202-234-4433

Neal R. Gross & Co., Inc.

Page 1

 U.S. ENVIRONMENTAL PROTECTION AGENCY (EPA)

 + + + + +

 FEDERAL INSECTICIDE, FUNGICIDE AND

 RODENTICIDE ACT SCIENTIFIC ADVISORY PANEL

 (FIFRA SAP)

 + + + + +

 REEVALUATION OF THE HUMAN HEALTH EFFECTS OF

 ATRAZINE: REVIEW OF EXPERIMENTAL ANIMAL

 AND IN VITRO STUDIES AND DRINKING WATER

 MONITORING FREQUENCY

 + + + + +

 DOCKET NO.: EPA-HQ-OPP-2010-0125

 + + + + +

 TUESDAY,

 APRIL 27, 2010

 + + + + +

The Panel convened at 8:30 a.m. in the

Hamilton Ballroom of the Hamilton Crowne Plaza

Hotel, located at 1001 14th Street, N.W.,

Washington, D.C., Steven G. Heeringa, Ph.D.,

Chair, and Kenneth M. Portier, Ph.D., Session

Chair, presiding.

FIFRA SAP MEMBERS PRESENT:

STEVEN G. HEERINGA, Ph.D., Chair

KENNETH M. PORTIER, Ph.D., Session Chair

JOHN R. BUCHER, Ph.D., DABT

JANICE E. CHAMBERS, Ph.D., DABT, ATS

GERALD A. LeBLANC, Ph.D.

DANIEL SCHLENK, Ph.D.
202-234-4433

Neal R. Gross & Co., Inc.

Page 2

FQPA SCIENCE REVIEW BOARD MEMBERS PRESENT:

SUSAN F. AKANA, Ph.D.

RICHARD H. COUPE, Ph.D.

KENNETH BARRY DELCLOS, Ph.D.

PENELOPE A. FENNER-CRISP, Ph.D., DABT

ROBERT J. GILLIOM, Ph.D.

RICHARD GREENWOOD, Ph.D.

WILLIAM L. HAYTON, Ph.D.

STEVEN D. HOLLADY, Ph.D.

TERESA H. HORTON, Ph.D.

KANNAN KRISHNAN, Ph.D.

HERBERT K.H. LEE, Ph.D.

KEVIN T. O'BYRNE, Ph.D.

NU-MAY RUBY REED, Ph.D., DABT

JEAN F.L. REGAL, Ph.D.

DANIEL J. SELVAGE, Ph.D.

CARMEN J. WILLIAMS, M.D., Ph.D.

LINDA J. YOUNG, Ph.D.

ALSO PRESENT:

JOSEPH E. BAILEY, Designated Federal Official
202-234-4433

Neal R. Gross & Co., Inc.

Page 3

 C-O-N-T-E-N-T-S

Opening Remarks and Introductions . . . . . . .4

EPA Presentation: Use of Bootstrapping

 Methods . . . . . . . . . . . . . . . . 11

 Mary Frankenberry

EPA Presentation: Artificial Neural

 Networks. . . . . . . . . . . . . . . . 35

 Mike Messner

EPA Summary - Nelson Thurman. . . . . . . . . 69

Public Comments . . . . . . . . . . . . . . . 96

Charge Questions . . . . . . . . . . . . . .340
202-234-4433

Neal R. Gross & Co., Inc.

Page 4

1 P-R-O-C-E-E-D-I-N-G-S

2 8:31 a.m.

3 MR. BAILEY: Let's get started

4 here this morning. My name is Joe Bailey

5 again. I'm serving as the designated federal

6 official for the meeting. This is the

7 Reevaluation of the Human Health Effects of

8 Atrazine.

9 I just want to make a couple of

10 quick comments. EPA's presentations from

11 yesterday are in the docket, so if anybody

12 wants to see them, they're there.

13 We will have a public comment

14 session this morning. And I would ask anybody

15 who has electronic copy of their comments to

16 e-mail them to me. I will get them -- I'll

17 give you the e-mail at break or sometime if

18 you do need that.

19 And the final reminder is just to

20 please state your name when you're making

21 comments so that we can get the name recorded

22 into the audio and written transcript. 
202-234-4433

Neal R. Gross & Co., Inc.

Page 5

1 And I think that's all I have to

2 announce this morning. And I'll turn the

3 meeting over to Ken Portier. Thank you.

4 SESSION CHAIR PORTIER: Good

5 morning. Welcome to day two of this EPA SAP

6 meeting on Reevaluation of Human Health

7 Effects of Atrazine: Review of Experimental

8 Animal and In Vitro Studies and Drinking Water

9 Monitoring Frequency.

10 We're continuing the presentation

11 from day one. But as we normally do with the

12 panel on the second day we're going to go

13 around and introduce everyone from the panel. 

14 I'm Ken Portier, the director of statistics at

15 the American Cancer Society national home

16 office in Atlanta. I'm a bio-statistician, a

17 member of the permanent panel. And we'll

18 start with John.

19 DR. BUCHER: John Bucher. I'm the

20 associate director of the National Toxicology

21 Program. I'm a toxicologist and a member of

22 the permanent panel.
202-234-4433

Neal R. Gross & Co., Inc.

Page 6

1 DR. SCHLENK: Dan Schlenk,

2 professor of aquatic ecotoxicology, Department

3 of Environmental Sciences, University of

4 California, Riverside, and a member of the

5 permanent panel. 

6 DR. CHAMBERS: I'm Jan Chambers, a

7 professor in the College of Veterinary

8 Medicine at Mississippi State University. I'm

9 a pesticide toxicologist and a member of the

10 permanent panel.

11 DR. LEBLANC: I'm Gerry LeBlanc. 

12 I'm head of the Department of Environmental

13 and Molecular Toxicology, North Carolina State

14 University. And I'm a permanent panel member.

15 DR. HOLLADAY: I'm Steve Holladay. 

16 I'm a professor at University of Georgia

17 College of Veterinary Medicine. I'm head of

18 Anatomy and Radiology Department. I'm an

19 immunotoxicologist, first time on the panel. 

20 DR. REGAL: Jean Regal, University

21 of Minnesota Medical School, Duluth --

22 immunotoxicology.
202-234-4433

Neal R. Gross & Co., Inc.

Page 7

1 DR. WILLIAMS: Carmen Williams,

2 National Institute of Environmental Health

3 Sciences, and I'm a reproductive endocrine and

4 fertility specialist and reproductive tract

5 biologist.

6 DR. DELCLOS: Barry Delclos from

7 the FDA, National Center for Toxicological

8 Research, toxicologist.

9 DR. HORTON: Teresa Horton,

10 Department of Neurobiology and Physiology,

11 Northwestern University, Evanston, Illinois,

12 reproductive neuroendocrinology, maternal

13 fetal interactions and development of the

14 reproductive system and neuroendrocine system.

15 DR. AKANA: Susan Akana from the

16 University of California, San Francisco. I'm

17 a research physiologist with a specialty in

18 the stress axis and its interaction with

19 energy balance.

20 DR. O'BYRNE: I'm Kevin O'Byrne

21 from King's College London, the Department of

22 Anatomy and Human Sciences. And I'm a
202-234-4433

Neal R. Gross & Co., Inc.

Page 8

1 reproductive physiologist.

2 DR. SELVAGE: Dan Selvage, Idaho

3 State University. I study stress and gonadal

4 hormones. I'm banished to the corner over

5 here.

6 SESSION CHAIR PORTIER: Dr.

7 Gilliom?

8 DR. GILLIOM: Bob Gilliom, U.S.

9 Geological Survey. And I am director of

10 pesticide studies for the National Water

11 Quality Assessment Program.

12 DR. COUPE: Richard Coupe, U.S.

13 Geological Survey.

14 DR. YOUNG: Linda Young,

15 Department of Statistics, University of

16 Florida.

17 DR. LEE: Herbert Lee, University

18 of California, Santa Cruz, Department of

19 Applied Mathematics and Statistics.

20 DR. REED: Nu-may Ruby Reed,

21 California Environmental Protection Agency. 

22 I do pesticide risk assessment and risk
202-234-4433

Neal R. Gross & Co., Inc.

Page 9

1 assessment issues.

2 DR. FENNER-CRISP: Penny Fenner3 Crisp, private consultant, Charlottesville,

4 Virginia. My area of interest is toxicology

5 and risk assessment.

6 DR. GREENWOOD: Richard Greenwood

7 from University of Portsmouth in the U.K. I'm

8 a toxicologist, pesticides and environment. 

9 DR. KRISHNAN: Kannan Krishnan,

10 professor and director of the Toxicology

11 Research Center of the University of Montreal,

12 Canada. My expertise is in pharmacokinetics

13 and risk assessment methods.

14 DR. HAYTON: William Hayton from

15 the College of Pharmacy at The Ohio State

16 University., professor of pharmacy. And my

17 area is pharmacokinetics.

18 CHAIR HEERINGA: Steve Heeringa,

19 the University of Michigan. I'm a

20 statistician for the Institute for Social

21 Research and I'm currently the Chair of the

22 FIFRA Science Advisory Panel.
202-234-4433

Neal R. Gross & Co., Inc.

Page 10

1 SESSION CHAIR PORTIER: Thank you. 

2 Dr. Selvage, I think after we do the public

3 comments we're going to move you around and

4 kind of give you a little room. We added two

5 more members since yesterday so -- as if it

6 wasn't tight enough. 

7 Just to give you a little idea of

8 how we readjusted the agenda, we're going to

9 spend the first period this morning before the

10 break on the remaining EPA presentation. 

11 Following the break we have six

12 groups of public commenters that have asked to

13 spend time. You have some documents added to

14 what was already in the docket. We'll take a

15 look at those during the break. I doubt if

16 we'll get through all the public commenters. 

17 We have about three hours of

18 public comment, so we'll do two hours of that

19 before lunch. We'll take an hour, hour-and20 fifteen-minute lunch, and then we'll come back

21 for public comments. And then we'll start on

22 the questions.
202-234-4433

Neal R. Gross & Co., Inc.

Page 11

1 And even though we -- our agenda

2 says we'll stop at 5:00 we'll probably push it

3 a little bit to 5:30 -- maybe a quarter to

4 6:00 today to kind of capture a little bit of

5 the time we've lost.

6 And, with that, I'm going to turn

7 it over to Mr. Thurman to continue the --

8 introducing the speakers for the water part.

9 MR. THURMAN: Okay. Where we're

10 going to go next is we presented two examples

11 in the white paper for you. And Mary

12 Frankenberry is going to give a presentation

13 on the use of bootstrapping methods to

14 evaluate how well different sampling frequency

15 estimate time average concentrations over

16 time. And then Mike Messner is going to talk

17 about how an application of artificial neural

18 networks to simulate pesticide concentration

19 patterns and how that might be applied. So

20 I'm going to turn this over to Mary.

21 MS. FRANKENBERRY: Thank you,

22 Nelson. As Nelson said, we'll be looking --
202-234-4433

Neal R. Gross & Co., Inc.

Page 12

1 I'll be looking this morning at evaluating

2 sampling strategies for estimating maximum

3 concentrations of different durations.

4 And as we also said yesterday, we

5 talked about recent work by Crawford and

6 others that have evaluated the performance of

7 different sampling strategies for estimating

8 the upper percentiles of true values -- so9 called true chemograph profiles -- and we'll

10 get into that a little more.

11 In the white paper that OPP did we

12 carried out two methods that were variations

13 on the Crawford bootstrapping approach, and

14 one of which I'll be presenting today. 

15 Crawford's work created synthetic

16 true chemographs using linear interpolation to

17 augment less than daily sampling up to 365-day

18 profiles. OPP, however, chose three -- I'm

19 sorry -- 30 community water system where

20 Crawford looked at Heidelberg data. We chose

21 these CWS data. They were normally sampled

22 weekly or biweekly and augmented again to 365
202-234-4433

Neal R. Gross & Co., Inc.

Page 13

1 days linearly. 

2 The systems that we chose were

3 taken to be representative of most of the

4 systems -- AMP systems over the five years. 

5 So they included some low, medium, and high

6 concentration systems.

7 While earlier work looked at the

8 upper percentiles of a single day

9 concentration, OPP is concerned with the

10 maximum value of several different durations

11 ranging from probably single day values all

12 the way up 90-day averages at the current

13 time. Similar to earlier methods, OPP looked

14 at the performance of different sampling

15 strategies for computing estimates of true

16 values for these maximum running averages.

17 Sampling was carried out using the

18 interpolated systems according to schedules

19 which ranged from quarterly sampling to

20 sampling every four days. We actually did put

21 in also a seasonal daily strategy where we

22 sampled every day during the season of April
202-234-4433

Neal R. Gross & Co., Inc.

Page 14

1 through August. I think it was biweekly

2 beginning and ends of the year.

3 Each sampling strategy was

4 implemented with replacement over 4,500 times

5 following a random start. Each resample was

6 interpolated to 365 days, and then the maximum

7 single day, maximum three-day, and so on

8 average of each run was compiled with the

9 range and percentiles of these maximum values

10 reported. And we took this range of maximum

11 values and presented it against the true value

12 for each chemograph and the tables that will

13 follow.

14 This is just a matrix of our

15 sampling strategies versus the concentration

16 durations that we looked at. On the left

17 you'll see the sampling intervals going from

18 daily down to quarterly. I put in single days

19 up to 90-day averages here. But we also

20 looked at 14-day averages, I think 28- or 30-

21 day averages, 60-day averages as well.

22 Okay. Chemograph number 21 is
202-234-4433

Neal R. Gross & Co., Inc.

Page 15

1 something -- well, I'll present some numbers

2 on that. But just to show you what it looks

3 like, it's an example of our middle group of

4 systems. It rises to its peak and then

5 declines somewhat slowly over the course of

6 several weeks. If you look at the notches on

7 the graph in the peak there those represent

8 weekly samples. So you'll see that the --

9 from beginning to end of this peak that's

10 somewhere along the order of five to six

11 weeks.

12 These are tables of the numbers

13 that we ran for them. You can see the true

14 value for single daily -- a single day value

15 sampled at either four-day intervals, seven16 day intervals, or 14-day intervals. The true

17 single day value for this chemograph was

18 31.25.

19 On the right-hand column here we

20 show how many times we actually captured that

21 mean in all of these 4- to 5,000 samples. And

22 actually it's kind of what you'd expect for
202-234-4433

Neal R. Gross & Co., Inc.

Page 16

1 sampling every four days. We might catch it

2 once in four days or 25 percent of the time,

3 sampling every seven days about 14 percent of

4 the time, sampling every two weeks about 7

5 percent of the time.

6 If we look at the mean returns or

7 the mean runs on these resamples we see that

8 we were 4 to 5 percent lower than the true

9 value for sampling every four days. If we

10 sampled every seven days we were 7 to 8

11 percent lower on average, every two weeks 14

12 to 15 percent lower on average. We also show

13 the lowest runs, and they run from 11 up to 27

14 percent underestimating. But, clearly, the

15 performance is best if the sampling is more

16 frequent than less frequent.

17 That was the first graph -- or

18 table represented single day values. These

19 are seven-day average concentrations --

20 maximum seven-day average concentrations for

21 the same chemograph. You can see that the

22 performance is a little better when we go from
202-234-4433

Neal R. Gross & Co., Inc.

Page 17

1 looking at a single-day peak to a seven-day

2 average. On average we get between 2 and 3

3 percent below, between 5 and 6 percent below,

4 or between 13 and 14 percent below. 

5 And up at the top we have

6 captured -- the top runs capture the maximum

7 fairly well. We're also starting to

8 overestimate it just a little bit as this

9 average gets a little longer than the single10 day or the three-day average.

11 Just to summarize the results for

12 this, which really are consistent with the

13 results for all of the other systems, it

14 appears that accuracy, as you might expect, is

15 best for strategies which employ the most

16 frequent sampling. Accuracy generally seems

17 to improve for longer-term averages, that is,

18 seven days, 30 days, 90 days, over the shorter

19 term averages of single days or three days for

20 instance.

21 Here I'm going to show two other

22 peaks that are representative of two other
202-234-4433

Neal R. Gross & Co., Inc.

Page 18

1 types of chemographs that we saw among those

2 that we looked at. Number 4 represents a

3 single narrow peak that is created and then

4 declined again in the course of two weeks. 

5 And the values here go from a little bit over

6 zero up to 73 parts per billion. And this is

7 down close to 10.7 -- I think 11 parts per

8 billion one week later. And that -- those

9 differences are important when we look at how

10 well we do with sampling.

11 Next graph is simply typical of a

12 series of broad -- one broader series of

13 extended peaks -- and we have a number of

14 those. So we looked at examples of all of

15 these in the exercise that we ran.

16 Okay. This is a busy table -- and

17 I apologize. I took some things out of it

18 actually that you may find yourselves asking

19 about later. But these are the results for

20 the three-day average concentrations sampled

21 at four-day intervals. That's -- everything

22 was done in that manner.
202-234-4433

Neal R. Gross & Co., Inc.

Page 19

1 This group at the top is the group

2 of thin, skinny peaks and the performance for

3 them. That's the average performance for four

4 of them if you look at the mean returns. 

5 Middle group is the -- some middle medium

6 broad peaks. Bottom are the broad peaks. And

7 if you look at our mean returns we go from a

8 most underestimating by 9 percent, 4 percent,

9 and then down to less than 2 percent. We do

10 much better with the broad peaks. If you look

11 at the right-hand -- these two columns at the

12 90 -- 99.9 -- we've estimated the maximum with

13 virtually no error there.

14 Okay. Next slide. That was the

15 three-day averages sampled every four days. 

16 This is three-day averages sampled daily

17 during the season, 14 days during the rest of

18 the year. And you can see the performance has

19 gotten even better as you might expect for

20 this kind of sampling. We're looking at, you

21 know, 2 to 3 percent underestimating in the

22 least performing group, 1 percent in the
202-234-4433

Neal R. Gross & Co., Inc.

Page 20

1 middle group, and less than 1 percent

2 recaptured on mean returns here. The lowest

3 returns are -- have a little more

4 underestimation, but they're not too bad.

5 Up at the top again at least 50

6 percent of the time in 50 percent of the runs

7 we capture the maximum with no error, and

8 sometimes even higher -- 60 percent or more.

9 The final graph here is the --

10 looks at 90-days averages for sampling every

11 seven days during the season, 14 days during

12 the remainder of the year. This is pretty

13 much the way the current AMP program is run

14 now. 

15 And if we look at the mean

16 returns, again -- and in all of these the mean

17 and the median were very similar. So you can

18 call it either way if you want to look at

19 middle values and say half the time we're

20 higher than these, half the time we do less

21 well, all of these are less than 1 percent of

22 the true value here and we still capture the
202-234-4433

Neal R. Gross & Co., Inc.

Page 21

1 maximum a large percentage of the time.

2 In terms of what we think these

3 indicate, well, as expected an end point with

4 a shorter duration will require more frequent

5 sampling than one with a longer duration to

6 capture corresponding monitoring values with

7 similar accuracy.

8 We think it also shows that the

9 current sampling scheme of weekly and biweekly

10 monitoring appears to perform adequately for

11 estimating 90-day averages with reasonable to

12 good accuracy.

13 Seasonally targeted sampling

14 strategies generally seem to perform better

15 than allocating samples equally throughout the

16 year. Sampling strategies appear to perform

17 differently according to the shape of the

18 chemograph, and because of this it might be

19 useful -- may not be practical. We're

20 considering it might be useful to examine the

21 shape properties of annual profiles and the

22 subset of systems with the highest peaks over
202-234-4433

Neal R. Gross & Co., Inc.

Page 22

1 the years -- across the years.

2 And here we're looking at -- I

3 think Nelson might have shown this yesterday. 

4 This table looks at these individual peaks,

5 not averages, above arbitrary cutoffs of 12

6 parts per billion, 20, 30 parts per billion. 

7 To the right are the CWSes where these peaks

8 were found -- 12, 20, 30. And, of course,

9 these 30 and 20 are nested in the systems that

10 are over 12. And as -- of course, as we go

11 from lower to higher concentration we see

12 fewer systems involved.

13 We also have seen that across the

14 years many of these systems are the same; they

15 repeat. There are some different ones each

16 year, but there are some that are consistently

17 among the higher ones each -- from year to

18 year.

19 The other thing to point out is

20 that sample sizes here on the left -- they're

21 in the thousands year by year. Across year

22 they're in the tens of thousands. But when we
202-234-4433

Neal R. Gross & Co., Inc.

Page 23

1 get to the individual system where we need to

2 do our risk assessments, it's system by system

3 year by year. What we're dealing with terms

4 of sample sizes here are 30 to 35 samples. 

5 And that's I think something to keep in mind.

6 So, as a result, what we're asking

7 the Agency for certainly is feedback on

8 looking at the population of systems from year

9 to year. Can we do something with this

10 exercise we've just done? Is there anything

11 practical to do, and especially logistically?

12 But, frankly, our biggest concern

13 in terms of doing our assessments is on the

14 individual system level where we need to look

15 at missing values that result from less than

16 daily sampling.

17 So, by way of summary, results of

18 this preliminary exercise agree with Crawford

19 and others in pointing to smart sampling

20 strategies that can target both spatial and

21 temporal patterns among the CWSes. We think

22 that more intense seasonal sampling among
202-234-4433

Neal R. Gross & Co., Inc.

Page 24

1 perhaps some of the higher concentration

2 systems in vulnerable areas might be a good

3 allocation of sampling resources and we'll be

4 seeking your feedback on that.

5 And Mike now will be talking about

6 a modeling approach to all of this if you

7 have -- after your questions.

8 SESSION CHAIR PORTIER: Questions? 

9 Dr. Young?

10 DR. YOUNG: We saw each other up

11 in the hall, so I'll start kind of where I

12 left off. You know the probability of hitting

13 a peak is almost zero so -- as you go through

14 here. So you -- as you say, everything is as

15 you'd expect it. You're always

16 underestimating that maximum value. But there

17 are a whole host of literature on like extreme

18 value theory. Have you -- is there a reason

19 why you guys don't want to go that way?

20 MS. FRANKENBERRY: No. I think we

21 just haven't gotten to it. But I think that

22 what we did see -- and this is that -- while
202-234-4433

Neal R. Gross & Co., Inc.

Page 25

1 we did not underestimate it on all of them the

2 highest runs did catch it. And they caught it

3 in percentages that you might expect. But

4 that's the next step. And that's a good

5 suggestion we're waiting to hear about.

6 MR. THURMAN: And, Dr. Young,

7 if -- in the report and such, if you could

8 provide us -- recommend some of those

9 literature we'd be happy to look at them.

10 DR. YOUNG: I just was -- you

11 know, just for information, I was wondering if

12 you guys had already looked at it and there

13 was a reason you were throwing it aside.

14 MS. FRANKENBERRY: No. This is

15 very preliminary -- 

16 DR. YOUNG: Okay.

17 MS. FRANKENBERRY: -- and it's

18 mostly descriptive statistics as you see.

19 DR. YOUNG: Okay. Thanks.

20 SESSION CHAIR PORTIER: Dr. Lee?

21 DR. LEE: One minor concern on

22 what you did is, in developing your, quote,
202-234-4433

Neal R. Gross & Co., Inc.

Page 26

1 true data sets you took the actual

2 observations and then interpolated them. If

3 you have a seven-day average -- you only have

4 observations every seven days on the water

5 sources that have very sharp peaks, like

6 number 4 that you presented and number 22 on

7 the white paper, you may be missing the actual

8 peak. And then the analysis you're doing -- 

9 MS. FRANKENBERRY: To begin

10 with -- 

11 DR. LEE: To begin with your

12 actual data may be missing the peak and

13 then -- so then this sort of analysis may be

14 further underestimating the actual truth -- 

15 MS. FRANKENBERRY: No. We

16 realized -- 

17 DR. LEE: -- and that might be a

18 concern.

19 MS. FRANKENBERRY: -- that and this

20 was the best we could do in this kind of

21 thing.

22 SESSION CHAIR PORTIER: Yes, Dr.
202-234-4433

Neal R. Gross & Co., Inc.

Page 27

1 Gilliom?

2 DR. GILLIOM: Well, I concur with

3 that last point, which it sounds like you guys

4 are already realizing that. But it could

5 have -- it's kind of the known/unknown problem

6 that -- was it Rumsfeld brought up. But

7 it's -- it's one -- it can really bias what

8 you end up with.

9 The other -- I guess a part of --

10 kind of a question more than anything is the

11 implication of the range of time frames you

12 guys are investigating is that on your

13 radar -- and I think the implication of what

14 you want to have us on the radar for giving

15 you comment is that it's possible their very

16 short-term estimates should be within what

17 we're looking at.

18 So this may be a simple point, but

19 the implication is is it's possible that the

20 biological arguments are going to say short

21 term closure is important and we may need to

22 include in our design a way to pick up a daily
202-234-4433

Neal R. Gross & Co., Inc.

Page 28

1 or one-week maximum type of thing. So that's

2 important for us to know.

3 MR. THURMAN: And that's exactly

4 why we're here.

5 DR. GILLIOM: Yes. Okay. And

6 then I guess related to that -- actually, you

7 know, I think -- I've got a couple of

8 questions related that I think are better to

9 come after we hear the stochastic. So I'll

10 defer and pass it on.

11 DR. COUPE: Richard Coupe. I just

12 wondered if the way the data are presented

13 here with the sharp peaks, medium peaks kind

14 of organizes itself along basin size or

15 intensity of agriculture within the basin.

16 MS. FRANKENBERRY: Along what? 

17 I'm sorry.

18 DR COUPE: Along the lines of --

19 so are these organized -- do they fall into

20 basin size, so sharper peaks, smaller basins,

21 or more intense --

22 MS. FRANKENBERRY: Yes. I think
202-234-4433

Neal R. Gross & Co., Inc.

Page 29

1 Crawford found that and we didn't go on to

2 look at that. But I'm sure that probably is

3 the case.

4 DR. COUPE: Yes. And his data you

5 looked at you saw that you could get better

6 statistics on the larger basins because they

7 had slower changing.

8 MS. FRANKENBERRY: Right.

9 MR. THURMAN: This is Nelson

10 Thurman. I just want to add, you know, as

11 we've done that analysis and found the trends

12 based on that I think there are some

13 general -- not just basin size but reservoir

14 versus stream intake. So I think those are

15 things we're looking at as ways of grouping

16 them.

17 DR. COUPE: Sure. I mean, there's

18 an awful lot to become interested in that. 

19 And also in Crawford's day, you know, although

20 he went from different sizes they all had the

21 same percentage agriculture. In general

22 that's not true the larger the basin the less
202-234-4433

Neal R. Gross & Co., Inc.

Page 30

1 agriculture you have. So you won't have the

2 same kind of distribution as you might have

3 with a smaller basin. Thank you.

4 SESSION CHAIR PORTIER: This is

5 Ken Portier. It's clear -- I just wanted to

6 clarify that it's clear that you feel like you

7 have enough information to be able to actually

8 stratify these -- the CWSes based on expected

9 profile or the variability of the profile

10 which you didn't take into account in the

11 simulation yet. Right? 

12 But there is that feeling that you

13 might be able to say these are very variable,

14 these are ones that have one peak a year and

15 it's very consistent. We don't know when that

16 peak's going to occur. That's going to depend

17 on climate and agricultural practice. But we

18 usually expect one versus this one that could

19 have two or three or some years it's -- right? 

20 So there's the opportunity for differential

21 sampling -- clearly differential sampling

22 there. 
202-234-4433

Neal R. Gross & Co., Inc.

Page 31

1 Any additional questions? Dr.

2 Krishnan?

3 DR. KRISHNAN: I just wonder if

4 you had calculated the areas under the curve. 

5 Or like, you know, I see the rolling averages

6 doing a particular period, but have you done

7 area-under-the-curve calculations even -- the

8 same one applies to the presentation of

9 yesterday; you know, taking the points,

10 calculating the area under the curve as well

11 as the interpolated or the modeled ones,

12 because the error on the basis of the area

13 under the curve may not be the same as what we

14 were talking about here.

15 And eventually AUC could also be

16 the more relevant one depending on the -- you

17 know, how we're going to proceed further.

18 MR. THURMAN: This is Nelson

19 Thurman. I just wanted to add on that, I

20 think that's something we've considered, but

21 I think this is also going to be where the

22 toxicologists have to come into play with the
202-234-4433

Neal R. Gross & Co., Inc.

Page 32

1 exposure folks to answer the questions, and an

2 area under the curve type exposure equivalent. 

3 I mean, can we do it in that regard? And I

4 think that's one of the reasons why we've got

5 a diverse group here.

6 SESSION CHAIR PORTIER: Dr.

7 Heeringa?

8 CHAIR HEERINGA: I'd like just to

9 second that point. I think that's something

10 that I struggled with a little bit yesterday

11 afternoon, and I think it's going to be an

12 issue. Clearly, if we're down to daily

13 averages or even three-day averages, it may

14 not make that much of a difference. But if

15 you go out to longer periods that profile --

16 that profile, which again, area under the

17 curve may not even capture the profile because

18 you could have several profiles that generate

19 the same integral.

20 But I think that's something for

21 us to keep in mind. And is it really acute

22 exposures? Is it sort of medium-term semi-
202-234-4433

Neal R. Gross & Co., Inc.

Page 33

1 acute exposures? Or is it longer term -- 90

2 day -- maybe even 90 days isn't chronic, but

3 it's more exemplary of a long-term exposure

4 through the water system. So I think that's

5 important for us to keep in mind over the next

6 two days.

7 SESSION CHAIR PORTIER: Any

8 additional questions?

9 (No response.) 

10 SESSION CHAIR PORTIER: Doesn't

11 look like it. We can move on to the next

12 presentation. 

13 Okay. Dr. Akana?

14 DR. AKANA: I just have a minor

15 question. In my mind what I imagine visually

16 is you have a map of the United States. And

17 on it we saw the blue map with the blue dots. 

18 Okay.

19 If you take that map -- okay --

20 and overlay it on the map with rainfall and on

21 top of that another map with basin, reservoir

22 size, and running water, can you factor an
202-234-4433

Neal R. Gross & Co., Inc.

Page 34

1 algorithm through 3D -- through the 3D map? 

2 MR. THURMAN: Actually, the blue

3 you saw took into account use, rainfall,

4 watershed size -- it took into account a

5 number of those factors which is how we got to

6 that map in the first place. So, yes, it can

7 be done.

8 DR. AKANA: So you have a massive

9 algorithm that just percolates everything

10 through.

11 MR. THURMAN: It was a regression12 based analysis, but it did take into account

13 the factors that on a national scale were

14 driving exposures. What we've learned in the

15 separate monitoring study I mentioned, the

16 ecological exposure monitoring study, is that

17 there are also localized factors that start

18 helping to explain the differences we're

19 seeing in exposures even within that most

20 vulnerable area. So those are things that

21 we're looking at and exploring.

22 Separately from this but will tie
202-234-4433

Neal R. Gross & Co., Inc.

Page 35

1 into this in terms of how can we zoom in maybe

2 in a corn belt scale and take into account

3 those additional local factors.

4 SESSION CHAIR PORTIER: Susan, the

5 panel looked at that. Was it October or

6 December last year? We went through a lot of

7 that on the ecological one. If you can look

8 at the SAP minutes on that you'll see that

9 it -- actually it's another level above what

10 you were talking about that they've actually

11 done.

12 MR. THURMAN: It was actually May

13 of last year.

14 SESSION CHAIR PORTIER: Was it

15 May?

16 MR. THURMAN: So it's been almost

17 a year.

18 SESSION CHAIR PORTIER: Time

19 flies. It seems like I was here yesterday. 

20 MR. MESSNER: My name's Michael

21 Messner. I'm a statistician in the Office of

22 Groundwater and Drinking Water. And I'm going
202-234-4433

Neal R. Gross & Co., Inc.

Page 36

1 to speak about one use of a -- of artificial

2 neural network modeling. I'm going to

3 describe the mathematical formula of this

4 particular kind of artificial neural network

5 model. Then I'm going to show how it can be

6 fit to some atrazine occurrence data. 

7 Then I'm going to show how this

8 model can be used to simulate different -- not

9 just different patterns but different levels

10 of atrazine occurrence to assess the

11 performance of some strategies -- in other

12 words, combinations of monitoring plans and

13 decisions based on the monitoring data. 

14 Finally, I'll provide a table with some pros

15 and cons of the artificial neural network

16 modeling. 

17 My presentation's going to differ

18 from Mary's in two ways. One is the kind of

19 model that I'm using -- the neural network

20 model. The other way though is the way the

21 model's used to assess performance. And I'm

22 kind of wishing I had a pros and cons table on
202-234-4433

Neal R. Gross & Co., Inc.

Page 37

1 this. I think it's maybe something you're

2 going to be struggling with.

3 And that is where Mary described

4 how the use of statistics and the probability

5 that a statistic would capture a true

6 parameter value -- a true percentile -- my

7 focus is going to be on the decision making

8 using the data to decide that we have a

9 positive or a negative -- sort of like do we

10 put a star on one of those bar charts you

11 looked at yesterday. So my focus is the

12 assessing the probabilities of false positives

13 and false negative. So that's a different way

14 of looking at performance.

15 This is one flavor of artificial

16 neural network model. The ugly looking

17 equation here at the top describes a moving

18 average of log concentration values. The

19 first term here on the right, b0, is an

20 intercept that sets the baseline occurrence --

21 that is, before any of the other fractions

22 here begin to take effect. 
202-234-4433

Neal R. Gross & Co., Inc.

Page 38

1 Each fraction is called a node. 

2 And a node can describe either an increase or

3 a decrease in the atrazine concentration. So

4 if you take the first fraction here and let it

5 describe an increase and then later on a

6 second fraction describes a decrease, together

7 they describe a peak in occurrence. So this

8 particular formula with four of these

9 fractions or nodes can very easily describe a

10 double peak during a source water's year of

11 atrazine occurrence.

12 So with -- if one can understand

13 what these different parameter values mean,

14 one can construct a formula here that will

15 mimic atrazine occurrence. And then with

16 particular values for these 12 parameters here

17 and also with the precision parameter that

18 describes how the measured values can depart

19 from the moving average. We can calculate the

20 log likelihood. I guess you could think of it

21 either as the likelihood of the data given the

22 parameters or the likelihood of the parameters
202-234-4433

Neal R. Gross & Co., Inc.

Page 39

1 given the data.

2 But with that in hand, there are

3 number of programs -- a number of algorithms

4 one could use then to optimize that -- to

5 maximize the log likelihood and find the best

6 fitting parameter values. 

7 All of these routines requires

8 though that you first have a decent initial

9 value. So this just shows how I went about

10 finding initial values for atrazine data from

11 the Maumee River in 1998. I think this -- was

12 this an ecological data set that you provided? 

13 Heidelberg? Ambient water. Okay. 

14 There are a lot of daily

15 measurement values here in the middle part of

16 the year. These out here I think are every

17 two weeks or so. 

18 So the way I went about fitting

19 this was first to say, okay, I'm going to let

20 the intercept -- the b0 term -- be this value

21 here. And then I wanted to have an increase

22 occur at this time of the year. I wanted the
202-234-4433

Neal R. Gross & Co., Inc.

Page 40

1 increase to be just so big and so fast that it

2 sort of fit the data. I'm just doing this

3 using my eyeball. Okay? And to my eye that

4 looks like an awfully good fit.

5 Next I attacked the parameters in

6 the second node and adjusted those until they

7 kind of fit this downturn here. Then I added

8 the parameters for the third node so that I

9 had this increase occurring at that time of

10 the year. And finally the fourth node

11 described this decrease bringing the atrazine

12 level back down. Now, that's not perfect. 

13 That's just an eyeball kind of fit to the data

14 which gave me some initial values for those

15 algorithms to do their thing.

16 This is the result. This is a

17 maximum likelihood model. It's actually a

18 three-node model rather than a four-node

19 model. And you can see that it does a very,

20 very nice job of following the data. 

21 The way the three-node model

22 works -- it's a little harder to understand
202-234-4433

Neal R. Gross & Co., Inc.

Page 41

1 the parameters in the three-node model. The

2 first node causes this increase here. The

3 third node causes this increase here. And the

4 second node actually causes the decrease that

5 you see spanning the time from here to about

6 here. So there's just one node that's sort of

7 describing both of those drops in atrazine

8 concentration.

9 I like this model because it

10 provided -- in terms of log likelihood it

11 provided this sort of the same quality of fit

12 as the four-node model with fewer parameters. 

13 And the appearance is a little nicer. There

14 was something funny going on in here with the

15 four-node model. So this was a nice maximum

16 likelihood model.

17 How do I go backwards? Okay. One

18 thing a little bit odd here is there are

19 couple of periods of time where there's a

20 string of four or five values -- maybe more

21 than four or five -- in a row that appear

22 either above or below the curve. And I was
202-234-4433

Neal R. Gross & Co., Inc.

Page 42

1 concerned that maybe that's a little more than

2 would occur by chance if each day's value was

3 sort of a -- had random error about the curve.

4 So I looked at the residuals and I

5 also looked at something called an

6 autocorrelation function. This figure here

7 plots the autocorrelation function. This is

8 called a correlogram, and you can there's a

9 sort of a cosine -- dampened cosine shape to

10 this that is characteristic of what's known as

11 an AR2 model. So I wanted to build in this

12 kind of autocorrelation.

13 This isn't the way one day's

14 concentration is dependent on the previous

15 day's so much as it is the way one day's

16 departure from expected is related to the

17 previous day's departure from expected.

18 So if you think of that artificial

19 neural network, the smooth curve, as

20 describing the expected log concentrations on

21 different days, what I'm talking about here

22 are the departures from that curve -- that
202-234-4433

Neal R. Gross & Co., Inc.

Page 43

1 there's still some autocorrelation going on

2 from day to day that you can't explain with --

3 you know, as ugly as that model is it still

4 doesn't quite account for everything that we

5 might want to account for.

6 So I found that of all the AR

7 models, AR2 did turn out to be the best

8 fitting. These two parameters here tell how

9 a day's departure from the expected value --

10 that is, from this equation -- are related to

11 the previous day and two days previous. So

12 today's departure would be -- 38 percent of it

13 would be -- or .38 times yesterday's departure

14 plus about .2 times two days' ago departure

15 would be added to some random error for today

16 to produce today's departure from expected. 

17 So with those two additional parameters we can

18 explain this additional autocorrelation

19 feature of the data.

20 Okay. So given those model

21 parameters we can drive some summary

22 statistics. We can calculate the mean, the
202-234-4433

Neal R. Gross & Co., Inc.

Page 44

1 median, we can calculate other percentiles of

2 interest if those are things that you care

3 about.

4 But what if we're concerned about

5 making decision errors when the occurrence is

6 either much higher or much lower than is given

7 by that particular model? Now, this is -- I'm

8 just going to pause right here. One thing we

9 could do with the parameter values is we could

10 estimate the concentrations on days where we

11 don't have data -- we could do things like

12 that. We could provide credible intervals,

13 say, for those concentrations. We could

14 provide credible intervals for these things,

15 the mean concentration, the median

16 concentration.

17 But the interest here -- I'm not

18 going to go down that path. Rather, I'm going

19 to be talking about how to use this particular

20 model to study how different monitoring

21 strategies behave. So if we just accept that

22 this model is true and that we care about
202-234-4433

Neal R. Gross & Co., Inc.

Page 45

1 occurrence when it behaves this way, we're

2 going -- I'm going to use this model to

3 evaluate how different monitoring strategies

4 behave. Okay?

5 I sense the statisticians in the

6 group might want to see uncertainty intervals

7 about the parameter estimates -- Markov chain

8 Monte Carlo samples. But, I'm sorry, but

9 you're not going to see those things.

10 If we take the model with those

11 parameter values and simply add a constant to

12 that intercept term we preserve the shape of

13 the occurrence pattern and we just -- we

14 simply are changing the scale of it. 

15 If the constant that we add is

16 negative then the result is a lower overall

17 level of occurrence. If K is positive then

18 adding it will increase all of the

19 concentration values. The mean, the median,

20 and other percentiles of interest are all

21 going to change by the same factor.

22 The error structure remains the
202-234-4433

Neal R. Gross & Co., Inc.

Page 46

1 same. The shape of the occurrence

2 distribution also remains the same. The only

3 thing that we're doing is changing the overall

4 occurrence level.

5 This next figure shows two

6 simulations, both with the same random number

7 seed but with different intercept values. And

8 you can see they're identical except for the

9 scaling of the vertical axis. I've drawn a

10 red line here at the 99th percentile given

11 this model. And in one case here on the right

12 the 99th percentile is 10, and on the left

13 it's 25. So there's a factor of two and a

14 half difference. Each point here on the left

15 is two and a half times as high as each point

16 on the right.

17 So if decision makers were

18 concerned about making a negative decision,

19 say, failing to take action given this kind of

20 occurrence pattern, we could simulate with

21 this model -- simulate many years of

22 occurrence to estimate the false negative
202-234-4433

Neal R. Gross & Co., Inc.

Page 47

1 rate. Similarly, if the decision makers

2 consider this to be a low occurrence pattern

3 and level we could simulate a year of

4 occurrence like this many, many times to

5 estimate the probability of the false

6 positive.

7 We could do this, say, 10,000

8 times if we wanted to get good estimates of

9 those error rates. And we could repeat this

10 using different levels of our trigger or our

11 critical value to see how the probabilities of

12 those decision errors depend on the decision

13 rule.

14 This figure shows how the

15 probabilities of false positives and false

16 negatives play against each other as the

17 critical value for determining a positive is

18 increased from zero here to some high value. 

19 If we set the critical value very high this

20 could be like a -- this value could be a limit

21 for, say, the maximum measured value or the

22 sample mean or the second highest -- whatever
202-234-4433

Neal R. Gross & Co., Inc.

Page 48

1 your test statistic is.

2 If it's high -- if your critical

3 value is high you'll be controlling the false

4 positive rate fairly well, but the price

5 you'll pay is a higher false negative error

6 rate. If you lower the critical value you'll

7 find that you increase the false positive rate

8 while decreasing the false negative rate.

9 Now, this curve sort of shows

10 there's a sweet spot in the middle around

11 about 10 percent. If you could tolerate 10

12 percent false positive and false negative

13 errors you might like a critical value here in

14 the middle. I don't want to suggest that

15 that's where the answer should be. It may be

16 that when you think about consequences of

17 decision errors you may be much more concerned

18 with one kind of an error than another, and

19 that -- what appears to be a sweet spot in the

20 middle might not be acceptable.

21 But my message here is that we can

22 use these kinds of models and these kinds of
202-234-4433

Neal R. Gross & Co., Inc.

Page 49

1 simulations to get an understanding of how a

2 strategy in a decision rule can perform so

3 that a strategy can be selected with open

4 eyes -- with an understanding of what these

5 error probabilities are.

6 So what often happens is that we

7 can't find a spot where we can craft a

8 decision rule that satisfies both constraints

9 for false positives and false negatives. This

10 almost always happens in my experience. We

11 find that decision makers can't tolerate the

12 idea of errors of any sort and they'll

13 initially want very small probabilities of

14 false positives and false negatives. And we

15 find that we can't achieve that without, say,

16 millions and millions of samples.

17 And so some back and forth is

18 required asking decision makers, well, what

19 can you really live with and what can really

20 be afforded in terms of sampling. Maybe we

21 can afford hundreds of samples or maybe we can

22 afford to challenge thousands of rats in a
202-234-4433

Neal R. Gross & Co., Inc.

Page 50

1 dose response study. But it's normal to have

2 this back and forth to arrive at a design that

3 one can live with.

4 So, in summary, artificial neural

5 network models are one kind of artificial

6 neural network model, but it's showing you it

7 can be used -- I'm sure there are others. 

8 There are also other time series models that

9 can be used to characterize these occurrence

10 patterns and simulate occurrence patterns that

11 are of concern to decision makers -- simulate

12 low occurrence patterns, low levels for which

13 there's concern for the false positive, also

14 simulate high levels where there's concern for

15 the false negative.

16 We can simulate high variability

17 patterns and low variability patterns --

18 simulate levels and patterns that are of

19 concern to the decision makers and use this to

20 assess candidate monitoring strategies for

21 their ability to satisfy the requirements for

22 false positive and false negative error rates.
202-234-4433

Neal R. Gross & Co., Inc.

Page 51

1 Here I've listed some of the

2 advantages and disadvantages of the artificial

3 neural network models. At the top on

4 disadvantage I listed the complexities. It

5 can be difficult to understand the parameters. 

6 A key advantage is that they can be very

7 flexible and they seem to do a nice job of

8 representing realistic occurrence patterns for

9 atrazine.

10 I've left some blanks here. You

11 all can read these yourselves. I'm sure you

12 have -- you can think of additional pros and

13 cons for this type of modeling. And I'll just

14 stop there and leave those blanks and

15 encourage you to add to those lists.

16 SESSION CHAIR PORTIER: So, Dr.

17 Lee, first question.

18 DR. LEE: I think this was a very

19 nice use of neural networks. Let me start

20 with a technical question. Did you look at

21 the autocorrelation plot for the four-node

22 network that you fit?
202-234-4433

Neal R. Gross & Co., Inc.

Page 52

1 DR. MESSNER: I don't think I did.

2 DR. LEE: Because I suspect there

3 will be much less autocorrelation there --

4 possibly none or close enough to none. The

5 four-node one -- I think this is the first

6 time I've had to try to convince someone to

7 use more nodes. Usually it's going the other

8 direction, trying to convince someone they're

9 using way too many nodes and it's not going to

10 predict well. 

11 Particularly in this case where

12 you're trying to look for potentially a peak

13 or a very short-term average. The three-node

14 model is smoothing a lot more than the four15 node model. And I suspect you'll get better

16 results if you look at the four-node model in

17 this case rather than the three-node model. 

18 It's going to fit much more tightly.

19 And one of the things is that sort

20 of the shape of a peak that you can

21 estimate -- the more nodes you use the more

22 variability you can get in different types of
202-234-4433

Neal R. Gross & Co., Inc.

Page 53

1 shape of peaks you can fit. And so by

2 restricting yourself to three nodes you're

3 forcing a more particular shape of peak that

4 doesn't quite fit the data as much. And for

5 sort of a longer term average that's fine, but

6 if you're looking at a very short-term average

7 or a peak that's going to make a bigger

8 difference. And so I suspect you can get even

9 better results here with a four-node network. 

10 And I can put some additional comments in the

11 minutes about model selection there.

12 DR. MESSNER: In the paper I

13 describe another data set that I modeled using

14 a two-node model. And interestingly I found

15 that an AR1 model fit quite well for that even

16 though it has fewer nodes than this.

17 SESSION CHAIR PORTIER: Dr.

18 Heeringa?

19 CHAIR HEERINGA: Thank you for

20 that presentation. One question for you and

21 then one for Nelson. In these models is there

22 any way to incorporate prior -- I know it's in
202-234-4433

Neal R. Gross & Co., Inc.

Page 54

1 your notes -- prior year information? In

2 other words, if we assume that these community

3 water systems had a response profile that was

4 dependent on intake and volume of water and

5 sources of water I would expect if peaks occur

6 they would subside with some type of profile

7 that may be unique to that water system. If

8 you had data like that how would you factor

9 that into this model?

10 DR. MESSNER: What I'd really like

11 to have first is an expert that I could sort

12 of interview. I would wonder is it -- do you

13 really expect only one peak? Would you expect

14 two peaks? How many peaks would you expect in

15 a year? I imagine that would depend on things

16 like rainfall, and I think it would be very

17 difficult to interview an expert and extract

18 from that expert prior knowledge about these

19 things.

20 But I suppose you could do it. 

21 And if you had many years of data also for

22 that particular source water you could perhaps
202-234-4433

Neal R. Gross & Co., Inc.

Page 55

1 roll all that together. This sort of speaks

2 to my third disadvantage here, year-to-year

3 variability. That's a lot of parameters, and

4 the hierarchal structure -- it would be really

5 fun to try, but I think it would be very

6 difficult. What's your feeling on that, Dr.

7 Lee?

8 DR. LEE: Neural networks are

9 really not the best model to try to

10 incorporate the year-to-year variability. It

11 probably could be done, but it would be really

12 difficult. I think you'd probably find better

13 success with like the kriging-type approach

14 that's also discussed in there or some other

15 sorts of models.

16 CHAIR HEERINGA: Second question

17 was for Nelson. I -- what data do we have

18 available? We know for a select set of

19 community water systems that we have the

20 registrant's data that's been provided as

21 being provided for the 90-day average. And

22 are there other sources in terms of the
202-234-4433

Neal R. Gross & Co., Inc.

Page 56

1 national water quality programs that produce

2 periodic or annualized estimates of atrazine

3 concentration for community water systems,

4 say, in atrazine use areas? What data could

5 we expect to have across the various sources?

6 MR. THURMAN: Well, NAWQA hasn't

7 really focused on community water systems. 

8 They focus on ambient waters, some of which

9 are similar to what you would see in community

10 water systems. They focused on flowing water. 

11 So for streams they have places where they

12 have monitored years to year-to-year that you

13 can start to get that.

14 This particular data set that we

15 have now on the community water systems, which

16 is a range from 130 to 150-some systems --

17 several of those we've got probably at least

18 five years of data that we can start looking

19 at.

20 Now, some of those systems have

21 been monitored historically back years longer

22 before that that we could also take a look at. 
202-234-4433

Neal R. Gross & Co., Inc.

Page 57

1 Some of it would be going back to -- you know,

2 part of the challenge is -- we've also in some

3 of these areas we now have the ability to

4 getting at land use patterns that change year

5 to year over the watersheds, probably more in

6 some of the more recent years than in previous

7 years. So we can start taking a look at that.

8 The one data set that comes to

9 mind that has a whole lot of years of history

10 is the Heidelberg University data set. That

11 is something that we're also going to take a

12 look at.

13 SESSION CHAIR PORTIER: I've got

14 the next question -- Ken Portier. Can you

15 slide back to your best-fitting ANN model

16 picture? I just had a couple of questions on

17 the best fit. So if we're interested in the

18 maximum annual concentration -- right? -- your

19 model underestimates that. And that was one

20 of the reasons you do a -- what is it? --

21 k0 -- or b0 shift. If you shift it up you

22 could do a better job -- that one -- do a
202-234-4433

Neal R. Gross & Co., Inc.

Page 58

1 better job of estimating that overall maximum. 

2 Right?

3 DR. MESSNER: I don't think I'd

4 put it that way. The reason I was adjusting

5 that intercept was to be able to simulate

6 occurrence levels that are of concern to the

7 decision makers, not to provide a better

8 estimate of the maximum. 

9 With -- so if we think of a year

10 as being -- say, this data set is being -- if

11 we could measure every day of the year we

12 would have 365 measurements -- some of them

13 may be missing here -- we could use this model

14 to estimate some missing values -- some

15 concentrations on days that weren't measured. 

16 So that's something that could be done

17 pretty -- it would be pretty straightforward

18 using this model, but it wasn't what I was

19 interested in.

20 SESSION CHAIR PORTIER: I guess I

21 see a lot of uses for the model. If you're

22 moving b0 up and down what does that do to the
202-234-4433

Neal R. Gross & Co., Inc.

Page 59

1 lowest level? Does that shift the whole

2 profile down? 

3 DR. MESSNER: Yes. Everything is

4 increased by the same factor.

5 SESSION CHAIR PORTIER: Because

6 your model fits well where there's stuff

7 happening. But if you look on both tails at

8 the BDL levels you're not fitting very well at

9 all -- right? -- because you don't put very

10 much weight on the winter -- I guess it would

11 be the winter time periods -- right? --

12 where -- I mean, if you look at this point

13 over here you've got a whole bunch of -- you

14 know, three or four levels of zero --

15 right? -- close to -- below detection limit or

16 near detection limit data that the model

17 doesn't fit very well.

18 DR. MESSNER: Right. Of course,

19 we're not terribly concerned about how -- 

20 SESSION CHAIR PORTIER: Concerned

21 about those.

22 DR. MESSNER: -- high those values
202-234-4433

Neal R. Gross & Co., Inc.

Page 60

1 might be. But the likelihoods actually are

2 fairly decent for those sensitized --

3 SESSION CHAIR PORTIER: And it's on

4 a low scale and they're very small. The other

5 thing is your 99th percentile line -- does

6 that take into account these BDL values here?

7 DR. MESSNER: Yes. When I use

8 this model to estimate a 99th percentile or a

9 95th percentile I'm -- 

10 SESSION CHAIR PORTIER: 

11 Integrating?

12 DR. MESSNER: -- integrating and

13 summing over the 365 days of the year. And so

14 it's not the percentile for this particular

15 year, it's actually a percentile for the

16 model. So if I could simulate 10,000 years

17 the percentile for that simulation, let's say,

18 would be very, very, very, very close to what

19 I get by integrating and summing across the

20 days of the year for the model.

21 SESSION CHAIR PORTIER: You talked

22 about credible intervals. Is it possible with
202-234-4433

Neal R. Gross & Co., Inc.

Page 61

1 this model to establish a credible interval of

2 time above a certain threshold? So if I were

3 to pick this curve and say I want to know the

4 time above zero -- log -- whatever -- on this

5 scale -- 

6 DR. MESSNER: Right. So -- 

7 SESSION CHAIR PORTIER: -- can you

8 estimate that time period -- how many weeks,

9 how many days -- 

10 DR. MESSNER: So, if like you were

11 to tell me that we want to know what fraction

12 of the time the levels above ten parts per

13 billion?

14 SESSION CHAIR PORTIER: Right.

15 DR. MESSNER: Yes. I could tell

16 you what percent of the time with this model

17 I'd expect the concentration to be above that

18 level. And, again, we're talking about

19 percent of the days.

20 SESSION CHAIR PORTIER: Yes. 

21 Because -- I mean, one of the things we heard

22 yesterday is they may not be the maximum
202-234-4433

Neal R. Gross & Co., Inc.

Page 62

1 concentration. They may be how many days a

2 person is exposed above a certain threshold. 

3 So if I'm drinking the water and I'm drinking

4 it for four weeks at above 10 parts per

5 mission that may cause the health effect. And

6 so I'd want to know for this site what's the

7 probability it's going to be more than ten

8 weeks or more than five weeks. That's turning

9 the model backwards. It's the inverse --

10 DR. MESSNER: Yes. Estimating the

11 percent of days above a level is very easy to

12 do. What's difficult to do and requires

13 simulation is estimating how many years --

14 during how many years one would have, say,

15 consecutive days above a level. That's -- 

16 SESSION CHAIR PORTIER: But it can

17 be done.

18 DR. MESSNER: -- something that can

19 be done -- 

20 SESSION CHAIR PORTIER: It may be

21 difficult -- 

22 DR. MESSNER: -- but -- 
202-234-4433

Neal R. Gross & Co., Inc.

Page 63

1 SESSION CHAIR PORTIER: Yes.

2 DR. MESSNER: -- you know, I sort

3 of cringe at the thought of programming that.

4 SESSION CHAIR PORTIER: Dr.

5 Gilliom.

6 DR. GILLIOM: As you've applied it

7 for evaluating sample strategies the value of

8 any of these interpolation models basically

9 producing an estimate of the true population

10 is -- it depends on how well it estimates

11 truth basically.

12 And when you have a really dense

13 data set, like almost every day like the

14 Heidelberg data, you're probably -- I mean,

15 frankly, you're probably better off just

16 connecting the dots because you've actually

17 then included these maximums that we're

18 worried about. You don't have to worry about

19 smoothing distorting things or anything.

20 And then, on the other hand, if

21 you apply the same method to sparser data then

22 you've got a severe problem with whether you
202-234-4433

Neal R. Gross & Co., Inc.

Page 64

1 can replicate truth because you're missing so

2 many true events. So you're kind of in a fix

3 there. 

4 So I think ironically for dense

5 data sets you can use the method to get a

6 pretty good quantitative tool for producing

7 test data sets for evaluating sample

8 strategies. And then, on the other hand, when

9 you get to all the more normal circumstances

10 we have it's got the problem of not being able

11 to fill things in because it's not related to

12 predictor variables that have connection to

13 what's really going on in the field, like used

14 patterns and precipitation events and all that

15 which are what's really creating the extreme

16 events.

17 So I guess what I'd like to see in

18 the direction of thinking when we're looking

19 at how to simulate reality for the purpose of

20 these sampling experiments is to get an

21 approach that takes into account the maximum

22 we can about cause and effect variables that
202-234-4433

Neal R. Gross & Co., Inc.

Page 65

1 actually simulate the type of distributions we

2 expect in reality.

3 And I think that direction would

4 give us a better foundation for defending how

5 it is and give you more reality in what the

6 concentrations are and incorporate error

7 better and yield a better sampling design.

8 SESSION CHAIR PORTIER: This is Ken

9 Portier. Kind of like filling in the blanks

10 with knowledge rather than just kind of

11 interpolating or smoothing is really what

12 you're talking about. Linda -- Dr. Young?

13 DR. YOUNG: Yes. Just kind of a

14 follow up on the whole discussion I think

15 because this what's bothering -- this is --

16 what's been bothering me is kind of in the

17 same realm. How many -- you're heavily

18 relying on getting a very dense data set to do

19 this -- right? -- to really get a good fit for

20 your ANN model. Right?

21 DR. MESSNER: I'll say yes. There

22 are many parameters here and it takes a lot of
202-234-4433

Neal R. Gross & Co., Inc.

Page 66

1 data to get good estimates when you have so

2 many parameters. But, on the other hand,

3 personally what I'm concerned with is trying

4 to model something that causes concern for the

5 decision maker -- something realistic such

6 that if this were the true case -- if you

7 really had a system that behaved like this

8 you'd be concerned about either false

9 positives or false negatives.

10 So I like it to be realistic, but

11 I'm finding I don't care so terribly much

12 about explaining a particular data set and

13 making predictions for a particular stream.

14 DR. YOUNG: So the key seems to be

15 to be able to get -- if that's the philosophy

16 then to get a series of what seems to be a

17 range of profiles that you might want to do. 

18 And then -- and if you do that then you

19 have -- then I'm not sure the value of the

20 model. I mean, once I begin to do that the

21 connect the dots does seem to be a reasonable

22 thing.
202-234-4433

Neal R. Gross & Co., Inc.

Page 67

1 So I -- so at one point I thought

2 you were going to try to do this for each

3 community water system. But you're not. 

4 You're just going to look at a range of these

5 types of things. Right?

6 DR. MESSNER: Perhaps.

7 DR. YOUNG: Okay. I'm just trying

8 to get where you're coming from.

9 SESSION CHAIR PORTIER: This is Ken

10 Portier. I was thinking the same thing as Dr.

11 Young was and realizing you have 13 plus three

12 parameters -- what is that? -- 16 parameters

13 and the average data set has 36 data points,

14 many of which are in the no-detect winter

15 range. Right? So you're down to almost a

16 data point per parameter. 

17 And you're trying to fit a two18 point autocorrelation structure, and you start

19 getting missing values and you just can't --

20 so the model, while nice mathematically -- we

21 love it, and it's pretty. When you've got a

22 lot of data, it's nice. When you have -- you
202-234-4433

Neal R. Gross & Co., Inc.

Page 68

1 start getting missing data and low counts,

2 you're starting to have faith rather than

3 data. 

4 Yes, Dr. Lee.

5 DR. LEE: If I could actually just

6 respond to that briefly. Known networks have

7 a particular structure such that the effective

8 number of parameters tend to be smaller than

9 the actual number of parameters. So you don't

10 necessarily need quite the same multiplier of

11 data points to parameters as you would in,

12 say, a standard regression model. 

13 And you can get fairly -- I mean,

14 you wouldn't need, say, this dense a data set

15 to get a fit like this. Just sort of

16 eyeballing it, guessing -- certainly I think

17 if you had a quarter as many data points, you

18 could still get it pretty much the same fit. 

19 I don't know how the fit would change beyond

20 that.

21 DR. YOUNG: But the point would be

22 that you'd probably miss some of those peaks,
202-234-4433

Neal R. Gross & Co., Inc.

Page 69

1 which is really what you want. So as soon as

2 you start doing that, yes, the fit's good to

3 what you saw, but you didn't see what you

4 wanted to see.

5 SESSION CHAIR PORTIER: Well, I

6 think the statisticians have had their fun

7 here.

8 Mr. Thurman, we'll send it back to

9 you for a summary.

10 MR. THURMAN: Okay. Actually, I

11 was having fun too. All right. What I'm

12 going to try to do is bring this back to --

13 all right -- what are we going to do with this

14 stuff. As Anna Lowit said yesterday, when the

15 rubber hits the road how are we going to use

16 this, so that when you folks are commenting on

17 our questions you kind of know where we're

18 coming from and where we're hoping to go on

19 that.

20 Let's start with what we know and,

21 hopefully, I can even fill in some of the

22 holes or questions that maybe didn't answer as
202-234-4433

Neal R. Gross & Co., Inc.

Page 70

1 well. Earlier -- we know pesticide

2 concentrations in water varies both spatially

3 and temporally. We're operating under the

4 feeling that the variability you're going to

5 see spatially and temporally outweigh the

6 other variability you'll see in sampling and

7 analytical.

8 And there may be -- I mean, I

9 think in general we believe that's true. If

10 there are more exceptions to that then,

11 General, I'm sure the USGS folks who have a

12 lot more experience in that are going to tell

13 us that and we can take that into account. 

14 But that's where we're operating right now.

15 We have -- and particularly for

16 those of you who have been on the SAP a year

17 ago and then those two years before that,

18 we've been doing a lot with the ecological

19 exposure monitoring wrapping our minds around

20 the spatial variability and how do we capture

21 that and how do we use that to particularly

22 focus on the areas where we think there's
202-234-4433

Neal R. Gross & Co., Inc.

Page 71

1 going to be more exposure concerns.

2 For this particular drinking water

3 assessment with atrazine we're zooming in on

4 community water systems that happen to be in

5 those areas that spatially are having a high

6 exposure history. And so we're looking at the

7 temporal pattern -- how do we get at the

8 temporal pattern.

9 And, now, we know when you start

10 looking at timing and frequency of sampling it

11 has to take into account that temporal

12 pattern. But it also needs to keep in mind

13 what is our exposure duration of concern. We

14 have a monitoring data set that for these

15 community water systems that took weekly

16 samples during the use season to characterize

17 a 90-day exposure. And that's -- and we think

18 that's been working pretty well for what we're

19 doing.

20 If we end up with a shorter

21 duration of exposure how well does that data

22 work for us? How well can we use it? What --
202-234-4433

Neal R. Gross & Co., Inc.

Page 72

1 how can we characterize the uncertainty we

2 have in that -- and if we do it to the point

3 where we don't have to have additional more

4 frequent monitoring, or do we need to go to

5 that approach. That's a question we're

6 wrestling with in that regard.

7 You heard in Mary Frankenberry's

8 presentation that obviously shape of the

9 exposure chemograph influences that. And we

10 are looking at ways that can we bend the

11 community water system based on whether it's

12 a flowing water body or a reservoir, and even

13 within that it's a -- you know, how big is the

14 basin size, how big is the use -- how great is

15 the use within that basin size. So we're

16 going to be taking a look at that in ways

17 that -- once again, it's helping us be smarter

18 in where -- in how we're going to do.

19 And the complexity of the approach

20 is going to depend on the quality of the

21 monitoring data. When I first started doing

22 this work we were really criticized as you
202-234-4433

Neal R. Gross & Co., Inc.

Page 73

1 guys are using deterministic models that

2 aren't real that generate exposures that don't

3 really happen. We did it for screening

4 purposes.

5 I would argue that when you've got

6 monitoring data that -- of any sample

7 frequency and when you're trying to interpret

8 that you're applying a model to interpret that

9 monitoring data. And some of the models can

10 be fairly simplistic; some of them are going

11 to be more complex.

12 All of them are going to have some

13 degree of wrongness in terms of estimating

14 your exposure. To the extent that we can

15 characterize the uncertainty in that that

16 helps us when we're making our decisions on

17 the assessment.

18 So you're going to see a question

19 I have later is how simple is -- when it

20 simple good enough. That's what we're taking

21 a look at -- you know, when can we live with

22 that and when do we need to go more complex.
202-234-4433

Neal R. Gross & Co., Inc.

Page 74

1 So here's what we plan to do. And

2 I think I want to emphasize a few things. And

3 Dr. Lee and others pointed this out. 

4 Whenever -- and particularly in the paper we

5 started looking at the community water systems

6 to start taking a look at how we might apply

7 this. And, once again, this wasn't the

8 drinking water exposure assessment itself. 

9 It's let's look at methods that we can use to

10 apply to interpret that exposure assessment.

11 And so, you know, we're already

12 starting out with a data set that's weekly

13 sampling. And when I gave you my presentation

14 and I started saying, well, let's try -- I

15 want to try to illustrate what this means. 

16 The first thing I did is I looked at that

17 weekly -- that data -- interpolated weekly

18 data set and said, All right, now if I show

19 you an example of weekly sampling then I've

20 got the same data set you have there.

21 So I went back to use what we had

22 the monitoring on the ecological exposure data
202-234-4433

Neal R. Gross & Co., Inc.

Page 75

1 set which is much more robust in sampling

2 frequency to illustrate that point. We know

3 that for the most part those are going to

4 occur farther up in the watershed. We tend to

5 expect higher concentrations from those

6 particular sites than we're going to find in

7 community water systems further downstream. 

8 But it still gives us an idea of how can we

9 put together an approach that we can then

10 apply to the community water systems.

