:11,12 476:15 477:17

484:11 485:6,7 486:6 487:17 489:19 495:9 499:2

500:11 501:21,22,25 502:12 542:8 543:5,22

546:17 565:6 574:18 577:21 585:16,17 586:12

592:25 595:16,18 600:20 604:8 628:26 630:16

673:24 676:23 682:23 685:25 687:13 694:4,5,22

701:20 714:2 724:3 728:21 732:24 734:10

probes 123:12

probit 451:18

problem 103:13 113:10 138:26 140:5 161:2 172:14

172:14 178:15 197:11 281:3 298:13 302:7 326:8

326:16 327:18 334:8 344:6 368:22 385:23 397:18

473:22 481:14 498:15,21,22,26 501:5 563:13

565:6 569:18 578:21 579:7,23 587:2,3,15 605:19

612:18 635:17 659:18 671:17,18,24 672:7 676:6

676:14,18 688:7 696:18,20 723:6 731:16

problematic 103:24 104:12 225:1 372:10 479:2

628:16 630:22 677:6 716:12 722:4

problems 102:9,10 103:12,25 104:1 129:22,24

130:26 131:9 136:16 196:12 219:11 224:20

237:20,22 249:18 279:16 301:10 316:20 319:3

331:13 370:18 498:5 499:7,19 560:24 571:16

581:2,26 586:15 604:24 608:19 610:17 629:9,20

641:17,18 670:23 677:8 727:25 741:5

probs 123:8

PROC 473:5

procedure 89:24 127:9,13,20 138:20,22,23 139:3

166:14 502:25,26 589:11 591:3 680:4

procedures 166:8 417:11 467:7 501:6 521:23

582:12 605:18 673:20 675:10 744:7

proceed 249:15 695:22

proceeded 50:9

proceedings 9:18

proceeds 158:24

process 4:11 9:16 11:12 12:6 14:15 27:8,16 28:3,9

32:7 33:17 41:14 83:18 93:13 120:21 141:18,21

142:18 143:4 159:11 160:18 190:10 206:13 231:2

241:8 248:5 251:8 252:1 253:20 256:1,11 261:1

283:4 310:13 321:13 326:16 337:2 369:1 387:19

388:15 389:24,25 392:25 393:26 394:11 401:7

422:8 430:4 454:14 471:21 502:15 507:23 522:15

539:14 573:15 592:5 607:13 611:1 617:21 621:16

623:11,18,19,26 626:11 628:5 633:22 634:23

635:9 642:12 649:1,7 650:1 652:10,25 656:11

663:4 670:26 697:13,20,24,25 699:20 704:5

713:18 717:25 731:25 738:15,16 739:22 743:25

746:11

processed 45:24

processes 14:18 30:9 82:10 98:5 139:26 259:8,23

261:5 357:13,26 364:8 451:17 452:16 461:15

463:2,14,15 504:24,26 512:19 550:3 556:7

625:16 626:1 637:2 666:8

processing 188:23 499:2 604:15

prochloraz 97:18 99:13 119:9 134:15 135:24

161:22 162:1,5 163:5,14,16 170:9,10,26 171:1

173:18,26 209:21 281:23 311:22 485:21 487:10

488:26 489:26 492:23,24 495:12,21 505:15

551:13 555:13 560:11,15 561:16 562:10 596:19

596:23 602:12,17,23 603:26 604:2 625:11 682:2

722:3,3

produce 23:25 53:2 88:8 91:22 143:15 172:10

177:11 181:24 201:5 273:13 290:12 348:2 389:6

482:14 489:17 502:23 513:23 519:19 650:20

produced 50:5 55:16 64:12 81:11 149:25 172:11

223:18 237:6 265:16 271:22 280:25 290:9 319:5

320:8 338:24 339:2,2,23 340:1,4 505:10 521:3

568:7 616:12 691:26 700:2

produces 46:25 162:6 171:16 172:8 478:22 479:24

producing 46:18,19 51:19 197:20,22 203:3 214:15

489:16 505:22 699:25,26

product 64:7 201:5 587:18 620:12

production 18:26 41:1 45:3 50:3,18,24 51:11,14

51:16 52:16,21,23 55:19 63:6 70:19 71:15 79:25

96:19,19 135:3 141:18 143:22 208:1 214:10,23

215:25 237:11 247:12,20 255:11,22 263:17,20

339:13 350:5 364:19 368:3 375:1 431:11 438:10

486:13 494:26 519:26 529:12 551:7 552:12

561:18 568:18 711:12

productive 51:19 68:7

products 29:7 168:2

professional 706:14

professor 5:13,16,20 6:20,24 7:1,4,11,16,20,23

243:2 401:9 679:22

proficiency 199:17 375:22 650:13 652:6,10 660:12

661:12 715:8 743:12

proficient 456:23,24 714:24

profiles 683:3

Progesterone 423:20,21

program 1:5 2:6 9:20 11:3,13 12:3 13:4,15,15,19

13:26 14:4,10,12 15:12,16 17:2 18:20 20:13,15

27:6 29:14 41:26 88:23,26 89:7 90:5 179:23

181:2 195:5 204:26 251:19 253:13 268:21 269:22

282:6 306:14,17 315:13 316:8 324:14 326:18

349:7 354:10 355:3,6 365:25,26 381:19 428:19

428:22 430:5 472:23 473:2,6,8,17 475:7 483:6,18

522:16 543:19 611:3 614:2 633:25 639:20 650:14

652:15 659:1,6,15 661:14 664:5 705:25 713:19

735:11 738:24 739:15,18,22 742:9,19 744:2

746:20 747:16
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programatic 742:6

programs 8:17 10:20 11:6 13:23 114:9 136:22

265:20 459:5 464:6 592:3 649:22 651:2 659:23

725:12 746:22

Program's 241:6

progress 182:8 330:2

progressed 505:19 659:14

progressing 86:3

progression 46:20 142:22 362:18 551:5

prohibitive 547:16

project 239:5 742:9

projected 53:20 545:6 721:16

prominent 56:25 185:2

promise 305:1 404:7 471:10 537:21

promote 319:25

prompt 549:13

promptly 105:3

prone 427:21

pronounce 595:9

pronounced 217:9 425:5

proof 597:15

proper 379:7 384:12 414:9 419:12 478:2 493:11

516:14 526:25 601:21 697:5

properly 138:5 195:14 282:23 544:12 566:11

728:25

properties 19:10 275:15 383:11 470:17 479:25

551:12 576:20 621:10 674:22

prophylactic 113:21

proportion 87:1 481:8,13 591:12

Proportional-Odds 578:20

proportionately 378:20

proportions 400:10,10

proposal 210:5 308:7 484:8 737:8,9

proposals 737:5

propose 122:12 153:5 386:23

proposed 1:4 2:22 9:21 21:17 25:2 112:11 152:9

187:13 195:20 202:8 210:6 222:7 241:6 251:26

253:9 299:9 300:2 301:21,24 302:4,8 307:14

349:12 352:2 377:1 383:12 444:10 454:3,18

456:6,9 459:5 470:18 475:20 507:25 528:25

533:26 534:3,17 576:21 664:4 674:23 687:19,22

716:13

proposing 73:21 75:23 87:9 107:22 108:2 121:13

122:24 124:1,14 136:15 209:15 475:8 528:22

649:14 707:21

Propper 6:12,12 66:22,23 67:8,17 68:12 180:23,24

181:20 182:3,7,17 183:4,9,13,26 198:10,11,20,25

242:20,20 460:8,8 545:23,25 549:15,16 554:8,9

563:7,8 565:1,2 568:26 569:1 573:24,25 574:15

574:16 575:8,23 598:14,15 600:25,26 606:13,14

607:18,19 608:22 609:3,12 610:9 611:9,10

654:15,16 655:9,10 660:24,25 665:10,10 733:1

proprietary 335:25 336:3

protect 15:4 323:10 619:9 642:20 687:12

protected 335:10 617:15 619:7

protecting 18:13 315:14 621:6 686:8 710:21

protection 1:13 16:14 18:13 244:19 285:6 323:10

459:16 619:18,24 620:8,15 621:4 708:9

protective 15:11 24:8,21 30:24 31:9,14 254:25

255:3 256:2,3,7,14,17,24,25,26 307:18,19 308:1

314:12 334:19 686:1 692:22

protects 686:11

protein 190:9 410:12 416:15

proteins 184:9 567:14

protocol 27:21 28:1,2 39:12 40:6 41:11 42:4,16

43:7,19 56:9 57:19 58:19 63:18 64:3 75:19 76:2

76:24 77:6 78:15,15,16,26 79:3,21,22 80:9,10,11

89:22 98:23 100:25 103:18 104:16 107:20,26,26

108:8 112:11 139:10,17 143:7 160:14,15 173:11

193:6,14 197:5 209:15 210:7,7 273:25 275:26

278:20 292:18,18 296:7,11 299:6 300:14 301:21

302:8,14 305:4,8 313:17 347:21 348:1 349:6,20

350:8,26 352:17 354:19 356:5 358:8,19 364:4,10

364:23 367:11 368:20 370:16 384:20,21 386:24

403:26 408:14,17 409:17 412:15,17 413:11 414:9

417:9 418:2 419:11 420:13 422:2,3 428:14

433:13 436:12 437:4,5,7 438:25 439:10 461:12

461:18 465:14,21 466:14 469:14 470:2 471:15,16

472:19 480:6,13 483:1 491:24 493:10 502:14,16

502:17,24 503:2,4,5 504:11 505:12 506:6,18

507:16,18,26 510:20,20,26 511:1,22 512:15

513:21 515:11 516:19 520:13,17 528:25 529:3

546:9 555:16 556:4,9 558:8 567:13 570:13

571:15,17 573:20 574:9 579:14 586:11,17 598:25

599:8,12 601:20 607:7,8 608:4 610:15,17 614:3

622:14 623:14 638:11 640:5,24 642:12 649:22

650:23 651:1,12,13 652:1,7,14,14 654:11,23

655:6,7,20,21,25 659:10,22 660:13,16 661:13

666:5,10,21 667:1 670:12,15 671:7 680:13

682:21 697:4 699:17 703:24,26 707:21,24 709:1

710:23 711:1 714:16 716:22 717:2 724:6

protocols 27:25 28:21 40:21 77:22 78:23 79:1

89:10 93:12,12,14 114:13 160:17 194:13 195:14

198:1 206:14,17 208:24 282:22 325:25 330:19,20

330:20 331:14 335:2 349:1,3 426:2 428:9 433:17

465:4,10 466:13 471:17 497:3 509:22 514:21

517:22 526:3,26 558:4,5 585:3,6 599:22 607:26

614:19 621:1 622:26 639:21 645:5 668:25 697:22

698:6,26 706:17

protracted 197:1
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protuberance 462:26 463:6,8,9,10,24

prove 157:16 436:13 440:19 489:24,25

proved 157:17

proven 287:8 375:12 428:1 710:20

provide 11:25 20:25 21:19 25:19 28:15 29:19 40:5

43:3 57:21 59:11 63:19 64:5 70:7 104:14 107:19

114:7,9 175:24 195:7 196:4 205:18 228:14

229:18 251:9 274:17 277:4 301:13 325:11 347:12

348:5 349:16 355:3 356:21 363:12,19 364:11

366:17 371:19,21 374:18 382:25 384:5 407:4

414:9 419:11,21 422:11 434:16 443:13 444:13

453:13,15 457:4,14,24 461:19 462:6 467:19

483:6 486:14 487:3 493:11 494:19 508:2 516:9

521:16 530:2 545:17 556:10 558:25 561:26

564:15,17 566:5,21 568:4 570:14 572:24 575:6

596:25 597:14 601:21 603:1 607:16 608:13

609:14,20 613:20 614:10 616:24 624:11 626:11

631:15 638:13 642:24 645:11 650:7,24 657:20

662:26 666:11,26 667:17 674:8 679:25 680:2

695:6 697:5 698:16,18 704:8 705:21 716:20

730:8 731:7 743:16

provided 10:8 90:24,25 122:2 155:1 160:14,17

173:11 179:11,17 244:19 250:19 282:7 296:24

300:13 302:9 309:21 346:5,10 347:23 366:25

367:18 376:13 418:8,13 434:24 437:12 442:10

446:3 448:22 449:11,14 465:4,13,17 469:16

493:25 510:21 511:13 528:25 533:23 561:26

571:14 578:18 580:21 627:6 636:11 666:24

670:17,24 676:4,22 678:8 712:10 719:1 736:2

provides 2:19 10:3 41:11 58:14 83:17,18,21 84:9

172:8 313:3 314:10,15 322:15 323:17 365:4

369:16,26 376:23 421:4,14 426:7 445:26 464:26

520:7 621:17,26 622:2 680:6 708:8 719:3

providing 9:9 10:1,25 25:15 62:17 172:22 251:7

277:17 296:2 346:17 350:15 372:25 377:12

455:16 459:4 492:17 518:15 534:26 566:1 572:7

620:14 686:24

proving 247:11 724:9

provisions 2:9 3:6

proxy 551:7 552:14

prudent 124:17 343:11 438:21 451:11

pseudo-replication 696:11,16,18

PTP 529:18

PTU 457:13

pubertal 233:6

pubertals 233:20

puberty 424:11,16

public 2:14,14 3:15,19,23 4:1,4,5,19 9:8,17 10:26

12:17 13:25 15:4,11 20:19 43:9 61:23 154:10

239:26,26 240:3,4 241:13,15,19 277:8,12 284:3

314:24 325:23,23 330:23 343:5 345:20,20 346:14

428:3 442:18 451:13 510:21 514:2 623:16 624:20

642:7 648:21 663:5 669:11 730:12 739:25 742:25

publically 514:6

publication 352:14 462:22 515:12

publicly 10:1 31:1

publish 489:23

published 27:17 94:19,22 95:13,22 97:6,9 98:10,20

157:10 163:16 174:22 181:16,16 225:15 248:17

431:24 547:11 560:13 567:24 738:8

pull 8:1 13:10 17:10 29:4 109:6 114:18 187:11

230:8 270:11 364:18 508:22 587:22 653:24

658:15,21 727:12 733:6,19

pulled 27:7 109:9 164:3 165:8 311:20

pulling 217:7 653:10,13

punctuated 634:22

Purdue 7:15 243:11 665:23

purely 228:9

purification 513:5

purity 214:7

purple 179:14

purpose 25:6,19 27:10,11,15 39:7,22 42:19 58:25

76:4 112:11 155:11 206:10 208:19 212:16 222:8

260:2 278:8 285:16 306:14 324:11 329:3 332:7,7

334:12 346:23 347:5,7,14 348:7,8 357:21 426:17

430:4 443:5,8,15 444:7,14 445:22 452:23 453:4

503:23 522:15 545:9,12,20 550:11,16 551:22

597:16 611:1 613:5,15 614:22 616:6,9 617:1,11

626:13 627:17,21,26 631:11 635:3 637:10,16

685:9,14 697:14 705:6 713:17 732:20,23

purposes 29:7 375:4 409:24 436:14 500:15 598:3

605:22 662:7 681:21 706:1

pursue 148:3 206:20 252:18 266:25 356:22 455:8

pursued 116:1 252:19 743:17

pursuing 743:22

purview 655:13

push 13:25 22:14 480:20 492:2 630:10 646:5

pushed 643:3

pushing 643:17 728:16 739:3

put 11:10 13:8 14:10,20,22 15:16 18:18 31:2 67:12

86:20 87:1 100:18 116:14 117:14 121:2 139:24

140:3 150:11 169:5 178:7 202:25 239:25 244:11

248:4 298:5 299:25 306:17 311:20 314:14 330:24

336:17 342:4 346:14,15 361:9 369:26 372:20

387:12 391:19,23 395:19 397:10 425:18 429:13

431:23 442:17 484:9 490:15,16 491:17 494:1

502:12 513:17 514:6 521:1 584:16 585:5,17

586:13 590:7 592:8,9 593:8,11,11 594:4 614:17

617:7 623:22 630:1 635:8 648:5,13,24 649:9

650:3 662:11 673:19,26 687:3,13 698:22 700:14
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700:15 721:10,12,26 725:21 728:20 730:11,13

731:6 738:7 739:10 741:12

putative 207:5 374:4 377:6 431:26 529:12 534:10

puts 598:19

putting 9:16 18:23 19:2 20:10 28:12 34:3 38:25

150:1,4 269:8 343:4 392:17 595:11 631:11

745:19

puzzle 22:20

P-chem 19:9

P0 528:21 529:11,13

Q

QA 104:9 113:10 346:15

QAQC 705:3,6

QA/QC 104:1 114:11 119:21 129:23 139:10

512:20 516:14,15,26 614:20

QC 346:16

qPCR 122:25,26 139:20 368:13 410:8,10,15,21

423:12,26 512:7,8 513:2,2,10,19,20 575:18 726:5

726:12,16

QSAR 11:16 19:3,4

quadratic 483:9

quail 25:3 29:21 39:3 42:5,10 43:20 45:19 46:17

51:18 53:1,7,26 54:11,19 55:15 56:15,16,18 57:7

57:10,17,21 58:18 59:7 62:2 64:8 65:12 66:11

75:8 267:13 277:26 285:26 286:17,21 287:3

293:13 295:18 338:16 340:13 346:19 347:16

349:24,26 350:2,12,13,16,23 351:5,22,24,26

352:3,9,12 353:17 355:26 356:18,18 359:10

360:25 361:7 362:9 366:15 367:5,25 373:5,11,20

376:11,16 377:5 381:23 383:20 384:11 386:5,20

388:17 390:26 393:10 406:10 413:17 414:13

415:6,15,26 416:7,16 417:18 418:20 419:7

422:12 424:11,13,22 425:18 427:17 430:10,14,23

430:26,26 431:1 432:8,8 433:4 435:11,17 472:22

488:20 489:12 518:14 526:11 559:12 569:20

646:22 647:10 648:13,17 659:17,17 661:10 737:1

quails 42:7

qualified 514:5

qualify 517:9

qualitative 274:14

qualitatively 33:9

qualities 675:17

quality 114:5 116:22,25 117:9 194:23 195:8 196:1

199:19,20,24 202:5 203:7 214:13 223:17 241:11

241:11 278:17 358:13 378:5 465:11,11,12,14,17

465:21 501:8 512:21 514:2 515:17,19 516:6,10

516:17 530:12 554:18 558:19,20 571:13 572:13

572:17 663:4 705:7 715:7 726:16 738:16

quantified 463:7,16

quantify 54:15 60:15 76:8 112:14 427:9,10 622:21

641:22

quantile 402:14

quantiles 402:12

quantitation 561:24

quantitative 17:15 33:10 60:23 177:21 377:23

380:14 449:19 457:3 555:7 578:23 609:14

quantitatively 33:9 309:12

quantity 171:2 288:26 396:3

quarter 544:20

question 17:5,13 22:11,26 27:24 29:10,15 30:17,20

31:17 34:15 36:1,16,22,25 37:3 54:17 64:1,2,20

67:22 68:23 71:13,21,22,26 73:13,17 74:2 75:2

115:1 122:21 124:7,13 126:11 130:3,8,11 131:3

132:23,25 133:12 134:18 135:14,20 136:15

138:12 139:23 143:8 153:11,23,24 164:3 173:20

175:6,7,7,11 177:12 179:21 184:19 187:14

188:17,21 191:17,22,26 192:18 193:3,9 196:16

198:17,26 203:16 208:15 218:13,15 227:17 231:5

232:22,25 233:16,24 234:1,14,15,20 239:1

241:12 243:24 245:14 246:8,9,11 247:6,24

250:16,17,26 256:16 257:2,21 258:24 259:25

265:9 268:15 272:1 274:23,26 275:7,15 281:12

286:17 287:10 297:18 298:9,9 304:17 306:15,18

308:9 316:4 323:13 325:16 326:2,14 330:25

332:1 335:17 336:21 338:14 344:22 346:11 347:3

353:13 354:14 355:14,19,21 356:2,8 357:3,10,11

359:14 360:4,5 361:23,24 362:13 363:3,23,26

364:2,3 370:25 373:9,23 375:16 376:2,3 382:9

383:5,6 385:6,10 389:2,19 391:7,12 395:4 403:22

404:2 406:14,20,22 409:9 410:4 411:6 412:5,21

414:2,4 421:20,26 422:1,16 429:14,23 430:1,3

434:9 435:4,5 436:15,16 437:18 441:2,19 442:9

443:3,22,24 452:6,12,14 458:10 459:9,14 461:9

461:11 466:20 467:12,19 468:9,22,24 469:8

470:10,11 471:4,7 475:25 476:1,15 484:25 485:5

485:7,8 487:15 490:16,25 491:11 492:20 493:2,4

493:5 496:10 501:1,22 502:1,2,12 507:2,14 508:8

510:4,13 511:17,25,25 514:15 518:5 521:8

522:13,14 523:4 526:7 528:16,16,18 529:9

532:25 533:20,21 535:21 539:6 541:6 545:4,6,7

550:1 556:1,3,3 559:11 561:16 564:7,12 565:9

566:7,9 567:20 569:18 570:7,8 575:5 576:14,16

585:22 592:4 593:26 595:26 596:8 597:5 601:13

601:15 602:3,4,5 603:24 607:4,6 608:26 609:10

610:19,25,26 612:8 613:4 615:12,22 616:1,2,4

618:5 623:18,19 627:8,17,17,25 628:8,11 632:18

633:3 635:3,18,20,23,23 636:7,8,24,26 639:16,17

642:24 645:1,14 646:18,19 647:20 649:12 656:2

656:20 660:2,4 662:4 666:1,2,3,4 667:16 668:14

669:22 670:9 671:10,11,17 674:15,17 675:7,11
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678:25 679:7 681:5 682:12,19 685:7 687:15,19

690:8,16,22 691:10 694:19 695:3,12 696:24,26

702:19 703:23 707:13,15,17,19 713:16 718:13,22

721:7 724:25 726:4 737:24 738:3 741:8 747:20

questionable 173:17 224:23 294:17 479:7 510:10

510:11 625:16

questioned 227:6 433:7 567:21

questioning 32:4 184:25 306:12,13 308:4 737:23

questions 3:25 4:9 8:8 22:1 29:25 30:1,5 34:6

38:10 45:26 59:4,8 62:6 63:16 70:17 88:20

114:23 115:16 128:8 130:20 134:6,7 137:12

145:20 153:15 176:6 184:2,8 186:7 198:19

203:14,24,25 204:1,9 205:11,21 210:17 227:13

233:13 234:24 239:16,21,25 243:23 244:1,3,9

248:7 265:14 267:9,12,15 270:2,2,4,7 276:1,5,6

276:16,19 280:2,16 295:24 316:22 324:1,15

333:13 335:24 343:13,15,17 345:19,23 346:19

347:24 353:14 360:8 372:16 375:16 382:10 384:1

386:20,23 387:7 403:25 409:12 411:12,13 412:16

421:21 429:24 434:19,21 441:12,21 442:5,22,24

442:26 443:23,26 449:12 452:7,9 458:3,4,20

461:5 468:20 470:7,8 471:23 484:20 492:12

493:19 507:7 521:9,12 528:14 533:18 541:4

544:12,16,18,24 549:23 555:22,25 564:13,15

570:1,4 575:3 577:16 595:25 596:1,5,16 601:11

607:3 608:17 610:23 615:16,26 625:5,6 626:26

627:24 629:24 633:7 634:11 642:21 645:4,22

647:18 655:13,16 656:19 657:1 662:26 664:14

673:11 695:1 702:22 703:14 707:10 713:12,14

714:5 718:8,9 724:21,23,24 733:8 737:25 738:1

746:4

quick 18:16 75:16 137:12 150:25 180:14 184:7

187:6 195:18 202:15,20 227:17 253:1 275:7

678:23

quicker 155:4 249:14

quickly 18:23 24:18 61:10 76:5 90:8 91:20 98:15

99:8 104:21 105:15 132:12,16 181:2,24 206:2

242:1 301:6 316:23 317:23 336:22,24 373:19

403:3 404:7 427:8 459:13 464:8 476:7 589:23

601:4 626:7 629:11 740:22

quiet 655:13

quite 12:10 26:21 40:14 65:10 76:18 79:13 87:5,11

87:18 97:8 98:26 99:16,16 111:16 118:2,14,15

120:15 123:20 131:22 137:15 160:14 180:19

183:16 192:24 200:7 204:21 243:22 246:3 264:6

274:16 275:11,23 291:18 311:17 320:13 329:14

331:2 352:23 355:25 371:3 373:7 386:16 408:11

410:25 416:26 417:3 423:15 426:1 474:9 478:26

552:8 565:3 569:10 572:21 617:18 641:1 647:3

680:13 682:15 683:18 692:1,7 719:21 728:2

744:4,19 745:23 747:13

quo 697:19

quorum 593:25

quote 29:7 288:15 301:16 329:2 520:5

quotes 323:12

quoting 402:21 433:24

Q&A 442:6

R

R 91:11 114:10 269:23

RAA 425:25 426:2

Racine 28:24

rack 415:16

racks 415:15,18

radio 283:21,22 459:7

radioimmunoassay 191:15 567:7

raise 350:3 364:23 569:22 590:2 649:21

raised 280:2 348:1 351:6 352:26 561:23 623:9,17

702:20 718:6

raises 54:17 81:25 265:9 281:11 340:19

raising 37:3

ramble 684:6

ran 95:25 96:16 117:26 137:23 162:3 224:16 291:4

489:14 656:11

Rana 549:10 596:23

random 137:19 145:21 146:5 153:19,22 228:9

303:19 387:19 388:15 389:25 393:19,26 394:2

402:15 429:3,9 472:9 475:8 512:25 578:15

592:14 594:19,20 595:11

randomization 62:7 386:11,14,16,19 387:10 388:8

388:13,26 389:17 392:2,2 394:2,11 502:15 588:4

588:5,5

randomizations 590:14

randomize 388:10

randomized 220:5 380:1,23 387:23 388:7 391:16

394:4 408:20 472:6,6,14 502:19,19 675:15,21

676:6,15,16

randomizing 471:21,21

randomly 109:21 289:25 387:10,13 389:13

range 16:7 31:11 48:26 53:24 55:20 64:17 68:2,4

86:9 87:7,24 117:9 148:6 151:3,5,8 200:11

312:17,25 313:1 337:9 349:1 365:4 383:11 390:9

397:24 446:22 455:26 470:17 511:8 518:9 531:16

558:10,21 567:10 576:19 582:21 654:12 674:21

682:25 691:3 698:8 702:18 709:22 712:12

ranged 506:10 551:15 560:16

ranges 218:18 391:12 501:8 506:7 689:8

range-finder 165:12

range-finding 47:16 61:12,15,21 278:7 599:3,20

647:25,26

range-findings 585:10
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rank 401:20,23 402:6 403:1 479:24 588:1

ranked 17:24

ranking 405:7

ranks 401:6,12 402:10,11,12,20,23 474:5,9

rank-order 478:24

Rao-Scott 481:19

Rao-Scott/Cochran-Armitage 89:21

rapid 224:14 431:7 448:7 453:8

rapidly 16:22 56:16 293:7

rare 129:7,8 130:10

rarely 87:1 130:10 722:10

rat 23:12 37:4 162:6 233:5,7,25 329:8 335:4,5

415:1

rate 25:5 55:22 60:9 172:12 197:18 211:8 275:22

291:12 481:15 483:12 551:18 558:23 701:20

rated 523:20

rates 170:21,23 171:8,8 172:23 212:9 222:17 403:8

586:5

rating 457:13

ratio 45:6 87:20 214:4 316:16 395:6 397:2 451:3

rationale 59:11,12 110:5 131:4 133:26 147:25

155:6 225:8 231:7 237:20 249:12 267:19 268:17

274:5 287:2 296:24 328:5 347:4,14 348:8 349:26

443:3,15 444:7,9,13 446:7,20 487:17,17 488:13

528:25 529:1 530:6,11 534:16,26 536:4 537:23

545:7,20 546:8 549:1 567:21 616:5 617:1,11

622:15 626:12 627:11,17,20,26 628:3 631:11,13

631:15 632:12 635:3 686:4 707:23 728:7

rationales 382:26

ratios 140:15,17 165:2,4 445:17,24 450:22 455:2,5

487:2 531:1 553:2 557:14,15 711:5

rats 37:18 381:21 486:11

raw 483:26 674:6

ray 462:26 463:2,12,13,15

Rayan 698:4

rays 463:1,1,14,25 464:2

reach 96:19 109:11 156:20 197:2 530:21 551:6

reached 10:15 51:17 187:25 568:5

reaching 13:23 187:10

react 348:12

reaction 82:16 413:16 512:26

reactive 102:15 644:4

read 28:8 29:5,6 45:25 50:13 76:5,5,5 129:16

130:24 136:11 146:10 166:23 173:25 174:2,2,3,8

179:15,16 197:5 198:1 230:1 235:19 241:13

277:10,11,16,24 283:11 324:7 348:4 369:14

381:16 421:17,26 442:16,25 470:11 493:2,3,4

522:14,25 528:16 554:23 556:1 570:7 574:6

580:11 598:25 599:1,10 601:13 604:3 607:4

616:2 632:17 635:6 639:25 658:18 674:15 676:8

680:12 681:16 703:23 741:18,24

readable 202:3

reader 446:3

readers 747:20

readily 131:7 173:8 187:23 188:15 501:24 515:26

523:19 524:3

readiness 28:5 430:4,6,15 433:26 441:15 522:16

522:17 533:8 611:2,3,16 614:1 697:22 713:18,19

reading 89:17 121:23 188:23,25 330:6 346:20

347:3 364:2 372:17 376:3 383:6 385:3 414:3

422:1 429:13 442:24 452:13 461:8,10 528:18

549:26 566:8 576:15 580:4 601:14 604:15 666:3

674:16 687:18 694:15

readings 189:22

ready 12:22 75:7 105:9 205:24 277:7 282:5,8

305:5 306:5 310:18 346:18 353:13 357:9 363:1

363:26 376:1 382:10 383:23 414:1 430:15 442:22

470:9 493:2 522:13 566:7 576:14 590:8 615:21

636:23 666:1 674:15 681:4,8 696:24 702:6

703:22 707:16 713:6 718:12 728:2,2,15

reaffirm 373:4

reagents 512:19,23,24

real 49:19 70:6 75:16 80:19 91:20 92:8 98:15 99:8

100:17 104:21 113:19 131:23 135:4 173:19

176:19 202:15 208:2 219:9 262:24 274:9 317:23

321:24 330:26 336:22,24 367:21 375:2 391:19

415:2 523:23 524:1 539:18 548:14 581:5 599:22

626:7 728:23

reality 193:17 290:20 310:19 404:23 672:5 743:24

realize 61:18 107:21 145:17 163:21 248:3 315:25

354:19 393:5 533:6 690:19

realized 531:25

realizing 290:15

really 9:4 11:11 12:7,12,18 16:11 19:23 23:9 25:5

26:6,6 27:22,26 28:14,14 30:19 39:16 42:6,15,23

42:23 46:15 48:17 49:1,18 50:6,17 51:11 52:8

53:26 59:22 60:2 61:21 64:4 70:12 73:5,21 77:12

79:20 83:12,12 86:18 94:13 112:7 115:18,19

117:11,11 119:12 121:16 126:5 127:16 132:10

134:20 135:8 139:20 141:11 142:11,22 152:8

154:12 157:21 165:19,20,25 173:16,17 175:10,15

176:19,22 179:8 180:26 181:8 184:21 187:8

188:14 190:26 192:8,13 194:8,15 195:23 196:5,7

196:14 197:15 198:1 199:18 203:2,11 207:4

208:19 210:7 211:22 212:3 213:4,19 214:7 218:8

218:17 224:25 226:19 228:13 238:15 239:12

244:4 245:20 246:9 247:16 248:5 249:1 253:6,20

254:1 256:8 262:22 269:25 283:22 291:9 292:16

293:10 294:14 296:7,13 297:10,17 298:2,14

299:26 301:8,13,22 304:19 306:15 307:2,6
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312:23 315:3,7 316:6 317:10 321:21,25 322:6,7

322:17,24 323:4,7,8,16 324:9 325:11,15 326:3,23

327:8,21,25 328:4 331:1,9 332:9,18 333:4,17

334:5,11 335:14,18 336:23 343:14,18 345:1

355:19 359:12 367:19 371:12,15,15,16,25 380:19

382:16 384:21 388:20 390:5 391:18 393:4 403:8

406:19 409:17 412:13 415:19 416:2 424:3 428:11

428:13 429:7 442:16 445:19,23 447:21 450:20

451:6 457:21 458:5,16 480:6 488:14 489:5,12

502:16 510:5 511:22 512:15 516:9 517:5,8

523:12 524:7 525:21 526:4,6,7 530:4 532:13

547:15 548:1,7 555:8 565:15,17 569:20 577:10

579:1 580:11 581:3,9 588:22 590:17,25 594:4

608:15 612:16 621:3 623:14 625:24 629:17 631:4

632:5,7,18 636:12 639:20 642:2 648:6 649:2

653:8,13,16,18,22,24 654:1,2,2 655:12,16 657:8

658:9,13,14 660:11 662:5 672:12 677:16 678:9

680:23 682:26 683:2 684:5,16,26 685:20 686:12

687:10 688:5 690:14 691:4 694:2 701:5 702:16

702:22 712:21 720:6 721:9,10 722:19 727:21

728:15,16 729:26 730:19,22 731:7 732:12,16

733:9,10,17 734:1,12,15,16 735:3 738:20,26

739:11,14 740:15,18 741:17 747:17 748:9

realm 35:23 336:18 341:13 355:5

real-time 514:2 573:26 575:10,18

reanalysis 93:21 94:6 162:24 205:12 276:17

595:10 671:23

reanalyze 90:23 595:19

reanalyzed 90:22 475:1 677:2

reanalyzing 741:1

rear 103:22

reared 109:11

rearing 558:2,8

reason 26:2 51:16 119:11 133:2 140:23 176:9

191:5 204:19 246:20 270:13 271:3,8 299:20

319:16 379:16 433:17 445:15 449:13 454:26

498:1 562:26 579:2 581:6 629:22 688:14 711:21

720:5 731:14

reasonable 94:20 110:26 156:3 195:13 304:15

312:26 377:14 395:25 420:9 465:1 504:2 517:13

543:1 649:25 654:6 659:11,13 706:13

reasonably 37:1 58:25 60:18 77:10 117:20 137:1

224:8 430:26 653:15

reasons 49:8 55:16 102:22 108:25 129:22 283:7

287:6 293:19 305:2 311:2 343:23 449:10 499:24

570:24 629:10 643:10 718:26

recall 62:24 67:8 165:8 237:3 675:12

recapitulate 465:5

recapitulated 445:11 454:25

receive 9:7 415:16

received 28:8 199:15 277:8 475:5

receiving 276:18 474:25

receptive 38:8

receptor 19:3 37:11 96:1 135:9 162:6 184:10

279:12 303:2,4 408:3 410:12,12 560:12,22 561:7

561:11 568:16 625:22 644:1,9 667:15,21

receptors 407:3,4 626:10,10 643:24 644:1

recess 458:21 663:12

recipient 206:25

recipients 284:23

recognition 16:15,25 29:9 539:13 696:17,20

708:26 710:19 723:17,21

recognize 55:12 73:10 111:4,18 267:5 273:4

520:15 522:1 601:3 741:12 742:10 744:4

recognized 16:22 59:25 90:21 171:19 547:12 551:4

630:17

recognizes 635:10 670:23 680:22 708:11

recognizing 185:26 186:15 260:18 521:17 540:1

587:24 634:14,23 673:12,17

recollect 62:18

recollection 381:24,25

recombinant 486:16

recommend 57:3 117:14,15 118:9 121:15 150:20

172:26 211:6 350:26 363:4 370:11 371:17 427:5

488:24 518:14 533:24 538:7 540:24 575:22

609:21 648:17 656:4 673:6 676:17 685:13 707:23

716:21 723:3 726:26

recommendation 17:1 206:19 251:16 354:2

363:25 365:21,24 366:10 367:3,23 368:1 413:19

415:7 416:1 417:26 424:18 489:3 527:10,12

539:12,20 540:2 549:11 565:9 575:13,24,26

576:6 609:1 647:5,24 648:4,15,20,24 655:15

674:11,13 696:3

recommendations 16:13 19:24 20:25 28:18 42:1

42:14 54:21 117:1 122:8,8 173:1 209:10 230:10

232:12 252:17 265:19 349:8 354:23 355:12 362:3

362:8 363:13,19 377:1,3 380:11 382:13,16

417:16 422:11,21 427:24 430:1 434:22 458:5

508:2 521:5 542:11 564:15,17 566:21 570:14

607:16 609:13,20 632:23 633:26 636:20,22 651:2

661:9 673:3,5 674:2 679:7 704:8 744:9

recommended 43:19 57:19 75:23 111:15 121:11

146:13 168:10 171:2 172:9 194:9 266:5 402:23

431:4 433:13 447:9 482:26 516:20 517:20 531:8

534:11 538:3,13 540:1 542:15 549:11 640:10

670:19 708:7 714:15

recommending 108:6 109:4 118:25 273:1 350:22

538:5 649:10 670:25 677:22 695:14

recommends 362:14 499:26 500:24 568:22 573:17
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reconcile 222:1

reconsider 673:16 688:3

reconsidered 120:16 314:10

reconvene 593:23 615:22

record 92:19 241:14 277:10 283:11 369:14 493:2

513:18 630:3 649:9 650:4 655:14 662:11 666:3

717:25 718:3

recorded 65:2 462:23 604:10

recording 38:20 243:18 680:17,18

recreational 619:13

recurrent 360:7 414:21

red 51:24 163:1,3 218:4 235:11 392:12 729:1

redesigns 582:15

redid 162:14

redistribute 150:15

redistributed 109:10

redone 246:4 720:23 722:3

reds 218:20

reduce 79:12 84:18 220:16 260:22 365:26 418:24

465:15 470:3 492:12 530:7,7,8,9 536:6 584:26

608:13 673:4 716:18 720:19,20

reduced 51:1 72:13 92:16,25 93:15 94:21 125:23

144:12 168:15 192:5 340:16 402:17 456:9 481:26

491:26 492:4,10 494:20,25,26 495:2,8 496:21,21

reducing 97:20 511:14 700:10

reduction 50:20,21 92:4,10 94:5 95:4,8 98:4 110:9

110:11,12,15,17,21,25 111:2 495:22 506:2,3

525:2,12 710:26 711:10

reductions 81:26 92:21,22,23 93:1 124:23 723:25

redundancies 444:3

redundancy 455:19 462:16 508:15 511:24 714:19

redundant 152:5 308:15 309:6 529:21

reemphasize 476:15

reevaluated 516:19

reevaluation 381:1 655:7

refer 42:18 372:13 386:4 417:1 471:12 506:24,24

602:3,4 670:9 723:17

reference 27:25 65:3 279:11,18,26 280:15,17

299:3 369:21 383:8 455:10 457:15 462:21 470:13

486:14 489:13,14,20 494:8,18 513:8,22,23 514:3

515:24 516:21,22 522:25 523:9 525:19 576:17

596:25 599:11 602:6,15 603:1 650:12 651:16

652:4 662:9 670:1,2 674:18 685:22 698:3 726:19

746:3

referenced 61:24 262:26 457:4 702:18

references 147:14 363:19 378:10 403:11,12 434:25

435:3 442:9 457:24 458:6,7 487:3 643:23

referred 249:23 437:3 670:3

referring 45:1 267:21 360:26 389:11 412:8,16

650:22

refers 229:9 436:20 504:15 563:19

refine 26:6 247:10

refined 291:24 292:19 453:13 511:1 651:1 652:2

refinement 23:2 282:18 292:18 426:18

refinements 302:8 517:21 716:13

refining 716:17

reflect 288:16 530:16 556:21 568:12

reflected 262:12 647:14

reflecting 464:14

reflection 412:14,15 539:17

reflections 194:14

reflective 31:15 364:8 461:15 556:7 666:8 667:2,4

667:6

reflects 417:13 730:15

reform 682:24

ref-tox 651:17,18 684:25

regard 118:17 444:7 446:5 448:12,21 455:19 465:6

495:4 500:20 537:21 539:13 604:19 688:26

689:16 725:10 729:5 746:2

regarding 2:21 78:11 90:7 98:10 105:14 109:17

112:22 118:1 152:26 279:20 314:25 409:6 419:21

421:14 445:5 446:3,10 475:12 484:18 529:15

530:23 566:21 570:14 571:5,10 711:12,15 728:5

regardless 252:6 667:11 692:18 740:11

regards 17:6 20:3 132:25 180:25 287:24 357:20

370:24 448:3 452:22 462:2 466:26 469:5 541:7

550:10 575:8 614:10 637:9 717:24 732:15

regime 44:12 442:13 444:20 558:17

regimen 43:24 301:9

regimens 88:2 99:4,5 281:20 282:17

regimes 558:16

region 463:18,19

register 194:19 329:2

registered 241:17 285:7 336:12

registrants 33:4 669:18

registrant's 657:20

registration 195:7 279:6 315:26 321:16 342:15

659:5

registrations 294:11

regresses 167:22

regression 167:24

regular 379:6 671:4

regularly 516:11 684:25

regulate 31:14 244:7 338:4 621:9 732:1

regulated 185:12 292:4,5 311:10 335:14 338:5,5,8

regulating 30:22 31:8,9 209:5 454:8 621:9,15

710:21

regulation 207:24 317:20 494:22 732:18

regulations 3:5,7

regulations.gov 4:1 663:6
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regulatory 2:22,24 3:1 28:21 29:7 35:6 185:9,14

195:5,15 198:16 199:8,14,20 200:26 204:10,18

204:22 208:22 241:8 262:20 284:26 285:3,8,13

286:10,22 292:11 295:14 305:18 306:2 315:19

316:10 319:17 321:5 322:10 324:14 326:18

337:25 341:13 343:6 347:5 348:7 375:3 413:23

441:16 443:5 444:9 492:6 545:9 565:13 613:2,15

614:11,17 616:6 638:9 650:1 667:9 706:1 728:20

733:10,13 738:11 744:2

reiterate 74:8 404:8 413:5 428:16 429:2 450:19

457:20 487:25 515:18 588:4 608:23 609:12

631:10 632:4 655:18 712:1 730:14 733:17 743:19

reiterated 428:9

reiterating 298:7 424:2 577:12

reject 147:11 399:8,9 581:4 582:7 658:1

rejected 399:2

rejection 222:17 582:8

relate 214:7 272:25 344:9 371:20 385:26 517:25

678:16

related 2:21 3:26 9:20 45:16 53:16 100:8 103:3,7

132:3 156:19 188:21 191:22 255:16 259:2 261:12

268:15 307:12,15 308:26 341:14 348:17 356:15

384:20 406:16 442:3,9,22 462:16 473:23 475:24

490:4 491:11 553:25 558:18,20 559:2 560:9

596:9 613:18 626:26 635:20 706:22 719:24 721:6

721:7 722:15

relates 133:5 276:26 344:25 394:23 541:13

relating 77:7 371:15 468:25 638:14 693:13

relation 139:14 227:23 345:3 448:24 562:2 734:12

relationship 17:15 30:8,12,13,15 31:18 32:2 71:19

76:12 96:3 121:17 128:24 263:12 280:7 289:2

316:26 329:4 347:8 371:11 443:9 447:11 448:1

448:21 450:5 451:18 545:13 561:17 611:14 613:7

616:10

relationships 32:19 204:11 292:24 448:23 450:3

450:22 613:14 617:26 621:16 622:21 646:25

relative 180:18,20 197:17 238:18 253:3 436:26

546:11 726:24

relatively 47:12 56:18 76:22,23 77:9 86:15,16

132:11 222:17 427:9 444:26 464:19 479:8 499:17

499:25 605:26

relatives 351:24

release 211:19 214:2,25 217:18 221:7,8,10,14,16

221:19 261:25 263:17 332:23

released 213:15,20 221:15,18 262:7 739:16

releases 212:2,8

releasing 184:12 261:26

relevance 27:9,14,23 28:20 282:20 287:11 307:10

326:12 328:14 357:20 361:16 364:22 365:2,23

367:9 447:8 452:22 453:2,2,6 462:1 550:10,24,25

553:17 555:2,6 637:9,14,18 697:13,21

relevancy 637:18

relevant 15:18 18:4 20:7 30:19 58:22 212:19

252:11 286:15 295:18 296:17 305:10 307:26

309:14 313:5,13 314:17 318:13 324:10,21 325:1

328:10 330:12 332:9 343:7 349:1 351:18 353:17

353:23 357:13,26 361:15,19 364:9 369:6,8

373:21 376:8 382:19 437:6 452:15 454:6,16

461:16 474:22 500:11 550:2 555:10,14 556:8

565:14,19 582:22 617:22 637:2 666:9 701:9

714:12 720:1 744:21

reliability 27:9,12 28:6,20 224:23 278:12 282:21

297:16 324:17 326:12 364:22 365:2 366:17 367:9

414:12 415:1,2 419:22 421:3,6,6,13,14 453:13

493:14,22 494:13 500:16,23 502:24 503:9,19

515:22 517:25 537:16,19 560:9 582:5 601:24

602:9 603:21 605:22 606:6 697:8,13,15,24

reliable 58:25 188:16 291:21 324:10 382:19 414:6

493:8 503:21 601:18 697:2

reliably 314:9 669:1

reliance 350:13

relied 403:12 572:10

relies 585:19

reluctant 576:5

rely 60:25 165:4 288:11 311:12 327:24 352:2

384:7,13 562:19

relying 350:23 402:22

remain 433:12 476:2 494:3 735:15

remaining 24:9,25 109:15 358:12 745:9 747:1

remains 420:15 446:15 498:2

remark 274:19 747:12

remarkable 269:15 379:14

remarkably 166:17 613:19 712:4

remarks 8:25 12:26 397:17 411:15 471:10,12,16

475:21 483:10 725:3

remedy 334:2

remember 52:8 62:20 130:1 134:6 172:13 188:2,4

231:10 238:14 318:7 323:13 355:11 577:10 599:9

660:1 681:12 691:5

remembered 600:1

remembering 325:12 328:1

remind 21:2,25 38:17 134:5 159:8 187:21 203:22

243:17 270:1 316:4,24 343:3 488:7 569:24

577:19

reminded 694:7 731:11

reminder 554:10 578:3

reminds 728:26

remotely 588:21

removal 450:5 529:8 676:1

remove 144:6 514:26 567:13 571:18 600:23 676:13
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removed 325:2 391:3 531:11 558:15 680:17

removing 213:5 466:10 530:2 675:23 724:5

renewal 390:8

reoccurs 564:13

repair 164:21

repeat 23:3 63:26 133:15 197:25 200:17 202:17

228:15 258:16 265:12 504:11 527:9 542:8 598:26

651:24 695:12 715:11

repeatability 297:16 301:15 311:8 414:12 464:15

493:14 503:2,9 504:4,4,14 517:25 570:15 601:24

697:8

repeatable 414:6,7,24 493:7,9 504:10 563:2

601:16,19 697:1,3

repeated 89:14 137:23 138:3,6,12,14,22,24 153:25

180:1 202:16 215:10,18 219:9 475:3,4,6 478:20

479:6 480:9 502:26 503:3 581:19

repeatedly 229:1 578:22 582:23 585:3 586:10

repeating 278:17

repetition 414:20 422:16

repetitions 581:20

repetitive 66:14

rephrase 720:8

replace 316:2 386:18

replaced 221:23,25 238:20

replacement 210:26 316:1

replacing 680:24

replicate 91:12 109:5,13,16,19,21 110:2,3,5 111:1

111:5,9,15,22,23 112:1,1,2,4 138:4 149:13

160:24 191:18 211:11 221:26 228:2 271:21

273:12 433:11 471:26 481:23 506:7 524:25

586:26 587:3 659:13 688:25 694:17,18,21 695:5

replicated 35:5

replicates 106:9 111:10,11,13,21 148:24,25 160:25

168:11,12,13,13 192:8,17,25 211:24 254:17

267:17 271:26 359:1,3 367:11 377:26 378:7

476:24 503:11,11,26 504:2 505:6 511:7 513:16

534:1 535:11 583:7,8 585:17 687:20,21 688:13

691:14 693:14,16,25 694:14,20 695:4,7,8,15,17

695:19 699:4 715:5 736:24

replication 90:13 98:16 101:5 106:9 108:6 109:4

112:7 137:25 152:19 191:18 271:17 297:7 299:13

300:2 301:25 302:19 464:15 526:20 535:2,8

583:25 584:6,8 687:23 688:3,8

report 3:9 4:8,11,13,15 8:12 9:3 12:16 13:5 16:11

33:11 47:9 63:23 93:23 129:16 130:2,25 150:20

154:23 160:15 161:16 165:9 172:20 200:15

268:16 289:15 300:15,22 301:2,17 329:1 348:10

349:25 363:19 365:3 368:7 369:15 379:10,15

382:14 384:26 385:1,8,9 388:20 389:4 396:4,20

397:13,15 398:26,26 399:25 401:5 422:7 425:4

429:15 430:18 431:25 433:21,24 438:2,11 447:9

455:11 470:5 471:13,24 472:4 474:14 477:11,19

477:24 480:26 485:22 486:2,14,24 487:18,19

491:17 498:11,13 502:25 503:14,20 504:16,18

505:2,3 506:14 507:22 520:5 522:26 547:19

575:7 590:10 591:5 595:16 596:2,26 597:3 603:2

603:25 604:3 607:12 627:13 635:20 636:3 660:7

662:24 679:19 691:26 699:6 704:4 725:21 727:12

746:3

reported 93:23 95:7 100:13 150:16 161:15 164:9

164:11 165:1 171:14 184:14 254:5,15 279:24

280:2 281:23,25 395:15 455:9 482:16 487:6,13

496:20 498:23 509:2 560:23 561:12 567:3,6,10

572:23 575:17 602:19 603:5 605:5 641:19 647:14

676:5 683:22 698:9 706:25

reporting 94:23 100:14,16 112:26 163:18 282:15

570:14

reports 3:13 38:25 65:26 76:6 346:24 361:11 365:5

379:15 392:9 394:24 399:1 402:11,19 504:15

735:21 738:7,8

represent 46:12 110:8 383:10 390:1 443:25 470:16

531:16 553:8 576:19 628:25,26 674:21 706:4

708:13 726:9

representation 16:7 271:16 629:15 740:7

representative 86:2 145:22 379:26 380:3 381:1,5

383:8 431:3 432:8 446:25 447:14 456:14 470:14

525:17,17 538:7 562:2 576:17 625:14 674:19

representatives 29:18

represented 21:23 37:14 271:6 553:9 559:17 630:6

630:22 705:2

representing 3:20 9:23 110:6 223:24 241:15 553:6

577:24 693:7

represents 48:4 110:10,25 420:26 506:20,21

594:13

repro 291:20

reproduce 58:5,9 116:12 157:4 165:5 297:25

472:17 533:8 700:9

reproduced 320:9 503:4 603:22 669:2

reproduces 467:8

reproducibility 27:12 53:1 100:21 114:25 196:11

198:2 293:21 301:15 304:9 311:7 412:4 414:12

420:11 488:3 491:1 493:14,23 496:9,10,11 497:4

499:22 500:12 503:3 504:12 509:13,15,20 526:19

537:16,19 560:10 566:20,22 567:18 570:16

582:18 585:5 601:25 603:25 697:8,16,26 698:12

712:2

reproducible 60:18 190:11 196:14 197:6 198:4,5

282:24 414:6,7,24 493:7,9 500:6 502:18 550:22

601:17,19 652:21 697:2,3 698:21 712:4

reproducing 117:8 221:22 223:3 582:11
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reproduction 20:24 21:19,20 23:21 24:13 25:21

27:1 40:22 42:11 45:3 47:17 49:23,26 50:18 51:7

52:8 54:3 55:2,4 64:8,14 66:25 67:1,3,4,17 68:3

72:8 74:1,26 75:25 78:23 79:23 81:7 88:4 91:5

92:17 93:17,18 97:11 99:9,18 102:25 103:1

107:22 108:7,11,16,17,20,23 109:21,25 110:2,5

115:24 118:5 142:3 144:18 155:14,25 175:21

185:20 186:20,23 206:6 207:14 212:15,24 213:24

214:12,26 215:15 222:3 236:7 237:4 255:4,14

260:12 263:6,7 273:16 279:21 280:12 281:4

288:13 296:3,8,11,16 297:8,11,15 298:5,19 299:8

302:13,20 304:22 305:1 309:1 316:13 318:3

325:15 329:16 334:15 338:8,17 339:7,8 342:17

344:17,19,20 351:17 354:6 430:24 431:20 433:5

438:24,25 440:7 444:26 448:5 479:15,16,16

480:7 481:6 496:21 505:24 507:26 524:11,12

528:20,22 529:8 530:3 532:15 533:7,24 534:2,6

535:3 537:3,6 541:9,13,21 542:26 543:1 552:17

557:14 565:25 612:2 616:15,20 618:8,11,18

624:19 625:23 631:5 638:20,25 639:3 640:8

643:5 645:9 664:13 667:6 689:1,7 699:23,25

700:1 701:4 706:8 716:25 719:24 721:6,20,20

722:15,17 732:14

reproductive 5:5,10,14 7:12 33:13 40:24 42:9 46:3

55:23 69:10 73:15 89:14 91:9 98:16 105:14,21,23

107:9 110:7 141:18 142:5,23 144:15,16 145:7

162:7 164:1 166:19 167:9 169:21 170:13,18

175:18 185:22 187:9 211:10 212:17 213:12

214:13 221:20,24 222:12 225:21 228:3,18 255:11

261:9 273:15,16 275:8 279:4 290:11 335:7

340:24 426:25 431:6 435:8 437:24 438:19,23

447:22 448:7,9 455:15 456:1 465:26 466:3,9

495:20 497:20 503:10 505:18,18 520:25 521:2

526:4 530:15 532:17 533:9,10 535:7,10,11,14

544:5 552:9,13 561:1,20 603:12 605:13 628:17

637:21 638:13 669:7 683:3,6 700:17 717:9

718:16 721:18 722:6,7 735:1

reproductively 214:4

request 283:5,12 309:2 332:23,25 363:12 382:12

382:12 658:12

requested 441:21 677:22 716:11,12

requests 615:10

require 166:11 194:24 225:9 366:16 530:4 533:10

576:9 626:5 641:16 649:9 687:21 718:1,2

required 9:2 15:23 35:19 56:9 152:20 220:6 227:2

287:5 294:10 313:13,16 314:10 332:17 341:17

342:3 391:11 449:9 464:25 625:20 675:15,20

711:20 715:16

requirement 4:12 197:8 222:13 328:1,5 341:13

342:12 405:1 534:22 660:18

requirements 3:13 104:8 127:18 194:18 241:9

331:23 341:21 342:15,16,21 465:14 508:9 525:10

624:14 625:10 626:2 666:22 743:22

requires 15:16 55:26 60:4 221:3 367:17 572:3

573:11

requiring 282:23 706:19 743:9

reran 137:22

rerun 138:10 194:24 706:19

research 5:9 6:2,9 10:13 13:24 16:9 27:18 29:19

49:3 75:18 78:15,15,26 79:21 107:17 108:1 148:8

160:7 204:9,11 242:5,18 322:3 323:6 354:9 356:9

365:15 366:17 367:17,24 368:2 377:22 380:13

382:22 383:2 419:6 430:22 434:16 436:5 441:11

459:18 465:14 639:13 664:20 665:8 734:16 742:6

746:23 747:4,6

researched 377:18

researcher 471:20 472:17

researchers 370:4 473:4,10,11 480:14

resembled 463:10

resembles 683:5

reserve 298:3 460:6

reside 169:14

residential-only 234:10

residuals 484:3

resistance 335:21,23 447:18 669:11,15,19 715:25

715:26

resistant 690:11

resolution 178:9

resolve 313:8

resolved 314:7

resonating 363:16

resource 534:12

resources 173:26 259:12 307:5 309:8 334:11 523:9

643:22 687:21 715:16 741:9

respect 76:9 96:11 99:9 109:16 112:14 151:24

271:11 275:18 279:7 281:16 313:11 385:19 387:4

403:2 492:21 503:5 504:4,12 534:17 539:21

586:21 587:1 594:14 612:9 671:3 676:17,20,23

respectable 219:3

respectful 308:2

respective 602:11

respectively 414:8 493:10 530:1 601:20 697:4

respond 62:9 64:20 184:15 227:14 299:22 322:1

351:13 368:4 409:17 411:19 416:22 520:21

610:16 617:17 696:15

responded 520:4 747:14

responding 178:6 666:1

response 23:4 30:8,12 39:24 48:9 51:2,21 60:21

68:10 69:13 72:17 85:14 89:5,15,19,25 95:22

96:3 112:17 125:21 126:2 132:5 133:2 134:26
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161:19 166:22 182:2,4 185:21 192:4 203:20,20

207:18 212:19 215:9 222:9 233:4 261:18 262:22

262:24 276:23 278:10 280:5 290:6 292:24 293:15

294:5 301:13,18 302:18 303:12 305:7 306:21

316:26 320:18 329:4 331:26 345:9,11,14,14,16

346:11 347:8,13 349:17 358:15,22 360:5 371:10

372:17 378:21,22 381:17 391:5 403:22,23 409:8

412:1,9 415:20 416:16 418:10 423:4 437:4

438:26 447:11,18 448:1,18,21,22 449:4,6,24

450:3 451:10,12,18 453:6 455:17,18 468:10

474:25 475:5 476:19 478:12,13,22 480:3 481:9

482:7,9,11,16,18 484:16 491:13 492:22 504:21

507:4 508:7 520:11 528:10 533:14 539:6,7 541:4

544:14 545:18 549:20 555:21 558:12 564:8 570:5

574:10 585:9,12,25 587:23 590:3 594:8 595:23

600:4,7 601:8 604:4 606:26 610:20 612:24 613:6

613:14 615:13 616:10,25 617:26 621:16 622:21

624:11,22,26 625:2,15 626:25 642:7 646:25

648:7 659:12 662:17 667:16 683:6 688:6 690:25

702:20,23,26 703:1,3 712:25 720:12 729:14,22

744:24

responses 4:9 60:7 63:4 131:19 145:10 217:11

269:4,15,16 276:21 277:4 280:2,23 288:2 290:22

293:2,3 297:20 303:10 308:1 318:10 322:26

325:9 329:17 351:20 357:8 377:6 383:11 412:3

413:12 416:10 432:17 448:14 449:21 453:8,9

454:22 455:13,14,23 467:4 470:17 482:14 484:1

499:14 500:5 516:22 520:13 526:18 529:18

530:15 531:17 537:3 554:10 564:1,1,5 574:24

576:20 608:16 627:8 668:9,11 674:21 675:26

691:1 698:12,14 704:18 705:16 712:26 713:1

729:14

responsibility 3:3

responsible 2:8 49:11 277:19 283:14 318:2 433:22

responsive 39:26 219:16 255:19 357:14 397:20

452:17 550:4 637:3 701:6 708:21

responsiveness 416:18 423:3 643:8 689:22

rest 4:26 33:24 180:19,20 256:7 383:24 442:16

484:25 511:26 542:2 633:7 635:25 658:15 672:17

672:21 745:20

restate 311:6

restricted 23:6 365:19

result 68:22 88:3 96:16 125:15 132:21 141:23

166:10 174:20 175:20 183:3 185:19 197:25

278:17 280:6 292:9,10 375:1 401:25 407:15

415:1,2 416:11,24 424:12,14 436:24 489:25,25

489:26 494:23 495:1,14,17 496:4 524:14 528:1

565:7 581:10 582:20 601:25 602:25 604:19 605:3

605:8 611:20 619:15 620:18,20 630:25 656:4

669:6 712:22 713:5 715:18,22

resulted 171:6 219:18 494:20 495:6 603:11

resulting 27:1 125:15 260:25 402:2 415:8 420:20

530:10 616:20 669:7 711:8

results 27:13 39:18 43:22 47:20 51:4 60:12,16,25

81:24 88:9 118:22,22 133:12 151:3 167:23

190:12 194:4 206:15,17 214:16 226:21,25 239:3

246:2 248:10 249:1 255:26 269:6 278:11 279:16

280:25 281:1,15,20,21 282:4 291:2,2 292:7

301:14 311:12 318:21 320:5,6,9,16 326:4,20

344:16 348:18 351:3 371:1,4 375:1,6,25 386:17

387:1 391:21 405:7 409:14 412:14 414:7,13

420:11 422:8 436:3 437:12,17 438:4,9,13 440:10

487:12,12 489:16,24,26 490:18 493:8,15,23

494:5 496:18,19 497:11 498:15 499:21 500:8

502:23,26 503:3 507:22 511:15 515:22 519:20

531:22 547:26 551:12 552:6 557:23 558:5 561:16

562:9,13,25 563:14,15 572:5 581:2 582:11 597:5

601:18 602:11,20 603:25 604:9,23 605:7,11,12

605:17 607:13 609:1 614:14 620:11 647:2 649:25

650:7,20 651:5,19 652:5 659:18 667:26 670:21

678:8 683:26 685:15 686:25 690:17,19 693:17

697:2,9,16 698:18,23 700:3 701:1 704:4 707:20

708:20 709:26 710:1,9,18 713:9 714:13 715:12

716:15

retained 109:17

retains 57:2

retention 40:16

rethink 584:20 639:4

rethinking 633:24

retinkering 655:7

retinoid 643:24

retired 736:5

retreat 28:24

retroactively 322:4

retrospective 24:4 269:20

return 213:5 345:21 441:26 492:4

returned 61:21 703:22

returning 80:22

reverify 225:3

reversal 80:14 81:13 85:3,10 86:13 92:12,16 165:1

364:19 377:13 495:10,14,17 496:23 499:11

541:14,21,22 553:22 603:17 605:9

reversals 91:22

reverse 726:16

reversed 80:15 85:9 547:2

reversible 213:1,4

review 1:4 2:4,20,25 8:10 10:1,6 11:12 14:20 20:17

21:4 27:20 28:17 31:4 42:13 75:19 76:3 81:5

83:11 91:14 93:5 109:1 147:11 209:8 210:10

241:4 248:17 254:7 284:14 306:5 318:4 330:24
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350:1 359:9 486:21 492:17 529:25 539:19,19,23