11 So we're going to take a look at

12 robust monitoring data sets. The Heidelberg

13 University has a lot of intensive sampling in

14 a number of streams in varying -- with varying

15 basins that we'll be able to look at. We're

16 going to be looking at several of the

17 ecological exposure monitoring sites that had

18 more intensive sampling. And the idea is to

19 evaluate the approach that we will then apply

20 to how well is the community water system data

21 working.

22 We're going to take a look at
202-234-4433

Neal R. Gross & Co., Inc.

Page 76

1 sampling these data sets at different

2 intervals. We'll use anywhere from three,

3 seven, 14, 30-day sampling. So some of the

4 discussions you've had before is how well do

5 some of these models perform when you have

6 less frequent monitoring, when your chemograph

7 shape from the monitoring data isn't as well

8 defined. So we're going to be taking a look

9 at that.

10 We're going to use -- then use as

11 the approaches that we described in Section

12 5.4 of our white paper we presented here any

13 other things that you folks think maybe we

14 need to take a look at in doing that

15 evaluation. And we're going to see to use

16 that to evaluate how well these different

17 approaches estimate exposures between sampling

18 points and generate exposures for varying

19 rolling average periods.

20 Right now for atrazine we're

21 working with a 90-day exposure period -- is

22 our exposure period of concern. We may end up
202-234-4433

Neal R. Gross & Co., Inc.

Page 77

1 looking at a shorter duration of exposure. 

2 Now we don't know yet -- this is part of what

3 we'll be hearing in this SAP and probably

4 continuing into September. If we have a much

5 shorter duration how well can we use the

6 existing weekly monitoring data. That's one

7 of the things we're trying to evaluate out of

8 that.

9 So when ultimately we come up with

10 an exposure duration of concern we can say,

11 all right, this is what we need to do to

12 provide your exposure estimates on that with

13 whatever confidence bounds we need to place on

14 that. 

15 And so our ultimate focus is on

16 taking into account the spatial, temporal,

17 and, in particular in this case, the temporal

18 nature of the pesticide concentrations to

19 answer those two questions you see at the

20 bottom -- how frequently does sampling need to

21 be in order to adequately characterize various

22 exposure durations with a specified confidence
202-234-4433

Neal R. Gross & Co., Inc.

Page 78

1 interval and how much can we derive from

2 existing less frequent sampling.

3 We're applying this to the

4 atrazine in community water system data. We

5 think this type of question we wrestle with

6 with other pesticides as well. And, to be

7 honest with you, we wrestle with it not just

8 for drinking water exposures but for

9 ecological exposures. And so these are things

10 that -- we're focusing on atrazine but have

11 applications beyond atrazine. And so we're

12 aware of that and we see the value of this not

13 just for atrazine but going beyond.

14 We have four questions that you

15 will be getting to later in the week. I tried

16 to simplify these into two main issues for you

17 to look at. Given the nature of the temporal

18 patterns, in this case atrazine in surface

19 water, but pesticide currents in general --

20 how do we best develop the confidence bounds

21 and exposure estimates for monitoring data of

22 different sampling frequencies.
202-234-4433

Neal R. Gross & Co., Inc.

Page 79

1 Now, a converse of this would be

2 if you're designing a monitoring study to

3 provide exposure estimates with -- for certain

4 duration how frequently do you have to sample

5 to give a level of confidence. So that's

6 where a couple of our questions are coming

7 from on that.

8 We presented approaches to you

9 that are common in literature. We've already

10 heard a couple of suggestions that we haven't

11 looked at yet and we may want to look at. And

12 so the questions we want you to -- as you look

13 at those approaches or any others what are the

14 advantages and disadvantages in -- for

15 estimating pesticide concentrations for either

16 short or moderate or long duration of

17 exposure.

18 And it comes down to that

19 question. When is simple good enough and when

20 do we need to look at more complex methods? 

21 And I think -- yes, you've heard enough of me. 

22 SESSION CHAIR PORTIER: Dr. Young? 
202-234-4433

Neal R. Gross & Co., Inc.

Page 80

1 DR. YOUNG: So you know in

2 designing a study there's often this tradeoff

3 in what we'd like and what we can actually do. 

4 Okay? So how much -- is there room for

5 tradeoff in one day would be good but we can't

6 really do that as well as what we would need

7 to to actually talk about one-day maximum. So

8 seven day -- is that part of this discussion

9 or not? You understand my question?

10 MR. THURMAN: I think I do, but

11 I'm going to respond to you. And if I'm wrong

12 you can say, No, that's not what I meant. I

13 think -- you know, for a lot of it we know

14 we're not going to get sampling, for the most

15 part, if it's intense enough to give you daily

16 peak. And we know that you're going to need

17 less frequent sampling.

18 And so I think there are tradeoffs

19 there. And this is where to the extent that

20 through an uncertainty analysis or through

21 supplemental modeling can we at least get an

22 idea of what that peak is given what we have? 
202-234-4433

Neal R. Gross & Co., Inc.

Page 81

1 I'm not sure if that's what you're talking

2 about and I may be off -- 

3 DR. YOUNG: Yes. And so what if

4 the answer's no? I mean, just what if. I'm

5 not saying it will be, but what if it's no? 

6 Does -- is there room for discussion of

7 broadening that to get -- so that at least

8 you're getting what you say you're getting?

9 MR. THURMAN: And that becomes a

10 conversation not just with us on the exposure

11 end but the folks in the tox end and our risk

12 managers as well. I mean, so I think

13 that's -- 

14 DR. LOWIT: That's basically what

15 I was going to add to this. I said from the

16 beginning yesterday this is really a rubber

17 meets the road analysis, and you're asking a

18 rubber meets the road question of a reality. 

19 What is the real situation of daily sampling

20 throughout the Midwest in every water system

21 is not going to happen. 

22 So the first question is: is what
202-234-4433

Neal R. Gross & Co., Inc.

Page 82

1 Syngenta is doing right now enough if it --

2 and what is the relevant confidence in that

3 and uncertainty in that. If the degree of

4 uncertainty is very high is there a way to get

5 a handle on that and how will more frequent

6 sampling improve that? The balance comes in

7 with the hazard assessment piece, the risk

8 managers, the cost -- all of that goes into

9 the decision process.

10 So the degree to which it's

11 possible to fully understand all those

12 components you can make a more full discussion

13 of the pros and the cons of a lot of

14 monitoring, smaller amounts of monitoring --

15 and then when you blend that with the hazard

16 assessment to come to the closest to ideal

17 that we possibly can. So the degree to

18 which -- I guess I don't really have anything

19 else.

20 MR. THURMAN: No, but this is --

21 honestly this is why we're here. And this is

22 what -- we want -- scientifically how far can
202-234-4433

Neal R. Gross & Co., Inc.

Page 83

1 we go -- how far can we take this. And your

2 feedback will then lead us to whatever

3 discussion we need to do in terms of -- Okay,

4 we can only go this far -- how do we deal with

5 that. And we want your honest feedback on

6 that so when we have that discussion we know

7 where we can go.

8 SESSION CHAIR PORTIER: Dr.

9 Heeringa?

10 CHAIR HEERINGA: I asked about data

11 earlier, Nelson. The other part in designing

12 any statistical process costs and technologies

13 come into play here. And I'm not sure I

14 understand -- and maybe it would be helpful

15 for the panel to understand -- where the costs

16 enter in these water samples. Are they in the

17 labor to physically visit the site each day or

18 each sampling point, pick the water sample and

19 transport it? Is it in the assay?

20 The reason I ask is that, you

21 know, one strategy which is hinted at here is

22 for more intensive sampling during periods of
202-234-4433

Neal R. Gross & Co., Inc.

Page 84

1 sort of high runoff and after application. 

2 And that's just intuitive from a lot of

3 statistical arguments.

4 But the cost of doing that -- I

5 mean, is it the assay? Is it the physical

6 labor -- you know, the assistance to do this

7 collection? Do you have a sense of where that

8 cost structure -- how that breaks down?

9 MR. THURMAN: I'm sure the USGS

10 and Syngenta as well can give you a much

11 better -- but I know there is a substantial

12 cost in physically going out, visiting the

13 site, taking the sample, transporting it back. 

14 So there is a substantial cost involved in

15 doing that in that regard.

16 CHAIR HEERINGA: Hopefully we can

17 hear from them. And the other thing I think

18 when we looked at the ecological side, in

19 addition to the point sampling -- the time

20 specific sampling -- there are integrated

21 samplers too, are there not? 

22 MR. THURMAN: That's correct.
202-234-4433

Neal R. Gross & Co., Inc.

Page 85

1 CHAIR HEERINGA: And from a

2 statistical standpoint the ability to sort of

3 integrate concentration over a period of time,

4 while it doesn't give you the profile it at

5 least gives you the mass. And that might be

6 actually useful in estimation. And I don't

7 think Dr. Lee and Dr. Young have been thinking

8 about this, but I think it would be useful if

9 we can have a little description of what those

10 measurement technologies and what those costs

11 look like as we think about this process.

12 MR. THURMAN: Yes, that's correct. 

13 And I know that the technology is improving in

14 water sampling in general and that there are

15 auto-samplers that will take sips over time,

16 and so you can get an integrated sample over

17 a time period.

18 Some of them will do this for a

19 number of periods. The samples will be stored

20 so that it reduces the number of trips you

21 have to make. So I -- and I think as Syngenta

22 has continued with the ecological monitoring
202-234-4433

Neal R. Gross & Co., Inc.

Page 86

1 they've gone more toward these because it

2 does -- there is a savings involved in that.

3 SESSION CHAIR PORTIER: Dr.

4 Gilliom?

5 DR. GILLIOM: I have a few

6 thoughts on those other things I can bring out

7 later, but not now I don't think. On the --

8 an issue we haven't talked about that I think

9 I'm interested in in having the biological

10 side of the panel understand, but also

11 understand from EPA how we might need to

12 factor this into sampling design evaluation,

13 is the whole area of co-occurrence with other

14 contaminants and mixtures.

15 It's kind of unrealistic in these

16 agricultural systems to isolate out one

17 chemical like atrazine and its degradants in

18 terms of exposure. And so if we're looking at

19 the sampling design problems and there's going

20 to be a need to even take the simplest case of

21 combining it with simazine which is part of

22 the total chlorinated triazine group it has
202-234-4433

Neal R. Gross & Co., Inc.

Page 87

1 different seasonal patterns than atrazine. 

2 And those are changing, in fact, as cropping

3 patterns change and GMO crops come in and

4 herbicide use patterns change.

5 So one of the things I was

6 thinking about as we look at our charge, which

7 is really to help give you guys a roadmap to

8 a decision making process for coming up with

9 monitoring we're not given a specific target

10 like we need the five-day moving average or

11 the one day. Ours is -- our charge is more --

12 given these circumstances what process should

13 we follow as an agency to come up with an

14 approach.

15 Well, you have more to deal with

16 than just atrazine, of course. And some of

17 the specific toxicological related studies

18 maybe we're talking about this week are

19 specific to atrazine. But then in the next

20 panel, which is also relevant to the

21 monitoring design problem, you're going to be

22 talking about the epidemiological studies more
202-234-4433

Neal R. Gross & Co., Inc.

Page 88

1 in there. 

2 And there's going to be a lot more

3 issues come up about how to separate out the

4 potential co-occurrence effects -- just simple

5 ones like there's nitrate in atrazine -- does

6 that interact to have a different concern than

7 just atrazine alone or does atrazine with

8 fungicides or whatever.

9 And the point being is that when

10 we look at the sampling design needs for that

11 it may totally change how we look at what

12 knobs are really -- need to be turned to get

13 the right frequency and the right moving

14 average values through the year. So we may

15 look at atrazine and say, Well, if we know we

16 need a seven-day moving average for atrazine

17 we can dial that right into a three-month

18 period and come up with a really efficient

19 design. 

20 We could even go into the sampling

21 tricks. We could do some -- since we're

22 moving average sampling composite sampling and
202-234-4433

Neal R. Gross & Co., Inc.

Page 89

1 use amino assays that are cheap and screen and

2 then analyze. But if the problem goes beyond

3 atrazine and it has to be captured in the same

4 monitoring thing then it changes how we

5 approach it.

6 So I guess it's a concern I have

7 to the process of how we give guidance on

8 monitoring design, but it's also kind of a

9 question of the rest of you looking at the

10 biology as we go through this how comfortable

11 are we with limiting the scope to atrazine

12 with making these kinds of decision.

13 MR. THURMAN: I just -- I want to

14 add one thing to what you were saying, Bob. 

15 When we're looking at community water system

16 monitoring they actually measure for total

17 chlorotriazines. And some of the figures

18 we've seen labeled atrazine -- and we do have

19 the atrazine measurements as well as simazine

20 measurements, but we also have total

21 chlorotriazines, and that's ultimately what

22 the drinking water assessment will be based
202-234-4433

Neal R. Gross & Co., Inc.

Page 90

1 on.

2 But you made some other points

3 that -- but as long as everyone knows we do

4 have -- for the drinking water exposure we are

5 considering total chlorotriazines.

6 DR. GILLIOM: I guess just one

7 follow up that I lose track of once in a while

8 too is that this monitoring design and purpose

9 is in supplement to the regular Safe Drinking

10 Water Act monitoring. So it's -- people may

11 not always realize that, that you have every

12 system being done quarterly just for the

13 regulated compound atrazine, and then you have

14 this special program layered onto it that's

15 required by FIFRA that Steve explained

16 yesterday what the trigger points are and

17 everything.

18 So I'm assuming that that is still

19 the conceptual model you're moving ahead with. 

20 There's kind of a double monitoring plan.

21 MR. THURMAN: That is correct.

22 SESSION CHAIR PORTIER: Very good. 
202-234-4433

Neal R. Gross & Co., Inc.

Page 91

1 Dr. Coupe?

2 DR. COUPE: Yes. Thank you. Hey,

3 I was just curious. I heard two of you

4 mention already that daily sampling is off the

5 table or whatever, but why would that be if

6 you can't answer the question as to what's the

7 cost of the collection or what-not? What if

8 human health says, you know, you need to

9 collect daily and that's what has to be. No

10 offense to the statisticians, but it's a

11 model, and all models are wrong -- some are

12 useful maybe.

13 DR. BRADBURY: Sometimes. Always

14 useful but how you interpret them. This is

15 Steve Bradbury speaking. I would suggest you

16 don't constrain yourself in terms of sampling

17 design frequency, different scenarios of how

18 you may have embedded sampling within a

19 different sampling design. Do what you think

20 is right in terms of the scientific

21 perspectives that we should consider.

22 At some point we'll have to decide
202-234-4433

Neal R. Gross & Co., Inc.

Page 92

1 how to implement the scientific perspectives

2 that you raise or you offer to us. I wouldn't

3 constrain yourself. I think Dr. Lowit though

4 made a good point yesterday when we were

5 talking about some of the toxicology, and it

6 would apply to the sampling design too. 

7 As you describe different

8 approaches that we should consider we would

9 respectfully request you also be very clear on

10 what it is -- what's the question that you

11 think we should have and that we should come

12 up with an answer for and perspectives on how

13 good an answer can be with different

14 approaches.

15 Because we also want to ensure

16 that we're focusing on issues that we need to

17 get resolved -- a resolution to. And with all

18 due respect to my previous history, as well as

19 my colleagues in the room, we're talking about

20 issues here that aren't in a research mode. 

21 We will learn things and will contribute to

22 research, but we'd also like a focus on what
202-234-4433

Neal R. Gross & Co., Inc.

Page 93

1 are the issues that we can take on tomorrow,

2 six months from now, 12 months from now which

3 will certainly contribute to the broad

4 scientific knowledge, but ought to focus on

5 real decisions that we have to be making in

6 real time. 

7 And we'll never know everything. 

8 And so it's how to make informed decisions

9 with what we do know. And some of Nelson's

10 comments and Anna's comments on helping us

11 think about uncertainty and variability

12 interpretation will be critical.

13 But the bottom line is don't

14 constrain yourself in terms of where you need

15 to go, but offer some insights into the

16 perspectives in terms of the range of options

17 that you may consider.

18 SESSION CHAIR PORTIER: Okay. I

19 think we're ready for the overall summary and

20 conclusion. Dr. Lowit.

21 DR. LOWIT: I don't have a slide

22 and I won't say much. I thought it just would
202-234-4433

Neal R. Gross & Co., Inc.

Page 94

1 make sense to go back to where we started.

2 Last fall a group of us sat in

3 front of the permanent panel and announced we

4 were doing this reevaluation and we would come

5 to the SAP several times in 2010. And we told

6 you yesterday we're going to add another one

7 in 2011 -- that our initial hope had been in

8 September of this year to have a harmonized

9 weight of the evidence across cancer/non10 cancer. But our colleagues at NCI are not

11 going to have their agricultural health study

12 reconsideration done in time, so we're going

13 to separate the cancer.

14 So we still have the exact same

15 two goals today that we did back last fall --

16 that we have two pretty practical questions. 

17 And the first one has to do with drinking

18 water monitoring frequency required of

19 Syngenta. And the second one is a basic

20 question -- do we need to reopen the human

21 health risk assessment. 

22 And we're doing a lot on two major
202-234-4433

Neal R. Gross & Co., Inc.

Page 95

1 fronts that you've heard a lot about. The

2 hazard assessment component -- we're in the

3 sort of mid -- a snapshot on time on the

4 update of the literature review of the hazard

5 assessment. And the new data since the last

6 risk assessment suggests that the HPA axis

7 happens on a -- maybe on a shorter temporal

8 scale than does the data that supported the

9 2003 risk assessment.

10 And so as we think about that HPA

11 data and its temporality and how you would

12 take that and think about linking key events

13 across that pathway of toxicity -- how to link

14 those together to -- across time and across

15 dose to think about the relationship of one to

16 the other and how to translate that -- the

17 temporal component of that into the drinking

18 water frequency, which we've talked a lot

19 about this morning and I won't reiterate.

20 And part of that hazard assessment

21 is to rethink things like the points of

22 departure, the uncertainty factors, the --
202-234-4433

Neal R. Gross & Co., Inc.

Page 96

1 whether it's a rolling average or some other

2 dose metric. So we're really looking forward

3 to the discussion over the next few days. I

4 guess that's -- do you have anything else? I

5 think that's it.

6 SESSION CHAIR PORTIER: Very well. 

7 I think at this point we'll take a 15-minute

8 break. I have 10:08. We'll return at 10:25.

9 MR. BAILEY: If I could ask all

10 the people who are making public comments to

11 please come up here and check with us so we

12 can get your files loaded into the laptop

13 ready to go. Thanks.

14 (Whereupon, the above-entitled

15 matter went off the record at 10:09 a.m. and

16 resumed at 10:27 a.m.)

17 SESSION CHAIR PORTIER: Okay. 

18 Let's begin. Before lunch we're going to have

19 presentations first by the Triazine Network

20 with a Mr. White, Laura Knoth, and Richard

21 Fawcett. And following that we're going to

22 have beginnings of the presentation from
202-234-4433

Neal R. Gross & Co., Inc.

Page 97

1 Syngenta of crop protection. So we'll start

2 with Mr. White.

3 MR. WHITE: Thank you, Mr.

4 Chairman, members of the Committee. My name

5 is Jere White. I live in east central Kansas. 

6 I'm the executive director of the Kansas Corn

7 Growers Association and also Kansas Grain

8 Sorghum Producers. And if you're not from the

9 Midwest you probably have no idea what grain

10 sorghum is. But it's a grain crop actually

11 similar to corn in some ways and, yet, very

12 different. I'm here on behalf of Kansas

13 farmers.

14 I also serve as chairman of the

15 Triazine Network. The network was formed in

16 1995 as a response by the grower community to

17 the EPA announcement of a special review and

18 the reregistration efforts.

19 We represent over 30 commodities

20 in over 40 states. Most of our members are

21 organizations like my own, but also includes

22 many individuals farmers. Network membership
202-234-4433

Neal R. Gross & Co., Inc.

Page 98

1 encompasses farm groups literally from border

2 to border and sea to sea -- in fact, some

3 across some seas. We have an executive

4 committee that's formed from states like

5 Kansas, Missouri, Florida, California, and

6 Hawaii.

7 We certainly are a very diverse

8 group focused on a single outcome. And our

9 objective has always been to ensure that EPA

10 understands the value and the utility of

11 atrazine. We also have assumed that role with

12 the registrants because, as you can imagine a

13 long process like this, it's not only

14 important that the people regulating the

15 product understand its value it's important

16 that the people that are producing the

17 product -- and I might add an off-patent

18 product -- understand the value to farmers. 

19 And so we've engaged on all points on that.

20 But the fact is that atrazine has

21 been the foundation of our weed control

22 programs for 50 years now. We know the
202-234-4433

Neal R. Gross & Co., Inc.

Page 99

1 product well. We know how to steward atrazine

2 in a way that provides safety for ourselves in

3 the environment. And that's the environment

4 that we live and farm in.

5 I'm going to turn over comments

6 now to Laura Knoth. Laura is the executive

7 director of Kentucky Corn Growers, and then

8 she'll pass the baton to Dr. Fawcett, and then

9 I'll wrap up. Laura?

10 MS. KNOTH: Thank you. Thanks,

11 Jere. Mr. Chairman and members of the panel,

12 as Jere said, my name is Laura Knoth. I'm the

13 executive director for the Kentucky Corn

14 Growers and Kentucky Small Grain Growers in

15 Kentucky.

16 I grew up on a Kentucky farm that

17 I still own and operate there today. 

18 Graduated the University of Kentucky with an

19 agriculture degree, natural resources major. 

20 I started my career as an environmental

21 consultant and then came home to agriculture. 

22 I wanted to just talk today about
202-234-4433

Neal R. Gross & Co., Inc.

Page 100

1 stewardship and conservation as it relates to

2 atrazine. And please pardon nervousness. 

3 This is obviously my first advisory panel.

4 It's because American agriculture

5 and American farmers are dependent upon the

6 integrity of their soil and other natural

7 resources for their livelihood that they work

8 to protect and improve the land. As a result

9 significant benefits to society have been

10 achieved and improvements and efficiencies

11 will continue to lessen the environmental

12 impacts of food production.

13 Crops cannot be produced without

14 disturbing the soil in some way. Tillage is

15 the farmer's way of preparing the ground for

16 planting, for breaking it up and smoothing it

17 out. Tillage also helps control weeds and

18 aerates the soil. Yet there are consequences

19 to tillage: Rain and wind carry loosened soil

20 off of the fields, adding silt to the

21 waterways and particulate matter in the air. 

22 Old school cultivating with a moldboard plow
202-234-4433

Neal R. Gross & Co., Inc.

Page 101

1 had even greater impacts on soil and water.

2 In today's agriculture when

3 tillage is necessary farmers have adapted from

4 the historical tillage practices of intensive

5 soil disruptive for weed control to simpler

6 practices, such as conservation tillage, which

7 minimizes soil disturbance. 

8 By leaving the crop residue on the

9 field for cover and eliminating additional

10 trips across the field for tilling farmers are

11 better able to protect the soil from water and

12 wind erosion. It conserves moisture, reduces

13 runoff, and improves the wildlife habitat, and

14 it limits our output as farmers of labor,

15 fuel, and machinery to produce a crop.

16 Several crop production systems

17 fall under the heading of conservation

18 tillage, including no till, ridge till, low

19 till, and minimum till. But common to all of

20 these is a crop mulch covering left on the

21 ground to provide a protective cover to the

22 soil between the seasons. And it improves the
202-234-4433

Neal R. Gross & Co., Inc.

Page 102

1 soil fertility by maintaining that nutrient

2 rich organic matter on the field.

3 Conservation tillage allows

4 organic matter to build up in the soil,

5 absorbing carbon dioxide, and reducing a

6 significant amount of greenhouse gas. Just

7 like our lawns and gardens, compost and mulch

8 are good things in agriculture.

9 As a result of increasing adoption

10 of conservation tillage and other soil

11 conservation practices soil erosion from U.S.

12 crop land has steadily declined. A natural

13 resources inventory report from 2007 published

14 by the Natural Resources Conservation Service

15 states that soil erosion resulting from

16 rainfall and runoff declined 42 percent

17 between 1982 and 2003. And, likewise, soil

18 erosion from high winds declined 44 percent

19 during that same time frame.

20 In 2008 atrazine was applied to

21 over 650 percent of the conservation and no

22 till corn acres. As America's original
202-234-4433

Neal R. Gross & Co., Inc.

Page 103

1 stewards of the environment growers value

2 atrazine for its capability with conservation

3 tillage. 

4 The ability of atrazine to move

5 off the surface crop residue of the top soil

6 to where the weeds germinate -- it moves down

7 into the soil as well as the length of that

8 weed control that makes atrazine the tool of

9 choice for no till farmers and adopters of

10 other conservation tillage practices.

11 Effective weed control -- it's not

12 a luxury. It's a necessity in conservation

13 tillage systems. So without herbicide use no

14 till agriculture becomes impossible. And

15 that -- you know, that causes additional

16 increased erosion estimated to be over 300

17 billion pounds of soil annually.

18 Much of this soil erosion would

19 enter waterways. Sediment is the largest

20 contaminate of surface water by weight and

21 volume. And it's identified by the states as

22 the leading pollution problem in rivers and
202-234-4433

Neal R. Gross & Co., Inc.

Page 104

1 streams and the fourth leading problem in

2 lakes. Sediment in surface water is largely

3 a result of soil erosion, which is influenced

4 by soil properties, and for agriculture the

5 production practices that farmers choose.

6 Farmers and industries have

7 heavily invested in research on BMPs for

8 pesticides, including atrazine. Stewardship

9 requirements on the label for atrazine are

10 unique. And they are not typical with other

11 pesticides. BMPs effective in reducing runoff

12 of atrazine into surface water, including

13 those conservation tillage practices, buffer

14 strips, vegetative filter strips, terraces,

15 contour planning, post-emergence application,

16 and mechanical incorporation. 

17 It should be noted that atrazine

18 BMPs and stewardship provide benefits, not

19 just for atrazine but for a variety of other

20 concerns, including nutrients and other

21 pesticides. BMPs and other stewardship

22 efforts used by farmers often depend on site
202-234-4433

Neal R. Gross & Co., Inc.

Page 105

1 specific conditions. And on our farms

2 stewardship is -- occurs every day. It's an

3 important part of continuing to be able to

4 farm.

5 I want to thank the panel for its

6 deliberations this week. As I said, this is

7 my first SAP and I have to say the volume of

8 science put in front of you all seems

9 staggering. So good luck on your

10 deliberations. I wish you well. At this time

11 I'll turn it over to Dr. Fawcett.

12 DR. FAWCETT: Thank you, Laura. 

13 My name is Richard Fawcett, and I'm here

14 appearing on behalf of the Triazine Network.

15 My background is as a weed

16 scientist and also working in water quality. 

17 I've done a lot of work in developing best

18 management practices and being involved in

19 educational efforts to try to get those on the

20 land.

21 I appreciate the opportunity to

22 share some information about atrazine use with
202-234-4433

Neal R. Gross & Co., Inc.

Page 106

1 this SAP panel that has a very important task. 

2 And it's very important to weigh carefully,

3 because atrazine remains a very important tool

4 to American agriculture.

5 It may seem surprising a product

6 that's been around for 50 years is so widely

7 used. But the reason it's so widely used is

8 that it works so well. It works better than

9 alternatives or, maybe better said, it makes

10 alternatives work better.

11 It's interesting when a new

12 herbicide active ingredient for corn is

13 registered and hits the market the first thing

14 that usually happens is it is marketed with a

15 combination of atrazine as a prepackaged

16 product or labeled as a tank mix in

17 combination with atrazine because usually a

18 relatively low rate of atrazine improves that

19 product; controls weeds maybe missed by the

20 alternative herbicide.

21 We've heard about the

22 environmental benefits that atrazine's
202-234-4433

Neal R. Gross & Co., Inc.

Page 107

1 important and with conservation tillage. 

2 Atrazine is also an essential tool in

3 herbicide resistance management that has

4 become so important as herbicide tolerant

5 crops have been planted over such a large

6 acreage.

7 We scientists routinely recommend

8 to farmers that they do not use the same

9 herbicide mode of action alone year after year

10 after year because that brings on weed --

11 herbicide resistant weeds. And atrazine is

12 very often in corn. That alternative mode of

13 action is used to manage weed resistance. 

14 I've been involved in several

15 analyses of very large databases to exam the

16 hypothesis that there is a yield benefit from

17 using atrazine. I'd like to briefly share a

18 little bit of that with you. There are

19 actually two ten-year analyses over a 20-year

20 period where we used the research report of

21 the North Central Weed Science Society as a

22 source of studies. 
202-234-4433

Neal R. Gross & Co., Inc.

Page 108

1 The North Central Weed Science

2 Society published the research report as an

3 outlet for universities for their annual

4 progress report. And it's by far the largest

5 published source of the herbicide efficacy

6 trials. 

7 In comparing treatments that

8 either had atrazine or atrazine alternatives

9 we haven't -- be very careful that they were

10 logical comparisons or fair comparisons. The

11 treatments had to have -- they had to control

12 both broadleaves and grass and so usually had

13 to have at least two active ingredients. They

14 had to be registered for use at the time of

15 the analysis -- they have been experimental

16 when the study was done. They had to be used

17 as registered rates. So there's a lot of

18 criteria that the studies and treatments had

19 to meet.

20 But over the period of 1986 to

21 `95, the first analysis that was done in

22 response to the special review for atrazine --
202-234-4433

Neal R. Gross & Co., Inc.

Page 109

1 over that time period atrazine -- treatments

2 containing atrazine yielded 6.3 bushels or 5.9

3 percent higher than alternatives -- treatments

4 without atrazine.

5 After ten years had passed to see

6 whether that benefit still remained we went

7 back to that research report, used the same

8 methods, and analyzed another ten years of

9 data from -- see you get the years -- I guess

10 it would be '96 to 2005. During that time

11 period the atrazine treatments yielded 5.4

12 bushels or 4.6 percent higher -- very similar

13 benefit despite the introduction of many new

14 herbicide active ingredients, new technologies

15 like herbicide tolerant crops -- that benefit

16 was really rather surprisingly constant. And

17 there were 236 studies and 5,811 treatments in

18 those analyses -- very, very large database.

19 Well, after 62 years of

20 publication the North Central Weed Science

21 Society terminated their publication of the

22 research report in 2006 for budgetary reasons. 
202-234-4433

Neal R. Gross & Co., Inc.

Page 110

1 And so in order to look at the most recent

2 years and do a similar analysis we had to come

3 up with a different way of getting studies.

4 We started by contacting all the

5 states that traditionally submitted studies to

6 the research report. And this is, again,

7 north center -- primarily corn belt. And we

8 got studies directly from them, often from

9 websites, sometimes directly from the

10 universities.

11 But to try to go on a broader

12 scale to get more data and actually to look at

13 regions outside the corn belt we also utilized

14 an electronic database, so it's Syngenta crop

15 protection have that contain mainly university

16 studies but also some company-sponsored

17 studies.

18 We actually analyzed those

19 separately -- took out the Syngenta studies

20 and we was -- actually there was less benefit

21 in the Syngenta studies than the university

22 studies. So we used the entire database. We
202-234-4433

Neal R. Gross & Co., Inc.

Page 111

1 wanted to make sure there wasn't any bias

2 there. We used the entire database.

3 For the three years 2006 to 2008

4 there were 310 qualifying studies of 4,293

5 treatments -- a huge stack data -- very large

6 analysis. There were 24 U.S. universities in

7 22 states that represent all the major corn

8 growing regions that did over 80 percent of

9 the studies. So for that three-year time

10 period corn yielded an average 5.3 bushels or

11 3.5 percent higher -- again, a very similar

12 benefit that remains despite all the new

13 technologies. And these -- all these were

14 statistics that we analyzed. They were

15 significant by t-test and Wilcoxton signed16 rank test.

17 Thus, despite the introduction of

18 many new herbicide active ingredients, new

19 technologies like herbicide tolerant crops,

20 the benefit of atrazine remains. It's the

21 most -- it's also cost effective. When you

22 consider both the lower cost and the yield
202-234-4433

Neal R. Gross & Co., Inc.

Page 112

1 increase the benefit from atrazine remains

2 over $30 per acre.

3 But besides just an economic

4 benefit we've heard about the environmental

5 benefits from the conservation tillage. And

6 one thing I am concerned about is when you

7 survey farmers and ask them if they're

8 considering to go to conservation tillage what

9 their biggest worry is, is always weed

10 control. They're worried that if they give up

11 that tried and true tillage method that they

12 won't be able to control weeds. 

13 If atrazine were not available I'm

14 afraid we would backslide. Some of the

15 farmers that are in conservation tillage would

16 start doing more tillage. Some of the farmers

17 considering that shift would not make that

18 shift.

19 Atrazine remains a valuable tool

20 in agriculture, producing both economic and

21 environmental benefits. With that I'd like to

22 shift it back to Jere White and have him make
202-234-4433

Neal R. Gross & Co., Inc.

Page 113

1 some closing comments.

2 MR. WHITE: Thanks, Dick. As of

3 now we've participated in every SAP concerning

4 atrazine since the beginning of this special

5 review in 1994. We missed the 1988 --

6 obviously special review was not in place, but

7 I must say my total of SAPs regarding atrazine

8 was somewhat higher than what was reported to

9 the panel yesterday. And I hope my wife

10 doesn't read the report because I've told her

11 I've been going to these SAPs in 2007, and I

12 don't think any of those were on the list.

13 But, regardless, we do want the

14 panel to know that although we see the value

15 and we understand the benefits and we've

16 shared them with EPA and actually EPA I think

17 has incorporated a lot of our work into their

18 analysis of benefits of the product.

19 We also understand that there are

20 other issues. And we certainly do not

21 casually dismiss activists' claims of harm

22 from the use of atrazine, but we do insist
202-234-4433

Neal R. Gross & Co., Inc.

Page 114

1 that sound scientific weight of evidence

2 prevail on regard to regulatory action further

3 lending its use.

4 In the course of time in my career

5 that I've been involved with atrazine we've

6 seen significant changes in labeled use rate. 

7 There is stewardship incorporated into the

8 labels, and, quite frankly, agriculture has

9 adapted and the product has maintained its

10 value.

11 Obviously there are issues we need

12 to be concerned. We're the primary handlers

13 of the product. Our kids and our grandkids

14 are a part of our farms, they're a part of our

15 land, they're a part of our lives. And we

16 need safe tools in our toolbox. And we

17 believe atrazine because of our experience and

18 simply -- you know, to put it -- simply put,

19 because of our participation in now 16 years

20 of scientific review we believe atrazine to be

21 one of the safest tools that we have in our

22 toolbox. That's important to us.
202-234-4433

Neal R. Gross & Co., Inc.

Page 115

1 Personally, I do understand the

2 implications of cancer. I buried my father a

3 few years ago after he lost his fight with

4 prostate cancer. Don't take it lightly. 

5 Cancer strikes unique fear in all of us, and

6 certainly I'm not exception.

7 I've also had experience with

8 birth defects. My first grandchild was born

9 still because he suffered severe developmental

10 birth defects. And so, you know, even though

11 there was no implications of pesticide

12 exposure or anything like that in that case

13 it's -- we understand what these tragedies in

14 life are. And if they were going to affect

15 anyone we would expect them to affect

16 ourselves and our families. And we do not

17 take it lightly at all.

18 If we believed that these were

19 scientifically weight of evidence issues that

20 we should be considering or that we believed

21 we wouldn't be here today. We don't believe

22 that.
202-234-4433

Neal R. Gross & Co., Inc.

Page 116

1 And, quite frankly, the EPA did

2 not believe it until the New York Times and

3 Huffington Post supplied their version of the

4 peer review to an NRDC report last summer. 

5 And that report was taken on by certain

6 political appointees at EPA. 

7 And you can simply go back to what

8 EPA was posting on its own website in July of

9 last year. Upon reregistration in 2006 EPA

10 determined that triazine herbicides post no

11 harm that would result in the general U.S.

12 population, infants, children, or other,

13 including consumers.

14 I know this process did not begin

15 a year ago, as was inferred yesterday, to take

16 a look at the new reports and the new science

17 that's out there. We're not opposed to that

18 obviously. If there's legitimate concerns

19 that surface they could surface at any time. 

20 You could finish a review one day. The next

21 day you could have issues. But there is a

22 certain amount of process that certainly comes
202-234-4433

Neal R. Gross & Co., Inc.

Page 117

1 under question at this point.

2 From the growers' standpoints our

3 growers often ask when is enough enough. You

4 know, take the case of water. Atrazine

5 certainly has the most comprehensive database

6 of any pesticide. Is it enough? I mean, the

7 discussions this morning -- at some point

8 people involved in science and policy have to

9 make determinations. But, certainly, when you

10 look at the substantial data on atrazine

11 compared to anything else that you look at out

12 there -- and I would argue any contaminant the

13 water system looks at -- the database is

14 certainly more robust.

15 When you further consider that the

16 levels of concern have multiple safety

17 factors -- tremendous safety factors backed by

18 decades of scientific review there seems to be

19 little doubt that the robust data -- and since

20 conservative safety factors provide confidence

21 for farmers, for regulators, and for

22 consumers.
202-234-4433

Neal R. Gross & Co., Inc.

Page 118

1 Mark Twain wrote in Life on the

2 Mississippi that there is something

3 fascinating about science. One gets such

4 wholesale returns of conjecture of such

5 trifling investment of fact. EPA, in fact,

6 has a process designed to avoid absence of

7 fact, and that's what this process is.

8 This week, however, you have 123

9 studies in front of you for your deliberations

10 and have less than a month to review them. 

11 For stakeholders the charge questions were

12 only released on the evening of the 8th,

13 allowing a mere eleven working days to develop

14 a response.

15 To have a re-review of atrazine

16 and stated two-thirds of the way through the

17 normal pesticide registration process when it

18 was scheduled for a 2013 registration review

19 is contrary to the process established under

20 FIFRA and not based on sound science.

21 I must give each of you special

22 thanks. You are being asked to review two
202-234-4433

Neal R. Gross & Co., Inc.

Page 119

1 completely separate disciplines in this SAP: 

2 toxicology on many fronts and drinking water

3 monitoring; requiring unusually wide span of

4 research expertise. And though the average

5 number of studies submitted for SAP review --

6 the average per session numbers around 15 --

7 the EPA has generously provided you with 123. 

8 Actually I think the number is slightly higher

9 than that.

10 And while that might just be a

11 sign of EPA's confidence in the scientific

12 capabilities of the panel -- and I'm sure it

13 is -- given the short time frame involved, it

14 does not -- it certainly does bring into

15 question the agency's definition of open and

16 transparent and inviting stakeholder

17 participation.

18 Regulators, government bodies, and

19 organizations around the world, including the

20 U.K. in 2000, Canada in 2004, WHO and FAO in

21 2007, Australia in 2008, and most recently the

22 state of Minnesota have recently given
202-234-4433

Neal R. Gross & Co., Inc.

Page 120

1 atrazine favorable reviews.

2 And despite activists' lies to the

3 contrary, no country has ever banned atrazine

4 for health concerns, none, including the

5 European Union. EU regulations do restrict

6 the use of any pesticide that has a potential

7 to occur in groundwater at amounts greater

8 than one part per billion -- or .1 part per

9 billion, but that is a political not a

10 scientific standard.

11 While EU farmers do not currently

12 use atrazine, they use a similar triazine

13 herbicide with nearly the same safety profile: 

14 terbuthylazine. But you don't hear that when

15 you read the reports or look at the news

16 articles; not because the reporters don't

17 know -- because I've told them -- but because

18 it doesn't fit into their story.

19 The continuous restatement of this

20 activist propaganda has taken on its own life

21 and has been declared as fact; probably be

22 declared as fact again before the day's over
202-234-4433

Neal R. Gross & Co., Inc.

Page 121

1 with, yet it is a falsehood.

2 Extensive monitoring show the

3 levels of atrazine in raw and finished water

4 is steadily decreasing due to a number of

5 factors, including label changes that occurred

6 in the early nineties and stewardship

7 activities on the farm which you've just heard

8 about.

9 But atrazine is one of many

10 regulated chemicals that are present in safe

11 water -- in safe amounts in our drinking

12 water. We know that it's safe because of the

13 extensive scientific body of research

14 available to us. Other chemicals like

15 fluoride added to drinking water to protect

16 our teeth and the byproducts of chlorination

17 trihalomethanes are also in our drinking

18 water, but they're there to do important jobs

19 like kill dangerous pathogens such as E. Coli. 

20 They're present in water and they're present

21 in safe amounts. However, most of these other

22 contaminants do not have the same massive body
202-234-4433

Neal R. Gross & Co., Inc.

Page 122

1 of science supporting their safe presence in

2 water as atrazine does.

3 On December 20, 2008, the

4 President-elect Obama said in his weekly

5 address, it's about ensuring facts and

6 evidence are never twisted or obscured by

7 politics or ideology. For more than 50 years

8 atrazine use has been conservatively regulated

9 and approved for use in agriculture by EPA and

10 its predecessor agencies through both

11 Democratic and Republication administrations.

12 For instance, one of the most

13 significant determinations made in the past

14 two decades regarding atrazine that atrazine

15 was not likely to be a carcinogen actually

16 occurred during the Clinton administration. 

17 The current politically driven, second

18 guessing of previous EPA and SAP decisions

19 suggest twisting and obscuring at levels that

20 should make a politician blush.

21 We would argue that those

22 decisions were not made in some black box. We
202-234-4433

Neal R. Gross & Co., Inc.

Page 123

1 are confident that the past -- that when the

2 dust settles the scientists who have been

3 working diligently for years -- many of you in

4 this room have been a part of many SAPs -- and

5 in this room today both have the agency and

6 the SAPs will be vindicated for their past

7 regulatory decisions.

8 And make no mistake about it. We

9 believe that the reviews that have been

10 done -- while we might not have agreed with

11 every turn or twist that EPA, the agency, and

12 their SAPs have made in the past collectively

13 we think it has been a very solid, very deep

14 process. It's not something that the agency

15 under any administration has taken lightly. 

16 It's not something that the

17 SAPs -- the previous SAPs have taken lightly. 

18 And, again, many of you have been involved in

19 those. And to suggest that somehow a SAP

20 process under a different administration was

21 somehow lacking I think is a disservice to

22 people that have dedicated a lot of their time
202-234-4433

Neal R. Gross & Co., Inc.

Page 124

1 and effort to this project.

2 But, having said that, we

3 certainly will roll up our sleeves. We'll be

4 back in September and we certainly look

5 forward to working with all parties as the

6 process continues. Thank you.

7 SESSION CHAIR PORTIER: Thank you. 

8 Any questions from the panel? Yes, Dr. Regal. 

9 DR. REGAL: Just is there any

10 number out there in terms of corn yields with

11 no herbicide versus corn yields with

12 herbicide?

13 DR. FAWCETT: I can't give you a

14 firm number. But just from looking at -- it

15 depends on how many weeds you have in a field. 

16 Some fields have a lot of weeds; some have

17 less. Typically in our research plots we

18 always have controls without any herbicide. 

19 They may not yield anything. They may be 20

20 bushels compared to 200 bushels for a

21 herbicide. But if you have a field that's

22 relatively clean it might yield 100 bushels. 
202-234-4433

Neal R. Gross & Co., Inc.

Page 125

1 It just depends on how many weeds are there.

2 SESSION CHAIR PORTIER: Dr.

3 Gilliom?

4 DR. GILLIOM: So is it reasonable

5 to assume given the continued need for

6 atrazine that the use should still remain

7 pretty constant in the corn belt total?

8 MR. WHITE: You know, we would

9 argue that the use has -- you know, certainly

10 it's changed in the 15, 16 years since the

11 special review was initiated. The use today

12 is really tied -- you know, if you go back 30

13 years ago atrazine might have been the main

14 component of weed control, and label grades

15 would have reflected it. They would have been

16 very high. 

17 Today it's more of a component in

18 a mixture of new chemistries. But as Dr.

19 Fawcett said the really issue is even some of

20 the most popular new chemistries like

21 glyphosate quickly found problems with weed

22 resistance. And so you have to have some
202-234-4433

Neal R. Gross & Co., Inc.

Page 126

1 alternative. And the reality is, is that, you

2 know, it's surprising to us. But the utility

3 and the need for atrazine today, although it's

4 in a different fashion, is probably just as

5 profound as it was 20 years ago or 30 years

6 ago. But it's different.

7 DR. FAWCETT: I'll just quickly

8 give an example from Iowa. Today in Iowa I

9 think atrazine's actually used on more acres

10 today than it was 20, 30 years ago. It's used

11 at lower rates, different ways, some post12 emergence and combinations. So the

13 environmental load is actually less, but it's

14 just as important, if maybe not more

15 important, than it was 20 or 30 years ago.

16 MR. WHITE: And I guess the other

17 point I'd add, in conservation tillage it is

18 absolutely critical.

19 SESSION CHAIR PORTIER: Dr.

20 Horton?

21 DR. HORTON: Yes. I have a

22 question. One of the things we're going to be
202-234-4433

Neal R. Gross & Co., Inc.

Page 127

1 considering on the biology panel is that the

2 health effects may occur at many different

3 levels and different stages of development. 

4 We may be looking at fetal periods, perinatal,

5 pubertal, adult, cancer, neurotoxicity. 

6 And so in your statement you

7 commented that the European Union safety

8 review said atrazine would not harm humans. 

9 Could you comment on what specific human

10 health effects the European Union evaluated --

11 what stages of development, which health

12 effects they were evaluating?

13 MR. WHITE: In fairness I probably

14 would need to go back and take a look at all

15 that. You know, I'm sure that those reviews

16 did not include some of the newer stuff that

17 this panel and future panels will be taking a

18 look at.

19 But I do believe that it certainly

20 included some of the things that we are

21 looking at and probably the Syngenta folks

22 might be better equipped. But I'd be happy to
202-234-4433

Neal R. Gross & Co., Inc.

Page 128

1 go back and specifically give a list of those

2 things that were reviewed. I don't know it

3 off the top of my head.

4 SESSION CHAIR PORTIER: Dr.

5 O'Byrne?

6 DR. O'BYRNE: Kevin O'Byrne. I

7 don't think any sane person would say that

8 there is not a requirement for herbicides, and

9 I get a sense that as the commitment to reduce

10 the amount of usage globally -- perhaps

11 globally.

12 But I have a simple question. The

13 application of these herbicides, are they

14 coordinated with weather forecasts and things

15 like that by the farmers?

16 MR. WHITE: Well, certainly one of

17 the BMPs that we encourage farmers to utilize

18 is if it looks like a significant rainfall

19 event's on the horizon, you don't go out and

20 spray the field. But having said that, you

21 know, Mother Nature is somewhat fickle

22 sometimes, and so you don't always know when
202-234-4433

Neal R. Gross & Co., Inc.

Page 129

1 those will occur. 

2 And having been involved in other

3 water issues in my state, I can tell you that

4 in spite of your best modeling, in spite of

5 your best efforts, there will be, you know,

6 acts of nature that will fall out of your

7 parameters. You know, I think farmers do a

8 much more precise job today. I mean, you

9 know, a lot of herbicide goes down using

10 satellites to drive the sprayer, not farmers. 

11 You know, the issues like over-applying

12 because of overlaps that were very common

13 were -- you know, a couple of decades ago are

14 somewhat unheard of on the most modern farms.

15 One of the challenges, though, is

16 that as a society we like the notion of small,

17 what we call family farms in this country. 

18 It's not -- you know, it's kind of a romantic

19 notion. But the bottom line is is that most

20 of the very smallest of farmers are the ones

21 doing the poorest job of applying herbicides

22 and things like that because they're using the
202-234-4433

Neal R. Gross & Co., Inc.

Page 130

1 economies of scale. They're not using GPS to

2 help them put the product where it needs to

3 be. They're probably using a sprayer that

4 they bought at a sale that had been used by

5 another farmer that might represent technology

6 that's 20 or 30 years old.

7 So there's -- it's like new cars. 

8 It takes a long time for the transportation

9 system to cycle through. It does the same on

10 farms. But everyone's about efficiency. And

11 the most efficient use of a herbicide is to

12 use the least amount that you need. But if

13 you use less than the least amount that you

14 need you've done a disservice because you

15 improve the chances for resistance, you've

16 spent money and you get no benefit. 

17 And there are levels -- you know,

18 you could go back -- with atrazine you can go

19 back as the labeled rate was reduced in the

20 nineties, you saw weed control come off the

21 label for different weeds just simply because

22 it wasn't efficacious anymore.
202-234-4433

Neal R. Gross & Co., Inc.

Page 131

1 SESSION CHAIR PORTIER: Dr.

2 LeBlanc?

3 MR. WHITE: I don't know if I

4 rambled too much on that.

5 DR. LEBLANC: You mentioned that

6 atrazine was banned in the U.K. not for health

7 concerns but because levels in drinking water

8 exceeded a somewhat arbitrary limit.

9 MR. WHITE: No. Actually I said

10 it was not banned in the U.K. It's use was

11 discontinued.

12 DR. LEBLANC: Okay.

13 MR. WHITE: And not because the

14 levels in drinking water -- because of

15 detections in groundwater.

16 DR. LEBLANC: Detection in

17 groundwater. And the -- and you also

18 mentioned that an alternative, or a

19 substitute, triazine is used in place of

20 atrazine.

21 MR. WHITE: Yes, terbuthylazine.

22 DR. LEBLANC: So I assume that
202-234-4433

Neal R. Gross & Co., Inc.

Page 132

1 levels of that herbicide are not found in

2 excess of that limit in groundwater?

3 MR. WHITE: I would assume that

4 they've not detected it in groundwater at that

5 level. So that it's a concern but, you know,

6 again, it's a very similar product. You know,

7 I'm not familiar with terbuthylazine because,

8 in fact, we don't have it registered in the

9 United States.

10 DR. LEBLANC: I'm not familiar

11 with it either.

12 MR. WHITE: Yes.

13 DR. LEBLANC: And perhaps I'm

14 going above your level of understanding and

15 expertise.

16 MR. WHITE: Well, other than my --

17 my understanding is that the tox and actually

18 the compound is very similar, as are the other

19 triazines.

20 DR. LEBLANC: I just wonder if

21 its -- presumably its levels are lower than

22 the limit I think you said was 0.1 parts per
202-234-4433

Neal R. Gross & Co., Inc.

Page 133

1 billion.

2 MR. WHITE: 0.1 parts, yes.

3 DR. LEBLANC: Is it because it

4 degrades more rapidly or that is -- is it used

5 effectively as a lesser concentration?

6 MR. WHITE: I don't think that

7 would be the case, but there's probably people

8 in the room that can answer that better than

9 I can.

10 SESSION CHAIR PORTIER: I think we

11 want to stop at this point. I really want to

12 get on to some of the other commentors. And

13 I appreciate the Triazine Network being

14 represented. I've heard you many times, and

15 it's always good to be reminded of where this

16 product fits into agricultural practice and

17 its key component. Thank you very much.

18 The next group that's up is the

19 Syngenta. Syngenta has seven presenters. And

20 they're going to begin their presentation and

21 we'll stop at some good point for lunch and

22 then they'll finish after lunch.
202-234-4433

Neal R. Gross & Co., Inc.

Page 134

1 Joe, I don't even see seven chairs

2 there so you're going to have to pick and

3 choose who gets to sit at the table. And I

4 guess first up is Dr. Simpkins. Is that

5 first? Or who's your lead on this. You are. 

6 Syngenta has provided a large packet of

7 material to the panel and to the online

8 archive.

9 DR. BRECKENRIDGE: Mr. Chairman,

10 I'm Charles Breckenridge. I'm a senior

11 toxicology fellow with Syngenta. I've been

12 responsible for the safety assessment of the

13 product since about 1994.

14 Today we have invited our

15 colleagues who assist us with the

16 interpretation of the endocrine effects of

17 atrazine. And we're going to be making multi18 part presentation. The other two Syngenta

19 presenters are, in fact, going to address the

20 water questions and so they'll come up later.

21 To give you an orientation of the

22 sequence of speakers today, and also, Mr.
202-234-4433

Neal R. Gross & Co., Inc.

Page 135

1 Chairman, just to inquire how you wish to

2 proceed, we have diverse topics that we are

3 going to be talking about and we have

4 approximately in this section 90 minutes of

5 presentation. I trust that it's most

6 convenient to ask questions after each

7 subsection. So if you choose to do that that

8 would be fine.

9 SESSION CHAIR PORTIER: With

10 questions we may only be able to get two11 thirds of that. So we'll -- 

12 DR. BRECKENRIDGE: Sure. It's as

13 you choose. So the first topics -- two topics

14 deal with one of the charge questions, the

15 cancer mode of action, and whether or not it

16 still pertains. 

17 The first speaker will be Dr. Jim

18 Swenberg from North Carolina in Chapel Hill. 

19 He will be dealing with the framework analysis

20 as it was conducted and presented to the SAP

21 in 2000 and refreshing our memories about the

22 conclusions reached at that time and the
202-234-4433

Neal R. Gross & Co., Inc.

Page 136

1 implications of any new research on that

2 topic.

3 Dr. James Simpkins from the

4 University of North Texas will then address

5 the question of aromatase and whether or not

6 atrazine is a -- first off, the evidence that

7 it induces aromatase in vitro and the

8 relevance of those observations to the whole

9 animal. As you know, aromatase is a

10 significant enzyme in the conversion of

11 testosterone to estrogen. 

12 And any time you would have

13 another mode of action that would lead to the

14 production of estrogen one would be concerned

15 that perhaps that mechanism would be operative

16 in humans. And so Syngenta initiated a

17 program of research with Dr. Simpkins more

18 than three years ago to investigate those

19 studies and try to understand exactly the

20 relevancy of those observations in vitro to

21 the human. And we'll be summarizing about

22 three years of research on that.
202-234-4433

Neal R. Gross & Co., Inc.

Page 137

1 You have in your packet three

2 papers that were submitted by us at the

3 beginning of the year. These are the papers

4 by Yee et. al. These have not yet actually

5 gone to publication. We've been adding new

6 data to them and we will be publishing those

7 shortly. 

8 Finally, I'm going to round up the

9 section where we're making a transition from

10 discussion of the cancer mode of actions and

11 the potential mechanisms to a discussion of

12 the endocrine effects of atrazine relative to

13 non-cancer end points. So that's the bridge

14 section there. It will be very short. 

15 We then turn to our Dr. Robert

16 Handa. And when we ended the more animal

17 descriptive toxicology around about the 2000

18 SAP, we turned our attention to the question

19 about the GnRH pulse generator and exactly

20 what were the mechanisms whereby atrazine was

21 affecting that system. 

22 Dr. Handa will be summarizing that
202-234-4433

Neal R. Gross & Co., Inc.

Page 138

1 program of research which also is being

2 carried out over about three years. There are

3 several publications, and those by Foradori

4 et. al. are the ones that are in your packet. 

5 But more importantly for us today, we're going

6 to share some of the new research that we have

7 conducted and for studies that are still

8 ongoing.

9 Finally, when we come out of that

10 particular section we're going to turn our

11 attention to the question of the toxicologic

12 consequences of effects on the endocrine

13 system, especially as it pertains to the HPA

14 axis. 

15 We're fortunate that Dr. Steve

16 Pruett has agreed to come and speak about his

17 research -- and this is not Syngenta funded

18 research -- that he began in 2003 looking at

19 the relationship between the area under the

20 curve exposure to corticosterone induced by

21 atrazine and immune system responses. 

22 In addition to that, Dr. Pruett is
202-234-4433

Neal R. Gross & Co., Inc.

Page 139

1 advising us on studies that we have ongoing

2 relative to the question of the

3 immunotoxicologic potential of atrazine and

4 we'll share preliminary results of those

5 investigations -- or at least their study

6 designs. We have committed to the agency we

7 will submit those two studies to the agency by

8 about end of July. 

9 And, incidentally, those studies

10 were conceptualized and initiated in December

11 of last year and in February I believe of this

12 year. So we're working on a fairly fast track

13 here to try to get data that is meaningful to

14 the questions that are in front of this group.

15 So, with that, I'll turn it over

16 to Dr. Swenberg.

17 DR. SWENBERG: Thank you very

18 much, Charles. And it's a pleasure to be here

19 with the SAP as a former member of this

20 organization.

21 And what I'd like to do today is

22 talk to you about -- since many of the members
202-234-4433

Neal R. Gross & Co., Inc.

Page 140

1 this is your first SAP on atrazine -- bring

2 you up to date on kind of where we are in

3 understanding the carcinogenic potential of

4 this very important compound.

5 So this slide just gives you an

6 overview of how comprehensive the

7 carcinogenicity database is on atrazine. 

8 There have been three different mouse cancer

9 bioassays in males and females. All of these

10 are negative at all sites. 

11 There has been a series of Sprague

12 Dawley cancer bioassays. Four of these have

13 been in female Sprague Dawleys and they are a

14 single site, single sex, single tumor type

15 positive. Ovariectomized females were also

16 evaluated, and they are negative at all sites. 

17 Sprague Dawley males are negative at all

18 sites. And the Fischer 344 rat, male and

19 females, are negative at all sites. 

20 Next please. So what I'd like to

21 do is -- you may or may not be familiar with

22 the international program for chemical safety
202-234-4433

Neal R. Gross & Co., Inc.

Page 141

1 and the EPA framework for evaluating

2 mechanistic data. This way of examining data

3 came about -- it started at a meeting in

4 Hanover, Germany in 1997, and one of your

5 panel members, Dr. Fenner-Crisp, and myself

6 were at that meeting. 

7 It then evolved into more

8 intensive study. A number of publications

9 have come out on it, and, most importantly to

10 you, it is part of the 2005 cancer risk

11 assessment guidelines of the EPA. So we'll

12 walk through these different bullets as they

13 pertain to atrazine.

14 Next please. So the first

15 thing -- go back please. The first thing one

16 does in this framework approach -- and it was

17 brought about to increase the transparency of

18 how decisions are made -- is to postulate the

19 mode of action after a thorough review of, you

20 know, the literature and the findings. 

21 And so the postulated mode of

22 action for atrazine is that it causes an
202-234-4433

Neal R. Gross & Co., Inc.

Page 142

1 increase in mammary tumors by a species,

2 strain, and sex specific mode of action that

3 is the result of prolonged exposure to

4 endogenous estrogen.

5 Next please. So we want to then

6 examine the key events that are driving this

7 phenomenon. So as you heard yesterday from

8 Dr. Cooper, and we'll expand upon today,

9 atrazine inhibits the LH surge in female

10 Sprague Dawley and, as he showed, Wistar rats.

11 The Sprague Dawley rat is well

12 known that its reproductive senescence is

13 characterized by constant estrus. And chronic

14 exposure to atrazine promotes early

15 reproductive senescence in female Sprague

16 Dawley rats. This results in prolonged

17 exposure to endogenous estrogen.

18 So we want to evaluate these key

19 events. According to the framework we'll be

20 looking at dose response relationships,

21 temporal relationships evaluating the

22 strength, the consistency, and the specificity
202-234-4433

Neal R. Gross & Co., Inc.

Page 143

1 of this association, as well as the biological

2 plausibility and coherence of the results.

3 Next please. So this is a

4 slide -- I think Ralph also showed this

5 yesterday. This is a very high dose cancer

6 bioassay that was done. And one of the things

7 you'll notice -- in the blue are the

8 controls -- that the Sprague Dawley female

9 rats has a very high background incidence of

10 mammary tumors, but that with exposure to

11 atrazine what we're doing is we are decreasing

12 the latency time, i.e. the curve shifts to the

13 left as we're looking at that screen, and we

14 have an increase in incidence. These are the

15 hallmarks of a dose response in

16 carcinogenicity. 

17 Next please. I think that Dr.

18 Cooper also showed one of these slides. Now,

19 this slide is very interesting. So he

20 explained how he used vaginal smears to

21 characterize the estrus cycle of the rat. 

22 What this slide represents is on the y-axis
202-234-4433

Neal R. Gross & Co., Inc.

Page 144

1 there are 90 individual lines of animal data. 

2 These are the 90 animals that were in this

3 study. This is the six-month period of time.

4 And what you see is this starting

5 of these red lines, and the red lines

6 represent persistent estrus. And what's

7 unusual about the Sprague Dawley rat is that

8 this is a control animal that this early

9 senescence, i.e. persistent estrus, starts

10 very early in their life. It's -- this is

11 about a 90-day into the study where we start

12 seeing this. That's markedly in contrast to

13 most other strains.

14 Next please. Now, if you expose

15 these rats to 400 parts per billion of

16 atrazine for six months and you're doing the

17 same thing -- this is actually the same study

18 now -- these are the 400 PPM atrazine

19 animals -- and you can readily appreciate that

20 the increase in reproductive senescence and

21 the persistent estrus has clearly increased.

22 Next please. This is shown more
202-234-4433

Neal R. Gross & Co., Inc.

Page 145

1 easily with a -- over on the y-axis we have

2 percent of days in estrus. And you can see

3 the controls are running around between 40 and

4 45 percent, and in a dose-related manner it's

5 going up to 65 percent. So this is the

6 evidence for chronic exposure to endogenous

7 estrogen.

8 Next please. Now, the mechanism

9 for this is this inhibition of the LH surge

10 that Dr. Cooper spoke of yesterday. And what

11 you can see -- it's probably most easily seen

12 in this repeat sample group -- that you have

13 a complete inhibition of the LH surge at high

14 doses. These are the same high doses that

15 result in the increase in mammary cancer. So

16 this shows the dose response relationship for

17 this key event.

18 Next please. Here is a comparison

19 of two bioassays, a second Sprague Dawley

20 bioassay and a Fischer 344 bioassay. These

21 are the female animals. And you see the much

22 higher incidence of the mammary cancer in the
202-234-4433

Neal R. Gross & Co., Inc.

Page 146

1 Sprague Dawleys and that there is a dose2 related increase. And now we're up to a 80

3 percent incidence in the control animals --

4 just showing you how susceptible these animals

5 are. The Fischer rat on the other hand has a

6 much lower background of mammary cancer and

7 there is no dose response relationship.

8 Next please. If one looks then at

9 the percent of days in estrus you also see a

10 very striking difference. We have the

11 increase and dose response relationship in the

12 Sprague Dawleys in days in constant estrus

13 compared to the Fischer rat where this effect

14 just does not happen. In fact, the amount of

15 estrogen days decreases with increasing age.

16 Next please. This is then

17 compared across the board for the Sprague

18 Dawley rat, the Fischer rat, and the human. 

19 So you can see I spoke about when senescence

20 starts. You can see that the Sprague stands

21 out here at 30 to 40 percent of the life span,

22 whereas the Fischer rat and the human are more
202-234-4433

Neal R. Gross & Co., Inc.

Page 147

1 in the 60 to 70 percent range. 

2 The principal cause of senescence

3 in the Fischer -- or the in Sprague Dawley rat

4 is this lack of the LH surge and the lack

5 of -- so it's a hypothalamic failure rather

6 than an ovarian failure.

7 In the Fischer rat we also have

8 hypothalamic failure, but now it's not for LH

9 release, it's for controlling prolactin

10 surges. And in the human it's depletion of

11 ovarian follicles that drive reproductive

12 senescence. 

13 So if we look at the LH surge

14 capacity the Sprague Dawley loses this, the

15 Fischer rat maintains it, and the human

16 maintains it. Estrogen and progesterone

17 ratios are elevated and prolonged in the

18 Sprague Dawley, they're reduced in the

19 Fischer, and they're reduced in the human.

20 The predominant cyclic pattern is

21 one of persistence estrus in the Sprague

22 Dawley rat, pseudopregnancy episodes in the
202-234-4433

Neal R. Gross & Co., Inc.

Page 148

1 Fischer rat, and menopause in the human. 

2 Prolactin secretion is persistency elevated. 

3 It's -- in the Sprague, it's episodically

4 elevated in the Fischer, and it's reduced in

5 women. And prolactin dependence is high in

6 the Sprague Dawley, medium in the Fischer, and

7 non-existent in the women. 

8 Next please. So pulling this all

9 together one can look at the human relevance

10 of this. The LH surge suppression does not

11 increase estrogen exposure in humans. The

12 ovary, not the hypothalamus, drives

13 reproductive senescence in humans, and

14 reproductive aging in human results in

15 estrogen deprivation, not increased exposure

16 to estrogen.

17 Next please. So the next part of

18 the framework mode of action analysis is to

19 consider are there other potential modes of

20 action that we need to be examining? And

21 three of them that have continually been

22 evaluated are shown here. Is the compound
202-234-4433

Neal R. Gross & Co., Inc.

Page 149

1 genotoxic, is it estrogenic, and does it

2 affect aromatase? Many of these issues on the

3 hormonal milieu were covered also by Dr.

4 Cooper yesterday.

5 Next please. So the next two

6 slides will just give you the large number of

7 in vitro studies that have been conducted on

8 atrazine for genotoxic potential. And they

9 cover virtually all of the types of assays

10 that are normally done for hazard I.D. You'll

11 notice that they're predominantly negative

12 with very few positive studies.

13 Next slide. You also see it in

14 this slide. This is the status of the data in

15 the year 2000.

16 Next please. So the weight of

17 evidence in the year 2000 strongly indicated

18 that atrazine is not genotoxic. This is

19 derived from two reviews by David Brusick in

20 1994 and again in 2000. Again, in 2000 Dr.

21 Andrew Kligerman from the Research Triangle

22 Park EPA did sister chromatid exchange in
202-234-4433

Neal R. Gross & Co., Inc.