540:12 541:25 591:24 648:16 664:4 706:23 713:1

729:1 735:24 737:5 738:10 743:26

reviewed 3:13 179:17 283:9 435:22 592:5,6,8

741:13

reviewers 689:20

reviewing 8:11 10:17 360:18 459:3 706:1

reviews 193:12 251:14 348:1

revise 437:5

revised 275:8 296:7 305:8 471:16 566:16,20

567:19 652:13

revising 521:21

revision 367:11

revisit 439:17 609:20 652:14 724:24

rewrite 595:18

Reynaldo 6:7,7 59:10 128:7,12,16 129:15,26 130:7

130:13,19,24 131:3 132:22 133:11,17 134:3

242:17,17 457:17 460:5,5 467:22 475:26 476:6,9

485:1,4 493:18 518:24 527:22 532:20 539:1

542:8 548:21 554:7 563:6 568:25 573:23 596:12

602:2 609:25 613:19 615:5 665:7,7 732:4

re-demonstrated 50:8

re-invited 745:9

re-registration 316:1 321:16

rich 24:4 523:8

right 11:10 24:20 28:6 34:3 35:7 38:22 39:2 46:4

61:7 62:9 64:6 67:19 74:19 105:12 116:15,16

119:23 125:4 126:1 127:12,12 128:10 130:13

131:2 133:8 136:14 142:24 143:2 148:13,14

149:11 150:13 158:12 169:13 170:1 176:14 183:6

183:12,26 187:16 190:2 191:8,9 198:25 199:7

200:7 201:2 228:5,23 229:6 234:20 239:10

241:20 250:4 255:24 266:16,17 270:19 271:19,20

271:20 275:6 277:7 283:24 285:24 304:10 306:3

306:9 310:20 311:16 313:17,18 334:1 336:2

337:21 339:13 342:13 343:20 362:15 382:9 396:4

399:21,23,25 407:12,15 409:15 413:11 414:1

473:24 492:26 502:10 512:13 521:13 549:24

574:20 576:11 580:5 583:18 588:13 590:4 593:15

607:1 624:17 628:23 636:5 653:5,26,26 654:4

658:15 674:14 679:20 682:17 686:2 713:7 732:11

743:11

rigor 454:8

rigorous 464:13

rigorously 291:23

ring 286:12 290:19,20 294:24 295:7 296:13,13

299:11,15 301:20 302:1 305:4,5,6,8 497:7,8,9,11

499:16,25 500:10 501:25 510:5,7 517:6 518:5

538:6,12 542:25 543:5 602:22 603:26 714:21

rise 736:1

risk 12:1,6 15:9,10 17:16 21:21,22 23:10 24:7

26:20 30:16 32:7,8,11 33:8,17 34:21 35:1,23

49:11,24 69:11 74:7 184:20,25 186:8 199:22

212:20 230:16 231:15 234:11,16 239:6 256:19

257:3 263:1,4 286:15 287:25 293:10,24 294:18

295:19 296:17 297:13 298:18 302:16 304:8

305:10 306:3,22,24,25 307:3,7,10 308:6,8,13

309:11,13 312:26 313:6,7,13 314:17 321:7

323:14 324:21 325:12,12,13 326:4 327:23 328:17

328:19 332:8,9 334:7,18 347:13 348:19,21

349:17 350:15,19 351:3,18,21 352:1,19 355:6

356:3 357:1 369:7 426:8 435:15 436:5,13 439:5

443:14 492:6 530:9,13 541:20 542:1,4 545:18

547:26 586:21 598:20 613:25 614:6,10,23 616:25

617:20 624:12 631:19 641:9 643:14 701:6 718:18

719:3 732:18 734:15,18 735:3

risk-based 15:12 621:14

river 155:3 748:2

Riverside 5:17 242:9 459:22 664:24

RNA 139:18,19 144:24 368:13 494:22 512:13,19

512:20,22,25 570:19 726:7,13,15,16,23

RNAs 184:9

road 183:24 312:20 343:3 684:7

roadmap 14:22,24 20:11

ROBERT 6:19 34:8 35:7 68:14 190:4,17,26 191:3

191:9,14,17,22 192:20 193:2,5 242:24 460:12

548:18 550:15 556:16 564:18 565:22 566:25

570:18 575:16 576:8 598:12 606:9 609:11 615:3

665:14 726:6 735:9

robin 409:24

robust 25:18 40:24 63:18 64:3 123:14 156:1,9

291:21 330:13 398:22,22 414:6 493:8 494:2,6

502:18 524:7 549:8 559:1,5 563:2 573:11 601:18

610:13 680:20 681:1 697:2

robustly 157:14

robustness 25:4 175:10 414:11 419:7 445:14

493:13,24 500:16,18,20,24 502:21 503:6 582:5

601:24 605:22,24 606:6 653:3,4 697:7,17

rocks 155:3

rodent 36:17

Rodney 30:5 75:9,10,10 104:26 105:12 115:8,12

115:26 117:4 118:8,19 119:12,15,24 120:2,9

121:1,8,15 122:6,26 123:3,7,15 124:3,11,16,21

124:26 125:5,11,25 126:7,20 127:4,8,12,17 128:2

128:4,11,14,21 129:19 130:6,11,16,23 131:2,6

133:1,19 134:12,24 135:17,21 136:10,19 137:5,8

137:16,21 138:9,15,21 139:5,9 140:2,25 141:12

143:2,23 144:25 145:24 146:7 147:7,20,25

148:19 149:2,12,17,22 150:6,9,13,19 151:21

152:3,14 153:2,13 154:19 161:22 174:10 189:2
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194:12 202:24 250:17,23 270:7,16,19 271:1,14

271:25 272:3,9,15 273:3,25 274:25 275:3,11

452:9 458:4 461:7 468:23 469:9 470:8 484:21

492:18,20 507:5,7 515:11 520:24 521:9,11

528:11,13 533:15,17 541:5,6 542:17 543:10

747:8

Rodney's 154:16 174:13 244:10

role 9:26 10:5 372:24,25 435:24 439:5 621:6,14

622:11 627:9

roll 515:1

roof 304:14

room 154:26 239:19 277:14 387:8,9,9 388:1,2,3,4

388:5 390:5 415:15 459:13 519:8 593:25 633:2

662:21 664:15 748:11

rooms 387:8 418:3

root 264:12 397:8,10 399:3 481:9 591:10

roster 4:3

rostral 463:5

rough 159:2

roughly 43:13 61:1

round 409:24 497:6,7 532:12 662:14

route 303:22 581:16 639:12

routes 685:24

routine 64:16 194:3 249:20 523:11

routinely 180:17 286:23 304:23 321:15 534:14

routines 725:7

rows 211:23 223:14

RSCABS 481:19,23

RT 512:26 513:1 726:4,12

RT-PCR 512:4,5

rubber 79:6 312:20

rule 483:4,5 489:6

rules 633:2

Rumsfeldian 727:24

run 88:26 99:5 100:23,23 101:20 122:22 127:22

144:22 152:20 161:4 166:26 181:24 183:23

201:23 204:4 233:20 291:1,6 292:26 294:22,24

295:8 299:17 300:5,7 303:17 305:22 307:17

309:24 310:1,16 314:13 316:23 321:26 322:4

326:25 349:22 394:18,19 409:13 489:14,15,20

501:25 503:4 509:25 512:7,22 516:26 517:10

526:16 573:2,18 592:2 641:11 653:9 654:18,22

661:1,11 683:12 687:4 690:24 704:17,23 724:1

726:21

runable 114:17

running 98:13 145:17 154:8 157:18 181:26 182:1

182:5 199:23 285:10 291:7,8 292:6,8 310:8

312:20 313:23 328:12 412:17 423:12 503:4

511:20 516:16,17 527:1 573:7 614:21 653:5

656:13 688:18 709:4,6 733:12

runoff 723:25

runs 293:8 351:23 513:13 517:1

rush 327:19

rushed 327:14

RXR 644:1 667:21

S

S 216:20

SAB 539:11

sack 288:23 289:1

sacrifice 144:8

sacrificed 221:10 228:21

safe 14:6 15:25 17:20 329:21 342:25

safeguard 446:15

safeguards 483:6

safely 335:15 621:8 639:16

safety 8:22 11:7 319:25 323:18

sagittal 145:7 146:19 274:8

sake 178:5 255:25 651:8 659:16

salt 129:25

sample 103:25 104:1,11 120:6 131:19 145:20

254:13,13 289:5,12 293:15,16,17 359:5 366:12

370:9 371:3 394:12 396:23 399:7,8,9 405:6

417:14 418:23 421:2,8 437:4,8 439:7,10,13,16

469:20 473:23 499:1 555:10 568:1 571:22 572:13

582:24 588:10 589:8 594:17,26 595:2 604:15

654:10 694:1 698:20 699:7 715:14 722:26 723:3

sampled 82:7 100:2,3,3 109:15,22 158:26 380:20

534:5

samples 26:1 104:4 113:8 145:12,22,26 146:26

166:17 181:24,26 182:5 188:23,23 254:17 268:5

268:8 287:17 288:16,25 289:14,24,25 293:17,18

295:6 366:5,12 367:14 376:9 379:22 382:3 402:7

566:17 569:22 571:14 572:9,17,21 573:1,3,15

656:12,13 723:4,5

sampling 85:22 91:13 93:16 99:4 100:9,11 145:23

254:18 268:9 289:3,23,26 418:4 424:2 571:18

sandpaper 515:1,2

Sandy 2:10 4:26 5:2,4 7:26 8:13,18 12:21 29:26

34:6 35:24 36:14 38:10,15 59:6 61:3,26 63:15

65:6 66:22 68:13 70:15 75:7 104:23 105:2,6,9

114:22 115:10,13 126:9,13 128:6 130:14 133:15

134:4 137:9 139:8 145:15 148:10 150:23 153:5

153:10 154:7 176:7 177:3,13 179:1,20 180:13,23

184:4 187:12 189:11 190:3 195:17 196:9 198:9

198:18 199:12 200:13 201:17 202:19 203:13,22

205:3,23 227:15 229:20 239:8 241:22,24,25

242:1 243:16,25 253:22 254:21 263:22 266:12

267:9 270:1 273:20 274:21 276:1,12 277:6 284:2

285:22 295:22 305:14 314:21 323:19 333:8,10

337:15 338:11 341:10 344:21 345:18,25 346:17
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346:25 347:18 352:20,24 353:1,4,7,12 354:12

355:10,17 356:6,11 357:2,9,23 359:19,26 360:3

360:11,15,21 361:3,8,22 363:10,22 368:8,16

369:12 370:14 372:4,15,15 373:2,17,22 374:9,21

375:8,15 376:1,20 380:15 381:8,14 382:5,8 383:4

383:21 403:15,21 405:10,13,25 406:1,4,6,12

407:6,13,16,19,24 409:4,22 410:2 411:1,3,7,11

412:20 413:15 414:1,14 418:26 419:4,8,15

421:19,25 422:13 428:5,15 429:10,16,19,22

430:2,12 434:1,5,7,10,18 435:2,18 439:24 440:2

440:21,24 441:1,7,18 442:21 443:1,18 450:9,14

450:17 451:25 452:1,3,5,11,25 456:17,20 457:16

457:18,26 458:2,9 459:11,12,15 461:4,24 467:13

467:17,21,23,26 468:3,7,16 469:10 470:6,9 471:2

475:22 476:4,7,11,13,21 477:2,5,8 484:5,13,24

485:2 487:22 488:5 490:5,12,21 491:2,5,7,10

492:18 493:1,16 501:12,16,19 502:3,5,8 507:1,9

507:13 508:4 518:11,20,23,25 519:1,4 520:20

521:7,13 522:9,22 527:9,14,18,21,23 528:3,5,8

528:15 529:4 532:2,6,9,19,21,23 533:12,19

534:23 538:15,18,21,24,26 539:3,5 540:22 541:3

542:6,16 543:11 544:13 545:1,2,23 548:16,20,22

548:24 549:13,18,24 550:13 554:5,8,14,20

555:19,26 556:15 563:4,7,9,11 564:6,26 565:21

566:6,24 568:24,26 569:2,4,7,16,23 570:6,17

573:21,24 574:1,4,15,21 575:1 576:13 577:4

589:26 592:10,22 593:2,19,23 595:21 596:3,6

598:11,14 599:23,25 600:25 601:6,12,26 606:2,8

606:11,13,24 607:4,18 608:22 609:9,24 610:1,7

610:18,24 611:9 615:1,4,6,8,11,17,24,25 617:4

628:10 631:8 632:2,16 633:11,15 636:6,23

637:12 643:1 644:17,20,23,26 645:21 646:2,17

647:9,17 648:10 649:8 650:2,21 651:14 652:16

654:8,15 655:1,9,23 656:7,17 657:2,6,15 659:26

660:21 661:15,25 662:3,15 664:9,10,11,16

665:26 666:16 669:21,26 670:4,7 671:9 672:8,23

672:26 674:14 675:6 679:12 681:4,13 682:13

683:15 684:10,13 685:2 687:14 688:23 690:5

693:10,19,22 694:26 696:23 697:10 702:7,11,14

703:7,10,17,21 704:10 707:1,3,5,7,12,16,26

711:23,26 712:8,15 713:11,15,26 717:12,15,17

717:19,21 718:7,12,21 723:9,13 724:12,15,18,22

725:23 726:1 727:4,7 741:10 743:1 745:14,25

sanguine 728:17

SAP 1:1,9 2:11,17 5:16 9:2,11,14,15,19 15:1 17:22

19:1 20:4 21:3 33:23 35:2 171:19 198:4 283:10

284:24 325:20 419:22 421:15 482:13 539:11

549:7 567:21 568:22 573:9,16 608:8 620:25

625:25 632:26 634:23 735:12 742:3,3,22 744:16

745:20

SAPs 14:22,26 633:20 735:23,25 745:10

SAP's 2:25

SAS 154:6 183:20 592:2,2,3 725:13,17,19 736:8

SAS-based 88:26

sat 639:16 735:11 747:23

satisfactory 221:26 236:8 374:20 651:13

satisfied 3:5 703:3

satisfying 530:16 734:1

Saturday 600:19

Saulnier 89:20 481:21

save 31:2 61:14 585:16 602:5 627:12

saw 39:19 52:17 123:12 132:2 138:26 139:15

161:18 163:13,23,24 166:4,5,20 167:16 169:1,2

169:17,22 170:10,12,13 172:18 197:13 227:18

238:24 249:1 253:22 254:19 284:19 294:14,15,16

297:5 311:2 320:5 321:3 323:1 330:20 341:5,6

365:5 377:16,20 433:17 438:9 509:26 586:15

587:2 588:13 613:16 651:9 662:12 672:12 702:18

saying 5:4 30:17 86:21 104:10 112:7 126:16,18

137:13 152:1,4 155:5 162:24 172:13 178:9

179:11 186:18 194:11 197:25 228:16,25 233:26

235:16 248:13 249:12 263:9 327:22 334:21

335:20 337:26 339:10 340:16 341:5 342:14

379:23 380:3 407:8,13 408:9 411:9 413:1 421:7

489:12 579:19 580:4 582:20 584:14,15 630:3

642:22 646:12 651:16 654:21 655:14 656:22

660:8 738:19

says 162:13 229:1 236:24 239:23 319:11 380:25

390:7 392:9 408:16 420:13 469:14 474:14 479:10

479:26 503:6,19,20,25 504:13,18 505:3,5,6

506:22 507:11 520:5 542:26 548:26 555:12

563:18 569:19 571:17 574:7 599:12 670:18

675:14,25 694:10,10

scale 46:11 350:10 463:7,17 537:14 564:3

scales 405:16

Scanes 7:3,3 36:14,15 37:1 38:7 63:15,16 64:2

145:18,19 146:4 147:2,15,21 177:14,15,18,25

178:10,14,19,24 180:15 203:14,15 204:19 205:2

243:4,4 263:22,23 264:5,16 267:14,15 268:3,15

269:7 338:11,12 339:5,14,18,22,25 340:3,7,21

347:22,26 352:24,25 354:12,13 356:6,7 357:23

357:24 359:20,24,26 360:2 362:13,20 363:6

364:16 368:10 372:16,21 373:26 374:15 375:23

376:20,21 380:18 381:4,7 405:25,26 407:25,26

408:12 411:1,2 413:15,16 419:4,5 428:15,16

430:12,13 440:2,3 460:18,18 569:16,17 646:21

646:23 648:3,11,12 649:18 650:4,26 651:22

652:11 654:6,21,26 655:17 659:7 661:5,8 665:18

665:18 672:23,24 736:13

scarified 195:2
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scars 731:20

SCC 483:20

scenario 306:3

scenarios 508:21

scenes 9:15

scheduled 3:21 252:23 276:8 507:9

schema 253:3

schematic 46:10 212:25 213:14 223:8

scheme 253:6 256:12 632:6 691:1

schemes 228:13

Schlenk 5:15,15 30:3 31:16 33:22 34:1,5 115:14,15

116:21 117:25 118:12 119:8,14,20,26 120:7,18

121:6,9,21 122:21 123:1,4,9,26 124:5,12,19,24

125:1,9,14 126:1,9,12,14,24 127:6,10,15,26

128:3,5 242:8,8 443:18,20 457:20 459:21,21

467:26 468:1 476:13,14 488:7,8 492:21,25

501:19,20 508:14 519:1,2 528:3,4 532:21,22

538:16,17 540:22,23 541:26 542:6 664:23,23

728:18 743:3

School 7:15

science 2:25 3:11 6:5 7:4 8:15,21 10:3,3 13:19

16:22 31:2 155:15 332:24 348:12 366:16 382:20

400:20 473:12,25 474:22 475:15,19 492:17

591:14 600:10 631:1 632:8 634:26 636:19 694:16

742:13 743:18 744:21 746:21 747:10

Sciences 7:15

scientific 1:1,4 2:3,4,18,20,26 3:24 4:8,21 5:1 9:26

10:4,8 11:5,9,12 20:10 27:8 241:3,4,5,18,20

284:9 306:12,16 323:25 326:5,10 343:4 347:5

348:7 400:5 435:15 443:5 444:9 459:3 534:16

545:9 616:6 635:14 664:7 697:13 718:18 732:7

743:26 744:7 746:1,4

scientifically 15:18 18:4 20:7 202:3 330:12 357:13

357:26 452:15 550:2 637:1

scientist 6:17 10:12,12 694:8 747:15

scientists 16:9 28:26 77:15 327:16,20 390:4 393:12

735:10

scissors 514:26

scoliosis 171:14,16,23,25,26 172:8,10,11

scope 63:21 162:1 198:26 439:14 631:23 636:15

score 84:5,6,7 89:18 90:1 91:22 177:20 378:9

379:1 462:24 463:22,23 464:3,5 478:15 562:25

scored 483:22 556:25

scores 85:5 379:1,9,9,11,12,20 402:8,15,16

scoring 147:4 269:8

scorning 84:10

Scott 5:26,26 148:11 150:23,24 152:1,4,25 153:4

184:5 187:12,13,18,20 188:1 189:5,9,14 190:1

198:10 202:19,20 203:13,16 235:2 236:19 237:17

238:5,10 239:8 242:14,14 243:25,26 248:2

249:21 250:2,5,9,16 253:1,18 266:12,13 333:15

333:16 335:13 336:16 337:14 460:1,1 569:8,9

617:5,6 627:16 632:16,17 633:17 635:1 636:2

637:12,13 643:19 645:1,2 646:11 656:7,8 659:2

665:3,3 669:26 670:1 672:9,10 681:14,16 682:14

682:15 683:26 684:5 685:19 686:15 690:5,6

702:14,15 703:15 707:5,6 712:8,9 717:13,14

723:13,14 730:26 748:4

Scott's 203:16 243:23 628:15 672:25 711:16

scratch 547:11

screaming 169:7

screen 9:20 14:8,10 15:23 16:4 17:3 22:5,9 121:2,5

148:13 187:15 234:17,23 299:12 349:14 368:3

470:12 594:1 622:16 623:10 626:8,13 634:16,17

screened 220:11 644:2,4

screening 1:5 2:6 11:3 12:3 13:15,19 14:13 15:4

17:2 18:1,2 21:3 22:3,15 41:26 96:4 241:6 282:6

336:7 355:3,6 407:11 459:4 622:14 628:5 630:12

630:20 631:12 637:23 638:1 644:2 664:5 682:6,7

682:21 686:18 702:3 705:5,25 716:15 717:10

739:15 742:18

screenings 623:13,24 624:7

screens 284:26

screw 586:13

SDWA 634:18 729:19

se 126:2 135:6 226:7 259:18 353:24 411:23 412:6

553:25 652:25 655:7 667:25

seamless 9:16

search 115:20 270:25

seated 276:12

seats 38:16 105:7 153:11 345:26 441:26

seawater 211:13,26 222:21 266:21

second 20:16 24:1,6 27:21 64:20 153:24 162:20

210:14 211:19,20 212:13,14 213:22,23 214:12

215:11 217:17 221:7,10 241:4 250:8 255:22

256:2,4,4,9 258:21 263:24 290:9 291:1 293:21

294:7,17 295:1 296:10 316:15 319:12 320:7,10

320:25,26 323:8 325:10 335:17,21 340:5 354:2

356:12 369:23 381:25 384:9 404:21 417:17 423:2

428:23 432:7 451:9 457:8 465:25 497:6,7,19

510:22 524:12 566:9 597:7 598:23 604:21 681:5

687:19 692:23,25 695:25 699:25,26 700:1,2,10

700:10 705:23 707:18 711:12,22 715:12,15,18

716:26 720:24,25 721:11 722:20 733:22 737:20

737:22 743:22 745:12

secondary 69:13 80:5 82:8,11 87:2 90:20 91:22

92:2,7 94:5 95:5 97:5,13,20 98:3 100:8,11 101:13

102:5 103:3,26 106:25 108:15 109:23,25,26

110:2,3 120:6 131:1,4,12 132:3 140:21 142:8

298:1 303:3 371:24 425:6 448:7 451:5 453:9,22
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462:14,17 464:16 494:21 495:8 509:4 511:9

514:13 524:5 541:23 543:16 552:3

secondly 63:19 127:20 577:8 710:5

secret 336:12

Secretary 742:23

section 15:26 146:22 164:18 178:2 274:8 300:14

348:10 361:10 368:21 369:16,18,26 370:2,6,11

376:14 379:26 380:2 381:2,5 397:16 410:16

420:21 421:4 422:8 430:10 444:17,20 445:3,26

452:24 453:20 461:22 462:3,11 464:26 469:14,19

503:13 504:16 506:14 507:23,25 509:10,14

528:26 534:26 545:22 566:16 607:13 637:11

660:6 704:4 707:25 713:24 728:6

sectioning 145:3,7 146:19 178:20 188:22

sections 146:5,10,11,18,24 158:4 159:6 174:13

179:12 274:10,11,12 347:16 357:22 364:14

380:25 422:26 443:17 511:1 523:4 550:12 556:14

562:2,20 617:3 666:15

secure 21:8

security 257:16

sediment 344:14,18 447:5 621:21,24 622:4

sediment-dwelling 641:10

see 22:15 24:9 30:10 32:1 38:11 40:18,19 47:19

48:11,12 51:23 65:5 67:14,16 69:8 72:13 79:5,23

85:11,13,26 86:17,26 88:6 89:10 92:12,13,15,23

92:24 94:25 96:2 97:19 98:11 99:15,19,24 100:9

100:19 101:3,21 102:1 110:21 111:1 115:21

117:23 124:9,9,23 125:6 126:23 127:1 131:13

133:2 140:16 143:5 145:16 160:17 162:16,17,18

165:14,22 166:1 169:6,7,9,14,18 170:2,2 172:3

172:16 174:16 179:1 182:5,16 184:5 185:3

186:19,22 190:19 194:4,20 196:7,25 197:2

201:22 202:16 203:19 215:16 216:12,25 217:8,11

218:6,16,17 226:15 230:4 231:5,11,13 232:5

233:8,26 234:18,19 238:1 239:1 241:19 244:17

252:16,20 254:8 257:2,18 258:5,7 260:24 262:2

262:23 263:18 266:9 270:11 279:21 290:13 292:3

294:15,24 297:21 298:12,22 299:12 300:19 303:2

303:7,11 306:19 307:15 308:7 311:23 314:14

319:6,15 321:21 332:22 333:14 334:6,12 335:5

335:20,24 336:6,17 338:23 344:9 346:10,23

360:19 365:20 371:1,12,16 372:11 375:6 378:8

380:21 381:3,11 394:3 406:15 413:20,23 416:22

416:24 418:16 421:20 425:18 431:24 441:2

450:21 468:5 473:9 479:10 480:1,10 490:2

491:14 492:13 506:1,2,4,15 508:6 526:21 555:16

565:3,23,26 569:14 575:9 576:3 584:17 585:18

588:6 589:7,13,15,24 592:9 593:21 594:4 607:25

610:20 624:17 626:19 633:11 634:7 635:13

639:13 642:4 646:21 648:11 650:24 651:24 652:5

652:19 653:2 654:12 655:24 659:24 661:10,11

663:10 677:17 678:1,6,9,17,18,19,23 701:23

703:11 722:5 723:7,20,25 724:10 726:5 729:16

730:2,23 735:8,14 736:1,1 737:5,6,7 746:25

seeing 32:9 47:22 67:13 179:13,14 195:10 200:6

214:22 215:14,19 216:9,24 217:24 252:8 267:6

267:11 276:3 330:3 365:6 379:20 412:24 431:17

444:25 473:7 491:14 507:5 528:11 533:15 539:8

555:22 564:9 581:6 599:9 601:9 615:14 618:24

689:25 693:2 713:12 726:3 745:7

seek 28:20 35:17 246:23 744:3

seeking 25:23 28:7 363:12

seen 47:5 48:8,11 49:19 51:10,21 52:18 53:8 56:6

60:14,19 63:6 72:4 74:4,21 87:5 108:8 130:12

198:9 203:9 206:17 207:22 214:17 215:23,24

216:11,19,26 217:18 218:12 223:7,25 228:11,14

229:5 239:3 248:22 262:5 264:20 269:4,16

280:17 294:16 315:2 318:10 319:13 326:19

331:17 334:25,25 340:23 358:17 364:25 413:3

419:13 420:16 432:14 435:26 436:4 479:13

524:11,14 546:26 551:14 552:3 553:6 558:19

560:23 562:10 563:26 564:2 582:4 588:6,7 590:4

598:17 651:10 667:18 668:23 669:8 676:25

678:20 685:15,15,16 693:13,15 708:17 712:2

sees 48:20 671:18

segways 124:7

seldom 595:6

select 35:3 268:8 280:14 387:11,14 501:23 519:25

709:13,24

selected 25:3,17 26:2,8,9,24 27:25 34:9 55:18,18

60:15 62:5 232:10 287:19 289:24,24,25 380:19

380:21,22 387:10,12,13,18 394:1 407:14,21

422:24 488:26 629:2 667:8 714:21

selecting 62:11 228:8 287:2 392:18 559:18 671:13

685:24 708:4 716:15

selection 33:2,15,21 219:5 292:14 293:6 294:22

298:10 321:10 364:7,13 368:17 372:23 383:13,17

384:1,8 406:15,22 410:3 425:9 461:14,21,26

462:13 470:19,24 478:3 484:26 491:12 556:6,12

556:17,18 576:22,26 596:10,14,17 623:13 646:22

666:7,13 674:23 675:2 679:13 681:5 682:8

686:10,12 716:1,16 717:25

selective 23:17 280:20

selfish 733:24

self-starting 220:9,12,15

semantics 660:5

semen 366:12,12,15

semi-domesticated 55:15

semi-static 211:21 223:12 225:2

send 117:4 406:7 569:25 585:4 657:8 680:15
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sending 205:16

Senior 6:17

sense 13:15 21:12 22:20 30:17 32:21 37:15 48:14

75:2 96:16 105:23 119:5 126:4 146:8 148:5

152:19,22 161:8,26 177:6 193:16 197:23 201:25

202:3 207:1,10 227:6 238:18 245:19 246:22

248:16 255:18 257:16 260:18 263:2 264:11

266:19 327:22 328:8 348:20 364:26 366:4 367:22

384:6 387:14 390:5 393:25 397:5 400:20 434:12

494:6 519:13 628:6 629:24 630:26 670:15 727:24

sensible 425:1

sensitive 26:14 40:1,8 41:6 56:26 63:13 72:10

76:11 84:25 107:9 112:16 175:17 197:21 207:2

208:10 215:3,24 216:2 218:1,5,6,7 221:3 223:25

225:17 226:14,18 231:6 244:12,23 245:3 246:6

246:19 247:5 248:23 249:6 254:24,25 255:6

257:9 258:2,6 259:5 263:19 280:13 282:11

302:15 306:8 317:20 318:13 324:20 342:17

351:12 376:5,8 413:10 424:17,21 433:4 450:24

454:2 526:4,9 534:13 546:13,17 549:8 558:1

571:7 576:3 609:19 620:23 622:9 626:14 643:13

669:4,6 680:20 684:16 687:11 690:10,12 691:22

692:9,9 700:22,23 702:19,26 708:6 713:7 715:23

716:1 719:5,9,10,12,18,19,23 720:5,7,22,24,25

721:5,22 724:1,4 726:9

sensitivities 226:20 518:9 633:18 708:21 726:24

sensitivity 48:10 97:8 197:19 224:23,26 226:11

231:12,26 245:25 246:18 255:15 258:20 278:24

279:1,4 286:13 295:17 306:7 309:4 324:17 325:4

326:12,23 328:14,15 332:3 364:10 390:2 413:9

435:8 436:18 437:16 438:15 439:1 446:19 461:17

483:11,24 503:22 518:3,8 531:7 534:18 535:14

535:22 537:22 556:9 568:15 612:23 620:22

622:11 634:22 635:8 666:10 684:23 686:7 690:23

708:17 709:18,22 715:2,3,23 718:16 719:4

sensitization 71:21 340:22 440:17

sent 8:7 137:13 152:14 188:24 239:21 277:23

326:13 392:12 442:4,13 654:11 657:10

separate 65:23 109:7,10 223:6 255:10 289:6

385:18 590:25,25 635:24 686:19

separated 100:26 228:24 677:7,9

separately 284:18 285:20 325:26 583:23 674:7

677:5,10 740:24

separation 362:14 385:19

SEPs 744:8

September 20:20

sequence 370:10

sequenced 129:3

sequences 523:20

sequential 31:24

sequentially 43:8

serial 451:23 572:6

series 93:18 137:24 158:7 325:19 346:18 356:25

376:26 403:11 592:2 599:13 648:18,25 662:9

736:22

serious 111:5 125:6 131:24 279:15 498:23 574:24

696:21 744:9

seriously 111:10 119:25 632:5

serotonin 317:26

Sertoli 158:13

serum 174:17,17 190:20 571:24 572:4

serve 22:9 305:17 306:20 342:6 503:23 644:13

731:1 732:5 733:23 735:19 736:12 740:17 743:3

served 184:23 337:1 735:24

serves 4:13

service 358:15

services 33:12

serving 2:10 735:7,25 743:2

session 2:11 5:3 241:25 459:11

set 2:4 12:18 33:5,6 44:4,13 46:23,26 47:18 57:19

80:11,16 82:5,6 93:9 109:14,22 112:3 116:14

147:26 151:11,18 188:25 189:26 198:16 219:6

260:15 262:25 267:2 277:9 284:12 288:8,9

312:10,21 345:22 351:17 366:3 369:26 381:11

413:9 442:22 480:11 496:13 513:2 522:10 573:5

573:6 579:5 585:12,15 586:4 587:25 604:21

634:2 646:18,20 664:14 677:14 696:19

Seth 7:6,6 139:9 140:6 141:6 142:24 143:5 144:21

184:7 185:1 186:18 243:6,6 450:18 456:21

460:20,20 467:18 477:3 490:13,23 501:14 508:6

520:24 522:23 527:11 532:10 538:25 542:17

662:4

sets 11:9 234:5 317:25 349:18 496:11 562:25 572:7

603:26 700:2

setting 25:4 35:6 56:23 156:2 247:13 285:3,14,18

312:6,8 354:20 363:14 393:16 613:2 744:24

settings 562:22

settlement 621:19

setup 32:10 82:6 227:25 695:24,26

setups 696:13

seven 51:15,19 56:21 85:23 87:8 100:3 148:15,22

148:25 149:20 211:20 221:7,15 223:24 250:7

256:7 271:4 405:19 538:8 673:8 691:15 692:25

693:1 701:16 713:16

seventh 46:22 389:5

seven-day 221:13 720:22,23

several-week 47:17

severe 164:18 378:24 455:15,21 728:22

severely 402:17

severity 84:5,6 85:5 89:18 90:1,2 171:17 378:9,11
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378:13 405:17 478:15 481:25 482:3

sex 26:1 40:13 45:6,16 80:5,10,13,18,20,24 81:2,12

81:13 82:8,11 85:3,9,10 86:13 87:2 90:20 91:22

91:22 92:2,7,9,12,16 94:5 95:5 97:5,13,20 98:3

100:8,11 101:13 102:5 103:4,12,23,26 106:25

108:15 109:23,25,26 110:2,3 113:3 120:6 128:8

131:1,1,4,4,5,12,13,15 132:4 139:17 140:15,18

140:18,21,23 142:8 156:2,12 160:2,3 165:1,2,3,4

169:24 214:4 268:12 281:2 297:26 298:1 303:3

316:16 348:14,15 364:19 377:13 431:12 448:7

451:5,6 453:10,12,22 462:14,17 464:16 466:26

468:25 469:6,20 487:2,6,6,7 494:21 495:8,10,14

495:17 496:22,22 498:21,22 499:11 509:4 514:13

515:11,14 523:23,25 524:1,5,5 526:23 541:13,14

541:21,21,22,23 543:16 546:25 547:2 552:26

553:2,22 557:14,15,16 570:20 603:17 604:12

605:8,9

sexed 26:11 109:13

sexes 131:12

sexing 102:9 103:13 104:17 122:18 130:18,26

131:10 155:24 280:26 301:10

sexual 25:8 40:11 45:19 66:15 156:21 157:22

223:5 371:24 372:23 423:16 425:6,8 426:11,14

427:2,13 433:5 454:15 530:21

sexually 76:25

sex-determining 77:3

sex-reversal 97:15 169:25

sex-reversals 82:20

sex-reversed 169:23

shape 180:18 404:24,25 405:2

shaped 178:16,17

Shapiro-Wilk 399:1

share 114:12 336:5 403:4 458:8 472:12 646:9

731:12 732:7,25 740:16

shared 465:22 732:8

sharing 258:18 731:3

shark 732:1

sharks 729:1 731:11

Sharlene 2:1,2 5:2 8:4 9:10 13:8 38:12 205:9

239:18 241:1,2 283:16 342:26 343:2 346:3

441:25 458:14 459:1,2 544:22 593:3,6,10,15

615:21 626:21 635:11 636:2 649:1 662:23 664:1

664:2 732:5 736:16 745:17,22

Shea 7:10,10 243:8,8 254:22 255:24 258:18 261:11

261:17 263:5 341:11 342:5,11,14,22 343:20

460:22,22 628:14 643:2 665:20,20 669:24 681:15

682:16 683:16 686:14 688:24 690:6 691:3,12

693:12 697:12 702:15 704:12 708:2 712:18

717:16 721:10,14 723:11,15 738:18

shed 499:24

sheer 291:13,17

shelf 204:13

shell 365:1 393:25 394:1 428:25 432:18

she'll 593:3

shift 599:4 606:17 720:15

shifts 440:4 720:14

ship 746:25

shipping 113:8

Shirley 748:6

shocked 488:17

short 47:12 77:9,10,12 134:8 155:26 205:5 238:25

350:4 352:10 367:5 396:11 415:20 427:18 463:17

485:12 523:10 564:18 593:19 687:25 708:23

740:16 742:1

shortcoming 302:2 638:18

shorten 233:4

shortening 152:26

shorter 22:4 42:10 43:2 77:10 210:19 222:12

301:24 415:21 471:9 622:18 694:5

shorter-term 319:24

shortly 16:5 19:6 89:3 743:23

short-term 22:14 281:4

should've 501:22 628:1

show 18:21 52:10 53:19 55:24 83:26 91:21 92:10

94:11 96:6,9 98:18 99:9 106:3,10,18 109:3

122:11 129:8,10 132:4,5,21 134:14 136:12 158:3

162:11 163:9 198:1 208:10 256:6 259:25 261:19

278:19 280:11 296:6 320:18,24 370:20,26 486:16

500:4 555:8 567:25 581:23 630:23 631:5 698:25

711:3 721:1 725:17,19 736:18

showed 95:9 105:18 106:2 133:23 159:7 162:25

163:7 164:12,16 165:18 188:9 202:23 251:3

255:23 261:13 270:15 279:13 281:6 320:17,19

374:18 379:17 431:13 436:1 440:6,10 476:16,18

498:11,21 504:22 537:21 560:7 562:8 591:14

604:4 605:11

showing 74:14 97:8 98:2 135:8 148:13 164:15

374:16 379:4 414:25 447:16 474:15 582:17

693:18 719:7 725:16

shown 66:18 128:9,25 129:3 133:24 140:12 168:3

168:4 170:22 251:3 252:16 254:3 282:10,21

376:8 395:12 401:11 402:16 438:14 481:3 506:1

537:22 548:3 568:17 631:20,26 669:15 693:15

shows 53:20 84:16 95:22 97:25 98:20 106:11,13,23

106:26 173:22 297:24 317:18 340:26 471:14

479:26 614:24 720:12

shrimp 173:8 213:17 220:19,20,21,22 237:8

244:22 315:21 323:3 442:20 620:3,4,4 638:8,10

699:10

shut 51:7
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shutdown 50:17

shutting 167:13

sic 525:17

side 23:19 37:14 48:12 72:25 120:15 146:25

149:12 185:26 186:1,2 220:13 231:10 237:25

249:6 252:14 268:20,24 272:16 335:15 337:25

432:10 488:11 497:15 565:10 729:6 733:21

744:21 747:16,23

side-by-side 166:16

sieve 234:21

Sig 194:12 196:10 201:19 747:1,8

sigma 594:24 595:3,4,6,13

sigmoidal 450:3 585:14

Sigmund 26:17 154:15 176:11,14,17,22,26 177:7

177:16,24 178:5,12,15,22,26 179:6,10,16,25

180:3,6,11,22 181:15,21 182:6,9,26 183:6,11,18

184:17 185:7 187:5,17,19,22 189:1,7 190:7,23

191:1,5,10,16,21,25 192:23 193:4,8 196:15 197:9

198:3,22 199:3 200:19,22 201:2,7,10 204:8,24

549:22 555:24 564:11 566:3 570:3 575:4,21

576:12 595:26 596:4 601:10 607:2 610:22 615:15

727:5

signal 119:21 219:17 553:21,22 557:6,9 711:5

signaling 354:13 371:20 557:2,17,21 570:20,21

645:25

signalling 720:14 726:10

signed 29:5 240:3

significance 89:24 90:1 162:25 226:16 262:16

297:10 300:12 320:23 361:20,20 675:26

significant 30:15 31:19,22,25 32:1 47:26 48:2 50:6

51:5 52:17,18 70:5,25,26 71:9 90:3,25 97:21 98:7

102:13 127:21,25 158:1 161:20 168:19 215:12,14

215:20 216:4,7,14,23 217:3,13,18 218:12,15

225:10 226:15 246:3 249:25 250:12,13 254:2

262:2,15 264:9 279:19,24 281:5,7,15,17 282:15

282:24 285:1 292:1 302:20 339:16,18 367:16

421:1 446:18 448:22,24 449:3,7 454:8,11 462:15

480:4 482:26 483:8,9 484:2 504:3 505:11 506:5

529:21 531:6,17 536:8 537:9 546:19 559:9,13

560:9 561:15 612:5 617:14 619:19 639:12 647:7

675:24 680:20 689:14 698:23 700:11,13 701:19

706:16 711:6 715:14 721:16 722:23

significantly 94:7 261:20 263:25 281:22 454:9

497:21 524:25 715:16

signs 45:7 159:19 600:16

Sig's 186:9

similar 15:19 51:4 95:13,23 97:15 101:8 102:20

117:24 118:23 128:17 130:4 139:24 174:26

177:11 189:16 208:4 215:5,15,20 217:2 222:8

223:16 225:24 226:12 231:2 248:10 252:5 275:20

291:3 303:11 317:26 318:2 319:13 322:21 331:13

374:7 400:11 404:11 466:14 471:16 472:21 474:1

474:15,21,23 481:15 487:25 500:5 509:2 512:1

514:4 518:13 520:18 531:23 536:12 543:19 548:7

559:12 591:20 604:8 610:16 618:19 624:20

630:15 640:26 684:1 698:7,8 708:21 721:24

722:2 726:14

similarities 132:20

similarity 683:11,12,13 684:6 705:20

similarly 206:16 233:8 337:13 455:9 471:18 472:8

562:23 603:7 624:2 709:4 715:2

simple 71:19 72:17 104:3 133:5,12 136:17 139:16

140:6 173:6 204:21 289:3 408:15 428:19 589:12

589:15,21 640:2 678:13 680:23 684:22 700:17

714:11

simpler 117:19 249:17

simplest 589:18

simplify 137:7,8

simplistic 142:19

simply 82:12 99:23 104:5,9 107:8,9 113:10 118:10

124:18 131:21 135:11 137:18 147:3,22 177:26

178:10 273:5 336:9 345:14 436:17 483:21 506:16

512:5,7 513:24 515:4 517:10 553:6 563:16

577:26 581:13 582:19 622:5 642:6,13 678:8

686:5 698:21 711:19

simulate 47:10

simulated 483:18

simulating 483:17

simulations 157:13

simultaneously 191:8

singing 283:21 427:2

single 30:14 31:18 32:2 33:23 64:13 156:15 211:4

222:7 279:11,18 291:8 293:2 313:16 320:24

345:15 375:11 381:1 389:9,10 415:26 417:18,20

423:6,6 449:4,14,19 472:2 475:4 530:17,19

568:21 573:1 609:15 613:26 704:24 718:25

siphons 220:9,12,15

sister 13:21

sit 284:5 327:21 440:3 626:5 739:8 746:19

site 1:9 622:10

sites 420:4,18 723:25

sits 295:15

sitting 28:26 205:17 580:4 584:16 679:17 745:25

situ 144:4 147:23 378:3

situation 32:26 66:19 199:7 200:2 245:1 344:18,19

367:4 380:1 495:13 600:9 659:15

situations 72:13,26 162:21 173:25 201:23 345:10

356:10 483:7 659:20

six 17:4 21:4 44:4 46:13,18 51:19 109:5,6,6,10

110:2,19,23 111:1,6 112:4 118:3 148:25,26 149:2
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149:4,5,5,7,11,13,13 223:1 271:21,22,26 272:4,6

272:6,11,12,13,16,17,18,20 273:12 464:2 505:6

688:5 693:1 698:8,8 707:19 713:24

sixth 46:22 389:5

six-week 46:14,21,25

size 40:15,15 55:14,20,20 63:3 76:21,21 88:3 100:6

172:18 178:21 204:5 226:3 254:13,13 259:19

262:2,10 263:10 289:12 293:15,16,17 370:9

396:23 399:7,8,9 417:14 421:2,8 425:10 426:25

426:26 431:5 437:4,8 439:14 466:3,8 471:26

473:23 497:24,25 498:3 517:8 562:4 568:1 574:9

588:10 594:17,26 595:2 647:4 668:19,19 699:7

715:14 722:26

sized 55:17 99:17

sizes 131:20 394:12 405:7 418:24 439:7,10,16

555:11 582:24 694:1 698:20 723:3

skew 487:2

skilled 658:17

skills 266:7

skirt 192:18

slapped 734:14

slices 89:22 481:19

slide 14:1 18:17 21:25 29:16 53:3,20 54:24 66:26

67:13,15 68:1 70:18 71:13 98:21 105:10 112:21

114:25 115:7 118:13 124:5,6 133:23 134:14

143:6 146:10 148:12 149:9,14 150:24 152:8

158:22 176:8 177:15 181:1 187:13 202:23 203:10

216:17,22 224:1 229:21,25 230:1 233:10 235:4

235:19 236:25 237:17 238:5 244:10,17 245:24

248:8 249:21 250:11 253:24 254:4,20,22,23

255:22 261:12 263:15,15,23 270:17,24 271:3,6,9

272:25 284:10 296:5 297:20 299:2 379:4 380:2

438:12,14 545:6 590:7 592:9 711:3 719:1

slides 115:4,6 146:11 154:24,24 174:8 179:8,15,17

181:1,3 188:25,25 251:3 253:24 270:12 286:2,6

314:26 323:21 335:16 336:22 338:15 343:11

370:26,26 445:18 507:14 562:25

slight 96:9 276:19 281:19 294:13 356:4 423:8

454:8 463:8 509:23 574:11

slightly 247:1 305:25 417:11 641:6 741:3

slope 182:15 451:21

small 67:26 68:5 76:17,21,22 80:21,21 99:17

100:10 102:20,23,26 119:13 121:1 146:16 165:19

177:20 192:14 219:20 235:23 236:12 238:17

247:3 391:19 394:12,13,13 399:9 405:6 408:2

431:5 439:10 445:1,11 454:26 462:4 464:19

465:20 466:12 483:18 492:20 496:18 497:24

519:9 567:24 568:1 606:15 624:2 630:18 641:18

650:16,17 691:21 693:25 698:21 721:15 722:24

723:2

smaller 25:17 76:18,23 99:26 178:18 222:22

254:16 641:11

smallest 36:8 172:23

smoking 702:5

SMV 215:8 216:10

soapbox 330:18

socialization 65:24

Society 277:19 283:14 332:22

soft 117:11,21 118:3 508:25

softer 187:10

software 398:16 403:6 464:8,9 725:8

sole 349:24 445:22 455:4

solely 41:15 350:24 529:11

solemnity 515:20

solid 19:2

solidify 276:22 300:8

solidifying 417:3

solubility 98:14 303:25

soluble 266:22

solution 132:13 161:5

solve 161:2 731:16

solvent 211:14,25 264:10 266:2,7 267:5 300:17,18

300:19,26 331:17,20 569:10,13

solvents 265:25 266:25

solvent-based 267:2

solvent-free 211:6,14 266:1,4,9,15,23

solvent-related 225:6

somatic 159:23 308:5

somebody 263:9 394:3 442:15 580:14 658:17

682:26

someday 329:25

something's 298:4

somewhat 37:17 38:3 40:7 41:21 42:3,15 43:1,14

45:1,11 49:1,16 50:23 51:17 52:10 54:10 55:15

56:9 57:1,10 60:2,6,8 62:15 66:20 68:11 69:21

70:1 74:7 102:2 132:21 141:22 151:25 201:20

215:3,4,9,11 227:8 256:15 257:17 265:22 325:4

327:14 362:16 415:25 446:6,8 478:9 488:26

490:1 510:9 703:3 746:2

song 57:9 180:16 355:1 356:16,23 360:25 373:8,12

425:5,10,10 426:26 427:18,19

songbirds 180:17

soon 16:22 65:14 157:24 344:19 470:12 493:4

662:21

sooner 156:21 187:24

SOP 139:11,12,22 146:8 416:3,4 521:17

sophisticated 166:12

SOPs 83:21 139:24 140:2 160:18 417:3,12,13

418:1,24 425:24

sorry 114:20 149:18 166:3 198:18 229:23 239:8
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579:22,26 585:7 650:10 651:23 695:11

sort 33:14 61:5 62:5 81:24 101:8 109:4 113:4

115:17 117:1,2 118:18 119:22 120:13 124:14

132:1 136:21 139:23,25,26 141:10,26 142:1,21

142:26 143:13,20,25 145:2 148:7 154:20 184:9

186:22 194:18 195:10 196:19 235:26 244:26

245:13 254:12 274:14 275:16,16,17 284:20

298:13 305:3 308:9 324:1 325:8 370:21 371:7,8,8

375:11 385:2 390:19,22 392:7 395:17 397:3,17

400:13 401:6,14 402:11,18 403:12 444:4,4 445:2

473:5 488:21,21,22 489:6 492:15 508:21,25

511:26 512:4 514:20 517:9,13 520:26 525:2

526:2 532:12 541:10 543:25 565:9 574:24 591:14

592:1,2 594:6 617:7 652:18 679:2 693:12 724:8

729:23 730:2 734:14 741:2

sorted 735:17

sorts 59:21 80:17 353:21 388:7 580:17 581:1,26

582:11 653:19

sought 17:5 325:24 354:1

sound 8:3 283:16 517:18 547:10 621:4 742:13

sounded 635:15

sounds 34:1

source 62:19 416:1 418:4 423:6 558:7,15 570:12

572:15 585:23 586:8 588:17 673:26

sources 16:1 279:14 281:14,17 394:25 415:5 447:6

477:13 492:13 503:8,16,18 546:7 558:9 572:2

583:13,17 584:21,23,23 587:12 608:2 677:25

South 1:15,16 248:18

southeast 77:18 620:1 622:10

southern 526:14

space 210:20 222:13

spacing 362:13 363:4

span 107:23 363:5

sparingly 433:17

spawn 446:23 447:4 454:4 523:12

spawned 505:19

spawning 454:9 523:11 536:4

speak 3:21 38:18 92:3 180:10,11 184:24 186:14

200:11 243:17 283:18 284:8 329:8 336:13,15

345:3 346:26 360:16 490:21 523:2 580:14 620:17

649:11

speaker 25:14 270:23 323:20

speakers 28:15 38:19,20 296:18 328:11

speaking 128:17 194:17 257:19 343:5 382:14

524:8 603:20 650:13

speaks 199:26

specialized 357:17 452:19 550:7 637:6

specially 701:10

species 25:17,25 26:2,20,24 33:11 36:24 38:9 42:2

42:7 43:19,21 56:20,20,22,25 57:1,22,23 60:9

67:23 76:17 151:18 152:2 155:6,13 156:1,4,7,11

156:21,26 167:2 172:7 186:6 188:20 206:26

207:9 210:8,9 211:9 213:26 218:3 224:11,14

225:13,18 234:15 248:24 287:7 289:11 293:5,7

296:25,26 297:4 302:21 303:16 315:21 317:5

323:4 324:19,20 328:15 341:17 350:3 351:7,11

351:17 352:1,6,14 353:18,22 354:4,11 355:7,13

356:2,16 357:15 361:2,7 373:14,15 424:5,7,8,10

424:20 425:17 427:6,14 430:25 431:4,5 434:13

434:15 446:26 447:14 452:18 453:26 454:10,17

537:26 543:15,16 547:21 549:10 550:6 575:11

614:13,14 619:13,14,16,26 620:1,8,14 622:9

628:25 634:13 637:4 641:9 648:13 693:7 697:18

698:1,3 707:20 708:12 715:1 734:20,21 737:2,3

species-specific 233:23

specific 4:10 15:13 21:6,14 25:9 27:10 31:17 33:16

62:10 66:21 118:25 146:8 153:15 199:19 205:11

213:25 255:19 259:22 260:8 267:20 268:3 283:17

287:2,4,22 291:24 292:16 297:17,21 298:8

299:26 301:18,25 302:1,7,25 303:1,4 313:8 318:1

319:1 325:16 328:6 331:16 334:17 339:3 343:23

347:11 348:20,23 349:6,16 357:14,17 362:3,8,14

362:18 363:4,13,18 369:9 407:18 417:13 418:17

420:7 430:20 437:19 442:12 443:12 448:15,19,20

449:22 452:17,20 453:8 458:6 464:8 466:9

493:24 494:19 500:25 501:8 521:18,26 537:10

545:17 550:4,8 557:2,21 564:14 566:13 569:14

569:19,21 578:1 597:13 616:24 624:10,18 632:24

635:18 637:3,6 640:15 645:10 647:22 650:17

667:24 673:5,19,23,25 674:5 678:15 682:21

685:13 697:14 705:2,9 707:9 708:25 723:23

743:8 744:6,18,19

specifically 25:26 62:18 137:15 159:14 206:14

207:6 209:17 225:24 258:26 287:10 290:2 297:19

306:7 308:20 317:22 319:1 321:4 322:9 337:10

411:19 447:25 448:2 462:4 496:10 501:4 525:1

554:24 575:12 602:16 603:24 611:16 613:5,20

618:10 627:23 638:26 670:9 681:2,26 689:16

701:12 742:11 744:10

specifications 194:6

specificity 21:9 278:25 279:2 324:17 326:12,23,26

382:14 672:20 683:4

specifics 136:3 194:8 349:12 469:7 625:5 635:4

649:16 673:18

specified 62:26 111:15 146:13 391:12 421:2

specifies 273:25

specify 196:5 274:2 522:10
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specimen 114:11 146:9,26

specimens 381:1

spect 166:11

spectrum 312:24 362:22,24,25 652:18 710:13

speculate 343:10

speculated 282:1

speculative 264:13 446:8 715:10

speed 311:3

Speedvac 572:4

spelled 627:23

spend 155:7 307:4 336:19 363:7 649:19

spent 18:20 623:14 692:11

sperm 157:8 433:14

spermatid 45:14

spermatogenesis 98:11

spermatogonia 158:13 378:16

spermatozoa 366:4,4,8,10,20

spiel 415:7

spiking 572:14

spillage 408:16,17

spilled 408:21,22

spinal 171:14

spleen 82:22 146:23

split 140:21 200:5 234:13 451:6 471:4 675:7

spoke 130:16

spoken 575:23

sporadic 49:19

spore 624:25

spot 336:17

spring 447:4

Springer 89:20 481:20

spurts 739:4

square 397:8,10 399:3 481:9 591:10 620:11

squared 594:25 595:3,4,6,13

squishy 555:3

SSC 469:24 474:24 482:22 495:2,24 496:21,24

498:6,12 509:13

SSC-androgen 469:23

staff 3:24 4:20 9:12,15 10:23 153:7 241:18,20

243:21 411:12 429:23 434:20 452:7 458:3 461:6

468:20 484:18 539:8 549:21 564:10 645:22

662:19 666:2 672:15 736:17 738:17 741:12

742:23 745:20

stage 26:14,14 43:5 47:14 48:19 82:4 85:22 86:9

88:6 89:7 142:15 147:1 157:24 158:5,11,16,25

159:1,3,20,26 161:10 164:4,5 165:24 166:2

174:26 175:5,5 190:19 191:6 197:2 284:12 305:3

419:14 438:21 519:16 523:16 536:9,10,24 547:14

550:21 567:1 568:6,9,14,21,22 587:16,21 604:10

612:4 620:2 654:21 657:25 658:4 708:25 715:4

stages 42:24 47:13 56:11 85:7 112:16 213:9 222:26

223:1,2 226:8 244:23 245:4 246:6 260:8 302:16

349:2 370:18 425:1 446:19 519:24 533:11 641:19

711:18

staggered 294:8 388:23

staging 178:6,8

staining 147:23

stakeholder 332:16 717:1

stakeholders 332:5,19

stamps 79:6

stand 22:22 311:15

standard 122:12 136:16 137:26 151:20,24 181:12

208:24,26 209:25 222:18 225:13 246:11 253:25

253:26 254:1,3,5,12,14,15 279:5 280:12 291:20

292:22 306:2 313:17 370:26 379:11 390:19 395:3

395:4,5,9,13,15,21,22,22,26,26 396:4,11,13,22

397:2,8 438:26 453:25 472:19 474:20 477:20,21

477:22 483:17 506:20,21 513:2,23 514:5 571:13

572:7 575:14 591:3 638:10 685:1 697:21 706:10

standardization 152:13 365:3 380:22 429:1

432:19 464:24 465:10,25 466:8,22,23,24 511:22

standardize 415:26 417:18 420:15 512:18

standardized 155:21 371:4 386:26 398:3 410:22

416:3 418:1,2,6,9,12 423:7 425:15 426:16 466:21

515:8 526:2 558:17 562:3 570:11 573:20 582:15

640:5

standardizing 418:8 424:1 558:9

standards 365:7 370:24 428:24 513:22 572:7,16

654:14

standing 32:6 606:15

standpoint 49:3 204:23 239:7 252:3 334:6 565:13

649:2 733:13

stands 126:17 262:19

standup 441:26

star 410:12

starlings 366:14

stars 263:24

start 5:3 12:25 30:18 36:20 77:25 104:17 105:3

122:19 131:17,18,19 142:10 146:13,14,18,19

155:5 157:23 181:8 183:15,16 185:24 192:8

195:4 196:24,24 197:2 198:22 229:22 233:17

240:5 264:16 271:21 277:11 286:3 305:24 313:23

314:2 315:2 324:1 333:20 340:10,14 345:22

346:18 357:24 388:21 403:21 406:16 434:2 442:1

444:25 451:19 458:18 469:16 476:8 485:3 518:4

518:5 544:11,17,24 577:12 581:22 593:25 599:14

599:16 615:26 617:8 675:10,10 693:2 695:4

727:16 731:17,20 733:10 745:24

started 75:12 100:11 105:8 229:24 291:17 296:9

323:3 346:1 392:25 403:13 441:20 617:10 646:20
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Starter 173:6