Page 150

1 chromosome analysis studies. They were

2 negative. 

3 And there are a number of studies

4 that have been done between 2000 and the

5 present time that were in your package. And

6 these also do not change the weight of

7 evidence. So atrazine is not genotoxic and we

8 can put away that mode of action.

9 Next please. The second was the

10 estrogenic potential of atrazine, and, there

11 again, have been a wide number of screening

12 studies that have been -- that have evaluated

13 this as well as some of the traditional

14 reproductive toxicology studies. They are all

15 negative. So estrogen is not -- or I should

16 say atrazine is not estrogenic.

17 Next slide. But we still do have

18 uncertainties. This is the last step in a

19 framework mode of action. So the mode of

20 action underlying the tumor response in the

21 Sprague Dawley females is very well

22 understood. However, the precise targets
202-234-4433

Neal R. Gross & Co., Inc.

Page 151

1 still have more to be known.

2 Since the 2000 review Dr. Handa's

3 laboratory has started to work on this, and he

4 is primarily focused on the HPG and the HPA

5 targets that Dr. Cooper also talked about

6 yesterday. And he will speak later on on this

7 new research so that you are brought right up

8 to date on where we're at.

9 Furthermore, the significance of

10 the in vitro induction of aromatase was

11 unknown. There has been research now on

12 amphibians and on fish that has been

13 conducted, and it did not show any evidence

14 for functional consequences in vivo.

15 Furthermore, Dr. Simpkins will

16 bring you up to date on his laboratory's

17 efforts to assess the mechanisms -- the in

18 vitro mechanisms and to explore why we're not

19 seeing these effects in vitro, again, to bring

20 you up to the very latest point.

21 Next please. So one can use the

22 cancer risk assessment guidelines, go through
202-234-4433

Neal R. Gross & Co., Inc.

Page 152

1 the human evidence, the animal evidence, the

2 mode of action, and the human relevance of

3 that mode of action, and you see the checks in

4 these boxes that is -- this was based back in

5 the year 2000 -- it has not changed.

6 Next please. So, in conclusion,

7 the mode of action underlying the increased

8 risk and incidence of mammary tumors to a

9 female Sprague Dawley rat is very well

10 characterized. The key events that are

11 leading to this carcinogenic response have

12 been elucidated and are dose dependent and

13 temporally consistent.

14 The mode of action has been

15 determined to not be relevant to humans. And

16 plausible alternative modes have been

17 experimentally discounted.

18 The U.S. EPA concluded that

19 atrazine is not likely to be carcinogenic in

20 humans. And other international groups,

21 including IARC, Australia, the EU, and the WHO

22 have reached similar conclusions. New
202-234-4433

Neal R. Gross & Co., Inc.

Page 153

1 research that has been conducted since 2000

2 has strengthened the confidence in this

3 conclusion. 

4 Now, I'd like to end with one last

5 thing that came up yesterday. Could we have

6 the next slide? There was a question raised

7 about polycystic ovary syndrome in women. 

8 This was a major focus in the 2000 SAP

9 assessment of atrazine. And Dr. John Marshall

10 from the University of Virginia, who is a

11 recognized expert on PCOS, put together this

12 slide and gave a presentation. 

13 So you can see the comparison here

14 of atrazine versus PCOS. Atrazine has reduced

15 LH secretion. PCOS is associated with

16 elevated LH secretion. Atrazine has decreased

17 LH pulse amplitude. PCOS has increased LH

18 pulse amplitude. There is no change in

19 pituitary sensitivity to GNRP -- or H I should

20 say -- whereas in PCOS there is increased

21 pituitary sensitivity.

22 There is -- it is correlated with
202-234-4433

Neal R. Gross & Co., Inc.

Page 154

1 weight loss -- atrazine exposure is -- in the

2 rat, whereas PCOS is commonly correlated with

3 both obesity and diabetes. Androgen levels

4 are decreased or unchanged with atrazine. 

5 They are markedly increased in PCOS. They are

6 not associated with endometrial cancer in

7 atrazine. PCOS has an increased association

8 with endometrial cancer.

9 Atrazine is associated, as you

10 heard, with mammary tumors. PCOS does not

11 have any association with breast cancer. 

12 Atrazine induces reproductive senescence. 

13 PCOS does not resemble menopause. And the

14 neuroendocrinology of atrazine is well

15 understood. It is not well understood for

16 PCOS.

17 Last slide please. So this is

18 just a quote out of the 2000 SAP on atrazine

19 cancer classification meeting. Further, if

20 this and this relied -- or was -- if you went

21 back to the previous sentence was associated

22 with LH -- flattening of the LH response --
202-234-4433

Neal R. Gross & Co., Inc.

Page 155

1 occurred in humans. It could not result in

2 PCOS since this condition is associated with

3 elevated LH. 

4 Therefore, the conclusion that

5 there could be a potential for human cancer

6 from elevated estrogen levels if atrazine had

7 effects on conserved hypothalamic mechanisms

8 in humans similar to those in Sprague Dawley

9 rats is at odds with the arguments and other

10 conclusions of the agency draft assessment.

11 So I hope this is helpful to the

12 discussions on PCOS. Thank you very much.

13 SESSION CHAIR PORTIER: I think at

14 this point we'll open it up to a couple of

15 questions. Is there some? Dr. Holladay.

16 DR. HOLLADAY: Nice overview. 

17 Thank you. One of your slides indicated there

18 were no functional differences found in fish

19 exposed to atrazine. I think there was a

20 review in EHP, or Environmental Health

21 Perspectives, in `10 -- 2010. There's a meta22 analysis of the literature available that
202-234-4433

Neal R. Gross & Co., Inc.

Page 156

1 suggested something like 30 out of 40 immune

2 end points were suppressed in freshwater fish

3 with atrazine exposure. Are you aware of why

4 that might be the case what these guys found

5 as compared to what you were suggesting?

6 DR. BRECKENRIDGE: Yes, sir. 

7 Charles Breckenridge from Syngenta. I'll

8 respond first with respect to the statements

9 on Cloath and Tanguay. Those comments were

10 pertaining to reproductive effects on

11 aromatase specifically as a mechanism whereby

12 a sex reversal could be accounted for by

13 atrazine exposure. 

14 And, in fact, this panel in

15 earlier times has reviewed the Cloath work,

16 and the evidence suggests that there's no

17 evidence of aromatase modification in those

18 animals. In regard to immunotoxicity in

19 native species, I think that would be more

20 appropriate at another SAP rather than here

21 because today we're going to be mainly

22 focusing on the endocrine mechanism. So if I
202-234-4433

Neal R. Gross & Co., Inc.

Page 157

1 could ask to defer any kind of a detailed

2 discussion on that, sir, I'd appreciate it.

3 SESSION CHAIR PORTIER: Dr. Reed?

4 DR. REED: I have two questions. 

5 One is in your slide 14. So you line up the

6 Sprague Dawley and Fischer 344 and then women. 

7 Since the mice bioassay also indicated

8 negative response for mammary gland tumors,

9 could you line up mice -- adding another

10 column for mice in this schematic of things,

11 or is it not -- 

12 DR. SWENBERG: I don't believe

13 those studies have been conducted because of

14 the negative bioassay, but certainly it could

15 be looked at. But the mouse doesn't go into

16 the same reproductive senescence that the

17 Sprague Dawley female does.

18 DR. REED: Right. That's what I

19 was asking about -- if you could line them up

20 together with that.

21 DR. SWENBERG: Yes. I don't think

22 that's been attempted -- 
202-234-4433

Neal R. Gross & Co., Inc.

Page 158

1 DR. REED: Mode of action -- 

2 DR. SWENBERG: -- but it's a good

3 suggestion.

4 DR. REED: Okay. My next question

5 is that in the agency's issue paper there are

6 two studies of Fukamachi 2004 and Ueda 2005

7 that allude to the possibility of promotional

8 potential. Would you care to comment on that? 

9 I mean, I understand that there's a lot of,

10 you know, issues about the study design and

11 also the interpretation of the data. 

12 But given that it -- what the

13 author is saying has merit, meaning that

14 there's a possibility of promotional

15 potential, how would that fit into the mode of

16 action scheme?

17 DR. SWENBERG: Yes. This -- these

18 two papers -- I've gone through them several

19 times. They are very complex. So if the

20 first paper, which is -- has the transgenic

21 animals being used, I have not yet been able

22 to find, you know, what are the numbers of
202-234-4433

Neal R. Gross & Co., Inc.

Page 159

1 copies of the transgene in different animals

2 and different cells. You'll remember this was

3 an issue with the TGAC mouse -- transgenic

4 mouse. 

5 So I'm having a difficult time

6 understanding it. The statistical analyses

7 that were done are somewhat unusual. The

8 model's quite unusual. If we go to the second

9 paper where they use the fairly standard DMBA

10 breast cancer induction model -- I am familiar

11 with that model. 

12 Again, the data are really

13 difficult to interpret. There is some

14 suggestion of a high dose effect there. But

15 what the mechanism is I don't think we

16 really -- I mean, those animals were

17 ovariectomized. So it's not a mechanism

18 that's similar to what we're seeing in these

19 bioassays.

20 But they do deserve further

21 consideration of all of the work that's been

22 published related to genotoxicity and
202-234-4433

Neal R. Gross & Co., Inc.

Page 160

1 promotion and that. Those are very

2 interesting papers and provocative papers. 

3 But I've been working in carcinogenicity

4 research for more years than I'd like to --

5 going back to before 1970 -- let's put it that

6 way -- including promotion and initiation

7 promotion studies, and I can't fully interpret

8 those studies.

9 SESSION CHAIR PORTIER: Dr.

10 Williams.

11 DR. WILLIAMS: I just wanted to

12 clarify. You said in on one of your -- 

13 SESSION CHAIR PORTIER: Pull the

14 mike closer.

15 DR. WILLIAMS: Sorry -- LH

16 suppression does not increase estrogen

17 exposure in humans. What it looked like you

18 were really saying was that PCOS is associated

19 with elevated LH levels. But if you actually

20 suppress LH is there any experimental evidence

21 to say that in humans, or maybe in primates,

22 that suppression of LH does not increase
202-234-4433

Neal R. Gross & Co., Inc.

Page 161

1 estrogen exposure?

2 DR. SWENBERG: I'm going to turn

3 that over to Dr. Simpkins. He's an expert in

4 this area.

5 DR. HANDA: I'm not sure I

6 understand the question completely. But since

7 estrogen is driven by LH changes with age --

8 and I'm assuming you mean changes with age in

9 at menopause there's decrease -- 

10 DR. WILLIAMS: No. I'm actually

11 not talking about menopause at all. So if you

12 think about younger women who are

13 anovulatory -- sometimes PCOS related but

14 sometimes for reasons that we can't -- 

15 DR. HANDA: Hypothalamic

16 amenorrhea or something like that.

17 DR. WILLIAMS: Or simply oligo18 ovulatory for, you know, unexplained reasons. 

19 That type of women -- is there any evidence

20 that their failure to ovulate potentially a

21 slight disregulation of LH, as opposed to

22 complete ovarian failure where you don't --
202-234-4433

Neal R. Gross & Co., Inc.

Page 162

1 you know, obviously estrogen has left.

2 DR. HANDA: Usually those are

3 associated -- for example, hypothalamic

4 amenorrhea will be associated with a decrease

5 in LH, and, as a result, there's no

6 corresponding increases in estrogen.

7 DR. WILLIAMS: But, again, so

8 complete hypothalamic amenorrhea is -- you

9 know, is again a complete failure. What about

10 oligomenorrhea -- women who periodically don't

11 ovulate? Where -- you know, here we're

12 talking about this potentially intermittent

13 suppression of LH release related to

14 intermittent high levels of atrazine. 

15 So if you see this happening how

16 can you say that that would not necessarily

17 increase estrogen exposure? Is there any

18 evidence for that?

19 DR. HANDA: As far as I know, no.

20 SESSION CHAIR PORTIER: Okay. I

21 think we'll move on to the next presentation.

22 DR. SIMPKINS: I'm Jim Simpkins
202-234-4433

Neal R. Gross & Co., Inc.

Page 163

1 from the University of North Texas Health

2 Science Center. And I think I can do this.

3 We will in the next few minutes

4 try to sort out the observation that in

5 certain transformed cell lines exposure to

6 relatively high doses of atrazine induce

7 expression of aromatase, enhanced aromatase

8 activity. Whereas that observation is not

9 made in vivo. The answer to that is we

10 believe embedded in the metabolism of atrazine

11 with exposure, certainly to mammals, and

12 likely to every species.

13 Atrazine is a relatively short

14 lived compound after dosing. It is converted

15 to the chlorotriazines which are shown in the

16 green area on that slide. Those

17 chlorotriazines, as well as atrazine itself,

18 are then conjugated as part of the process of

19 their elimination. What is also shown on that

20 slide is the percent of those metabolites that

21 are present in the urine. This happens to be

22 in humans, but the numbers are very similar in
202-234-4433

Neal R. Gross & Co., Inc.

Page 164

1 primates as well as in rodents. Where the

2 dominant form are the conjugated triazine and

3 triazine metabolites. Ammeline and

4 hydroxytriazine are plant metabolites shown

5 here and here.

6 The point I will make is that we

7 have an opportunity with cell lines to ask

8 questions that simply cannot be asked in vivo

9 because cell lines are not good at

10 metabolizing atrazine so when we put atrazine

11 in, we should, at the end of our studies have

12 predominantly atrazine with very little

13 metabolism occurring.

14 Now what I will show you are what

15 I think are the most comprehensive studies

16 done to date. For concentration and duration

17 assessment of the effects of atrazine, and a

18 variety of its metabolites, in an H295R cell,

19 this is an adrenal cortical tumor cell line

20 that was shown by Sanderson to be responsive

21 to atrazine with increased expression in

22 activity of aromatase. So we wanted to take
202-234-4433

Neal R. Gross & Co., Inc.

Page 165

1 a careful look at that cell line. I will also

2 point out to you that the data you see, will

3 see, were presented in one of the manuscripts

4 that Dr. Breckenridge refers to. The data you

5 will see is essentially identical to data that

6 we have in a placental tumor cell line which

7 also responds to atrazine. So you will see

8 part of the data but I think, as you will

9 agree, it is an extensive data set.

10 What is shown here is aromatase

11 messenger RNA levels in H295R cells that were

12 treated from as short as a quarter of an hour

13 to 72 hours at four doses, 0.100 nanomolar, 1

14 micromolar, 10 micromolar. As you can see

15 atrazine causes a consistent two- to three16 fold increase in expression of aromatase

17 messenger RNA that begins at about two hours

18 and persists through 72 hours.

19 So with this high concentration of

20 atrazine in a cell type that cannot metabolize

21 you can induce, you can demonstrate a highly

22 reliable consistent induction of aromatase. 
202-234-4433

Neal R. Gross & Co., Inc.

Page 166

1 If we look at two of the chlorinated

2 metabolites, the desethyl and desisopropyl

3 forms of atrazine, we see a modest response

4 that is delayed relative to atrazine and is

5 reduced in amplitude. You can notice, this is

6 for both of those metabolites this is less

7 than a two-fold induction of the message.

8 In contrast to that,

9 hydroxyatrazine, ammeline, and

10 diaminochlorotriazine are negative at all

11 times and all doses, short of a sporadic hit

12 here or there, but I will point out to you

13 that for each of these metabolites there are

14 essentially 44 samples, so we're not surprised

15 that we get an occasional hit. Statistically

16 we ought to see that happen occasionally. But

17 we don't think these small occasional hits are

18 significant. So these metabolites appear to

19 be inactive.

20 We also then looked at two model

21 conjugates of atrazine -- that is, the

22 glutathione and the mercapturate form of
202-234-4433

Neal R. Gross & Co., Inc.

Page 167

1 atrazine, keeping in mind that the atrazine is

2 the active, and we wanted to know when they're

3 conjugated does atrazine lose its activity.

4 Not only is atrazine negative when

5 it's conjugated, there appears to be a

6 reduction in aromatase message, at least at

7 the 24-hour sampling point. So these

8 metabolites, which are the predominant form

9 soon after administration of atrazine, are not

10 inducing aromatase in this cell line.

11 The doubling that we saw in

12 message is very consistent with measurements

13 of activity conducted by Dr. Sanderson, as

14 well as with an increase in this case estrone

15 concentrations in the media. And his

16 metabolite response is very similar to that

17 which we see. Two of the chlorometabolites

18 show an increase in activity of aromatase,

19 whereas the hydroxymetabolites are completely

20 negative. So we think there's good

21 concordance in the data sets to date.

22 So this message increase is
202-234-4433

Neal R. Gross & Co., Inc.

Page 168

1 consistent with activity increase, and all of

2 them are happening at or around ten micromolar

3 atrazine in vitro. And we've identified those

4 metabolites which are inactive in vitro, and

5 those are the predominant metabolites that

6 show up when atrazine exposure occurs in vivo.

7 An additional question we asked

8 was the extent to which the induction of

9 expression of aromatase could be secondary to

10 cytotoxic effects of atrazine or its

11 metabolites. And we undertook these series of

12 studies because we had been studying nervous

13 tissue, and it's very clear that chemical or

14 physical insults to brain cells result in

15 expression of aromatase, and that's probably

16 part of a protective mechanism there.

17 So -- but before we do that I want

18 to make a comment about all of these in vitro

19 studies -- and it is for you the panel just a

20 word of caution. Atrazine is very insoluble. 

21 What's shown here are photomicrograms of

22 atrazine at 3 micromolar, 10, and 30
202-234-4433

Neal R. Gross & Co., Inc.

Page 169

1 micromolar in culture. And in many of the

2 documents that you've had to read folks are

3 using in vitro exposures up in this range.

4 In PBS we see atrazine come out of

5 solutions somewhere between 1 and 3

6 micromolar. That's shown in these slides as

7 the dark aggregates. Those aggregates are

8 larger in number and in size at higher

9 concentrations of atrazine. And at 30

10 micromolars you can see they're completely

11 covering the cells in this dish. If you

12 solubilize in DMSO you can enhance the

13 solubility of atrazine. But I'll point out to

14 you, at 30 micromolar we start seeing atrazine

15 come out of solution. So keep that in mind in

16 interpreting data.

17 Despite those aggregates, when we

18 look at solubility using any of three methods

19 at times ranging from a few minutes all the

20 way out to 72 hours -- and what's shown here

21 is representative data at 24 hours -- even at

22 extraordinarily high concentrations -- in this
202-234-4433

Neal R. Gross & Co., Inc.

Page 170

1 case up to 300 micromolar -- in which cells

2 are literally coated with atrazine we get no

3 toxicity. 

4 And we don't see toxicity for this

5 which is a cell viability assay. We don't see

6 it if we look at assays that require

7 functioning mitochondria to report the health

8 of cells. We don't see it if we look at

9 production of reactive-oxygen species. We get

10 a straight line over an extraordinarily wide

11 range of atrazine concentrations.

12 We see no toxicity with any of the

13 metabolites that I showed you in the previous

14 slides at any of the times we assessed. And

15 we see no interaction between atrazine and, in

16 this case, three known cytotoxic agents,

17 glutamate, succinate dehydrogenase inhibitor,

18 NPA, and then exogenous administration of

19 hydrogen peroxide.

20 So atrazine is neither protecting

21 these cells nor enhancing the toxicity of

22 known insults. So atrazine and its
202-234-4433

Neal R. Gross & Co., Inc.

Page 171

1 metabolites are not cytotoxic in the cell

2 lines, and they don't potentiate toxicity of

3 other agents.

4 We then turn our attention to

5 potential effects of triazines on

6 phosphodiesterases. And we did that because

7 in the literature there were a number of hints

8 that atrazine may be interacting with the

9 breakdown of cyclic AMP, which is one of the

10 jobs of phosphodiesterases. 

11 For example, dating way back to

12 1997 there's a report that a high single dose

13 of atrazine increase cyclic AMP levels in

14 livers of rats. Leroux reported on a series

15 of chemicals that he had synthesized using

16 atrazine as a scaffold for the purpose of

17 producing phosphodiesterase for inhibitors and

18 showed very good evidence that he could

19 improve the phosphodiesterase-4 inhibiting

20 activity of atrazine.

21 Sanderson reported in 2002 that

22 there was a strong correlation between
202-234-4433

Neal R. Gross & Co., Inc.

Page 172

1 atrazine's effects on cyclic AMP

2 concentrations and their ability to induce

3 expression of aromatase. And then in 2004

4 Roberge reported that atrazine inhibited

5 phosphodiesterase using a bovine liver

6 preparation. And there's a great deal of data

7 from a variety of sources indicating that

8 cyclic AMP is critical to aromatase gene

9 expression.

10 So what we set about doing was

11 asking the question, does atrazine or any of

12 the metabolites that I showed you interfere

13 with phosphodiesterase activity and thereby

14 increase cyclic AMP and then signal to

15 aromatase through this PKA CREB

16 phosphorylation pathway.

17 And then I'll finish by showing

18 you data that resulted from us identifying the

19 phosphodiesterases in H295R cells as well as

20 in the JEG3 cells and then designing and using

21 small inhibitory RNAs to knock down message

22 levels for those species and the effects that
202-234-4433

Neal R. Gross & Co., Inc.

Page 173

1 that has on the atrazine signaling.

2 These are all results for atrazine

3 inhibition of phosphodiesterase activity. 

4 IBMX is a well-described phosphodiesterase

5 inhibitor that has an IC50 value of about two

6 micromolar. Atrazine does indeed repeatedly

7 reduce phosphodiesterase activity. It's about

8 20-fold less potent than IBMX.

9 And then the other metabolites

10 that we assessed really had very little

11 activity in this assay. Their IC50s, if we

12 could calculate them, were in the millimolar

13 or molar range, which means that they are very

14 ineffective PDE inhibitors.

15 We then designed primer pairs to

16 ask questions about what species of

17 phosphodiesterase is made by the H295R cells,

18 and we did the same with the JEG3 cells

19 because both of those cell types essentially

20 showed the same time course and magnitude of

21 aromatase induction in response to atrazine.

22 We made the assumption that, that
202-234-4433

Neal R. Gross & Co., Inc.

Page 174

1 being the case, the two cell lines should show

2 comparable phosphodiesterases, and we could

3 use those to identify the site of action of

4 atrazine. And it turns out four

5 phosphodiesterases were picked up by our

6 primer pairs in both of those cell types. 

7 But only two of those were present

8 in the same relative concentration -- and that

9 is the PDE4D and the PDE8. We then built

10 siRNA constructs to knock down those species,

11 and these are those data.

12 The siRNAs for PDE4 reduced PDE4D

13 levels by about 50 percent. The siRNAs for

14 PDE8 reduced levels by about 60 percent. In

15 both cases reducing levels of these

16 phosphodiesterases essentially blocked the

17 ability of aromatase to induce -- excuse me --

18 of atrazine to induce aromatase expression,

19 suggesting that these PDE4s were indeed

20 mediating the ability of atrazine to induce

21 expression of aromatase.

22 What we haven't yet done, because
202-234-4433

Neal R. Gross & Co., Inc.

Page 175

1 this is an ongoing set of studies, is

2 transfected into these cells scrambled forms

3 of the siRNA to make sure that we're not doing

4 something else to the cells that's off target. 

5 Those things will be done and hopefully will

6 be reported to you this summer.

7 So let me finish and conclude. 

8 The in vitro-in vivo dichotomy and the effects

9 of atrazine on aromatase we believe are

10 because atrazine is very rapidly metabolized

11 in vivo with the formation of essentially

12 inactive or weakly active chlorotriazines as

13 well as conjugates, and, therefore, atrazine

14 doesn't stay around long enough to induce

15 aromatase expression.

16 That's in contrast to what we

17 believe is happening in cell types where the

18 triazine that we put in is either not

19 metabolized or only metabolized very slowly,

20 is able to stay around and, likely through a

21 PDE inhibition, able to increase expression of

22 aromatase.
202-234-4433

Neal R. Gross & Co., Inc.

Page 176

1 And, finally, I would point out

2 that in the in vitro system these effects of

3 the triazines happen at very high

4 concentrations. Thank you.

5 SESSION CHAIR PORTIER: Thank you. 

6 We'll open it up for some questions. Dr.

7 Schlenk?

8 DR. SCHLENK: I'm actually really

9 intrigued by your conjugate data because I

10 guess I wouldn't have expected the conjugates

11 to cross the cell membrane and get into the

12 cells. But, yet, the glutathione conjugate

13 does appear to actually impair transcription. 

14 I'm just curious what your thoughts are in

15 particularly the glutathione conjugate and how

16 that may actually be impairing.

17 DR. SIMPKINS: Yes. Keep in mind

18 in all of our study -- in vitro studies we do

19 these assessments with I think -- with 0.01

20 percent DMSO, which is probably affecting

21 solubility of membrane and maybe enhancing the

22 movement of the conjugates into the cells. I
202-234-4433

Neal R. Gross & Co., Inc.

Page 177

1 don't know.

2 DR. GREENWOOD: Richard Greenwood. 

3 You in your figures say that I think it was 85

4 percent was conjugated to glutathione and 15

5 percent not. Which -- what's the source of

6 the data? Because it's sort of flipped on

7 that in quite a few of the rodents. That is

8 where I think it's more or less exactly the

9 converse.

10 DR. BRECKENRIDGE: Okay. There

11 are several sources of the data. In fact,

12 there are regulatory studies in radiolabeled

13 compounds in regard to metabolism in rodents. 

14 And so when Dr. Simpkins mentioned there was

15 a concordance between rodents and primates and

16 humans, there is approximately -- and it's

17 fairly well described, at least in our data

18 and others -- that the chlorals represent only

19 about a maximum 15 percent, and the rest are

20 conjugates.

21 I'm sorry. I don't know the

22 discrepancy between information you have and
202-234-4433

Neal R. Gross & Co., Inc.

Page 178

1 what we understand. But that's roughly the

2 situation. The rates of metabolism and the

3 elimination kinetics are described in some

4 rodent studies. We have done primate studies

5 and we intend to do actually a much more

6 thorough characterization of that on a shorter

7 time scale given the concerns about short time

8 frame events, especially as it relates to the

9 HPA axis.

10 So to some extent we're going to

11 come back with some pharmacokinetic data that

12 gives a proper description of the early time

13 events in the biotransformations using

14 specific analyte techniques to get at all the

15 metabolites that are pertinent I think to the

16 toxicities we're discussing here. So perhaps

17 if you'd give us a chance at the next

18 occasion, we'll have a lot more data on that.

19 SESSION CHAIR PORTIER: Dr.

20 Williams?

21 DR. WILLIAMS: I just have a short

22 question about the different
202-234-4433

Neal R. Gross & Co., Inc.

Page 179

1 phosphodiestrases. Is there any evidence that

2 atrazine affects various PDEs differently? So

3 you looked at 4 and 8, but what about PDE3 or

4 others that are more prevalent function in

5 other tissues?

6 DR. SIMPKINS: We have not

7 assessed specific interaction of atrazine

8 on -- to date on any of the specific

9 phosphodiesterases, some of which, as you

10 know, are cloned and the proteins are

11 available. So those studies are doable -- we

12 just don't have the data on them.

13 The data that I showed is a liver

14 homogenate phosphodiesterase preparation. So

15 atrazine could be affecting any of a number of

16 phosphodiesterases. The other thing you would

17 note is that the maximum suppression produced

18 by atrazine, as well as IBMX, is only about a

19 60, 65 percent reduction in phosphodiesterase

20 activities. So there are other

21 phosphodiesterases that appear to be resistant

22 to both of those compounds.
202-234-4433

Neal R. Gross & Co., Inc.

Page 180

1 SESSION CHAIR PORTIER: Dr.

2 Chambers?

3 DR. CHAMBERS: Back to Dr.

4 Schlenk's question, is it possible in your

5 system that those conjugates were being

6 hydrolyzed?

7 DR. BRECKENRIDGE: Just as a

8 further comment on the aspect of the uptake of

9 chemicals into the cells, we are actually

10 going to do a metabolite profiling in the

11 media and intracellularly with these H295R

12 cells just so we can ascertain whether any

13 biotransformations are occurring.

14 And, secondly, we want to know

15 what the component of, lets say,

16 bioconcentration due to the DMSO is. 

17 Obviously we're interested in biological

18 processes under physiological conditions and

19 not these kinds of model systems that are used

20 for other purposes. So we have those

21 investigations in plan as well.

22 DR. SELVAGE: Dan Selvage. I just
202-234-4433

Neal R. Gross & Co., Inc.

Page 181

1 had a question about the PDEs of 4B and 4D? 

2 Perhaps you can help me out here. Do you know

3 their expression levels in the pituitary or

4 hypothalamus? I thought 4B was in the

5 pituitary but I'm not 100 percent sure on

6 that.

7 DR. SIMPKINS: I don't know

8 relative expression levels of the various

9 subtypes in all the tissues. I don't know

10 about the pituitary.

11 DR. DELCLOS: I'm not sure this is

12 the appropriate place for the question. But

13 since you mentioned pharmacokinetic studies

14 and you mentioned -- you were discussing the

15 solubility issue, I'm wondering if this is --

16 are there pharmacokinetic studies being done

17 closer to levels of human exposure? 

18 There's some things that strike me

19 as unusual about the pharmacokinetic data,

20 either if there's double peak of absorption in

21 the mouse and rat or with the flat level of

22 atrazine appearing over time. And I'm
202-234-4433

Neal R. Gross & Co., Inc.

Page 182

1 wondering if solubility in the gut has

2 something to do with this, these higher dose

3 levels.

4 DR. BRECKENRIDGE: Okay. Thank

5 you very much. We are going to do really a

6 study design prep work after this meeting, and

7 we're going to kick those studies off so that

8 we're welcoming any suggestions that would

9 inform risk characterization as far as

10 biotransformation over short time periods,

11 including lower doses. And actually I think

12 Dr. Swenberg is -- low doses are near and dear

13 to his heart. And we tend to want to do

14 studies in relative -- in meaningful dose

15 ranges, both for animal modeling as well as

16 human risk characterization. So we'll

17 certainly take that into consideration, sir.

18 SESSION CHAIR PORTIER: For the

19 record that was Dr. Delclos asked the

20 question. Any additional comments? 

21 This is probably a good point to

22 break for lunch, but I -- but if you want to
202-234-4433

Neal R. Gross & Co., Inc.

Page 183

1 have a closing remarks on this section,

2 because I know your transition. I looked

3 through your slides and it's more of the non4 cancer stuff.

5 DR. BRECKENRIDGE: I don't think

6 we need to make a closing statement. I'll

7 make an introductory statement for the next

8 two parts then when we return. Thank you very

9 much, sir.

10 SESSION CHAIR PORTIER: So the

11 panel's been sucking this all in. We need

12 biological material to keep going. So we'll

13 break until -- I have 12:02. We'll break till

14 1:15. We'll reconvene at 1:15.

15 (Whereupon, the meeting adjourned,

16 to reconvene this same day, April 27, 2010, at

17 1:15 p.m.)

18

19

20

21

22
202-234-4433

Neal R. Gross & Co., Inc.

Page 184

1 A F T E R N O O N S E S S I O N

2 1:15 p.m.

3 SESSION CHAIR PORTIER: So, Dr.

4 Breckenridge, I guess we'll continue with

5 your -- what was it? -- transition from cancer

6 to non-cancer.

7 DR. BRECKENRIDGE: Thank you very

8 much, Mr. Chairman. So the next sections deal

9 with the toxicologic consequences of the

10 endocrine effects of atrazine, especially as

11 it relates to non-cancer end points.

12 And the first thing we want to

13 talk about is -- you will recall in November

14 we postulated that there was a dose duration

15 phenomenon relative to the responsiveness of

16 the GnRH system to atrazine's effects. And

17 this exhibit -- it appeared in handouts. 

18 Short duration studies of Dr.

19 Cooper referred to these the other day, and

20 these are his data. Single day or three day

21 leads to no effect levels on the LH surge

22 suppression. In our own hands we have shown
202-234-4433

Neal R. Gross & Co., Inc.

Page 185

1 that for five days we get effects as low as 50

2 milligrams per KG. Longer duration studies of

3 the no effect levels progressively get lower

4 and lower. 

5 The Stoker 2000 is an important

6 study, and it didn't specifically measure LH

7 in this particular study, but it measured the

8 time to I believe vaginal opening, which is

9 presumed to be attributed to some effect on

10 pulsatile GnRH release.

11 And then the Morseth study is the

12 six-month study that Dr. Swenberg was speaking

13 of earlier where we measured LH in female

14 Sprague Dawley rats after they had been

15 treated for six months.

16 We recognize that there's an

17 interaction between duration of treatment and

18 sensitivity to the chemical. And so we wanted

19 to postulate or try to separate those

20 components.

21 This slide is a recapitulation of

22 similar kind of characterization, so that the
202-234-4433

Neal R. Gross & Co., Inc.

Page 186

1 shorter duration types of studies that usually

2 set short duration toxicologic end points for

3 the purpose of risk assessment tend to have

4 higher no effect levels. Not all of the

5 responses on this slide are relating to the LH

6 effect, so that there's information here about

7 pregnancy loss and so on.

8 But quite a number of them

9 actually are relating presumably to effects on

10 the LH pulsatile release. And so you go from

11 shorter duration early in life time at no

12 effect levels to older animals that are, in

13 the case of the female Sprague Dawley rat,

14 particularly sensitive.

15 Next slide. So we designed a

16 study that would try -- would ask the question

17 as to whether or not the young animal is more

18 sensitive than the older animal relative to

19 the effects on -- atrazine's effects on LH. 

20 And for this study we had two cohorts of

21 animals. The first cohort called Cohort 1 the

22 dams were treated from the point of conception
202-234-4433

Neal R. Gross & Co., Inc.

Page 187

1 all the way through end of lactation. The

2 pups were receiving atrazine in their milk

3 through the mother at that time. And post4 lactationally we started gavaging the pups

5 with the appropriate dose.

6 These cohorts run for different

7 durations of time. And the first time that we

8 were interested in assessing them was five

9 days after vaginal opening. And this was the

10 earliest time that we thought we could

11 challenge the system to find out if the LH

12 surge would be suppressed. 

13 So that subgroup is the group that

14 we are looking at today. And this subgroup

15 is -- they were treated identical except the

16 dams were not exposed and the pups only were

17 treated post-lactationally. So we can assess

18 whether something's special about continuous

19 treatment versus a short-term treatment. This

20 study is still ongoing and you see we have

21 sacrifices coming out in the middle to late

22 June.
202-234-4433

Neal R. Gross & Co., Inc.

Page 188

1 And these animals who will then

2 have additional 90 days of treatment -- again,

3 we'll assess the LH surge. And at that point

4 we're going to have a recovery subgroup just

5 to see -- or test the hypothesis of

6 reversability, which you'll hear about later

7 from Dr. Handa.

8 And, likewise, we get a window on

9 whether the vulnerability at this time is, in

10 fact, due to continuous treatment or due to,

11 in fact, the age of the animal. So the intent

12 is to try to separate out those factors that

13 are, let's say, a buildup of effect or a

14 deterioration of a function as a function of

15 dose duration versus a simple vulnerability

16 change as the animals age. Obviously we have

17 the hypothesis that it's a vulnerability

18 change that has actually occurred.

19 Next slide. And today we are

20 providing you the information on this. And we

21 have committed to the agency to provide the

22 full report by the end of July.
202-234-4433

Neal R. Gross & Co., Inc.

Page 189

1 Next slide. This is more cut-down

2 version of that particular schema focusing

3 just on the animals that we're going to talk

4 about today. So we're going to be discussing

5 Cohort 1 animals, subgroup A. But, in fact,

6 for certain metrics we're going to give data

7 for group A, B, and C. At this point in age

8 they're all identical. Some of those animals

9 continue on and are dosed for additional

10 duration of time.

11 And in addition to that we were

12 interested in putting in a control group that

13 would show, in fact, that laboratory and this

14 process of treatment and so on could detect LH

15 surge suppression. So we took animals that

16 were 12 weeks of age and dosed them for five

17 days at 100 mgs per kg. We selected that does

18 because we wanted to make sure we were going

19 to get an LH effect. It would have been

20 tidier if we'd have used 50 mgs per kg because

21 that is the high dose in this study. But,

22 nevertheless, we wanted to prove that, in
202-234-4433

Neal R. Gross & Co., Inc.

Page 190

1 fact, in this lab and in this operation we

2 could actually elaborate an effect of atrazine

3 treatment on the LH surge.

4 Next slide. So the first results

5 are from that first cohort of animals that are

6 only treated when they're 12 -- approximately

7 12 weeks of age, and they're given five days

8 of atrazine treatment. They're

9 ovariectomized, and LH surge is induced by the

10 implantation of an estrogen pellet. 

11 The light cycles are set such that

12 we can sample them and get appropriate blood

13 and measure LH. And what we see is, in fact,

14 there is and has been described many times the

15 LH suppression of atrazine at 100 mgs per kg

16 as measured by both mean peak height and area

17 under the curve.

18 Next slide. The animals that were

19 treated either throughout entirely from

20 conception to the point of LH effect at

21 termination were unaffected by atrazine at any

22 dose up to 50 mgs per kg. And, likewise, the
202-234-4433

Neal R. Gross & Co., Inc.

Page 191

1 cohort that was only exposed from end of

2 lactation to onset of sexual maturation were

3 unaffected by atrazine treatment at that age. 

4 The statistics are shown below giving area

5 under the curve and peak height.

6 Next slide. Now, because we took

7 these animals to sexual maturity we were

8 smearing -- or looking for vaginal opening --

9 onset of sexual maturation. That was the

10 entry into the cohort effectively -- they had

11 to reach that end point. So effectively we

12 knew what the vaginal opening dates were or

13 duration to. 

14 And these are the three subgroups

15 in Cohort 1, and this is the combination of

16 the subgroups. And we can see that while we

17 have an LH surge suppression -- or we don't

18 have an effect of atrazine on the LH surge

19 suppression we do have an effect in these

20 young animals on the presumed pulsatile LH

21 release that's attributed to regulating sexual

22 maturation -- or at least vaginal opening.
202-234-4433

Neal R. Gross & Co., Inc.

Page 192

1 So we observe that there's an

2 effective treatment, and the no-effect is 25

3 mgs per kg in this study. That's

4 approximately equivalent to what had been

5 reported previously by Laws et al., Ashby et

6 al. So there's several studies. And we

7 weren't terribly surprised by this result, but

8 it is confirmation of what had been given

9 before.

10 Next slide. As one nuance around

11 this is this is the cohort that were actually

12 treated for the inception onward and that

13 difference was statistically significant. 

14 This difference was not and we don't

15 understand why that is. It's trending up, but

16 it's not increased enough to be significant.

17 So that's just a piece of

18 information that -- next slide. Okay. That

19 is addressing the question of age dependent

20 sensitivity to this particular system, perhaps

21 elaborated by both the LH surge mechanism and

22 the pulsatile LH mechanism. Dr. Handa will
202-234-4433

Neal R. Gross & Co., Inc.

Page 193

1 have considerable more to say about those

2 processes and the effects of atrazine on them. 

3 So, with that, I would stop and

4 maybe if there's any quick questions I could

5 take them. But perhaps we could put them all

6 together after the next piece if that's

7 suitable.

8 SESSION CHAIR PORTIER: Dr.

9 Chambers?

10 DR. CHAMBERS: This is real quick. 

11 Just on the dosing, the dams were given those

12 doses and then the pups were given the very

13 same doses?

14 DR. BRECKENRIDGE: That's correct.

15 DR. CHAMBERS: Okay. 

16 SESSION CHAIR PORTIER: Okay. Dr.

17 Handa.

18 DR. HANDA: Okay. Good afternoon. 

19 My name is Bob Handa. I'm a professor in the

20 Department of Basic Medical Sciences at the

21 University of Arizona, College of Medicine.

22 Today what I'd like to do is spend
202-234-4433

Neal R. Gross & Co., Inc.

Page 194

1 a little time telling you about some of the

2 studies that are ongoing in my lab regarding

3 the examination of the mechanisms that might

4 underlie atrazine suppression of LH secretory

5 patterns. I think Dr. Breckenridge showed you

6 a number of these questions that we'll try and

7 answer today regarding the LH -- the

8 hypothalamic-pituitary-gonadal axis and its

9 regulation or its modification by atrazine

10 exposure.

11 What I'd like to do today is spend

12 a brief period of time addressing these first

13 six questions. I want to thank Ralph Cooper

14 for introducing the topic. This will allow me

15 to go a little faster here. 

16 And then bulk of my time today

17 will be spent hopefully addressing these lower

18 two questions, and that is whether atrazine

19 alters the secretion of other hormones, most

20 notably those involved in the hypothalamic21 pituitary-adrenal axis, and whether these

22 changes in the HPG axis that we see following
202-234-4433

Neal R. Gross & Co., Inc.

Page 195

1 atrazine administration are secondary to the

2 changes in the HPA axis.

3 So you've seen this type of data

4 before showing that atrazine can reduce the

5 hormonal induced LH surge in a female rat --

6 rodent. This is data taken from Wistar

7 females -- young adult Wistar females. And on

8 the left side you can see that, in fact, under

9 hormonal stimulation there's an increase in LH

10 that occurs in the afternoon. And when

11 atrazine is administered by gavage at three

12 different doses, 5100 and 200 milligrams per

13 kilogram, we can obliterate that LH surge.

14 We went one step forward. And,

15 again, just to verify that this, in fact,

16 might be due to effects generated at the GnRH

17 neuron, or above the pituitary, we asked the

18 question as to whether these effects are

19 mediated by changes in pituitary sensitivity

20 to GnRH.

21 But these data show that, in fact,

22 if we take estrogen treated ovariectomized
202-234-4433

Neal R. Gross & Co., Inc.

Page 196

1 female rats and we obliterate their endogenous

2 GnRH by passive immunization that we can then

3 have them respond to a synthetic GnRH agonist,

4 D-ALA-6 GnRH, and, in fact, there's no

5 difference in the response to following

6 several different doses of atrazine.

7 So these results suggest that the

8 pituitary sensitivity to GnRH has not changed

9 and that the effects we see on the HPG axis

10 are actually due to changes in hypothalamic

11 function.

12 Now, to address this we used a

13 GnRH EGFP transgenic rat model -- and in this

14 rat model has been engineered such that GnRH

15 neurons express enhanced green fluorescent

16 protein which allows you to visualize these

17 neurons in situ. We can couple this

18 immunocytochemistry for a protein called cFos,

19 and cFos is commonly used to demonstrate

20 neuronal activity. When neurons are active

21 cFos is expressed within the nucleus. And

22 what we have known for several years now is
202-234-4433

Neal R. Gross & Co., Inc.

Page 197

1 that during the LH surge that GnRH neurons

2 express cFos during the LH surge but not

3 before and not after the LH surge.

4 So we embarked on an experiment to

5 determine if cFos was expressed in GnRH

6 neurons and if atrazine modified that

7 expression of cFos. What you can see is the

8 panel on the right -- is that during the LH

9 surge you get about 75 percent of GnRH neurons

10 that express cFos indicating that their

11 active, and that at high doses of atrazine,

12 doses that inhibit the LH surge, you actually

13 see a much reduced activation of GnRH neurons,

14 suggesting that GnRH neurons -- changes in

15 GnRH neuron activity may, in fact, underlie

16 the deficits in LH secretion that we see in

17 those animals treated with atrazine.

18 Now, based on these findings we

19 also hypothesized that perhaps GnRH neurons

20 might be altered in terms of their ability to

21 produce GnRH. As a result, we examined GnRH

22 mRNA as shown in this slide as well as other
202-234-4433

Neal R. Gross & Co., Inc.

Page 198

1 primary transcript for GnRH. And as this

2 slide shows we found no evidence that there

3 are alterations in GnRH synthesis or

4 expression.

5 Also, we counted the number of

6 cells using in situ hybridization, as well as

7 following immunocytochemistry, and found no

8 changes in GnRH expression throughout the rat

9 brain.

10 As a result of these studies we

11 hypothesized that the GnRH neuronal system may

12 be, in fact, not altered by GnRH and that --

13 or may not be altered by atrazine and, as a

14 result of the response of the GnRH system to

15 estrogen, may, in fact, recovery following

16 cessation of atrazine treatment.

17 So in this study we examined the

18 ability to induce an LH surge in animals that

19 were tested two days and four days after the

20 cessation of atrazine treatment which occurred

21 for four days prior. What you can see, again,

22 is that when tested immediately at the end of
202-234-4433

Neal R. Gross & Co., Inc.

Page 199

1 those four daily gavages with atrazine that

2 you can see a decrease in the peak of the LH

3 surge, and that's coupled with a decrease in

4 the activity of GnRH neurons as measured by

5 Fos expression -- that within two days after

6 the termination of treatment that the peak of

7 the LH surge begins to recover, and it's

8 normal by four days following cessation of

9 treatment, and that the same occurs in the

10 activity of GnRH neurons.

11 These data suggest then that the

12 inhibition of GnRH and the LH surge is

13 transient and is very rapidly recovered

14 following the loss of exposure to atrazine.

15 We also examined pulsatile hormone

16 secretion -- and Dr. Cooper mentioned this

17 briefly yesterday. In the absence of hormone

18 administration LH and GnRH are released in a

19 pulsatile fashion. And they are coupled in

20 time such that each quanta of GnRH that's

21 released from the hypothalamus initiates a

22 small pulse of LH in response.
202-234-4433

Neal R. Gross & Co., Inc.

Page 200

1 This pulsatile pattern of LH and

2 GnRH are of physiological importance. If GnRH

3 is released in a constant fashion it very

4 quickly loses the ability to drive LH

5 secretion.

6 So in these data you can see that,

7 in fact, if we sample quickly enough we can

8 identify discrete increases in LH secretion

9 with a pulse period of about 30 minutes. And

10 if we treat animals with atrazine ultimately

11 what we see is something that looks like this

12 where the number of pulses in a sampling

13 session is dramatically reduced. And when we

14 do see those pulses they're much larger. This

15 suggests that there is a disruption of the

16 GnRH pulse generator, and that's a very

17 important component of the hypothalamic18 pituitary-gonadal axis.

19 Now, just to show you that this

20 is -- my last slide showed you pulses of LH. 

21 Just to show you that the same changes occur

22 in regard to GnRH secretion we began to
202-234-4433

Neal R. Gross & Co., Inc.

Page 201

1 examine GnRH release in an ex vivo system

2 where hypothalamic slices were peri-fused with

3 media, and the results are shown here. 

4 Animals were treated with 200 milligrams per

5 kilogram atrazine for four days. 

6 The hypothalami were removed. 

7 They were put into the superfusion chamber and

8 then we examined pulsatile GnRH release from

9 these hypothalami. And what you can see is

10 hypothalami taken from control animals have

11 normal looking pulses of GnRH, which, again,

12 occur about every 30 minutes. And hypothalami

13 taken from animals that were treated with 200

14 milligrams per kilogram atrazine you can see

15 that the GnRH pulses are dramatically reduced

16 in frequency, and when they do show up they're

17 dramatically elevated in amplitude.

18 As a result we see no real change

19 in mean GnRH levels or area under the curve. 

20 It's really just the dynamics of the system. 

21 Peak amplitude is increased and the number of

22 peaks is reduced by atrazine.
202-234-4433

Neal R. Gross & Co., Inc.

Page 202

1 Okay. So I want to change gears

2 just a little bit. I think those data

3 indicate that there are dramatic effects of

4 atrazine on the hypothalamic-pituitary-gonadal

5 axis, both in terms of pulsatile hormone

6 secretion, as well as the generation of the

7 pre-ovulatory surge of LH. 

8 And we embarked on a series of

9 studies -- I'll tell you a little bit about

10 this one. Looking for change -- potential

11 changes in endocrine and immunological

12 function within male Sprague Dawley rats the

13 design of the experiment looks like this where

14 we have animals that were treated with three

15 different doses of atrazine -- 6.5, 25, and

16 100 milligrams per kilogram per day for either

17 1, 7, 14, or 28 days. The animals at 28 days

18 will be taken for an examination of

19 immunological function by Steve Pruett. 

20 We were able to take these animals

21 on 1, 7, and 14, and that's the data I'll

22 report to you today. We have not yet analyzed
202-234-4433

Neal R. Gross & Co., Inc.

Page 203

1 these -- the blood from these animals at 28

2 days.

3 So the design of the experiment

4 then was to treat the animals with atrazine or

5 a vehicle control and then to sacrifice them

6 30 minutes after the end of gavage and examine

7 the influence of that atrazine -- acute

8 treatment of atrazine on a number of different

9 hormones.

10 Now, I have to tell you a little

11 bit about our approach to measuring these

12 hormones. We use classical antibody based

13 approaches to measure hormones such as

14 corticosterone, prolactin, and progesterone,

15 as well as ACTH. I won't report the data on

16 ACTH -- we don't have it yet. 

17 We also used a different

18 approach -- liquid chromatography tandem mass

19 spectrometry was used to measure the serum

20 estrogens -- estrone, estradiol, and

21 estriol -- as well as the androgens --

22 androstenedione, testosterone, and
202-234-4433

Neal R. Gross & Co., Inc.

Page 204

1 dihydrotestoterone -- and we have not yet

2 obtained the data on aldosterone levels.

3 Now, why would we want to use the

4 LCMS to measure steroid hormones? I think

5 there's been recently a controversy regarding

6 the ability to measure estrogens and androgens

7 using antibody based approaches. There's some

8 evidence that there's very low fidelity in the

9 different assays that are used to measure

10 these hormones, particularly when you get to

11 low levels. And by low levels we mean about

12 100 picograms or less. We also used

13 deuterated steroids as internal standards so

14 that we could identify exactly where peaks of

15 these hormones ended up on the chromatograms.

16 Let me just briefly describe the

17 biosynthesis of steroid hormones so you can

18 which of these hormones we began to examine. 

19 All steroid hormones are derived from a common

20 precursor, and that precursor is cholesterol,

21 which is taken up into steroid synthesizing

22 tissues. It's then transferred into the
202-234-4433

Neal R. Gross & Co., Inc.

Page 205

1 mitochondria by this steroidogenic acute

2 regulatory protein, and then it's acted upon

3 by a number of enzymes. The rate limiting

4 enzyme is P450 side chain cleavage. And, as

5 a result, you can see that then flows downhill

6 depending on the presence of these different

7 enzymes in different tissues.

8 But what you can immediately see

9 here is these hormones that are -- these

10 steroids that are indicated in green are the

11 progestins, and they actually give rise to all

12 the other steroid hormones -- the

13 mineralocorticoids, such as aldosterone, the

14 glucocorticoids, such as cortisol, the

15 androgens, such as androstenedione,

16 testosterone, and DHT, which are the

17 predominant androgens found in the rat. And

18 androgens give rise to the estrogens --

19 estrone, estradiol, and estriol -- which are

20 the predominant estrogens also found in the

21 rat.

22 So we measure plasma levels of
202-234-4433

Neal R. Gross & Co., Inc.

Page 206

1 progesterone, of aldosterone, of

2 corticosterone, androstenedione, testosterone,

3 dihydrotestosterone, estrone, estradiol, and

4 estriol. 

5 And I'll show you the results of

6 these studies. I should first tell you a

7 little bit about some of the other results

8 from this series of studies. Again, animals

9 were kept on atrazine at least three doses for

10 up to 28 days, and this is the result of

11 results showing changes in body weight. I

12 think someone from the panel asks whether

13 atrazine alters body weight, and what you can

14 see here is at the highest dose -- 100

15 milligrams per kilogram -- per day there's a

16 significant decrease in body weight. The

17 changes that you see at the 6.5 and the 25

18 milligram per kilogram dose did not reach

19 significance.

20 Someone else also asked whether

21 there are effects of atrazine on adrenal

22 weight yesterday, and we will report now that
202-234-4433

Neal R. Gross & Co., Inc.

Page 207

1 there was no effect of atrazine at any one of

2 the three doses on adrenal weight, whether

3 calculated on absolute adrenal weight or an

4 adrenal weight to body weight ratio.

5 What we find if we look at some of

6 these hormones -- for example,

7 corticosterone -- we find that, in fact,

8 following the initial treatment with atrazine,

9 we see a very dramatic increase in

10 corticosterone. Again, this is 30 minutes

11 after gavage, and we see that there is a dose

12 response curve. We have not yet found the no13 effect level because 6.5 milligrams per

14 kilogram effectively increases corticosterone,

15 but albeit not to the degree seen following

16 100 milligrams per kilogram.

17 Of interest is the fact that after

18 seven daily doses of atrazine we see no

19 increase in corticosterone in these animals. 

20 And this is maintained throughout day 14 -- or

21 14 daily doses -- suggesting that the response

22 of these animals -- of male Sprague Dawley
202-234-4433

Neal R. Gross & Co., Inc.

Page 208

1 rats to atrazine is habituated somewhere

2 between the first and the seventh day of

3 treatment.

4 We continue on. We can measure

5 progesterone, and progesterone is another

6 hormone -- steroid hormone of adrenal origin

7 in the male. Progesterone is largely secreted

8 in response to ACTH administration. And what

9 you can see is the same thing that happens for

10 corticosterone also occurs for progesterone. 

11 Within 30 minutes after the first injection of

12 atrazine, we see dramatic increases in

13 progesterone, which are lost by the seventh

14 and the fourteenth exposure to atrazine.

15 Interestingly, there are no

16 changes in prolactin to response to atrazine

17 administration at any of the doses for any of

18 the time periods. And this suggests that the

19 effects of atrazine on corticosterone and

20 progesterone are not a generalized stress

21 response because in most instances prolactin

22 rises following a stressor.
202-234-4433

Neal R. Gross & Co., Inc.

Page 209

1 We also examined the androgens, as

2 I indicated, using LCMS. These are the

3 results shown here. We find no effect of

4 atrazine at 1 or 7 or 14 days of treatment at

5 any of the doses on plasma levels of

6 androstenedione, testosterone or

7 dihydrotestosterone. There is a trend for the

8 longest time point examined -- 14 days -- and

9 the highest dose -- 100 milligrams per

10 kilogram -- to reduce androstenedione levels. 

11 But these did not reach significance.

12 Similarly, we also measured the

13 estrogens -- estradiol, estrone, and estriol. 

14 And what we report now is that for all the

15 groups there was a substantial number of them

16 below assay sensitivity -- more than 50

17 percent in every group. And as a result we

18 have to report that the average values are

19 below assay sensitivity.

20 The sensitivity of this assay is

21 very low in terms of milligram -- or picograms

22 per ML. For estradiol and estrone it's 2
202-234-4433

Neal R. Gross & Co., Inc.

Page 210

1 picograms per ML. For estriol it's 10

2 picograms per ML. And for the rodent that's

3 at the very low physiological end of the

4 scale.

5 These chromatograms then show

6 standards for each of these hormones -- and

7 you can see it's very selective in terms of

8 what we're measuring. And so we have an assay

9 that is very specific and very sensitive, and

10 we aren't measuring changes in any of the

11 estrogens. This assay was originally

12 developed by Steve Soldin at Georgetown

13 University to measure low levels of estrogens

14 and androgens.

15 So this suggests that, in fact,

16 there are some changes in response to atrazine

17 in the male. To be honest, we have not yet

18 run as extensive an experiment in females. 

19 This is a little bit harder to address just

20 because of the cyclicity of females. But,

21 nonetheless, I'll try and piece together the

22 results of several different studies to
202-234-4433

Neal R. Gross & Co., Inc.

Page 211

1 demonstrate to you that, in fact, there may be

2 similar changes that occur in females.

3 So in this study we examined the

4 acute response of the components of the HPA

5 axis, particularly corticosterone on --

6 following atrazine administration at two

7 different doses of 50 and 200 milligrams per

8 kilogram -- and what we see, in fact, in

9 females -- ovariectomized females that we find

10 a very rapid increase in corticosterone that

11 peaks within 20 minutes. And at the highest

12 dose these levels of corticosterone are

13 actually maintained elevated for up to 12

14 hours. So this is a very unusual type of

15 corticosterone response in that it's extended

16 for a long period of time. At lower doses,

17 the response is much briefer, and we get a

18 peak which rapidly drops down to the levels of

19 the controls. You can also see here that

20 gavage by itself causes an increase in

21 corticosterone.

22 Results taken from a different
202-234-4433

Neal R. Gross & Co., Inc.

Page 212

1 study in which animals were exposed to five

2 daily doses of atrazine at the 100 milligram

3 per kilogram dose show that, in fact, after

4 five daily doses, we seem to find also that

5 there's an increase in corticosterone. In

6 particularly, these animals were also measured

7 for LH responses to estrogen, and we find no

8 LH surge in response to estrogen.

9 And, lastly, in this series of

10 slides, I want to introduce some studies

11 performed by Dr. Pruett who will address these

12 in more detail shortly in that he used female

13 mice -- not rats. These animals were

14 administered atrazine for up to 28 days. They

15 were administered in a different route --

16 intraperitoneally. But what you can see if

17 after a single exposure to atrazine that

18 there's a dramatic rise in corticosterone --

19 the levels in mice are much greater than those

20 achieved in rats -- and that this is

21 maintained for over four hours and that 28

22 days later -- after 28 daily exposures to
202-234-4433

Neal R. Gross & Co., Inc.

Page 213

1 atrazine you find the same effect is present,

2 suggesting that in females there's very little

3 habituation to atrazine exposure in that

4 corticosterone responds in a similar fashion

5 through at least four weeks.

6 Okay. So this begs the question

7 as to whether or not the product of the HPA

8 axis -- that is corticosterone -- can

9 influence the HPG axis. And we know from a

10 number of studies that corticosterone and

11 perhaps CRH can negatively impact the

12 hypothalamic-pituitary-gonadal axis.

13 So we asked the question as to

14 whether or not if we disrupted this axis,

15 particularly if we adrenalectomized the

16 animals, whether this would affect the ability

17 of atrazine to disrupt the hypothalamic18 pituitary-gonadal axis.

19 The design of the study was to

20 adrenalectomize or sham adrenalectomize these

21 animals and then begin treatment with 200 or

22 50 milligrams per kilogram atrazine. And at
202-234-4433

Neal R. Gross & Co., Inc.

Page 214

1 the end, initially we examined these animals

2 for pulsatile hormone secretion. We used a in

3 dwelling right atrial cannula to collect

4 samples every five minutes for a three-hour

5 period. If you remember from our previous

6 results, atrazine normally decreases the

7 frequency of these pulses and increases the

8 amplitude of these pulses. 

9 These are the results of this

10 study in which in the sham animals, again, you

11 can see following exposure to -- well,

12 following exposure to atrazine at these two

13 doses you see a decrease in pulse frequency

14 coupled with an increase in pulse amplitude. 

15 And if we remove the adrenals you see no

16 effects of atrazine on those hormonal changes,

17 suggesting that pulsatile LH is driven by

18 changes in corticosterone levels.

19 Of course, we also examined the

20 hormonal induced surge of LH and asked the

21 question as to whether adrenalectomy can block

22 the effects of atrazine on the estrogen
202-234-4433

Neal R. Gross & Co., Inc.

Page 215

1 induced LH surge. And remember that atrazine

2 very potently inhibits the LH surge.

3 These are the results of that

4 study. We can induce an LH surge with

5 estrogen and progesterone. That treatment

6 with atrazine reduces the peak amplitude and

7 the area under the curve of LH secretion, and

8 that in the adrenalectomized animals those

9 effects of atrazine are identical, suggesting

10 that for the LH surge generation that adrenal

11 secretions are not responsible for the

12 inhibition seen by atrazine treatment.

13 Okay. So let me summarize very

14 quickly here. What we know is that atrazine

15 increases corticosterone and progesterone

16 secretion within 30 minutes after a single

17 treatment. But this disappears by 7 or 14

18 days in male Sprague Dawley rats. That in

19 females, atrazine also increases

20 corticosterone following a single dose and

21 that this -- these levels may remain elevated

22 for a long period of time and that females do
202-234-4433

Neal R. Gross & Co., Inc.

Page 216

1 not seem to habituate to this effect as do

2 males.

3 We find no effect of atrazine on

4 prolactin, on any of the androgens measured,

5 or on any of the estrogens measured. And this

6 suggests that changes in vitro -- reported in

7 in vitro studies showing increases in

8 aromatase may not actually correspond to

9 physiological changes in hormone levels.

10 And, lastly, we show that

11 adrenalectomy blocks the effects of atrazine

12 on the LH pulses, but doesn't block the

13 effects of atrazine on the LH surge. And this

14 suggests that the effects of corticosterone on

15 the HPG axis may not come into play in terms

16 of the loss of cyclicity seen in these animals

17 with age.

18 So some key points I want you to

19 remember when driving home tonight, and that

20 is that high doses of atrazine can inhibit the

21 HPG axis -- I know you're all going to be

22 thinking about this on the way home -- and
202-234-4433

Neal R. Gross & Co., Inc.

Page 217

1 that the effects on the HPG axis are reversed

2 following cessation of treatment, that the

3 effects of atrazine on the HPG axis are

4 secondary, perhaps to changes in HPA axis

5 activity in that adrenalectomy can block the

6 effects of atrazine on pulsatile hormone

7 secretions but not on the LH surge.

8 And the fact is that at this point

9 in time we find no effects of atrazine on

10 circulating androgen or estrogen levels in

11 male rats using LCMS.

12 So some of the things that we're

13 approaching in terms of identifying how this

14 might work, we are testing the ability of

15 atrazine to activate CFR neurons or the

16 hypothalamic centers -- neurons involved in

17 regulating the HPA axis. We can passively

18 immunoneutralize HCTH and get HCTH out of the

19 picture, and that will tell us whether

20 atrazine can effectively activate the adrenal

21 directly.

22 And we also want to determine
202-234-4433

Neal R. Gross & Co., Inc.

Page 218

1 whether the effects of atrazine we see on

2 hormone secretion are differences between our

3 measurements and those of other labs are due

4 to methodological considerations, and so we're

5 going to measure using antibody based assays

6 as well as LCMS assay to measure these

7 changes. Are there any questions?

8 SESSION CHAIR PORTIER: Yes. Dr.

9 Horton?

10 DR. HORTON: Thank you. Theresa

11 Horton, Northwestern University. In the

12 female estrocycle or menstrual cycle,

13 whichever group of species you want to

14 consider, there's a switch between a period of

15 negative feedback and positive feedback. And

16 it appears that what you have delineated in

17 your work here is potentially a need to focus

18 on those two separate components of the cycle

19 independently, and that the HPA axis

20 components may be, in fact, impacting the

21 negative feedback components, if that is

22 influencing the pulsatile secretion. Would
202-234-4433

Neal R. Gross & Co., Inc.

Page 219

1 you agree with that?

2 DR. HANDA: I'm not sure -- 

3 DR. HORTON: Or suggest -- 

4 DR. HANDA: I'm not sure I would

5 agree with that completely. The pulses that

6 we measured in terms of pulsatile LH

7 secretion, of course, were measured in the

8 absence of negative feedback.

9 DR. HORTON: Right.

10 DR. HANDA: That's the only way we

11 can actually see that.

12 DR. HORTON: Okay.

13 DR. HANDA: Okay. So we don't

14 really know if, in fact, there's a change in

15 sensitivity to estrogen negative feedback. We

16 have to address that -- 

17 DR. HORTON: That's true.

18 DR. HANDA: -- by actually looking

19 at the ability of estrogen to inhibit those

20 pulses and then we will be able to answer

21 that.

22 DR. HORTON: Okay. And the second
202-234-4433

Neal R. Gross & Co., Inc.

Page 220

1 part to that is then the LH surge mechanism is

2 a positive feedback mechanism. And since

3 there's ample evidence that there is something

4 altering that positive surge mechanism it

5 appears the potential of that needs to be

6 investigated independently and maybe a

7 component of impacts that may be part of

8 modification of the HPG regulatory mechanisms.

9 DR. HANDA: And I agree with that

10 completely.

11 DR. HORTON: Okay.

12 DR. HANDA: I think we are looking

13 at two different, although related, mechanisms

14 driving LH and GnRH neural activities -- 

15 DR. HORTON: Okay.

16 DR. HANDA: -- and that just

17 because we influence one doesn't necessarily

18 say that we can influence the other. 

19 DR. HORTON: Okay. Thank you.

20 DR. HANDA: So it appears that

21 corticosterone may -- the system may be much

22 more sensitive to corticosterone when we
202-234-4433

Neal R. Gross & Co., Inc.

Page 221

1 measure pulsatile hormone secretion.

2 DR. HORTON: Thank you. 

3 SESSION CHAIR PORTIER: Dr.

4 Selvage?

5 DR. SELVAGE: Yes. That was very

6 nice. You addressed a lot of the questions

7 that I've been asking myself in the past

8 couple of weeks here. I just had a couple of

9 questions, and you went pretty fast so if I

10 just missed something forgive me.

11 So in your chronic studies when

12 you gave atrazine chronically, did you ever

13 measure cort levels, you know, like, say,

14 prior to the atrazine application?

15 DR. HANDA: No, we didn't. 

16 DR. SELVAGE: Okay. 

17 DR. HANDA: No, we didn't. All

18 the analyses were done as compared to the

19 vehicle treated control at the same time.

20 DR. SELVAGE: Okay. I think that

21 would be a useful measure.

22 DR. HANDA: It would add a number
202-234-4433

Neal R. Gross & Co., Inc.