starting 14:3 18:19 23:19 52:10 79:15 110:11

154:10 163:20 193:18 203:6 206:1 239:11 273:6

296:5 430:13 447:3 466:4 547:11 601:4

starts 78:18 88:4 108:9 122:14 159:11 197:4

262:12 341:8

stat 192:6 733:21

StatCHARMMS 137:13

StatCHARRM 327:15

StatCHARRMS 88:23 90:4,9 91:1,12 138:4,5,23

153:14 160:22,26 161:7 205:13 268:21,26 269:22

277:1 472:23 473:1,7,17 475:13 477:26 478:18

480:15,23 481:10,18 483:5 579:8 581:12 587:3

592:1 725:14

statCHARRMS's 580:26

state 7:1,7,11 16:8 36:6 91:11 155:15 182:11 197:9

213:6 243:3,7,9 259:12 273:4 284:1 365:13

390:18 424:23 446:6 460:17,21,23 509:15 531:4

532:10 665:17,21 684:9 689:21 743:9

stated 29:13 30:20 34:19 121:23 280:10 316:8

357:20 371:9 389:4 418:2 430:18 444:18 449:23

452:10,22 475:15 526:11 527:3,3 535:1 537:4

550:10 581:4 587:4 614:22 618:5 624:8 637:9,19

641:4 642:23 660:24 744:3

statement 15:3 29:5 176:10 347:5 348:7 385:8

386:12 389:12,24 390:7 392:9 393:8,22 394:12

400:18 402:14 430:21 443:4 444:10,13 447:15

468:24 532:26 545:9 554:23 600:3 616:6 617:8,8

656:19 671:6 672:4 697:15

statements 10:22 237:21 394:14 445:5 446:8 591:8

states 1:12 277:20 283:15 315:20 378:10 403:10

436:18 447:9 498:9 544:6 613:5

static 120:23,24 238:11

statically 30:15

stating 618:5

statistic 114:9 162:19 183:2,3 222:5 269:23 300:10

470:25

statistical 7:19 31:13 32:10 50:4 53:24 58:22 70:19

71:16 83:24 88:25 89:11,19 90:7,14 91:13 97:22

101:7 127:18 162:14 179:23 205:13 234:24

239:22 243:13 246:1 262:22 264:17 265:6,17

268:17,23 269:10,21 276:16 287:14 289:12 290:1

291:5,8,10 300:4,11,14,25 311:5 320:22,23

331:23,24,25 361:19 374:26 377:25 379:1,6

383:13,15,18 384:16 385:18,20,23 392:22 394:13

397:16,22,23 398:16 402:17 403:26 404:3,14

405:24 406:13 409:18 414:18,19 422:7,25 436:11

437:2 442:2,9 449:9 450:1,4 461:1 464:13 470:20

470:22 471:5 474:2 475:13,24 476:5 484:15,18

505:25,26 507:21 525:7 526:24 530:7 534:13,18

535:6 537:15,21 554:12 555:15 572:24 574:8

576:23,24 577:1,13 578:2,22,25 580:9 582:2

583:18 584:13,16,24 590:2 595:22 607:12 609:22

665:25 670:13,24 673:10,15 674:12,24,26 675:3

675:8,10 676:2,21 679:10,13,21 680:12,19 681:1

683:19 685:6 687:23 688:20 689:2 691:25 692:18

694:8 696:18 698:26 704:3 705:19 706:14 707:9

716:19 725:8 735:20 744:10

statistically 31:19,22,23,25 32:1 33:24 36:3,5

52:26 70:4 71:8 73:1 94:7 97:21 102:13 127:5,21

127:23 250:15 262:15 287:12,16 290:4,6 291:26

339:16 448:25 449:7 478:9 504:23 506:4 524:7

555:10 582:22 683:12 700:3 705:16 706:10

statistical-based 705:21

statistician 7:2 182:21 331:24 381:26 394:5 519:22

580:3 587:9 665:17 670:20,26 672:4 673:18

statisticians 62:7 177:2 182:22 480:18,25 484:6

579:24 594:4 647:4 671:13

statistician's 582:9

statistics 7:21 88:22 137:12,22 147:18 163:2

176:18,24 183:19 249:25 268:16,20,21 301:4

380:7 402:3 403:1 406:9,10 476:16 579:18,26

584:20 587:7 672:11,12,20 673:11 679:23 733:17

735:26 736:23

stats 126:25 182:19 183:16 254:7 406:3 735:21

736:2

statue 11:8

status 250:20 315:18 372:2 697:19

statutes 14:5

stay 512:18 514:10 582:20 632:25

staying 24:16

steady 634:3

stealth 594:15

steeper 451:21

steer 57:4 176:4

step 19:18 20:12,16 27:17,21,22 28:4 30:16 60:5

146:18 182:22 296:14 337:6,12 354:22 546:1

600:3 654:24 695:16 733:12 740:8,26 741:4,6

744:1

STEPHEN 5:19 200:14,21,24 201:6,8,14 202:15

229:21 231:25 233:2 235:1 242:10 459:23 632:3

644:21 651:15 664:25 670:5 671:11,19 684:14

693:20 702:9 707:2 711:24 714:2 718:4 724:13

730:10

stepped 632:21

stepping 743:2,3

steps 12:2,13 20:11 127:24 259:10 634:25 647:25

712:11,12 743:25 744:5

stepwise 126:26 260:14
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step-by-step 12:7

step-down 89:23 138:20,22 589:11

stereotypical 427:9

sterile 123:6

sterilize 117:18

steroid 45:21 119:10,11,19 207:26 289:8,8 297:26

423:19 457:21 527:4 557:16 570:20 626:1,9

644:6

steroidal 456:3 553:23

steroidogenesis 37:12 85:14 92:19 119:9 134:20

135:25 136:4 209:22 230:26 231:1 303:10 449:22

488:25 525:25 531:18 538:10

steroidogenic 134:16 135:15

steroidogensis 448:16

steroids 288:15,18,21 289:4 318:4 337:20 416:14

416:15

Steve 5:19 8:19,20 32:4 33:26 34:4 242:10 459:23

664:25 747:21

Steven 8:14,15,18 10:19,21 194:11 491:16 543:12

652:17 657:16

Steve's 10:23

stick 249:13 343:11 648:19

sticking 239:15

stimulant 263:2

stimulating 184:13

stipulates 15:26

stirring 392:18

stock 273:5 417:20,22 429:3 516:3

stocking 558:24

stocks 259:14

stone 437:9

stop 108:3 129:5 239:9 518:10 626:18 642:22

646:18 736:11

stopped 247:18

stopping 615:17

stops 187:24

storage 139:18 393:15,17,20 415:10 423:6

store 723:26

stored 391:11 393:14 512:12

story 101:8 131:24 155:2

straight 237:8 356:4 368:26 431:22 677:8

straightened 390:14

straightforward 368:19 437:14 559:20 575:19

607:26

straight-forward 56:24 123:5 159:25

strain 55:22 57:2 62:5,23,24,25 63:9,9,13 118:24

118:25 119:5,6 128:18 129:8 156:16,17 279:15

292:13,16 385:17 415:6 416:7 417:18 422:19

423:3,3,5 432:3 526:12

strained 385:10 415:24

strains 55:13,16,21 56:4 57:17,19 62:2,3,5,11,18

63:2,7 119:2 128:19 282:16 385:11,12,14,21

394:16 495:8 526:13,16,17 612:24

strain-dependent 128:13,15

strange 474:23

strangely 180:18

strategic 17:19

strategically 714:20

strategies 439:18

strategy 479:10

stratified 389:25

streamline 25:21 711:1

streamlining 23:22 120:11

strength 45:4 262:1 393:25 420:24 525:24 611:18

strengths 293:5 302:13,15 313:2 322:8,9 430:9,16

430:17 431:10 432:2 522:20 523:1 524:17 525:1

547:4 611:7 639:23 640:4 708:5 709:5 713:23

714:4,6 718:10

stress 11:4 416:8,9 418:10 423:4

stressed 117:12 297:23,24,25 298:4 303:11 407:5

stressor 416:10,22

stressors 701:21

stretch 441:22 517:13

strict 88:2 515:19 517:21

stricter 417:26 418:24

strictly 69:12 173:1 260:19 261:9 350:2 482:15

stride 51:18

strident 635:16

striking 167:17

stringent 195:24 197:26 362:17

stringently 614:20

strong 80:9 125:10 135:4,6,12 177:4 187:9 301:13

330:13 349:6 376:24 377:10 380:9,10 409:19

412:18 423:15 430:22 431:12 433:7 437:25

448:13 451:21 483:4 485:15,21 489:10 490:1,24

505:23 518:6 523:24,24,26 524:2,14 525:19,24

525:26 530:2 548:2 553:18 561:6 581:9 585:20

597:9,21,24,25 603:11 611:12 661:9

stronger 305:7 372:25 438:5 488:1,16,23 489:1,2

517:22 546:26

strongest 509:7

strongly 449:25 535:12 603:14 625:8 670:19

struck 235:6 338:12

structurally 318:8

structure 22:23 27:21 83:22 109:4 138:13 142:16

152:19 167:22 175:19 271:17 401:22 529:6 531:2

569:20 587:12 588:18 589:1,17 590:13,23,23,24

590:24 591:1,2

structures 142:8

struggle 735:2
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struggled 176:17 396:18 629:11

students 391:25 392:4 628:23 739:1

studied 118:11 387:2 425:23 464:20

studies 6:24 18:7,9 20:5,24 21:11 22:14 23:3,11

24:12,19 25:11 31:6 33:3 39:14,18 42:26 43:8

47:8,22 50:11 59:17 61:19,24 63:20,21 72:4

91:18 99:2,5 100:26 101:21 102:2,19 103:14

107:12,15,16,16 110:8 111:26 113:17 118:20

121:10 128:22,23 129:23 130:9,18 131:23 132:17

132:18,25 144:23 145:25,25 150:16,20 151:22

157:9 158:15 162:3 163:14,15,20 166:7 170:8,26

173:18 174:1,2,3 181:9,24 182:1,20 189:22 193:1

195:2 196:1 197:26 200:12 202:11,12 219:4,12

220:8 226:12 230:20 248:17 264:15,25 278:7,11

280:1,21 282:7,26 285:9 286:10 290:16 291:21

292:10 294:16 295:14 297:3 298:13 307:17,20,20

308:1 310:1,10,16,24 311:9,15 312:2,2,14,20

313:22 314:25 315:15,19,25 316:10,25,25 317:14

317:21 319:20 320:1 324:10,12,18 325:19 326:6

326:10,21 328:9,17 329:10 332:26 334:23 338:1

342:1 356:18 366:25 368:24 370:6 371:3 381:20

382:4 384:22 386:1,6,24 390:18 399:14 401:11

420:12 422:9 428:21 431:11 438:18,22 447:16

449:2 454:25 456:9 465:16 466:10 479:14 488:1

488:20 501:4 503:11,11,26 504:19 507:24 519:9

520:7,26 525:9 527:8,13 537:10,18 544:5 547:13

547:15 558:4 560:5 574:9 585:10 597:17 598:17

602:22 607:14 650:16 669:14 670:12 683:20

689:6,26 699:18 704:6 712:20 714:26 723:7,24

744:18

study 21:19,20 23:21,22 24:6,13,14,24 25:9 26:3

27:21 29:24 42:2,18 43:13,22 47:17 51:3 52:18

57:14 63:23 66:1 67:9 87:20 94:24 95:2,4,9,14,25

96:16,25 97:25 98:13,15 100:10,25 101:2,4,5,11

101:12,14,14,17 102:2,3,6 103:6,11,23 105:19

106:1,3,5,8,11,13,17 107:25 108:4,12,23,25,26

111:13,23 112:5,6 113:15 118:21 123:12 129:11

129:11,11,15,21 130:4,4,12,20 132:6,7,8,26

133:2 138:26 142:7,10 157:13,17,19,20,21 158:6

158:7,24,24 160:5 165:5 167:12 168:15 171:1

172:17 173:26 185:21 192:1 194:22,23,24 195:6

197:15 199:9,15,16,19,20,21,23,24 200:1 201:24

201:25 202:3,4,10 210:19 212:6 220:25 233:21

254:8 265:12 267:22 269:14 278:3,4 279:11,19

280:11 281:18,20,22,23 288:13 290:4 293:1,5

294:9 297:15 299:10 305:4 306:3 308:17,20,25

309:16 310:5,6,8 311:10,12,15,17,18,22 312:10

313:23 315:8,9,13 316:6,11 317:2 318:11,21,23

318:24 319:10,24 320:11 321:15,21 322:17,20

323:16 329:8 335:4 339:12 340:9,17,18,26

341:20,20,21,22,25 342:3,19 346:5,10 358:12

359:6 378:10 380:26 383:15 385:25 386:5,7,20

387:5 388:17,23 393:22 395:8 403:26 420:7

431:24 438:13 442:10,19 448:8 456:7,9,11,13,14

470:21 477:13 480:6 484:2 486:11,15 487:5

498:18 499:6,13 500:3 504:6,6 505:8 506:6,9

510:4,16 521:6 532:1,26 557:24 576:24 577:15

580:7 582:4 587:8 590:26 591:1,22 595:10,19

596:22 599:3,15,16,20 600:15 602:22 622:8

650:23 652:1,6 657:22,23,25 660:13 674:25

683:22 687:19 688:12 699:21 716:17,26 722:3

723:4 735:24

studying 77:20,20 142:20

study-to-study 520:18

stuff 154:21 196:13 222:21 256:22 270:11 303:20

401:5 403:13

stunning 733:3

subadult 482:20 506:15,16

subadults 469:22 479:2 480:1 506:10 536:2

subcomponents 406:19

subcontracted 160:11

subcontractors 201:16

subject 28:17 228:10 349:24 350:18 409:2 478:12

478:13 547:5 744:16

subjective 274:14 380:4 537:13

subjectively 274:17

submerged 120:24

submission 199:24 200:26 744:22

submissions 200:26

submit 200:12 429:20

submits 200:15

submitted 19:22 277:10,13,16,22 283:12 289:16

289:26 657:23

subpanel 445:4 738:20

subpanels 734:7

subparts 559:10

subpopulations 669:5

subsampled 146:1,1

subsequent 44:18 72:14 210:10 245:12 422:25

435:12 441:12 449:12 451:2 518:1 533:3 596:15

625:5,6 627:24 642:21 645:3,14,20 687:23

690:21 706:25 723:6

subsequently 415:17 566:15

subset 64:17 75:24 107:24,25 158:25 443:25 646:4

substance 36:2 76:7 112:12 279:12,18 280:4

294:22 321:10 326:22 347:12 409:6,19 410:3

411:15 443:12 484:26 545:17 616:24 624:10

substances 219:24 282:11 321:9 349:16 383:9,10

383:18 384:2,8 406:16,23 430:8 470:14,16,25

484:25 491:12 512:12 522:19 567:14 576:18,19
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577:1,6 596:10 611:5 674:19,20 675:3 679:11

681:6,11,18 713:21

substandard 174:7

substantial 16:2 224:17 225:11 227:1,4 281:1

374:15 716:7

substantially 101:14 112:8

substantiate 531:3

substantiated 464:22 543:6

substitute 508:13

substituted 91:1

subtle 31:13 407:3 415:9 426:1

sub-adult 82:4 85:21 87:3 88:6 89:7 90:19 93:16

94:4,9,18 95:17 97:3,4,13,20 98:8 99:1,22 100:6

100:9 108:22 145:3

sub-adults 85:20 95:20 99:4 301:26

sub-sampling 267:25,26

success 45:6 74:10 82:25 87:23 275:22 433:15

435:8 453:18 454:10 462:9 515:21 528:20 643:11

718:16

successful 60:14 103:17 142:3 166:9 174:19 198:7

202:24 351:17 365:18 504:25 509:15

successfully 58:5,9,20 104:20 280:24 335:14 350:9

428:8 656:22

succession 108:17

succinctly 429:26

sudden 8:5 197:18

suddenly 581:24

suffered 510:6,7

suffice 342:6

sufficient 43:26 50:18 55:3 56:5 66:2 74:5 107:19

191:18 192:21,22 221:1,5 222:19 237:6,14,15

247:1,25 268:11 279:1 297:7 323:9 324:15 337:9

347:11,12 349:15,16 350:15 354:18,21 377:26

414:9 419:11 420:8,19 430:19 435:6 438:3

443:11,13 465:4 493:11 503:6,17 514:18,22

528:18 529:2 545:16,17 567:23 568:7,13 601:21

616:23,24 624:9,11 666:21 668:19,20,22 697:5

698:16 699:1 716:21 718:14

sufficiently 40:23 63:18 64:3 131:23 256:26

282:11,21 320:8 364:4 461:12 510:22 556:4

563:2 651:12 666:5

suggest 63:13 69:23 107:18 239:9 309:2 320:2

321:1 323:5 335:21 363:22 372:19 399:1 428:18

431:18 497:1 505:23 506:12 529:20 531:23 551:1

610:6 614:16 636:1 659:26 685:20 714:20 720:18

725:12 728:1

suggested 49:13 207:25 353:26 398:8,8,9 403:23

429:26 441:5 451:10,13 455:7 456:3 467:18

486:16 508:23 514:19 515:10,25 518:1 525:11

562:14 565:5 614:9 659:12 660:10 666:20 668:16

685:18 690:1

suggesting 96:21 350:21 356:8 366:25 373:23

375:19,20,23 436:5 483:23 486:12 492:3 505:17

540:14 574:12 609:4 651:1 659:21 685:10 687:18

712:6 714:23 715:26 725:6

suggestion 152:6 353:20 362:17 368:1 417:19

520:21 521:4 522:2 597:1 608:7 733:21 737:16

suggestions 230:19 231:19 353:26 354:7 357:4

362:8,10 364:17 421:24 422:11,21 434:25 441:6

508:2 543:26 544:9,11 607:16 608:15 696:10,14

704:8 724:26 728:11 737:16

suggests 225:16 282:5 313:18 483:3 510:17 530:11

551:13 557:25 613:24 614:12 625:25 658:20

662:8 668:24 711:19

suitable 306:22 315:18 316:1 356:1 391:10

suite 159:13 279:5

suited 426:17

summaries 93:6

summarize 55:11 56:14 58:18 93:7 98:22 102:17

107:7 217:15 226:21 426:15 705:16 722:18

summarized 8:8 47:8 166:22 352:23 494:10

504:19 520:8

summarizes 723:8

summarizing 349:25 648:16

summary 8:10 38:25 39:12 43:18 47:9 49:23 59:7

67:12 69:1 76:5 93:23 98:24 102:18 132:1 134:15

154:23 158:19 160:15 161:16 165:9 172:20 222:5

244:15 277:21 296:5 300:15,22 301:2,17 346:5

346:10,24 368:7,23 379:14 380:8 421:5 438:2

447:9 451:10 505:2 518:5 520:5 522:26 529:25

547:19 611:11 614:19 617:7 676:19

summer 28:25 31:4 189:25

Sunday 600:19

suntan 168:2

super 117:21 432:9

superficially 158:2

superfund 622:10

Superior 116:24 117:5,17

supervising 433:22

supplemental 352:5 652:2

supplementary 670:17

supplemented 741:14

supplier 62:13 415:26 417:19 423:6 429:8

suppliers 706:21

supply 309:24 370:25

support 112:25 351:10 370:1 371:25 372:26 411:8

435:21 436:20 437:13 536:10 551:11,22 553:2,20

554:3 557:18 558:25 560:13 565:17 568:8 609:6

631:9 706:21 722:19 742:17

supported 43:20 283:9 304:24 348:9,10 488:14
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535:12

supporters 135:6

supporting 27:10 38:26 93:24 95:3 367:3,4 411:9

559:6 561:20 697:14 738:6

supportive 358:2 643:16

supports 94:19 251:14 419:6 712:6 721:9

supposed 30:8 198:19 228:24 386:13,16,17 542:22

565:17 627:18 643:7 732:6

supposedly 299:21 712:5

suppress 486:17

sure 22:8 24:16,19 50:13 64:21,23 67:22 86:22

104:16,16 105:1,2 113:19 118:12 119:6 120:24

121:10,22 122:7 126:8 133:22,25 134:12 148:5

148:19 149:10 150:2,25 152:17 165:20 174:8

188:11 194:26,26 195:2 229:11 235:15,24 238:3

241:9 245:6 261:14 262:14 276:9,23 277:4

298:14 309:12 310:11 313:26 324:6 326:5 327:21

327:23 332:14 333:9 334:23,23 335:10 337:7

353:23 355:24 357:3 360:9 381:22 383:24 384:5

385:7 386:1 388:7 393:10,17 394:19 396:16

397:14 399:10 411:20 412:13 424:19 429:15

450:18 456:21 458:9 489:16 490:3 501:11 514:1

514:10 519:26 527:1,11 542:9 543:7 563:23

581:8 582:2 606:5 627:13 634:3,24 636:5 653:20

654:2,14 655:12 656:2 662:24 671:7 672:1 674:7

677:8 678:1,6 683:9,19 684:21 688:7 693:4 701:8

729:8,8,9 734:19 742:24 743:8,11 746:15 747:14

747:26

surely 18:22

surface 117:17

surprised 154:11 198:13 236:24 248:9,11,16

332:22 374:7 583:20

surprising 169:1 249:4,5,8 406:8 506:4

surrogate 157:8

surrogates 548:13

surrounding 448:24

Survey 6:5,8 242:16,18 460:4,6 665:6,8

survival 44:14,23 49:25 64:15 82:26 83:1,3 88:10

108:14,15,20 109:25 110:1 186:6 202:6 212:23

214:1,14,26 221:21 222:1 223:14,17,17 263:16

297:12 313:5 316:13,15 325:14 329:16 334:15

338:8,9 481:16 515:7 542:20 587:19,20,20

618:11 624:19 637:21 638:25 691:2 700:18 706:8

716:25 721:21 722:10

survived 72:12 257:11 268:9

survives 691:8

surviving 116:11 469:15,17

survivorship 690:1 722:11

susceptible 45:11 58:2 63:14 257:12

suspect 49:1,14 57:10 278:13 436:19

suspected 37:21 282:12

suspended 121:3

suspicion 41:18

suspicious 83:8

sustained 71:3

Sweden 210:5 251:26

swiftly 277:4

swimming 169:4

switched 183:16

switching 222:6

symmetry 373:8 425:9

sympathetic 374:23

syncs 317:7

syntheses 231:3

synthesis 168:5 207:26 348:3 512:23,24

synthesize 678:26 679:2

synthesized 8:8

syringe 267:3

system 11:23 12:4 17:26 18:15 19:4,7,12,16 21:10

21:13,18 22:12 23:7 57:6 114:1,5 120:19,23,24

121:5 132:11 147:4 148:6 175:18 178:6,8 180:16

192:14 211:6,14 219:25 222:19 226:1 241:11

266:15,21,24 267:2 269:8 283:16 286:9 294:6,7

317:13,17 323:7 340:24 371:8,13 373:12 378:20

407:22,23 427:18 430:18 432:3 456:5 471:26

472:1,3 487:1 494:2,6 534:10 552:13 557:19

617:16 622:20 623:2 626:17,23 641:23 667:12

670:19 696:2 697:18

systematic 88:25 146:5,12 178:19 381:4,6 478:10

systematically 88:24 380:1

systemic 69:13 327:5

systemically 178:2

systems 10:6 11:16 15:17 76:9 79:18 112:15

113:18,20,21,25,26 114:5,6 168:9 207:7,12,16

220:10 223:13 230:14 266:1,4,10,15 267:3 294:2

317:6,10,23 337:11 370:7 379:7 407:1 426:26

430:19 432:26 471:25 486:23 617:17,19 618:16

619:25 622:17 623:6 631:16 637:26 640:16

681:25 696:14

T

T 20:8,16 36:23 138:18 139:1 233:17 315:8 316:26

337:4 449:22 455:18 457:7 618:19,22 619:4

623:7,12 630:17 634:14 637:23,25 678:13 680:24

683:4 700:26 701:7 713:7

table 30:4 49:22 66:26 145:18 161:26 170:5,19

216:6,12 241:20 311:19,21,23 319:13 395:11,12

395:18 396:18,21 435:13 436:20 437:13,18

471:14,16,19 474:11,11 504:20 520:8 548:3,5,7

600:12 633:4 648:14,24 691:16 696:2 700:17

721:14 723:18 746:20 747:23
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tables 162:10,25 369:20 529:24 555:8 687:3 719:1

719:13

tackle 441:22 544:10

tackling 492:15

tactful 635:13

tadpole 567:4 586:1 594:9

tadpoles 190:18 567:1,9,15,24 654:20

tail 151:13 680:25

tails 680:23

take 3:6 9:6 38:11,16 49:4 58:6 59:4 104:24 108:10

108:11 114:18 118:5 120:20 127:21,23 129:24

131:23 134:4,20 145:20 146:9,23 149:25 152:23

152:24 153:6 154:6,26 162:9 166:23 176:5

178:10 182:14 183:21 191:25 205:5 210:25

212:12 236:21 258:7 273:12,13 276:7 284:17

285:19 291:13 295:26 296:14 311:5 316:15

318:15 321:4 330:5 332:18 344:19 345:11,26

354:14 364:3 380:2 381:25 384:24 391:4 396:11

397:2 403:16 404:13 412:9 419:16 441:21 468:11

468:16 484:24 494:4 507:10 520:26 533:4 542:10

544:16 547:14 576:3 577:20 578:15 593:19

595:10 615:18 628:24,24 629:12 635:25 646:19

677:12 678:23 680:4 681:12,14 703:17,18 722:4

723:18,21,24 730:7 733:23 740:8,26 741:4 742:1

taken 24:12 38:14 46:5 78:22 82:4 89:6 105:5

110:2,3 144:9 146:26 153:9 169:15 182:24

203:10 205:8 276:11 288:24 345:24 387:11 388:6

403:19 441:24 458:21 507:12 544:21 591:12

593:22 594:8 607:26 615:20 618:12 663:12

668:18 674:9 699:11 703:20 708:15 721:16

takes 9:14 73:21 86:19 138:4 152:21 153:11 159:2

173:9 195:5,12 197:1 210:19 285:7 323:2 424:10

739:17,18

take-home 170:4 172:24 174:17 196:17

talk 8:2 14:13,15,23,23 15:22 20:1 21:23 33:4 39:6

39:14,19 43:2 72:24 87:22 88:21 100:7 122:18

130:17 133:21 134:1 141:7 148:14 151:25 155:6

162:9 163:10 165:20 177:19 196:16 206:4,7,13

206:16,18 210:20,21 213:26 224:24 271:18 318:5

323:22 327:6 333:1 334:6 343:9 346:12,24

363:23 385:8 386:4,4 393:2 397:15 398:6 421:5

464:10 484:12 508:7 543:7 546:24 555:14 628:3

640:23 672:22 686:12 687:2,8,8 692:7

talked 29:1 179:22 203:16 209:20 259:13 270:13

271:4,9 290:24 324:22 338:15,16 359:9 373:26

378:14 394:23 414:24 416:26 418:7,18 423:26

424:1,20 432:4 487:26 490:8 526:22,24 532:11

584:22 588:7 591:26 592:12 635:11 643:10 658:6

659:2 682:22 700:22 701:15 704:14 725:18

talking 11:15 21:1 35:26 71:5 141:16 154:13 155:8

157:20 183:24 192:26,26 200:19,22,25,25 206:22

216:23 307:19 311:25 379:2 432:26 543:23

591:16,17 623:15 646:25 656:21 692:15,26 693:1

693:6 723:19 738:23

talks 212:25 323:24 337:17 346:22 401:6 402:11

432:21 433:11 479:12 481:1 632:18

Tamoxifen 96:25 107:4 485:19 494:24,26 495:2

506:11

tank 80:22 90:15 91:13 109:7,9,10,14,14 121:5

132:16 137:18 153:19,21,21 160:26 161:3,4,4,6

171:8,9 173:3 182:21,23 183:14,17,21 192:4,15

211:9,11 228:3,17,26 229:9 273:13,14,14 395:8,8

472:3,8,10,11,13 475:7,10 477:21 519:14,15,18

519:24 584:8 586:20,23 587:3,24 592:14,16

594:12,14,18,19,21 673:26 694:24 696:4,17

699:11,13,13

tanks 108:26 109:10,14 191:18,19 192:26 273:12

471:12 472:13 502:19,20 583:1,15,15 584:8

594:17 595:11 674:1 695:6,23 696:1,2,4

tank-to-tank 477:14

target 336:1 355:7 410:11 488:22 522:11 691:15

704:26

targeted 34:19 232:16 356:22 376:7 513:7

targets 47:14 125:20 457:10,11 489:8

task 39:1 520:6 584:16 730:16 733:7 747:20

tasked 308:20

taxa 20:25 21:23 23:12 34:25 35:18 36:18,26 37:2

37:5,7,17 63:10 72:5 189:23 206:22 348:17,20

550:26

teach 391:24 399:20 590:11 702:1 738:24

Teacher 5:24

team 2:15 205:17 428:17 742:5 746:19

tech 5:24 6:9 113:1,12 136:26 137:1 242:13 459:26

665:2

technical 224:17 227:1 301:9 497:12,15 499:19

500:21 511:6 513:16 566:19 567:18,23 568:19

604:14,24 605:17 726:13 747:24

technicality 662:5

technically 313:25 726:17

technique 54:10 174:19 269:24 289:3 370:18

402:18 583:24

techniques 11:15 100:16 103:17 123:6 174:22

189:19 266:6 269:21 372:10 379:8 509:22 546:5

578:17 582:15 630:5 644:11

technologies 13:25 181:6

technology 19:16 26:12 181:1 382:20 646:6,14

tedious 714:19

teleconferenced 251:2

Telephone 1:10

tell 35:9 69:11 87:15,16 88:16 104:5 117:24 123:16
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130:14 133:25 135:10 147:12 171:20 177:17

196:21 240:4 297:18 338:3 355:16 388:17,20

391:25 392:4 404:24 405:1 414:26 475:12 543:12

653:21 678:3 692:4,21 694:2

telling 22:21 173:11 188:26 327:1 330:26 692:7

tells 703:2

temper 292:16

temperature 99:5 118:15,20,21 122:1 123:22

391:9,11 393:18 415:10 423:8 451:3 454:5

513:12,12,13 515:20

temperatures 118:17 128:10

tempered 292:9

ten 3:17 175:12 276:8 340:13 615:19

tend 86:10 100:8,11 117:9,12 147:11 166:10 185:1

248:23 317:6 363:7 401:17 479:3 481:25 482:3

526:13

tended 99:24 129:24 175:22 294:12

tendencies 132:4

tendency 578:22 585:8

tends 76:18 84:25 87:11,19,23 88:11,13 91:22,23

178:17 185:8 262:23 274:7 374:6 400:3 581:3

tenets 624:13

tentative 189:25 376:26 380:11

tentatively 252:23

Tenuiremis 208:25

ten-minute 276:9

teratogenic 552:4

term 20:24 23:3 25:10 43:2 47:12 354:5 547:13

594:25 622:18,22 650:21 660:22 661:12 690:12

691:7

termed 22:4 481:19

terminated 44:14 108:12 221:6,16

terminates 528:24

terminating 304:24 534:2

termination 23:24 159:21,22 433:12 532:18 535:4

701:16 723:1

terminologies 471:13 660:10

terminology 432:20 579:24,24,25 590:15

terms 10:1 14:12 16:24 17:12,19 19:11,17 22:21

32:7 34:3 39:12,16 40:10,14,17 41:2,3 42:9,11,20

43:4,7 44:23,25 45:5,6,7,9,23,26 46:13,20,26

47:20,24 48:24 49:14,22,24 50:22,23 51:1,10,11

51:13 52:7,12,23,26 53:4,15,21,23 54:2,17 55:9

55:14 56:1 57:19 58:8,16,23 59:16,26 60:1,13,19

60:23 61:11,19,20 62:11 63:3,4,5,5 64:19,21 65:1

65:4,12,21,22 67:2,3,23 68:10 69:3,12,15 70:13

71:26 72:8 73:5,20 74:3,6,22,25,26 101:3,24

121:26 123:5 147:8 150:26 157:20 159:7 170:15

170:17 171:19 173:17 174:18 175:18 176:18

182:9 185:13 186:6,13 187:6,11 189:19,22 191:6

192:1 193:24 194:6,7,21 196:1,1,4,18,19 200:23

202:11 206:14,19,21 209:3,11,11,18 213:12,26

214:22 215:15 216:23 217:7,20 218:2 219:6

220:3,6 221:19 222:1,2 223:20 224:8,17 226:25

227:23 229:4 230:10 231:2,7,11,14,26 232:8,19

232:26 233:22 236:2 237:7 238:8,18,23 239:1,3

245:12 246:2,5,8,23 247:11,26 249:19 250:21,21

251:4,9 254:9 256:16 257:5,18 260:2,8 262:10,17

264:10,14,24,26 265:5,19 267:21 269:19 309:9

311:7 339:7 340:23 348:18 353:18,26 354:8

356:3 358:8 359:15 362:1,3,9,9 372:23 378:11

382:17 386:26 400:2 408:5 409:1 410:20 411:10

411:15,24,26 412:24 413:3 423:12 426:18,20

427:24 431:10,19 434:22,22,24,24,26 441:6

444:1 453:1 472:5 491:19,23 492:4,5 493:19,21

500:21 501:22 503:15 506:23 508:25 519:26

542:4 566:4 586:25 588:24 590:11 598:4 602:4

605:9 617:19 620:24 632:10 636:13,19,19 645:12

647:4,20,22,26 651:5 673:3,5,15,17,18,19,20

674:11 685:8 695:19 706:7 716:5 729:13 732:16

741:2 743:7,10 744:1

terrestrial 619:20

terribly 167:10 670:15

test 2:6 9:22 10:6 14:8,16 15:17 16:1 17:11 18:24

20:1,17 21:17 23:17,20 24:9,15,25 25:1,13,15,16

25:20,21,22 26:6,8,22,22,23,24 27:5,14 28:13,21

29:9,12,14 30:7,20 31:7,11 32:3 35:19 36:2,16,17

37:6 39:3,5,7,16,21,22 40:5,5,19,22 41:7,9,10,23

42:5,19 43:10,22,26 44:8,14 45:3 47:18 49:4,5,18

50:9,11 54:26 56:20,21 58:10,14,17,25 59:7,13

60:3,11,13 61:5,16,22 62:22 64:7,8,10 74:2,25

75:14,18,20,22,23,23,24,25 76:2,4,6,7,17,23,23

77:6,10 78:2,3,3,16,24 79:13 80:8,17,18,19 81:1

81:3,3,4,4,6 83:1,2 86:3,4 88:9,17,25 89:20,22

91:16 93:6,18 98:23 99:7 104:15,17,19 105:16

107:18,20,21,22,23,26,26 110:16 112:12,12,13

113:7,21 114:2,8 121:2 122:19 127:13,18,26

132:15,23 133:24 134:1 136:2 139:1 142:5 148:6

151:7 152:9,11,12,21 155:6,6,7,7,11,17,21,23

156:1,26 157:2,14,25,26 159:21,22 160:1 161:21

164:15 165:13,16 167:3,4 168:17,21 169:18,19

175:2 177:8,10 188:3 194:6,7,18,18,19,20 195:19

195:20,20 196:7 199:26 200:10,12,16,18 202:16

202:17 206:5,6,11,15 208:17,19,24 209:6,7,12,21

210:6 211:7,22 212:3,5,5,10,12,13 213:14,15,22

214:6,7,18 217:26 219:2,20,24,25 220:4,5 221:1

221:16 222:18 223:16,20 224:12 225:4,17 226:5

227:9,24 230:8 232:2,4,5,9 234:14 237:16 245:17

245:20 246:10 247:3,11,16,18 248:5 249:14,26

251:9,14,18,20 252:4,25 253:8,10 258:16 260:5
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265:14,24 266:2 268:26 278:5,21 279:12 280:4

280:15,25 282:9 284:23 286:1,7,9,13,18,19,23,26

287:1,1,7,11,13,14,20 290:13,19,20 291:4,4,9,9

292:6,7,23 293:7,8,14,22 294:1,4,9,21,23,24,25

295:7 296:3,3,8,9,11,16,25,26 297:6,6,15 299:8,9

299:17 300:6,11,19 301:12,20 302:13,15 303:6

303:16 304:2 305:1,2,5,6,8 306:20 307:5,8

308:23 311:7 313:16,17 321:9,9 322:5,13 325:7

325:24 326:13,22 336:1,23,25 338:19,25 339:1,8

339:9,17 347:4,6,6,16 348:9,22,23 349:2,6,11,12

349:18 350:1,8 352:3 353:19,19,23 354:21

355:13 356:1,3,13,24 357:12,25 358:24 364:4,10

366:2 369:24 372:9 373:15 374:12,19 375:19,24

375:25 376:12,16 377:5,17 381:10 383:7,9,10,12

383:17 384:2,8 385:23 389:7 393:9 399:1,2 402:8

406:15,22 407:11,15,18,21 408:2,5,13 409:1,2,6

410:3 411:15,22 413:22,26 414:5,5,6,8,11,24

415:1 417:9 419:7,11,22 420:3,4,5,8,13,17,18,22

421:6,8,12,14 422:2 424:4,4 426:15,20 427:7,17

427:17 430:5,6,7,7,14,20 431:4,19 433:12 435:11

436:25 437:1,18 438:4 440:6 442:4,23 443:3,4,6

444:9 446:1 447:10 452:14,14 453:3 456:10

457:4 458:19 459:5,9 461:6,11,12,18 467:5

470:12,15,16,18,24 471:14,15,26 472:1 482:7

483:7,7 484:25,26 485:5,11 487:16 490:17

491:12 493:6,6,8,13 494:14 496:16 497:1,3,7,8,9

497:9,10,11 499:16,25 500:2,10 501:25 503:5,19

504:4,12,13 505:12,13 507:16,26 510:5,7 511:7

517:7 518:2,5,8 521:17,18,21 522:7,8,16,17,18

522:19 528:22 529:3,10,13 530:5,10,19 533:6,24

533:24 534:2,11,17 535:4 537:23 539:16,21

540:18 542:26 543:5 544:16 545:8,10,11 550:1

556:3,9,26 563:20 576:16,18,19,20,26 577:6

580:2 581:3,5 582:10 583:3,18 584:11 587:26

588:1,15 589:15 596:10,14,17,18 598:7,9,25

599:5,13 600:3 601:16,16,18,20,23 602:10 604:2

604:8 605:10 607:7 610:16 611:2,3,4,5 614:2

616:4,5,7,8 618:16 619:23 621:8,13,17 622:9,16

622:19,20 623:4 624:3,14,18 625:13 626:13,14

632:11,13,22,22,25 634:1 636:13,24,26,26

637:15 638:11,24 639:15,18,22,23 640:2,7,8,10

640:11,16,17,26 641:2,5,7,11,13,14,16,17,21

643:2,4,5 644:14 645:5 647:22,26 648:17 649:14

649:24 651:3,17,18,18,26 652:3 654:11 655:8,22

656:26 657:13,21,24,25 659:12,22 662:8 664:12

664:13 666:5,10,21,22 667:22 668:6 669:1,13

670:16,21 674:17,19,20,22 675:2 676:22 678:13

679:11,23 681:5,11,18,18,25 684:18,18,20,21,25

688:15 690:25 695:18 696:10 697:1,1,2,4,7,26

700:13,20 702:3 703:24 705:11,16,17 706:4,7,13

706:17,21 709:5,13,14 710:4,6,24 711:20 713:10

713:18,20,21,21 714:10,21,25,25 715:8,21 716:3

716:5,5,15,16,24 717:1,2 722:21 734:22 737:2,2

743:8,10,17,21,21,23 744:2,6,14

tested 76:11,21 105:26 112:17 134:16 161:22,23

164:22,24 165:11,11,14 167:1,2 219:8 224:5

287:7 296:13,14 299:11,11,13,15 300:3 302:1

305:4 313:4 321:11 361:7 382:2 423:25 424:17

430:8 457:12 485:13 490:19 510:8 522:19 561:7

561:9,12 564:25 596:23 611:6 623:7 659:24

681:20 682:5 685:15 700:21 704:25 705:18

713:22 721:26

testes 379:17,17 380:12 410:10,11 469:22 559:4

561:8 562:5

testicular 84:4 85:4 378:17 546:14 552:20 557:10

testing 15:4 17:2,11,19 18:5,6,10 19:14 22:26

23:16 25:3 27:23 29:7 31:12,12 34:19 42:15

43:16 76:19 77:22 90:25 103:18 114:1 132:11

138:25 150:26 151:18 156:17 182:14 184:19

208:22 224:13 225:13 226:16 230:2,3,4,5 232:1

234:3 244:21 245:1 249:20 251:13 253:6 283:6

284:24 285:8,9 286:8,8,12,22 287:5,9,25 290:18

302:4 306:1 310:4 316:10 322:10 324:16 325:10

332:17 334:9 341:12,14,17 347:15 349:7 352:15

354:11 356:26 369:11 374:8 377:15 378:14 383:8

406:25 407:5 428:26 430:5 433:22 437:20 439:7

442:12 443:16 444:19 462:6 470:13 478:5 500:13

522:16 529:8 534:15 538:6,12 539:15 545:21

546:8 558:16 570:11 576:17 582:17 584:9 589:1

589:12 599:7 611:2 614:2 617:2 618:20,23 621:5

622:10 623:7,13 624:23 625:9 629:15 634:20

638:9,10,16 641:3,14 642:17 648:22,26 649:4,13

649:15,15,17 650:10,12,22 651:25 652:4 655:19

655:20 659:22,23 660:11 661:2 666:25 668:24

669:3 674:18 680:5,5 681:20 682:8,9 693:13,15

698:5,6 700:5,6 708:11,16,18,26 709:7,16,20,20

709:25 710:2 713:19 714:9,16,24 717:8 724:6

testis 84:2,3 85:3 158:12 169:26 170:1

testosterone 207:24 626:3

tests 1:6 17:12 40:26 47:10 56:13,24 57:11 58:19

59:22 60:1 62:23 64:7,8 73:18 79:2,17 85:21,24

88:26 89:8,8,11 93:7,8,9,16 104:8,9 112:24,25

113:5,8,12,22 114:6,17 138:18 194:3 203:19

208:21,23,26 209:14 219:7,9 222:15 224:15

229:26 230:5 231:8 232:14,26 235:23 241:7

244:12 247:2 253:8,9 259:16 265:18 278:2,8,12

278:17,26 279:5,6 280:8 282:9,16,21,22 283:9

284:18 285:1,15,19 290:26 291:8,10 295:3,5

300:7,26 310:25 314:14 324:3,14 328:6 331:23

332:6,14 338:14 347:10,11 348:19 349:14,15,24
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350:9,14,14,23 351:3 352:2,17 354:5 361:11

372:12 377:15 387:16,16,17 397:21 398:2,2,6

399:7,10 406:26 407:7,8,15 409:11,13 413:1,6

417:6 421:11,11 435:26 436:1,3,9,23 438:8

439:21 443:7,10,11 473:18,18,20,22 482:8,25

505:9,26 508:10 510:2 524:26 537:17 545:15,16

547:22 551:13 582:8 588:24 603:26 616:22,23

620:20 624:8,9 625:9,21 627:2 629:18 630:17

664:5 667:23 668:25 669:20 671:5 675:23 679:21

679:24 682:7 684:17 698:2 699:2,19 701:1

705:13 708:8 719:2 721:17 724:1 730:23 734:5

test-to-test 624:25

Texas 6:8,9 242:18 460:6 665:8

TH 486:13

thank 2:10,11,13,15 4:18 5:1,2 7:26 8:12,18 9:8,10

10:16,21,24 12:9,14,17,21,24,26 13:7 29:24,26

38:13,24 59:6 68:12,14 70:16 75:8 105:4 114:22

115:13 130:13 133:11 134:3 139:7 149:8 150:22

153:4,8 154:6,7,14 155:2 176:7,25 177:13,16

182:17 190:2 203:13 205:2,4,7,23,26 227:15

229:19,20 239:12,14 240:6 241:23 243:25 244:1

248:2 253:18,21,23 261:11 270:5 274:22 276:3

276:10,14,15,24,25 277:3,5,6 284:2,7,13 285:21

285:22 295:22 305:13,14 314:19,21,23 315:1

323:18,19 333:8,16 337:14 342:5 345:17,19,23

346:1,1,17 352:24 353:12 357:8 363:21 373:22

380:15,17 382:5 383:3,4 403:15,24 405:9 407:25

414:16 421:19 428:5 430:2 434:1 439:24 441:17

441:19 442:21 443:20 452:11 456:17 458:11,15

458:20 459:12 461:4 467:13 468:19 469:9 470:6

470:8 475:22 484:5,22 487:21 488:5 491:10

501:11,15 507:1,11,13 518:11 520:20 521:13

527:22 528:15 532:2 533:19 539:1 543:10 544:20

545:2 549:24 555:19,26 563:4 570:6 576:12

589:26 590:6 594:3 595:20 596:6 598:10 605:26

606:2 609:9,26 615:16,25 616:3 617:6,7 631:7

634:26 635:1 637:13 642:26 644:16 645:24,25

646:3,16 648:8 662:18,19,23 663:7 664:8 674:10

674:13 677:19 702:7 703:6,22 707:12 717:12

718:3,11 723:9 724:10 725:2 726:3 727:5,7

730:10,12,26 731:2,5,8 732:3,4,25 733:1 734:3

735:18 738:19,20 740:17,19,21 741:9,24,26

745:13,14,15,15,17,21,24 746:18 748:4

thanking 8:26 12:25 745:25

thanks 12:20 68:23 116:21 130:19 179:19 201:14

235:1,2 245:8 250:25 263:5 323:21 354:15,15

601:11 632:3 662:2 687:17 730:25 736:12 740:12

748:3

than/equal 214:14

theirs 74:18

theme 337:17,21 349:22 360:7 372:11 414:21

729:15 746:18

themes 323:23 727:10 736:22 737:15

theoretical 287:8

theoretically 287:6 302:18 453:7

theory 366:23

thereof 668:11

thermocycler 123:6,7

they'd 254:15 288:22 727:16

thickness 45:4 358:3 365:1 394:1 432:1,18

thin 174:12

thing 51:12 55:12 56:4 68:24 73:2 81:19 87:13

92:14 96:6 111:4 112:2 114:3 119:4,24 122:17

127:6,11 132:2,9 140:7 150:11,16,17 156:20

164:26 167:17 178:7 188:26 190:15 193:16 215:8

215:13 219:12 236:11 245:3 246:17 247:8,15

249:7 260:16 271:14 285:4 294:3,15 306:26

307:16 322:11 323:13 330:19 331:3 334:5 335:4

345:5 354:17 360:22 362:7 371:6 377:9 388:22

390:21 392:17 393:1 398:21 401:4 404:21 405:4

408:16 427:23 428:7 429:2,12 433:1 444:1

450:10 457:8 472:12 474:24 477:11 485:15 489:4

490:8,14 501:20 514:20 521:26 524:8 532:10

542:17 549:6 554:9,15 567:20 574:17,23 579:13

579:15,18 580:5,19 581:11,24 582:1 585:2

589:12 590:10 591:16 600:12 610:2 631:2 639:13

655:26 662:6 663:9 677:21,26 678:11,21,24

680:11 687:4 690:14 691:12 693:11 695:25 726:3

729:3 735:2 739:25 743:11

things 39:9 46:6 47:21 48:22 62:7 69:1 79:1 82:19

83:17 86:5 102:25 104:2,10 113:14 116:22 117:2

117:18 120:19 121:26 122:1,2 123:23,24 125:24

131:26 139:10,17 143:6 147:26 153:17 155:10

157:9 158:19 159:20 174:11 181:3 184:12 185:16

185:18 186:11,15 190:16 193:13,15,17 194:5,8,8

194:9,14 195:19 196:7,7,23 204:12,21 208:1

209:21 216:13 218:25 239:6 244:8 256:12,20

257:1 259:11 260:9 262:6,8 267:4 274:15,16

291:7 297:13 298:5,21 307:1 309:18 310:15

311:14 313:19 315:23 319:4 326:15 327:17

329:18 330:5 331:1,14,16 335:3,18 337:20,22,25

338:2,8 346:2,7 353:25 370:21,21,21 371:7,8,12

371:17,26 372:2 385:6 386:25,25 387:3 388:9,10

388:10,13,15,25 390:10,25 391:1,24,26 392:2,8

393:5 400:24 401:18 405:6 408:19 414:17 433:1

440:5 445:2,13 456:22 469:13 472:5,16 473:2

480:4 491:26 501:11 502:13,14 514:8,10 518:17

518:19 522:3,11 523:18 542:21 546:14 549:3

577:11,26 578:2,8,24 579:3 580:17 583:20,23

584:20 585:7,8,10 586:5 588:6 590:19 591:12,13
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594:5 598:16 600:2,13,15 627:11 632:12 635:19

640:20 643:19,21 653:22 657:17,22 658:3 659:1

673:1 679:1 686:15 689:18 690:26 695:25 696:6

710:3 721:18,18 722:1 728:23 729:26 731:21

732:24 733:21,25 734:21 736:10,21 737:4,12

739:23 748:9

think 11:11 12:7,12 17:19 30:25 32:5,6,23,25 33:8

33:10,14,15 35:12 38:11 48:22 49:3 53:3,20

55:12 58:18 59:14 60:17 63:23 64:17 65:26 68:21

68:24,24 69:18 71:2,4 72:4,24 73:7 74:14,19 75:7

75:12 80:9 87:10 88:18 96:6,12 98:12,22 101:6,9

105:6,8 110:24 112:1 113:13 114:4,17,19 116:13

116:17 117:11,19 118:21 121:16,25 122:10,14

124:21 125:9,11,12 126:9,14,17,20 129:19,22

131:6,22 132:20 133:1,4,8,12 134:8,10,26 135:5

135:7,8,9,11,21,22,23 136:1,24,26 137:9 139:11

140:13,25,26 141:1,12,20,24,25,26 142:12,19

145:13,26 146:15 147:10,13 154:9 157:20 163:15

163:20 164:23 171:5 173:4 175:11,12 176:2,9

177:7,25 180:7,12 181:18 183:12 186:9,12

188:14 194:13 195:4,10 196:2,18 197:5,25

198:26 201:11,11 203:24 216:5 228:12 229:6,6

229:14 231:17 232:21 234:22 235:17 236:20,22

237:4 238:7,13 240:3 243:23 244:6,10 245:9,18

246:22,23 247:23 248:15 249:1,7 254:1 255:9

256:15,23 257:1,17 258:6,12,13,19 259:25 261:3

264:8,23 266:14,16 268:19 269:21,23 270:16,18

271:15 273:18 274:18 275:13,23 276:7 277:7,11

285:14 295:23 296:22,24 297:5 298:6 299:8

301:5 303:23 304:26 306:15 307:1 308:10 311:2

311:4 312:7 316:5 317:1,18 318:12 323:15,23

325:3,16 326:14,15,19,24 328:15 329:25 330:4,7

330:9,15 331:3,17 332:2 333:18 334:2,16,17,20

334:22,25 335:8,9,13,16 336:10,13,18 337:4,9,11

338:20 340:7,8,16,21 345:18,20 346:1 353:10,13

353:22 355:17 357:9 358:19 359:2 361:21 362:7

363:5,23 366:17 369:21 370:16 371:6 372:6,21

373:12 375:6,10,11,24 382:10 384:12 385:26

386:6,8,18,19,23,25 387:5,18 388:5,24 389:11,23

390:10,13 391:22 392:23 394:16,20,22 395:12

396:18 398:23 399:11,16,20 400:19 402:10,21

403:13,15 404:22 407:7 408:1,13,18 409:5,8,11

409:24 410:19 411:4 413:6,8,16 415:25 418:11

419:2,9 421:18,22 423:9 424:4 426:15,20 428:10

429:6,25 431:13 432:22 433:19 435:19,20 440:12

440:15 441:4,5,20 444:3 450:10,20,23 451:13

454:21 457:5,21,22 458:10,11 465:9 466:21

468:15,21 472:26 473:8,11,24 475:17,19 476:1

476:19,23,24 477:1,10,26 485:1 487:25 488:9,9

488:10,14,15 489:5,11,12,13,26 492:5,25,26

501:14,17 502:22 503:6,20 508:7 509:19 510:4

510:13,26 512:15 513:2,22 514:19 515:15 517:6

517:7,16 518:4 520:24 522:1,3 523:3,15 525:3

527:24 537:4 538:20 540:13,23,25 541:19,20,26

542:10,15,24,26 543:2,3 544:3 564:2,20 565:10

567:11 573:5 574:19,23 575:19 580:1 581:2,12

581:26 582:14,23 583:2,10,16 584:22 585:21,22

586:2,9,13,25 587:9,15,17 588:24 590:9,22,25,26

591:2,18 592:10 595:9,16 599:21 600:7,9,20

608:18 610:24 615:18 620:16 621:3 627:17

628:12,15,18 629:5,18,22 630:1,7,9 631:25,25

632:13,25 633:1 636:2,15,20,22,23 639:5,12

640:11,11,19 642:2,2,7,9,10 643:3,13,18,21

645:15,17,18 646:1,13,17 647:3,12,14,19 648:16

650:4 652:17 653:1,3,23 655:3,22 657:5,13,18

658:10 659:19 660:9,23 661:8,15,23 662:13

671:17 674:11,14 679:18,25 682:15,16,19,20,24

683:9 684:6,8,26 685:20,21,23,25 686:15 687:12

688:20,22 689:21,26 690:6,8,14,24,25 691:3

694:15,17,21,25 695:4,14 696:23 698:11 702:5,5

702:16,24 703:1,4,5,15,17,21 712:10,11 713:2,5

713:8 718:10 722:16 723:15,16,20 724:8 725:2,5

725:11,20 727:10,23 728:21,21 729:5,6,7,10,11

729:22 730:1,5,8,14,22 731:4,25 732:10 734:10

734:13,17,25 735:21 736:11,15,18 737:11,13,21

737:26 738:6,11 739:9,10,11 740:1,8,14 741:2,8

thinking 37:16 47:15 59:15 63:24 135:14 136:6

146:4 147:3 178:8 190:2 202:23 204:20 229:6

244:17 256:10 257:19,19 285:13 302:11 308:23

330:25 336:20 342:6 390:16 405:3 429:5 472:2

472:15,24 492:23 544:4 636:10 646:11 651:16

670:11 694:7 728:18 735:20 737:20 741:6 747:25

thinks 180:9 542:3

thiourea 559:23

third 9:19 63:23 102:3 364:2 410:4 429:2 459:7

463:13 498:5 517:1 651:24

thorough 570:5

thoroughly 315:12 332:18 361:4

thoroughness 636:10

thought 30:5 34:12 36:1,4 70:18 141:14 168:6

180:4 188:18 189:9 203:10,14 222:13,23 230:6

249:13 263:12 270:6 271:8,9 272:26 305:24

309:15 348:3,23 349:19 359:24 388:6 397:6

409:1 410:6 423:11 427:26 428:2 444:20 446:2

515:25 516:25 549:3,10 574:16,25 582:16 605:2

628:8 630:2,2 631:10 645:15 648:6,20 670:11

678:10 679:1 680:19 725:1 731:16 732:25

thoughtful 648:6 741:23

thoughtfulness 646:4 647:14

thoughts 184:3 275:7 419:1 484:7 533:13 653:4
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thousand 118:4 128:26 266:22

thousands 22:5

threads 514:26

three 4:6 43:15 50:14 52:13,15 53:8,8 67:25 78:17

78:17 79:11,17 84:6 87:18 89:4 90:9 93:9 95:10

100:23 101:21 102:8,19 103:4 107:12 108:16

109:2 117:26 151:5,9 162:2 163:24 165:22,26

169:2 170:13,14 174:26 175:3,8 177:10 184:4

191:7 192:4,11,12 210:7 211:12,23,24 212:10

214:18 223:14,14,23,23 224:3,6 238:20 244:16

267:19 277:25 278:26 279:20 280:1 281:6,10,25

282:4,8 292:1,8 309:9 311:11 320:1 367:15 378:2

379:22 383:26 394:15 405:16,18,19 412:12 419:7

421:10 429:5,6 463:7,17 477:16,17 478:11

483:13 492:11 497:7,22 505:21 508:21 551:14

566:9 578:3 589:5 599:13 602:21 604:1 630:6

648:26 653:3,22 666:2,3 668:18 678:25 679:7

687:20,26 688:1,24 691:17,20 699:3 700:18

707:10 719:10 737:22 746:24

three-generation 648:17

three-way 498:7

threshold 415:23 574:24 688:9

threw 389:2

throughput 19:16

throw 150:2 336:9 520:2 588:2 686:13 694:24

thrown 683:17

Thursday 459:6

thyroid 11:23 16:20 17:7 37:14,24 38:4 45:21 46:4

49:15 57:6 76:9 145:8 158:26 159:9,24,26 161:12

163:23,26 165:25 167:8,9 168:4,5 169:10,12

170:12,15 175:8,10 177:26 178:1,14,16,16

180:15,20 184:10,12,12 185:5 186:23 187:6

191:4 204:2,5 231:17 273:23,24 274:3,6,10

309:11 317:13 329:5 347:9 376:10 377:17,19

430:24 433:6 443:10 447:19,20,20 448:15,19,20

456:25 457:10 485:25 486:4,4,5,12,17,19,23

487:1 488:20,21,22 499:12 538:9 545:14 546:4

546:13,19 547:7 551:7,10,12,14,20,24 552:2,3,7

553:19 556:20 557:5,6 558:12 560:13,15,22

561:11,12,17,17 562:5,5 567:26 568:5,7,10,12,13

568:15,16,18 596:24 597:1,11,21 602:13,21

603:18 604:22 605:12 611:14,17,22 613:8 616:11

616:14 618:1 622:17 625:15 626:3 628:17 630:23

682:4

thyroidal 377:24

thyroids 208:5 230:15

thyroid-related 175:6 314:17

thyroxine 566:11 604:16 609:21

tie 405:10

tied 420:26

tier 1:5 2:6 9:21 11:20 14:13,13,16,17 16:26,26

17:3,11,12,26 18:2,5,6,10,21 19:14,21,22 20:1,6

20:17,23 21:2,3,17,26,26 22:3,15,26 23:14,15,17

24:9 25:1,3,16 26:8,10 30:7,20 31:7,10,11 32:3

32:14,14,15,16,20 33:2 34:2,9,9,10,12,15,18,19

34:25 35:4,4,8,10,13,19 36:16,18,21,26 37:3,6,20

38:1,2 60:1 68:16,16,25 69:15 75:23 76:4,11

78:16 79:21 91:15 96:4,5 104:15,19 107:20

112:12,17 115:17,18 119:10,11 133:24 142:4

187:15 189:16 206:11 229:26,26 230:2,3,4,5,5,7

230:8 231:8 232:2,3,5,9,14,14,26 233:5,7,9,9,12

233:12,15,22 234:3,17,19,20,20,24 241:7 247:26

253:2,3 278:22 280:8 281:4 282:6,9 283:4,6,8

285:1 286:8,8 287:5,7,25 292:7 294:9 295:8,8

296:26 297:1,1,4 298:17 299:1 302:11,12 303:14

303:15 307:13,14 308:14,18,20 310:3,4,24,25

314:25 315:10 316:24,25 317:14 322:5,5 325:12

325:21 326:4,22 328:1,2,3,16 329:3 330:11,11,23

331:6,7 332:6,14,17 334:21,23 337:23 341:12

342:7,10,12,16 346:23 347:6,10,15 348:19 349:7

349:14 351:3 352:17 363:17 371:9 374:11 406:25

406:26 407:5,7,8,11,15,15,18,21 408:2,7,8

435:26 443:7,10,16 447:10,26 449:16 450:13

459:5 516:24 527:7,13 528:23 529:3 533:25

534:11,17,22 537:11 538:3,4,12 539:13,18,20

540:1,3,8,17 543:15,17 545:11,15,21 550:17

551:25 553:10 559:17,21 560:2 563:2 564:21

565:17,23,24,26 608:25,25 609:1,5 612:1 613:5

613:21 614:22 616:8,22 617:2,25 620:20 622:15

622:16,19 623:1,4,10,13,24,25 624:6,7,8,13

625:13,14,14,20,21 626:4,4,5,5,8,8,8,9,13,26

627:2 629:3,6,8,14,18 630:21 631:12 632:22,22

632:24 633:22,24,24 634:2,4,5,6,9,10,11,20

635:21,25 637:23 638:1,16 641:21 643:5 644:13

646:11,15 664:5 669:20 682:7,8 686:18 702:2,4

705:13 707:22,24 708:16 716:9 717:24,25,26

728:7,9,12,12,15 729:25 732:13,15,22,23 734:12

734:14 735:12,13,14,16,16 739:4,26 740:19

742:4 743:5,21,23 744:8,13,15,20

tiered 17:2

tiers 253:11 326:22

ties 405:6,8,9,12,12,20 421:7

tight 24:22 440:14

tightened 390:10,14 416:4 472:7

tighter 440:16

tightly 371:15 454:7

tilted 114:25

Tim 285:23,25,25 338:14,20 339:10,15,20,23,26

340:4,16,25 341:5
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38:24 42:17 43:13 44:23 46:14,14,21 47:15 48:7

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65:23 79:8,19 80:19 82:5 85:26 87:26 88:4 94:2

98:1 99:4 100:5 104:24 113:9 114:19,20,23 116:2

124:22 133:7 138:8,9,25 143:14 147:8 153:26

154:1,16 155:8 156:3 159:1,25 161:10 164:2

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188:3 197:1 199:8 201:22,26 204:17 209:13

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221:7,11 224:15,19 225:21 227:2,20,20 232:5

238:19 245:15 246:7 247:17 258:8,9,15 276:18

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367:21 372:13 375:21 383:25 384:24 388:21