Page 222

1 of animals to the group -- 

2 DR. SELVAGE: Yes.

3 DR. HANDA: -- and so, you know, in

4 order to save some animal -- 

5 DR. SELVAGE: Okay.

6 DR. HANDA: -- life we --

7 DR. SELVAGE: And just a technical

8 question, in your ex vivo GnRH studies what

9 was the media that you dissolved the atrazine

10 in? You know, I've been thinking, well, you

11 can shoot this stuff in ICV or whatever, but

12 you'd probably get a -- you know, a vehicle

13 effect if you tried to do that.

14 DR. HANDA: Right.

15 DR. SELVAGE: So what media did

16 you use for that?

17 DR. HANDA: So that was using

18 artificial CSF. Again, that was all in

19 vitro -- 

20 DR. SELVAGE: Right.

21 DR. HANDA: -- that tissue was

22 taken out.
202-234-4433

Neal R. Gross & Co., Inc.

Page 223

1 DR. SELVAGE: Right.

2 DR. HANDA: We made sagittal

3 slices and then placed them into the

4 superfusion chambers and ran artificial CSF

5 over the top of that, either containing --

6 well, in this case there was nothing else in

7 that CSF. So the atrazine exposure became --

8 came earlier than the examination of pulses. 

9 Okay?

10 DR. SELVAGE: That's it for now.

11 DR. HANDA: Okay. Thank you.

12 SESSION CHAIR PORTIER: Dr.

13 LeBlanc?

14 DR. LEBLANC: That was a very nice

15 presentation. So one of the take-home

16 messages that that I got from it was that

17 males habituate and females don't with respect

18 to corticosteroid -- or atrazine treatment

19 with respect to corticosteroid levels. It

20 wasn't obvious to me as to why. Is there an

21 obvious mechanism there or reason?

22 DR. HANDA: I don't think there's
202-234-4433

Neal R. Gross & Co., Inc.

Page 224

1 an obvious mechanism. Obviously sex

2 differences in stress responses have been

3 explored for years and years. And one of the

4 things that has come about if you look at the

5 literature is that there are some sex

6 differences in habituation to a stressor. 

7 And, again, females don't necessarily

8 habituate as fast as males. That's stressor

9 specific. It usually involved estrogen and

10 testosterone being onboard. In this case

11 we're using a very different system so we

12 can't really say why males may habituate and

13 females won't.

14 DR. LEBLANC: At least at face

15 value, it seems that there's some lack of

16 concordance between what we saw just now and

17 some of the studies we were exposed to

18 yesterday -- things like testosterone levels

19 being affected or not being affected,

20 estradiol levels being affected or not being

21 affected. 

22 And, again, I guess my question's
202-234-4433

Neal R. Gross & Co., Inc.

Page 225

1 a general one. Have you -- is it obvious to

2 you why we see these inconsistencies? Is

3 it -- if I look more closely at the dosages,

4 would that explain it or the sex differences

5 or the duration of exposure?

6 DR. HANDA: What we believe is

7 that when measuring -- and I should go back to

8 a couple of recent reports, one by the College

9 of American Pathologists, where they did a

10 test and submitted samples to a number of

11 different companies for measuring estrogen,

12 testosterone by antibody based assays. They

13 left it up to the companies. 

14 They found a variation within what

15 those companies produced using different

16 antibodies of up to ninefold from the lowest

17 to the highest, the idea being that when you

18 use the antibody to measure something you're

19 really dependent on the specificity and the

20 sensitivity of that antibody to detect it.

21 And there have been some reports

22 that antibodies may -- especially steroid
202-234-4433

Neal R. Gross & Co., Inc.

Page 226

1 antibodies because they're directed against a

2 very small molecule -- that they may interact

3 with something that we call a matrix. Okay? 

4 And that's really a fancy word for something

5 in the assay tube. And as a result we don't

6 necessarily get the exact right levels,

7 especially when we're looking at low levels.

8 But, again, we're going to test

9 that by trying to directly compare our

10 measurements using LCMS versus an antibody

11 based approach.

12 SESSION CHAIR PORTIER: Dr. Schlenk

13 and then Dr. Regal.

14 DR. SCHLENK: Yes. Just to follow

15 up on Jerry's comment there. Am I right in

16 terms of your slide saying that you didn't

17 detect any estrogen in any group in the

18 controls?

19 DR. HANDA: The majority of

20 animals had levels below detectability. We

21 did detect some.

22 DR. SCHLENK: Even the controls.
202-234-4433

Neal R. Gross & Co., Inc.

Page 227

1 DR. HANDA: Even in the controls. 

2 And the idea is that circulating estrogen

3 levels are not very high in males. They are

4 in females -- well, in rats the highest level,

5 physiological levels are in about the 50

6 picogram per ml range and basal levels are in

7 the 10 to 15 picogram range for estradiol,

8 which means in males they're going to be way

9 down there in the 2 picogram range -- very

10 difficult to detect.

11 DR. SCHLENK: Okay. Because, I

12 mean, if I compare slide 13 -- I mean, they're

13 in the 20 to 40 picogram per ml range, and

14 you're saying there's that much discrepancy

15 between an RIA and your LCMS method?

16 DR. HANDA: I mean, basically

17 there's that much discrepancy between RIAs. 

18 Okay. So if you examine different places that

19 use different assays -- different

20 antibodies --

21 DR. SCHLENK: Sure.

22 DR. HANDA: -- to detect estrogen
202-234-4433

Neal R. Gross & Co., Inc.

Page 228

1 levels sometimes you'll see a 10 and sometimes

2 you'll see an 80. And even if it's the same

3 sample -- 

4 DR. SCHLENK: Well, I understand

5 that. That's -- 

6 DR. HANDA: -- it's got much

7 variation.

8 DR. SCHLENK: That's a pretty

9 small variation I would think as opposed to a

10 tenfold difference. That seems pretty

11 dramatic actually, I mean, particularly when

12 you're not seeing any estrogen in a -- 

13 DR. HANDA: And, again, I can only

14 say, again, we measured estrogen levels in

15 those females that we showed following -- up

16 to 12 hours of increases following

17 corticosterone. And, again, we see no

18 detectable estrogen -- estradiol levels in

19 those animals.

20 DR. BRECKENRIDGE: Mr. Chairman, I

21 submit that this is an experimental question

22 that we'll elucidate, and we'll do it by
202-234-4433

Neal R. Gross & Co., Inc.

Page 229

1 making comparisons between the two

2 methodologies and then we'll come back. Thank

3 you.

4 SESSION CHAIR PORTIER: Dr. Regal

5 and Dr. O'Byrne.

6 DR. REGAL: Just back to that

7 habituation thing for a second, you know that

8 male rats habituate but female mice don't. 

9 You don't know about -- so you've got two

10 species in there too. Right?

11 DR. HANDA: Exactly. And that was

12 the caveat with the study. We piecemealed

13 together evidence from a number of different

14 studies. We know that five days of treatment

15 in rats -- they still show a response. But

16 that's not inconsistent with the data we

17 showed in the male because we only looked at

18 seven.

19 DR. O'BYRNE: I was about to say a

20 mouse is not a small rat. But can I ask you

21 a question about Fos. It's a very powerful

22 tool. And you showed complete obliteration of
202-234-4433

Neal R. Gross & Co., Inc.

Page 230

1 the LH surge with your 50 milligram dose, but

2 very little -- well, you don't show

3 significant tenuation of the Fos activation. 

4 How do you account for that?

5 DR. HANDA: So we don't know for

6 sure. And the idea with the Fos is we're

7 taking a snapshot of Fos expression at one

8 particular time. Okay?

9 DR. O'BYRNE: Of course.

10 DR. HANDA: Which was 1700 hours,

11 which is what we estimated beforehand about

12 the peak of the LH surge. Now, when the LH

13 surge is reduced in amplitude, but perhaps not

14 all the way, sometimes it shifted --

15 oftentimes we see it shifted in time. Okay? 

16 So the induction of Fos expression in GnRH

17 neurons is time locked so that it doesn't just

18 pop on in all neurons. It slowly creeps up on

19 all those neurons and you kind of see a wave

20 of Fos expression occurring through the

21 population of GnRH neurons. If we miss the

22 peak we're going to hit something that's not
202-234-4433

Neal R. Gross & Co., Inc.

Page 231

1 necessarily as robust.

2 DR. O'BYRNE: So delay in the

3 surges is reasonable, and there's evidence for

4 that as well.

5 DR. HANDA: In fact, every time

6 we've measured the presence of an LH surge but

7 reduce in size that surge is delayed in onset. 

8 The samples I showed you today -- it was

9 basically obliterated so you couldn't tell

10 that.

11 DR. O'BYRNE: I have one other

12 question relating to the adrenalectomy. I

13 mean, I'm fascinated that you can't block the

14 effects of the surge. What do you think is

15 going on there?

16 DR. HANDA: We were very surprised

17 when that happened. I don't know. We have no

18 idea -- 

19 DR. O'BYRNE: So it's quite

20 critical, isn't it?

21 DR. HANDA: It is. It is. And so

22 we are very happy when we saw that there was
202-234-4433

Neal R. Gross & Co., Inc.

Page 232

1 a decrease of the obliteration of the effect

2 on LH pulses. We expected to see a

3 decrease -- or prevention of the inhibition of

4 the LH surge following adrenalectomy -- didn't

5 show up. And so we're scratching our heads

6 and we've got to go back and figure out why

7 that is.

8 DR. O'BYRNE: You're not only one.

9 SESSION CHAIR PORTIER: Last two

10 questions and then we'll move on. Let me

11 start with -- actually let's do Dr. Akana. 

12 You had one already.

13 DR. AKANA: On your reversal with

14 the removal of atrazine did you measure

15 corticosterone in those animals?

16 DR. HANDA: Unfortunately we

17 didn't -- 

18 DR. AKANA: Okay.

19 DR. HANDA: -- because it was

20 much earlier on before we began to explore the

21 contribution of corticosterone.

22 DR. AKANA: And Part B, on your
202-234-4433

Neal R. Gross & Co., Inc.

Page 233

1 long body weight studies where you had the

2 per-fed controls -- okay, so these are running

3 28 days -- have you started considering to

4 measure leptin or to measure nasal-anal length

5 to see what kind of growth you are getting in

6 the two different -- the control food

7 restricted and the high dose?

8 DR. HANDA: That's an excellent

9 idea. I think we should consider that.

10 DR. SELVAGE: Dan Selvage again. 

11 Just one question. Hopefully I'm not going

12 off on too much of a tangent. But considering

13 your expertise in HPA and HPG axes, do you

14 think there could be a disruption of the

15 neuradrenergic -- central neuradrenergic

16 system going on that could explain both of

17 these effects?

18 DR. HANDA: That's a possibility. 

19 That's been suggested before actually by Dr.

20 Cooper in terms of some of the data that he's

21 been able to garnish over the last ten years. 

22 And so I would not want to misinterpret his
202-234-4433

Neal R. Gross & Co., Inc.

Page 234

1 data. I guess I'd have to ask him more

2 specifically after this.

3 SESSION CHAIR PORTIER: I think at

4 this point we'll move on and forewarn Dr.

5 Pruett that he's going to face a lot of

6 questions. Linda, do you have a quick

7 question? I kept -- I thought I kept seeing

8 something but I -- 

9 DR. YOUNG: No. I just -- I'm

10 really surprised that some of these results

11 aren't significant. So I was just questioning

12 kind of what's -- whether you powered your

13 study and what kind of numbers you had in your

14 treatment groups along the way.

15 DR. HANDA: Which experiment

16 are -- in particular are you referring to?

17 DR. YOUNG: This is one. I mean,

18 over time when it -- the two lines are

19 consistently below the control you would think

20 you'd be picking up significance for most

21 analyses, yes.

22 DR. BRECKENRIDGE: Okay. So
202-234-4433

Neal R. Gross & Co., Inc.

Page 235

1 that's about a 5-1/2 percent reduction in gain

2 on those ten animals in the group when you

3 start to -- I mean, we're not discounting that

4 those are probably treatment-related effects. 

5 But it's a statistical detection phenomenon. 

6 And so that's typical -- that statistical

7 significance with that kind of -- and on body

8 weight reaches levels when you start to get

9 around 8, 9 percent reduction. 

10 And that's -- clearly is a trend

11 there that indicates there's a compound effect

12 on body weight. It's interesting that they

13 don't discriminate between each other at 25 or

14 6.5 mgs per kg. And so there's other

15 possibilities.

16 Mr. Chairman, if I could, I just

17 want to kind of give a segue now to the

18 immunotox presentation. So we've been

19 spending a lot of time describing

20 endocrinological characterization. And the

21 immunotox area is a somewhat unique advantage

22 to actually do a translational research from
202-234-4433

Neal R. Gross & Co., Inc.

Page 236

1 end point measurement into -- things really

2 matter for the well-being of the animal or the

3 human. And this is systems impacts and

4 adverse effect outcome.

5 So Dr. Pruett is especially in

6 that topic, and I think you'll see a

7 connection between the work on the cort

8 systems and the immune system response.

9 DR. HANDA: We need Joseph Bailey

10 here to to unlock the computer.

11 DR. YOUNG: It's just that it

12 makes me question what methods you used in the

13 analysis. If you have something with that

14 many time points straight that's consistently

15 below, to not pick that up indicates that --

16 it makes me question statistical methodology. 

17 So just for a comment.

18 DR. BRECKENRIDGE: Thank you. 

19 We'll look at that very carefully. I should

20 say that most of this data has come in over

21 the last days, so that it's not exactly well22 examined at this point. Thank you.
202-234-4433

Neal R. Gross & Co., Inc.

Page 237

1 DR. PRUETT: Ready? Okay. Thank

2 you very much for the opportunity to speak to

3 you today. I want to thank Dr. Bob Luebke for

4 setting the stage for this presentation. He

5 gave us a very nice overview yesterday of the

6 effects of atrazine on immunological

7 parameters.

8 I'm not going to try to reiterate

9 all of that, but I would like to focus on a --

10 narrow down just a little on one of the

11 components of that, particularly because it

12 also ties into the discussion that we've just

13 been having about endocrine effects of

14 atrazine.

15 I should also tell you that these

16 studies were not done on behalf of Syngenta. 

17 These were studies that were from an NIH18 sponsored grant, and then also one of the

19 experiments I'll show is from a project with

20 Pfizer.

21 And really the basis for those

22 studies -- the reason for those studies is
202-234-4433

Neal R. Gross & Co., Inc.

Page 238

1 that, quite often, when chemicals are being

2 tested for safety near the maximum tolerated

3 dose there's often a non-specific stress

4 response associated with that. And the exact

5 quantitative contribution of that response to

6 immunological end points had not been

7 investigated previously. And that's really

8 what this -- these experiments were designed

9 to do. But it turns out I think they do have

10 something to say about the questions at hand

11 today with regard to atrazine.

12 You've seen the diagram of the HPA

13 axis, so I won't go into that again. Suffice

14 it to say that corticosterone, in addition to

15 feeding back and controlling that axis and

16 also contributing to effects in the HPG axis,

17 also affects the immune system at both the

18 organ, cellular, and functional levels.

19 I've indicated a couple of

20 parameters by underlining them that I will

21 mention again as we go through some data. 

22 Natural killer cell function and antibodies --
202-234-4433

Neal R. Gross & Co., Inc.

Page 239

1 both of those have been selected. In fact,

2 those are two of the ones that have been

3 identified by EPA and FDA in their guidance

4 documents as functional immunological

5 parameters that would be useful in evaluating

6 immunotoxicity.

7 First, the approach that we've

8 taken has been a little different than what

9 most investigations have done in terms of

10 evaluating, not single time points of the

11 corticosterone level, but rather looking at

12 the area under the curve. 

13 And the value of that can be

14 illustrated in these graphs. You can see that

15 we've dosed mice with 100, 200, and 300

16 milligrams per kilogram, single dose, and then

17 we follow them over a period of hours and

18 measure corticosterone in the blood. 

19 And what you can see is that, if

20 you look at the hour one values, they are

21 between 8- and 900 for all three dosages. 

22 That -- if that was the only information you
202-234-4433

Neal R. Gross & Co., Inc.

Page 240

1 had you would conclude that corticosterone is

2 affected very, very similarly by all three

3 doses and that there was not really a dose

4 response pattern. And, therefore, if you saw

5 a dose response in immunological effects you

6 would assume that it wasn't glucocorticoid

7 related.

8 However, if you measure area under

9 the curve -- and you can see those values up

10 at the top of each graph -- those are very

11 nicely dose dependent. And we have graphed

12 those and they are for a number of different

13 stressors over a considerable time period.

14 So one of the interesting

15 parameters that we've evaluated is the

16 expression of the major histocompatibility

17 complex class 2 protein on spleen cells. This

18 protein is involved in presenting foreign

19 antigens to T cells and it's a critical

20 component, both of the antibody response and

21 the T cell mediated acquired immune response. 

22 And it turns out to be one of the most
202-234-4433

Neal R. Gross & Co., Inc.

Page 241

1 sensitive parameters in terms of the effects

2 of glucocorticoids. 

3 What you can see on these graphs

4 is that at 100, 200, and 300 milligrams per

5 kilogram on the left, if we plot those data in

6 terms of corticosterone area under the curve

7 instead of using the milligram per kilogram

8 dosage you can see that the effects of

9 exogenous corticosterone, which are indicated

10 by the dotted line, and restraint stress,

11 those effects are quite similar to the effects

12 that we see on an equivalent area under the

13 corticosterone concentration versus time curve

14 as we see with atrazine. Those values -- or

15 the elevations and the slopes of those lines

16 are not significantly different.

17 If we look at dose values and

18 compare one-day and 28-day exposures, we also

19 see there that there is really no difference

20 in the corticosterone production one day as

21 compared to 28 days. So, as has already been

22 mentioned, no sign of habituation in these
202-234-4433

Neal R. Gross & Co., Inc.

Page 242

1 female mice in this particular study.

2 I'd also point out that the low

3 dose at the 28-day -- in the 28-day group is

4 37.5 milligrams per kilogram, and if we just

5 did a simple -- ANOVA comparing the doses in

6 either of these studies we find that that

7 difference between the control and the low

8 dose is not significant. And, in fact, it

9 doesn't get significant until we get to about

10 100 milligrams per kilogram. And that's

11 generally the case in -- for all of the

12 immunological parameters that we've measured. 

13 This is the most sensitive immunological

14 parameter that we've measured in this series

15 of studies.

16 Natural killer cells I mentioned

17 are an important parameter in determining

18 overall immunotoxicity, and they are also

19 decreased in a linear manner predictable by

20 area under the corticosterone concentration

21 versus time curve, and also quite similarly to

22 the effects of corticosterone, exogenous
202-234-4433

Neal R. Gross & Co., Inc.

Page 243

1 corticosterone, and restraint, which were

2 designed to give similar area under the curve

3 values.

4 This is an experiment where we're

5 changing gears a bit and looking at male

6 Sprague Dawley rats. And this is a project

7 that I did with Pfizer. And the idea here was

8 that new guidance documents indicated that the

9 FDA would no longer take investigators' word

10 for it that thymus decreases and other things

11 that are typical -- other changes typical of

12 stress responses are due to a non-specific

13 stress response. They indicated that from now

14 on there would need to be evidence of a stress

15 response along with those immunological

16 changes to make the case that there was, in

17 fact, a stress response causing those

18 immunological effects.

19 So one of the easy ways to measure

20 a stress response, rather than bleeding, to

21 get blood values for corticosterone is to take

22 urine samples. And what we found is that a
202-234-4433

Neal R. Gross & Co., Inc.

Page 244

1 six-hour urine sample from zero to six hours

2 after dosing gives a very nice indication of

3 the excess corticosterone produced during that

4 period of time.

5 I think it's very analogous to

6 area under the curve and we do have data on

7 that. I don't have a slide here with me

8 today. But we have compared area under the

9 curve and urinary glucocorticoids and they do

10 have a nice correlation.

11 So in this particular experiment

12 we've evaluated several stressors. The

13 experiments labeled 20 and 25 involve atrazine

14 at quite high doses. But what you can see

15 here is that they decrease lymphocyte

16 differential and stressors in general --

17 glucocorticoids in general have this effect. 

18 But you can see that the relationship is quite

19 similar for all the different stressors and

20 that the urine corticosterone values seem to

21 be quite predictive of that effect. 

22 And there are several other
202-234-4433

Neal R. Gross & Co., Inc.

Page 245

1 parameters. In the interest of time I don't

2 have those shown. But there are several other

3 parameters with similar relationships.

4 So you've seen this before. This

5 is a study that is in progress that Syngenta

6 has commissioned. And the last column under

7 28 days will be used for evaluation of dose

8 response relationship in the -- in terms of

9 immunological effects. 

10 And we have some groups in

11 there -- the NK assay anti-asialo GM1 and the

12 AFC assay cyclophosphamide. Those are

13 positive control groups where we should

14 definitely see an effect just to make sure

15 that the assay's working properly, and we

16 should be able to get a much better idea than

17 we have now about the dose response pattern.

18 As already mentioned, these

19 animals will also be used to evaluate

20 endocrine effects, and we will take urine

21 samples from these animals to evaluate

22 corticosterone. So those results, the plan is
202-234-4433

Neal R. Gross & Co., Inc.

Page 246

1 to report those in July.

2 And similarly, one of the areas

3 where there is some uncertainty because the

4 two major studies didn't reach quite the same

5 conclusion is the effects of atrazine on

6 development of the immune system. And a

7 developmental study was already planned when

8 I came -- when I became involved in this

9 process. And it became clear that we could

10 probably get some useful immunological

11 information from this study as well.

12 Now, the ovariectomy, admittedly,

13 may have some effects on immune system

14 parameters, but the parameters that I've been

15 talking about typically are not drastically

16 affected in adult animals by ovariectomy. And

17 I think this would give us some useful

18 information. So we would check urine

19 corticosterone shortly before the ovariectomy

20 in both the animals that are exposed

21 throughout gestation and early development,

22 and then we would do the same thing for
202-234-4433

Neal R. Gross & Co., Inc.

Page 247

1 animals on the -- in Cohort 2 that are exposed

2 for a lesser period of time.

3 Then we would measure

4 immunological parameters, both immediately

5 after that at the time indicated on the chart

6 there for blood collection, and then we would

7 also let them recover for three weeks and then

8 also evaluate immunological parameters at that

9 time.

10 I believe we can evaluate not

11 perhaps exactly the same parameters that were

12 evaluated in the Rooney and Rowe studies, but

13 we can evaluate parameters that are very

14 comparable that should give us a similar

15 answer -- like instead of the -- we can do

16 mixed lymphocyte culture, which should give us

17 an idea of the T cell -- specific T cell

18 response, even though that different assay was

19 done in those investigations, as well as we

20 can measure all of the typical immunological

21 basal data that was measured in both of those

22 studies. And that, again, should be available
202-234-4433

Neal R. Gross & Co., Inc.

Page 248

1 in July.

2 To summarize, high doses of

3 atrazine increase circulating levels of

4 corticosterone in rodents. Elevated

5 corticosterone levels have been reported for

6 atrazine doses associated with immunological

7 effects. The effects of the immune system may

8 be caused by elevated corticosterone levels --

9 the effects of atrazine on the immune system.

10 Urinary corticosterone at zero to

11 six hours and area under the plasma

12 corticosterone concentration versus time curve

13 are good predictors of immune system effects. 

14 And studies in progress should provide us a

15 description of the dose response relationship

16 for the urinary corticosterone levels and

17 immunological changes, and also should give us

18 some more information about developmental

19 effects of atrazine exposure in terms of dose

20 response.

21 And I'd be glad to address any

22 questions that you might have.
202-234-4433

Neal R. Gross & Co., Inc.

Page 249

1 SESSION CHAIR PORTIER: Dr. Regal.

2 DR. REGAL: Yes. So are you

3 planning on looking at the male and female

4 offspring?

5 DR. PRUETT: I think if you go to

6 those -- back to those -- 

7 DR. BRECKENRIDGE: I can address

8 that quickly. Charles Breckenridge. So we're

9 only carrying the females because originally

10 that was designed as a study to look at the

11 impact on LH mechanisms. So that's all we

12 have running in that study.

13 DR. REGAL: Yes. Because the

14 other two studies in the literature saw the

15 primary effects in the males.

16 DR. PRUETT: Right.

17 DR. BRECKENRIDGE: We understand

18 that. And we may have to go back.

19 DR. REGAL: And then is -- how --

20 so 111-day old rat -- most of the other

21 studies also use slightly younger animals. 

22 Right?
202-234-4433

Neal R. Gross & Co., Inc.

Page 250

1 DR. BRECKENRIDGE: Yes.

2 DR. REGAL: Okay.

3 DR. BRECKENRIDGE: Yes. Yes. 

4 This won't be an exact replica of either of

5 those studies -- absolutely. 

6 DR. REGAL: Okay.

7 DR. BRECKENRIDGE: No, it won't. 

8 And that -- and it may be necessary to do an

9 additional study to clarify what's still

10 uncertain.

11 SESSION CHAIR PORTIER: Dr. Akana?

12 DR. AKANA: Is the urine

13 collection -- is it a nighttime or a light

14 time collection? What time of the day in the

15 cycle?

16 DR. BRECKENRIDGE: Yes, the urine

17 will be collected during the interval of time

18 from lights on for six hours. And it

19 coincides with the time when dose is

20 administered actually.

21 DR. PRUETT: We've evaluated other

22 times, and we've also evaluated longer
202-234-4433

Neal R. Gross & Co., Inc.

Page 251

1 collection times -- zero to 24 hours. And

2 zero to 6 gives us the least noise in the

3 system -- less error within groups. So that's

4 the basis for that.

5 DR. AKANA: That probably also

6 gives you the least urine volume.

7 DR. PRUETT: Yes, it does. Yes. 

8 That's right. We do adjust. We also -- we do

9 an adjustment for creatinine, which should

10 account for differences in urine output. And

11 that also does help to decrease the

12 variability in the numbers.

13 SESSION CHAIR PORTIER: Dr.

14 Selvage?

15 DR. SELVAGE: I have

16 catecholamines on the brain. But some of

17 these -- some of the results you've seen, like

18 with NK cells in the spleen, could that be

19 explained by catecholamine action at the level

20 of the spleen?

21 DR. PRUETT: Yes. Some portion of

22 the effects of these stressors is almost
202-234-4433

Neal R. Gross & Co., Inc.

Page 252

1 certainly due to catecholamines. There are

2 some data that I didn't show you where the

3 effects of the chemicals and restraint stress

4 depart considerably from the effect of

5 exogenous corticosterone. Now, exogenous

6 corticosterone -- there isn't a big

7 catecholamine response because the only stress

8 there is brief handling to do the dosing. 

9 The others -- the chemicals

10 themselves where the restraint induce is a

11 major catecholamine response -- and for some

12 parameters we see that all the chemicals and

13 the restraint track much closer to each other

14 than they do to corticosterone, suggesting

15 that probably it's catecholamines or some

16 other stress mediator that is driving that

17 particular effect.

18 Most of these we didn't -- we ones

19 that I showed you we didn't see that pattern,

20 but we have seen it for other parameters.

21 SESSION CHAIR PORTIER: Dr.

22 LeBlanc?
202-234-4433

Neal R. Gross & Co., Inc.

Page 253

1 DR. LEBLANC: You showed rather

2 nicely that at these dosages the area under

3 the curve is very informative with respect to

4 corticosterone levels. But these are high

5 dosages. And I was just wondering if you

6 titrated down to levels that are more

7 representative -- the NOEL, the LOEL for

8 atrazine -- whether the peaks might be more

9 informative -- or at least as informative.

10 DR. PRUETT: That is possible. We

11 have done some studies at lower levels and

12 also a number of vehicle studies. And below

13 a certain value -- and it's not far from the

14 lowest value that I showed you up there -- the

15 100 milligram per kilogram -- it's not far

16 from that. We don't see any immunological

17 effects.

18 However, we have not looked at

19 some parameters that some people have reported

20 to be enhanced if there is a very brief stress

21 response. So one example might be cocaine,

22 which induces a very brief, but high
202-234-4433

Neal R. Gross & Co., Inc.

Page 254

1 amplitude, stress response in rodents. And

2 that actually doesn't cause immuno

3 suppression; it enhances some immunological

4 end points, similarly with certain physical

5 stressors or restraint stress if it's just a

6 very short duration.

7 SESSION CHAIR PORTIER: Dr. Akana?

8 DR. AKANA: Have you carried

9 adrenalectomized animals through this

10 protocol?

11 DR. BRECKENRIDGE: No, we have

12 not. I think it's a rather arduous task to

13 maintain animals for any duration of time. 

14 And actually it's the in-dwelling catheters

15 that Dr. Handa uses that permit us to do those

16 kind of experiments because we can provide

17 back saline to those animals.

18 DR. PRUETT: We've done some

19 experiments like that with other chemicals,

20 not atrazine. And for a number of the

21 parameters that I've shown you here,

22 particularly MHC Class 2, the effect
202-234-4433

Neal R. Gross & Co., Inc.

Page 255

1 completely disappears in the adrenalectomized

2 animals. We've also used glucocorticoid

3 synthesis inhibitor and a glucocorticoid

4 antagonist and we get the same answer using

5 all three methods.

6 SESSION CHAIR PORTIER: Okay. I

7 think we're going to move the token to the

8 other side of the table for a while.

9 DR. BRECKENRIDGE: That's correct. 

10 And I had a summary. I'll just give that out

11 in paper copy. 

12 And I'm only going to make two

13 comments just making an introduction. Dr.

14 Pastoor is going to present a margin of

15 exposure assessment using NOELs that exist at

16 the present time in the regulation of

17 atrazine. So the acute NOEL is 10 mgs per kg

18 short term, 6.25 in long term, 1.8 mgs per kg. 

19 These are obviously subject to a new research

20 when some of these other parameters get

21 investigated.

22 And, just in general, to say that
202-234-4433

Neal R. Gross & Co., Inc.

Page 256

1 Syngenta's committed to continuing to conduct

2 research to elucidate the mechanisms of

3 actions of atrazine on all aspects of

4 endocrine and impact functions in toxicology.

5 So, with that, I'll ask the

6 colleagues from the other side to come up and

7 we'll abandon the table. Thank you very much

8 for your patience.

9 SESSION CHAIR PORTIER: Before you

10 all leave I think Dr. Bucher's got a quick

11 question.

12 DR. BUCHER: Sorry. I just wanted

13 a quick question. Is this the only study the

14 one that's going on now with the in utero

15 exposure that you know of with atrazine?

16 DR. BRECKENRIDGE: Could you

17 repeat please?

18 DR. BUCHER: You have a study

19 going on with in utero lactational exposure. 

20 Is this only one that you're aware of that's

21 been done this way so far?

22 DR. BRECKENRIDGE: I'm the only
202-234-4433

Neal R. Gross & Co., Inc.

Page 257

1 one that -- I haven't directly involved with

2 conducting so I don't know what others may be

3 doing. I think there could be programs

4 similar to that elsewhere, but I don't know of

5 those.

6 DR. BUCHER: So there's no --

7 given the neurological effects that have been

8 seen in all of these other studies has any

9 thought been given to neural behavioral

10 studies or assessments of the developing

11 nervous system in these animals?

12 DR. BRECKENRIDGE: We're listening

13 with interest to the discussion of the neural

14 toxicity sections that were brought forward in

15 this meeting in the last few days. And we

16 have been investigating that model for two

17 years for other reasons. We are aware --

18 brutally aware of its limitations, and so

19 we're reluctant to do those kind of high dose

20 types of experimentations. If we thought

21 there was a need to investigate those kinds of

22 outcomes we would. Thank you.
202-234-4433

Neal R. Gross & Co., Inc.

Page 258

1 DR. HENDLEY: Okay, thank you, Mr.

2 Chairman. My name is Paul Hendley. I'm a

3 senior Syngenta fellow from the product safety

4 department in Syngenta. My interests are in

5 exposure assessment. And what Dr. Pastoor and

6 I will be talking about over the next few

7 minutes is moving from toxicology into the

8 exposure assessment and leading to talking

9 about the risk assessment part of the

10 challenge to understand that.

11 Okay. We're looking forward to

12 giving the panel some more information that's

13 going to compliment that they've already seen. 

14 And the first part of that is going to refer

15 back to the atrazine drinking water monitoring

16 databases and show how they indicate that the

17 current monitoring frequency is sufficient to

18 estimate exposure profiles for the end points

19 and duration.

20 And then we'll move to Dr. Pastoor

21 talking about the margins of safety -- and

22 they're extremely large for the exposures in
202-234-4433

Neal R. Gross & Co., Inc.

Page 259

1 toxicological end points. And he will go into

2 the issues of some of the thresholds in

3 detail. 

4 But I would like to take the

5 opportunity now to talk about a little sort of

6 safety warning because I'm going to use the

7 word peaks and maxima and high centiles. But

8 the important thing to bear in mind when I'm

9 saying that is the drinking water supply is

10 indicated as safe. No finished drinking water

11 samples have ever exceeded 100 parts per

12 billion, which is the one-day HAL. And Dr.

13 Pastoor will go into the background of that.

14 In the atrazine monitoring program

15 with high frequency, no system has had a

16 finished water annual average greater than 3

17 parts per billion of atrazine from 2006 to

18 2009, which is the current MCL or MCLG as the

19 running annual average. And also, no 90-day

20 rolling average has exceeded the drinking

21 water level of -- I always want to say

22 concern, and, of course, it isn't -- it's
202-234-4433

Neal R. Gross & Co., Inc.

Page 260

1 comparison -- of 37.5 parts per billion total

2 chlorotriazines in rural finished water in the

3 atrazine monitoring program. So that's the

4 background against which we're going to talk

5 about peaks and maxima.

6 So the two features of my talk are

7 about the atrazine monitoring programs and the

8 magnitude, the focus, and the frequency that

9 has produced a massive database, and then

10 some of the analyses that have been done on

11 that database that have indicated the

12 trends -- and they're trending downwards --

13 the confidence with which we understand the

14 peak values and the high centiles, and some

15 sampling performance of some alternative and

16 additional sampling designs to those that

17 you've seen already from EPA.

18 But I think the keynote word for

19 my presentation is going to be database

20 because when I sum up I'm going to use the

21 word exceptional for this database. And I

22 hope by then you'll realize why I'm absolutely
202-234-4433

Neal R. Gross & Co., Inc.

Page 261

1 certain that's the right word to use in how it

2 provides a unique resource for actually

3 answering some of the questions that the panel

4 has been challenged with.

5 So the magnitude of the surface

6 water programs is where I'd like to start. 

7 There's essentially two types of surface

8 water, non-drinking water and drinking water. 

9 Let's deal with the non-drinking water first. 

10 And by that I'm talking about all water

11 bodies -- streams, rivers, lakes -- not

12 associated with drinking water finished

13 analyses.

14 There's about 180,000 atrazine

15 analyses in that database. Probably one of

16 the best and most consistent databases,

17 particularly because it analyzes multiple

18 compounds, is the NAWQA program that Dr.

19 Gilliom supervises.

20 You've heard about the Heidelberg

21 College program -- we'll come back to it

22 briefly. That is characterized by superb
202-234-4433

Neal R. Gross & Co., Inc.

Page 262

1 daily information, so very relevant to sample

2 frequency work.

3 The ecomonitoring program many of

4 you know quite well from several presentations

5 in the past. And, in addition, there's a

6 massive database on over 100,000 analyses on

7 other non-drinking water.

8 However, the rest of the

9 presentation is going to be on the topic of

10 drinking water -- community water systems that

11 are using surface water as sources. And

12 there's going to be three sets of acronyms

13 that we'll use extensively. The Safe Drinking

14 Water Act program, which is a program managed

15 by the states -- and we will keep calling it

16 SDWA from now on -- and the Syngenta managed

17 voluntary monitoring program, which we will be

18 calling VMP, and the Syngenta managed atrazine

19 monitoring program, AMP.

20 So what do I mean by that? The

21 SDWA program has been running since 1993, and

22 it's a regulatory compliance program that's
202-234-4433

Neal R. Gross & Co., Inc.

Page 263

1 roughly 90 regulated compounds -- atrazine is

2 one of them. And the 12,000 community water

3 systems on surface water have to meet

4 compliance.

5 Typically sampling for atrazine is

6 up to four samples per year, and that has led

7 to the accumulation of 50,000 or so samples

8 since 1993. And that's coming from 4,400

9 community water systems. The SDWA program

10 only looks at finished water. 

11 So the next part of the program is

12 the Syngenta voluntary monitoring program. 

13 And that little icon has appeared at the top

14 of the pyramid for a reason -- because the

15 community water systems that move into the

16 voluntary program are moved in because this

17 ran from `93 to 2003 are those with a

18 significant history of atrazine detections. 

19 And 132 community water systems

20 eventually became part of the voluntary

21 monitoring program, covering ten states. And

22 the voluntary monitoring program typically
202-234-4433

Neal R. Gross & Co., Inc.

Page 264

1 used 26 to 30 samples a year. And those

2 samples were stratified towards the peak

3 runoff time in the growing season. 

4 Now, in addition to finished

5 water, sort of -- let's say 26 raw samples and

6 26 finished samples taken a year. And that

7 has accumulated a database of over 22,500

8 finished water samples. 

9 The second program is the atrazine

10 monitoring program, and that was mandated in

11 2003. The way it was set up was it had to

12 capture all the community water systems with

13 annual average atrazine concentrations in

14 excess of 1.6 parts per billion, which is half

15 of this MCL level.

16 But that was going back to 1997,

17 so it looked back over the records for the

18 last six years to pull in all the community

19 water systems that had a history of

20 atrazine -- interesting atrazine residues --

21 and that's why we also have an icon at the top

22 of the pyramid. We're focusing in on those
202-234-4433

Neal R. Gross & Co., Inc.

Page 265

1 sites of most interest.

2 In the end, 152 community water

3 systems are in that database from ten states. 

4 The sampling frequency is higher than the

5 voluntary program. The timing is very

6 similar. And, again, you have a full set of

7 raw and finished samples. And that's amassed

8 about 26,500 finished water samples.

9 Some people find this format

10 easier to look at, and I think it deals

11 directly with the word Dr. Gilliom used early,

12 which was density of sampling. What this

13 shows is a -- the time course from zero to 365

14 days. And what it shows is for each day

15 within the entire SDWA database how many

16 measurements there are in the database.

17 And because atrazine runoff is

18 stochastic -- it's due to the timing of

19 rainfall application, complex interrelated

20 factors. What we see here is how well we've

21 sampled that universe of possible actions. We

22 also happen to see that apparently in the
202-234-4433

Neal R. Gross & Co., Inc.

Page 266

1 Midwest, sampling on Christmas Day and New

2 Year's Day are not popular activities.

3 In addition to the SDWA

4 distribution we see the same thing for the VMP

5 and AMP. And what you can see here is April

6 1 to July 31 is -- if you like the growing

7 season when we know you're likely to get

8 higher residues -- higher runoff occurring --

9 and you can see how VMP, AMP selectively

10 samples from that period.

11 But think about the total sum of

12 the database. If you think of that, say,

13 April 15 you have around 200 -- well, 150

14 samples to 250 samples perhaps. And, again,

15 you add in the AMP samples, the amount of data

16 and information in our database is really

17 quite remarkable.

18 So that gives us a database of

19 enormous magnitude. It's focused on sites of

20 interest and it has high frequency monitoring

21 data. So Dr. Heeringa used the phrase "prior

22 information." This is a pool of prior
202-234-4433

Neal R. Gross & Co., Inc.

Page 267

1 information of some considerable power. And

2 it also perhaps also ought to be in the list

3 of what do we know now, which is a question

4 that Dr. Thurman asked in one of his slides. 

5 So the first thing we did with the

6 database was we looked at trends. And what we

7 have here -- what are we doing from now is

8 giving you a one-page summary, a very short

9 introduction and some conclusions, and you

10 will see the reference, in fact, up in the top

11 left-hand corner here. That is on the

12 docket -- that report -- but with just too

13 detail and too little time to go through it in

14 detail I'm afraid.

15 So what we see here is across the

16 period 1994 to 2006 there is a downward trend

17 in time weighted annual mean concentrations of

18 atrazine. And there's two lines on there

19 because the sites where the raw annual mean

20 greater than 3 at some point within that

21 historical database are actually seen to be

22 trending downwards at a faster trend than
202-234-4433

Neal R. Gross & Co., Inc.

Page 268

1 those without such a history. 

2 And that's encouraging. And one

3 of the reasons to note is that across the top

4 line here you see the total atrazine use in

5 millions of pounds. And that hasn't changed

6 significantly over the 1994 to 2006 period. 

7 So the decrease and the trend downwards

8 largely reflects activities by the growers,

9 improvements in stewardship for atrazine use.

10 In addition to this for the annual

11 mean is a similar and somewhat steeper trend

12 for the high centile peak values. And USGS

13 also reported a similar trend in their NAWQA

14 decadal review. 

15 The second thing we did with the

16 database was look at both peak and high

17 centile atrazine drinking water

18 concentrations. And here obviously with

19 sample sizes in the order of 50,000 data

20 points they're large enough to estimate the

21 high centile population measurements with

22 extreme confidence.
202-234-4433

Neal R. Gross & Co., Inc.

Page 269

1 And what this table does is

2 summarize those measurements. The actual

3 confidence limits are in the detailed report. 

4 And Dr. Mosquin from RTI International is with

5 us on the table should you have any questions

6 on the detail of this piece of work. Dr.

7 Mosquin is down there.

8 On the top line you can see the

9 SDWA data. And what you have is a 99.9th

10 centile value of 6.7 parts per billion from

11 our 4,400 community water systems. For the

12 VMP, AMP we have 22.7 ppb as the 99.9th

13 centile.

14 Interestingly, if you take from

15 SDWA the data corresponding to those community

16 water systems in VMP and AMP you see that the

17 99.9th centile moves from the 6.7 of the total

18 population to 17 parts per billion. In other

19 words, SDWA sampling across the years does a

20 good job of capturing the community water

21 systems with a history of higher atrazine

22 occurrence.
202-234-4433

Neal R. Gross & Co., Inc.

Page 270

1 The panel also asked the question

2 earlier about sourcing for community water

3 systems. So what was done here was the

4 database was broken down into subsets, the

5 first of which is community water systems

6 using static water bodies as the source. 

7 There's 134 -- about two-thirds of that

8 population in the VMP and AMP. And there, the

9 99.9th centile and the 99th are higher than

10 the whole population, and correspondingly the

11 flowing water subset, the high centile values

12 are lower. In other words, the static water

13 bodies actually tend to have higher residue

14 patents, and that is unsurprising when you

15 think about the hydrology.

16 Additionally on here, and

17 unsurprisingly as well, is when you look at

18 the April to July period, you come out with

19 centile estimates that are considerably higher

20 than the whole population. 

21 Okay. That led us to the question

22 before -- some of the questions before the
202-234-4433

Neal R. Gross & Co., Inc.

Page 271

1 panel about sampling frequency and confidence

2 intervals. What I'm going to do now is show

3 three more approaches in addition to those

4 you've seen from EPA.

5 The first of those deals with the

6 longer term exposures -- the 90-day exposures. 

7 And in all these cases again I'm going to give

8 a very brief summary. The reference -- or the

9 handout material you have will provide details

10 should you want more, or, indeed, our experts

11 around the table with us.

12 So in this approach we used raw

13 water from 400 community water systems from

14 the AMP program from 2003 to 2006, and we

15 looked at four sampling frequencies. Thirty16 five samples a year stratified to the runoff

17 season, 17, nine and six. We looked at both

18 the full data set and the flowing water/static

19 water subsets -- either source subsets. We

20 simulated a set of samples, calculated the

21 maximum 90-day rolling averages, and compared

22 the predictions with the measured values.
202-234-4433

Neal R. Gross & Co., Inc.

Page 272

1 The conclusions from that work are

2 for the 90-day rolling averages maxima both

3 the AMP schedule of 35 samples a year and the

4 17 samples per year approach are almost

5 indistinguishable. And what's more the static

6 water systems' lower frequency sampling is

7 effective.

8 But I think to answer a thought

9 that was almost voiced by the panel, the

10 correct -- I think it was -- the correct sort

11 of suitable frequency for community water

12 systems can be a sign based on history and

13 knowledge about the source water, the atrazine

14 trends, flows, et cetera.

15 The next two approaches deal with

16 the question of the confidence associated with

17 various sampling approaches if shorter

18 exposures become of interest. And the first

19 of these -- and here Dr. Sielken from Sielken

20 Associates is on the table with us to answer

21 any questions -- the approach was using 202

22 community water system finished water profiles
202-234-4433

Neal R. Gross & Co., Inc.

Page 273

1 which were up to eleven years in duration. 

2 Each of those was linear

3 interpolated across the entire length of the

4 multi-year profile. And then fixed sampling

5 intervals -- two day intervals, three day

6 intervals, four day intervals -- were applied. 

7 And the sampled maxima from those samples were

8 compared with the observed peak maxima for the

9 entire community water system profile.

10 The combined ratios of those

11 comparisons for each of the 202 individual

12 profile analyses were built into a

13 distribution. And that led to conclusions

14 across all the community water systems. And

15 I'll give you the example for the seven-day

16 frequency. And you can be 95 percent

17 confident that the sampled maximum from your

18 seven-day sampling profile is greater than or

19 equal to 78 percent of the maximum.

20 You can turn that around into what

21 you could call a performance factor and say,

22 if I multiply my sampled value by 1.28, which
202-234-4433

Neal R. Gross & Co., Inc.

Page 274

1 is the reciprocal of .78 or 78 percent, you

2 would be confident that you were trying to

3 estimate -- managing to estimate the observed

4 maxima.

5 Similar conclusions are in the

6 handout for various intervals and time

7 weighted periods. And the approach was also

8 applied to daily data sets -- and I'll come to

9 how that's helpful in a moment.

10 The second and last experiment I'm

11 going to tell you about also deals with the

12 question of the shorter exposure periods and

13 sampling frequency. In this case, rather than

14 using the power of the history from the VMP

15 and database what was used were the daily data

16 sets that are available, where true daily day

17 after day after day samples were taken.

18 And I'll just sort of briefly

19 dwell on these because there is one very late

20 breaking item, as Dr. Breckenridge sort of

21 mentioned. Data always seem to come in at the

22 last minute. And a few weeks ago we became
202-234-4433

Neal R. Gross & Co., Inc.

Page 275

1 aware of a community water system in Missouri

2 between 1993 and 2000 took -- well, I think

3 five years with more than 300 samples. 

4 This is truly a valuable resource. 

5 If you really want to understanding drinking

6 water variability you want to deal with

7 measurements made in drinking water. This was

8 something we were unaware of until very

9 recently. This is a community water system

10 that samples the Missouri River. It's a big

11 system. So that's the most relevant one we

12 have.

13 The Heidelberg College data set

14 you've heard about from EPA. The key point I

15 want to make is that there are four watersheds

16 and they vary in area between about 34 square

17 miles and 6,300. And somebody mentioned basin

18 area earlier. And so this is a very

19 convenient set for understanding some of the

20 implications of basin area -- all of these

21 heavily agricultural 60 to 80 percent raw

22 crop.
202-234-4433

Neal R. Gross & Co., Inc.

Page 276

1 But the important think to note is

2 both Heidelberg College and the eco programs

3 are raw water. They're not drinking water.

4 So the last group of data is the

5 eco program data. And, remember, for those

6 who haven't spent many hours on this these are

7 very small streams -- some of them are six

8 feet wide -- but certainly they're all very

9 small on small watersheds -- typically nine

10 to -- well, up to 100 square miles is the very

11 most. 

12 We looked at 2009 data taken

13 daily -- composite samples actually -- ten

14 sites. And in addition, of course, there are

15 some ongoing programs -- there will be 30-plus

16 sites of daily data coming in in 2010. I

17 won't dwell more on that.

18 But I will raise a point that a

19 panel member made yesterday. And the question

20 was are there drinking water community water

21 systems in some of these upstream small

22 watersheds? And the answer is yes there is. 
202-234-4433

Neal R. Gross & Co., Inc.

Page 277

1 But there's a twist because the community

2 water system's job is to provide a continuous

3 supply to their customers. So if you have a

4 very small stream that's prone to drying up or

5 going very low you have to do something about

6 it. And what you can see on this diagram is

7 a watershed that's actually coming down

8 here -- there's a creek -- doesn't look unlike

9 some of the eco creeks.

10 But what they do is they pump from

11 this creek, typically not in the worst runoff

12 events because they don't want to put sediment

13 and stuff in there, and they pump into a 30

14 mega gallon kind of settling reservoir --

15 that's million gallons. And then they pump it

16 into a 90 mega gallon storage reservoir. 

17 That's actually got 150 days of supply for

18 that community water system in there.

19 And so the raw water that actually

20 feeds the treatment plant comes from this

21 storage reservoir. It doesn't come from the

22 creek. Now, the reason why I raise that is
202-234-4433

Neal R. Gross & Co., Inc.

Page 278

1 because in May 2006 -- May 23 actually -- was

2 a residue reported that 227 parts per billion

3 in the creek. On the same day the raw water

4 that was going into the plant was .09 parts

5 per billion. And then the finished water that

6 came out was .05 parts per billion.

7 In your handout there are two high

8 numbers. One of them is this 227 as raw water

9 feeding a community water system. This is in

10 the white paper -- sorry -- from the EPA. And

11 so the value that is of 227 is a true value

12 for the creek. It is not relevant for what is

13 going into the plant. And, in fact, another

14 value -- 73 ppb -- was reported in 2008. And

15 that is also in the database. It's got a

16 different CWS code number in the EPA white

17 paper. And that was also not representative

18 by about the same ratio of the true raw water

19 that's actually feeding the plant.

20 So the important thing to realize

21 is these what are called side channel

22 reservoirs have no watershed of their own. 
202-234-4433

Neal R. Gross & Co., Inc.

Page 279

1 There's no water drains in there barring

2 actual rainfall. You've got to pump water

3 uphill to get them in there.

4 So looking at our daily data set

5 the same sort of simulation approach that Dr.

6 Frankenberry discussed generated simulated

7 sampling ranges including the every seven days

8 AMP ranging. And then it compared the

9 simulated data with the true values -- the

10 full daily data set -- and estimated the

11 ratios between the true and the simulated

12 values.

13 And these, again, are sampling

14 performance factors -- and Dr. Mosquin

15 actually did this work and so, again, he can

16 answer the questions on this. And those

17 performance factors for estimating maximum

18 daily values from seven day in season sampling

19 programs range for the community water system

20 between 1.2 and 1.8 and for the Heidelberg

21 College set of watersheds -- there's four,

22 remember -- between 1.1 and 2.6. But I'll
202-234-4433

Neal R. Gross & Co., Inc.

Page 280

1 hasten to point out that's not drinking water,

2 so its relevance has to be carefully

3 considered.

4 And, again, for the eco programs,

5 which I believe are representative merely of

6 understanding how extremely flashy systems

7 behave and really have very little relevance

8 to drinking water, the range was 2.1 to 2.6.

9 So what can we conclude about

10 sampling frequency and confidence? The data

11 support lower frequencies than current AMP

12 programs, but 90 day for the longer averaging

13 periods, community water systems characterized

14 by static source water require fewer samples

15 than flowing, and you can use knowledge --

16 prior knowledge to come up with suitable

17 frequencies for sampling such watersheds.

18 The huge available monitoring

19 database address current SAP questions, and

20 the daily data sets are very important to help

21 us kind of validate our thinking about

22 sampling frequency. 
202-234-4433

Neal R. Gross & Co., Inc.

Page 281

1 We used two complimentary sampling

2 performance estimation approaches. One was

3 the one from the VMP data looking down at the

4 entire profiles across history, sampling many

5 years, many circumstances. The other was

6 working up from true daily measured data sets. 

7 Both of those gave similar results for the

8 daily data sets that are available. And those

9 two converged to suggest that sampling

10 performance factors for estimating maximum

11 daily values from seven-day sampling regimes

12 range between 1.1 and 2.6.

13 And why is that a useful number? 

14 Well, it's consistent with the 3X sampling

15 uncertainty factor that's already in the

16 drinking water level of comparison. So the

17 numbers are coming out and, again, making

18 sense within the context of the existing risk

19 assessment process. And Dr. Pastoor will

20 mention that later.

21 Additionally, in turning very

22 briefly to the modeling questions, a variable
202-234-4433

Neal R. Gross & Co., Inc.

Page 282

1 data permit confident analyses of sampling

2 uncertainty. Direct analysis of relevant data

3 sets that have historical relevance are

4 true -- feasible. And so I would suggest that

5 developing models to interpolate between less

6 frequent samples may be unnecessary given the

7 unique qualities of the atrazine database.

8 I would also suggest -- just

9 popping back to the first point on community

10 water systems, I would submit to the panel

11 that considering the source of the community

12 water systems may be an easier way of

13 classifying than worrying about peak shape,

14 but I think both need to be examined

15 carefully.

16 So, in wrapping up, we have a

17 truly exceptional database of monitoring that

18 provides very effective way of getting into

19 some of the questions being asked due to its

20 temporal and spatial coverage. It gives us

21 high certainty that 99.9th centile can be

22 determined and other high centile values, and
202-234-4433

Neal R. Gross & Co., Inc.

Page 283

1 we can understand trends that are declining.

2 The current seven-day regime being

3 used in AMP is sufficient for estimating

4 short, intermediate, and long-term exposures

5 with high confidence using some of the

6 approaches we've show you. No additional

7 uncertainty factor is needed based on sampling

8 performance.

9 We recommend that you should use

10 finished drinking water for all aspects of

11 human exposure assessment, and we would also

12 suggest that the data available -- the

13 relevant data available, and full use of that

14 is preferable to developing models.

15 And, with that, after questions I

16 will turn you over to Dr. Pastoor.

17 SESSION CHAIR PORTIER: Panel? 

18 Questions? Dr. Horton.

19 DR. HORTON: I have a question for

20 you. I'm a neuroendocrinologist so I'm going

21 to ask you some naive question regarding

22 hydrology. Most of what I've heard regard
202-234-4433

Neal R. Gross & Co., Inc.

Page 284

1 surface water runoff -- what is done regarding

2 sampling of deeper water supplies -- aquifers,

3 wells, and such things?

4 DR. HENDLEY: Okay. Very good

5 question. There are approximately 212,000

6 ground water samples also from the SDWA

7 program. So from that database plus some very

8 focused studies done on both community water

9 systems on drinking water, as well as rural

10 wells, which are the smaller wells that maybe

11 an individual family may use, there was a

12 great deal of work done between the mid13 nineties and the early 2000s.

14 EPA reviewed all of the water data

15 in roughly 2002. They concluded that the

16 exposure through groundwater was limited. And

17 then they actually -- that's -- they then

18 moved towards these programs on surface water. 

19 So the data show that surface water is what to

20 concentrate on.

21 As I pointed out, none of the

22 surface water samples are exceeding the health
202-234-4433

Neal R. Gross & Co., Inc.

Page 285

1 standard. Groundwater residues are so much

2 lower than surface water residues that EPA

3 judged surface water to be the focus.

4 SESSION CHAIR PORTIER: Dr.

5 Gilliom.

6 DR. GILLIOM: On the -- on that

7 trend analysis graph, which was slide eight,

8 is -- I wasn't able to catch up totally on the

9 background documents so I apologize for that. 

10 But how was the -- was the trend analysis that

11 Syngenta did based on the aggregated data or

12 was it a site-by-site analysis that this is

13 summarizing?

14 DR. HENDLEY: This is summarizing

15 the data from 103 water -- raw water bodies

16 from 96 community water systems. And I think

17 it was -- there were two communities we looked

18 at. One was a population with I think more

19 than nine years of continuous data and the

20 other was a population with more than five

21 years of continuous data.

22 And so those were looked at within
202-234-4433

Neal R. Gross & Co., Inc.

Page 286

1 the categories, as we said, of source water

2 type and also the, if you like, the low

3 series, the ones with history never exceeding

4 three ppb, and ones with the high, higher than

5 three ppb. So those community water system

6 data sets were looked at in various clusters.

7 DR. GILLIOM: So the lines on the

8 graph are kind of a generalization of that?

9 DR. HENDLEY: They are absolutely

10 a generalization. If you saw the original

11 graphics you would all have screamed and run

12 out of the room because they are a series of

13 distributions for each of the years of the

14 data within the year. And you really can't

15 explain them in a 20-minute presentation.

16 However, I would commend you to

17 have a look at the report, because it's very

18 relevant to some of the work that you and

19 Recio have done.

20 DR. GILLIOM: Yes. Because you --

21 I mean, you guys are -- I haven't caught up to

22 the fact of trying to resolve the differences. 
202-234-4433

Neal R. Gross & Co., Inc.

Page 287

1 But you're aware that that's a different

2 result than what that work shows for the large

3 rivers?

4 DR. HENDLEY: I think that was

5 actually why you probably wanted to talk to

6 Dr. Chen.

7 DR. GILLIOM: Yes.

8 DR. HENDLEY: I would defer to Dr.

9 Herschel and Dr. Chen who are both in the

10 room. And perhaps after this session it will

11 be best if you spoke to them about that.

12 DR. GILLIOM: Okay. And one other

13 just while I have the mike here. On the slide

14 12 where you did the comparison for the 90-day

15 exposures, was -- am I correct in

16 understanding that this was done by

17 essentially resampling distributions from the

18 AMP data which was on the order of 30 to 35

19 samples a year?

20 DR. HENDLEY: Correct.

21 DR. GILLIOM: So I guess -- and

22 the only point, which was the same point we
202-234-4433

Neal R. Gross & Co., Inc.

Page 288

1 talked about some with the EPA simulated data

2 experiments, was that -- and it's mostly

3 relevant to shorter-term occurrences. But you

4 don't have -- in the original data set used to

5 build the relations, you don't have the

6 highest peak from more frequent sampling. In

7 other words, it's limited by the original

8 data.

9 DR. HENDLEY: I think that's an

10 accurate statement. Of course, this analysis

11 was looking at the success against the 90-day

12 rolling average end points.

13 DR. GILLIOM: True enough. It's

14 just that some of the 90-day ones can be

15 pretty affected by relatively short-term

16 runoff events and so forth. That would be the

17 only -- 

18 DR. HENDLEY: But, again, this is

19 over 440 community water system years. So

20 it's sampled pretty thoroughly across time.

21 DR. GILLIOM: With all the sites

22 aggregated, you mean, or you mean -- 
202-234-4433

Neal R. Gross & Co., Inc.

Page 289

1 DR. HENDLEY: I'm just trying

2 to -- 

3 DR. GILLIOM: I was getting a

4 little confused at some points in here where

5 we had aggregated across sites -- kind of

6 the -- 

7 DR. HENDLEY: Right.

8 DR. GILLIOM: -- grand data set,

9 because it's a mixture of approaches that

10 makes a difference. Because some of these

11 questions are being targeted to the most

12 vulnerable systems, so it's more that

13 individual system versus the global.

14 DR. HENDLEY: I think the point --

15 well, one of the points here is, of course,

16 the AMP programs are already the most

17 vulnerable systems.

18 DR. GILLIOM: Right.

19 DR. HENDLEY: So, you know, the

20 entire analysis was on the most vulnerable

21 systems.

22 DR. GILLIOM: Right.
202-234-4433

Neal R. Gross & Co., Inc.

Page 290

1 SESSION CHAIR PORTIER: Dr. Young?

2 DR. YOUNG: But just to follow up,

3 if I understand correctly you took these and

4 you did just linear interpolations. So any

5 peaks that were missed are still missed. And

6 so, in fact, it's a very great smoothing. And

7 so -- and then things are aggregated, so we're

8 losing things over space and time. And then

9 to draw conclusions like that seems just very

10 counter-intuitive. So -- 

11 DR. MOSQUIN: Paul Mosquin, RTI. 

12 There's two different approaches used. One

13 uses the linear interpolation. And when the

14 linear interpolation was used that was for

15 data sets that were, let's say, essentially --

16 had a large number of observations relative to

17 the others -- the Heidelberg, the ECHO and the

18 St. Louis. So there was less interpolation,

19 let's say, than you would if you'd used a VMP

20 or an AMP site.

21 For -- in the case of aggregation

22 the interpolation, if you will, was by a
202-234-4433

Neal R. Gross & Co., Inc.

Page 291

1 weighting, which is a constant value weighting

2 over a particular interval centered on the

3 measurement, which is then a finite population

4 approach. So they are separate techniques in

5 that sense.

6 DR. SIELKEN: Can I add to that

7 response a little bit? This is Bob Sielken. 

8 When we did the analysis looking at the CWSes

9 it is true that we used the available

10 realistic data, interpolated it linearly

11 stepwise and it didn't make much difference

12 which we did to create a test profile for the

13 202 CWSes over about 1,400 years. So we had

14 a large CWS/year combination.

15 So we took those real data set --

16 realistic data set for year-to-year

17 variability that was discussed earlier -- took

18 those as tests to see how well our performance

19 would do -- not necessarily how well they

20 would have captured some unknown maximum. But

21 when we went back and sampled those profiles

22 whatever scheme we used -- would it have
202-234-4433

Neal R. Gross & Co., Inc.

Page 292

1 captured what was known to be there.

2 So they're really just used as

3 test cases when we're going back to those 202

4 CWSes. And, you know, we got really good

5 performance. Paul Hendley mentioned that, you

6 know, you got at the 95 percent confidence

7 limit -- the ratio of the sample max to the

8 true profile max in your test case was 78

9 percent. And that was at one day. We got --

10 we did the analysis for three-day rollings and

11 seven-day and ten-day and others. And we

12 could show that -- of course, the level of

13 performance improved. So there were none of

14 those that were really bad even looking at the

15 short durations down to one day.

16 The follow up to that is is that

17 in addition to using those representatives

18 test cases based on real data we went to the

19 Heidelberg, the Missouri, and then looked at

20 partitioning the data sets. And we found that

21 in all of those separate analysis, which were

22 closer to having a raw database that was
202-234-4433

Neal R. Gross & Co., Inc.

Page 293

1 nearly daily sampling, that we got the same

2 sort of performance. 

3 And I think that that's really

4 germane to the question about what about short

5 durations. And in that analysis we show that

6 even down to one day your current sampling

7 plan is really on top of things.

8 DR. YOUNG: But the -- I need to

9 do some more reading on it because there's a

10 logical disconnect here. The things that you

11 see -- if you sample every fourth day and your

12 peaks occur in between you're going to miss

13 something. It -- that's just a fact. You

14 know, you can't get away -- that if you want

15 short time scales you've got to sample on

16 short time scales. 

17 So -- now, that's a fact. It's

18 just -- it doesn't matter whether you're in

19 space or time. If you want fine resolution

20 you've got to sample at that fine resolution. 

21 Now, I can't quite -- haven't quite figured

22 out what's happening here, but that fact
202-234-4433

Neal R. Gross & Co., Inc.

Page 294

1 can't -- it's just solid. So help me

2 understand why all of a sudden this is going

3 to work when I know that last fact's a fact.

4 DR. HENDLEY: I'll make out,

5 though, is I think that's why I'm happy that

6 we had the two-pronged approach working

7 upwards from true daily data, which addresses

8 your question, as well as downwards from the

9 extent of data collected across many years and

10 many sites.

11 Dr. Mosquin?

12 DR. MOSQUIN: Thank you. Yes,

13 certainty at a given site you're not going to

14 capture all the peaks if you sample

15 intermittently. The argument for the

16 weighting approach is -- relies on a belief

17 that the sites are comparable in some manner

18 so that for some sites you do sample the peak. 

19 For other sites you don't. And yet because

20 there are enough sites that are of comparable

21 nature the times when you do sample the peaks

22 the weight is going to contribute that site's
202-234-4433

Neal R. Gross & Co., Inc.

Page 295

1 maximum a certain number of days.

2 DR. YOUNG: But aren't you then --

3 I mean, that seems to me to be changing the

4 question, because aren't -- I mean, and maybe

5 I need guidance here. I just don't know. I

6 don't think we're trying to find the peak of

7 the whole population. Aren't we trying to

8 think about each site and what we can say

9 about a site? Or are we just kind of talking

10 about for all of those blue dots out there? 

11 I mean, because that's -- I mean, you're

12 looking at all blue dots.

13 DR. MOSQUIN: Right. That's -- 

14 DR. YOUNG: And I thought we

15 wanted to be able to draw inferences at

16 locations. And those are two very different

17 questions.

18 DR. MOSQUIN: Right. So you

19 might -- I mean, that's why the two analyses

20 are performed. And one way to view it perhaps

21 in terms of the -- you know, the finite

22 population methods is that you ultimately
202-234-4433

Neal R. Gross & Co., Inc.

Page 296

1 arrive at estimates that might represent, say,

2 your prior belief for sites of this kind in

3 terms of centiles.

4 DR. SIELKEN: I'd like to add two

5 things here. One is that in the profile that

6 you're sampling from those data are related to

7 each other. I mean, if you have admittedly a

8 peak that went up in a minute and came back

9 down by the end of the day you'd probably miss

10 it even with daily sampling.

11 But you've got a profile where

12 yesterday is a little bit related to the day

13 before and today is a little bit related to

14 yesterday and tomorrow will have this relation

15 over days. So that's one point -- that this

16 data is related and not isolated from each

17 other.

18 The second point regards to this

19 aggregation point -- really what we did was --

20 when we were, say, talking about the 202 CWSes

21 was we tested the procedure individually on a

22 CWS, got an answer, and then said, well,
202-234-4433

Neal R. Gross & Co., Inc.