391:2,14,15,19 400:25,25 401:8 403:16 418:3

424:1,14 427:18 431:20 438:2 458:15 459:13

492:5 505:13 508:12,13 510:16 512:26 523:10,11

525:2,11 530:8 544:11 546:18 547:16 551:6

560:24 561:13 575:19 581:21 585:21 587:15,16

591:10,11 600:21,22 602:5 604:10 614:26 623:15

630:2 639:2 643:18 651:24 653:10 654:10 661:10

662:6 668:18 673:6,7 677:9 683:25 689:17

692:14,22 699:8,10 700:9,23,24 701:2,4,15 708:4

715:12,16 719:22,25 720:22,24 721:20 731:13

735:12 737:8 739:5 740:3 742:24 746:10 748:10

timeframe 252:24 304:16

timeline 18:16 107:24 327:20

timelines 78:12 465:2

timely 742:22

times 14:21 32:1 33:19 85:22 89:16 100:23,24

111:20 219:14 228:22 245:5 254:24 258:9 259:4

272:22 307:12 350:20 394:24 401:18 404:17

478:13 537:5 564:19 599:1 607:24 620:11,12

660:24 691:17 692:8,13 717:5 722:26 732:12

738:21

timing 259:16 420:22,24 446:7 511:10 551:10

557:4 600:14 633:20

Timothy 481:20

tinker 655:25

tired 646:17

tirelessly 10:14

tissue 82:21 120:1,3,4,5 141:24 144:2 145:6 146:18

164:21 274:24 275:1,3 289:17 376:4,9,17 378:18

378:22 381:2,20,23 422:25,26 423:13 424:1

457:23,23 512:10,13 531:23 562:20 568:8,15,16

tissues 32:22 45:23,23 46:3 53:13,17,22 82:23

83:26 144:4 145:9,12 146:23 289:16 293:26

376:7 378:21 531:25 576:10

today 8:6,23 10:15 11:1 12:18 14:18,23 16:12

19:25 20:2,22 23:13 25:12 26:17 28:15 29:1,6,19

34:26 42:4 72:24 99:15 154:8 202:22 203:10

204:14 205:7,24 209:20 229:24 239:10 240:5

251:4 276:21 289:12 296:2 305:19 332:26 336:9

385:7,26 459:6 575:25 579:18 591:18 615:26

623:22 634:1 636:4 661:16 662:12 663:10 721:13

742:14,21

today's 13:2 17:18 28:17 240:1

told 122:4 155:2 366:13 475:2

tolerance 258:21 669:10,15

tolerant 117:9 446:22

Tom 248:18

tomorrow 154:10 239:11 240:4,4 277:1 363:24

575:25 616:1 646:19 649:2 661:17,26 662:1,16

662:20 663:11

tone 444:21

tool 25:2 84:8,10 189:3,4 253:16 478:18 480:15

481:10 483:5 614:6 646:14

toolbox 31:7 253:15 642:16 705:22 708:11

tools 104:13 113:6 174:14 570:19

top 4:3 14:4 109:24 133:19 150:4 164:15 287:15

358:21,22 363:15 382:25 415:16 441:8 572:6

577:18 646:26 647:6

topic 9:20 10:17

topics 9:18 633:19 634:25

toppling 186:14

toss 201:9

total 3:20 79:12 81:9 111:23 149:7 178:21 214:3

437:19,23 456:10 463:4 510:26 519:9 542:24

591:13 699:21,22 719:17,23 720:6

totality 22:21 32:23 34:22

totally 542:2 588:16

toto 618:12

Touart 25:14 27:4 29:20,23 35:12 36:9,21 37:8

38:22,23 59:14 61:8 62:9 63:26 64:6 65:11 66:17

67:2,14,19 68:23 70:24 71:2,24 73:17 74:19

135:23 185:10 189:12,13 193:10 195:17,18

199:17 201:17,18 205:24,26 227:22 228:7,20

229:3,10,14,17 230:9 232:6 234:4 235:17 237:3

237:24 238:7,13 245:8 248:15 249:24 250:4,6,11

250:25 253:4 254:5 255:5 256:15 259:24 261:16

261:23 263:14 264:2,8,23 266:18 267:12,20

268:7,19 269:11 270:5,24 346:4,9,20,21 347:1

353:15 357:5,7,11 361:25 362:7 363:3 364:2

375:17 376:3 383:6 411:14 412:20,21 414:3

421:22 422:1 429:25 430:3 434:21 435:5 441:2,4

442:25 443:2 452:13 461:7,8,10,10 470:11 493:3

493:4 507:15,16 521:14,15 522:14 528:17,17
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570:6,8 576:13,15,15 601:12,14,14 607:5,6

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647:19 648:8 666:2,4 673:1,2 674:10,15,16

675:12 685:3,5 687:16 695:1,2,13 696:8,24,25

703:11,13,22,24 707:13,15,18 713:12,13,17

718:7,9,14 724:19,20 742:10,15 745:15

Touart's 233:4 346:12 711:3

touch 39:4

touched 140:10 414:17

tough 334:5

toxic 81:21 82:1 83:9 96:20,23 163:14,22 165:9

170:12 326:25 329:17 440:8 467:4 554:19 630:24

715:22 720:17

toxicant 83:9 209:26 379:20 489:13,15,20 525:20

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685:22 689:9

toxicities 638:4

toxicity 24:25 25:20 45:7 59:7 63:10 75:14 81:25

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116:5,20 124:8,8,23 125:3,6,8,10,12,18,19

159:14,17,19 163:17,19 169:4 208:21 225:6,15

225:21 275:18 279:6 280:13 287:23 288:4,4

293:9,22 304:3,4 313:3 319:2 327:5 333:24

363:16 381:13 450:6 453:17 455:25 456:15 462:6

462:8 465:3 467:8 517:18 523:9,16 525:22,22

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548:12 550:4 551:4 553:6,12 554:4 556:25

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614:9 616:4 620:25 621:21 624:16 636:24 637:4

638:6,12 640:19 664:12 667:2,6 684:3,4 686:20

687:9 689:8 690:8 697:26 698:2 701:9 702:17

708:18

toxicodynamics 744:25

toxicokinetics 21:12 744:24

toxicological 5:9 242:5 287:11 341:23 357:17,20

409:26 431:22 452:20,22 453:2,6 459:18 462:1

550:8,10,24 553:16 555:2 611:21 620:22 637:6,9

637:18 664:20

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665:2

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toxicology 5:11,17,21,24 7:8,16 11:15 42:14

304:12 306:1 313:4 370:22 381:18,20 482:13

654:9 738:25 739:14

toxico-dynamics 21:12

toxico-kinetics 33:1

toxins 482:12 692:13

TPO 184:13,18 486:16 603:19

track 86:4 137:1 368:26 745:20 746:1

tracking 104:1,11 114:11 224:14

tract 162:7 556:21

trade 439:6

tradeoff 439:9,13 689:2 695:18

tradeoffs 268:13

traditional 308:13 482:12 639:9

traditionally 294:1 306:25 308:6

trail 52:25

train 136:18

trained 113:22 382:2 509:21

training 3:6 104:14 113:3 114:18 136:22 365:7,25

365:26 366:20,21 416:4 417:4 418:12 428:10,12

428:12,18 526:25 641:17 649:22 651:1 652:2,15

659:23 661:13 709:3 743:13,15

transactivation 454:1

transcribed 254:8

transcript 4:15,16

transcription 726:16

transcriptional 37:11 457:10,11

transcriptomic 432:16

transcriptomics 371:18,19 434:12

transfer 25:10 40:16,19 41:4 48:23 58:5 72:2

73:10 76:14 103:16 112:19,24 113:1,12 126:21

136:26 137:1 145:2 156:17 412:12 415:20 446:11

446:13,18 524:19,20,21,22,23 527:7 529:12,14

639:11

transferability 292:13 297:16 304:10 309:17

313:11 321:2 422:10,19 426:19 441:15 485:9

508:1,18 509:8,20 511:16,23 515:23 517:26

518:9 537:19 605:20 607:15 608:18,19 656:21

704:7

transferable 422:3 507:17 510:17 520:14,16 534:8

610:11 703:25

transferrable 187:23 301:23 607:8

transferred 46:22 113:23 394:10 398:12 400:1

427:25 428:8 627:4 656:23,25

transferring 656:25

transform 478:24 479:24

transformation 399:3,4,4,22 400:8,11 401:3 402:7

402:16 404:17 451:17 478:22 479:21 481:9

574:13

transformations 398:7,7,11,14,18 400:9,17 401:21

568:8 578:24

transformed 400:11 574:13 591:10

transforming 402:10,11,12 479:9

transforms 401:23

transgenerational 78:3

translate 48:25 50:1 57:9 116:6 515:22 746:7

translated 17:18
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translator 116:5

transparency 2:26

transparent 4:11 10:1 11:12

transport 448:10

transporter 185:5

trans-generational 58:15

travel 746:13

traveled 2:12

travels 742:24

treat 142:10 401:7 578:23 587:22

treated 47:2 56:17 167:22 272:7 368:21 379:20

388:2 396:10 397:9 425:20 472:9 487:7 591:11

608:6 677:4 699:24

treating 388:1 396:15 431:16

treatment 41:11 43:23,23 44:2 46:13 48:15,16

53:22 64:10 90:3,17 98:3 101:6,18,22,23 102:4

102:12,13 103:3,7,9 111:8,21,21 112:5,5 127:22

127:24 132:3 144:14 146:2 148:15 149:5 164:14

164:17 168:14,18 183:22 211:12,25 216:14,15

217:3 221:9 250:6 264:4 271:10,10,11,17 272:17

273:1,1 277:18 283:13 300:12 368:25 378:8

387:24 388:18 389:5,13 392:12 401:22 404:25

414:25 416:25 439:19 449:7 463:23 472:11 481:6

483:8 484:1 521:3 534:1 568:2 584:9,10 585:16

590:13,18,23,24 591:1 673:20 675:25 687:20

688:4,4,14,17 698:22

treatments 67:10 129:18 130:2 131:18 148:16

168:12,12,14 211:13,24 216:24 264:3 268:10

300:23 388:4 481:17 505:4,10,15 511:6 547:2

580:25 583:16 585:18 590:18 679:4 695:8,9

715:5,15

treatment-related 224:20 279:13 281:24 300:6

301:7

tree 186:14 579:7,9 581:13,16

trees 580:23

tremendous 9:13 366:6

tremendously 684:23

trenbolone 43:11,13 47:23 48:12 50:11 51:3,22

60:12,25 62:22 63:20 66:1 68:20 70:20 72:2

97:10 106:13 107:5,13 133:13,17 161:22 162:3

166:26 167:11,21,23 181:18 290:23 339:6,11

374:4,17 409:1 412:22 413:6 431:14,24 438:9,22

440:7 479:17 485:21 506:9 561:6 597:9,25

603:11 651:8

trend 31:25 48:2 50:23 71:6,9 127:13,15 216:23,25

218:13 291:9 292:11 320:22 345:16 483:7,8

505:17,23 588:15 600:3

trended 292:12

trends 32:9 215:15 320:21 449:15 506:4

trend-based 482:5

trial 43:15 509:1,2,25 517:1,9,11 535:12 562:10

573:2

trials 250:20 503:1 508:14,19 509:5,9,11 511:18

511:20 516:10,26 525:14,16 529:19 533:23 543:3

682:4 697:25

trick 169:13

tricky 199:2 303:26

triclosan 188:9,13

tried 102:21 112:23 119:16,20,25 120:2 131:13

132:10 144:14 190:13,15 314:1 635:5 677:24

687:3 730:22 731:22,23

tries 46:11

trigger 19:13 123:20 231:8 634:6

triggered 286:8

triggering 717:26

triggers 632:10

trigging 716:9

tripping 363:15

trivial 114:12

tropicalis 156:6,8,20 165:5 167:3,12 171:22,26

172:2,3 547:23,24 549:2

trops 191:11

trouble 38:19 88:17,17 131:18 571:12 629:11

troubleshoot 370:21

troublesome 714:22

Trout 173:6

true 70:20 81:19 110:14 117:6 140:26 151:17

186:19 207:21 262:22 263:13 310:5 322:16 328:2

402:22 404:9,15,16 465:6 472:4 477:1 481:6

499:14 519:12 529:7,17 552:8 581:17 655:3

659:19 676:8 680:2,7 740:7

truly 98:22 143:2 144:1 429:9 433:8 529:10 578:5

578:7 680:16 733:3

trust 311:18

try 32:9 37:8 38:26 39:4,8,10 47:17,19 56:14 57:13

67:15 69:18,22 71:24 77:21 79:3,12,21 93:2

96:15 99:6 105:7 107:7 116:3 119:6 122:15

131:10,14 132:15 137:11 142:11 151:25 161:26

164:21 180:24 185:10 186:14 189:18,19 193:10

193:14,23 194:24,25 202:11 205:21 206:2,3

207:9 216:13 230:9 232:24,26 245:16 249:19

258:10 269:15 284:15 319:8 326:26 328:25

337:12 345:11 347:2 369:9 381:12 386:21 388:14

391:20,24 392:4 398:19 399:2,4,9 400:23 414:19

429:15 435:1 472:17 517:12 590:25 591:19

592:20 593:3 607:21 631:2 635:2 639:3 640:1

642:2 648:4 659:13 683:14 728:19 731:21 732:2

733:19,25 734:5 747:25

trying 39:2 49:26 55:11 56:2 59:16 62:20 66:12

67:12 70:5 72:16 94:11 114:16 116:5,14 133:18
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151:10,15 152:10 154:20 162:11 181:23 182:1

187:14,20 192:19 194:13 217:15 222:19 225:8

226:21 228:12 229:23 231:16 232:2 235:18 236:4

237:3,20 238:12 249:16 250:18 263:14 268:8,12

285:12 308:2 317:11 327:2,19 330:16 337:7

361:21 362:2 380:5 392:24 394:3 397:4 404:9

412:5 425:21 434:26 469:1 473:2 491:17 502:17

546:21 563:13 580:14 588:9,22,23 591:16 600:24

619:9 626:25 628:2 630:22 633:3 634:5,15

635:15,16,24 636:1 642:20 678:26 684:6 688:17

721:7 723:16 727:22 728:12 730:8 731:18,26

732:1 733:4,6 736:8 741:4

TSH 184:18

TT 447:12

tub 121:2

Tuberty 7:10,10 243:8,8 254:21,22 255:24 258:18

261:11,17 263:5 341:10,11 342:5,11,14,22

343:20 460:22,22 628:13,14 643:1,2 645:22

646:8 665:20,20 669:23,24 681:6,10,15 683:15

683:16 686:14 688:23,24 691:12 697:11,12

704:10,12 707:9,26 708:2 712:18 717:15,16

721:10,14 723:10,11 738:18

Tuberty's 703:9

tubes 571:18 572:3

tubing 113:25

tubules 558:19

tubulin 513:9

Tuesday 80:2

turkeys 366:13

turn 4:25 8:13 10:19 12:20 118:24 132:16 154:12

183:21 241:21 243:24 249:22 272:5 277:7 284:3

352:20 361:23 404:3 419:17 421:20,25 426:7

428:5 442:22 459:10 468:20 501:12 507:5 518:11

521:9 527:14 528:11 533:15 538:15 539:8 544:26

549:21 554:5 555:22 564:9 565:1 595:24 596:7

609:10 610:20 615:1,14,24 633:16 645:22 646:21

660:3 664:8 681:6 685:3 703:11 724:19 741:25

turned 104:11 166:25 190:14 671:24 675:11

turning 360:3 391:9 439:24 532:2 660:2

turns 90:21 136:19 175:17 401:10 676:15

turn-around 113:10

tweak 500:21,21 542:12

twentyfold 321:6

twice 100:24 505:14 579:12

two 4:17 15:13 17:2 23:20 25:19 42:8 43:8 46:16

64:12 77:23 79:18,19 84:5 86:17 87:1 100:26

101:6 102:2,8,19,20 107:15 110:8 111:20,25

115:4 118:4 121:11 122:4 123:24 137:18 145:16

146:23 148:15,23 149:4,6,20 151:4 153:7 154:9

156:4,22 158:3 159:1,3 160:11 161:17 166:15

168:7 170:17 174:1,2,2,19,21 175:23 176:24

188:8 192:25 198:9 199:7 210:13 211:3 212:1,8

214:18 215:7,22,26 217:6 222:26 224:6 226:10

228:5,17 237:21 244:16 247:14,19 248:7 249:8

254:11 256:6 263:17,26 271:5 272:22 273:13,13

277:8,26 279:22 280:1 291:7,10 292:10,12,12

309:10 310:14 311:11 314:14 317:24 319:26

321:16,22 322:21 324:19 331:14,26 341:17 345:4

346:7 353:25 363:5 364:3 368:17 369:25 374:12

393:15 394:9 397:2,8,10 402:7 403:7 404:26

405:11,16,18,18 408:13 412:22 430:25 437:21

442:3,6 449:7 464:1 469:13 474:12,15,18 480:7

481:18 487:9 489:21 492:11 496:11,17,19,25

504:22,26 506:23 509:5,6,18,24 512:3 513:7

523:1 533:26 535:1 547:21 562:11,15 571:2,5

572:16 577:6 580:11 588:21 589:3,4,8 594:16,16

594:18 600:15 603:26 629:18 630:12 633:8

643:25,25 659:8 666:1,18 667:16 668:18,20

671:12 677:9 680:26 682:12,19 686:13,15 690:19

691:20 692:14 695:22 698:18 699:23 704:12

707:20 710:3 711:5,14 712:20,24 719:9,18 723:8

734:13 737:16,21 746:20

twos 379:21,21

two-factor 484:3

two-gen 29:21 247:9 292:26 294:4 315:16 318:26

321:21,23 322:11 335:5,5 338:25 339:1 340:10

344:24

two-generation 21:19 23:20 24:3,24 29:23 31:6

39:3,7 41:7 42:3 49:5 58:19 59:7 61:16 206:5

211:4 212:3,11 213:14 234:12 245:9,22 286:1,18

286:26,26 287:1 290:24 291:4 293:14 294:5,26

295:5 319:23 320:11,12 322:18 347:16 348:9

376:12 377:5 636:24 637:11 664:12

two-sample 588:14

two-tiered 253:14

two-way 484:2 655:22

two/one 111:19

tying 621:3

type 41:23 49:15 57:2,4 60:6 83:6 116:2 117:2

139:4 140:23 145:2 150:16 157:18 177:11 185:12

185:14 201:21 204:10 209:5 213:1 218:3 248:21

249:14,20 257:10 266:4,9 327:5 336:2 357:14

374:11 395:8 402:2,4,16 403:2,7 405:15 452:17

463:10 465:21 512:26 513:1 514:20 517:5 550:4

553:1 581:17,19,25 591:6,21 608:3 637:3 649:14

687:1 698:23 702:3 719:26

types 108:9 135:12 159:20 172:4 174:16 224:15

232:8 233:23 258:25 317:25 361:12,12 371:11

376:17 383:8 397:25 401:13 421:10 430:7 464:7

467:2,3 470:14 472:20,21 478:11 498:24 516:7
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522:19 526:15 542:21 573:7 576:18 578:4 606:21

611:5 626:10 671:5 674:19 686:25 713:21 723:15

typical 85:17 86:6 87:4 345:9 450:2 494:23 589:3

696:13

typically 92:10 199:14,23 212:8 237:26 238:22

249:6 261:26 265:3 313:4 331:21 337:24 338:4

344:13 353:18 479:18 494:20 533:8 560:7 572:8

661:20

typing 205:13,14 544:7 679:17

t-test 588:14 589:3,8,21

T2 134:20

T4 159:1,26 163:24 166:7,9 174:17,17,20,20,25

175:9,11 181:10,21 182:10 187:8,14,16 188:5,10

188:11 190:5,22,24 191:6 204:3,5 308:11,17,19

309:10 310:14 314:7 546:23 551:10,17 557:6

559:10 566:11,14,17,20,22,26 567:3,6,8,15,19,22

567:24 568:3,11,20,23 569:19,19,21 604:23

U

U 216:20 243:14

ultimate 68:9 246:23 582:16 586:21

ultimately 42:16 73:10 142:16 252:17 259:9

584:19 608:11 613:4,14 734:20

unable 190:4 280:22 437:16 566:26 570:25 698:16

unacceptable 200:16 202:5

unanimously 251:21

unbiased 396:17

uncertain 301:18 551:2 710:10 715:9,11 735:15

uncertainness 732:20

uncertainties 492:9 537:24

uncertainty 22:2 233:14 239:7 280:5 344:16

409:11 448:22 491:23,23,25 492:4,10,12,13

531:17 562:26 584:21,23,26 585:1 586:7 720:20

unclear 120:20 313:24 437:16 467:3 488:26

531:19 538:4 618:20 622:14 649:13 659:10

698:26 734:15

uncommon 51:18 169:7 171:8

undefined 481:12 499:17

undergo 517:5

undergone 431:2

undergraduates 702:1

underlies 625:24

underlying 163:19 337:17 371:19 402:24 404:9

473:14

undermines 624:24

underpowered 563:16 582:24

underrepresentation 629:2

underscores 11:11

understand 11:16 14:25 30:7,23 35:15 40:8 48:19

49:10 74:16 102:21 123:23,24 131:11 133:7

149:10,26 150:25 173:13 183:9,10,13,16 187:15

192:10 201:2 204:11,26 207:8 224:26 227:20

228:16 229:4 230:25 231:16 237:20 245:6 252:12

299:25 312:19 326:5,11 327:6,8 328:4,20,25

342:22,23 353:16 357:7 361:24,25 375:18 382:20

394:19 404:9 411:17 412:26 421:22 469:1 492:15

493:20 546:7 567:16 576:9 580:2,6,8,22 586:11

629:20,26 633:23 634:4 635:7 655:16 660:7

668:5 682:8 687:18 700:8 717:2 718:5,10 720:19

739:5 743:25

understandable 660:16 688:25

understanding 21:8 22:7 32:19 63:9 68:15 77:19

121:19 125:12 160:25 225:19 229:26 235:15

236:6 238:26 253:21 286:18 315:4 316:3,7

318:16 319:20 322:24 328:13 330:14 332:13

338:7 340:9 343:12 413:9 491:24 492:3,13

540:10 547:25 550:24 592:1,22 597:18 614:23

650:16 654:25 660:25 685:9 695:10

understands 446:20

understood 57:6 77:16 109:18 238:4 317:10 323:8

355:20 429:25 441:4 490:11 636:16 695:4 701:13

undertaken 465:23

underutilized 454:18

underway 638:24

underwent 376:11

undeveloped 424:23

undirected 427:1

unequal 398:10,14 402:20 403:6,9 473:21 474:8

475:9 725:19

unequivocally 490:17

unexpected 487:12 602:13,20,24

unexplained 505:3

unexposed 42:22

unfolding 160:18

unfortunately 156:15 301:20 321:23 325:3 327:12

330:5 419:25 421:17 728:22

uniform 416:23 423:5

uniformed 84:9

uniformly 379:21 679:22

unique 17:21 41:11 90:4 116:23 168:6 172:19

357:15 427:15 450:21,25 452:18 453:4 462:4

550:6 637:4 745:4

uniquely 350:17

unit 6:9 90:14 91:13 100:17 147:5,6 153:18,22

242:19 254:18 400:15,24 460:7 586:22 665:9

677:19

United 1:12 277:19 283:15 544:5

units 154:1 227:25 399:16,18 578:6 586:16,17,18

589:16 590:19 699:13

universally 188:6 258:8

universe 14:7 17:21 19:5 634:18
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University 5:13,17,20 6:10,13,21,25 7:1,4,7,11,15

7:20 242:7,8,11,21,24,26 243:3,4,7,9,11 390:18

459:20,21,24 460:9,12,14,17,18,21,23,25 664:22

664:23,26 665:11,14,17,18,21,23

unknown 60:2 282:17 388:11,14 536:15 570:25

727:25 728:5

unknowns 727:24,25,25 728:5

unproven 526:19

unreasonable 428:21

unrelated 53:14

unreliable 55:25

unresolved 504:13 517:5

unsolvable 310:15

unsuccessful 566:10

UNT 215:8

untreated 47:1 50:26 71:4 74:9

unusual 111:12 129:13,13 359:12 468:14 612:20

680:26

unusually 497:24

update 250:22 251:4

updated 84:12

updates 594:2

upfront 563:19

upgraded 181:7

upper 396:25

uptake 260:22

urge 582:14

urines 190:21

urogenital 462:26 463:6,9,10,24 552:10 556:21

USDA 654:12

use 19:4 21:25 27:10 35:10 46:4 57:12 59:16 62:4

65:2 70:8 76:23 77:4 88:24 104:13 113:5 114:10

116:24 117:17,20 118:26 120:13 131:7 133:4,14

135:11 137:3,3,19 144:2,23 145:1 148:17,18

154:20 166:10 171:24 177:21 181:23 183:2 185:1

187:1 188:8 190:13 193:18 202:6,10 220:20

222:21 232:24 234:9 236:17 244:6 257:1 272:26

273:1 278:3,20 279:26 280:17 282:5,9 295:12

298:18 305:5 306:22 307:2 308:4 310:23 312:15

313:7 317:21 322:18 324:4 329:20 332:7 338:15

344:4 358:18,24,25 362:15,17 367:21 370:8

372:26 374:12 379:5,6,7 386:9 404:10,11,11,12

404:14 405:8 414:22,25 415:2 417:17,18 418:24

420:5 422:20 426:13 427:6 432:26 441:16 444:10

445:13 446:21 450:26 451:17 454:17 479:6

488:20 490:26 493:20 494:23 512:20,21 514:26

515:1 524:6 525:11 526:14 533:24 542:4 548:6

549:1,2 579:2 583:3,25 585:8 587:21 589:3

590:24 592:9 595:1,13 614:5 622:25 625:13

626:7 633:3 639:9 642:9 645:5 647:5 650:21

662:9 668:2 669:18 671:26 674:3 679:1 682:9

683:21 686:11 697:15 704:14 705:4,17 708:7,8

708:15 714:8 716:18 718:23 722:19 724:10

734:18 735:4 736:7 738:14 740:3,9 744:2,26

useful 27:14 54:18,23 60:1,7,10 61:12 66:12 83:16

85:2 176:3 181:9 269:24 293:10 296:17 302:10

302:16 305:10 312:23 325:6 366:26 367:6,8

382:1 398:12 399:10 413:6 418:22 449:21 453:3

560:3 570:19 580:7 599:22 608:2,11 613:20

627:12,14 631:21,26 637:15,22 647:26 717:9

732:17 736:25,26 737:3,15,21,22

usefulness 280:19 430:6 522:18 524:17 608:13

611:4 713:20

user 510:23

uses 91:12 196:21 278:3,4,5 293:6 302:20 355:14

478:18 481:18 592:3 687:20 709:11

USGS 25:14 43:12 50:12 51:10,22 60:26 74:14,18

usual 45:1 746:12

usually 44:3 199:23 220:11 236:12 238:17 270:26

306:4 309:11 344:14 345:10 350:4 391:23 489:14

586:23

uterotropic 233:19

utility 39:11 48:25 181:25 246:22 433:6 447:15

457:7 547:12 718:23

utilization 15:17 29:12 445:10 454:24

utilize 54:20 56:23 57:24 58:22 59:24 201:19 221:5

222:5 225:12 265:19 620:8

utilized 34:2 43:25 44:3 57:12 58:19 62:19,23

64:22 175:23 189:20,21 211:11 214:6 220:7

221:11 223:18 224:4 228:8 235:24 237:13 247:26

268:22 353:20 445:16 455:1 526:23 529:12

utilizing 160:6 548:1 744:25

UV 168:2

U-shaped 215:9 482:14

U.C 7:23 461:2

U.S 6:5,8,17 25:14 101:4,5 152:22 160:6 242:15,17

242:22 252:17 253:13 310:20 429:4 431:2 460:3

460:5,10 474:13,17 509:1,12 549:9,10 620:9,11

622:10 640:13 650:23 665:5,12 707:14

V

vacuoles 147:6

vague 369:2 423:1

valcuolization 290:8

valid 32:3 282:9 285:16 289:18 293:2 324:14 490:3

577:14 669:11

validate 182:1 542:14 596:18 597:17,26 743:23

validated 15:17 16:23 23:20 27:7 155:18 286:9

288:2 289:11 290:21 293:12 295:12,13 314:6

315:17 320:15 332:7 457:12,12 518:7 539:21

541:11 542:21,25 546:5 551:22 580:8 657:25
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726:11

validating 18:21 409:24 541:7 542:18,19 562:21

validation 2:5 9:21 10:5 14:15 18:22 19:3 20:1,23

25:12 26:3,17 27:3,8,16 28:3,5,9 37:20 39:9

44:20 59:11 73:25 132:24 181:12 189:22 194:1

206:13 208:9 209:20 251:12,13 252:22 253:2

278:11 280:4 281:16 282:26,26 287:9 292:8

295:3 296:9,12 299:6,18 302:2 312:19 319:26

320:3 324:10 326:3 376:12 383:15,19 384:11

430:4 433:21 444:8 454:13 464:21 470:21,26

487:26 491:1 494:9,9 508:9 510:19 522:15 537:6

537:12,17 538:1 539:13,14 540:14,17 541:8

543:4,8,8 547:9 548:6 551:13 560:5 566:10 576:3

576:24 577:2 597:22,25 598:7 602:7 611:1

614:25 633:22 650:11 652:19,25 656:12 662:7

670:12 674:25 675:4 697:12,22,23 707:19 713:18

716:16,26 727:26 743:21,25

validations 130:9

validity 214:6 223:15 280:9 306:12,16 324:2,13

326:6,10 367:24 562:23 598:22 625:17 648:18

validly 496:18

valuable 12:19 16:16 176:5 245:11 423:11 438:1

547:3,5 614:10 738:4

value 23:5 39:20 66:26 67:3 68:9 74:24 87:15

88:11,12 107:19 138:7 151:16 157:18 162:22

245:14 246:23 247:24,24 257:17,20,20 258:14,14

291:17 294:7,18 307:6 314:10,15,16 319:17

321:5,21 322:25 328:12 338:13 376:15 405:7,17

435:16,21 436:16 479:19 528:20 529:2 572:23

574:17,20 605:5,5 629:5,6 669:20 690:2 695:8

710:3,5 718:19

valued 420:14

values 23:6 85:18 86:10,11 99:1 100:12 101:10

110:10 115:3 118:13 120:20 138:6 190:10 275:20

295:4 304:19,23 345:2 365:6 420:3 496:25 509:3

529:26 543:2 567:5,9 680:23

variabilities 218:14 417:10 418:4

variability 55:24 66:5,24 67:6,11,13,23,26 139:14

157:14 190:18 196:13 198:12,14 199:10 200:3

278:14 281:14,17 282:15 292:16,21 293:14

294:12 309:22 322:25 385:9 390:23 391:4 394:25

394:26,26 395:1 398:1 414:4 416:5 417:9,10

418:25 420:15,20 421:10,12 422:17 427:24

436:25 449:1,11 454:12 465:15 466:11 476:25

480:3 493:5 494:3 497:23 498:1 503:14,21,25

504:17 505:10 506:7 508:11 511:15 520:14,15,17

520:19 530:9 536:8 537:25 557:23 560:14 572:2

584:8 594:13,14 600:23 601:15 604:17 605:16

607:25 610:15 668:23 671:20 692:5 693:13,15

696:26 697:18 714:21,22 715:4,5,6,10,13

variable 54:9,16 78:25 157:11 289:20,21 297:3

390:15 399:26 400:3 404:18 417:3 451:3 455:22

472:26 474:24 475:18 478:23 479:8 481:10

500:19 510:5 558:24 561:1 567:5 571:25 578:12

587:23,25 714:25

variables 55:24 291:13,19 292:21,22 361:14

395:17 397:3 400:13 405:21 437:21,23 473:9,13

473:26 474:1,16 475:16,20,20 479:2 501:8 520:8

578:8,15 725:4

variance 85:25 90:17,17,17 98:26 146:16 229:2,5

235:5 237:18 394:18,20 395:2 396:3,6 401:6

417:4 571:21 578:25 583:13,15,17 584:5 594:24

594:24,25 595:3 677:24 678:19

variances 89:9 398:10,14,17 402:20,24 403:6,9

404:26 473:19,21 474:7,8 574:11 584:2,4 725:19

variants 676:25

variation 89:21 151:4,8 181:14 199:2 218:18,21,26

235:12,13 279:14 311:2 366:1,6,19 372:12

394:23 395:2,7,7,8,10 396:5 415:5 417:14 436:22

437:14,21 438:2 440:15,16 477:12,13,14,14,15

477:16,18 503:8,16,18 520:8 546:7 558:7,8,11,16

558:18 560:8 567:26 570:22,25 571:25 572:20,21

573:4 585:23 608:3,4,14 613:16,23,24 614:11,24

670:21 673:26 677:25 733:5

variations 52:3 65:8 218:22 236:1 579:13 680:8

varied 62:3 145:24 164:12 561:2,4 573:1

varies 580:20 684:23

variety 8:8,9 36:18 37:21,24 200:8 209:10,16

216:16 217:22 235:26 287:20 494:3 619:6 638:12

658:11 710:9

various 85:5 217:1 299:12 302:6 446:19 504:14

533:23 564:19 580:20 582:8 597:16 700:6 747:22

vary 57:17 390:2 446:19 466:7 508:11 698:1

varying 581:22 695:24

vast 351:25

vastly 281:24

vector 513:25

vehicle 249:26 250:2,8 263:26 264:4,6,11,19,26

265:5,7,8,10 358:24 671:3

vehicles 201:19

vehicle's 264:26

Velcha 306:6

ventral 463:3,16,17,19,20,26

verbal 382:13,16

verbatim 4:15

verbose 482:10

verification 238:16 322:10,12 344:6,7

verify 229:11

versa 361:21

version 83:16 89:1 91:12
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versus 66:14 75:5 123:11 133:18 134:19 141:7,9