Page 297

1 either put up a table of 202 answers or

2 characterize the distribution of those

3 answers, which you would do in a statistical

4 analysis. You'd look at the performance of

5 your procedure and you'd evaluate it

6 individually and then report the distribution

7 of those performances. And that's all we're

8 doing.

9 DR. YOUNG: Again, I think -- I'd

10 like clarification. Are we going to be

11 looking at locations and want to be able to

12 draw inference at locations or across the

13 whole area?

14 SESSION CHAIR PORTIER: Linda, I

15 think you can save that question. When EPA

16 reads the question to the panel you can ask

17 for clarification again at that time and we'll

18 get staff to come up and clarify it, because

19 I understand what you're talking about. It's

20 a matter of exactly what data you're

21 estimating for the decision role and there's

22 a lot of ways to look at that. 
202-234-4433

Neal R. Gross & Co., Inc.

Page 298

1 I don't want to drag this out for

2 too much longer, but Dr. Horton's been waiting

3 patiently for her question.

4 DR. HORTON: I have another

5 question of just how community water systems

6 are making decisions about how to pump and

7 when to pump. And going back to your slide

8 16, you're showing the model system and you're

9 saying, you know, the communities are not

10 pumping all the time from their sources, but

11 yet they're sampling -- or oftentimes their

12 samples are being taken even when pumping is

13 not occurring.

14 I'm wondering if information from

15 sampling can be used by communities to make

16 decision as to when to pump or not in order to

17 keep their side channel reservoirs cleaner.

18 DR. HENDLEY: I think there's two

19 answers to that. It depends on the community

20 water system. If you're on the Missouri or

21 the Mississippi, you have a constant stream of

22 water that you're pretty confident about, and
202-234-4433

Neal R. Gross & Co., Inc.

Page 299

1 I understand it's efficient for them to pump

2 continuously under those circumstances.

3 For this type of smaller community

4 water system on a more variable water source

5 my understanding -- and these are individuals. 

6 They all have their own characteristics,

7 communities, numbers of people to supply, and

8 jurisdictions within which they work -- but I

9 understand that the reason why they don't like

10 to pump during runoff events is sediment

11 organic matter, as well as synthetic organic

12 chemicals. 

13 And so they will try and avoid

14 that where they can. And in fact you could do

15 it by chemical analysis. I think it's an

16 interesting suggestion. The problem is time,

17 because you've got to get that information

18 somehow in real time if you're going to use it

19 in real time. I think with what we know and

20 what they know about runoff they know, and

21 that's why they do tend to avoid peak events.

22 So they're very clever guys. 
202-234-4433

Neal R. Gross & Co., Inc.

Page 300

1 They're good at doing their job. And they

2 minimize their own difficulties. And that's,

3 in fact, why there's this sedimentation

4 reservoir.

5 And we didn't go into it, but

6 there are aspects to do with blending. And I

7 understand from people much more expert than

8 I that there are actually other legislations

9 to do with blending and getting a more sort of

10 homogenous supply in terms of the way their

11 plant's going to work. You know, they don't

12 want peaks going into the plant -- much easier

13 to have a smooth constant set of

14 concentration. So they do their best to

15 simplify things for themselves like all of us.

16 VOICE: Just to add one little

17 thing to that, that points out the difference

18 between using finished water concentrations to

19 characterize the drinking water than it would

20 to take an ambient water out of the creek as

21 a measure of drinking water -- just greatly

22 illustrates that difference.
202-234-4433

Neal R. Gross & Co., Inc.

Page 301

1 SESSION CHAIR PORTIER: Dr.

2 Heeringa asked for the last question here.

3 CHAIR HEERINGA: I'll do this

4 quickly, and if it doesn't come across, I'll

5 have a side conversation. So getting back to

6 Linda's question, think of the 202 community

7 water systems. I'll start out and I'll take

8 the maximum observed value in that community

9 water system and I'll plot that on -- one to

10 202. There are 202 points and it's 78 percent

11 of the maximum value. I plot that for every

12 community water system, and those are the --

13 and then I do a bootstrap sample from the

14 observed sample data for each community water

15 system. And I resample according to

16 frequency. So I get a distribution for each

17 of those community water systems.

18 And what you're saying is that in

19 95 out of 100 community water systems that the

20 sample point exceeded that 78 percent of the

21 observed maximum, not that five percent of the

22 generated samples for a community water system
202-234-4433

Neal R. Gross & Co., Inc.

Page 302

1 exceeded -- 

2 DR. HENDLEY: Dr. Sielken will

3 address that.

4 DR. SIELKEN: Thank you, Paul. 

5 This is Dr. Sielken. Yes, Dr. Heeringa, you

6 have the example right. You're looking at all

7 202, how they performed, simulated each one,

8 and then you looked to see the -- what you

9 would have expected across that group. And

10 you would have expected in 95 percent of the

11 cases that your sample max would have been

12 bigger than about 80 percent of that.

13 CHAIR HEERINGA: So you're looking

14 along this axis. I think Linda's looking

15 vertically at the distribution of the

16 simulation samples within a community.

17 DR. SIELKEN: Yes. I am looking

18 across the CWS axis. 

19 SESSION CHAIR PORTIER: Frigid

20 factor is going up in the audience. We're

21 looking for a break, but we have about 13

22 slides -- right? -- for the last presentation
202-234-4433

Neal R. Gross & Co., Inc.

Page 303

1 here.

2 DR. PASTOOR: I promise not to be

3 too boring. Okay? All right. Let me just

4 first of all get my slides up here. Just one

5 second here.

6 (Pause.)

7 DR. PASTOOR: All right. 

8 Everybody stand up and do calisthenics now. 

9 Okay. 

10 Tim Pastoor. I'm a toxicologist

11 and principal scientist with Syngenta, and I'm

12 batting cleanup today. And it's my pleasure

13 to try and bring us back to what this all

14 means in context.

15 And, Dr. Portier, you've been

16 mentioning that there seems to be one side of

17 the table over here that gets all excited

18 about things like corticosterone increases,

19 whereas the other side of the table gets

20 excited about, you know, 95th percentile of

21 the 99.9 percentile of confidence. So what my

22 job has been over the last number of weeks is
202-234-4433

Neal R. Gross & Co., Inc.

Page 304

1 to spent time in both of those rooms, and I

2 think I get a higher place in heaven for that.

3 But the point is is that I was

4 able to sit with the neurobiologists, Dr.

5 Handa and Dr. Simpkins, as well as to sit with

6 Dr. Chen and with Dr. Hendley and also with

7 Bob Sielken, RTI. And, believe me, I've

8 learned more about statistics than I've ever

9 wanted to know. And I know more about

10 neurobiology than I should know.

11 But collectively we're trying to

12 answer a question that I think Anna Lowit --

13 Dr. Lowit brought up the other day is -- where

14 does the rubber hit the road. And someone

15 else -- I think it was Dr. Hayton -- asked,

16 well, what's coming out of my tap and what

17 does that mean for health? And for me it's a

18 matter of how do you answer your friends and

19 family. And when they say, well, atrazine --

20 you find it in water -- what does that really

21 mean.

22 So my job is to take the
202-234-4433

Neal R. Gross & Co., Inc.

Page 305

1 neurobiology and the statistics and bring that

2 together in something that resembles something

3 of a risk assessment. Now, this is a risk

4 characterization, and it's a contextual one. 

5 It's not a true risk assessment. It's just a

6 way of giving you some sense of where we are.

7 Now, one of the first things we've

8 got to do is take a look at exposure. I'm

9 going to take a look at exposure and I'm going

10 to bring the toxicology back in again. And in

11 order to do that we have to take a look at all

12 those data points. And Dr. Sielken graciously

13 gave me one example of how we do the linear

14 interpolation to get at how much exposure

15 might occur in a particular CWS or overall

16 anyway.

17 And the first thing you have to do

18 to establish an exposure is take all of these

19 data points and do a linear interpolation. 

20 Why would you do that? It's because you now

21 have from these data points -- this is over

22 quite a number of years you can see here on
202-234-4433

Neal R. Gross & Co., Inc.

Page 306

1 the X axis. You can actually do a linear

2 interpolation and you can begin getting an

3 idea of the 365-day pattern -- or 365-day

4 profile for the atrazine concentrations in

5 each individual CWS.

6 The value to that is that you can

7 then start taking slices of that and saying,

8 well, what's the average concentration that

9 you'd have for 90 days, for 30 days, for 10

10 days, and, of course, you can also take a look

11 at it on the basis of single-day peaks. And

12 you can compare that then to no-effect levels,

13 which is what I'm going to do next.

14 So the real thing here is when you

15 take a look at all the information that we

16 have -- and this is in finished water from the

17 CWSes primarily in the Midwest that have been

18 in this intensive monitoring programs. So

19 these are the CWSes where you'd expect to find

20 atrazine in the first place. And, indeed,

21 that's where we focus our attention. 

22 We've got over 40,000 individual
202-234-4433

Neal R. Gross & Co., Inc.

Page 307

1 data points which are then taken, and we've

2 made this linear interpolation for 365 yearly

3 points. That gives us a half-a-million points

4 to work with from the standpoint of

5 understanding what the distribution of

6 concentrations are in water in the Midwest in

7 finished drinking water.

8 So when we do that you can then

9 take a look at what that shows us. And I like

10 this because this box and whiskers diagram

11 kind of pulls it back from all the stuff that

12 we've been listening to -- well, how much is

13 where. 

14 If you take a look at the one-day

15 concentrations and you look at the -- this is

16 the mean right here which is less than 1. You

17 look at the 75th percentile, of course, and

18 99.9th percentile -- you can look at that over

19 these rolling averages as well -- so 10 day,

20 30 day, and 90 day.

21 And you get an idea of what the

22 concentrations actually are in the water
202-234-4433

Neal R. Gross & Co., Inc.

Page 308

1 supplies that we've been sampling with a large

2 degree of intensity that we've been doing. 

3 And you can see here that the -- we know with

4 a significant degree of confidence that the

5 top value there -- 99.9th percentile anyway --

6 is right around 15. Fifteen drops down as you

7 start averaging your 365-day profile. So you

8 can kind of get an idea of how much atrazine

9 is in the water. 

10 Now, that's all well and good,

11 but, again, what does that really mean? What

12 kind of context can we put that in relative to

13 water limits that EPA has already set? And

14 there are actually limits that you can compare

15 that to. Let me give you that so that you can

16 put it in relative context.

17 EPA has two sets of values that

18 have been developed. One is by the Office of

19 Water, and these are health advisory limits. 

20 And they're duration specific. You can see

21 here seven-day HAL for adults, one-day HAL for

22 children, and, of course, you've heard already
202-234-4433

Neal R. Gross & Co., Inc.

Page 309

1 about the three part per billion maximum

2 contaminant limit which is a lifetime health

3 advisory as well. That was established back

4 in 1988.

5 That particular value is based on

6 .5 milligram per kilogram per day no-effect

7 level, a thousandfold uncertainty factor, and

8 also a contribution of 20 percent of water to

9 a dietary contribution. I'll get back to

10 that. But, anyway, that's where that number

11 comes from.

12 And in actuality it is the

13 statutory limit for atrazine. If you look at

14 the SDWA code you'll actually see that the

15 maximum contaminant limit, or the maximum

16 contaminant limit goal of three parts per

17 billion, actually has a statutory statement

18 that surrounds it. And I'll leave it at that,

19 but that's what the official level is that we

20 cannot exceed. But there are other levels

21 that are duration specific.

22 The Office of Pesticide Programs
202-234-4433

Neal R. Gross & Co., Inc.

Page 310

1 also had what are called drinking water levels

2 of comparison, which are also duration

3 dependent. This is a 90-day DWLOC -- this for

4 infants. We have an annual one here -- 90-day

5 and so forth. So these are duration specific.

6 This particular value here is

7 based on a ten -- I'm sorry -- a 1.8 milligram

8 per kilogram per day study. That was the

9 Morseth study that Dr. Breckenridge referred

10 to. I believe it's also a study that Dr.

11 Mendez referred to as one of the end points of

12 interest.

13 That also has a thousandfold

14 safety factor built into it. Why? There's

15 the typical hundredfold safety factor built

16 in, and then there's an additional tenfold

17 safety factor. That tenfold safety factor

18 that's added onto it is for two reasons --

19 threefold for uncertainties with a drinking

20 water program and threefold for uncertainties

21 with regard to children. Now, I think we're

22 answering both of those question, but suffice
202-234-4433

Neal R. Gross & Co., Inc.

Page 311

1 it to say that's the basis for a thousandfold

2 uncertainty factor. So that's 12.5 parts per

3 billion. 

4 There's also a value here of 298. 

5 That's a one-day drinking water level of

6 comparison that's used by EPA as a way of

7 giving yourself a level to compare against

8 measurements that you would find in community

9 water systems. That value is based on a 10

10 milligram per kilogram no-effect level. It

11 also has a one thousandfold safety factor

12 applied to it.

13 Okay. So what happens when you

14 take what I showed you before on the box and

15 whiskers diagram here? And if we rescale the

16 y-axis now, because we've got to put in those

17 larger values -- so we now have this axis

18 here -- same box and whiskers diagram. But

19 now we're going to put the values in for the

20 one day or the shorter term values. 

21 You can see that the information

22 that we've achieved from the water monitoring
202-234-4433

Neal R. Gross & Co., Inc.

Page 312

1 program in the Midwest has clearly shown that

2 we're well below these levels which already

3 have a one thousandfold safety factor built

4 in.

5 Now, if you take a look at the

6 longer term, which is the 90-day -- here's

7 some values just to give you some context to

8 it -- there is, of course, the seven-year

9 adult health advisory limits, seven-year

10 child, and, of course, the 90-day drinking

11 water level comparison just to give you an

12 idea of where these 90-day rolling average

13 values are falling out relative to EPA's

14 already established comparison levels. That's

15 just to give you some comparison to

16 established levels that EPA can use.

17 The other way as a toxicologist

18 that I can do it -- and you've been hearing a

19 lot of about corticosterone up and down and

20 no-effect levels and dosages at 200 milligram

21 per kilogram, 50, and so forth. I've also

22 mentioned that there's a short term limit for
202-234-4433

Neal R. Gross & Co., Inc.

Page 313

1 NOEL for 10 and longer term NOELs. 

2 How do they all fit together? 

3 Well, they fit together in the following way. 

4 If you look at the top over here you can see

5 the no-level levels of 10 for short term, 6.25

6 for intermediate, and longer term is the 1.8

7 milligram per kilogram per day. These are

8 values that Dr. Mendez was referring to

9 yesterday and are the values that underpin

10 these limits down here.

11 And I did a calculation just to

12 get an idea of what that means in parts per

13 billion. So what -- if someone were to

14 actually be drinking water that would give

15 them a 10 milligram per kilogram dosage on

16 that particular day what does that convert to? 

17 And the conversion factor that I use, which is

18 down here on the bottom, is a 70 kilogram body

19 weight, somebody drinking two liters a day. 

20 And what that converts to in this particular

21 case is 350,000 parts per billion. And,

22 likewise, if you look at the lower value for
202-234-4433

Neal R. Gross & Co., Inc.

Page 314

1 longer term exposure of 1.8 milligrams per

2 kilogram per day -- if you do that same

3 conversion it comes out to 63,000 parts per

4 billion.

5 Why am I doing this? I'm trying

6 to give us a context for a question that

7 actually came up yesterday, which is what's

8 the gap between what we're finding in these

9 water supplies with great confidence and the

10 toxicological testing levels that we're using. 

11 What can we say about that? That's why I did

12 these calculations.

13 And I went one step further and I

14 said, Well, what if you compared the gap

15 here -- what kind of levels do you get? If

16 you compare the 99.9th percentile in the case

17 of one day you're dealing with something close

18 to 23,000 fold difference. If you look at the

19 75th percentile the gap is obviously quite a

20 big larger to the tune of 410,000 for one day,

21 69,000 for 90 day.

22 And if you take the mean values
202-234-4433

Neal R. Gross & Co., Inc.

Page 315

1 and compare that against those no-effect

2 levels you're talking about nearly a

3 millionfold difference between the no-effect

4 level that's seen here and the mean value

5 that's found in the water supplies that we're

6 detecting, which is somewhere less than one

7 part per billion.

8 Likewise, for the longer term

9 value you're still looking at something like

10 150,000 fold difference between the no-effect

11 level and the 50th percentile that's found in

12 the 90-day rolling average.

13 So overall what we can say by this

14 that you've heard from Dr. Hendley, that

15 you've heard from the neurobiologists and so

16 forth, is that with regard to the water

17 monitoring programs it's extraordinary

18 extensive. We've got 100,000 finished

19 drinking water samples to work with, which for

20 the statisticians has been like kids in a

21 candy store with all that data to work with. 

22 They've had a great time with it.
202-234-4433

Neal R. Gross & Co., Inc.

Page 316

1 It's also been focused. We've got

2 50,000 samples from CWSes that have been

3 triggered into the extensive monitoring

4 program, which is based on these criteria of

5 half the MCL. And on top of that it's a

6 frequent program. It's weekly during the

7 planting season when you're likely to find

8 atrazine in the water. 

9 On top of that the results are

10 then showing that there's a sufficient

11 database to start making estimates of what you

12 believe to be the highest values. And we can

13 say with certainty what we know they are at

14 the 99.9th percentile because of the extensive

15 database.

16 On top of that, and because of

17 this large database that we're working with,

18 it's our opinion -- and I think Dr. Hendley

19 articulated this -- is that models are really

20 unnecessary. If you've got the data you don't

21 need the models.

22 Finally, I think I'd like to leave
202-234-4433

Neal R. Gross & Co., Inc.

Page 317

1 us all with this final message here -- that

2 the drinking water is safe. The margins of

3 exposure are very, very large and substantial. 

4 And I think that is a good indication that for

5 as much as we want to know a great detail

6 about the mode of action and the levels at

7 which it can occur, we need to put that in a

8 context of exactly what can people be exposed

9 to.

10 And I got to thinking as well the

11 other day when I was talking to Dr. Simpkins

12 about that one slide -- I don't know if you

13 remember that where they saw the atrazine

14 precipitating out in solution. And so I asked

15 our chemist, well, what's the water solubility

16 limit for atrazine? 

17 And so I went back to this

18 slide -- and I'm going to leave you with this

19 last slide here. And I said, well, what's the

20 water solubility limit if I were to look at it

21 on this scale. And that's what the atrazine

22 water solubility limit is; it's about 33,000
202-234-4433

Neal R. Gross & Co., Inc.

Page 318

1 parts per billion.

2 And I sat there and thought about

3 that for a minute, and I thought, you know,

4 the fact of the matter is we're establishing

5 that no one is getting exposed to atrazine in

6 excessive amounts, nor could they because you

7 simply can't get enough atrazine in the water

8 to get that kind of a dose.

9 Now, the last thing I want to

10 leave you with too is that we are still in the

11 process of refining these no-effect levels. 

12 We will understand them better and better as

13 we go through the information. And I think

14 when it comes to atrazine there has been a lot

15 of work, not just on the mode of action level,

16 but I believe on the mechanistic level to

17 understand how it works. And we'll get a

18 better idea of what these no-effect levels are

19 and how they compare with these water

20 monitoring limits.

21 So I hope what I've done here

22 today is give us a contextual reference to
202-234-4433

Neal R. Gross & Co., Inc.

Page 319

1 what we're finding in these toxicology studies

2 relevant to the levels that we're finding in

3 the monitoring program. Okay? And I'll take

4 questions.

5 SESSION CHAIR PORTIER: Questions? 

6 Dr. Krishnan?

7 DR. KRISHNAN: I have a couple of

8 questions. Maybe with this slide. Here, this

9 is similar to your slide on page five also

10 where you showed the margin of exposure of

11 about 6,000 at the last set of data --

12 right? -- 1.8? Maybe -- just to be sure that

13 you mentioned -- would this set of data, the

14 1.8 as the RfD, the uncertainty factor was

15 thousand.

16 DR. PASTOOR: Yes, that's correct.

17 DR. KRISHNAN: And the relative

18 source contribution used was 20 percent as in

19 other risk assessments. So it should be about

20 a 5,000 is the expected margin of safety

21 because we can't just compare those 63,000

22 directly with the water concentration that
202-234-4433

Neal R. Gross & Co., Inc.

Page 320

1 doesn't have any -- I'm trying to -- 

2 DR. PASTOOR: Yes.

3 DR. KRISHNAN: -- be sure that I

4 understand it correctly.

5 DR. PASTOOR: You're absolutely

6 correct. And, in fact, I think it's more

7 appropriate to go back to this slide right

8 here. And if, for example, we compare to the

9 12.5 -- just for an example -- this is based

10 on 1.8 milligrams per kilogram per day divided

11 by the 1,000 uncertainty factor and multiplied

12 by the 20 percent contribution of water to the

13 diet. Now, the other 80 percent is considered

14 to be foodborne atrazine. And I should point

15 out there is no atrazine exposure through the

16 food. 

17 What EPA has in their document to

18 account for food is actually -- let's see, the

19 number is .000041 milligrams per kilogram per

20 day by food contribution. So the source

21 contribution is actually going to be 100

22 percent water. If you're going to get exposed
202-234-4433

Neal R. Gross & Co., Inc.

Page 321

1 it will be by this route. Does that answer

2 your question?

3 DR. KRISHNAN: Thanks for the

4 clarification on the reassessing of the water

5 source contribution. But what I wanted to be

6 sure is that the margin of about 6,000 or so

7 that you showed with the last one that

8 compares with an expected margin of 5,000

9 based on the same assessments that you were

10 comparing to that's what I wanted to be sure

11 about -- thousand of uncertainty factor and

12 five for the water source contribution, which

13 would mean -- because there's no benchmark of

14 what is expected, so that's why I'm saying. 

15 Because the margin of exposure is usually

16 interpolated based on the applied uncertainty

17 factors to an assessment. So -- 

18 DR. PASTOOR: That would be true --

19 well, yes. You're absolutely right. I think

20 we're saying the same thing here unless I'm

21 wrong here. These already have built-in

22 they're uncertainty factors. So you're
202-234-4433

Neal R. Gross & Co., Inc.

Page 322

1 comparing uncertainty factored levels to the

2 existing concentrations. 

3 Let me correct this. This does

4 not have the 20 percent correction here. It's

5 actually built in to the 298. Do I have that

6 one right?

7 DR. KRISHNAN: The 20 percent

8 is -- 

9 DR. PASTOOR: Right. Only in the

10 MCL values that are calculated is a 20 percent

11 calculation made for relative source

12 contribution in the health advisories -- the

13 Office of Water calculations that are made.

14 DR. KRISHNAN: Not with these

15 numbers that are presented as the pbbs?

16 DR. PASTOOR: No. This has got

17 a -- this is a 1.8 divided by what the 1,000

18 for the uncertainty factor, and then the

19 according body weight for either an adult or

20 a child. So the adult would be 70 divided by

21 two liters -- 70 kilograms divided by two

22 liters. For the child it's 10 kilograms
202-234-4433

Neal R. Gross & Co., Inc.

Page 323

1 divided by one liter per day consumption. So

2 sorry for that.

3 SESSION CHAIR PORTIER: I think the

4 rest of us are going to take a break and

5 you're going to keep working on this.

6 DR. PASTOOR: Right.

7 SESSION CHAIR PORTIER: Dr.

8 Breckenridge, do you have any final words? 

9 Because after this we're going to switch to

10 the rest of the public commentors.

11 DR. BRECKENRIDGE: No, Mr.

12 Chairman. Thank you very much for your

13 continued patience and the time you spent with

14 us this afternoon. We greatly appreciate the

15 opportunity to share new information. Thank

16 you very much.

17 SESSION CHAIR PORTIER: We're going

18 to take a 15-minute break and reconvene at

19 four o'clock.

20 (Whereupon, the above-entitled

21 matter went off the record at 3:45 p.m. and

22 resumed at 4:02 p.m.)
202-234-4433

Neal R. Gross & Co., Inc.

Page 324

1 SESSION CHAIR PORTIER: Okay. We

2 have -- yes, I know -- my bell. I left my

3 bell at the other meeting room. We have

4 remaining four public commentors. It will

5 take a little over a half-hour. These are

6 relatively short compared to the two sets of

7 public comments we've heard before. So, Joe,

8 what's the order here? Just like it is?

9 MR. BAILEY: Yes.

10 SESSION CHAIR PORTIER: So I guess

11 the first commentor is Alan Roberson with the

12 American Water Works Association.

13 MR. ROBERSON: Well, my comments

14 start off by saying good morning, but I'll

15 switch that to good afternoon. And while I

16 was in the audience, I made up 60 slides that

17 I'll start off with. 

18 (Laughter.)

19 MR. ROBERSON: I do have one thing

20 that's real short. 

21 SESSION CHAIR PORTIER: As long as

22 you can do it in the five minutes we've got.
202-234-4433

Neal R. Gross & Co., Inc.

Page 325

1 MR. ROBERSON: I'll talk really

2 fast. So, anyway, I want to thank EPA and SAP

3 for the opportunity to present the perspective

4 of the American Water Works Association. This

5 is an important issue to us.

6 We're an international non-profit

7 scientific and technical association

8 representing drinking water -- primarily

9 drinking water utilities. So some of the

10 questions I've heard earlier about water

11 utilities I'd be happy to answer. We've got

12 almost 50,000 members, 4,200 member utilities

13 that serve about 80 percent of the water in

14 the U.S. 

15 So we're dedicated to safe water

16 and we appreciate the opportunity to comment

17 on the statistical approaches for the drinking

18 water monitoring frequency.

19 Our staff -- me -- and our member

20 utilities -- we're not expert on the health

21 effects issues, and we'll leave those issues

22 to the appropriate experts -- and a lot of
202-234-4433

Neal R. Gross & Co., Inc.

Page 326

1 those are around the table.

2 We have reached out to some of our

3 members that have statistical expertise to

4 develop these verbal comments. And these

5 comments focus on the drinking water

6 monitoring frequency as opposed to the

7 ecological monitoring. There's two separate

8 issues there and we recognize that.

9 So -- and the other caveat I want

10 to make is that I'm not a statistician but I

11 play one on T.V. I'm an engineer by training

12 so I've had to go out to my members to do

13 this. So these verbal comments mirror our

14 written comments that we submitted to EPA on

15 April seven that are in the docket -- little

16 more detail in those comments.

17 So we've been following the

18 regulatory process for atrazine, simazine, and

19 the other triazines since the MCLs came out in

20 the early nineties. As you know, compliance

21 with the atrazine MCL of three parts per

22 billion is based on annual average of
202-234-4433

Neal R. Gross & Co., Inc.

Page 327

1 quarterly samples.

2 And one sort of side comment I

3 want to make is that sampling is taken as what

4 is known as the entry point to the

5 distribution system -- so it's the finished

6 water after any treatment. If you hear about

7 raw water samples -- utilities may take those

8 as part of the participation in the Syngenta

9 program or they may be doing that as part of

10 their process control if they're trying to

11 determine how much carbon they might need to

12 add to comply with the MCL.

13 So atrazine and the other triazine

14 herbicides have been a major issue for us and

15 our members for the past 20 years due to this

16 potential to contaminate groundwater and

17 surface water. Over the past 20 years we've

18 written many comments to EPA as part of the

19 atrazine re-registration process. 

20 In the past we were concerned

21 about the lack of data to make regulatory

22 decisions. But the recent IRED has been a
202-234-4433

Neal R. Gross & Co., Inc.

Page 328

1 step in the right direction in requiring the

2 registrants to conduct monitoring in source

3 water. We think that's very important to have

4 that information. And we think those

5 registrant monitoring requirements should be

6 a requirement for any pesticide with the

7 potential to contaminate drinking water.

8 So our past written comments have

9 focused on several issues, but primarily our

10 main concern has been the additional treatment

11 burden that our utility members and ultimately

12 their customers continue to shoulder in order

13 to comply with the current atrazine standard.

14 Many service water utilities

15 installed additional treatment, either

16 granular activated carbon or powdered

17 activated carbon or both, soon after the MCL

18 was finalized in 1991 to comply with the new

19 standard. In fact, I would say from sort of

20 anecdotal evidence most service water

21 utilities now have carbon treatment in one

22 form or another to comply with the atrazine
202-234-4433

Neal R. Gross & Co., Inc.

Page 329

1 MCL and to address other issues such as taste

2 and odor issues.

3 The registrant and the growers

4 have made some progress in controlling

5 atrazine at the source. And this stewardship,

6 combined with the additional carbon treatment

7 by utilities, have contributed to atrazine

8 violations in community water systems

9 decreasing from 81 in 1995 to three in 2007. 

10 And that data came from the EPA's data system.

11 Any potential change in compliance

12 monitoring has implication for our members,

13 particularly the small and medium systems in

14 the Midwest that are subject to the wide

15 variability of atrazine concentrations in

16 their source waters. 

17 Without restating the obvious in

18 the extensive literature you've seen the

19 service water utilities get that flush in the

20 runoff, and that depends on a lot of factors

21 such as the application date, the intensity

22 and direction of the rainfall, the type of the
202-234-4433

Neal R. Gross & Co., Inc.

Page 330

1 watershed, several other factors. In some

2 Midwestern reservoirs the spring flush will

3 remain in the reservoir so that the atrazine

4 levels remain elevated year round.

5 I want to talk a little bit about

6 the economic implications for a required

7 increase in compliance monitoring. For

8 example, assume you have a small service water

9 system in the Midwest that serves 500

10 connections -- that's about 1,500 people. And

11 for this example let's assume an average

12 monthly water bill of $30 a month, so that the

13 total annual revenue for this system would be

14 about $180,000 a year.

15 So the analysis for a single

16 compliance sample using the approved GCMS

17 method costs about $200. And it should be

18 noted that the amino assay analytical method

19 has not been approved for drinking water

20 compliance monitoring. It's good for

21 screening. It's just not sensitive and

22 accurate enough for the compliance monitoring. 
202-234-4433

Neal R. Gross & Co., Inc.

Page 331

1 Assume an additional $100 for sample

2 collection. That gives a total cost of $300

3 for each additional compliance sample. 

4 So doubling the samples for this

5 water system from four to eight would increase

6 the monitoring cost by about $1,200 a year. 

7 That's about one percent of the revenue for

8 that system. So there's a significant impact

9 for any change in compliance monitoring. And,

10 again, that's for the drinking water side. 

11 The ecological from my point of view will be

12 done by the registrant and our utilities would

13 not be involved with that.

14 At the same time, due to this

15 application in right-of-ways and other

16 widespread uses, atrazine is ubiquitous is

17 almost all surface waters and many

18 groundwaters, albeit in low concentrations. 

19 In a recent study by USGS out of 4,600 samples

20 only eight were non-detects. In other words,

21 atrazine was detected in 99.8 percent of the

22 USGS samples of potential sources in drinking
202-234-4433

Neal R. Gross & Co., Inc.

Page 332

1 water. This study did have a relatively low

2 detection limit of .004 parts per billion, and

3 the samples were taken in might be considered

4 vulnerable areas.

5 EPA's recent release of finished

6 water monitoring data from 44 states is part

7 of it's six-year review of drinking water

8 regulations. So EPA's required to go back and

9 look at the drinking water standards every six

10 years. That basically parallels the

11 previously mentioned violation data.

12 Atrazine was detected in 4.23

13 percent of about 161,000 samples. And I'm

14 going to give some ranges of the median, the

15 90th percentile, and the maximum levels from

16 that data set. And that's from each state, so

17 when you see the range it's -- you know,

18 here's the lowest in one state and here's the

19 highest.

20 The median range from .1 to 1.61

21 part per billion, the 90th percentile range

22 from 0.4 to four parts per billion, and the
202-234-4433

Neal R. Gross & Co., Inc.

Page 333

1 maximum range from .1 to 27 parts per billion. 

2 Generally atrazine is found less frequently in

3 finished drinking water as opposed to drinking

4 water sources due to existing carbon treatment

5 to comply with the MCL.

6 So we have four general

7 recommendations for the EPA and the SAP. EPA

8 should use its authority under FIFRA to

9 require environmental monitoring for any

10 pesticide with the potential for being water

11 contamination. EPA should complete this

12 reevaluation of atrazine as soon as possible

13 and to continue to hold to its principles of

14 good science and transparency.

15 EPA should use its toxicological,

16 epidemiological, and cost benefit expertise to

17 develop the appropriate risk management option

18 for drinking water as soon as possible. And

19 the registrants, or the users, should pay for

20 any additional compliance monitoring if needed

21 for water utilities.

22 Finally, I want to comment a
202-234-4433

Neal R. Gross & Co., Inc.

Page 334

1 little bit on the basis of drinking water

2 monitoring frequency due to the potential

3 implications to compliance monitoring for

4 service water utilities. 

5 A two-step process is needed to

6 make the decision if any additional monitoring

7 is necessary. First, EPA needs to determine

8 the appropriate concentration and time frame

9 that protects public health. It is important

10 to decide what criteria are before any attempt

11 to made to define a sampling strategy.

12 To do this some basic decisions

13 need to be made regarding what causes the

14 risks that are being mitigated. Are the risks

15 acute, chronic, sub-chronic, semi-chronic --

16 there are different words you can use. If

17 chronic then what levels over what duration

18 are implicated in the health end point of

19 concern.

20 These fundamental questions need

21 to be answered, and we are looking at EPA to

22 answer these questions. Then a sampling plan
202-234-4433

Neal R. Gross & Co., Inc.

Page 335

1 can be developed to meet those objectives.

2 Again, the current atrazine MCL of

3 3 parts per billion in compliance is based on

4 an annual average of quarterly samples. This

5 may or may not be the bright line for shorter

6 term exposures to atrazine. I have seen other

7 numbers mentioned such as 12, 12-1/2, 20, 30,

8 37-1/2 parts per billion and others -- there

9 was a whole list in the previous

10 presentation -- along with time frames of one,

11 seven, 30, and 90 days.

12 At this point we recommend that

13 the EPA make the decision as soon as possible

14 on a bright line for the appropriate

15 concentration and time frame for public health

16 protection noting that the ecological issues

17 may require a different set of criteria.

18 Then once the appropriate

19 concentration and time frame is set the

20 appropriate monitoring strategy can be

21 designed for water utilities, noting that the

22 current framework may or may not be
202-234-4433

Neal R. Gross & Co., Inc.

Page 336

1 appropriate.

2 We think that the artificial

3 neural network approach developed by EPA is

4 too complex for water utilities and state

5 primacy agencies to use in developing

6 compliance monitoring strategies for specific

7 utilities.

8 It may be useful in evaluating

9 possible sampling structures during the

10 development of alternative rules, but even

11 before this application there are other

12 methods that have been explored, like

13 bootstrap sampling that are as effective and

14 much simpler than the ANN approach.

15 We've developed an approach that

16 focuses on developing a high confidence that

17 a measured value does not exceed specific

18 values established in the regulation, whatever

19 those values might be.

20 This approach found that the

21 number of samples needed to characterize a

22 spring flush and understand its potential
202-234-4433

Neal R. Gross & Co., Inc.

Page 337

1 impacts on compliance for a specific level of

2 concern -- in this case we used three parts

3 per billion on an annual average because

4 that's what we were -- we knew to be a set

5 value for drinking water. That level of

6 concern is weighted to the mean and the

7 standard deviation of historical data for each

8 site. 

9 These potential monitoring

10 requirements can be very succinctly and

11 summarized on the approach shown on the next

12 page -- and that's the graph -- I think it's

13 on the back of what everybody's gotten.

14 The results from this approach

15 suggest that using historical data -- and,

16 again, that's really looking at the mean and

17 standard deviation -- sampling can be designed

18 so that surface water systems that are

19 vulnerable with high means and high

20 variability sample more frequently while

21 systems that have low means and are little

22 variability sample less frequently. And I've
202-234-4433

Neal R. Gross & Co., Inc.

Page 338

1 given a electronics copy of this spreadsheet

2 on stick drive to EPA staff, and that's

3 available for anyone to use. It's really a --

4 I think a pretty simply tool that might be

5 effective.

6 So that's the end of my comments. 

7 If there are any questions?

8 SESSION CHAIR PORTIER: Any

9 questions from the panel? 

10 MR. ROBERSON: Wow.

11 SESSION CHAIR PORTIER: I don't see

12 any. Could be a factor that you're very clear

13 or a factor that we're very tired.

14 MR. ROBERSON: You've had a long

15 day. Okay.

16 SESSION CHAIR PORTIER: I suspect

17 it was clear.

18 MR. ROBERSON: Okay. And I will

19 be around for some of the discussion tomorrow

20 afternoon and Thursday when you start talking

21 about this issue. My toxicological background

22 is pretty thin so I'm going to pass on that
202-234-4433

Neal R. Gross & Co., Inc.

Page 339

1 tomorrow morning.

2 SESSION CHAIR PORTIER: An

3 opportunity to learn more.

4 MR. ROBERSON: I learned a lot

5 today.

6 SESSION CHAIR PORTIER: The next

7 speaker is Dr. Sass with the National

8 Resources Defense Council. Dr. Sass provided

9 two handouts this morning to the panel.

10 DR. SASS: Thank you very much for

11 your indulgence. I know it's been a really

12 long two days and I know it's not going to get

13 shorter.

14 My name is Jennifer Sass. I'm a

15 scientist with the National Resources Defense

16 Council, which is an environmental non-profit. 

17 And I'm in the health program. I'm located

18 here in Washington, D.C., and my background is

19 molecular, developmental, and toxicology.

20 I have two handouts for you. So

21 one is this report. It's an update from a

22 report that we actually did, I think, in
202-234-4433

Neal R. Gross & Co., Inc.

Page 340

1 August of `09 -- doesn't seem that long ago to

2 me. And we analyzed the data and crunched it

3 and sort of came up with different tables of

4 the individual community water systems as well

5 as drinking water and watershed both from the

6 Syngenta -- I'll call it Syngenta EPA data --

7 from the monitoring programs that you've been

8 hearing about.

9 So we presented that in a report

10 in August and this is an update of the more

11 recent years from that data set. But the

12 summary of that -- there's some executive

13 summary, but the summary of that is in my

14 comments.

15 My comments, which have also been

16 passed to you -- I'm not going to read all of

17 it obviously; I think it's about 17 pages. 

18 But I did respond to the actual charge

19 questions that you've been given and I slotted

20 my answers -- I read those charge questions

21 and tried to answer those and give you what I

22 think are my answers. So feel free to sleep
202-234-4433

Neal R. Gross & Co., Inc.

Page 341

1 through this presentation, and then when you

2 get to those charge questions you can pop open

3 this handy list and see what Jennifer would

4 have said.

5 And a lot of them -- again, the

6 toxicology is really very expert, and you know

7 that, and you've heard from the experts

8 actually. So in a lot of them, I just say

9 that I don't have an answer for that and I

10 look forward to seeing what you guys come up

11 with. So I haven't stretched myself -- I

12 haven't made up anything -- I haven't

13 stretched myself beyond what I actually can

14 comment on.

15 So question 1.1 -- this is the

16 issue of the carcinogen -- whether -- how to

17 classify it as -- what its potential is to be

18 a human carcinogen. This is something that

19 EPA and I have been going back and forth on

20 since I started this job in 2001, in January. 

21 So my answer to this hasn't

22 changed that much, which is that I think
202-234-4433

Neal R. Gross & Co., Inc.

Page 342

1 classifying it as not likely a human

2 carcinogen, which is what EPA is proposing

3 here, is inconsistent with EPA's cancer

4 guidelines and supplemental, which were

5 published and finalized in `05, and as well

6 with the criteria of the International Agency

7 for Research on Cancer, IARC, which is

8 probably the premier cancer classification

9 body located in France. They've reviewed

10 atrazine twice. And I also say it would be

11 scientific ignorance and dangerous public

12 health policy to classify atrazine in the way

13 that EPA proposes here. 

14 I want to read you the IARC

15 guidelines for classifying something as not

16 likely -- that would be a Group 4 for IARC. 

17 IARC has evaluated about 900 agents since the

18 mid -- since 1971 -- since the 1970s. And of

19 those only one has ever been categorized as a

20 Group 4, not likely. And that's because the

21 burden of proof required to put something into

22 not likely, to actually know, to be able to
202-234-4433

Neal R. Gross & Co., Inc.

Page 343

1 state with scientific certainty -- scientific

2 confidence that you know that it's not going

3 to cause human cancer is a threshold that's

4 very hard to meet for the World Health

5 Organization, which is who IARC advises.

6 On the other hand the EPA doesn't

7 seem to have too much trouble bumping things

8 into that category. And I think that's really

9 poor public health policy for an agency that's

10 charged with protecting public health. 

11 Atrazine, as I said, has been

12 twice reviewed by IARC in `91 and again in

13 `99. It's classified as Group 3, unable to

14 determine. And that's based on sufficient

15 evidence of causing cancer in experimental

16 animals, but inadequate evidence in humans. 

17 It was downgraded in -- from its

18 1991, from its earlier classification, which

19 was a 2B -- Group 2B, possibly carcinogenic to

20 humans. And that's because in the `99 the

21 second work group felt that the mechanism by

22 which it was causing tumors in animals might
202-234-4433

Neal R. Gross & Co., Inc.

Page 344

1 not be relevant to humans. Nobody questions,

2 though, that it caused cancer in animals. 

3 Okay. Dr. Lorenzo Tomatis, who, I

4 point out here, was probably one of the

5 greatest public health experts of our

6 lifetime, a former director of the IARC, as

7 well as chief of the IARC monograph program,

8 wrote, in referring to the decision to

9 classify atrazine as unable to determine,

10 Group 3, "Evidence of carcinogenicity provided

11 by the results of experimental bioassays

12 appears too often to have been disregarded on

13 the basis of suggested mechanistic hypotheses. 

14 Ominous consequences on public health may

15 follow if such hypotheses, once actually

16 tested experimentally, are shown to be

17 incorrect or if they do not account adequately

18 for the wide range of susceptibility that is

19 known to exist in human populations."

20 Atrazine has not been adequately

21 tested in studies covering a full range of

22 human exposures, including pre-birth and early
202-234-4433

Neal R. Gross & Co., Inc.

Page 345

1 life exposures for its potential to cause

2 cancer in humans and it should not be

3 classified as not likely. 

4 I have some more on that but I'm

5 not going to read it to you at this time

6 except to say that we would like EPA to follow

7 the advice of earlier scientific advisory

8 panels in 2000 and in 2003 that reviewed this

9 question. In both those panels the final

10 report recommended that EPA consider all of

11 the relevant science relating to atrazine

12 carcinogenicity including whether hormonal

13 effects in childhood or adolescence may have

14 an impact on cancer occurrence in later years. 

15 But, unfortunately, this panel has

16 not been asked to look at all that data and

17 has not been charged, actually, with a full

18 review of data for that question. And so I

19 would suggest that the suggestions of earlier

20 panels be heeded.

21 The regulation of hazardous

22 materials in the U.S. is generally based on
202-234-4433

Neal R. Gross & Co., Inc.

Page 346

1 the scientific dogma that the dose makes the

2 poison -- the notion that a larger doses are

3 more toxic and that smaller doses are less

4 toxic. This belief leads to the presumption

5 that a chemical is safer at lower doses. And

6 for this reason, and also because testing with

7 low doses is often not done for practical

8 reasons, which you all know, we rarely have

9 reliable toxicity data of potential hazardous

10 effects at very low doses.

11 Moreover, for chemicals that

12 interfere with the development of critical

13 organ systems, such as the immune system,

14 reproductive systems, and the nervous system,

15 there is now a substantial amount of

16 scientific proof than when exposure occurs at

17 the same time as these critical systems are

18 developing during early life stages even

19 transient low dose exposures can cause

20 irreparable harm -- that is, the timing of

21 exposure may be as important or even more

22 important than the dose.
202-234-4433

Neal R. Gross & Co., Inc.

Page 347

1 For example, pre-birth exposure to

2 mercury at levels so low that you can't detect

3 effects in the pregnant mother could cause

4 improper connections between the brain cells

5 that are forming synapses in her fetus and

6 result in lifelong alterations in brain

7 structures and function. We know that.

8 Exposure to endocrine or hormone9 disrupting chemicals such as atrazine during

10 critical windows of development has also been

11 shown to have permanent effects in laboratory

12 animal studies. Some of these effects, such

13 as infertility or cancer, may not be obvious

14 until adulthood even though the exposure

15 occurred during fetal or neonatal life.

16 This phenomenon has been

17 demonstrated for atrazine in laboratory

18 animals, such as amphibians, where short

19 exposures during early development stages have

20 had long-lasting or even permanent

21 irreversible effects on adult behavior and

22 longevity, including increased susceptibility
202-234-4433

Neal R. Gross & Co., Inc.

Page 348

1 to infection -- the immune tox data --

2 alternations in survival behavior -- also

3 shown there -- and reduced long-term survival. 

4 Some of these data were mentioned by your

5 expert speakers that addressed you yesterday. 

6 I know because I was here -- something to

7 consider.

8 Jumping to question 1.4,

9 commenting on the agency's preliminary

10 conclusion regarding the aromatase mechanism,

11 I just want to point out there is data, and I

12 don't want to go through the data -- actually

13 Pia summed it up for you and as well I'm sure

14 some of you know it independently.

15 I just want to point out that

16 atrazine is associated with numerous diverse

17 health end points related to endocrine

18 disruption. For example, it's an endocrine

19 disruptor. It impairs the immune system and

20 it is associated with birth defects. It's

21 associated -- it's been associated in male

22 frogs with eggs in their testes, with impaired
202-234-4433

Neal R. Gross & Co., Inc.

Page 349

1 sperm production and frogs -- in animals --

2 sorry -- and in humans and with birth defects

3 in human infant gut wall.

4 Giving the experimental evidence

5 of such diverse outcomes it is unclear to me

6 how EPA can determine with confidence that a

7 particular mechanism of toxicity is not

8 relevant until it has been thoroughly tested

9 in multiple, mutually consistent, adequately

10 powered studies that exclude with reasonable

11 certainty bias, confounding, and chance. And

12 I don't think that bar has been met.

13 A 2010 article published by

14 University of South Florida researchers Rohr

15 and McCoy analyzed the findings of over 125

16 independently published research studies of

17 atrazine effects on freshwater fish and

18 amphibians. Their meta-analysis found that

19 many of the studies reported the same health

20 outcomes, even though the studies were in

21 several different wildlife species and used

22 different research methods.
202-234-4433

Neal R. Gross & Co., Inc.

Page 350

1 In particular, atrazine affected

2 the hormone system of freshwater fish and

3 amphibians in most studies, including effects

4 such as altered time of metamorphosis --

5 sometimes it was a delayed and sometimes it

6 was accelerated, it wasn't always the same --

7 impaired sperm production and abnormal gonadal

8 development.

9 These reports of diverse endocrine

10 disruption effects on atrazine across diverse

11 species and in different independent studies

12 suggests that the mechanisms of toxicity for

13 atrazine are likely also diverse, including

14 several mechanisms of action potentially and

15 possibly including aromatase catalysis.

16 Question 1.6, the agency has also

17 concluded that there's no association between

18 atrazine and the development of Parkinson's

19 disease. You heard their presentations

20 yesterday; I also did. Please comment. 

21 So my comment is this. A

22 potential association between atrazine and
202-234-4433

Neal R. Gross & Co., Inc.

Page 351

1 Parkinson's disease has not yet been tested

2 with multiple, mutually consistent, adequately

3 powered studies covering a full range of human

4 exposures that exclude, with reasonable

5 certainty bias, confounding, and chance to

6 provide individual and pooled estimates of

7 risk in near unity with narrow confidence

8 intervals. It has not been tested, in other

9 words.

10 However, a number of well11 conducted studies have reported a

12 statistically significant link between

13 Parkinson's disease and exposure to pesticides

14 generally, and including some herbicides and

15 organochlorines like atrazine.

16 For example, in 2010 a Portuguese

17 team reported that among 86 cases of

18 Parkinson's disease, sex- and age-adjusted

19 logistic regression showed a potential risk

20 factor, included pesticides, herbicides, and

21 insecticides. It's in my comments: The odds

22 ratio is 2.6, and the 95% confidence interval
202-234-4433

Neal R. Gross & Co., Inc.

Page 352

1 range is 1.17 to 5.86.

2 The 2009 epi study from California

3 reported a correlation between well water

4 possibly contaminated with agriculture

5 pesticides, possibly because they used GIS6 based models and state pesticide use reports

7 in order to estimate contamination levels, and

8 Parkinson's disease specifically.

9 Although atrazine was not one of

10 the pesticides that was selected for

11 examination in that study, they did find a

12 high level of possible well water

13 contamination with methomyl -- odds ratio is

14 1.67 -- it was statistically significant --

15 with chlopyrifos -- and the odds ratio of

16 1.87, again statistically significant -- and

17 propargite -- odds ratio 1.92, again

18 statistically significant -- resulting in

19 approximately 70 to 90 percent increases in

20 relative risk of Parkinson's disease.

21 And more recently a 2010 study

22 published in the Journal of Neurology reported
202-234-4433

Neal R. Gross & Co., Inc.

Page 353

1 on a correlation between blood serum levels of

2 dieldrin, which is also an organochlorine like

3 atrazine, and Parkinson's disease. I also

4 have the odds ratios here. Atrazine may also

5 be found to have similar health outcomes if it

6 were examined properly. 

7 These findings are in agreement

8 with an NIH agriculture health study which

9 reported in 2006 a nearly twofold increase in

10 the risk of Parkinson's among applicators. 

11 Incident Parkinson's was associated with

12 cumulative days of pesticide use at

13 enrollment. So the more days that you applied

14 the more your risk went up. And they divided

15 them into quartiles. And when the highest

16 quartile, which was 400-plus days, was

17 compared to the lowest the odds ratio was 2.3,

18 and it was statistically significant for

19 persons applying pesticides.

20 For persons applying pesticides

21 more than half the time the odds ratio is 1.9. 

22 While this study reported a link between
202-234-4433

Neal R. Gross & Co., Inc.

Page 354

1 atrazine and incident Parkinson's it was not

2 significant at the 95th percentile. It

3 probably would have been significant at the

4 90th percentile -- the odds ratio is 1.1 and

5 the confidence sensor was -- spanned .5 to

6 2.2, but they didn't present 90th

7 percentile -- or 90 percent confidence

8 intervals. I wish they did. Moreover, the

9 authors reported a link between the

10 organochlorine lending and also incident

11 Parkinson's -- statistically significant.

12 Unfortunately, there was a low

13 number of cases -- 43 percent with atrazine

14 and 19 cases with lindane. It's -- because

15 it's a prospective epidemiology study they're

16 still following it. And it compromises the

17 statistical power of the study at this point,

18 but they're still following up these

19 applicators and I think there may be things we

20 can learn in future iterations -- future

21 reports.

22 These studies and others provide
202-234-4433

Neal R. Gross & Co., Inc.

Page 355

1 suggestive evidence, though, that a possible

2 link may exist between atrazine exposure and

3 risk of Parkinson's disease. And therefore,

4 a lack of an association cannot be asserted

5 with scientific confidence.

6 I have some answers to 1.8 on the

7 immune system but I actually don't think I'm

8 going to read them for you today. Oh, except

9 to sum up at the end because I read a little

10 bit of the Jason Rohr study -- the meta11 analysis and a few things -- except to sum up,

12 that atrazine has been shown to act

13 synergistically with other chemicals to

14 increase their toxic impairing of the immune

15 system.

16 In a 2009 study when tiger

17 salamander larvae were raised for two weeks in

18 atrazine containing -- or in water containing

19 atrazine at 20 or 200 part per billion or

20 chlorpyrifos at 220 or 200 part per billion,

21 no increase in death was observed. But when

22 the larvae were exposed to the combination of
202-234-4433

Neal R. Gross & Co., Inc.

Page 356

1 atrazine and chlorpyrifos together there was

2 a significant increase in larval death from

3 increased viral infection and disease, so

4 presumably a compromised immune system.

5 This study suggested the two

6 chemicals acting together can harm immune

7 function, whereas either one alone may not. 

8 This is significant because these pesticides

9 are commonly found together. USGS finds them

10 up to seven or more at the time routinely in

11 water bodies, and, as well, because many of

12 the pesticide products, including atrazine,

13 are packaged and sold as mixtures with other

14 pesticides.

15 Question 1.9: I sort of use that

16 question as an opportunity to jump in and say

17 that I think EPA failed to consider a number

18 of important studies that are directly

19 relevant to the understanding of human health

20 risks associated with atrazine, and I've

21 identified some of those.

22 One of them that I think is really
202-234-4433

Neal R. Gross & Co., Inc.

Page 357

1 important and that was mentioned at the -- by

2 the scientific advisory panel you guys in --

3 I think February was your last meeting -- is

4 one on poor birth outcomes -- it's a 2009

5 study. It found a significant correlation

6 between prenatal atrazine exposure and reduced

7 birth weight -- reduced body weight at birth.

8 They reviewed over 24,000 babies

9 born in Indiana and localized them to

10 particular water systems. I've summed it up

11 for you with a little more data. I think it

12 should be considered.

13 There's also a 2009 study that

14 analyzed over 30 million births across the

15 U.S. and reported an increased risk of birth

16 defects associated with mothers who became

17 pregnant in April to July, when pesticides are

18 at their highest in the waterways. These

19 ecological epidemiology studies, as you know,

20 are more like hypothesis testing rather than

21 conclusive.

22 But what I think is interesting
202-234-4433

Neal R. Gross & Co., Inc.

Page 358

1 about this is that in spring when other --

2 where pesticides may fluctuate in the water

3 and might be considered to be highest, other

4 contaminants wouldn't be expected to fluctuate

5 in spring. So, for example, pharmaceuticals

6 that are in our water, other kinds of

7 endocrine disruptors that are in drinking

8 water -- so you wouldn't be expecting them to

9 have seasonal spikes like pesticides. So I

10 think it's worth looking at. I think it's

11 significant. They found a birth defect in the

12 gut wall of newborn babies associated with

13 early life exposures during this time.

14 The other thing I want to point

15 out -- in digging through the RED, which is

16 the Registration Eligibility Decision that

17 EPA -- EPA's final decision to register

18 atrazine -- is that they actually determined

19 that it was likely that workers and homeowners

20 and their families may be exposed to unsafe

21 levels of atrazine because of lawn uses. For

22 workers, that included farmers who mix, load,
202-234-4433

Neal R. Gross & Co., Inc.

Page 359

1 and apply the pesticides like atrazine -- this

2 is in atrazine RED -- and I have page numbers

3 and references.

4 What they found -- we know that

5 exposures can occur from accidental spills and

6 splashes onto skin or clothing as well as

7 inhalation and droplets being applied directly

8 to the skin. And EPA noted all that and that

9 exposure can also occur when applicators

10 follow all the label requirements and use

11 protection clothing and equipment, that you

12 can still have exposures.

13 We also know that because we have

14 data showing that farmers have it in their

15 urine when they apply atrazine -- and that's

16 data from CDC researchers -- and I've cited

17 all of those studies. As well, we know that

18 there's been links between atrazine levels in

19 the urine and poor sperm quality in rural men

20 associated with atrazine exposure --

21 statistically significant.

22 And we also know that if you
202-234-4433

Neal R. Gross & Co., Inc.

Page 360

1 reduce the amount of atrazine that's being

2 applied then you can actually reduce the

3 amount that's in the urine. So we know that

4 there's a direct correlation if we take action

5 that we can actually protect direct exposures

6 to those workers.

7 And in 2006 in its RED in its

8 final decision EPA acknowledged concerns about

9 these human exposures. EPA classified

10 atrazine as an RUP, or restricted use

11 pesticide. That's because of its hazards to

12 ground and surface water, and it means that

13 only applicators can apply it.

14 But there's an exception for lawn

15 care, turf, and conifer trees and it allows

16 homeowners to apply the chemical themselves. 

17 And according to EPA's own assessment in its

18 RED, this exception may lead to unsafe

19 exposures that exceed EPA's own levels of

20 concern for homeowners who apply the products

21 for their lawn. And EPA also expressed

22 concern that children who play on these
202-234-4433

Neal R. Gross & Co., Inc.

Page 361

1 atrazine-treated lawns may also be at risk for

2 potentially unsafe exposures. So the idea of

3 needing to consider these early life exposures

4 is very relevant. It's very real.

5 We have some recommendations. I

6 used question 2.1 to take advantage of some

7 extra space at the end and make some

8 recommendations. So, first of all, the

9 watershed and monitoring programs of watershed

10 and drinking water -- actually our analysis of

11 EPA's atrazine monitoring program taken from

12 20 watersheds between 2007 and 2008, which is

13 our updated report which I've provided to you,

14 confirms that waters are contaminated. They

15 continue to be contaminated. 

16 Actually they did 20 watersheds

17 and they all showed detectable levels of

18 atrazine. 16 had average concentrations above

19 1 part per billion. 1 part per billion is the

20 level that EPA has been determined to show --

21 is determined to be harmful and plants and

22 wildlife -- aquatic plants and wildlife.
202-234-4433

Neal R. Gross & Co., Inc.

Page 362

1 18 of the monitored watersheds

2 were intermittently severely contaminated

3 where at least one sample was above 20 -- this

4 is watersheds, not drinking water, remember --

5 above 20 part per billion. And nine had peaks

6 that were above 50 part per billion. Three

7 had maximums that exceeded 100 part per

8 billion. One, the Big Blue River watershed in

9 Nebraska, had 147 part per billion in May of

10 2008.

11 The drinking water data, which we

12 also analyzed and provided an update, was

13 taken between 2005 and 2008. 80 percent of

14 the samples from 153 drinking water systems

15 contain atrazine -- that's raw and finished --

16 that's 80 percent of total samples. But of

17 the 153 drinking water systems that were

18 monitored, 100 had peaks in the raw water that

19 exceeded 3 part per billion, and two-thirds of

20 these 100 systems also had peaks that exceeded

21 3 in the finished water. Again, they don't

22 exceed the MCL because it's averaged over a
202-234-4433

Neal R. Gross & Co., Inc.

Page 363

1 year, but they're spiking.

2 Six had high enough atrazine

3 levels to actually exceed the MCL. But what

4 happens is, the utilities put in all the data,

5 and so when you get a lot more non-detects or

6 low levels of the year, you can average away

7 those peaks. But if they just took the

8 quarterlies it would actually exceed the MCL

9 in the finished tap water.

10 But the problem -- and my

11 recommendations as a scientific advisory panel

12 that you can pass on to the EPA -- is that EPA

13 actually identified 1,172 watersheds that were

14 at high risk. They, however, negotiated a

15 monitoring program with the registrant that

16 would only monitor 40 of the 1,172. So

17 everything you've been hearing about and all

18 the data we've collected and everything that

19 EPA has is on about 4 percent of what EPA

20 recognizes as the high risk watersheds.

21 Okay. So we think that that

22 monitoring program should be broadened. For
202-234-4433

Neal R. Gross & Co., Inc.

Page 364

1 one thing EPA doesn't have any plan to deal

2 with the other ones. So actually everything

3 they're finding and everything they're telling

4 you that they're taking action on is in that

5 40 and the other, you know, 1,060 are being

6 ignored. And I agree with Alan Roberson that

7 that should be paid for by the registrant.

8 And we also think that the

9 scientific advisory panel could recommend to

10 EPA that they publish the monitoring data so

11 that we don't have to keep doing report

12 updates. And they should publish it in a way

13 that the public can look up their data, like

14 they can on our website now, and they can look

15 up their community water system and see what

16 the numbers are. That shouldn't be our job. 

17 Thank you.

18 SESSION CHAIR PORTIER: Any

19 questions from the panel? Yes, Dr. Gilliom.

20 DR. GILLIOM: It's partly a

21 question on protocol. Are the reports that

22 are referenced in Dr. Sass' testimony with
202-234-4433

Neal R. Gross & Co., Inc.

Page 365

1 your notes -- are those automatically included

2 in the record? And part of this is also

3 intermixed with a question that some of the

4 input we've received is based on the

5 epidemiological type of studies -- more the

6 correlation things and sometimes involving

7 mixtures, which is kind of in this gray area

8 of scope for our panel. So it makes it kind

9 of difficult of how to factor everything in. 

10 Is there some guidance from -- is it included

11 in -- 

12 MR. BAILEY: I don't have the

13 studies. I only have the citations that

14 Jennifer has provided. If you -- I suppose if

15 you want copies of them we could try to get

16 them for you -- work with Jennifer to get

17 copies of them.

18 DR. SASS: I was being polite I

19 guess. I can provide those with you. I think

20 that your rules are that you don't put

21 published manuscripts in the docket because it

22 violates the copyright of the journal. So --
202-234-4433

Neal R. Gross & Co., Inc.

Page 366

1 but I can e-mail them to you and then you can

2 provide them to the committee I believe. Is

3 that correct?

4 MR. BAILEY: We do provide them on

5 an as-requested basis. 

6 DR. SASS: So, tomorrow morning

7 I'll bring our thumb drive with all of the

8 atrazine papers as well as the report. Is

9 that okay with you?

10 SESSION CHAIR PORTIER: And the --

11 I think as of today the minutes from our

12 February meeting are on the February docket

13 where we discuss a lot of the epidemiology

14 studies, the exploratory correlation studies

15 that the panel reviewed back in February. And

16 I think that -- a draft of that report was

17 provided to the panel. Is that right? I

18 think that's right. Dr. Fenner-Crisp?

19 DR. FENNER-CRISP: The report from

20 the February meeting is in the docket and I

21 downloaded it last night. So it's publicly

22 available.
202-234-4433

Neal R. Gross & Co., Inc.

Page 367

1 DR. GILLIOM: So just -- did we

2 get that one yet for the panel package or is

3 it just on the docket that I should get it if

4 I want it?

5 MR. BAILEY: Actually, I think

6 that one just came out last week.

7 DR. GILLIOM: Okay.

8 MR. BAILEY: Within the last few

9 days. We can e-mail that -- I can e-mail that

10 to you because it is in the docket -- should

11 be in the docket.

12 SESSION CHAIR PORTIER: I think you

13 have to pull it down because it's a pretty big

14 report. We were having problem e-mailing even

15 the panel members the report. So it's better

16 to go to the docket and download it at that

17 point.

18 DR. FENNER-CRISP: It's 80-

19 something pages -- 82 pages.

20 SESSION CHAIR PORTIER: Yes, it

21 was -- we reviewed it pretty thoroughly.

22 DR. FENNER-CRISP: The other point
202-234-4433

Neal R. Gross & Co., Inc.

Page 368

1 is, I think we have been asked today, in this

2 panel, to focus on the animal studies and not

3 on the epidemiology at this point in time,

4 because it isn't all available.

5 SESSION CHAIR PORTIER: And I think

6 we'll be ruling on that as the conversation

7 goes along. I'll help say whether this is in

8 the discussion. I mean, again, we're going to

9 have more discussion later on in the year,

10 it's going to come back to epidemiology. So

11 thank you very much. 

12 The next public commenter is Scott

13 Slaughter, the Center for Regulatory

14 Effectiveness. And then following him is Jay

15 Vroom of Crop Life America. So maybe the two

16 of you can stage up.

17 I do want to get one question in

18 today. We're going to do one question if it

19 kills me. 

20 MR. SLAUGHTER: I'm Scott

21 Slaughter and I'm commenting today on behalf

22 of the Center for Regulatory Effectiveness. 
202-234-4433

Neal R. Gross & Co., Inc.

Page 369

1 I met Dr. Portier in the hall at the beginning

2 of the last break, and he looked at me and

3 said, "Well, you wanted to be last." And I

4 told him it was one of my many errors in

5 judgment.

6 I beg your indulgence for just a

7 few minutes while I talk to you briefly about

8 the quality of the information that EPA's

9 asked you to consider in this SAP. The

10 February minutes of the February -- the

11 minutes of the February atrazine SAP were

12 mentioned just a minute ago. And those

13 minutes state, and I quote, "Like all

14 information considered in risk assessments,

15 the quality and reliability of the information

16 provided by epidemiologic studies needs to be

17 closely scrutinized."

18 That's good advice, and it's not

19 limited to epi studies. It also applies to

20 the other studies which EPA has asked this SAP

21 to review and which EPA is considering for use

22 in a new atrazine risk assessment.
202-234-4433

Neal R. Gross & Co., Inc.

Page 370

1 There are several regulatory

2 requirements and guidelines that govern EPA's

3 use of these studies. EPA cannot base an

4 atrazine risk assessment on studies that do

5 not meet and comply with these requirements

6 and guidelines. The guidelines and

7 requirements include reproducibility. 

8 For example, EPA's information

9 quality guidelines require EPA to "ensure the

10 reproducibility of influential information." 

11 An atrazine risk assessment is influential

12 information.

13 As another example, EPA is a

14 member of the international coordinating

15 committee for the validation of analytical

16 methods. We groupies know it as ICCVAM. 

17 ICCVAM has established test validation and

18 acceptance criteria which require that -- and

19 I quote -- "the extent of within-test

20 variability and the reproducibility of the

21 test within and among laboratories must have

22 been demonstrated. Data must be provided
202-234-4433

Neal R. Gross & Co., Inc.

Page 371

1 describing the level of inter- and intra2 laboratory reproducibility and how it varies

3 over time. The degree to which biological

4 variability affects the test reproducibility

5 should be addressed."

6 As still another example, EPA's

7 FIFRA data rules state that EPA "will evaluate

8 the conduct of each experiment in terms of

9 whether the study was conducted in conformance

10 with the design, good laboratory practices

11 were observed, and the results were

12 reproducible."

13 As a final example, CRE's comments

14 on the 2002 atrazine risk assessment raised

15 validation issues about some of the test data

16 which EPA wanted to use and which allegedly

17 showed endocrine effects from exposure to

18 atrazine. EPA's response to CRE's comments

19 said, and I quote, "In determining whether

20 data are acceptable the agency considers a

21 number of factors: for example, study design,

22 including sample size, replication, use of
202-234-4433

Neal R. Gross & Co., Inc.

Page 372

1 appropriate controls, et cetera."

2 In light of these regulatory data

3 quality requirements we ask EPA to answer two

4 questions. First, where is the record

5 demonstrating the reproducibility of the

6 studies the agency wants to use in atrazine

7 risk assessment? Second, where is the record

8 demonstrating that these studies meet EPA's

9 other data quality requirements?

10 EPA has dumped a large number of

11 studies and data on the public and has

12 provided very little time for the public to

13 review and comment on these studies and data. 

14 EPA's answers to these two questions would

15 greatly improve the transparency and user

16 friendliness of this proceeding. 

17 Thank you. And I'll try to answer

18 any questions you might have.

19 SESSION CHAIR PORTIER: Any panel

20 questions? 

21 (No response.)

22 MR. SLAUGHTER: Oh, that's good. 
202-234-4433

Neal R. Gross & Co., Inc.

Page 373

1 Thank you.

2 SESSION CHAIR PORTIER: Thank you,

3 Scott. The last public commenter is Jay

4 Vroom.

5 MR. VROOM: Thank you, Mr.

6 Chairman and members of the panel. My name is

7 Jay Vroom. I'm president of Crop Life

8 America, a trade association that represents

9 virtually all of the manufacturers, discovery

10 firms, distributors, and formulators of crop

11 protection products used by American farmers.

12 I submitted my four-page letter

13 for the staff to put in the record and I'd

14 prefer to summarize if I might.

15 I begin by saying good evening,

16 taking a different temporal approach --

17 perhaps more optimistic with the notion that

18 some of us may have been contemplating

19 exposing ourselves to water contaminated by

20 alcohol after a day like this.

21 Crop Life does not advocate on

22 behalf of any one individual member company or
202-234-4433

Neal R. Gross & Co., Inc.

Page 374

1 any one particular product provided by our

2 industry to be a tool for American farmers. 

3 We do try to take opportunities like this

4 public comment period to articulate specific

5 thoughts with regard to policy that has a

6 precedent that is important for our members

7 and others in the industry from a policy

8 standpoint and of ultimate impact and

9 importance to our customer, the American

10 farmer.

11 With respect to our understanding

12 at Crop Life of what's before this SAP, I was

13 struck by Dr. Sass's presentation that also

14 mentioned a number of other issues that, from

15 my understanding are not before this

16 committee, but, in fact, are also very

17 important issues. And it just, to me,

18 reinforced the reaction that we had when this

19 administration decided to convene the SAP on

20 these issues particular to atrazine, which we

21 believe have been rather thoroughly examined

22 by the agency and with input from previous
202-234-4433

Neal R. Gross & Co., Inc.

Page 375

1 SAPs in the past.

2 And our preference, of course,

3 would be that the kinds of resources

4 represented by you on this panel and the

5 support of the agency would be actually

6 attentive to many of those other issues that

7 Dr. Sass referenced. And so, in fact, we

8 believe that this is an unfortunate focus of

9 resource that otherwise could perhaps

10 otherwise be allowed to have you spending time

11 on that range of other issues that are on down

12 the road and in front of us.

13 The one specific issue that I'd

14 like to address here is the importance of

15 focusing on those sources of data that are

16 specific to finished drinking water. That is

17 our understanding as to what the issues are

18 really in front of this SAP with regard to

19 that route of exposure and that particular

20 concern with regard to human health effects

21 and the compound atrazine.

22 And I really think it really gets
202-234-4433

Neal R. Gross & Co., Inc.

Page 376

1 down to the matter of communication. And I

2 would draw another parallel to activity that

3 EPA has undertaken with regard to the

4 requirement under the Safe Drinking Water Act

5 as well as the Food Quality Protection Act

6 passed by Congress in 1996 and that has to do

7 with the evaluation of endocrine effects and

8 crop protection products and other chemicals

9 that humans may be exposed to.

10 And the parallel that I think is

11 important to draw here is how careful EPA has

12 been to explain what they are doing with

13 regard to the very careful and deliberate

14 science-based stepwise approach to

15 implementing the endocrine disruption

16 provisions in the Safe Drinking Water and Food

17 Quality Protection Acts, and more particularly

18 what they are not doing at particular stages.

19 And I think that's been a model

20 with regard to communication by the agency and

21 their advisors that would serve this SAP

22 working with the agency going forward to be
202-234-4433

Neal R. Gross & Co., Inc.

Page 377

1 extra careful about how you communicate, what

2 you're doing, what you're considering, and,

3 just as importantly, what is not on the table

4 in front of you.

5 And so, again, I would go back to

6 the core presentation that we wish to

7 emphasize here today with these remarks, which

8 is that -- the matter before this SAP has

9 nothing to do with human consumption of raw

10 water and that the data that should be

11 examined and the risk factors that should be

12 considered by this SAP in providing advice

13 back to the agency have to do with the risk

14 factors associated with the presence of this

15 compound in finished drinking water.

16 The record is clear with regard to

17 Congressional intent. In our view, having to

18 do with the scientific validity of information

19 that comes before and consideration of the

20 agency, that it needs to be science-based, and

21 when it is science-based there are a number of

22 criterion that the law is specific with regard
202-234-4433

Neal R. Gross & Co., Inc.

Page 378

1 to how the agency should consider it and how

2 it should communicate to the public. 

3 And I believe, again, that that

4 kind of consideration with regard to

5 communication is an obligation that the SAP

6 over the years has taken seriously and been

7 effective with. And at the end of the day,

8 with regard to the kinds of risks that are

9 being assessed and advice being given to the

10 agency with regard to exposure in water to

11 human beings with regard to atrazine, it's

12 only around the exposure on finished drinking

13 water and that humans are not intentionally

14 drinking unfinished or raw water provided to

15 them by community water systems.

16 So, once again, I thank you for

17 this opportunity to share that perspective. 

18 I would also share with you that having been

19 here for much of today I was struck by how

20 lucky we are as citizens of the United States

21 to have the service of committed volunteers,

22 such as yourselves, with scientific expertise
202-234-4433

Neal R. Gross & Co., Inc.

Page 379

1 that you represent for the SAP in looking at

2 these kinds of issues, serving not only the

3 interest of the general public but my industry

4 and ultimately the American farmer who uses

5 these tools effectively to conduct modern

6 agriculture and to provide us with a safe and

7 abundant food supply. Thank you very much.

8 SESSION CHAIR PORTIER: Thank you. 

9 Any additional questions from panelists? Yes,

10 Dr. Coupe.

11 DR. COUPE: I just had a comment

12 mostly, and it's kind of a little bit of

13 different thinking. I think I was more in

14 line with you last week before I got here and

15 heard the discussion.

16 But now I think I understand why

17 we want to look at surface water as opposed to

18 just concentrating on finished water, now that

19 I'm thinking about it, because the sampling

20 the surface water -- examining the surface

21 water gives us a conservative estimate. And

22 it also shows us, you know, sometimes these
202-234-4433

Neal R. Gross & Co., Inc.

Page 380

1 treatment plants can fail, and it kind of

2 gives us a backup on how much we need to be

3 careful with.

4 But, additionally, listening to

5 some of the other people I was struck by, why

6 should these treatment plants have to install

7 equipment to take care of something that

8 shouldn't naturally be in there? They do

9 their work looking on -- you know, they're

10 meant to take out chemicals that absorb to

11 sediment and to disinfect. They're not meant

12 to take out water-soluble chemicals.

13 So we're applying these water14 soluble chemicals. And there's always a

15 chance that they might get through. I know

16 I'm not a toxicologist, so I can't say what

17 the epidemiological work on it is, but it just

18 seems to me that perhaps we should be looking

19 and continue to look in the surface water, as

20 well as the drinking water.

21 MR. VROOM: Thank you. It should

22 be clear that as an industry we are not
202-234-4433

Neal R. Gross & Co., Inc.

Page 381

1 opposed to testing of other water besides

2 finished drinking water for certain crop

3 protection products. 

4 But in terms of the level of the

5 magnitude of testing that has been reviewed

6 here today by any number of presenters,

7 particularly all those representing the data

8 that Syngenta have produced, we think that

9 that's the right place where that kind of

10 resource has been focused in addition to all

11 of the resources that have been put into,

12 number one, innovating other products that

13 have displaced older compounds, but, number

14 two, also allowing for education on how older

15 compounds like atrazine can be more

16 effectively used by growers and other

17 pesticide users -- that the combination of all

18 of those resources coming together have

19 allowed the kinds of trend lines with regard

20 to reduction of exposure and reduction of risk

21 to occur over time.

22 And there are other reasons to
202-234-4433

Neal R. Gross & Co., Inc.

Page 382

1 sample water for other considerations,

2 including ecological effects, but the level of

3 resource and intensity of volume of sampling

4 seems to be, you know, completely different in

5 those contexts and ought to be approached that

6 way.

7 Lastly, I would say that it's my

8 understanding that community water systems do

9 filter for a wide range of contaminants and

10 that crop protection products, including

11 atrazine, are only, you know, one category,

12 and there are other soluble chemicals that are

13 out there.

14 We think that, you know, the

15 systems are adequate and are working quite

16 well -- always room for improvement. And,

17 once again, the kind of scientific robustness

18 that a group such as the SAP can bring to

19 thinking through those sorts of considerations

20 in EPA's context here as well as for the

21 Office of Water and the Safe Drinking Water

22 programs continues to, I think, advance the
202-234-4433

Neal R. Gross & Co., Inc.

Page 383

1 science and the practical safety of our water

2 supply.

3 SESSION CHAIR PORTIER: Thank you. 

4 We did receive electronically -- and you have

5 copies of a comment from an M. J. Quinn -- no

6 address, no other information -- recommending

7 banning of atrazine. And you can read this.

8 At this point I'm going to make a

9 last call for any additional public

10 commenters. Anyone wishing to provide a

11 comment before the panel? Going once, going

12 twice.

13 I think at this point I'm going to

14 close the period of public comment and we're

15 going to proceed to the panel discussion. 

16 We're about three hours late. I guess that's

17 not too bad. For an important meeting like

18 this, that isn't that bad. 

19 So I'm going to invite EPA back up

20 to the front table here to read the first

21 question. For the panel, we're going to begin

22 the discussion. I don't think we're going to
202-234-4433

Neal R. Gross & Co., Inc.

Page 384

1 probably end the discussion, but we're going

2 to at least spend the next 45 minutes on this

3 topic. When we get to about 4:40 we're going

4 to reconsider how much we've got left and then

5 move on -- 5:40 -- I'm sorry. Yes, 45 more

6 minutes -- 5:40 -- 5:40, 5:45.

7 DR. MENDEZ: Good afternoon. This

8 is Elizabeth Mendez again for the U.S. EPA. 

9 I'm going to start reading the charge

10 questions to the FIFRA Scientific Advisory

11 Panel.

12 Charge question 1.0 -- in 2003 the

13 human health risk assessment was based on a

14 mode of action in which exposure to atrazine

15 leads to a reduced release of gonadotropin

16 releasing hormone (GnRH) from the

17 hypothalamus, thereby lessening the afternoon

18 pituitary luteinizing hormone (LH) surge in

19 female Sprague Dawley rats. As a result, the

20 estrous cycle lengthens. This, in turn, leads

21 to increased estrogen levels and an increased

22 incidence of mammary tumors in female Sprague
202-234-4433

Neal R. Gross & Co., Inc.

Page 385

1 Dawley rats.

2 EPA determined that atrazine's

3 cancer mode of action (i.e., premature

4 reproductive aging) in the Sprague Dawley rat

5 is not likely to be operative in humans (a

6 conclusion consistent with the SAP

7 recommendation in 2000). The agency will be

8 evaluating the cancer classification further

9 as the experimental toxicology data are

10 integrated with the epidemiology data. New

11 experimental toxicology studies (Section 3.6

12 of the draft issue paper) do not suggest a

13 change to the agency's previous conclusion

14 that atrazine is not likely to be carcinogenic

15 to humans.

16 Next please. Although the cancer

17 mode of action may not be operative in humans,

18 it is not unreasonable to assume that atrazine

19 might cause adverse effects on hypothalamic20 pituitary function in humans. Thus, the same

21 endocrine perturbations that induce tumors in

22 rats may play a role in at least some
202-234-4433

Neal R. Gross & Co., Inc.

Page 386

1 reproductive/developmental effects (not

2 associated with reproductive aging) that may

3 be relevant to humans.

4 Accordingly, the agency identified

5 disruption of estrous cyclicity and delays in

6 puberty onset (males and females) occurring as

7 a consequence of disruptions to the

8 hypothalamic-pituitary-gonadal (HPG) axis as

9 the critical end points of concern (Sections

10 2.0 and 3.2 of the draft issue paper).

11 The agency continues to believe

12 that this disruption of the HPG axis is

13 critical to affecting estrous cyclicity and

14 delayed puberty onset. However, as discussed

15 in questions 1.2 through 1.6 below (see figure

16 3 of the draft issue paper), new information

17 has been published that indicates a more

18 expanded understanding of how atrazine may

19 perturb the pathway leading to reproductive

20 effects.

21 These new data indicate that

22 atrazine disrupts the hypothalamic-pituitary-
202-234-4433

Neal R. Gross & Co., Inc.

Page 387

1 adrenal (HPA) axis altering the central

2 nervous system's control of the pituitary and

3 adrenal which, in turn, disrupts the HPG axis. 

4 Furthermore, a hypothesis for a direct effect

5 of atrazine on the adrenal cortex and the

6 gonads which may impact steroidogenesis

7 directly is also supported by the available

8 data.

9 Now we get to the question. With

10 the caveat that the review of the

11 epidemiological literature is still ongoing,

12 please comment on the agency's preliminary

13 conclusion that new experimental data from in

14 vitro and in vivo laboratory animal studies do

15 not support a change in the conclusions from

16 the 2003 risk assessment that atrazine is

17 unlikely to be a human carcinogen.

18 SESSION CHAIR PORTIER: So we start

19 with a big question here. Dr. Fenner-Crisp as

20 the lead discussant.

21 DR. FENNER-CRISP: I could give a

22 very short answer, but now being on a panel,
202-234-4433

Neal R. Gross & Co., Inc.

Page 388

1 I can't do that. I've got to write a lot,

2 give folks more to read.

3 I'll essentially summarize two

4 documents, and I'll start out with saying, in

5 the 2000 draft risk assessment, MOA thing --

6 that's this one that went to the 2000 SAP and

7 later finalized in the IRED and RED -- the

8 agency presented its analysis of the human

9 carcinogenic potential of atrazine. Rodent

10 bioassay results revealed no increase in any

11 tumor type in male Sprague Dawley rats or in

12 Fischer 344 rats or CD1 or other mice strains

13 of either sex. However, there was an

14 increased incident and/or early onset of

15 mammary adenomas, carcinomas, fibroadenomas,

16 and pituitary adenomas in the females of

17 Sprague Dawleys.

18 EPA conducted an in-depth analysis

19 of these tumors, particularly the mammary

20 adenomas and carcinomas, which included the

21 development of additional data used to

22 describe possible modes of action by which
202-234-4433

Neal R. Gross & Co., Inc.

Page 389

1 these tumors arose and presentation and

2 conclusions as to the relevance of the

3 hypothesized modes of action with regard to

4 the chemical's human carcinogenic potential.

5 Based primarily on the work of the

6 ORD researchers, a non-genotoxic mode of

7 action was postulated for effects of atrazine

8 on components of the HPG axis and the

9 development of the mammary and to some extent

10 the pituitary tumors in these -- the strain of

11 female rats.

12 An alternative possible genotoxic

13 mutagenic mode of action was not supported

14 based upon the weight of evidence evaluation

15 of a variety of short-term genotoxicity

16 assays.

17 The figure and the text below

18 taken from the 2000 document present the key

19 postulated events. And I pulled the figure

20 off of page 33 in here and the text that

21 surrounds that particular page. I won't go

22 through the figure but remind folks they don't
202-234-4433

Neal R. Gross & Co., Inc.

Page 390

1 have that document of what the key events

2 were.

3 Key event one was: atrazine

4 exposure affects either directly or indirectly

5 the hypothalamus, leading to decreased

6 secretion of hypothalamic norepinephrine. 

7 The second key event: hypothalamic

8 norepinephrine normally modulates the release

9 of gonadotropin-releasing hormone, but with

10 the decreased norepinephrine, one saw a result

11 in a decreased release of GnRH.

12 Key event three: GnRH is

13 responsible for inducing the pituitary gland

14 to release LH. If you have decreased GnRH,

15 you have decreased LH. We've been hearing

16 this from several parties the last two days.

17 Fourth key event: LH normally

18 provides a signal to the ovaries prompting

19 ovulation, but below some critical level the

20 decreased serum levels of LH are insufficient

21 to stimulate ovulation.

22 The fifth key event essentially
202-234-4433

Neal R. Gross & Co., Inc.

Page 391

1 describes that the feedback loop has been

2 interrupted and you don't get the normal

3 feedback.

4 Key event six was with the

5 increased -- circulating estrogen acts on the

6 mammary gland to increase the risk of mammary

7 tumors.

8 And the last step in there dealt

9 with the fact that there was not a genotoxic

10 mode of action.

11 As will be discussed at greater

12 length below much of the EPA research

13 conducted since that time has been targeted

14 toward improving the understanding of the

15 nature of the reproductive and developmental

16 effects of atrazine. Pursuit of the

17 characterizations of the mode of mechanisms by

18 which these effects occur has revealed at

19 least one major aspect not identified earlier

20 while characterizing the mammary tumor mode of

21 action. And that's activation of the HPA

22 axis.
202-234-4433

Neal R. Gross & Co., Inc.

Page 392

1 The key events for this phenomenon

2 have now been described -- and that's on

3 page -- I think it's 33 in the current

4 document -- those four key events.

5 Hypothalamic changes resulting in

6 an increase in CRH release -- 

7 DR. KRISHNAN: Which document are

8 you referencing? Is that a white paper or -- 

9 DR. FENNER-CRISP: The current

10 document, yes, white paper. Key event two:

11 increased release of adrenocorticotropic

12 hormone (ACTH) from the pituitary. Key event

13 three: increased production of the

14 corticosterone and progesterone by the

15 adrenals. And key event four: decrease in GNR

16 release from the hypothalamus as a result of

17 one or more of those key events. So we're

18 seeing a circling back.

19 I've stuck Liz's favorite figure

20 in here because I couldn't find that just

21 shows the key events numbered in anybody's

22 presentation for the HPA. And unfortunately
202-234-4433

Neal R. Gross & Co., Inc.

Page 393

1 the numbers in your favorite thing don't match

2 up with the four key events, but that's okay.

3 The bottom line here is the

4 convergence of key events two and three in the

5 original mammary tumor mode of action and key

6 event four in the HPA activation pathway

7 component are obvious. Discovery that an

8 additional component or pathway may be in play

9 neither negates nor diminishes the conclusions

10 reached earlier with regard to the

11 characterization of the mammary tumor mode of

12 action, or its relevance to human health risk

13 assessment.

14 Even though I was listening very

15 carefully to the presentations today, I'm not

16 completely satisfied with the answer we got. 

17 So I kept in here a statement that says, One

18 aspect that remains unclear is whether

19 atrazine can directly affect the hypothalamus,

20 setting off the cascade of effects, or

21 indirectly through the HPA access or both. 

22 That's still unclear, so I'm raising that as
202-234-4433

Neal R. Gross & Co., Inc.

Page 394

1 something to be -- 

2 And then I ask another question to

3 think about. Is it possible to incapacitate

4 the HPA pathway and determine if the HPG axis

5 is impacted in same or different manner? I

6 think some of the discussion studies ongoing

7 today led some light on that possibly. That's

8 what I've done so far.

9 SESSION CHAIR PORTIER: Okay. Dr.

10 Bucher as an associate discussant.

11 DR. BUCHER: So I'm in general

12 agreement with the findings from the recent

13 studies on the interrelationships of the HPA

14 and HPG axes do not fundamentally change the

15 conclusions of the 2003 risk assessment

16 concerning the likelihood that atrazine is or

17 is not a human carcinogen.

18 The critical role of inhibitor of

19 LH surge in suppressing the estrous cycling in

20 the SD rat seems to be supported by the recent

21 literature in the sensitivity of the SD rat to

22 increases in mammary tumors with increases in
202-234-4433

Neal R. Gross & Co., Inc.

Page 395

1 endogenous estrogens or from treatment with

2 estrogenic substances is well recognized.

3 However, I'd caution that the

4 demonstration of the involvement of the HPA

5 axis and the mammary tumor response of the SD

6 rat may, in the eyes of some, diminish its

7 biological significance as an indicator of

8 disruption of the HPG axis.

9 EPA's clearly not doing this in

10 their background document, and the recent

11 findings that Dr. Handa reported today

12 concerning the maintenance of the suppression

13 of the LH surge in adrenalectomized animals

14 certainly supports the EPA's stance here. But

15 I wouldn't be a bit surprised to see papers

16 begin showing up in the literature

17 hypothesizing that this particular event is

18 maybe just a rat strain specific effect that

19 results from a unique or abnormal sensitivity

20 to a general stress response.

21 I've seen a strong tendency, when

22 interpreting chronic rodent cancer studies, to
202-234-4433

Neal R. Gross & Co., Inc.

Page 396

1 attribute difficult to explain tumor

2 incidences to, quote-unquote, "differential

3 stress across treatment groups." But, in

4 fact, there's hardly ever any data to back

5 this up.

6 Different routes of exposure do

7 result in different rates of tumors in some

8 sites in control animals. And there is a

9 general perception that the levels of stress

10 to animals might be different or greater in

11 inhalation studies where animals are housed

12 for two years in metal chambers with

13 significant air flows and presumably more

14 noise than, say, in studies where animals were

15 simply held in enclosed polycarbonate cages.

16 And we do see somewhat higher

17 rates of adrenal chromocytomas in control male

18 Fischer rats in our NTP studies in inhalation

19 studies than in dose feed studies. The

20 mammary tumors in female Fischer rats are not

21 different. And, of course, according to the

22 prevailing hypothesis they wouldn't be
202-234-4433

Neal R. Gross & Co., Inc.

Page 397

1 expected to differ as they don't senesce into

2 a persistent estrous state.

3 Unfortunately we don't have any

4 NTP studies in our database where we're used

5 SD rats in inhalation studies. But a brief

6 review of the SD rat inhalation studies in the

7 literature comparing the mammary tumor rates

8 in control females with the rates that were

9 reported in the controls in the dose feed

10 studies that were done by Syngenta on

11 atrazine, you'll find that the tumor rates are

12 actually very similar.

13 So what's the point of this? 

14 Well, again, if considerations of the

15 potential health affects of atrazine are in

16 any way diminished by the perception that the

17 responses seen in the Sprague Dawley rat are

18 because of an enhanced susceptibility of this

19 animal to non-specific stress. And not that

20 EPA is doing this in this current review, but

21 strange things do happen over time. I would

22 simply urge that the agency resist this line
202-234-4433

Neal R. Gross & Co., Inc.

Page 398

1 of reasoning.

2 But the bottom line is, I don't

3 see anything in the recent information that's

4 come out that would change my perception of

5 the original 2003 conclusion. Thank you.

6 SESSION CHAIR PORTIER: Dr.

7 Krishnan?

8 DR. KRISHNAN: In considering the

9 new lab animal studies that the agency's

10 preliminary conclusion is the same as in 2003

11 assessment, which is, atrazine is not likely

12 to be a human carcinogen, this descriptive of

13 "not likely to be a human carcinogen" is

14 appropriate when there's evidence that the

15 mode of action in experimental animals does

16 not operate in humans.

17 The white paper lays -- well,

18 emphasizes the initial reasoning that my

19 colleagues just referred to, which is mammary

20 tumors in female SD rats being a consequence

21 of elevated levels of circulating estrogen and

22 prolactin. And that remains unchanged.
202-234-4433

Neal R. Gross & Co., Inc.

Page 399

1 So the fundamental question of

2 human relevance still remains. And so

3 basically the new studies included here do not

4 provide any compelling evidence to change the

5 classification of atrazine.

6 There's no new evidence that I see

7 that changes the conclusions regarding

8 atrazine's ability to act as an initiator;

9 that is, evidence of mutagenicity or

10 genotoxicity. And also the new studies on

11 tumor promotion progression, in my opinion, do

12 not provide sufficient strength of evidence to

13 change the classification for atrazine.

14 So I agree with the agency's

15 initial conclusion -- or preliminary

16 conclusion that it still be considered not

17 likely to be carcinogenic to humans.

18 However, one aspect that I think

19 could be of concern is the temporal

20 relationship of the early life exposure in

21 light of the revised cancer guidelines of

22 2005. That doesn't come out very well, and I
202-234-4433

Neal R. Gross & Co., Inc.

Page 400

1 hope in the -- based on epi studies, the

2 analysis that might be ongoing -- I'm not

3 aware of the details of where those studies

4 are -- and I hope there will be some

5 consideration of time since first exposure,

6 duration of window of exposure, and so on, in

7 such analysis to be able to address whether

8 there's any concern for early-life exposures

9 and their relationship to tumorigenesis or the

10 overall weight of evidence. And that's

11 something that I'll be looking forward in the

12 future work.

13 So the answer is yes.

14 SESSION CHAIR PORTIER: Okay. Dr.

15 Reed.

16 DR. REED: It's true that I also

17 have not heard of any presentation that could

18 support any new mode of action within the

19 framework that we're talking about -- I mean,

20 the HPG and HPA axis.

21 However, I'm not I guess ready to

22 rule out the relevance of the two in vivo
202-234-4433

Neal R. Gross & Co., Inc.

Page 401

1 studies reviewed by the agency -- the

2 promotional -- possible promotional mechanism

3 of atrazine as presented in the Fukamachi et.

4 al., 2004 and Ueda et. al., 2005 pertaining to

5 mammary gland tumors.

6 I don't disagree with some of the

7 uncertainties and limitations presented by the

8 agency review. But I have a slightly

9 different opinion about these two studies. 

10 With the Fukamachi study, it's true that

11 there's uncertainties about the transgenic

12 nature of these rats. 

13 But the agency considered this

14 study presented no dose response, and I looked

15 at it and I felt sure -- you know, there's

16 this small sample size, and particularly the

17 high control incidence -- I think it's around

18 about 50 percent -- that could cloud the

19 conclusion coming from this study. 

20 But when I look at it, I felt

21 there is an apparent increase with the

22 atrazine treatment in both female and male
202-234-4433

Neal R. Gross & Co., Inc.

Page 402

1 rats. And I guess I'm a little bit more

2 concerned because it was at the range of 5 and

3 50 milligrams per kilogram.

4 I, frankly, am not as concerned

5 about the high dose being -- the incidence at

6 the high dose being dropped off because it's

7 500 milligrams per kilometer and that could be

8 some kind of a high dose effect that I'm not

9 sure that I'm willing to take it in and to say

10 that there is no dose response. The other

11 study -- the other 2005 study was also very

12 interesting, although the effects are now at

13 50 to 500 milligrams per kilogram. 

14 It's -- so looking at these two

15 studies I come with a different -- slightly

16 different conclusion. EPA concluded that

17 these two sets of data suggest that estrogen

18 does not induce mammary gland tumors in

19 transgenic rats or ovariectomized rats treated

20 with a mammary tumor inducer, thus

21 highlighting the importance of ovarian

22 hormones in the development of tumors in the
202-234-4433

Neal R. Gross & Co., Inc.

Page 403

1 mammary gland.

2 And I would have said that you

3 highlighted the possibility of a promotional

4 potential -- not initiating, but promoting. 

5 So, frankly, I'm kind of curious that these

6 two studies were from 2004 and `5, but there's

7 no -- as far as I know no other follow-up

8 studies along the same line of promotion.

9 Nevertheless, I would suggest that

10 the agency expand on the discussion to include

11 the significance of these two datasets on

12 tumor promotion and perhaps look at the data

13 in the context of mode of action and perhaps

14 that house might have a little shed or

15 something. I don't know.

16 Anyway, a possibility of, you

17 know, branching it out that might not, you

18 know, at least partly -- not directly

19 associated with the endogenous estrogen at

20 this point.

21 I would also suggest that, you

22 know, if it is possible that the agency would
202-234-4433

Neal R. Gross & Co., Inc.

Page 404

1 park these issues until all data for cancer

2 risk assessment is ready next year. At such

3 time, I'm sure these data and any other data

4 fitting within the HPG/HPA mode of action

5 would all come into play together with the epi

6 data.

7 SESSION CHAIR PORTIER: Thank you. 

8 At this point we open it up to any comments

9 from anyone on the panel. Anybody want to

10 jump in on this question? Add to it? The

11 promoter issue? Dr. Williams?

12 DR. WILLIAMS: I just wondered

13 what you really mean by not likely. So I

14 thought there were some good points raised by

15 the NRDC person regarding, you know, this

16 other commission specific designations. They

17 have a certain definition of not likely or

18 likely or whatever. What do you really mean

19 by not likely?

20 DR. MENDEZ: Our definition for

21 not likely or unlikely to be carcinogenic to

22 human is when they have a mode of action that
202-234-4433

Neal R. Gross & Co., Inc.

Page 405

1 we feel it's not appropriate to the human

2 condition. Having said that, let's keep in

3 mind that we're going to be looking at the

4 cancer issue in greater detail in 2011. So

5 let's just kind of keep that in mind.

6 SESSION CHAIR PORTIER: Dr.

7 Krishnan, did you want to comment?

8 DR. KRISHNAN: I'm going to leave

9 it off. No, that's fine.

10 SESSION CHAIR PORTIER: But it --

11 this is Ken Portier. But the definition means

12 you've looked and you don't see anything

13 rather than not look. One of the implications

14 is, you know, you haven't looked for it. And

15 you can't say it's not likely if you haven't

16 looked for it. And I think what EPA is

17 saying, we've looked for it and we see

18 something; we discount it because it's a

19 mechanism that's not related, therefore, it's

20 something else.

21 DR. MENDEZ: And we have also not

22 only looked at the mode of action, we've also
202-234-4433

Neal R. Gross & Co., Inc.

Page 406

1 evaluated other potential modes of action and

2 have ruled them out. So it's important to

3 keep in mind that we don't just look at one

4 component and just latch onto that. We then

5 move on to disprove, so to speak, other

6 potential MOAs.

7 DR. LOWIT: And the one piece to

8 add to that. The 2003 SAP on epidemiology and

9 atrazine really dealt with some of the

10 epidemiology data at the time for cancer. And

11 so that finding at that time incorporated both

12 the animal mode of action and the epi data

13 available at the time. And as we move towards

14 what will now be 2011 we'll rethink that whole

15 process again.

16 SESSION CHAIR PORTIER: Dr.

17 Holladay?

18 DR. HOLLADAY: I think we kind of

19 said it, but I want to make sure that I

20 understand. My understanding is that, as

21 least formally, EPA's definition of

22 classification of carcinogens is different
202-234-4433

Neal R. Gross & Co., Inc.

Page 407

1 than IARC's. And we had an IARC definition

2 read to us earlier, but EPA's definition is

3 not the same. Is that correct? So we're not

4 working under the IARC definition per se?

5 DR. MENDEZ: It's important to

6 point out that in 1999 -- I believe it's

7 1999 -- IARC evaluated this very same mode of

8 action and they consider that it was not

9 likely to be relevant to humans. But in their

10 definitions and in their framework they think

11 about it a little bit different. Having said

12 that, the MOA was actually considered and

13 considered to be believable by the IARC.

14 SESSION CHAIR PORTIER: Dr.

15 LeBlanc?

16 DR. LEBLANC: Considering our

17 increasing understanding of the mode of action

18 of atrazine my -- the only recommendation that

19 I have is that as we reevaluate the

20 epidemiological data that perhaps we're

21 sensitive to cancers that might be more

22 prominent in immunosuppressed individuals, not
202-234-4433

Neal R. Gross & Co., Inc.

Page 408

1 suggesting that atrazine is going to cause

2 immunosuppression in the population, but that

3 would be consistent with the mode of action

4 and it should be considered.

5 SESSION CHAIR PORTIER: Anyone

6 else wish to join in? The issue of promotion,

7 the issue of initiation -- those are always

8 good ones. I don't see -- Dr. Reed.

9 DR. REED: Would it help if you

10 have a copy of the 2005 cancer risk assessment

11 guideline that has the narratives for -- there

12 is a category called not likely to be

13 carcinogen to humans? Because we can provide

14 the panel with that. And I have a copy of the

15 guidelines so maybe I'll give it to Joe.

16 DR. HORTON: I think it would be

17 helpful.

18 SESSION CHAIR PORTIER: Dr.

19 Horton?

20 DR. HORTON: Yes. I was going to

21 say I think it would be helpful to know

22 exactly which guidelines we're working under
202-234-4433

Neal R. Gross & Co., Inc.

Page 409

1 if we're defining whether it's carcinogenic or

2 not. And also, if this is going to be

3 evaluated under the 2011 SAP under the

4 carcinogen -- under another set of data what

5 other -- I mean, how is this going to be

6 reevaluated? Could you -- what will happen in

7 2011 if we say -- or make a recommendation now

8 that it may not be? What are the consequences

9 of anything -- any recommendations that we

10 make at this point?

11 DR. LOWIT: Well, I'm actually

12 hearing -- I've heard from the -- from most of

13 the respondents that as the question's worded,

14 what's new since 2003 that would change those

15 conclusions. And I've actually heard a

16 consensus that no one's heard anything that

17 really changes those conclusions. So I'd be

18 surprised if we turn that upside down.

19 But in 2011, we will do some more

20 update to the animal. We'll continue to

21 monitor the literature to see if anything new

22 appears in the animal literature that does
202-234-4433

Neal R. Gross & Co., Inc.

Page 410

1 turn that upside down. And we will also be

2 bringing the epidemiology with the animal to

3 the degree to which there's a concordance with

4 those conclusions.

5 DR. KRISHNAN: Can I -- 

6 SESSION CHAIR PORTIER: Yes, Dr.

7 Krishnan?

8 DR. KRISHNAN: Yes. 

9 DR. LOWIT: Yes. So, I mean, the

10 one thing that's important for people to know

11 about the atrazine risk assessment, it is a

12 very dynamic thing. It's -- the agency's

13 constantly monitoring the literature on

14 atrazine. So we'll keep up.

15 SESSION CHAIR PORTIER: Yes. I

16 would also say that all we're doing is

17 recommending to the agency. We're not making

18 any final decisions here. And the agency

19 takes our recommendations and incorporates it

20 with everything else and then moves forward

21 from that. So just because we recommend that

22 that holds doesn't necessarily mean they
202-234-4433

Neal R. Gross & Co., Inc.

Page 411

1 believe us for the next two or three stages of

2 this.

3 DR. LOWIT: And it is worth adding

4 as -- it is worth adding to this piece that a

5 major component of the decision to separate

6 the cancer 2011 was -- with our colleagues at

7 NCI -- and if you're not aware of the

8 Agricultural Health Study, it's a very large

9 robust study upwards of 90,000 people. And I

10 don't know the exact number of people who are

11 atrazine users, but I think it's upwards of

12 50,000, maybe 60,000.

13 So it's a very large data set and

14 it's a very large robust. So we feel like

15 because of the large size of that it will

16 really play a lot of weight, and it's really

17 worth waiting to make sure that the results

18 are very strong and robust.

19 SESSION CHAIR PORTIER: Dr.

20 Krishnan?

21 DR. KRISHNAN: We talked about the

22 revised guidelines of the EPA and this
202-234-4433

Neal R. Gross & Co., Inc.

Page 412

1 particular clause of not likely to be

2 carcinogenic. The classical example of the

3 description that they give is that there can

4 be positive results in experimental animals

5 when there's consistent evidence -- consistent

6 and strong evidence that the mode of action in

7 experimental animals do not operate in humans,

8 in which case it can be concluded as not

9 likely.

10 But we can draw essentially a

11 parallel between this and the IARC group. I

12 suggest that we don't mix both of them even in

13 our comments. I don't think it's in our

14 purview. Because in IARC's number 4 you have

15 to have evidence suggesting lack of

16 carcinogenicity in experimental animals and

17 lack of carcinogenicity in humans, which is

18 not the case.

19 So I wouldn't -- but this

20 classification -- the way it's termed is

21 consistent with the criteria that EPA

22 considers. And we haven't really seen any
202-234-4433

Neal R. Gross & Co., Inc.

Page 413

1 convincing animal data that changed the

2 classification, I don't think. The promoter

3 studies can add to the strength of existing

4 evidence, but I don't see how, by in itself,

5 would suggest the chemical to be a carcinogen

6 as a free-standing evidence of information.

7 SESSION CHAIR PORTIER: Dr. Reed?

8 DR. REED: Well, maybe I didn't

9 make myself clear. Let me try it again. What

10 I think we're looking at is that we have found

11 a mode of action that looked very good to

12 interpret the Sprague Dawley rats data. But

13 I'm not ruling out the possibility that other

14 mode of action which is not initiation, but

15 promotion, might be a part of the oncogenic

16 potential that have been sort of indicated by

17 the two promotional studies.

18 The two promotional studies have

19 some problems, but in my mind they do not

20 deserve, I guess, to be put aside to say that,

21 ah, you know, there's a lot of problem with

22 this; we didn't see any potential or support
202-234-4433

Neal R. Gross & Co., Inc.

Page 414

1 for initiation. 

2 But how about promotion, is what I

3 guess I'm saying. That I'd much rather that

4 we take a better look at these two studies and

5 see if we can or cannot fit these two pieces

6 of information into the overall -- this well7 put-together mode of action. And if we cannot

8 then we need to set aside another possibility

9 with a lot of question marks.

10 But I felt that these two studies

11 can be looked at in a slightly different light

12 than the way they have been looked at right

13 now according to their reviews by the agency. 

14 So as I say, when I look at the Fukamachi

15 study I didn't feel like it was a clear-cut no

16 dose response. And going from a background

17 control incidence and since these are, you

18 know, transgenic in their prime, that the

19 background is 50 percent.

20 When you get to the atrazine dose

21 the lowest dose of 5 milligrams per kilometer

22 you're seeing 100 percent. And so from 50
202-234-4433

Neal R. Gross & Co., Inc.

Page 415

1 percent to 100 percent with small sample size

2 you could say that there is no statistical

3 significance. But, you know, how statistics

4 come into play is, you know, kind of not very

5 relevant with the sample size.

6 But I am alarmed that there is a

7 50 percent to 100 percent increase from zero

8 to five. And since you have the 5 milligram

9 per kilogram at 100 percent when you get to a

10 50 milligram per kilogram data of course it's

11 another 100 percent. And so to say that, you

12 know, 50, 100, 100 is no dose response it's a

13 little bit kind of not satisfying to me.

14 And then, you know, the agency

15 review saying that, okay, when you get another

16 tenfold increasing dose it dropped off a

17 little bit. Well, I mean, you might have some

18 high dose effects. So I'm not putting a lot

19 of weight on the lack of quote-unquote, "lack

20 of dose response" at the high dose and the

21 fact that, you know, you have high background. 

22 So I'd like to see a little bit
202-234-4433

Neal R. Gross & Co., Inc.

Page 416

1 more rigor in this discussion. I don't know

2 what is this going to lead to -- whether it's

3 going to fit into the current beautiful house

4 or whether it's going to be a slightly

5 different version or possibility. I just

6 personally like to see a little bit more

7 rigor. 

8 And apparently, you know, my take

9 on the promotional -- the two promotional

10 studies will be different from Dr. Krishnan's

11 comments. But maybe I'd like to hear, you

12 know, other people who have looked at these

13 two studies in terms of what they think about

14 whether they fit or not fit or irrelevant or

15 anything of that sort.

16 SESSION CHAIR PORTIER: This is Ken

17 Portier. I make the recommendations that the

18 statisticians are thinking, which is, do the

19 study right. Power it up and do it right. 

20 And then we don't have these kinds of

21 uncertainties and interpretation. I mean, I

22 agree, a 50 to 100 percent increase is -- you
202-234-4433

Neal R. Gross & Co., Inc.

Page 417

1 know, it's a big jump. And the fact that it's

2 not statistically significant tells me it was

3 way underpowered. So why did they do the

4 study?

5 And what you're saying is, they

6 did the study because they had some hypothesis

7 that they really think needed to be looking

8 at, and I wish they would have looked at it

9 with sufficient resources to be able to answer

10 the question the first time around.

11 It comes back to this whole

12 repeatability of information issue. EPA can't

13 look at it because we can't assess the

14 repeatability of the experiment with the

15 sample size and the power that they did. My

16 recommendation would always be do it right, do

17 it right the first time.

18 DR. REED: Well, I mean, as a risk

19 assessors, I mean, you know, I feel like -- a

20 lot of times I feel like -- I don't know --

21 that robot's name is R2D2? It's always

22 information, information, I need more.
202-234-4433

Neal R. Gross & Co., Inc.

Page 418

1 But when you look at what's out

2 there and, you know, there's times that you

3 say, well, I feel very comfortable to dismiss

4 this -- you know, it's not there. But in this

5 case I feel like there might be something

6 there and I just can't say go back and do some

7 more study because, you know, the agency is

8 not in the business of going and do some study

9 in that sense.

10 But as a risk assessor I feel like

11 I cannot ignore that kind of information. To

12 say that, well, nobody repeated it and so

13 forth and, you know, it's low powered so let's

14 forget about it.

15 SESSION CHAIR PORTIER: Dr.

16 Horton?

17 DR. HORTON: This is a general

18 comment that I would like to add that tags on

19 with Ruby's, is, I felt that there were many

20 times that I understand the need for looking

21 at the quality of data. But as a practicing

22 scientist, there are two things I run into. 
202-234-4433

Neal R. Gross & Co., Inc.

Page 419

1 One is, molecular biologists don't

2 understand statistics, but having been trained

3 as an ecologist generally I do. And so I beat

4 my head against the wall against this all the

5 time. And the other is that you often just

6 don't get the chance to repeat the study. 

7 However, the more that the agency

8 can ask people to go out and repeat the study

9 that's great. But I felt that in the review

10 of many of these papers, many of them were

11 dismissed as not providing information when

12 they were being dismissed on a technical basis

13 of not meeting some criterion when they had

14 passed scientific review.

15 And, in fact, they do suggest

16 something and you have to take into this

17 criterion of what is your risk assessment. So

18 I think -- 

19 SESSION CHAIR PORTIER: Anyone

20 else? Having spent a lot of time working both

21 with microbiologists and trying to get them to

22 understand statistics, but also reviewing a
202-234-4433

Neal R. Gross & Co., Inc.

Page 420

1 lot of these kinds of studies it's very

2 frustrating to -- because, you know, EPA did

3 look through the literature, and I think two4 thirds of the studies kind of got laid aside.

5 And the fact that this study got

6 considered tells me that the ones they laid

7 aside were even poorer. And that -- you know,

8 as a scientist that really discourages me

9 because that tells me we're doing a lot of

10 under-powered research that's not answering

11 the questions. It just raises more questions. 

12 And I get very frustrated with this though. 

13 Linda?

14 DR. YOUNG: I have to agree. And

15 I just wonder if there's some way to go back. 

16 Because even the studies that I've seen

17 presented by both EPA and Syngenta here, you

18 look at it, and unless they have about two

19 animals in the group, something's happening

20 there.

21 I'm also concerned about the

22 statistical methods they're using, given the
202-234-4433

Neal R. Gross & Co., Inc.

Page 421

1 study design. I think a lot of this period2 by-period analysis is being done, and that --

3 when you start out with small numbers you

4 shoot yourself in the foot.

5 SESSION CHAIR PORTIER: Dr.

6 Krishnan?

7 DR. KRISHNAN: I'm going to agree

8 with the statisticians. I'm not going to

9 argue. I just want to get back to your

10 comment.

11 Given the experimental design and

12 the animals models and the lack of dose

13 response, that's what really stuck me. And I

14 see your point. I don't think I mentioned my

15 reading -- it was not a question of dismissing

16 an evidence, you know, that clearly indicates

17 that something is a carcinogen, because I

18 don't see these studies showing that the

19 atrazine in itself is a carcinogen.

20 But there's some questionable

21 evidence of it being a promoter. So what I

22 would suggest is that essentially in the
202-234-4433

Neal R. Gross & Co., Inc.

Page 422

1 weight of evidence -- in the overall weight of

2 evidence, they do park these animal studies

3 and do reconsider in rating the overall weight

4 of evidence when they develop the narrative.

5 Whether it fits a specific mode of

6 action -- whether it fits this house or not I

7 think, as you alluded to, I think sometimes

8 that the incidence itself sometimes just

9 catches the eyes, rather than whether you can

10 explain it by an MOA or not. I don't think

11 that's -- at least for me that's not a major

12 consideration.

13 So how about recommending that

14 these studies be considered in developing an

15 overall weight of evidence narrative as the

16 other human data and so on come in? All I was

17 saying -- that I would agree, but all I'm not

18 agreeing to is that these studies in itself,

19 you know, take them from the current class and

20 make them a likely carcinogen, which is not

21 the case.

22 DR. REED: Well, that was -- 
202-234-4433

Neal R. Gross & Co., Inc.

Page 423

1 DR. KRISHNAN: We are on the same

2 side, maybe not on the same page.

3 DR. REED: Right. Same side of

4 the table? No, that's what I'm saying. I

5 mean, I would recommend that we park this

6 rather than coming with an affirmative as, you

7 know, what I thought what we're saying.

8 Rather than coming with

9 affirmatives, saying, yes, we do not see any

10 evidence. I'm saying -- and that's what I was

11 reading before -- that we park this issue

12 because of this. And I think it's a -- you

13 know, I guess you and I now agree. But that

14 was my recommendation. Let's park it. 

15 

16 SESSION CHAIR PORTIER: I think we

17 have a good sense of what we're going to do in

18 writing this up. So I think it's kind of --

19 I'm reading general agreement with the EPA

20 statement and with a caveat -- kind of a

21 minority report that we're going -- that when

22 we write up the report we'll make sure that's
202-234-4433

Neal R. Gross & Co., Inc.

Page 424

1 in there. Yes. Dr. Reed?

2 DR. REED: Can I make just a very

3 brief comment? I don't think it's reasonable

4 to separate out a chemical whether it is an

5 initiator or, you know, being a promoter would

6 be of less important or less relevant to

7 humans because there's all kinds of initiators

8 out there already.

9 And so if a chemical is a promoter

10 it is a cause for concern in that it could be

11 enhancing or increasing or shortening the

12 latency or whatever of something that is

13 already out there that if initiated that

14 possibly be a tumor.

15 So that's why I'm putting a lot

16 of, you know, credence to also a promoter. 

17 And I don't think, in cancer risk assessment

18 guideline, the promoter/initiator has been

19 parted out in a way that promoter is not

20 important.

21 DR. KRISHNAN: No, I guess. But

22 the way you would do an assessment would be
202-234-4433

Neal R. Gross & Co., Inc.

Page 425

1 different, because in one case you might end

2 up having -- identifying a threshold for

3 promotion, but as for initiation you'd rather

4 to go with a unit risk kind an approach or

5 something. But we're not getting into any of

6 the -- those response modeling.

7 SESSION CHAIR PORTIER: I think at

8 this point I'm going to close this question

9 and close today's session. I want to thank

10 the panel for sitting through all these

11 presentations and being patient, and let you

12 know that 8:30 tomorrow morning we're going to

13 start on question 1.2, and we're going to run

14 through these questions.

15 Thank you very much.

16 (Whereupon, at 5:48 p.m., the

17 above-entitled matter was concluded.)

18

19

20

21

22
202-234-4433

Neal R. Gross & Co., Inc.

Page 426

A

abandon 256:7

ability 50:21 57:3

85:2 103:4 172:2

174:17,20 197:20

198:18 200:4

204:6 213:16

217:14 219:19

399:8

able 30:7,13 58:5

64:10 66:15 75:15

101:11 105:3

112:12 135:10

158:21 175:20,21

202:20 219:20

233:21 245:16

285:8 295:15

297:11 304:4

342:22 400:7

417:9

abnormal 350:7

395:19

above-entitled

96:14 323:20

425:17

absence 118:6

199:17 219:8

absolute 207:3

absolutely 126:18

250:5 260:22

286:9 320:5

321:19

absorb 380:10

absorbing 102:5

absorption 181:20

abundant 379:7

accelerated 350:6

accept 44:21

acceptable 48:20

371:20

acceptance 370:18

access 393:21

accidental 359:5

account 30:10 34:3

34:4,12 35:2 43:4

43:5 60:6 64:21

70:13 71:11 77:16

230:4 251:10

320:18 344:17

accounted 156:12

accumulated 264:7

accumulation

263:7

accuracy 17:14,16

21:7,12

accurate 288:10

330:22

achieve 49:15

achieved 100:10

212:20 311:22

acknowledged

360:8

acquired 240:21

acre 112:2

acreage 107:6

acres 102:22 126:9

acronyms 262:12

act 1:4 90:10

262:14 355:12

376:4,5 399:8

acted 205:2

ACTH 203:15,16

208:8 392:12

acting 356:6

action 46:19 107:9

107:13 114:2

135:15 136:13

141:19,22 142:2

148:18,20 150:8

150:19,20 152:2,3

152:7,14 158:1,16

174:3 251:19

317:6 318:15

350:14 360:4

364:4 384:14

385:3,17 388:22

389:3,7,13 391:10

391:21 393:5,12

398:15 400:18

403:13 404:4,22

405:22 406:1,12

407:8,17 408:3

412:6 413:11,14

414:7 422:6

actions 137:10

256:3 265:21

activate 217:15,20

activated 328:16

328:17

activation 197:13

230:3 391:21

393:6

active 106:12

108:13 109:14

111:18 167:2

175:12 196:20

197:11

activist 120:20

activists 113:21

120:2

activities 121:7

179:20 220:14

266:2 268:8

activity 163:8

164:22 167:3,13

167:18 168:1

171:20 172:13

173:3,7,11 196:20

197:15 199:4,10

217:5 376:2

acts 129:6 376:17

391:5

actual 26:1,7,12,14

68:9 269:2 279:2

340:18

actuality 309:12

acute 32:21 33:1

203:7 205:1 211:4

255:17 334:15

adapted 101:3

114:9

add 29:10 31:19

45:11,15 51:15

81:15 89:14 94:6

98:17 126:17

221:22 266:15

291:6 296:4

300:16 327:12

404:10 406:8

413:3 418:18

added 10:4,13 40:7

43:15 121:15

310:18

adding 45:18

100:20 137:5

157:9 411:3,4

addition 84:19

138:22 189:11

238:14 262:5

264:4 266:3

268:10 271:3

276:14 292:17

381:10

additional 31:1

33:8 35:3 43:17

43:18 51:12 53:10

72:3 101:9 103:15

168:7 182:20

188:2 189:9 250:9

260:16 283:6

310:16 328:10,15

329:6 331:1,3

333:20 334:6

379:9 383:9

388:21 393:8

additionally 270:16

281:21 380:4

address 122:5

134:19 136:4

196:12 210:19

212:11 219:16

248:21 249:7

280:19 302:3

329:1 375:14

383:6 400:7

addressed 221:6

348:5 371:5

addresses 294:7

addressing 192:19

194:12,17

adenomas 388:15

388:16,20

adequate 382:15

adequately 21:10

77:21 344:17,20

349:9 351:2

adjourned 183:15

adjust 251:8

adjusted 40:6

adjusting 58:4

adjustment 251:9

administered

195:11 212:14,15

250:20

administration

122:16 123:15,20

167:9 170:18

195:1 199:18

208:8,17 211:6

374:19

administrations

122:11

admittedly 246:12

296:7

adolescence 345:13

adopters 103:9

adoption 102:9

adrenal 164:19

206:21 207:2,3,4

208:6 215:10

217:20 387:1,3,5

396:17

adrenalectomize

213:20,20

adrenalectomized

213:15 215:8

254:9 255:1

395:13

adrenalectomy

214:21 216:11

217:5 231:12

232:4

adrenals 214:15

392:15

adrenocorticotro...

392:11

adult 127:5 195:7

246:16 312:9

322:19,20 347:21

adulthood 347:14

adults 308:21

advance 382:22

advantage 51:6

235:21 361:6

advantages 51:2
202-234-4433

Neal R. Gross & Co., Inc.

Page 427

79:14

adverse 236:4

385:19

advice 345:7

369:18 377:12

378:9

advises 343:5

advising 139:1

advisories 322:12

advisors 376:21

advisory 1:4 9:22

100:3 308:19

309:3 312:9 345:7

357:2 363:11

364:9 384:10

advocate 373:21

aerates 100:18

AFC 245:12

affect 115:14,15

149:2 213:16

393:19

affirmative 423:6

affirmatives 423:9

afford 49:21,22

afforded 49:20

afraid 112:14

267:14

afternoon 32:11

193:18 195:10

323:14 324:15

338:20 384:7,17

age 146:15 161:7,8

188:11,16 189:7

189:16 190:7

191:3 192:19

216:17

agencies 122:10

336:5

agency 1:1 8:21

23:7 87:13 123:5

123:11,14 139:6,7

155:10 188:21

342:6 343:9

350:16 371:20

372:6 374:22

375:5 376:20,22

377:13,20 378:1

378:10 385:7

386:4,11 388:8

397:22 401:1,8,13

403:10,22 410:17

410:18 414:13

415:14 418:7

419:7

agency's 119:15

158:5 348:9

385:13 387:12

398:9 399:14

410:12

agenda 10:8 11:1

agents 170:16

171:3 342:17

age-adjusted

351:18

aggregated 285:11

288:22 289:5

290:7

aggregates 169:7,7

169:17

aggregation 290:21

296:19

aging 148:14 385:4

386:2

ago 43:14 70:17

115:3 116:15

125:13 126:5,6,10

126:15 129:13

136:18 274:22

340:1 369:12

agonist 196:3

agree 23:18 165:9

219:1,5 220:9

364:6 399:14

416:22 420:14

421:7 422:17

423:13

agreed 123:10

138:16

agreeing 422:18

agreement 353:7

394:12 423:19

agricultural 30:17

86:16 94:11

133:16 275:21

411:8

agriculture 28:15

29:21 30:1 99:19

99:21 100:4 101:2

102:8 103:14

104:4 106:4

112:20 114:8

122:9 352:4 353:8

379:6

ah 413:21

ahead 90:19

air 100:21 396:13

Akana 2:2 7:15,15

33:13,14 34:8

232:11,13,18,22

250:11,12 251:5

254:7,8

al 137:4 138:4

192:5,6 401:4,4

Alan 324:11 364:6

alarmed 415:6

albeit 207:15

331:18

alcohol 373:20

aldosterone 204:2

205:13 206:1

algorithm 34:1,9

algorithms 39:3

40:15

allegedly 371:16

allocating 21:15

allocation 24:3

allow 194:14

allowed 375:10

381:19

allowing 118:13

381:14

allows 102:3

196:16 360:15

allude 158:7

alluded 422:7

alterations 198:3

347:6

altered 197:20

198:12,13 350:4

altering 220:4

387:1

alternations 348:2

alternative 106:20

107:12 126:1

131:18 152:16

260:15 336:10

389:12

alternatives 106:9

106:10 108:8

109:3

alters 194:19

206:13

amassed 265:7

ambient 39:13 56:8

300:20

amenorrhea

161:16 162:4,8

America 368:15

373:8

American 5:15

100:4,5 106:4

225:9 324:12

325:4 373:11

374:2,9 379:4

America's 102:22

amino 89:1 330:18

ammeline 164:3

166:9

amount 102:6

116:22 128:10

130:12,13 146:14

266:15 346:15

360:1,3

amounts 82:14

120:7 121:11,21

318:6

AMP 13:4 20:13

171:9,13 172:1,8

172:14 262:19

266:5,9,15 269:12

269:16 270:8

271:14 272:3

279:8 280:11

283:3 287:18

289:16 290:20

amphibians 151:12

347:18 349:18

350:3

ample 220:3

amplitude 153:17

153:18 166:5

201:17,21 214:8

214:14 215:6

230:13 254:1

analogous 244:5

analyses 107:15,19

109:18 159:6

221:18 234:21

260:10 261:13,15

262:6 273:12

282:1 295:19

analysis 26:8,13

29:11 34:12 80:20

81:17 108:15,21

110:2 111:6

113:18 135:19

148:18 150:1

155:22 236:13

282:2 285:7,10,12

288:10 289:20

291:8 292:10,21

293:5 297:4

299:15 330:15

355:11 361:10

388:8,18 400:2,7

421:2

analyte 178:14

analytical 70:7

330:18 370:15

analyze 89:2

analyzed 109:8

110:18 111:14

202:22 340:2

349:15 357:14

362:12

analyzes 261:17

Anatomy 6:18 7:22

Andrew 149:21

androgen 154:3

217:10

androgens 203:21

204:6 205:15,17

205:18 209:1

210:14 216:4

androstenedione
202-234-4433

Neal R. Gross & Co., Inc.

Page 428

203:22 205:15

206:2 209:6,10

and/or 388:14

anecdotal 328:20

animal 1:7 5:8

136:9 137:16

144:1,8 152:1

182:15 186:17,18

188:11 222:4

236:2 347:12

368:2 387:14

397:19 398:9

406:12 409:20,22

410:2 413:1 422:2

animals 144:2,19

145:21 146:3,4

156:18 158:21

159:1,16 186:12

186:21 188:1,16

189:3,5,8,15

190:5,18 191:7,20

197:17 198:18

200:10 201:4,10

201:13 202:14,17

202:20 203:1,4

206:8 207:19,22

212:1,6,13 213:16

213:21 214:1,10

215:8 216:16

222:1 226:20

228:19 232:15

235:2 245:19,21

246:16,20 247:1

249:21 254:9,13

254:17 255:2

257:11 343:16,22

344:2 347:18

349:1 395:13

396:8,10,11,14

398:15 412:4,7,16

420:19 421:12

ANN 57:15 65:20

336:14

Anna 69:14 304:12

Anna's 93:10

announce 5:2

announced 94:3

announcement

97:17

annual 21:21 57:18

108:3 259:16,19

264:13 267:17,19

268:10 310:4

326:22 330:13

335:4 337:3

annualized 56:2

annually 103:17

ANOVA 242:5

anovulatory

161:13

answer 32:1 48:15

69:22 77:19 91:6

92:12,13 133:8

163:9 194:7

219:20 247:15

255:4 272:8,20

276:22 279:16

296:22 304:12,18

321:1 325:11

334:22 340:21

341:9,21 372:3,17

387:22 393:16

400:13 417:9

answered 334:21

answering 261:3

310:22 420:10

answers 297:1,3

298:19 340:20,22

355:6 372:14

answer's 81:4

antagonist 255:4

antibodies 225:16

225:22 226:1

227:20 238:22

antibody 203:12

204:7 218:5

225:12,18,20

226:10 240:20

antigens 240:19

anti-asialo 245:11

anybody 4:11,14

404:9

anybody's 392:21

anymore 130:22

anyway 305:16

308:5 309:10

325:2 403:16

apologize 18:17

285:9

apparent 401:21

apparently 265:22

416:8

appear 21:16 41:21

166:18 176:13

179:21

appearance 41:13

appeared 184:17

263:13

appearing 105:14

181:22

appears 17:14

21:10 48:19 167:5

218:16 220:5,20

344:12 409:22

application 11:17

84:1 104:15

128:13 221:14

265:19 329:21

331:15 336:11

applications 78:11

applicators 353:10

354:19 359:9

360:13

applied 8:19 11:19

63:6 102:20 273:6

274:8 311:12

321:16 353:13

359:7 360:2

applies 31:8 369:19

apply 63:21 74:6

74:10 75:10,19

92:6 359:1,15

360:13,16,20

applying 73:8 78:3

129:21 353:19,20

380:13

appointees 116:6

appreciate 105:21

133:13 144:19

157:2 323:14

325:16

approach 12:13

24:6 55:13 64:21

72:5,19 75:9,19

87:14 89:5 141:16

203:11,18 226:11

239:7 271:12

272:4,21 274:7

279:5 291:4 294:6

294:16 336:3,14

336:15,20 337:11

337:14 373:16

376:14 425:4

approached 382:5

approaches 76:11

76:17 79:8,13

92:8,14 203:13

204:7 271:3

272:15,17 281:2

283:6 289:9

290:12 325:17

approaching

217:13

appropriate 156:20

181:12 187:5

190:12 320:7

325:22 333:17

334:8 335:14,18

335:20 336:1

372:1 398:14

405:1

approved 122:9

330:16,19

approximately

135:4 177:16

190:6 192:4 284:5

352:19

April 1:13 13:22

183:16 266:5,13

270:18 326:15

357:17

aquatic 6:2 361:22

aquifers 284:2

AR 43:6

arbitrary 22:5

131:8

archive 134:8

arduous 254:12

area 9:4,17 31:10

31:12 32:2,16

34:20 86:13

138:19 161:4

163:16 190:16

191:4 201:19

215:7 235:21

239:12 240:8

241:6,12 242:20

243:2 244:6,8

248:11 253:2

275:16,18,20

297:13 365:7

areas 24:2 31:4

56:4 57:3 70:22

71:5 246:2 332:4

area-under-the-c...

31:7

argue 73:5 117:12

122:21 125:9

421:9

argument 294:15

arguments 27:20

84:3 155:9

Arizona 193:21

aromatase 136:5,7

136:9 149:2

151:10 156:11,17

163:7,7 164:22

165:10,16,22

167:6,10,18 168:9

168:15 172:3,8,15

173:21 174:17,18

174:21 175:9,15

175:22 216:8

348:10 350:15

arose 389:1

arrive 50:2 296:1

article 349:13

articles 120:16

articulate 374:4

articulated 316:19

artificial 3:13

11:17 36:1,4,15

37:15 42:18 50:4

50:5 51:2 222:18

223:4 336:2
202-234-4433

Neal R. Gross & Co., Inc.

Page 429

AR1 53:15

AR2 42:11 43:7

ascertain 180:12

Ashby 192:5

aside 25:13 413:20

414:8 420:4,7

asked 10:12 83:10

118:22 164:8

168:7 182:19

195:17 206:20

213:13 214:20

267:4 270:1

282:19 301:2

304:15 317:14

345:16 368:1

369:9,20

asking 18:18 23:6

49:18 81:17

157:19 172:11

221:7

asks 206:12

aspect 180:8

391:19 393:18

399:18

aspects 256:3

283:10 300:6

assay 83:19 84:5

170:5 173:11

209:16,19,20

210:8,11 218:6

226:5 245:11,12

247:18 330:18

assays 89:1 149:9

170:6 204:9 218:5

225:12 227:19

389:16

assay's 245:15

asserted 355:4

assess 36:10,21

50:20 151:17

187:17 188:3

417:13

assessed 170:14

173:10 179:7

378:9

assessing 37:12

187:8

assessment 8:11,22

9:1,5,13 71:3

73:17 74:8,10

82:7,16 89:22

94:21 95:2,5,6,9

95:20 134:12

141:11 151:22

153:9 155:10

164:17 186:3

255:15 258:5,8,9

281:19 283:11

305:3,5 321:17

360:17 369:22

370:4,11 371:14

372:7 384:13

387:16 388:5

393:13 394:15

398:11 404:2

408:10 410:11

419:17 424:17,22

assessments 23:2

23:13 176:19

257:10 319:19

321:9 369:14

assessor 418:10

assessors 417:19

assist 134:15

assistance 84:6

associate 5:20

394:10

associated 153:15

154:6,9,21 155:2

160:18 162:3,4

238:4 248:6

261:12 272:16

348:16,20,21,21

353:11 356:20

357:16 358:12

359:20 377:14

386:2 403:19

Associates 272:20

association 97:7

143:1 154:7,11

324:12 325:4,7

350:17,22 355:4

373:8

assume 54:2 125:5

131:22 132:3

240:6 330:8,11

331:1 385:18

assumed 98:11

assuming 90:18

161:8

assumption 173:22

as-requested 366:5

Atlanta 5:16

atrazine 1:7 4:8 5:7

36:6,10 38:3,11

38:15 39:10 40:11

41:7 51:9 56:2,4

71:3 76:20 78:4

78:10,11,13,18

86:17 87:1,16,19

88:5,7,7,15,16

89:3,11,18,19

90:13 98:11,20

99:1 100:2 102:20

103:2,4,8 104:8,9

104:12,17,19

105:22 106:3,15

106:17,18 107:2

107:11,17 108:8,8

108:22 109:1,2,4

109:11 111:20

112:1,13,19 113:4

113:7,22 114:5,17

114:20 117:4,10

118:15 120:1,3,12

121:3,9 122:2,8

122:14,14 125:6

125:13 126:3

127:8 130:18

131:6,20 134:17

136:6 137:12,20

138:21 139:3

140:1,7 141:13,22

142:9,14 143:11

144:16,18 149:8

149:18 150:7,10

150:16 152:19

153:9,14,14,16

154:1,4,7,9,12,14

154:18 155:6,19

156:3,13 162:14

163:6,10,13,17

164:10,10,12,17

164:21 165:7,15

165:20 166:3,4,21

167:1,1,3,4,9

168:3,6,10,20,22

169:4,9,13,14

170:2,11,15,20,22

171:8,13,16,20

172:4,11 173:1,2

173:6,21 174:4,18

174:20 175:9,10

175:13 179:2,7,15

179:18 181:22

184:10 187:2

190:2,8,15,21

191:3,18 193:2

194:4,9,18 195:1

195:4,11 196:6

197:6,11,17

198:13,16,20

199:1,14 200:10

201:5,14,22 202:4

202:15 203:4,7,8

206:9,13,21 207:1

207:8,18 208:1,12

208:14,16,19

209:4 210:16

211:6 212:2,14,17

213:1,3,17,22

214:6,12,16,22

215:1,6,9,12,14

215:19 216:3,11

216:13,20 217:3,6

217:9,15,20 218:1

221:12,14 222:9

223:7,18 232:14

237:6,14 238:11

241:14 244:13

246:5 248:3,6,9

248:19 253:8

254:20 255:17

256:3,15 258:15

259:14,17 260:3,7

261:14 262:18

263:1,5,18 264:9

264:13,20,20

265:17 267:18

268:4,9,17 269:21

272:13 282:7

304:19 306:4,20

308:8 309:13

316:8 317:13,16

317:21 318:5,7,14

320:14,15 326:18

326:21 327:13,19

328:13,22 329:5,7

329:15 330:3

331:16,21 332:12

333:2,12 335:2,6

342:10,12 343:11

344:9,20 345:11

347:9,17 348:16

349:17 350:1,10

350:13,18,22

351:15 352:9

353:3,4 354:1,13

355:2,12,18,19

356:1,12,20 357:6

358:18,21 359:1,2

359:15,18,20

360:1,10 361:11

361:18 362:15

363:2 366:8

369:11,22 370:4

370:11 371:14,18

372:6 374:20

375:21 378:11

381:15 382:11

383:7 384:14

385:14,18 386:18

386:22 387:5,16

388:9 389:7 390:3

391:16 393:19

394:16 397:11,15

398:11 399:5,13

401:3,22 406:9

407:18 408:1

410:11,14 411:11

414:20 421:19

atrazine's 106:22

126:9 172:1

184:16 186:19

385:2 399:8
202-234-4433

Neal R. Gross & Co., Inc.