141:13 185:4 195:12 200:26 217:4,14 225:9

257:4 258:25 265:5 286:19 287:23 294:4 300:12

319:15 425:23 431:22 451:7,8,20,22 455:2

490:18 522:5 526:14,14 543:8 546:21 554:4

564:14 580:13 624:16 626:22 669:13 686:20

690:18 692:1 700:18 721:23

vertebrate 37:17 207:20,22 208:11,15 209:21

212:17 217:23 230:14 246:16,18 252:12 277:25

282:8 318:6,9 322:1,2 337:11 598:8 615:23

616:19 617:18 618:23 619:18 620:7 623:13 628:5

631:20 637:26 640:16 641:22 667:10

vertebrates 40:12 207:17 246:20 317:24,26 611:13

616:14 618:4,22 619:5,20 625:12,24 628:18

630:16 687:12

vertebrate-active 209:17 258:4 260:4

verts 337:13

vessel 481:23

vetted 315:12 332:15

viability 44:12 45:5 74:11 213:13 214:4 394:7

466:25 515:4,5,9 521:2

viable 141:19 481:8 576:2

vice 361:20

vice-versa 143:17

Vicki 6:4,4 179:1,3,7,15,19 242:15,15 273:21

450:10 456:19 460:3,3 467:15 477:6 491:3 502:4

518:26 527:24 532:4 534:25 539:26 540:13

548:23 554:15 563:10 569:3 574:2 599:24 606:12

610:2 615:7 665:5,5

Vicky 147:12

videos 370:20 372:13 514:19 518:17 522:3 743:13

view 247:23 323:15 335:26 362:20 363:8 371:8

380:8 413:24 495:1,3 555:15 582:9 654:26 655:3

676:3 678:13 721:4

viewed 440:8

viewing 359:3

viewpoint 44:22 380:4,5 414:19

views 258:19

vigor 533:7

vinclozolin 43:17 52:12,17,19 53:4,6,14,16 59:13

59:16,18,25,26 60:4,20 63:20 67:9 97:25,25

98:13 101:20 102:15 103:1,6,11 121:10 129:16

130:5 131:24 132:12,24 151:1 210:1,24 226:19

238:25 255:10,13,21 266:3 269:14 279:13,18,26

280:15,17 281:11 292:1,25 295:7 301:7,11,18

395:13 409:9 411:15,17 412:6,14 414:25 436:1

438:8 486:2,8,12,16,19,21 487:5,8,10,18,25

492:23,25 494:17 496:7 497:9,16 498:19 499:9

499:14,16 500:8,10 501:21,24 503:14,17 504:15

510:1,4,8,9,16 517:6

violated 402:25

violation 402:26

Virginia 1:17 5:24 242:12 459:25 664:8 665:2

virtually 51:7 319:9 350:16 736:23

visibility 483:1

visible 463:8,15

vision 14:4 18:17,18 29:13

visit 597:4

visual 17:17 482:25 577:8

visualization 122:25 514:21

visually 582:6

vital 620:6 621:14

vitellogenic 143:14 169:6

vitellogenin 80:4 82:15 83:7 84:20 87:12,17 90:20

91:21,26 92:11,21,24 94:10,18 95:8,17,18,19,22

96:9,10,18,19 97:2,2,3,14,16 98:9,21 100:13,14

100:15,17 101:16 102:11,12,15 103:9,26 106:20

107:1,3,4,6 108:15,21,21 109:25 110:1 124:9,10

125:22,22 133:14 135:3 139:18 140:7,8,8,9

143:11,12,16,19,20,22 144:1,6,9,19 160:3 161:10

164:4 169:5,6,8,17 170:17 173:15,21 175:26

176:3 181:11,15 185:2,4 186:19 187:1,11 298:1

298:23 299:25 303:1 308:11,13 309:23 314:8

338:6,6 367:18,25 368:3,12,12,13 433:16 453:9

453:24 454:2 466:23 468:25 479:1 482:21 494:22

494:26 495:2,23 496:22,24 512:2,6,18 522:11

523:19 532:15,17 536:2,9,13,16 541:23 543:14

546:23 552:11,20,21,23 553:21 557:7,9 559:10

560:19 570:9,18,19,23,26 571:3,4,6,11,23 572:15

573:10,18,19,26 575:10,18 603:5,14 609:15,19

726:7,12,15,21,23,23

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vivo 17:5 21:5,11 22:19 23:7,11 29:8 96:23 233:20

376:6 462:5

vocal 427:14 741:10

vocalizations 373:9,11

volume 18:26 180:19 181:4 204:11 716:4

volumes 132:16 238:17

Vtg 185:19,22 467:1 483:26 513:6,23,25,26 515:25

524:4 570:9,12,14,16 574:7

VTG1 504:24 513:7

VTG2 513:7

vulnerability 56:10 309:16

vulnerable 57:26 351:19 354:25

W

W 156:12

wait 539:22 589:9 594:1

waiting 663:3

waiver 589:21
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walked 14:17

walking 634:24

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117:6 118:9 121:21 123:24 132:9 134:5 137:12

140:11 145:15 148:1 149:10 150:25 157:21

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233:3 234:22 239:18 247:7,16 252:18 253:16

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729:13,21 730:10,12,17,23 731:4,7 733:1,21,25

735:18 738:5,13 739:2,13 741:2 742:4,10,16

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wanted 8:10 21:2 50:13 59:15 92:14 98:25 100:18

105:13 121:9 124:12 145:5 152:18 179:3 181:10

205:11 261:12 284:11 324:1 325:22 334:1 337:2

346:3 359:23 371:17 392:26 401:4 404:6,8,21

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599:2 600:2 626:21 631:9 633:20 634:24 635:19

645:19 646:19 647:11 648:13,24 656:6,18 658:24

658:26 673:24 680:11 681:2 692:19 693:24 700:8

703:5 719:14 721:1 726:2 732:25 733:16 734:3

740:9,16,23 742:14 746:18

wanting 369:6

wants 369:15 413:23 692:12 724:8 729:5,8 738:11

warmed 747:7

warrant 18:8 107:20 247:25 430:19 529:3

warranted 531:6 639:22 642:18 708:3

warrants 17:11 18:5

washes 512:11

Washington 154:18

wasn't 179:8 183:7 249:5 253:19 263:18 294:5

339:1,18 378:8 380:19 388:16,16 389:8,10

409:17 416:6 442:11 488:17 490:1 527:25 547:11

586:17 587:4 598:25 599:4 628:7 633:21 655:12

655:17 662:6 687:5 720:5 722:4 740:6

waste 360:20 640:17 641:12 716:4,5,6

wasted 334:11 375:4 585:20

watch 739:8

water 13:23 14:7 15:25 16:1 17:20 116:22,25

117:5,9,17,20 118:1,2 121:3 214:13 220:12

222:20 223:16 265:1,9,21 275:4 303:24 322:13

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696:1 737:6

waterborne 303:20

waters 116:24 117:11,15,16,17,24 325:8

water-only 344:19

Watts 462:21

wax 747:25

way 23:12 24:15,20 56:2 61:13 64:24 70:6 73:20

74:17 78:23 88:25 103:22 108:10 110:12,23

111:6 116:6 134:21 140:13 145:21 146:5,5,8

152:24 153:3 154:18 160:21 162:26 175:24 180:7

180:9 182:23 183:19,23 186:22 190:2 193:16

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270:26 279:13 292:12 307:17 311:8 312:11,16

321:20 322:2,12 325:7 326:16 331:10 340:17

342:4 345:3 348:13 355:24 356:13 358:15 366:21

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396:16,17 397:6 398:12 400:13,14 401:7 410:11

414:25 420:10 424:23 425:20 431:18 436:23

440:19 442:3,3,18,18 473:10,11,24 474:26 475:9

477:19 478:1 481:4 485:6,9 489:6 494:5,5 502:22

544:10 550:23 559:15 579:5,25 580:13,19 584:14

584:18 586:4,13 587:17 590:13,26 591:15 592:14

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392:1 440:15 475:16 477:17 513:20 544:1 564:21
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weak 177:5 281:13 299:13 485:16,17 488:14

490:24 496:14 549:1 560:21 597:8 603:3 673:18

682:3

weaker 451:22

weakness 432:7,15 506:17

weaknesses 430:17 432:3 448:4 639:24 640:14

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weanling 23:23

weary 206:2

web 1:9 365:26 366:20,21 428:12 514:20

webcast 243:18 283:17 664:16

webcasts 514:22

website 20:19 331:9

web-based 189:3,4,18

week 10:6 12:16,19 13:3 44:15 46:20,22 49:13

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weekly 51:20 505:4

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weighs 257:24

weight 17:9,10 18:3 20:4,18 34:2 99:23,23,24

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weights 45:13 100:6,17 172:18 180:2,3 261:18

291:14 533:5 592:17,20 595:13

weight-of-evidence 356:22 371:21 411:10 434:16

608:8,10,24,25 623:20 624:15

welcome 8:23 241:25 323:20 421:23 434:25 545:3

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well-characterized 597:21

well-constructed 570:4

well-defined 445:7 500:7

well-documented 575:10

well-established 226:6 523:6,25 524:3

well-known 184:14 559:22

well-sequenced 523:18

well-studied 302:21

well-validated 294:6

went 66:26 90:22 98:4,6 155:20 163:4 166:10,13

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weren't 269:15 288:18 325:22 404:26 409:18

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358:7 364:3 378:8 399:24 404:3 410:3 416:2

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we're 195:2 234:20 241:26 250:18 253:23 260:10

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worldwide 317:5

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xenopus 26:9 156:5,5,24 167:3 171:25 190:21,25

191:15 547:22,22 549:1,4,7 556:20 567:11

570:12 572:14 574:7 575:11 579:12 601:3 610:14

611:12 612:18,25

XX 86:14,14 94:9 128:11,18 129:7,9,13 469:22

498:24

XY 94:4 95:6 98:5 128:9,19,23 129:1,7,17 130:1

130:10,12 469:23 495:10 496:23 498:24

Y

YARD 1:15

yeah 63:11 115:12,12 121:8,8 123:3 148:9 177:3

192:4 231:12 245:8,8 257:20 260:21 266:18

268:7 269:11 270:15 271:1,2,23 273:18 275:1

336:21,25 337:8 343:1,24 484:11 488:8 581:7

591:25 593:6 658:19 662:2 684:14 730:21

year 9:20 20:9 31:4 116:11,12 446:23 543:22

738:24 745:10

years 9:23 29:9 77:4 100:26 120:17 155:20 156:22

208:21,23 286:22 291:22 338:16 341:26 359:22

381:21 424:11 447:7 482:14 543:24,24 599:9

638:10 641:15 643:25 731:13 734:5,13 735:5,25

yellow 93:22 162:14

yep 652:23

yesterday 241:12 243:20 244:2 255:25 256:10

259:1 263:9 270:14 276:19 283:3 286:7 289:20

290:3,24 296:23 297:5 298:7 299:6 303:23

307:11 309:15 315:6 317:16 320:5 339:12 344:26

347:25 374:18 379:7 384:25 385:7 386:22 392:17

406:23 409:8 428:11 431:14 438:12,14 440:11

442:1 459:8 461:5 471:9,10,18 472:25 473:21

474:5 475:5 558:6 564:22 565:11 586:15 591:17

592:12 672:12,15 682:12,22,26

yes/no 22:13 614:4

yield 502:26 603:4

yielded 604:22 605:10

yields 581:1

Yij 594:21

yolk 85:12 447:4

young 76:26 214:3,15 351:5 424:21,22 425:2 447:5

699:21 715:19 747:9

young/control 214:11

YouTube 370:20 372:6 514:19 518:17 743:13

y'all 32:5

Z

zebra 352:6,7,12 354:3,3,10,24 355:14,24 356:9,19

372:22,24,26 373:6,9 418:21 425:10 427:2,6,11

427:19 431:9 457:1,9,14

zero 87:18 96:20 158:7 172:8 379:17 479:3,20

480:12 481:5,11,11,14 483:19,19,21,22 503:18

595:4,6,14 673:7 691:18 700:8 723:7

zeros 480:5,20,21,23 483:20

zone 276:20

zoologist 259:7 628:22 629:16

zoology 7:12 628:24

$

$10 310:10

$1000 716:6

$350,000 313:20

$500,000 310:7 313:21

0

0 269:9 595:4

0s 236:12

0.1 221:3

0.18 365:5

0.21 365:5

0.5 506:2

0.550 370:9

0.7 506:3

0008 421:4

05 589:4

07 95:16

09 181:18

1

1 11:20 14:13,16 16:26 17:3,26 18:2,21 19:14,21

19:22 20:6,17 21:3,26 22:3,15 23:14 26:10 31:10

32:14,16,20 34:9,15 36:16,21 37:3,20 38:2 68:16

69:15 76:11 91:15 95:23 96:4 112:17 119:10

133:24 140:8 142:4 160:25 171:12 177:20 187:15

189:16 229:26 230:2,4,7 232:2,5,14 233:5,9,12

233:15,22 234:17,19,20 253:3 269:8,9 281:4
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283:4 284:23 285:5 286:8 287:5 295:8 296:26

297:1 302:11 303:14 307:14 308:14,18,20 310:24

322:5 325:21 328:2 330:11,23 331:7 334:21

342:7,10,12,16 357:3 363:17 378:14 395:15,20

403:22 406:25,26 407:5,7,8,15,18 408:2,7 443:3

444:8 450:13 452:6 463:7,17 479:15,15,16

499:17 512:10 516:24 523:4 527:7,13 535:2

538:3 539:13,19 540:2,8 543:15,17 545:4,7

550:18 551:25 553:10 559:17,21 560:2 565:24

571:2 584:9 589:16 600:12 608:25 615:26 616:2

622:16 623:1,10,25 624:6 625:14,21 626:4,5,8,8

626:9 629:8,14,18 630:21 633:24 634:2,5,9,11

637:23 638:2 639:17 642:24 646:11 682:7 686:18

698:23 702:2 709:15,25 716:9 717:24 728:9,12

729:25 732:22 735:12,16 739:26 743:21 744:8,15

744:20

1s 739:4

1's 331:6

1,500 278:4

1-gen 233:8

1.1 693:7

1.3 97:1

1.5 168:17 169:3,22,25 177:9

1.8 395:22

1.82 395:26

1.9 95:15 106:12,12

1:00 544:20,23

1:15 345:22

1:30 105:3

1:300 572:9

10 38:12,13,13 51:6,8 52:9 54:4 68:3 71:15,17 87:4

155:20 158:8,18 159:5 176:23 181:18 211:24

223:14 224:6 235:12 258:8 289:5 469:16 479:4

482:20 505:7 531:15 544:23 571:1,6 589:14

687:5 692:14,14,22,24 693:5 700:20 706:11,12

719:7 735:5

10,000 630:19

10,341 17:21 634:18

10-fold 605:4

10-minute 153:6 507:10

10-week 158:12 552:22

10:10 703:18

10:15 276:10

10:25 703:19

10:35 507:11

100 88:7 95:1,10 106:4 112:6 115:3,7 167:11,14

272:17,18,18,19 307:22 402:3 510:17 535:16,16

538:3 539:16,22 540:1,8 603:16 656:2 678:1

701:2 746:7

100-level 272:7,11,12,13

1000 692:15

102 101:11 106:2 115:3 479:16

103 68:4

106 287:11 288:10 291:16

11 17:4 18:21 21:3 22:22 23:14 36:21 48:9,11,13

48:16 98:21 254:24 256:3 284:26 330:23 369:20

396:18 421:4 503:23 634:2

11.4 95:8

11:00 577:22

113 395:24

113.4 395:22

12 44:1,8 68:8 106:9 109:10,21 110:3,5,19,23

111:6,8,15,22,26 112:2 144:13 149:4,7,11 158:7

181:17 229:22 267:23 271:8,9,10 272:23,26

273:1,15,15 276:10 289:5 358:12 387:5 388:18

388:19 433:13 437:8 474:19 691:5

120 716:6

125 177:15

13 95:4,6 106:24 257:4 369:20 480:11

130 506:11

136 97:26

14 44:3 47:3 73:22 99:13 261:24 262:1 289:6 290:9

320:25 395:11,18 503:24 540:4,14,19,25 542:11

543:21 673:8 701:16 720:4

14-day 41:2 44:14,23 64:12,15 216:18 255:20

1457 15:26

148 187:13

15 3:21 211:9 285:24 286:6 323:3 369:17 394:10

536:3 540:20 555:12 692:3 703:18 735:5

15-day-old 288:21,22,25

150 88:8

155 287:14 291:16,18

158 26:23 744:22 745:4

16 44:3 99:12 158:25 433:12 503:24 540:20

160 716:6

163 236:25 263:15

164 311:24

168 261:12,12

17 67:23 98:4,6 99:12 102:5 474:17 589:4,13

620:10

17B 561:6

17-beta 94:3,15,21 97:10 99:10 107:10 410:13

17.3 505:20

170 88:5 311:23,26 312:3

170.4 395:23

171 249:21 263:23

172 244:17 245:24 254:22 719:2

173 235:4 237:1

174 237:17

18 143:18 262:1 506:8
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18S 512:22 513:8

18-week 143:10

180 162:23 312:13 551:15 560:16

19 108:4 456:10

19-week 108:23

190 395:24

190.6 395:21

193 218:11

197 102:6

1985 698:4

1990s 365:12

1991 28:25 29:6

1993 747:4

1996 15:14,25 16:5,10 697:15

1997 747:4,17

1998 14:11 16:10

1999 18:19 165:1 539:11

2

2 2:6 9:21 14:13,17 16:26 17:11,12 18:5,6,10 20:1

20:23 21:2,17,26 22:26 23:15,17 24:9 25:1,3,16

26:8 30:7,20 31:7,11 32:3,15,15 33:2 34:3,9,10

34:12,18,19,25 35:4,4,19 36:18,26 37:6 38:1 60:1

63:22 68:16,25 75:23 76:4 78:16 79:22 96:5

102:23 104:15,19 107:20 112:12 115:17,18

119:11 140:8 160:25 177:20 206:11 229:26 230:3

230:5,5,8 231:8 232:3,9,14,26 233:7,9,12 234:3

234:20,24 241:7 247:26 253:3 269:9,9 278:22

280:8 282:6,9 283:4,6,8 285:1 286:8 287:7,10,25

292:7 294:9 295:8 297:1,4,18 298:17 299:1

302:12 303:15 307:13 310:3,4,25 314:25 315:10

316:25,25 317:14 322:5 325:12 326:4,22 328:1,3

328:16 329:3 330:11 332:6,14,17 334:23 337:23

347:6,10,15 348:10,19 349:7,14 351:3 352:17

357:12 358:18,20 360:5 361:23 362:22 371:9

374:11 378:15 395:21 407:11,15,21 408:8 420:21

423:15 435:26 443:7,10,16 444:20 445:3 447:10

447:26 449:17 452:12,14 459:5 463:8,18 480:2

499:18 505:2 517:7 528:23 529:3 533:25 534:11

534:17,22 535:4 537:12 538:4,12 539:20 540:3

540:17 545:11,15,21 549:25 550:1 559:10 563:2

564:21 565:17,23,26 584:10 608:25 609:1,5

612:1 613:5,21 614:22 615:26 616:8,22 617:2,25

620:20 622:15,19 623:4,13,24,25 624:7,8,13

625:13,14,20 626:4,5,8,13,26 627:2 629:3,6

631:12 632:22,22,24 633:22,24 634:4,6,10,20

635:21,25 636:24,26 637:11 638:1,3,16 639:22

641:21 643:5 644:13 645:1 646:15,26 664:5

669:20 682:8 702:4 705:13 707:22,24 708:16

709:24 717:25,26 728:7,13,15 732:13,15,23

734:12,14 735:13,14,16 740:19 742:5 743:5,23

744:14

2s 346:23

2's 35:8,10,13

2-gen 233:7

2-1/2-generation 296:15 297:6 301:22

2.1 443:17 444:17 617:3

2.15 250:14 255:23 264:21

2.2 443:17 453:20 462:3,11 617:3

2.5 63:22 98:12 347:16 358:18,20 646:26

2.6 347:16

2.8 94:4

2:30 134:6

20 29:9 50:20,21 51:1,5,7 54:1,2 70:20,24 71:10,12

71:15,17,18 80:1 86:9 87:7,11 96:26 110:12

150:3,4 218:21,23,24 219:2 220:26 235:7,12,21

238:1 255:1 258:8 266:21 286:22 291:22 312:3

312:14 318:25 338:16 361:18 374:17 378:16

433:11 442:11 444:24 457:13 572:20 582:20

589:13 666:19 692:14,22 693:5 700:19,24

20-part 52:9

200 146:10,24 719:21

2000s 365:12

2001 209:7

2002 430:18

2003 462:21

2004 209:9,13 210:13 318:5

2005 42:15

2006 331:19

2007 455:9 509:2

2008 676:5 735:12 739:5

2009 19:1 157:10 209:14 457:5 514:4 515:13 739:5

2011 20:20

2012 167:5 359:13

2013 1:18 19:6 20:9 742:3 745:10

202 395:24

202.7 395:21

21 19:22 108:17,23 218:25 483:16 689:11 744:18

21st 11:15 743:14

21-day 716:15

21-say 181:26

22 55:3 67:20,22,24 506:9

222 445:26

22202 1:17

23 98:19 99:12 106:10 112:3,3 506:11 535:15

24 111:23 173:9 211:17,17,26 218:25 307:23

388:18 482:21 497:10 548:3 585:25

24-hour 212:7

240 506:12

25 86:7,8 97:18,24 101:17 320:17,19 506:8 692:3

712:20,22
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25-28 1:18

250 98:12 223:13

26 99:11 218:25 310:3 482:22

27 99:10,12 506:12

27th 459:6

27.9 94:22

2777 1:16

28 94:15,22 105:19,20 106:21 192:26 212:7 247:18

316:11 506:8

28th 664:6

28-day 212:6 316:19

29 78:24 99:12 456:7 474:18 497:11

29.5 396:1

3

3 63:24 168:17 169:22 223:22 254:24 256:3 269:9

269:9 298:9 312:13 357:22 358:19,20 359:14

362:23 363:7 364:1 378:17 395:22 452:24 459:9

459:14 461:9,11,22 463:9,19 464:26 468:9

504:20 511:25 514:15 520:9 545:22 550:12 556:1

556:3,14 559:11 566:9 616:1 638:5 646:19,26

666:5,15

3B 421:4

3,000 278:4

3-4 548:3

3.1 375:16 471:14,19 556:3 559:11 564:7

3.2 370:25 376:2,4 382:9 566:7,9

3.3 570:7,8 726:4

3.3.3 469:14

3.3.5 469:19

3.36 395:21,26

3.9 217:25

3:00 615:19

3:00:42 345:25

3:10 403:16

3:15 153:8

3:20 154:7

3:25 403:17

30 97:23 110:21 170:25 171:6,6 196:20 197:15

208:23 211:9,23 264:18 378:23,23 381:21 506:1

638:10 641:15 675:22 694:18 722:23

300 24:5 76:20 313:19 572:9

305-5805 1:10

32 97:10,12 106:13 107:6 193:1 257:4 520:6

594:25 595:4,6,14

32E 595:4

33 98:4,6,7 599:19

33.7 217:19 250:13

36 193:1 212:1 217:24

36-day 211:22

360 102:14

37 70:18 438:12

38 438:14 731:13

39 99:12

4

4 48:12,13,15 269:9,9 357:22 364:15 365:6 369:16

378:19 383:5,6 397:5 404:2 406:14 429:14

469:16 470:10,11 491:12 501:22 502:1,12 507:23

550:12 556:14 576:14,16 593:26 596:8 662:5

666:15 687:19

4tOP 480:1 482:22,23

4-Chloro-3-methlyphenol 485:18

4-chloro-3-methylphenol 95:24

4-octylphenol 603:3

4-tert-octylphenol 560:21

4-t-octylphenol 95:7 98:17 115:2,5 509:1,11,16

4.1 674:15,17 681:5,7

4.1.13 376:14

4.1.4 509:10

4.1.6 368:21

4.19 250:14

4.2 687:15,19

4.3 474:11

4.35 395:26

4.357 395:22

40 44:5 50:17 110:25 111:2 389:6,9,9 506:2 694:18

40-week 157:17

400 694:3

408P 15:15,23

41-milligram 505:20

42 699:21

43 689:20 699:6

44 66:26 67:13,15

45 110:23

46 480:2

463 446:6

48 506:8

485 446:6

5

5 54:5 269:9 312:15 362:24 364:15 369:26 370:2

378:19,22,23 379:4 411:6 414:2,4 421:20 485:7

487:15 493:2,5 502:2 507:3,25 510:4,13 511:25

528:26 534:26 597:5 601:13,15 607:13 621:1

640:11 641:2 642:18 662:8 696:26 704:4

5-DCP 223:23

5.1.4 506:14

5.2 620:11

5.3.1 368:24

5.3.2 566:16

5.4 421:4 506:15
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5.5 370:11 420:21

5.7 379:3 529:24

5.8 529:25

5.87 620:12

50 106:7 165:11 258:9 272:21 505:8 538:3 539:16

539:22 540:1,8 561:4 599:12,14,18 653:6

50/50 378:18

500 76:20 370:9

51 94:26 101:13,16 106:3,23 112:6 115:6

52 19:22 744:18

523.2 370:7

53 620:9

54 68:4 288:16

55 653:5,8,9

55-gallon 716:6

56 479:15

58 18:25

59 312:3

6

6 112:1 168:18 169:23 205:20 271:7,10 273:2

411:6 421:26 422:1,8 429:23 466:20 467:12,20

469:8 482:20 485:8 501:1 506:9 507:14 526:7

561:14 607:4,6,13 609:10 610:19 656:20

6th 44:5,13,15

6-4 435:13

6.1 503:13 504:16 703:23

6.2 707:17,19

6.25 561:4

6.4 436:20 437:18 707:25

6.5 95:11 107:1

6.7 551:15 560:16

60 118:13 165:11 202:23 203:10 211:7 312:14

324:6 332:25 378:23,24 666:19

60-day 316:19

6000 716:4

62 158:25 159:1,5,20,26 161:10 165:24 166:2

174:26 175:5,12 190:19 191:6 197:2 568:6,14,22

587:17,21 604:10 654:21

625 97:6

65 88:12 124:5 653:6

66 134:14 158:5,11,16 159:3

67 18:25 692:26 714:5

675 694:4

69 114:25,25 150:24 380:24 714:5

7

7 149:18 430:1,3,11 483:17 522:13,15 566:17

610:25,26

7s 236:13

7th 44:5,13,15

7-day 255:20

7.21 719:20

70 87:4,25 110:11,12 479:15 701:3

703 1:10

72 105:11

74 244:10

75 86:11 111:2,7 214:14,15

750 278:5 310:7

76 143:6

77 270:24

78 589:16

78-week 157:20

8

8 304:17 359:5 435:5 441:2 482:5 528:16,18

535:22 589:4

8th 46:19

8.9 94:8,17 106:21

80 54:26 67:21 86:10 87:23 88:10 361:12 378:15

378:24,24 396:24 683:11

80,000 694:11

8000 716:4

83 110:23

84 111:24

85 270:17

850 26:22

850.1350 212:5

86 620:1

870 325:19

88 96:10 689:26 691:3

890 325:19

9

9 52:24 68:4 239:11 240:6 273:10,11,12 365:6

458:18 482:24 518:5 533:20,21

9th 46:20

9.87 250:8

9:00 662:20 663:10

90 4:14 211:24

94 366:8

95 88:6 284:22 317:5 628:26

96 223:9 689:12

96-well 344:15 641:8

98 230:11

99 33:19 629:1