Page 430

atrazine-treated

361:1

atrial 214:3

ATS 1:22

attacked 40:5

attempt 334:10

attempted 157:22

attention 137:18

138:11 171:4

306:21

attentive 375:6

attribute 396:1

attributed 185:9

191:21

AUC 31:15

audience 302:20

324:16

audio 4:22

augment 12:17

augmented 12:22

August 14:1 340:1

340:10

Australia 119:21

152:21

author 158:13

authority 333:8

authors 354:9

autocorrelation

42:6,7,12 43:1,18

51:21 52:3 67:18

automatically

365:1

auto-samplers

85:15

available 55:18

112:13 121:14

155:22 179:11

247:22 274:16

280:18 281:8

283:12,13 291:9

338:3 366:22

368:4 387:7

406:13

average 11:15 14:8

16:11,12,19,20

17:2,2,9,10 18:20

19:3 26:3 37:18

38:19 52:13 53:5

53:6 55:21 67:13

76:19 87:10 88:14

88:16,22 96:1

111:10 119:4,6

209:18 259:16,19

259:20 264:13

288:12 306:8

312:12 315:12

326:22 330:11

335:4 337:3

361:18 363:6

averaged 362:22

averages 13:12,16

14:19,20,21,21

17:17,19 19:15,16

20:10 21:11 22:5

31:5 32:13,13

271:21 272:2

307:19

averaging 280:12

308:7

avoid 118:6 299:13

299:21

aware 78:12 156:3

256:20 257:17,18

275:1 287:1 400:3

411:7

awful 29:18

awfully 40:4

axes 233:13 394:14

axis 7:18 46:9 95:6

138:14 178:9

194:8,21,22 195:2

196:9 200:18

202:5 211:5 213:8

213:9,12,14,18

216:15,21 217:1,3

217:4,17 218:19

238:13,15,16

302:14,18 306:1

311:17 386:8,12

387:1,3 389:8

391:22 394:4

395:5,8 400:20

a.m 1:15 4:2 96:15

96:16

B

B 189:7 232:22

babies 357:8

358:12

back 10:20 40:12

49:17 50:2 56:21

57:1,15 69:8,12

74:21 84:13 94:1

94:15 109:7

112:22 116:7

124:4 125:12

127:14 128:1

130:18,19 141:15

152:4 154:21

160:5 171:11

178:11 180:3

225:7 229:2,6

232:6 238:15

249:6,18 254:17

258:15 261:21

264:16,17 282:9

291:21 292:3

296:8 298:7 301:5

303:13 305:10

307:11 309:3,9

317:17 320:7

332:8 337:13

341:19 366:15

368:10 377:5,13

383:19 392:18

396:4 417:11

418:6 420:15

421:9

backed 117:17

background

105:15 143:9

146:6 259:13

260:4 285:9

338:21 339:18

395:10 414:16,19

415:21

backslide 112:14

backup 380:2

backwards 41:17

62:9

bad 20:4 292:14

383:17,18

Bailey 2:22 4:3,4

96:9 236:9 324:9

365:12 366:4

367:5,8

balance 7:19 82:6

Ballroom 1:16

banished 8:4

banned 120:3

131:6,10

banning 383:7

bar 37:10 349:12

barring 279:1

Barry 2:3 7:6

basal 227:6 247:21

base 370:3

based 29:12 30:8

34:12 36:13 72:11

89:22 118:20

152:4 197:18

203:12 204:7

218:5 225:12

226:11 272:12

283:7 285:11

292:18 309:5

310:7 311:9 316:4

320:9 321:9,16

326:22 335:3

343:14 345:22

352:6 365:4

384:13 389:5,14

400:1

baseline 37:20

basic 94:19 193:20

334:12

basically 63:8,11

81:14 227:16

231:9 332:10

399:3

basin 28:14,15,20

29:13,22 30:3

33:21 72:14,15

275:17,20

basins 28:20 29:6

75:15

basis 31:12 237:21

251:4 306:11

311:1 334:1

344:13 366:5

419:12

baton 99:8

batting 303:12

BDL 59:8 60:6

bear 259:8

beat 419:3

beautiful 416:3

beg 369:6

began 138:18

200:22 204:18

232:20

beginning 14:2

15:9 81:16 113:4

137:3 369:1

beginnings 96:22

begins 165:17

199:7

begs 213:6

behalf 97:12

105:14 237:16

368:21 373:22

behave 44:21 45:4

280:7

behaved 66:7

behaves 45:1

behavior 347:21

348:2

behavioral 257:9

beings 378:11

belief 294:16 296:2

346:4

believable 407:13

believe 70:9 114:17

114:20 115:21

116:2 123:9

127:19 139:11

157:12 163:10

175:9,17 185:8

225:6 247:10

280:5 304:7

310:10 316:12

318:16 366:2

374:21 375:8

378:3 386:11

407:6 411:1

believed 115:18,20
202-234-4433

Neal R. Gross & Co., Inc.

Page 431

bell 324:2,3

belt 35:2 110:7,13

125:7

benchmark 321:13

bend 72:10

benefit 107:16

109:6,13,15

110:20 111:12,20

112:1,4 130:16

333:16

benefits 100:9

104:18 106:22

112:5,21 113:15

113:18

best 16:15 17:15

26:20 39:5 43:7

55:9 57:17 78:20

105:17 129:4,5

261:16 287:11

300:14

best-fitting 57:15

better 16:22 19:10

19:19 21:14 28:8

29:5 52:15 53:9

55:12 57:22 58:1

58:7 63:15 65:4,7

65:7 84:11 101:11

106:8,9,10 127:22

133:8 245:16

318:12,12,18

367:15 414:4

beyond 68:19

78:11,13 89:2

341:13

bias 27:7 111:1

349:11 351:5

big 40:1 72:13,14

252:6 275:10

314:20 362:8

367:13 387:19

417:1

bigger 53:7 302:12

biggest 23:12 112:9

bill 330:12

billion 18:6,8 22:6

22:6 61:13 103:17

120:8,9 133:1

144:15 259:12,17

260:1 264:14

269:10,18 278:2,5

278:6 309:1,17

311:3 313:13,21

314:4 315:7 318:1

326:22 332:2,21

332:22 333:1

335:3,8 337:3

355:19,20 361:19

361:19 362:5,6,8

362:9,19

bioassay 143:6

145:20,20 157:7

157:14 388:10

bioassays 140:9,12

145:19 159:19

344:11

bioconcentration

180:16

biological 27:20

86:9 143:1 180:17

183:12 371:3

395:7

biologist 7:5

biologists 419:1

biology 89:10

127:1

biosynthesis

204:17

biotransformation

182:10

biotransformatio...

178:13 180:13

bio-statistician

5:16

birth 115:8,10

348:20 349:2

357:4,7,7,15

358:11

births 357:14

bit 11:3,4 17:8 18:5

32:10 41:18

107:18 202:2,9

203:11 206:7

210:19 243:5

291:7 296:12,13

330:5 334:1

355:10 379:12

395:15 402:1

407:11 415:13,17

415:22 416:6

biweekly 12:22

14:1 21:9

black 122:22

blanks 51:10,14

65:9

bleeding 243:20

blend 82:15

blending 300:6,9

block 214:21

216:12 217:5

231:13

blocked 174:16

blocks 216:11

blood 190:12 203:1

239:18 243:21

247:6 353:1

blue 33:17,17 34:2

143:7 295:10,12

362:8

blush 122:20

BMPs 104:7,11,18

104:21 128:17

board 2:1 146:17

Bob 8:8 89:14

193:19 237:3

291:7 304:7

bodies 119:18

261:11 270:6,13

285:15 356:11

body 72:12 121:13

121:22 206:11,13

206:16 207:4

233:1 235:7,12

313:18 322:19

342:9 357:7

bootstrap 301:13

336:13

bootstrapping 3:8

11:13 12:13

border 98:1,2

boring 303:3

born 115:8 357:9

bothering 65:15,16

bottom 19:6 77:20

93:13 129:19

313:18 393:3

398:2

bought 130:4

bounds 77:13

78:20

bovine 172:5

box 122:22 307:10

311:14,18

boxes 152:4

Bradbury 91:13,15

brain 168:14 198:9

251:16 347:4,6

branching 403:17

break 4:17 10:10

10:11,15 96:8

182:22 183:13,13

302:21 323:4,18

369:2

breakdown 171:9

breaking 100:16

274:20

breaks 84:8

breast 154:11

159:10

Breckenridge

134:9,10 135:12

156:6,7 165:4

177:10 180:7

182:4 183:5 184:4

184:7 193:14

194:5 228:20

234:22 236:18

249:7,8,17 250:1

250:3,7,16 254:11

255:9 256:16,22

257:12 274:20

310:9 323:8,11

bridge 137:13

brief 194:12 252:8

253:20,22 271:8

397:5 424:3

briefer 211:17

briefly 68:6 107:17

199:17 204:16

261:22 274:18

281:22 369:7

bright 335:5,14

bring 69:12 86:6

119:14 140:1

151:16,19 303:13

305:1,10 366:7

382:18

bringing 40:11

410:2

brings 107:10

broad 18:12 19:6,6

19:10 93:3

broadened 363:22

broadening 81:7

broader 18:12

110:11

broadleaves 108:12

broken 270:4

brought 27:6

141:17 151:7

257:14 304:13

Brusick 149:19

brutally 257:18

Bucher 1:21 5:19

5:19 256:12,18

257:6 394:10,11

Bucher's 256:10

budgetary 109:22

buffer 104:13

build 42:11 102:4

288:5

buildup 188:13

built 174:9 273:12

310:14,15 312:3

322:5

built-in 321:21

bulk 194:16

bullets 141:12

bumping 343:7

bunch 59:13

burden 328:11

342:21

buried 115:2

bushels 109:2,12

111:10 124:20,20

124:22
202-234-4433

Neal R. Gross & Co., Inc.

Page 432

business 418:8

busy 18:16

byproducts 121:16

by-period 421:2

b0 37:19 39:20

57:21 58:22

C

C 189:7

cages 396:15

calculate 38:19

43:22 44:1 173:12

calculated 31:4

207:3 271:20

322:10

calculating 31:10

calculation 313:11

322:11

calculations 31:7

314:12 322:13

California 6:4 7:16

8:18,21 98:5

352:2

calisthenics 303:8

call 20:18 129:17

226:3 273:21

340:6 383:9

called 12:9 38:1

42:5,8 186:21

196:18 278:21

310:1 408:12

calling 262:15,18

Canada 9:12

119:20

cancer 5:15 94:10

94:13 115:2,4,5

127:5 135:15

137:10 140:8,12

141:10 143:5

145:15,22 146:6

151:22 154:6,8,11

154:19 155:5

159:10 183:4

184:5 342:3,7,8

343:3,15 344:2

345:2,14 347:13

385:3,8,16 395:22

399:21 404:1

405:4 406:10

408:10 411:6

424:17

cancers 407:21

cancer/non 94:9

candidate 50:20

candy 315:21

cannula 214:3

capabilities 119:12

capability 103:2

capacity 147:14

capture 11:4 17:6

20:7,22 21:6

32:17 37:5 70:20

264:12 294:14

captured 15:20

17:6 89:3 291:20

292:1

capturing 269:20

carbon 102:5

327:11 328:16,17

328:21 329:6

333:4

carcinogen 122:15

341:16,18 342:2

387:17 394:17

398:12,13 408:13

409:4 413:5

421:17,19 422:20

carcinogenic 140:3

152:11,19 343:19

385:14 388:9

389:4 399:17

404:21 409:1

412:2

carcinogenicity

140:7 143:16

160:3 344:10

345:12 412:16,17

carcinogens 406:22

carcinomas 388:15

388:20

care 44:2,22 66:11

158:8 360:15

380:7

career 99:20 114:4

careful 108:9 165:1

376:11,13 377:1

380:3

carefully 106:2

236:19 280:2

282:15 393:15

Carlo 45:8

Carmen 2:17 7:1

Carolina 6:13

135:18

carried 12:12

13:17 138:2 254:8

carry 100:19

carrying 249:9

cars 130:7

cascade 393:20

case 29:3 46:11

52:11,17 66:6

77:17 78:18 86:20

115:12 117:4

133:7 156:4

167:14 170:1,16

174:1 186:13

223:6 224:10

242:11 243:16

274:13 290:21

292:8 313:21

314:16 337:2

412:8,18 418:5

422:21 425:1

cases 174:15 271:7

292:3,18 302:11

351:17 354:13,14

casually 113:21

catalysis 350:15

catch 16:1 25:2

285:8

catches 422:9

catecholamine

251:19 252:7,11

catecholamines

251:16 252:1,15

categories 286:1

categorized 342:19

category 343:8

382:11 408:12

catheters 254:14

caught 25:2 286:21

cause 62:5 64:22

147:2 254:2 343:3

345:1 346:19

347:3 385:19

408:1 424:10

caused 248:8 344:2

causes 41:2,3,4

66:4 103:15

141:22 165:15

211:20 334:13

causing 243:17

343:15,22

caution 168:20

395:3

caveat 229:12

326:9 387:10

423:20

CDC 359:16

CD1 388:12

cell 163:5 164:7,9

164:18,19 165:1,6

165:20 167:10

170:5 171:1

173:19 174:1,6

175:17 176:11

238:22 240:21

247:17,17

cells 159:2 165:11

168:14 169:11

170:1,8,21 172:19

172:20 173:17,18

175:2,4 176:12,22

180:9,12 198:6

240:17,19 242:16

251:18 347:4

cellular 238:18

center 7:7 9:11

110:7 163:2

368:13,22

centered 291:2

centers 217:16

centile 268:12,17

268:21 269:10,13

269:17 270:9,11

270:19 282:21,22

centiles 259:7

260:14 296:3

central 97:5 107:21

108:1 109:20

233:15 387:1

certain 61:2 62:2

79:3 116:5,22

163:5 189:6

253:13 254:4

261:1 295:1 381:2

404:17

certainly 23:7

68:16 93:3 98:7

113:20 115:6

116:22 117:5,9,14

119:14 124:3,4

125:9 127:19

128:16 157:14

163:11 182:17

252:1 276:8

395:14

certainty 282:21

294:13 316:13

343:1 349:11

351:5

cessation 198:16,20

199:8 217:2

cetera 272:14

372:1

cFos 196:18,19,21

197:2,5,7,10

CFR 217:15

chain 45:7 205:4

Chair 1:18,18,20

1:21 5:4 8:6 9:18

9:21 10:1 24:8

25:20 26:22 30:4

32:6,8 33:7,10

35:4,14,18 51:16

53:17,19 55:16

57:13 58:20 59:5

59:20 60:3,10,21

61:7,14,20 62:16

62:20 63:1,4 65:8

67:9 69:5 79:22

83:8,10 84:16

85:1 86:3 90:22

93:18 96:6,17
202-234-4433

Neal R. Gross & Co., Inc.

Page 433

124:7 125:2

126:19 128:4

131:1 133:10

135:9 155:13

157:3 160:9,13

162:20 176:5

178:19 180:1

182:18 183:10

184:3 193:8,16

218:8 221:3

223:12 226:12

229:4 232:9 234:3

249:1 250:11

251:13 252:21

254:7 255:6 256:9

283:17 285:4

290:1 297:14

301:1,3 302:13,19

319:5 323:3,7,17

324:1,10,21 338:8

338:11,16 339:2,6

364:18 366:10

367:12,20 368:5

372:19 373:2

379:8 383:3

387:18 394:9

398:6 400:14

404:7 405:6,10

406:16 407:14

408:5,18 410:6,15

411:19 413:7

416:16 418:15

419:19 421:5

423:16 425:7

chairman 97:4,14

99:11 134:9 135:1

184:8 228:20

235:16 258:2

323:12 373:6

chairs 134:1

challenge 49:22

57:2 187:11

258:10

challenged 261:4

challenges 129:15

chamber 201:7

chambers 1:22 6:6

6:6 180:2,3 193:9

193:10,15 223:4

396:12

chance 42:2 178:17

349:11 351:5

380:15 419:6

chances 130:15

change 45:21 57:4

68:19 87:3,4

88:11 150:6

153:18 188:16,18

201:18 202:1,10

219:14 329:11

331:9 385:13

387:15 394:14

398:4 399:4,13

409:14

changed 125:10

152:5 196:8 268:5

341:22 413:1

changes 89:4 114:6

121:5 161:7,8

194:22 195:2,19

196:10 197:14

198:8 200:21

202:11 206:11,17

208:16 210:10,16

211:2 214:16,18

216:6,9 217:4

218:7 243:11,16

248:17 392:5

399:7 409:17

changing 29:7

45:14 46:3 87:2

243:5 295:3

channel 278:21

298:17

Chapel 135:18

characteristic

42:10

characteristics

299:6

characterization

178:6 182:9,16

185:22 235:20

305:4 393:11

characterizations

391:17

characterize 50:9

71:16 72:1 73:15

77:21 143:21

297:2 300:19

336:21

characterized

142:13 152:10

261:22 280:13

characterizing

391:20

charge 3:22 87:6

87:11 118:11

135:14 340:18,20

341:2 384:9,12

charged 343:10

345:17

Charles 134:10

139:18 156:7

249:8

Charlottesville 9:3

chart 247:5

charts 37:10

cheap 89:1

check 96:11 246:18

checks 152:3

chemical 86:17

140:22 168:13

185:18 299:15

346:5 360:16

413:5 424:4,9

chemicals 121:10

121:14 171:15

180:9 238:1 252:3

252:9,12 254:19

299:12 346:11

347:9 355:13

356:6 376:8

380:10,12,14

382:12

chemical's 389:4

chemist 317:15

chemistries 125:18

125:20

chemograph 12:9

14:12,22 15:17

16:21 21:18 72:9

76:6

chemographs

12:16 18:1

Chen 287:6,9 304:6

chief 344:7

child 312:10

322:20,22

childhood 345:13

children 116:12

308:22 310:21

360:22

chlopyrifos 352:15

chlorals 177:18

chlorinated 86:22

166:1

chlorination

121:16

chlorometabolites

167:17

chlorotriazines

89:17,21 90:5

163:15,17 175:12

260:2

chlorpyrifos

355:20 356:1

choice 103:9

cholesterol 204:20

choose 104:5 134:3

135:7,13

chose 12:18,20 13:2

Christmas 266:1

chromatid 149:22

chromatograms

204:15 210:5

chromatography

203:18

chromocytomas

396:17

chromosome 150:1

chronic 33:2

142:13 145:6

221:11 334:15,17

395:22

chronically 221:12

circling 392:18

circulating 217:10

227:2 248:3 391:5

398:21

circumstances 64:9

87:12 281:5 299:2

citations 365:13

cited 359:16

citizens 378:20

claims 113:21

clarification

297:10,17 321:4

clarify 30:6 160:12

250:9 297:18

class 240:17 254:22

422:19

classical 203:12

412:2

classification

154:19 342:8

343:18 385:8

399:5,13 406:22

412:20 413:2

classified 343:13

345:3 360:9

classify 341:17

342:12 344:9

classifying 282:13

342:1,15

clause 412:1

clean 124:22

cleaner 298:17

cleanup 303:12

clear 30:5,6 92:9

168:13 246:9

338:12,17 377:16

380:22 413:9

clearly 16:14 30:21

32:12 144:21

235:10 312:1

395:9 421:16

clear-cut 414:15

cleavage 205:4

clever 299:22

climate 30:17

Clinton 122:16

Cloath 156:9,15

cloned 179:10

close 18:7 52:4

59:15 60:18
202-234-4433

Neal R. Gross & Co., Inc.

Page 434

314:17 383:14

425:8,9

closely 225:3

369:17

closer 160:14

181:17 252:13

292:22

closest 82:16

closing 113:1 183:1

183:6

closure 27:21

clothing 359:6,11

cloud 401:18

clusters 286:6

coated 170:2

cocaine 253:21

code 278:16 309:14

coherence 143:2

cohort 186:21,21

189:5 190:5 191:1

191:10,15 192:11

247:1

cohorts 186:20

187:6

coincides 250:19

Coli 121:19

colleagues 92:19

94:10 134:15

256:6 398:19

411:6

collect 91:9 214:3

collected 250:17

294:9 363:18

collection 84:7 91:7

247:6 250:13,14

251:1 331:2

collectively 123:12

304:11

College 6:7,17 7:21

9:15 193:21 225:8

261:21 275:13

276:2 279:21

column 15:19

157:10 245:6

columns 19:11

combination

106:15,17 191:15

291:14 355:22

381:17

combinations

36:12 126:12

combined 273:10

329:6

combining 86:21

come 10:20 28:9

31:22 77:9 82:16

83:13 87:3,13

88:3,18 92:11

94:4 96:11 110:2

130:20 134:20

138:9,16 141:9

169:4,15 178:11

216:15 224:4

229:2 236:20

256:6 261:21

270:18 274:8,21

277:21 280:16

297:18 301:4

341:10 368:10

398:4 399:22

402:15 404:5

415:4 422:16

comes 57:8 79:18

82:6 116:22

277:20 309:11

314:3 318:14

377:19 417:11

comfortable 89:10

418:3

coming 67:8 69:18

79:6 87:8 187:21

263:8 276:16

277:7 281:17

304:16 381:18

401:19 423:6,8

commend 286:16

comment 4:13

10:18 27:15 127:9

158:8 168:18

180:8 226:15

236:17 325:16

327:2 333:22

341:14 350:20,21

372:13 374:4

379:11 383:5,11

383:14 387:12

405:7 418:18

421:10 424:3

commented 127:7

commenter 368:12

373:3

commenters 10:12

10:16 383:10

commenting 69:16

348:9 368:21

commentor 324:11

commentors

133:12 323:10

324:4

comments 3:20

4:10,15,21 10:3

10:21 53:10 93:10

93:10 96:10 99:5

113:1 156:9

182:20 255:13

324:7,13 326:4,5

326:13,14,16

327:18 328:8

338:6 340:14,15

351:21 371:13,18

404:8 412:13

416:11

commission 404:16

commissioned

245:6

commitment 128:9

committed 139:6

188:21 256:1

378:21

committee 97:4

98:4 366:2 370:15

374:16

commodities 97:19

common 79:9

101:19 129:12

204:19

commonly 154:2

196:19 356:9

communicate

377:1 378:2

communication

376:1,20 378:5

communities

285:17 298:9,15

299:7

community 12:19

54:2 55:19 56:3,7

56:9,15 67:3 71:4

71:15 72:11 74:5

75:7,10,20 78:4

89:15 97:16

262:10 263:2,9,15

263:19 264:12,18

265:2 269:11,15

269:20 270:2,5

271:13 272:11,22

273:9,14 275:1,9

276:20 277:1,18

278:9 279:19

280:13 282:9,11

284:8 285:16

286:5 288:19

298:5,19 299:3

301:6,8,12,14,17

301:19,22 302:16

311:8 329:8 340:4

364:15 378:15

382:8

companies 225:11

225:13,15

company 373:22

company-sponso...

110:16

comparable 174:2

247:14 294:17,20

compare 226:9

227:12 241:18

306:12 308:14

311:7 314:16

315:1 318:19

319:21 320:8

compared 117:11

124:20 146:13,17

156:5 221:18

241:21 244:8

271:21 273:8

279:8 314:14

324:6 353:17

compares 321:8

comparing 108:7

242:5 321:10

322:1 397:7

comparison 145:18

153:13 260:1

281:16 287:14

310:2 311:6

312:11,14,15

comparisons

108:10,10 229:1

273:11

compelling 399:4

compiled 14:8

complete 145:13

161:22 162:8,9

229:22 333:11

completely 119:1

161:6 167:19

169:10 219:5

220:10 255:1

382:4 393:16

complex 73:11,22

79:20 158:19

240:17 265:19

336:4

complexities 51:4

complexity 72:19

compliance 262:22

263:4 326:20

329:11 330:7,16

330:20,22 331:3,9

333:20 334:3

335:3 336:6 337:1

compliment 258:13

complimentary

281:1

comply 327:12

328:13,18,22

333:5 370:5

component 95:2,17

125:14,17 133:17

180:15 200:17

220:7 240:20

393:7,8 406:4

411:5

components 82:12
202-234-4433

Neal R. Gross & Co., Inc.

Page 435

185:20 211:4

218:18,20,21

237:11 389:8

composite 88:22

276:13

compost 102:7

compound 90:13

132:18 140:4

148:22 163:14

235:11 375:21

377:15

compounds 177:13

179:22 261:18

263:1 381:13,15

comprehensive

117:5 140:6

164:15

compromised

356:4

compromises

354:16

computer 236:10

computing 13:15

concentrate 284:20

concentrating

379:18

concentration

11:18 13:6,9

14:15 22:11 24:1

37:18 38:3 41:8

42:14 44:15,16

45:19 56:3 57:18

61:17 62:1 85:3

133:5 164:16

165:19 174:8

241:13 242:20

248:12 300:14

306:8 319:22

334:8 335:15,19

concentrations

11:15 12:3 16:19

16:20 18:20 42:20

44:10,13 58:15

65:6 70:2 75:5

77:18 79:15

167:15 169:9,22

170:11 172:2

176:4 264:13

267:17 268:18

300:18 306:4

307:6,15,22 322:2

329:15 331:18

361:18

conception 186:22

190:20

conceptual 90:19

conceptualized

139:10

concern 23:12

25:21 26:18 50:11

50:13,14,19 58:6

66:4 71:13 76:22

77:10 88:6 89:6

117:16 132:5

259:22 328:10

334:19 337:2,6

360:20,22 375:20

386:9 399:19

400:8 424:10

concerned 13:9

42:1 44:4 46:18

48:17 59:19,20

66:3,8 112:6

114:12 136:14

327:20 402:2,4

420:21

concerning 113:3

394:16 395:12

concerns 71:1

104:20 116:18

120:4 131:7 178:7

360:8

conclude 175:7

240:1 280:9

concluded 152:18

284:15 350:17

402:16 412:8

425:17

conclusion 93:20

152:6 153:3 155:4

246:5 348:10

385:6,13 387:13

398:5,10 399:15

399:16 401:19

402:16

conclusions 135:22

152:22 155:10

267:9 272:1

273:13 274:5

290:9 387:15

389:2 393:9

394:15 399:7

409:15,17 410:4

conclusive 357:21

concordance

167:21 177:15

224:16 410:3

concur 27:2

condition 155:2

405:2

conditions 105:1

180:18

conduct 256:1

328:2 371:8 379:5

conducted 135:20

138:7 149:7

151:13 153:1

157:13 167:13

351:11 371:9

388:18 391:13

conducting 257:2

confidence 77:13

77:22 78:20 79:5

82:2 117:20

119:11 153:2

260:13 268:22

269:3 271:1

272:16 280:10

283:5 292:6

303:21 308:4

314:9 336:16

343:2 349:6 351:7

351:22 354:5,7

355:5

confident 123:1

273:17 274:2

282:1 298:22

confirmation 192:8

confirms 361:14

conformance 371:9

confounding

349:11 351:5

confused 289:4

Congress 376:6

Congressional

377:17

conifer 360:15

conjecture 118:4

conjugate 176:9,12

176:15

conjugated 163:18

164:2 167:3,5

177:4

conjugates 166:21

175:13 176:10,22

177:20 180:5

connect 66:21

connecting 63:16

connection 64:12

236:7

connections 330:10

347:4

cons 36:15,22

51:13 82:13

consecutive 62:15

consensus 409:16

consequence 386:7

398:20

consequences

48:16 100:18

138:12 151:14

184:9 344:14

409:8

conservation 100:1

101:6,17 102:3,10

102:11,14,21

103:2,10,12

104:13 107:1

112:5,8,15 126:17

conservative

117:20 379:21

conservatively

122:8

conserved 155:7

conserves 101:12

consider 47:2

91:21 92:8 93:17

111:22 117:15

148:19 218:14

233:9 345:10

348:7 356:17

361:3 369:9 378:1

407:8

considerable 193:1

240:13 267:1

considerably 252:4

270:19

consideration

159:21 182:17

377:19 378:4

400:5 422:12

considerations

218:4 382:1,19

397:14

considered 31:20

280:3 320:13

332:3 357:12

358:3 369:14

377:12 399:16

401:13 407:12,13

408:4 420:6

422:14

considering 21:20

90:5 112:8,17

115:20 127:1

233:3,12 282:11

369:21 377:2

398:8 407:16

considers 371:20

412:22

consistency 142:22

consistent 17:12

30:15 152:13

165:15,22 167:12

168:1 261:16

281:14 349:9

351:2 385:6 408:3

412:5,5,21

consistently 22:16

234:19 236:14

constant 45:11,15

109:16 125:7

142:13 146:12

200:3 291:1

298:21 300:13
202-234-4433

Neal R. Gross & Co., Inc.

Page 436

constantly 410:13

constrain 91:16

92:3 93:14

constraints 49:8

construct 38:14

constructs 174:10

consultant 9:3

99:21

consumers 116:13

117:22

consumption 323:1

377:9

contacting 110:4

contain 110:15

362:15

containing 109:2

223:5 355:18,18

contaminant

117:12 309:2,15

309:16

contaminants

86:14 121:22

358:4 382:9

contaminate

103:20 327:16

328:7

contaminated

352:4 361:14,15

362:2 373:19

contamination

333:11 352:7,13

contemplating

373:18

context 281:18

303:14 308:12,16

312:7 314:6 317:8

382:20 403:13

contexts 382:5

contextual 305:4

318:22

continually 148:21

continue 11:7

100:11 184:4

189:9 208:4

328:12 333:13

361:15 380:19

409:20

continued 85:22

125:5 323:13

continues 124:6

382:22 386:11

continuing 5:10

77:4 105:3 256:1

continuous 120:19

187:18 188:10

277:2 285:19,21

continuously 299:2

contour 104:15

contrary 118:19

120:3

contrast 144:12

166:8 175:16

contribute 92:21

93:3 294:22

contributed 329:7

contributing

238:16

contribution

232:21 238:5

309:8,9 319:18

320:12,20,21

321:5,12 322:12

control 98:21

100:17 101:5

103:8,11 108:11

112:10,12 125:14

130:20 144:8

146:3 189:12

201:10 203:5

221:19 233:6

234:19 242:7

245:13 327:10

387:2 396:8,17

397:8 401:17

414:17

controlling 48:3

147:9 238:15

329:4

controls 106:19

124:18 143:8

145:3 211:19

226:18,22 227:1

233:2 372:1 397:9

controversy 204:5

convene 374:19

convened 1:15

convenient 135:6

275:19

converged 281:9

convergence 393:4

conversation 81:10

301:5 368:6

converse 79:1

177:9

conversion 136:10

313:17 314:3

convert 313:16

converted 163:14

converts 313:20

convince 52:6,8

convincing 413:1

Cooper 142:8

143:18 145:10

149:4 151:5

184:19 194:13

199:16 233:20

coordinated 128:14

coordinating

370:14

copies 159:1

365:15,17 383:5

copy 4:15 255:11

338:1 408:10,14

copyright 365:22

core 377:6

corn 35:2 97:6,11

99:7,13 102:22

106:12 107:12

110:7,13 111:7,10

124:10,11 125:7

corner 8:4 267:11

correct 84:22 85:12

90:21 193:14

255:9 272:10,10

287:15,20 319:16

320:6 322:3 366:3

407:3

correction 322:4

correctly 290:3

320:4

correlated 153:22

154:2

correlation 171:22

244:10 352:3

353:1 357:5 360:4

365:6 366:14

correlogram 42:8

correspond 216:8

corresponding

21:6 162:6 269:15

correspondingly

270:10

cort 221:13 236:7

cortex 387:5

cortical 164:19

corticosteroid

223:18,19

corticosterone

138:20 203:14

206:2 207:7,10,14

207:19 208:10,19

211:5,10,12,15,21

212:5,18 213:4,8

213:10 214:18

215:15,20 216:14

220:21,22 228:17

232:15,21 238:14

239:11,18 240:1

241:6,9,13,20

242:20,22 243:1

243:21 244:3,20

245:22 246:19

248:4,5,8,10,12

248:16 252:5,6,14

253:4 303:18

312:19 392:14

cortisol 205:14

cosine 42:9,9

cost 82:8 84:4,8,12

84:14 91:7 111:21

111:22 331:2,6

333:16

costs 83:12,15

85:10 330:17

Council 339:8,16

counted 198:5

counter-intuitive

290:10

country 120:3

129:17

counts 68:1

Coupe 2:2 8:12,12

28:11,11,18 29:4

29:17 91:1,2

379:10,11

couple 4:9 28:7

41:19 57:16 79:6

79:10 129:13

155:14 196:17

221:8,8 225:8

238:19 319:7

coupled 199:3,19

214:14

course 15:5 18:4

22:8,10 59:18

87:16 114:4

173:20 214:19

219:7 230:9

259:22 265:13

276:14 288:10

289:15 292:12

306:10 307:17

308:22 312:8,10

375:2 396:21

415:10

cover 101:9,21

149:9

coverage 282:20

covered 149:3

covering 101:20

169:11 263:21

344:21 351:3

co-occurrence

86:13 88:4

craft 49:7

Crawford 12:5,13

12:20 23:18 29:1

Crawford's 12:15

29:19

create 291:12

created 12:15 18:3

creating 64:15

creatinine 251:9

CREB 172:15

credence 424:16
202-234-4433

Neal R. Gross & Co., Inc.

Page 437

credible 44:12,14

60:22 61:1

creek 277:8,11,22

278:3,12 300:20

creeks 277:9

creeps 230:18

CRE's 371:13,18

CRH 213:11 392:6

cringe 63:3

Crisp 9:3

criteria 108:18

316:4 334:10

335:17 342:6

370:18 412:21

criterion 377:22

419:13,17

critical 47:11,17,19

48:2,6,13 93:12

126:18 172:8

231:20 240:19

346:12,17 347:10

386:9,13 390:19

394:18

criticized 72:22

crop 97:1,10 101:8

101:15,16,20

102:12 103:5

110:14 275:22

368:15 373:7,10

373:21 374:12

376:8 381:2

382:10

cropping 87:2

crops 87:3 100:13

107:5 109:15

111:19

cross 176:11

Crowne 1:16

crunched 340:2

Cruz 8:18

CSF 222:18 223:4

223:7

cultivating 100:22

culture 169:1

247:16

cumulative 353:12

curious 91:3

176:14 403:5

current 13:12

20:13 21:9 122:17

258:17 259:18

280:11,19 283:2

293:6 328:13

335:2,22 392:3,9

397:20 416:3

422:19

currently 9:21

120:11

currents 78:19

curve 31:4,10,13

32:2,17 41:22

42:3,19,22 48:9

61:3 138:20

143:12 190:17

191:5 201:19

207:12 215:7

239:12 240:9

241:6,13 242:21

243:2 244:6,9

248:12 253:3

customer 374:9

customers 277:3

328:12

cutoffs 22:5

cut-down 189:1

CWS 12:21 278:16

296:22 302:18

305:15 306:5

CWSes 22:7 23:21

30:8 291:8,13

292:4 296:20

306:17,19 316:2

CWS/year 291:14

cycle 130:9 143:21

218:12,18 250:15

384:20

cycles 190:11

cyclic 147:20 171:9

171:13 172:1,8,14

cyclicity 210:20

216:16 386:5,13

cycling 394:19

cyclophosphamide

245:12

cytotoxic 168:10

170:16 171:1

C-O-N-T-E-N-T-S

3:4

D

D 2:8

DABT 1:21,22 2:4

2:14

daily 12:17 13:21

14:18 15:14 19:16

23:16 27:22 32:12

39:14 80:15 81:19

91:4,9 199:1

207:18,21 212:2,4

212:22 262:1

274:8,15,16

276:13,16 279:4

279:10,18 280:20

281:6,8,11 293:1

294:7 296:10

dampened 42:9

dams 186:22

187:16 193:11

Dan 6:1 8:2 180:22

233:10

dangerous 121:19

342:11

DANIEL 1:23 2:16

dark 169:7

data 12:20,21 26:1

26:12 28:12 29:4

36:6,13 37:8

38:21 39:1,10,12

40:2,13,20 43:19

44:11 53:4,13

54:8,21 55:17,20

56:4,14,18 57:8

57:10 58:10 59:16

63:13,14,21 64:5

64:7 65:18 66:1

66:12 67:13,13,16

67:22 68:1,3,11

68:14,17 71:14,21

72:21 73:6,9

74:12,17,18,20,22

75:12,20 76:1,7

77:6 78:4,21

83:10 95:5,8,11

109:9 110:12

111:5 117:10,19

137:6 139:13

141:2,2 144:1

149:14 158:11

159:12 165:2,4,5

165:8,9 167:21

169:16,21 172:6

172:18 174:11

176:9 177:6,11,17

178:11,18 179:12

179:13 181:19

184:20 189:6

195:3,6,21 199:11

200:6 202:2,21

203:15 204:2

229:16 233:20

234:1 236:20

238:21 241:5

244:6 247:21

252:2 266:15,21

268:19 269:9,15

271:18 274:8,15

274:21 275:13

276:4,5,12,16

279:4,9,10 280:10

280:20 281:3,6,8

282:1,2 283:12,13

284:14,19 285:11

285:15,19,21

286:6,14 287:18

288:1,4,8 289:8

290:15 291:10,15

291:16 292:18,20

294:7,9 296:6,16

297:20 301:14

305:12,19,21

307:1 315:21

316:20 319:11,13

327:21 329:10,10

332:6,11,16 337:7

337:15 340:2,6,11

345:16,18 346:9

348:1,4,11,12

357:11 359:14,16

362:11 363:4,18

364:10,13 370:22

371:7,15,20 372:2

372:9,11,13

375:15 377:10

381:7 385:9,10

386:21 387:8,13

388:21 396:4

402:17 403:12

404:1,3,3,6

406:10,12 407:20

409:4 411:13

413:1,12 415:10

418:21 422:16

database 109:18

110:14,22 111:2

117:5,13 140:7

260:9,11,19,21

261:15 262:6

264:7 265:3,15,16

266:12,16,18

267:6,21 268:16

270:4 274:15

278:15 280:19

282:7,17 284:7

292:22 316:11,15

316:17 397:4

databases 107:15

258:16 261:16

datasets 403:11

date 140:2 151:8,16

164:16 167:21

179:8 329:21

dates 191:12

dating 171:11

David 149:19

Dawley 140:12,17

142:10,11,16

143:8 144:7

145:19 146:18

147:3,14,18,22

148:6 150:21

152:9 155:8 157:6

157:17 185:14

186:13 202:12

207:22 215:18

243:6 384:19
202-234-4433

Neal R. Gross & Co., Inc.

Page 438

385:1,4 388:11

397:17 413:12

Dawleys 140:13

146:1,12 388:17

day 5:5,11,12 13:8

13:11,22 14:7,21

15:14,16,17 16:18

17:10 29:19 33:2

43:2,2,11 58:11

63:13 80:5,8

83:17 87:11 105:2

116:20,21 183:16

184:19,20,20

202:16 206:15

207:20 208:2

241:20 250:14

265:14 266:1,2

273:5,5,6 274:16

274:17,17 278:3

279:18 280:12

292:9,15 293:6,11

296:9,12 304:13

307:19,20,20

309:6 310:8

311:20 313:7,16

313:19 314:2,17

314:20,21 317:11

320:10,20 323:1

338:15 373:20

378:7

days 13:1,20 14:6

14:18 16:1,2,3,9

16:10 17:18,18,18

17:19,19 19:15,17

20:11,11 26:4

33:2,6 42:21

43:11,14 44:10

58:15 60:13,20

61:9,19 62:1,11

62:15 96:3 118:13

145:2 146:9,12,15

185:1 187:9 188:2

189:17 190:7

198:19,19,21

199:5,8 201:5

202:17,17 203:2

206:10 209:4,8

212:14,22 215:18

229:14 233:3

236:21 241:21

245:7 257:15

265:14 277:17

279:7 295:1

296:15 306:9,9,10

335:11 339:12

353:12,13,16

367:9 390:16

day's 42:2,13,15,15

42:17 43:9 120:22

deal 83:4 87:15

135:14 172:6

184:8 261:9

272:15 275:6

284:12 364:1

dealing 23:3

135:19 314:17

deals 265:10 271:5

274:11

dealt 391:8 406:9

dear 182:12

death 355:21 356:2

decadal 268:14

decades 117:18

122:14 129:13

December 35:6

122:3 139:10

decent 39:8 60:2

decide 37:8 91:22

334:10

decided 374:19

decision 37:7 44:5

46:17,18 47:1,12

47:12 48:17 49:2

49:8,11,18 50:11

50:19 58:7 66:5

82:9 87:8 89:12

297:21 298:16

334:6 335:13

344:8 358:16,17

360:8 411:5

decisions 36:13

73:16 93:5,8

122:18,22 123:7

141:18 298:6

327:22 334:12

410:18

declared 120:21,22

declined 18:4

102:12,16,18

declines 15:5

declining 283:1

decrease 38:3,6

40:11 41:4 161:9

162:4 199:2,3

206:16 214:13

232:1,3 244:15

251:11 268:7

392:15

decreased 153:16

154:4 242:19

390:5,10,11,14,15

390:20

decreases 146:15

214:6 243:10

decreasing 48:8

121:4 143:11

329:9

dedicated 123:22

325:15

deep 123:13

deeper 284:2

defect 358:11

defects 115:8,10

348:20 349:2

357:16

defending 65:4

Defense 339:8,15

defer 28:10 157:1

287:8

deficits 197:16

define 334:11

defined 76:8

defining 409:1

definitely 245:14

definition 119:15

404:17,20 405:11

406:21 407:1,2,4

definitions 407:10

degradants 86:17

degrades 133:4

degree 73:13 82:3

82:10,17 99:19

207:15 308:2,4

371:3 410:3

dehydrogenase

170:17

delay 231:2

delayed 166:4

231:7 350:5

386:14

delays 386:5

Delclos 2:3 7:6,6

181:11 182:19

deliberate 376:13

deliberations 105:6

105:10 118:9

delineated 218:16

Democratic 122:11

demonstrate

165:21 196:19

211:1

demonstrated

347:17 370:22

demonstrating

372:5,8

demonstration

395:4

dense 63:12 64:4

65:18 68:14

density 265:12

depart 38:18 252:4

department 6:2,12

6:18 7:10,21 8:15

8:18 193:20 258:4

departure 42:16,17

43:9,12,13,14,16

95:22

departures 42:22

depend 30:16

47:12 54:15 72:20

104:22

dependence 148:5

dependent 42:14

54:4 100:5 152:12

192:19 225:19

240:11 310:3

depending 31:16

205:6

depends 63:10

124:15 125:1

298:19 329:20

depletion 147:10

deprivation 148:15

derive 78:1

derived 149:19

204:19

describe 36:3 38:2

38:5,7,9 53:13

92:7 204:16

388:22

described 37:3

40:11 76:11

177:17 178:3

190:14 392:2

describes 37:17

38:6,18 391:1

describing 41:7

42:20 235:19

371:1

description 85:9

178:12 248:15

412:3

descriptive 25:18

137:17 398:12

deserve 159:20

413:20

desethyl 166:2

design 27:22 50:2

65:7 86:12,19

87:21 88:10,19

89:8 90:8 91:17

91:19 92:6 158:10

182:6 202:13

203:3 213:19

371:10,21 421:1

421:11

designated 2:22 4:5

designations

404:16

designed 118:6

173:15 186:15

238:8 243:2

249:10 335:21

337:17

designing 79:2 80:2
202-234-4433

Neal R. Gross & Co., Inc.

Page 439

83:11 172:20

designs 139:6

260:16

desisopropyl 166:2

despite 109:13

111:12,17 120:2

169:17

detail 212:12 259:3

267:13,14 269:6

317:5 326:16

405:4

detailed 157:1

269:3

details 271:9 400:3

detect 189:14

225:20 226:17,21

227:10,22 347:2

detectability

226:20

detectable 228:18

361:17

detected 132:4

331:21 332:12

detecting 315:6

detection 59:15,16

131:16 235:5

332:2

detections 131:15

263:18

deterioration

188:14

determinations

117:9 122:13

determine 197:5

217:22 327:11

334:7 343:14

344:9 349:6 394:4

determined 116:10

152:15 282:22

358:18 361:20,21

385:2

determining 47:17

242:17 371:19

deterministic 73:1

deuterated 204:13

develop 78:20

118:13 326:4

333:17 422:4

developed 210:12

308:18 335:1

336:3,15

developing 25:22

105:17 257:10

282:5 283:14

336:5,16 346:18

422:14

development 7:13

127:3,11 246:6,21

336:10 346:12

347:10,19 350:8

350:18 388:21

389:9 402:22

developmental

115:9 246:7

248:18 339:19

391:15

deviation 337:7,17

DHT 205:16

diabetes 154:3

diagram 238:12

277:6 307:10

311:15,18

dial 88:17

diaminochlorotri...

166:10

dichotomy 175:8

Dick 113:2

dieldrin 353:2

diet 320:13

dietary 309:9

differ 36:17 397:1

difference 32:14

46:14 53:8 146:10

192:13,14 196:5

228:10 241:19

242:7 289:10

291:11 300:17,22

314:18 315:3,10

differences 18:9

34:18 155:18

218:2 224:2,6

225:4 251:10

286:22

different 11:14

12:3,7 13:10,14

22:15 29:20 36:8

36:9,9 37:13

38:13 42:21 44:20

45:3 46:7 47:10

52:22 76:1,16

78:22 87:1 88:6

91:17,19 92:7,13

97:12 110:3

123:20 126:4,6,11

127:2,3 130:21

140:8 141:12

159:1,2 178:22

187:6 195:12

196:6 202:15

203:8,17 204:9

205:6,7 210:22

211:7,22 212:15

220:13 224:11

225:11,15 227:18

227:19,19 229:13

233:6 239:8

240:12 241:16

244:19 247:18

278:16 287:1

290:12 295:16

334:16 335:17

340:3 349:21,22

350:11 373:16

379:13 382:4

394:5 396:6,7,10

396:21 401:9

402:15,16 406:22

407:11 414:11

416:5,10 425:1

differential 30:20

30:21 244:16

396:2

differently 21:17

179:2

difficult 51:5 54:17

55:6,12 62:12,21

159:5,13 227:10

365:9 396:1

difficulties 300:2

digging 358:15

dihydrotestoster...

206:3 209:7

dihydrotestotero...

204:1

diligently 123:3

diminish 395:6

diminished 397:16

diminishes 393:9

dioxide 102:5

direct 282:2 360:4

360:5 387:4

directed 226:1

direction 52:8

64:18 65:3 328:1

329:22

directly 110:8,9

217:21 226:9

257:1 265:11

319:22 356:18

359:7 387:7 390:4

393:19 403:18

director 5:14,20

8:9 9:10 97:6

99:7,13 344:6

disadvantage 51:4

55:2

disadvantages 51:2

79:14

disagree 401:6

disappears 215:17

255:1

disciplines 119:1

disconnect 293:10

discontinued

131:11

discount 405:18

discounted 152:17

discounting 235:3

discourages 420:8

discovery 373:9

393:7

discrepancy 177:22

227:14,17

discrete 200:8

discriminate

235:13

discuss 366:13

discussant 387:20

394:10

discussed 55:14

279:6 291:17

386:14 391:11

discussing 178:16

181:14 189:4

discussion 65:14

80:8 81:6 82:12

83:3,6 96:3

137:10,11 157:2

237:12 257:13

338:19 368:8,9

379:15 383:15,22

384:1 394:6

403:10 416:1

discussions 76:4

117:7 155:12

disease 350:19

351:1,13,18 352:8

352:20 353:3

355:3 356:3

dish 169:11

disinfect 380:11

dismiss 113:21

418:3

dismissed 419:11

419:12

dismissing 421:15

displaced 381:13

disprove 406:5

disregarded 344:12

disregulation

161:21

disrupt 213:17

disrupted 213:14

disrupting 347:9

disruption 200:15

233:14 348:18

350:10 376:15

386:5,12 395:8

disruptions 386:7

disruptive 101:5

disruptor 348:19

disruptors 358:7

disrupts 386:22

387:3

disservice 123:21
202-234-4433

Neal R. Gross & Co., Inc.

Page 440

130:14

dissolved 222:9

distorting 63:19

distribution 30:2

46:2 266:4 273:13

297:2,6 301:16

302:15 307:5

327:5

distributions 65:1

286:13 287:17

distributors 373:10

disturbance 101:7

disturbing 100:14

diverse 32:5 98:7

135:2 348:16

349:5 350:9,10,13

divided 320:10

322:17,20,21

323:1 353:14

DMBA 159:9

DMSO 169:12

176:20 180:16

doable 179:11

docket 1:10 4:11

10:14 267:12

326:15 365:21

366:12,20 367:3

367:10,11,16

document 320:17

389:18 390:1

392:4,7,10 395:10

documents 10:13

169:2 239:4 243:8

285:9 388:4

dogma 346:1

doing 23:13 26:8

31:6 40:2 46:3

69:2 70:18 71:19

72:21 76:14 82:1

84:4,15 94:4,22

112:16 129:21

143:11 144:16

172:10 175:3

257:3 267:7 297:8

300:1 308:2 314:5

327:9 364:11

376:12,18 377:2

395:9 397:20

410:16 420:9

dominant 164:2

dosage 241:8

313:15

dosages 225:3

239:21 253:2,5

312:20

dose 50:1 95:15

96:2 142:20 143:5

143:15 145:16

146:1,7,11 152:12

159:14 171:12

182:2,14 184:14

187:5 188:15

189:21 190:22

206:14,18 207:11

209:9 211:12

212:3 215:20

230:1 233:7 238:3

239:16 240:3,5,11

241:17 242:3,8

245:7,17 248:15

248:19 250:19

257:19 318:8

346:1,19,22

396:19 397:9

401:14 402:5,6,8

402:10 414:16,20

414:21 415:12,16

415:18,20,20

421:12

dosed 189:9,16

239:15

doses 145:14,14

163:6 165:13

166:11 182:11,12

193:12,13 195:12

196:6 197:11,12

202:15 206:9

207:2,18,21

208:17 209:5

211:7,16 212:2,4

214:13 216:20

240:3 242:5

244:14 248:2,6

346:2,3,5,7,10

dose-related 145:4

dosing 163:14

193:11 244:2

252:8

dots 33:17 63:16

66:21 295:10,12

dotted 241:10

double 38:10 90:20

181:20

doubling 167:11

331:4

doubt 10:15 117:19

downgraded

343:17

downhill 205:5

download 367:16

downloaded

366:21

downstream 75:7

downturn 40:7

downward 267:16

downwards 260:12

267:22 268:7

294:8

Dr 5:19 6:1,6,11,15

6:20 7:1,6,9,15,20

8:2,6,8,12,14,17

8:20 9:2,6,9,14

10:2 24:9,10 25:6

25:10,16,19,20,21

26:11,17,22 27:2

28:5,11,18 29:4

29:17 31:1,3 32:6

33:13,14 34:8

51:16,18 52:1,2

53:12,17 54:10

55:6,8 58:3 59:3

59:18,22 60:7,12

61:6,10,15 62:10

62:18,22 63:2,4,6

65:12,13,21 66:14

67:6,7,10 68:4,5

68:21 74:3 79:22

80:1 81:3,14 83:8

85:7,7 86:3,5 90:6

91:1,2,13 92:3

93:20,21 99:8

105:11,12 124:8,9

124:13 125:2,4,18

126:7,19,21 128:4

128:6 131:1,5,12

131:16,22 132:10

132:13,20 133:3

134:4,9 135:12,17

136:3,17 137:15

137:22 138:15,22

139:16,17 141:5

142:8 143:17

145:10 149:3,20

151:2,5,15 153:9

155:15,16 156:6

157:3,4,12,18,21

158:1,2,4,17

160:9,11,15 161:2

161:3,5,10,15,17

162:2,7,19,22

165:4 167:13

176:6,8,17 177:2

177:10,14 178:19

178:21 179:6

180:1,3,3,7,22

181:7,11 182:4,12

182:19 183:5

184:3,7,18 185:12

188:7 192:22

193:8,10,14,15,16

193:18 194:5

199:16 212:11

218:8,10 219:2,3

219:4,9,10,12,13

219:17,18,22

220:9,11,12,15,16

220:19,20 221:2,3

221:5,15,16,17,20

221:22 222:2,3,5

222:6,7,14,15,17

222:20,21 223:1,2

223:10,11,12,14

223:22 224:14

225:6 226:12,13

226:14,19,22

227:1,11,16,21,22

228:4,6,8,13,20

229:4,5,6,11,19

230:5,9,10 231:2

231:5,11,16,19,21

232:8,11,13,16,18

232:19,22 233:8

233:10,18,19

234:4,9,15,17,22

236:5,9,11,18

237:1,3 249:1,2,5

249:7,13,16,17,19

250:1,2,3,6,7,11

250:12,16,21

251:5,7,13,15,21

252:21 253:1,10

254:7,8,11,15,18

255:9,13 256:10

256:12,16,18,22

257:6,12 258:1,5

258:20 259:12

261:18 265:11

266:21 267:4

269:4,6 272:19

274:20 279:5,14

281:19 283:16,18

283:19 284:4

285:4,6,14 286:7

286:9,20 287:4,6

287:7,8,8,9,12,20

287:21 288:9,13

288:18,21 289:1,3

289:7,8,14,18,19

289:22 290:1,2,11

291:6 293:8 294:4

294:11,12 295:2

295:13,14,18

296:4 297:9 298:2

298:4,18 301:1

302:2,2,4,5,5,17

303:2,7,15 304:4

304:5,6,6,13,15

305:12 310:9,10

313:8 315:14

316:18 317:11

319:6,7,16,17

320:2,3,5 321:3

321:18 322:7,9,14

322:16 323:6,7,11

339:7,8,10 344:3
202-234-4433

Neal R. Gross & Co., Inc.

Page 441

364:19,20,22

365:18 366:6,18

366:19 367:1,7,18

367:22 369:1

374:13 375:7

379:10,11 384:7

387:19,21 392:7,9

394:9,11 395:11

398:6,8 400:14,16

404:11,12,20

405:6,8,21 406:7

406:16,18 407:5

407:14,16 408:8,9

408:16,18,20

409:11 410:5,6,8

410:9 411:3,19,21

413:7,8 416:10

417:18 418:15,17

420:14 421:5,7

422:22 423:1,3

424:1,2,21

draft 155:10

366:16 385:12

386:10,16 388:5

drag 298:1

drains 279:1

dramatic 202:3

207:9 208:12

212:18 228:11

dramatically

200:13 201:15,17

drastically 246:15

draw 290:9 295:15

297:12 376:2,11

412:10

drawn 46:9

drinking 1:8 5:8

35:22 62:3,3 71:2

74:8 78:8 89:22

90:4,9 94:17

95:17 119:2

121:11,15,17

131:7,14 258:15

259:9,10,20 261:8

261:12 262:10,13

268:17 275:5,7

276:3,20 280:1,8

281:16 283:10

284:9 300:19,21

307:7 310:1,19

311:5 312:10

313:14,19 315:19

317:2 325:8,9,17

326:5 328:7

330:19 331:10,22

332:7,9 333:3,3

333:18 334:1

337:5 340:5 358:7

361:10 362:4,11

362:14,17 375:16

376:4,16 377:15

378:12,14 380:20

381:2 382:21

drive 43:21 129:10

147:11 200:4

338:2 366:7

driven 122:17

161:7 214:17

drives 148:12

driving 34:14

142:6 216:19

220:14 252:16

droplets 359:7

dropped 402:6

415:16

drops 41:7 211:18

308:6

drying 277:4

due 92:18 121:4

180:16 188:10,10

195:16 196:10

218:3 243:12

252:1 265:18

282:19 327:15

331:14 333:4

334:2

Duluth 6:21

dumped 372:10

duration 21:4,5

71:13,21 77:1,5

77:10 79:4,16

164:16 184:14,18

185:2,17 186:1,2

186:11 188:15

189:10 191:13

225:5 254:6,13

258:19 273:1

308:20 309:21

310:2,5 334:17

400:6

durations 12:3

13:10 14:16 77:22

187:7 292:15

293:5

dust 123:2

dwell 274:19

276:17

dwelling 214:3

DWLOC 310:3

dynamic 410:12

dynamics 201:20

D-ALA-6 196:4

D.C 1:17 339:18

E

E 1:22 2:22 121:19

184:1,1

earlier 13:7,13 70:1

83:11 156:15

185:13 223:8

232:20 270:2

275:18 291:17

325:10 343:18

345:7,19 391:19

393:10 407:2

earliest 187:10

early 121:6 142:14

144:8,10 178:12

186:11 246:21

265:11 284:13

326:20 344:22

346:18 347:19

358:13 361:3

388:14 399:20

early-life 400:8

easier 265:10

282:12 300:12

easily 38:9 145:1

145:11

east 97:5

easy 62:11 243:19

ECHO 290:17

eco 276:2,5 277:9

280:4

ecological 34:16

35:7 39:12 70:18

74:22 75:17 78:9

84:18 85:22 326:7

331:11 335:16

357:19 382:2

ecologist 419:3

ecomonitoring

262:3

economic 112:3,20

330:6

economies 130:1

ecotoxicology 6:2

education 381:14

educational 105:19

effect 37:22 62:5

64:22 146:13

159:14 184:21

185:3,9 186:4,6

186:12 188:13

189:19 190:2,20

191:18,19 207:1

207:13 209:3

213:1 216:1,3

222:13 232:1

235:11 236:4

244:17,21 245:14

252:4,17 254:22

387:4 395:18

402:8

effective 68:7

103:11 104:11

111:21 192:2

272:7 282:18

336:13 338:5

378:7

effectively 133:5

191:10,11 207:14

217:20 379:5

381:16

Effectiveness

368:14,22

effects 1:6 4:7 5:7

88:4 127:2,10,12

134:16 137:12

138:12 151:19

155:7 156:10

164:17 168:10

171:5 172:1,22

175:8 176:2

184:10,16 185:1

186:9,19,19 193:2

195:16,18 196:9

202:3 206:21

208:19 214:16,22

215:9 216:11,13

216:14 217:1,3,6

217:9 218:1

231:14 233:17

235:4 237:6,13

238:16 240:5

241:1,8,11,11

242:22 243:18

245:9,20 246:5,13

248:7,7,9,13,19

249:15 251:22

252:3 253:17

257:7 325:21

345:13 346:10

347:3,11,12,21

349:17 350:3,10

371:17 375:20

376:7 382:2

385:19 386:1,20

389:7 391:16,18

393:20 402:12

415:18

efficacious 130:22

efficacy 108:5

efficiencies 100:10

efficiency 130:10

efficient 88:18

130:11 299:1

effort 124:1

efforts 97:18

104:22 105:19

129:5 151:17

EGFP 196:13

eggs 348:22

EHP 155:20

eight 285:7 331:5
202-234-4433

Neal R. Gross & Co., Inc.

Page 442

331:20

either 15:15 20:18

38:2,21 41:22

44:6 66:8 79:15

108:8 132:11

175:18 181:20

190:19 202:16

223:5 242:6 250:4

271:19 297:1

322:19 328:15

356:7 388:13

390:4

elaborate 190:2

elaborated 192:21

electronic 4:15

110:14

electronically

383:4

electronics 338:1

elevated 147:17

148:2,4 153:16

155:3,6 160:19

201:17 211:13

215:21 248:4,8

330:4 398:21

elevations 241:15

eleven 118:13

273:1

Eligibility 358:16

eliminating 101:9

elimination 163:19

178:3

Elizabeth 384:8

elucidate 228:22

256:2

elucidated 152:12

embarked 197:4

202:8

embedded 91:18

163:10

emergence 126:12

emphasize 74:2

377:7

emphasizes 398:18

employ 17:15

enclosed 396:15

encompasses 98:1

encourage 51:15

128:17

encouraging 268:2

ended 137:16

204:15

endocrine 7:3

134:16 137:12

138:12 156:22

184:10 202:11

237:13 245:20

256:4 347:8

348:17,18 350:9

358:7 371:17

376:7,15 385:21

endocrinological

235:20

endogenous 142:4

142:17 145:6

196:1 395:1

403:19

endometrial 154:6

154:8

ends 14:2

energy 7:19

engaged 98:19

engineer 326:11

engineered 196:14

enhance 169:12

enhanced 163:7

196:15 253:20

397:18

enhances 254:3

enhancing 170:21

176:21 424:11

enormous 266:19

enrollment 353:13

ensure 92:15 98:9

370:9

ensuring 122:5

enter 83:16 103:19

entire 110:22 111:2

265:15 273:3,9

281:4 289:20

entirely 190:19

entry 191:10 327:4

environment 9:8

99:3,3 103:1

environmental 1:1

6:3,12 7:2 8:21

99:20 100:11

106:22 112:4,21

126:13 155:20

333:9 339:16

enzyme 136:10

205:4

enzymes 205:3,7

EPA 1:1 3:8,13,18

5:5 10:10 86:11

97:17 98:9 113:16

113:16 116:1,6,8

116:9 118:5 119:7

122:9,18 123:11

141:1,11 149:22

152:18 239:3

260:17 271:4

275:14 278:10,16

284:14 285:2

288:1 297:15

308:13,17 311:6

312:16 320:17

325:2 326:14

327:18 333:7,7,11

333:15 334:7,21

335:13 336:3

338:2 340:6

341:19 342:2,13

343:6 345:6,10

349:6 356:17

358:17 359:8

360:8,9,21 361:20

363:12,12,19,19

364:1,10 369:20

369:21 370:3,9,13

371:7,16 372:3,10

376:3,11 383:19

384:8 385:2

388:18 391:12

397:20 402:16

405:16 411:22

412:21 417:12

420:2,17 423:19

EPA's 4:10 119:11

312:13 329:10

332:5,8 342:3

358:17 360:17,19

361:11 369:8

370:2,8 371:6,18

372:8,14 382:20

395:9,14 406:21

407:2

EPA-HQ-OPP-2...

1:10

epi 352:2 369:19

400:1 404:5

406:12

epidemiologic

369:16

epidemiological

87:22 333:16

365:5 380:17

387:11 407:20

epidemiology

354:15 357:19

366:13 368:3,10

385:10 406:8,10

410:2

episodes 147:22

episodically 148:3

equal 273:19

equally 21:15

equation 37:17

43:10

equipment 359:11

380:7

equipped 127:22

equivalent 32:2

192:4 241:12

erosion 101:12

102:11,15,18

103:16,18 104:3

error 19:13 20:7

31:12 42:3 43:15

45:22 47:9 48:5

48:18 49:5 50:22

65:6 251:3

errors 44:5 47:12

48:13,17 49:12

369:4

especially 23:11

138:13 178:8

184:10 225:22

226:7 236:5

essential 107:2

essentially 165:5

166:14 173:19

174:16 175:11

261:7 287:17

290:15 388:3

390:22 412:10

421:22

establish 61:1

305:18

established 118:19

309:3 312:14,16

336:18 370:17

establishing 318:4

estimate 11:15

44:10 46:22 47:5

52:21 58:8,14

60:8 61:8 63:9

76:17 258:18

268:20 274:3,3

352:7 379:21

estimated 19:12

103:16 230:11

279:10

estimates 13:15

27:16 45:7 47:8

56:2 63:10 66:1

77:12 78:21 79:3

270:19 296:1

316:11 351:6

estimating 12:2,7

21:11 58:1 62:10

62:13 73:13 79:15

279:17 281:10

283:3 297:21

estimation 85:6

281:2

estradiol 203:20

205:19 206:3

209:13,22 224:20

227:7 228:18

estriol 203:21

205:19 206:4

209:13 210:1

estrocycle 218:12

estrogen 136:11,14
202-234-4433

Neal R. Gross & Co., Inc.

Page 443

142:4,17 145:7

146:15 147:16

148:11,15,16

150:15 155:6

160:16 161:1,7

162:1,6,17 190:10

195:22 198:15

212:7,8 214:22

215:5 217:10

219:15,19 224:9

225:11 226:17

227:2,22 228:12

228:14,18 384:21

391:5 398:21

402:17 403:19

estrogenic 149:1

150:10,16 395:2

estrogens 203:20

204:6 205:18,20

209:13 210:11,13

216:5 395:1

estrone 167:14

203:20 205:19

206:3 209:13,22

estrous 384:20

386:5,13 394:19

397:2

estrus 142:13

143:21 144:6,9,21

145:2 146:9,12

147:21

et 137:4 138:4

192:5,5 272:14

372:1 401:3,4

EU 120:5,11

152:21

European 120:5

127:7,10

evaluate 11:14 45:3

75:19 76:16 77:7

142:18 245:19,21

247:8,10,13 297:5

371:7

evaluated 12:6

127:10 140:16

148:22 150:12

240:15 244:12

247:12 250:21,22

342:17 406:1

407:7 409:3

evaluating 12:1

63:7 64:7 127:12

141:1 142:21

239:5,10 336:8

385:8

evaluation 76:15

86:12 245:7 376:7

389:14

Evanston 7:11

evening 118:12

373:15

event 145:17 390:3

390:7,12,17,22

391:4 392:10,12

392:15 393:6

395:17

events 64:2,14,16

95:12 142:6,19

152:10 178:8,13

277:12 288:16

299:10,21 389:19

390:1 392:1,4,17

392:21 393:2,4

eventually 31:15

263:20

event's 128:19

Everybody 303:8

everybody's 337:13

everyone's 130:10

evidence 94:9

114:1 115:19

122:6 136:6 145:6

149:17 150:7

151:13 152:1,1

156:16,17 160:20

161:19 162:18

171:18 179:1

198:2 204:8 220:3

229:13 231:3

243:14 328:20

343:15,16 344:10

349:4 355:1

389:14 398:14

399:4,6,9,12

400:10 412:5,6,15

413:4,6 421:16,21

422:1,2,4,15

423:10

evolved 141:7

ex 201:1 222:8

exact 94:14 226:6

238:4 250:4

411:10

exactly 28:3 136:19

137:19 177:8

204:14 229:11

236:21 247:11

297:20 317:8

408:22

exam 107:15

examination 194:3

202:18 223:8

352:11

examine 21:20

142:6 201:1 203:6

204:18 227:18

examined 197:21

198:17 199:15

201:8 209:1,8

211:3 214:1,19

236:22 282:14

353:6 374:21

377:11

examining 141:2

148:20 379:20

example 15:3 74:19

126:8 162:3

171:11 207:6

253:21 273:15

302:6 305:13

320:8,9 330:8,11

347:1 348:18

351:16 358:5

370:8,13 371:6,13

371:21 412:2

examples 11:10

18:14

exceed 309:20

336:17 360:19

362:22 363:3,8

exceeded 131:8

259:11,20 301:20

302:1 362:7,19,20

exceeding 284:22

286:3

excellent 233:8

exception 115:6

360:14,18

exceptional 260:21

282:17

exceptions 70:10

excess 132:2 244:3

264:14

excessive 318:6

exchange 149:22

excited 303:17,20

exclude 349:10

351:4

excuse 174:17

executive 97:6 98:3

99:6,13 340:12

exemplary 33:3

exercise 18:15

23:10,18

exhibit 184:17

exist 255:15 344:19

355:2

existing 77:6 78:2

281:18 322:2

333:4 413:3

exogenous 170:18

241:9 242:22

252:5,5

expand 142:8

403:10

expanded 386:18

expect 15:22 17:14

19:19 24:15 25:3

30:18 54:5,13,13

54:14 56:5 61:17

65:2 75:5 115:15

306:19

expected 21:3 30:8

42:16,17,20 43:9

43:16 176:10

232:2 302:9,10

319:20 321:8,14

358:4 397:1

expecting 358:8

experience 49:10

70:12 114:17

115:7

experiment 197:4

202:13 203:3

210:18 234:15

243:4 244:11

274:10 371:8

417:14

experimental 1:7

5:7 108:15 160:20

228:21 343:15

344:11 349:4

385:9,11 387:13

398:15 412:4,7,16

421:11

experimentally

152:17 344:16

experimentations

257:20

experiments 64:20

237:19 238:8

244:13 254:16,19

288:2

expert 54:11,17,18

153:11 161:3

300:7 325:20

341:6 348:5

expertise 9:12

119:4 132:15

233:13 326:3

333:16 378:22

experts 271:10

325:22 341:7

344:5

explain 34:18 43:2

43:18 225:4

233:16 286:15

376:12 396:1

422:10

explained 90:15

143:20 251:19

explaining 66:12

exploratory 366:14

explore 151:18

232:20
202-234-4433

Neal R. Gross & Co., Inc.

Page 444

explored 224:3

336:12

exploring 34:21

expose 144:14

exposed 62:2

155:19 187:16

191:1 212:1

224:17 246:20

247:1 317:8 318:5

320:22 355:22

358:20 376:9

exposing 373:19

exposure 32:1,2

33:3 34:16 70:19

71:1,6,13,17,21

72:9 73:14 74:8

74:10,22 75:17

76:21,22 77:1,10

77:12,22 78:21

79:3,17 81:10

86:18 90:4 115:12

138:20 142:3,14

142:17 143:10

145:6 148:11,15

154:1 156:3,13

160:17 161:1

162:17 163:5,11

168:6 181:17

194:10 199:14

208:14 212:17

213:3 214:11,12

223:7 225:5

248:19 255:15

256:15,19 258:5,8

258:18 274:12

283:11 284:16

305:8,9,14,18

314:1 317:3

319:10 320:15

321:15 346:16,21

347:1,8,14 351:13

355:2 357:6 359:9

359:20 371:17

375:19 378:10,12

381:20 384:14

390:4 396:6

399:20 400:5,6

exposures 32:22

33:1 34:14,19

73:2 76:17,18

78:8,9 169:3

212:22 241:18

258:22 271:6,6

272:18 283:4

287:15 335:6

344:22 345:1

346:19 347:19

351:4 358:13

359:5,12 360:5,9

360:19 361:2,3

400:8

express 196:15

197:2,10

expressed 196:21

197:5 360:21

expression 163:7

164:21 165:16

168:9,15 172:3,9

174:18,21 175:15

175:21 181:3,8

197:7 198:4,8

199:5 230:7,16,20

240:16

extended 18:13

211:15

extensive 121:2,13

165:9 210:18

315:18 316:3,14

329:18

extensively 262:13

extent 73:14 80:19

168:8 178:10

294:9 370:19

389:9

extra 361:7 377:1

extract 54:17

extraordinarily

169:22 170:10

extraordinary

315:17

extreme 24:17

64:15 268:22

extremely 258:22

280:6

eye 40:3

eyeball 40:3,13

eyeballing 68:16

eyes 49:4 395:6

422:9

e-mail 4:16,17

366:1 367:9,9

e-mailing 367:14

F

F 2:2 184:1

face 224:14 234:5

fact 87:2 98:2,20

118:5,5,7 120:21

120:22 132:8

134:19 146:14

156:14 177:11

188:10,11 189:5

189:13 190:1,13

195:8,15,21 196:4

197:15 198:12,15

200:7 207:7,17

210:15 211:1,8

212:3 217:8

218:20 219:14

231:5 239:1 242:8

243:17 267:10

278:13 286:22

290:6 293:13,17

293:22 294:3

299:14 300:3

318:4 320:6

328:19 374:16

375:7 391:9 396:4

415:21 417:1

419:15 420:5

factor 33:22 45:21

46:13 54:8 59:4

86:12 273:21

281:15 283:7

302:20 309:7

310:14,15,17,17

311:2,11 312:3

313:17 319:14

320:11 321:11

322:18 338:12,13

351:20 365:9

factored 322:1

factors 34:5,13,17

35:3 95:22 117:17

117:17,20 121:5

188:12 265:20

279:14,17 281:10

321:17,22 329:20

330:1 371:21

377:11,14

facts 122:5

fact's 294:3

fail 380:1

failed 356:17

failing 46:19

failure 147:5,6,8

161:20,22 162:9

fair 108:10

fairly 17:7 48:4

60:2 68:13 73:10

139:12 159:9

177:17

fairness 127:13

faith 68:2

fall 28:19 94:2,15

101:17 129:6

falling 312:13

false 37:12,13

46:22 47:5,15,15

48:3,5,7,8,12,12

49:9,9,14,14

50:13,15,22,22

66:8,9

falsehood 121:1

familiar 132:7,10

140:21 159:10

families 115:16

358:20

family 129:17

284:11 304:19

fancy 226:4

FAO 119:20

far 82:22 83:1,4

108:4 162:19

182:9 253:13,15

256:21 394:8

403:7

farm 98:1 99:4,16

105:4 121:7

farmer 130:5

374:10 379:4

farmers 97:13,22

98:18 100:5 101:3

101:10,14 103:9

104:5,6,22 107:8

112:7,15,16

117:21 120:11

128:15,17 129:7

129:10,20 358:22

359:14 373:11

374:2

farmer's 100:15

farms 105:1 114:14

129:14,17 130:10

farther 75:4

fascinated 231:13

fascinating 118:3

fashion 126:4

199:19 200:3

213:4

fast 40:1 139:12

221:9 224:8 325:2

faster 194:15

267:22

father 115:2

favorable 120:1

favorite 392:19

393:1

Fawcett 96:21 99:8

105:11,12,13

124:13 125:19

126:7

FDA 7:7 239:3

243:9

fear 115:5

feasible 282:4

feature 43:19

features 260:6

February 139:11

357:3 366:12,12

366:15,20 369:10

369:10,11

federal 1:3 2:22 4:5

feed 396:19 397:9

feedback 23:7 24:4
202-234-4433

Neal R. Gross & Co., Inc.

Page 445

83:2,5 218:15,15

218:21 219:8,15

220:2 391:1,3

feeding 238:15

278:9,19

feeds 277:20

feel 30:6 340:22

405:1 411:14

414:15 417:19,20

418:3,5,10

feeling 30:12 55:6

70:4

feet 276:8

fellow 134:11 258:3

felt 343:21 401:15

401:20 414:10

418:19 419:9

female 140:13

142:9,15 143:8

145:21 152:9

157:17 185:13

186:13 195:5

196:1 212:12

218:12 229:8

242:1 249:3

384:19,22 389:11

396:20 398:20

401:22

females 140:9,15

140:19 150:21

195:7,7 210:18,20

211:2,9,9 213:2

215:19,22 223:17

224:7,13 227:4

228:15 249:9

386:6 388:16

397:8

Fenner 9:2

Fenner-Crisp 2:4

9:2 141:5 366:18

366:19 367:18,22

387:19,21 392:9

fertility 7:4 102:1

fetal 7:13 127:4

347:15

fetus 347:5

fewer 22:12 41:12

53:16 280:14

fibroadenomas

388:15

fickle 128:21

fidelity 204:8

field 64:13 101:9

101:10 102:2

124:15,21 128:20

fields 100:20

124:16

FIFRA 1:4,19 9:22

90:15 118:20

333:8 371:7

384:10

Fifteen 308:6

fifteen-minute

10:20

fifth 390:22

fight 115:3

figure 42:6 46:5

47:14 232:6

386:15 389:17,19

389:22 392:19

figured 293:21

figures 89:17 177:3

files 96:12

fill 64:11 69:21

filling 65:9

filter 104:14 382:9

final 4:19 20:9

317:1 323:8 345:9

358:17 360:8

371:13 410:18

finalized 328:18

342:5 388:7

finally 36:14 40:10

137:8 138:9 176:1

316:22 333:22

find 18:18 39:5

48:7 49:7,11,15

55:12 75:6 158:22

187:11 207:5,7

209:3 211:9 212:4

212:7 213:1 216:3

217:9 242:6 265:9

295:6 304:20

306:19 311:8

316:7 352:11

392:20 397:11

finding 39:10 66:11

314:8 319:1,2

364:3 406:11

findings 141:20

197:18 349:15

353:7 394:12

395:11

finds 356:9

fine 53:5 135:8

293:19,20 405:9

finish 116:20

133:22 172:17

175:7

finished 121:3

259:10,16 260:2

261:12 263:10

264:4,6,8 265:7,8

272:22 278:5

283:10 300:18

306:16 307:7

315:18 327:5

332:5 333:3

362:15,21 363:9

375:16 377:15

378:12 379:18

381:2

finite 291:3 295:21

firm 124:14

firms 373:10

first 6:19 10:9

16:17 34:6 37:19

38:4 39:8,19 41:2

51:17 52:5 54:11

72:21 74:16 81:22

94:17 96:19 100:3

105:7 106:13

108:21 115:8

134:4,5 135:13,17

136:6 140:1

141:14,15 156:8

158:20 184:12

186:21 187:7

190:4,5 194:12

206:6 208:2,11

239:7 258:14

261:9 267:5 270:5

271:5 272:18

282:9 303:4 305:7

305:17 306:20

324:11 334:7

361:8 372:4

383:20 400:5

417:10,17

Fischer 140:18

145:20 146:5,13

146:18,22 147:3,7

147:15,19 148:1,4

148:6 157:6

388:12 396:18,20

fish 151:12 155:18

156:2 349:17

350:2

fit 36:6 40:2,4,7,13

41:11 51:22 52:18

53:1,4,15 57:17

59:17 65:19 67:17

68:15,18,19

120:18 158:15

313:2,3 414:5

416:3,14,14

fits 59:6 133:16

422:5,6

fitting 39:6,18 43:8

59:8 404:4

fit's 69:2

five 13:4 15:10

41:20,21 56:18

62:8 185:1 187:8

189:16 190:7

212:1,4 214:4

229:14 271:16

275:3 285:20

301:21 319:9

321:12 324:22

415:8

five-day 87:10

fix 64:2

fixed 273:4

flashy 280:6

flat 181:21

flattening 154:22

flavor 37:15

flexible 51:7

flies 35:19

flipped 177:6

Florida 8:16 98:5

349:14

flowing 56:10

72:12 270:11

271:18 280:15

flows 205:5 272:14

396:13

fluctuate 358:2,4

fluorescent 196:15

fluoride 121:15

flush 329:19 330:2

336:22

focus 37:7,11 56:8

70:22 77:15 92:22

93:4 153:8 218:17

237:9 260:8 285:3

306:21 326:5

368:2 375:8

focused 56:7,10

98:8 151:4 266:19

284:8 316:1 328:9

381:10

focuses 336:16

focusing 78:10

92:16 156:22

189:2 264:22

375:15

fold 165:16 314:18

315:10

folks 32:1 69:16

70:11 76:13 81:11

127:21 169:2

388:2 389:22

follicles 147:11

follow 14:13 65:14

87:13 90:7 226:14

239:17 290:2

292:16 344:15

345:6 359:10

following 10:11

14:5 40:20 96:21

194:22 196:5

198:7,15 199:8,14

207:8,15 208:22
202-234-4433

Neal R. Gross & Co., Inc.

Page 446

211:6 214:11,12

215:20 217:2

228:15,16 232:4

313:3 326:17

354:16,18 368:14

follow-up 403:7

food 100:12 233:6

320:16,18,20

376:5,16 379:7

foodborne 320:14

foot 421:4

Foradori 138:3

forcing 53:3

forecasts 128:14

foreign 240:18

forewarn 234:4

forget 418:14

forgive 221:10

form 164:2 166:22

167:8 328:22

formally 406:21

format 265:9

formation 175:11

formed 97:15 98:4

former 139:19

344:6

forming 347:5

forms 166:3 175:2

formula 36:3 38:8

38:14

formulators 373:10

forth 49:17 50:2

288:16 310:5

312:21 315:16

341:19 418:13

fortunate 138:15

forward 96:2 124:5

195:14 257:14

258:11 341:10

376:22 400:11

410:20

Fos 199:5 229:21

230:3,6,7,16,20

found 22:8 29:1,11

43:6 53:14 125:21

132:1 155:18

156:4 198:2,7

205:17,20 207:12

225:14 243:22

292:20 315:5,11

333:2 336:20

349:18 353:5

356:9 357:5

358:11 359:4

413:10

foundation 65:4

98:21

four 13:20 16:1,2,9

19:3,15 38:8

41:20,21 52:14

59:14 62:4 78:14

140:12 165:13

174:4 198:19,21

199:1,8 201:5

212:21 213:5

263:6 271:15

273:6 275:15

279:21 323:19

324:4 331:5

332:22 333:6

392:4,15 393:2,6

fourteenth 208:14

fourth 40:10 104:1

293:11 390:17

four-day 15:15

18:21

four-node 40:18

41:12,15 51:21

52:5,16 53:9

four-page 373:12

FQPA 2:1

fraction 38:1,4,6

61:11

fractions 37:21

38:9

frame 102:19

119:13 178:8

334:8 335:15,19

frames 27:11

335:10

framework 135:19

141:1,16 142:19

148:18 150:19

335:22 400:19

407:10

France 342:9

Francisco 7:16

Frankenberry 3:11

11:12,21 24:20

25:14,17 26:9,15

26:19 28:16,22

29:8 279:6

Frankenberry's

72:7

frankly 23:12

63:15 114:8 116:1

402:4 403:5

free 340:22

free-standing

413:6

frequencies 78:22

271:15 280:11,17

frequency 1:8 5:9

11:14 71:10 73:7

75:2 88:13 91:17

94:18 95:18

201:16 214:7,13

258:17 259:15

260:8 262:2 265:4

266:20 271:1

272:6,11 273:16

274:13 280:10,22

301:16 325:18

326:6 334:2

frequent 16:16,16

17:16 21:4 72:4

76:6 78:2 80:17

82:5 282:6 288:6

316:6

frequently 77:20

79:4 333:2 337:20

337:22

freshwater 156:2

349:17 350:2

friendliness 372:16

friends 304:18

Frigid 302:19

frogs 348:22 349:1

front 94:3 105:8

118:9 139:14

375:12,18 377:4

383:20

fronts 95:1 119:2

frustrated 420:12

frustrating 420:2

fuel 101:15

Fukamachi 158:6

401:3,10 414:14

full 82:12 188:22

265:6 271:18

279:10 283:13

344:21 345:17

351:3

fully 82:11 160:7

fun 55:5 69:6,11

function 42:6,7

179:4 188:14,14

196:11 202:12,19

238:22 347:7

356:7 385:20

functional 151:14

155:18 238:18

239:4

functioning 170:7

functions 256:4

fundamental

334:20 399:1

fundamentally

394:14

funded 138:17

FUNGICIDE 1:3

fungicides 88:8

funny 41:14

further 26:14

31:17 75:7 114:2

117:15 154:19

159:20 180:8

314:13 385:8

Furthermore 151:9

151:15 387:4

future 127:17

354:20,20 400:12

F.L 2:15

G

G 1:17,20

gain 235:1

gallon 277:14,16

gallons 277:15

gap 314:8,14,19

gardens 102:7

garnish 233:21

gas 102:6

gavage 195:11

203:6 207:11

211:20

gavages 199:1

gavaging 187:4

GCMS 330:16

gears 202:1 243:5

gene 172:8

general 29:13,21

70:9,11 78:19

85:14 116:11

225:1 244:16,17

255:22 333:6

379:3 394:11

395:20 396:9

418:17 423:19

generalization

286:8,10

generalized 208:20

generally 17:16

21:14 242:11

333:2 345:22

351:14 419:3

generate 32:18

73:2 76:18

generated 195:16

279:6 301:22

generation 202:6

215:10

generator 137:19

200:16

generously 119:7

genotoxic 149:1,8

149:18 150:7

389:12 391:9

genotoxicity

159:22 389:15

399:10

Geological 8:9,13

Georgetown

210:12

Georgia 6:16
202-234-4433

Neal R. Gross & Co., Inc.

Page 447

GERALD 1:22

germane 293:4

Germany 141:4

germinate 103:6

Gerry 6:11

gestation 246:21

getting 57:4 65:18

67:19 68:1 78:15

81:8,8 110:3

233:5 282:18

289:3 300:9 301:5

306:2 318:5 425:5

Gilliom 2:5 8:7,8,8

27:1,2 28:5 63:5,6

86:4,5 90:6 125:3

125:4 261:19

265:11 285:5,6

286:7,20 287:7,12

287:21 288:13,21

289:3,8,18,22

364:19,20 367:1,7

GIS 352:5

give 4:17 10:4,7

11:12 65:4,5 79:5

80:15 84:10 85:4

87:7 89:7 112:10

118:21 124:13

126:8 128:1

134:21 149:6

178:17 189:6

205:11,18 235:17

243:2 246:17

247:14,16 248:17

255:10 271:7

273:15 308:15

312:7,11,15

313:14 314:6

318:22 332:14

340:21 387:21

388:2 408:15

412:3

given 38:21 39:1

43:20 44:6 46:10

46:19 78:17 80:22

87:9,12 119:13,22

125:5 158:12

178:7 190:7 192:8

193:11,12 257:7,9

282:6 294:13

338:1 340:19

378:9 420:22

421:11

gives 75:8 85:5

140:5 178:12

244:2 251:2,6

266:18 282:20

307:3 331:2

379:21 380:2

giving 27:14 191:4

258:12 267:8

305:6 311:7 349:4

glad 248:21

gland 157:8 390:13

391:6 401:5

402:18 403:1

global 289:13

globally 128:10,11

glucocorticoid

240:6 255:2,3

glucocorticoids

205:14 241:2

244:9,17

glutamate 170:17

glutathione 166:22

176:12,15 177:4

glyphosate 125:21

GMO 87:3

GM1 245:11

GNR 392:15

GnRH 137:19

184:16 185:10

195:16,20 196:2,3

196:4,8,13,14

197:1,5,9,13,14

197:15,19,21,21

198:1,3,8,11,12

198:14 199:4,10

199:12,18,20

200:2,2,16,22

201:1,8,11,15,19

220:14 222:8

230:16,21 384:16

390:11,12,14

GNRP 153:19

go 5:12 11:10 16:22

18:5 19:7 22:10

24:13,19 29:1

32:15 41:17 44:18

69:18 72:4 73:22

83:1,4,7 88:20

89:10 93:15 94:1

96:13 110:11

112:8 116:7

125:12 127:14

128:1,19 130:18

130:18 141:15

151:22 157:15

159:8 186:10

194:15 225:7

232:6 238:13,21

249:5,18 259:1,13

267:13 300:5

318:13 320:7

326:12 332:8

348:12 367:16

377:5 389:21

418:6 419:8

420:15 425:4

goal 309:16

goals 94:15

goes 82:8 89:2

129:9 368:7

going 5:12 10:3,8

11:6,10,12,16,20

14:17 17:21 27:20

30:16,16 31:17,21

32:11 35:22 36:2

36:5,7,17 37:2,7

39:19 41:14 43:1

44:8,18,18 45:2,2

45:9,21 52:7,9,18

53:7 57:1,11 62:7

64:13 67:2,4

69:12,13,15 70:4

70:12 71:1 72:16

72:18,20 73:10,12

73:18 75:3,6,11

75:16,22 76:8,10

76:15 78:13 80:11

80:14,16 81:15,21

84:12 86:19 87:21

88:2 94:6,11,12

96:18,21 99:5

113:11 115:14

126:22 132:14

133:20 134:2,17

134:19 135:3

137:8 138:5,10

145:5 156:21

160:5 161:2

178:10 180:10

182:5,7 183:12

188:4 189:3,4,6

189:18 216:21

218:5 226:8 227:8

230:22 231:15

233:11,16 234:5

237:8 255:7,12,14

256:14,19 258:13

258:14 259:6

260:4,19,20 262:9

262:12 264:16

271:2,7 274:11

277:5 278:4,13

283:20 292:3

293:12 294:2,13

294:22 297:10

298:7 299:18

300:11,12 302:20

305:9,9 306:13

311:19 317:18

320:21,22 323:4,5

323:9,17 332:14

338:22 339:12

340:16 341:19

343:2 345:5 355:8

368:8,10,18

376:22 383:8,11

383:11,13,15,19

383:21,22 384:1,3

384:9 405:3,8

408:1,20 409:2,5

414:16 416:2,3,4

418:8 421:7,8

423:17,21 425:8

425:12,13

gonadal 8:3 350:7

gonadotropin

384:15

gonadotropin-rel...

390:9

gonads 387:6

good 5:4 21:12 24:2

25:4 40:4 47:8

64:6 65:19 66:1

69:2 73:20 79:19

80:5 90:22 92:4

92:13 102:8 105:9

133:15,21 158:2

164:9 167:20

171:18 182:21

193:18 248:13

269:20 284:4

292:4 300:1

308:10 317:4

324:14,15 330:20

333:14 369:18

371:10 372:22

373:15 384:7

404:14 408:8

413:11 423:17

gotten 19:19 24:21

337:13

govern 370:2

government 119:18

GPS 130:1

graciously 305:12

grades 125:14

Graduated 99:18

grain 97:7,9,10

99:14

grand 289:8

grandchild 115:8

grandkids 114:13

grant 237:18

granular 328:16

graph 15:7 16:17

18:11 20:9 240:10

285:7 286:8

337:12

graphed 240:11

graphics 286:11

graphs 239:14

241:3

grass 108:12
202-234-4433

Neal R. Gross & Co., Inc.

Page 448

gray 365:7

great 72:14 172:6

284:12 290:6

314:9 315:22

317:5 419:9

greater 101:1

120:7 212:19

259:16 267:20

273:18 391:11

396:10 405:4

greatest 344:5

greatly 300:21

323:14 372:15

green 163:16

196:15 205:10

greenhouse 102:6

Greenwood 2:6 9:6

9:6 177:2,2

grew 99:16

ground 100:15

101:21 284:6

360:12

groundwater 35:22

120:7 131:15,17

132:2,4 284:16

285:1 327:16

groundwaters

331:18

group 15:3 19:1,1,5

19:22 20:1 32:5

45:6 86:22 94:2

98:8 133:18

139:14 145:12

187:13 189:7,12

209:17 218:13

222:1 226:17

235:2 242:3 276:4

302:9 342:16,20

343:13,19,21

344:10 382:18

412:11 420:19

groupies 370:16

grouping 29:15

groups 10:12 98:1

152:20 209:15

234:14 245:10,13

251:3 396:3

grower 97:16

growers 97:7 99:7

99:14,14 103:1

117:2,3 268:8

329:3 381:16

growing 111:8

264:3 266:6

growth 233:5

guess 27:9 28:6

38:20 58:20 59:10

64:17 82:18 89:6

90:6 96:4 109:9

126:16 134:4

176:10 184:4

224:22 234:1

287:21 324:10

365:19 383:16

400:21 402:1

413:20 414:3

423:13 424:21

guessing 68:16

122:18

guidance 89:7

239:3 243:8 295:5

365:10

guideline 408:11

424:18

guidelines 141:11

151:22 342:4,15

370:2,6,6,9

399:21 408:15,22

411:22

gut 182:1 349:3

358:12

guys 24:19 25:12

27:3,12 73:1 87:7

156:4 286:21

299:22 341:10

357:2

H

H 2:2,9 153:19

habitat 101:13

habituate 216:1

223:17 224:8,12

229:8

habituated 208:1

habituation 213:3

224:6 229:7

241:22

HAL 259:12

308:21,21

half 20:19,20 46:14

46:15 264:14

316:5 353:21

half-a-million

307:3

half-hour 324:5

hall 24:11 369:1

hallmarks 143:15

Hamilton 1:16,16

hand 39:2 63:20

64:8 66:2 146:5

238:10 343:6

Handa 137:16,22

161:5,15 162:2,19

188:7 192:22

193:17,18,19

219:2,4,10,13,18

220:9,12,16,20

221:15,17,22

222:3,6,14,17,21

223:2,11,22 225:6

226:19 227:1,16

227:22 228:6,13

229:11 230:5,10

231:5,16,21

232:16,19 233:8

233:18 234:15

236:9 254:15

304:5 395:11

Handa's 151:2

handle 82:5

handlers 114:12

handling 252:8

handout 271:9

274:6 278:7

handouts 184:17

339:9,20

hands 184:22

handy 341:3

Hanover 141:4

happen 71:4 73:3

81:21 146:14

166:16 176:3

265:22 397:21

409:6

happened 231:17

happening 59:7

162:15 168:2

175:17 293:22

420:19

happens 49:6,10

95:7 106:14

163:21 208:9

311:13 363:4

happy 25:9 127:22

231:22 294:5

325:11

hard 343:4

harder 40:22

210:19

harm 113:21

116:11 127:8

346:20 356:6

harmful 361:21

harmonized 94:8

hasten 280:1

Hawaii 98:6

Hayton 2:7 9:14,14

304:15

hazard 82:7,15

95:2,4,20 149:10

hazardous 345:21

346:9

hazards 360:11

HCTH 217:18,18

head 6:12,17 128:3

419:4

heading 101:17

heads 232:5

health 1:6 4:7 5:6

7:2 62:5 91:8

94:11,21 120:4

127:2,10,11 131:6

155:20 163:1

170:7 284:22

304:17 308:19

309:2 312:9

322:12 325:20

334:9,18 335:15

339:17 342:12

343:4,9,10 344:5

344:14 348:17

349:19 353:5,8

356:19 375:20

384:13 393:12

397:15 411:8

hear 25:5 28:9

84:17 120:14

188:6 327:6

416:11

heard 61:21 72:7

79:10,21 91:3

95:1 106:21 112:4

121:7 133:14

142:7 154:10

261:20 275:14

283:22 308:22

315:14,15 324:7

325:10 341:7

350:19 379:15

400:17 409:12,15

409:16

hearing 77:3

312:18 340:8

363:17 390:15

409:12

heart 182:13

heaven 304:2

heavily 65:17 104:7

275:21

heeded 345:20

Heeringa 1:17,20

9:18,18 32:7,8

53:18,19 55:16

83:9,10 84:16

85:1 266:21 301:2

301:3 302:5,13

Heidelberg 12:20

39:13 57:10 63:14

75:12 261:20

275:13 276:2

279:20 290:17

292:19

height 190:16

191:5

held 396:15
202-234-4433

Neal R. Gross & Co., Inc.

Page 449

help 87:7 130:2

181:2 251:11

280:20 294:1

368:7 408:9

helpful 83:14

155:11 274:9

408:17,21

helping 34:18

72:17 93:10

helps 73:16 100:17

Hendley 258:1,2

284:4 285:14

286:9 287:4,8,20

288:9,18 289:1,7

289:14,19 292:5

294:4 298:18

302:2 304:6

315:14 316:18

Herbert 2:12 8:17

herbicide 87:4

103:13 106:12,20

107:3,4,9,11

108:5 109:14,15

111:18,19 120:13

124:11,12,18,21

129:9 130:11

132:1

herbicides 116:10

128:8,13 129:21

327:14 351:14,20

Herschel 287:9

Hey 91:2

hierarchal 55:4

high 13:5 46:15

47:18,19 48:2,3

50:14,16 59:22

71:5 82:4 84:1

102:18 125:16

143:5,9 145:13,14

148:5 159:14

162:14 163:6

165:19 169:22

171:12 176:3

189:21 197:11

216:20 227:3

233:7 244:14

248:2 253:4,22

257:19 259:7,15

260:14 266:20

268:12,16,21

270:11 278:7

282:21,22 283:5

286:4 336:16

337:19,19 352:12

363:2,14,20

401:17 402:5,6,8

415:18,20,21

higher 20:8,20

22:11,17 24:1

44:6 48:5 75:5

109:3,12 111:11

113:8 119:8

145:22 169:8

182:2 186:4 265:4

266:8,8 269:21

270:9,13,19 286:4

304:2 396:16

highest 21:22 25:2

47:22 206:14

209:9 211:11

225:17 227:4

288:6 316:12

332:19 353:15

357:18 358:3

highlighted 403:3

highlighting

402:21

highly 165:21

Hill 135:18

hinted 83:21

hints 171:7

histocompatibility

240:16

historical 101:4

267:21 282:3

337:7,15

historically 56:21

history 57:9 71:6

92:18 263:18

264:19 268:1

269:21 272:12

274:14 281:4

286:3

hit 166:11,15

230:22 304:14

hits 69:15 106:13

166:17

hitting 24:12

hold 333:13

holds 410:22

holes 69:22

Holladay 6:15,15

155:15,16 406:17

406:18

HOLLADY 2:8

home 5:15 99:21

216:19,22

homeowners

358:19 360:16,20

homogenate

179:14

homogenous

300:10

honest 78:7 83:5

210:17

honestly 82:21

hope 94:7 113:9

155:11 260:22

318:21 400:1,4

hopefully 69:21

84:16 175:5

194:17 233:11

hoping 69:18

horizon 128:19

hormonal 149:3

195:5,9 214:16,20

345:12

hormone 199:15,17

202:5 208:6,6

214:2 216:9 217:6

218:2 221:1 347:8

350:2 384:16,18

390:9 392:12

hormones 8:4

194:19 203:9,12

203:13 204:4,10

204:15,17,18,19

205:9,12 207:6

210:6 402:22

Horton 2:9 7:9,9

126:20,21 218:9

218:10,11 219:3,9

219:12,17,22

220:11,15,19

221:2 283:18,19

298:4 408:16,19

408:20 418:16,17

Horton's 298:2

host 24:17

Hotel 1:17

hour 10:19 165:12

239:20

hours 10:17,18

165:13,17,18

169:20,21 211:14

212:21 228:16

230:10 239:17

244:1 248:11

250:18 251:1

276:6 383:16

hour-and 10:19

house 403:14 416:3

422:6

housed 396:11

HPA 95:6,10

138:13 151:4

178:9 195:2 211:4

213:7 217:4,17

218:19 233:13

238:12 387:1

391:21 392:22

393:6,21 394:4,13

395:4 400:20

HPG 151:4 194:22

196:9 213:9

216:15,21 217:1,3

220:8 233:13

238:16 386:8,12

387:3 389:8 394:4

394:14 395:8

400:20

HPG/HPA 404:4

Huffington 116:3

huge 111:5 280:18

human 1:6 4:7 5:6

7:22 91:8 94:20

127:9 136:21

146:18,22 147:10

147:15,19 148:1,9

148:14 152:1,2

155:5 181:17

182:16 236:3

283:11 341:18

342:1 343:3

344:19,22 349:3

351:3 356:19

360:9 375:20

377:9 378:11

384:13 387:17

388:8 389:4

393:12 394:17

398:12,13 399:2

404:22 405:1

422:16

humans 127:8

136:16 148:11,13

152:15,20 155:1,8

160:17,21 163:22

177:16 343:16,20

344:1 345:2 349:2

376:9 378:13

385:5,15,17,20

386:3 398:16

399:17 407:9

408:13 412:7,17

424:7

hundredfold

310:15

hundreds 49:21

hybridization

198:6

hydrogen 170:19

hydrology 270:15

283:22

hydrolyzed 180:6

hydroxyatrazine

166:9

hydroxymetaboli...

167:19

hydroxytriazine

164:4

hypothalami 201:6

201:9,10,12

hypothalamic

147:5,8 155:7
202-234-4433

Neal R. Gross & Co., Inc.

Page 450

161:15 162:3,8

194:20 196:10

200:17 201:2

213:17 217:16

385:19 390:6,7

392:5

hypothalamic-pit...

386:22

hypothalamic-pit...

194:8 202:4

213:12 386:8

hypothalamus

148:12 181:4

199:21 384:17

390:5 392:16

393:19

hypotheses 344:13

344:15

hypothesis 107:16

188:5,17 357:20

387:4 396:22

417:6

hypothesized

197:19 198:11

389:3

hypothesizing

395:17

H295R 164:18

165:11 172:19

173:17 180:11

I

IARC 152:21 342:7

342:14,16,17

343:5,12 344:6,7

407:1,4,7,13

412:11

IARC's 407:1

412:14

IBMX 173:4,8

179:18

ICCVAM 370:16

370:17

icon 263:13 264:21

ICV 222:11

IC50 173:5

IC50s 173:11

Idaho 8:2

idea 10:7 49:12

75:8,18 80:22

97:9 225:17 227:2

230:6 231:18

233:9 243:7

245:16 247:17

306:3 307:21

308:8 312:12

313:12 318:18

361:2

ideal 82:16

identical 46:8

165:5 187:15

189:8 215:9

identified 103:21

168:3 239:3

356:21 363:13

386:4 391:19

identify 174:3

200:8 204:14

identifying 172:18

217:13 425:2

ideology 122:7

ignorance 342:11

ignore 418:11

ignored 364:6

Illinois 7:11

illustrate 74:15

75:2

illustrated 239:14

illustrates 300:22

imagine 33:15

54:15 98:12

immediately

198:22 205:8

247:4

immune 138:21

156:1 236:8

238:17 240:21

246:6,13 248:7,9

248:13 346:13

348:1,19 355:7,14

356:4,6

immunization

196:2

immuno 254:2

immunocytoche...

196:18 198:7

immunological

202:11,19 237:6

238:6 239:4 240:5

242:12,13 243:15

243:18 245:9

246:10 247:4,8,20

248:6,17 253:16

254:3

immunoneutralize

217:18

immunosuppress...

407:22

immunosuppress...

408:2

immunotox 235:18

235:21

immunotoxicity

156:18 239:6

242:18

immunotoxicolo...

139:3

immunotoxicolo...

6:19

immunotoxicology

6:22

impact 213:11

249:11 256:4

331:8 345:14

374:8 387:6

impacted 394:5

impacting 218:20

impacts 100:12

101:1 220:7 236:3

337:1

impair 176:13

impaired 348:22

350:7

impairing 176:16

355:14

impairs 348:19

implantation

190:10

implement 92:1

implemented 14:4

implementing

376:15

implicated 334:18

implication 27:11

27:13,19 329:12

implications 115:2

115:11 136:1

275:20 330:6

334:3 405:13

importance 200:2

374:9 375:14

402:21

important 18:9

27:21 28:2 33:5

98:14,15 105:3

106:1,2,3 107:1,4

114:22 121:18

126:14,15 140:4

185:5 200:17

242:17 259:8

276:1 278:20

280:20 325:5

328:3 334:9

346:21,22 356:18

357:1 374:6,17

376:11 383:17

406:2 407:5

410:10 424:6,20

importantly 138:5

141:9 377:3

impossible 103:14

improper 347:4

improve 17:17 82:6

100:8 130:15

171:19 372:15

improved 292:13

improvement

382:16

improvements

100:10 268:9

improves 101:13

101:22 106:18

improving 85:13

391:14

inactive 166:19

168:4 175:12

inadequate 343:16

incapacitate 394:3

inception 192:12

incidence 143:9,14

145:22 146:3

152:8 384:22

401:17 402:5

414:17 422:8

incidences 396:2

incident 353:11

354:1,10 388:14

incidentally 139:9

include 27:22

127:16 370:7

403:10

included 13:5

63:17 127:20

351:20 358:22

365:1,10 388:20

399:3

includes 97:21

including 101:18

104:8,12,20

116:13 119:19

120:4 121:5

152:21 160:6

182:11 279:7

344:22 345:12

347:22 350:3,13

350:15 351:14

356:12 371:22

382:2,10

inconsistencies

225:2

inconsistent 229:16

342:3

incorporate 53:22

55:10 65:6

incorporated

113:17 114:7

406:11

incorporates

410:19

incorporation

104:16

incorrect 344:17

increase 38:2,5

39:21 40:1,9 41:2

41:3 45:18 48:7
202-234-4433

Neal R. Gross & Co., Inc.

Page 451

112:1 141:17

142:1 143:14

144:20 145:15

146:2,11 148:11

160:16,22 162:17

165:16 167:14,18

167:22 168:1

171:13 172:14

175:21 195:9

207:9,19 211:10

211:20 212:5

214:14 248:3

330:7 331:5 353:9

355:14,21 356:2

388:10 391:6

392:6 401:21

415:7 416:22

increased 47:18

59:4 103:16

144:21 148:15

152:7 153:17,20

154:5,7 164:21

192:16 201:21

347:22 356:3

357:15 384:21,21

388:14 391:5

392:11,13

increases 162:6

200:8 207:14

208:12 214:7

215:15,19 216:7

228:16 303:18

352:19 394:22,22

increasing 102:9

146:15 407:17

415:16 424:11

independent

350:11

independently

218:19 220:6

348:14 349:16

Indiana 357:9

indicate 21:3 202:3

258:16 386:21

indicated 149:17

155:17 157:7

205:10 209:2

238:19 241:9

243:8,13 247:5

259:10 260:11

413:16

indicates 235:11

236:15 386:17

421:16

indicating 172:7

197:10

indication 244:2

317:4

indicator 395:7

indirectly 390:4

393:21

indistinguishable

272:5

individual 22:4

23:1,14 144:1

273:11 284:11

289:13 306:5,22

340:4 351:6

373:22

individually 296:21

297:6

individuals 97:22

299:5 407:22

induce 163:6

165:21 172:2

174:17,18,20

175:14 198:18

215:4 252:10

385:21 402:18

induced 138:20

190:9 195:5

214:20 215:1

inducer 402:20

induces 136:7

154:12 253:22

inducing 167:10

390:13

induction 151:10

159:10 165:22

166:7 168:8

173:21 230:16

indulgence 339:11

369:6

industries 104:6

industry 374:2,7

379:3 380:22

ineffective 173:14

infant 349:3

infants 116:12

310:4

infection 348:1

356:3

inference 297:12

inferences 295:15

inferred 116:15

infertility 347:13

influence 203:7

213:9 220:17,18

influenced 104:3

influences 72:9

influencing 218:22

influential 370:10

370:11

inform 182:9

information 25:11

30:7 54:1 105:22

177:22 186:6

188:20 192:18

239:22 246:11,18

248:18 258:12

262:1 266:16,22

267:1 298:14

299:17 306:15

311:21 318:13

323:15 328:4

369:8,14,15 370:8

370:10,12 377:18

383:6 386:16

398:3 413:6 414:6

417:12,22,22

418:11 419:11

informative 253:3

253:9,9

informed 93:8

ingredient 106:12

ingredients 108:13

109:14 111:18

inhalation 359:7

396:11,18 397:5,6

inhibit 197:12

216:20 219:19

inhibited 172:4

inhibiting 171:19

inhibition 145:9,13

173:3 175:21

199:12 215:12

232:3

inhibitor 170:17

173:5 255:3

394:18

inhibitors 171:17

173:14

inhibitory 172:21

inhibits 142:9

215:2

initial 39:8,10

40:14 94:7 207:8

398:18 399:15

initially 49:13

214:1

initiated 125:11

136:16 139:10

424:13

initiates 199:21

initiating 403:4

initiation 160:6

408:7 413:14

414:1 425:3

initiator 399:8

424:5

initiators 424:7

injection 208:11

innovating 381:12

input 365:4 374:22

inquire 135:1

INSECTICIDE 1:3

insecticides 351:21

insights 93:15

insist 113:22

insoluble 168:20

install 380:6

installed 328:15

instance 17:20

122:12

instances 208:21

Institute 7:2 9:20

insufficient 390:20

insults 168:14

170:22

intake 29:14 54:4

integral 32:19

integrate 85:3

integrated 84:20

85:16 385:10

integrating 60:11

60:12,19

integrity 100:6

intend 178:5

intense 23:22 28:21

80:15

intensity 28:15

308:2 329:21

382:3

intensive 75:13,18

83:22 101:4 141:8

306:18

intent 188:11

377:17

intentionally

378:13

inter 371:1

interact 88:6 226:2

interacting 171:8

interaction 7:18

170:15 179:7

185:17

interactions 7:13

intercept 37:20

39:20 45:12 46:7

58:5

interest 9:4 44:2,17

45:20 207:17

245:1 257:13

265:1 266:20

272:18 310:12

379:3

interested 29:18

57:17 58:19 86:9

180:17 187:8

189:12

interesting 106:11

143:19 160:2

235:12 240:14

264:20 299:16

357:22 402:12
202-234-4433

Neal R. Gross & Co., Inc.

Page 452

interestingly 53:14

208:15 269:14

interests 258:4

interfere 172:12

346:12

intermediate 283:4

313:6

intermittent

162:12,14

intermittently

294:15 362:2

intermixed 365:3

internal 204:13

international

140:22 152:20

269:4 325:6 342:6

370:14

interpolate 282:5

interpolated 13:18

14:6 26:2 31:11

74:17 273:3

291:10 321:16

interpolating 65:11

interpolation 12:16

63:8 290:13,14,18

290:22 305:14,19

306:2 307:2

interpolations

290:4

interpret 73:7,8

74:10 91:14

159:13 160:7

413:12

interpretation

93:12 134:16

158:11 416:21

interpreting

169:16 395:22

interrelated 265:19

interrelationships

394:13

interrupted 391:2

interval 61:1 78:1

250:17 291:2

351:22

intervals 14:17

15:15,16,16 18:21

44:12,14 45:6

60:22 76:2 271:2

273:5,5,6,6 274:6

351:8 354:8

interview 54:12,17

intra 371:1

intracellularly

180:11

intraperitoneally

212:16

intrigued 176:9

introduce 5:13

212:10

introducing 11:8

194:14

introduction

109:13 111:17

255:13 267:9

Introductions 3:6

introductory 183:7

intuitive 84:2

inventory 102:13

inverse 62:9

invested 104:7

investigate 136:18

257:21

investigated 220:6

238:7 255:21

investigating 27:12

257:16

investigations

139:5 180:21

239:9 247:19

investigators 243:9

investment 118:5

invite 383:19

invited 134:14

inviting 119:16

involve 244:13

involved 22:12

84:14 86:2 105:18

107:14 114:5

117:8 119:13

123:18 129:2

194:20 217:16

224:9 240:18

246:8 257:1

331:13

involvement 395:4

involving 365:6

in-depth 388:18

in-dwelling 254:14

Iowa 126:8,8

IRED 327:22 388:7

ironically 64:4

irrelevant 416:14

irreparable 346:20

irreversible 347:21

isolate 86:16

isolated 296:16

issue 32:12 86:8

125:19 158:5

159:3 181:15

325:5 327:14

338:21 341:16

375:13 385:12

386:10,16 404:11

405:4 408:6,7

417:12 423:11

issues 9:1 78:16

88:3 92:16,20

93:1 113:20

114:11 115:19

116:21 129:3,11

149:2 158:10

259:2 325:21,21

326:8 328:9 329:1

329:2 335:16

371:15 374:14,17

374:20 375:6,11

375:17 379:2

404:1

item 274:20

iterations 354:20

I.D 149:10

i.e 143:12 144:9

385:3

J

J 2:5,16,17,18

383:5

James 136:3

Jan 6:6

JANICE 1:22

January 341:20

Jason 355:10

Jay 368:14 373:3,7

Jean 2:15 6:20

JEG3 172:20

173:18

Jennifer 339:14

341:3 365:14,16

Jere 97:5 99:11,12

112:22

Jerry's 226:15

Jim 135:17 162:22

job 40:20 51:7

57:22 58:1 129:8

129:21 269:20

277:2 300:1

303:22 304:22

341:20 364:16

jobs 121:18 171:10

Joe 4:4 134:1 324:7

408:15

John 1:21 5:18,19

153:9

join 408:6

Joseph 2:22 236:9

journal 352:22

365:22

judged 285:3

judgment 369:5

July 116:8 139:8

188:22 246:1

248:1 266:6

270:18 357:17

jump 356:16

404:10 417:1

Jumping 348:8

June 187:22

jurisdictions 299:8

K

K 45:17

Kannan 2:10 9:9

Kansas 97:5,6,7,12

98:5

keep 23:5 32:21

33:5 71:12 169:15

176:17 183:12

262:15 298:17

323:5 364:11

405:2,5 406:3

410:14

keeping 167:1

Ken 5:3,14 30:5

57:14 65:8 67:9

405:11 416:16

Kenneth 1:18,21

2:3

Kentucky 99:7,13

99:14,15,16,18

kept 206:9 234:7,7

393:17

Kevin 2:13 7:20

128:6

key 51:6 66:14

95:12 133:17

142:6,18 145:17

152:10 216:18

275:14 389:18

390:1,3,7,12,17

390:22 391:4

392:1,4,10,12,15

392:17,21 393:2,4

393:5

keynote 260:18

kg 185:2 189:17,20

190:15,22 192:3

235:14 255:17,18

kick 182:7

kids 114:13 315:20

kill 121:19

killer 238:22

242:16

kills 368:19

kilogram 195:13

201:5,14 202:16

206:15,18 207:14

207:16 209:10

211:8 212:3

213:22 239:16

241:5,7 242:4,10

253:15 309:6

310:8 311:10

312:21 313:7,15

313:18 314:2
202-234-4433

Neal R. Gross & Co., Inc.

Page 453

320:10,19 402:3

402:13 415:9,10

kilograms 322:21

322:22

kilometer 402:7

414:21

kind 10:4 11:4

15:22 19:20 24:11

26:20 27:5,10

28:13 30:2 36:4

36:18,22 40:7,13

42:12 46:19 48:18

50:5 64:2 65:9,10

65:13,16 69:17

86:15 89:8 90:20

129:18 140:2

157:1 185:22

230:19 233:5

234:12,13 235:7

235:17 254:16

257:19 277:14

280:21 286:8

289:5 295:9 296:2

307:11 308:8,12

314:15 318:8

365:7,8 378:4

379:12 380:1

381:9 382:17

402:8 403:5 405:5

406:18 415:4,13

418:11 420:4

423:18,20 425:4

kinds 48:22,22

89:12 180:19

257:21 358:6

375:3 378:8 379:2

381:19 416:20

420:1 424:7

kinetics 178:3

King's 7:21

Kligerman 149:21

knew 191:12 337:4

knobs 88:12

knock 172:21

174:10

Knoth 96:20 99:6

99:10,12

know 19:21 24:12

25:11 28:2,7

29:10,19 30:15

31:5,9,17 43:3

53:22 55:18 57:1

59:14 61:3,11

62:6 63:2 68:19

69:17,20 70:1

71:9 72:13 73:21

74:11 75:2 77:2

80:1,13,13,16

83:6,21 84:6,11

85:13 88:15 91:8

93:7,9 98:22 99:1

103:15 113:14

114:18 115:10

116:14 117:4

120:17 121:12

125:8,9,12 126:2

127:15 128:2,21

128:22 129:5,7,9

129:11,13,18

130:17 131:3

132:5,6 136:9

141:20 158:10,22

161:18 162:1,9,11

162:19 167:2

177:1,21 179:10

180:14 181:2,7,9

183:2 213:9

215:14 216:21

219:14 221:13

222:3,10,12 229:7

229:9,14 230:5

231:17 256:15

257:2,4 262:4

266:7 267:3

289:19 292:4,6

293:14 294:3

295:5,21 298:9

299:19,20,20

300:11 303:20

304:9,9,10 308:3

316:13 317:5,12

318:3 324:2

326:20 332:17

339:11,12 341:6

342:22 343:2

346:8 347:7 348:6

348:14 357:19

359:4,13,17,22

360:3 364:5

370:16 379:22

380:9,15 382:4,11

382:14 401:15

403:7,15,17,18,22

404:15 405:14

408:21 410:10

411:10 413:21

414:18 415:3,4,12

415:14,21 416:1,8

416:12 417:1,19

417:20 418:2,4,7

418:13 420:2,7

421:16 422:19

423:7,13 424:5,16

425:12

knowledge 54:18

65:10 93:4 272:13

280:15,16

known 42:10 68:6

142:12 151:1

170:16,22 196:22

292:1 327:4

344:19

known/unknown

27:5

knows 90:3

kriging-type 55:13

Krishnan 2:10 9:9

9:9 31:2,3 319:6,7

319:17 320:3

321:3 322:7,14

392:7 398:7,8

405:7,8 410:5,7,8

411:20,21 421:6,7

423:1 424:21

Krishnan's 416:10

K.H 2:12

k0 57:21

L

L 2:7

lab 190:1 194:2

398:9

label 104:9 121:5

125:14 130:21

359:10

labeled 89:18

106:16 114:6

130:19 244:13

labels 114:8

labor 83:17 84:6

101:14

laboratories

370:21

laboratory 151:3

189:13 347:11,17

371:2,10 387:14

laboratory's

151:16

labs 218:3

lack 147:4,4 224:15

327:21 355:4

412:15,17 415:19

415:19 421:12

lacking 123:21

lactation 187:1

191:2

lactational 256:19

lactationally 187:4

laid 420:4,6

lakes 104:2 261:11

land 57:4 100:8

102:12 105:20

114:15

laptop 96:12

large 21:1 107:5,15

109:18 111:5

134:6 149:6

258:22 268:20

287:2 290:16

291:14 308:1

316:17 317:3

372:10 411:8,13

411:14,15

largely 104:2 208:7

268:8

larger 29:6,22

169:8 200:14

311:17 314:20

346:2

largest 103:19

108:4

larvae 355:17,22

larval 356:2

lastly 212:9 216:10

382:7

latch 406:4

late 187:21 274:19

383:16

latency 143:12

424:12

latest 151:20

Laughter 324:18

Laura 96:20 99:6,6

99:9,12 105:12

law 377:22

lawn 358:21 360:14

360:21

lawns 102:7 361:1

Laws 192:5

layered 90:14

lays 398:17

LCMS 204:4 209:2

217:11 218:6

226:10 227:15

lead 83:2 134:5

136:13 360:18

387:20 416:2

leading 103:22

104:1 152:11

258:8 386:19

390:5

leads 184:21 346:4

384:15,20

learn 92:21 339:3

354:20

learned 34:14

304:8 339:4

leave 51:14 256:10

309:18 316:22

317:18 318:10

325:21 405:8

leaving 101:8

LeBLANC 1:22

6:11,11 131:2,5

131:12,16,22
202-234-4433

Neal R. Gross & Co., Inc.

Page 454

132:10,13,20

133:3 223:13,14

224:14 252:22

253:1 407:15,16

led 263:6 270:21

273:13 394:7

Lee 2:12 8:17,17

25:20,21 26:11,17

51:17,18 52:2

55:7,8 68:4,5 74:3

85:7

left 14:16 22:20

24:12 46:12,14

51:10 101:20

143:13 162:1

195:8 225:13

241:5 324:2 384:4

left-hand 267:11

legislations 300:8

legitimate 116:18

lending 114:3

354:10

length 103:7 233:4

273:3 391:12

lengthens 384:20

leptin 233:4

Leroux 171:14

lessen 100:11

lessening 384:17

lesser 133:5 247:2

letter 373:12

let's 4:3 60:17

69:20 74:9,14

96:18 160:5

188:13 232:11

261:9 264:5

290:15,19 320:18

330:11 405:2,5

418:13 423:14

level 23:14 35:9

40:12 45:17 46:4

47:3 59:1 61:18

62:11,15 79:5

132:5,14 181:21

207:13 227:4

239:11 251:19

259:21 264:15

281:16 292:12

309:7,19 311:5,7

311:10 312:11

315:4,11 318:15

318:16 337:1,5

352:12 361:20

371:1 381:4 382:2

390:19

levels 36:9 47:10

50:12,14,18 58:6

59:8,14 61:12

117:16 121:3

122:19 127:3

130:17 131:7,14

132:1,21 154:3

155:6 160:19

162:14 165:11

171:13 172:22

174:13,14,15

181:3,8,17 182:3

184:21 185:3

186:4,12 201:19

204:2,11,11

205:22 209:5,10

210:13 211:12,18

212:19 214:18

215:21 216:9

217:10 221:13

223:19 224:18,20

226:6,7,20 227:3

227:5,6 228:1,14

228:18 235:8

238:18 248:3,5,8

248:16 253:4,6,11

306:12 309:20

310:1 312:2,14,16

312:20 313:5

314:10,15 315:2

317:6 318:11,18

319:2 322:1 330:4

332:15 334:17

347:2 352:7 353:1

358:21 359:18

360:19 361:17

363:3,6 384:21

390:20 396:9

398:21

LH 142:9 145:9,13

147:4,8,13 148:10

153:15,16,17,17

154:22,22 155:3

160:15,19,20,22

161:7,21 162:5,13

184:21 185:6,13

186:5,10,19

187:11 188:3

189:14,19 190:3,9

190:13,15,20

191:17,18,20

192:21,22 194:4,7

195:5,9,13 197:1

197:2,3,8,12,16

198:18 199:2,7,12

199:18,22 200:1,4

200:8,20 202:7

212:7,8 214:17,20

215:1,2,4,7,10

216:12,13 217:7

219:6 220:1,14

230:1,12,12 231:6

232:2,4 249:11

384:18 390:14,15

390:17,20 394:19

395:13

lies 120:2

life 115:14 118:1

120:20 144:10

146:21 186:11

222:6 345:1

346:18 347:15

358:13 361:3

368:15 373:7,21

374:12 399:20

lifelong 347:6

lifetime 309:2

344:6

light 190:11 250:13

372:2 394:7

399:21 414:11

lightly 115:4,17

123:15,17

lights 250:18

likelihood 38:20,21

38:22 39:5 40:17

41:10,16 394:16

likelihoods 60:1

likewise 102:17

188:8 190:22

313:22 315:8

limit 47:20 59:15

59:16 131:8 132:2

132:22 292:7

309:2,13,15,16

312:22 317:16,20

317:22 332:2

limitations 257:18

401:7

limited 284:16

288:7 369:19

limiting 89:11

205:3

limits 101:14 269:3

308:13,14,19

312:9 313:10

318:20

Linda 2:18 8:14

65:12 234:6

297:14 420:13

lindane 354:14

Linda's 301:6

302:14

line 46:10 60:5

93:13 129:19

157:5,9,19 164:19

165:1,6 167:10

170:10 241:10

268:4 269:8 335:5

335:14 379:14

393:3 397:22

398:2 403:8

linear 12:16 242:19

273:2 290:4,13,14

305:13,19 306:1

307:2

linearly 13:1

291:10

lines 28:18 144:1,5

144:5 163:5 164:7

164:9 171:2 174:1

234:18 241:15

267:18 286:7

381:19

link 95:13 351:12

353:22 354:9

355:2

linking 95:12

links 359:18

liquid 203:18

list 113:12 128:1

267:2 335:9 341:3

listed 51:1,4

listening 257:12

307:12 380:4

393:14

lists 51:15

liter 323:1

literally 98:1 170:2

literature 24:17

25:9 79:9 95:4

141:20 155:22

171:7 224:5

249:14 329:18

387:11 394:21

395:16 397:7

409:21,22 410:13

420:3

liters 313:19

322:21,22

little 10:4,7 11:3,4

12:10 16:22 17:8

17:9 18:5 20:3

32:10 40:22 41:13

41:18 42:1 85:9

107:18 117:19

164:12 173:10

194:1,15 202:2,9

203:10 206:7

210:19 213:2

230:2 237:10

239:8 259:5

263:13 267:13

280:7 289:4 291:7

296:12,13 300:16

324:5 326:15

330:5 334:1

337:21 355:9

357:11 372:12

379:12 402:1
202-234-4433

Neal R. Gross & Co., Inc.

Page 455

403:14 407:11

415:13,17,22

416:6

live 49:19 50:3

73:21 97:5 99:4

lived 163:14

livelihood 100:7

liver 172:5 179:13

livers 171:14

lives 114:15

Liz's 392:19

load 126:13 358:22

loaded 96:12

local 35:3

localized 34:17

357:9

located 1:17 339:17

342:9

locations 295:16

297:11,12

locked 230:17

LOEL 253:7

log 37:18 38:20

39:5 41:10 42:20

61:4

logical 108:10

293:10

logistic 351:19

logistically 23:11

London 7:21

long 79:16 90:3

98:13 130:8

175:14 211:16

215:22 233:1

255:18 324:21

338:14 339:12

340:1

longer 17:9 21:5

32:15 33:1 53:5

56:21 185:2 243:9

250:22 271:6

280:12 298:2

312:6 313:1,6

314:1 315:8

longer-term 17:17

longest 209:8

longevity 347:22

long-lasting 347:20

long-term 33:3

283:4 348:3

look 10:15 15:6

16:6 18:9 19:4,7

19:10 20:15,18

23:14 25:9 29:2

33:11 35:7 51:20

52:12,16 56:22

57:7,12 59:7,12

67:4 72:16 73:21

74:6,9 75:11,15

75:22 76:8,14

78:17 79:11,12,20

85:11 87:6 88:10

88:11,15 110:1,12

116:16 117:10,11

120:15 124:4

127:14,18 147:13

148:9 165:1 166:1

169:18 170:6,8

207:5 224:4 225:3

236:19 239:20

241:17 249:10

265:10 268:16

270:17 277:8

286:17 297:4,22

305:8,9,11 306:10

306:15 307:9,14

307:15,17,18

309:13 312:5

313:4,22 314:18

317:20 332:9

341:10 345:16

364:13,14 379:17

380:19 401:20

403:12 405:13

406:3 414:4,14

417:13 418:1

420:3,18

looked 12:20 13:7

13:13 14:16,20

18:2,14 25:12

29:5 35:5 37:11

42:4,5 74:16

79:11 84:18

157:15 160:17

166:20 179:3

183:2 229:17

253:18 264:17

267:6 271:15,17

276:12 285:17,22

286:6 292:19

302:8 369:2

401:14 405:12,14

405:16,17,22

413:11 414:11,12

416:12 417:8

looking 11:22 12:1

17:1 19:20 22:2

23:8 27:17 29:15

34:21 37:14,16

53:6 56:18 64:18

71:6,10 72:10

74:5 75:16 77:1

86:18 89:9,15

96:2 124:14 127:4

127:21 138:18

142:20 143:13

187:14 191:8

201:11 202:10

219:18 220:12

226:7 239:11

243:5 249:3

258:11 279:4

281:3 288:11

291:8 292:14

295:12 297:11

302:6,13,14,17,21

315:9 334:21

337:16 358:10

379:1 380:9,18

400:11 402:14

405:3 413:10

417:7 418:20

looks 15:2 20:10

22:4 40:4 117:13

128:18 146:8

200:11 202:13

263:10

loop 391:1

loosened 100:19

Lorenzo 344:3

lose 90:7 167:3

loses 147:14 200:4

losing 290:8

loss 154:1 186:7

199:14 216:16

lost 11:5 115:3

208:13

lot 29:18 35:6

39:14 52:14 55:3

57:9 58:21 65:22

67:22 70:12,18

75:13 80:13 82:13

84:2 88:2 94:22

95:1,18 105:17

108:17 113:17

123:22 124:16

129:9 158:9

178:18 221:6

234:5 235:19

297:22 312:19

318:14 325:22

329:20 339:4

341:5,8 363:5

366:13 388:1

411:16 413:21

414:9 415:18

417:20 419:20

420:1,9 421:1

424:15

Louis 290:18

love 67:21

low 13:5 47:2 50:12

50:12,17 60:4

68:1 101:18

106:18 182:12

185:1 204:8,11,11

209:21 210:3,13

226:7 242:2,7

277:5 286:2

331:18 332:1

337:21 346:7,10

346:19 347:2

354:12 363:6

418:13

lower 16:8,11,12

22:11 44:6 45:16

48:6 111:22

126:11 132:21

146:6 182:11

185:3,4 194:17

211:16 253:11

270:12 272:6

280:11 285:2

313:22 346:5

lowest 16:13 20:2

59:1 225:16

253:14 332:18

353:17 414:21

Lowit 69:14 81:14

92:3 93:20,21

304:12,13 406:7

409:11 410:9

411:3

luck 105:9

lucky 378:20

Luebke 237:3

lunch 10:19,20

96:18 133:21,22

182:22

luteinizing 384:18

luxury 103:12

lymphocyte 244:15

247:16

M

M 1:18,21 383:5

machinery 101:15

magnitude 173:20

260:8 261:5

266:19 381:5

main 78:16 125:13

328:10

maintain 254:13

maintained 114:9

207:20 211:13

212:21

maintaining 102:1

maintains 147:15

147:16

maintenance

395:12

major 94:22 99:19

111:7 153:8

240:16 246:4

252:11 327:14
202-234-4433

Neal R. Gross & Co., Inc.

Page 456

391:19 411:5

422:11

majority 226:19

maker 66:5

makers 46:17 47:1

49:11,18 50:11,19

58:7

making 4:20 37:7

44:5 46:18 66:13

73:16 87:8 89:12

93:5 96:10 134:17

137:9 229:1

255:13 281:17

298:6 316:11

410:17

male 140:18 202:12

207:22 208:7

210:17 215:18

217:11 229:8,17

243:5 249:3

348:21 388:11

396:17 401:22

males 140:9,17

216:2 223:17

224:8,12 227:3,8

249:15 386:6

mammals 163:11

mammary 142:1

143:10 145:15,22

146:6 152:8

154:10 157:8

384:22 388:15,19

389:9 391:6,6,20

393:5,11 394:22

395:5 396:20

397:7 398:19

401:5 402:18,20

403:1

manage 107:13

managed 262:14

262:16,18

management

105:18 107:3

333:17

managers 81:12

82:8

managing 274:3

mandated 264:10

manner 18:22

145:4 242:19

294:17 394:5

manufacturers

373:9

manuscripts 165:3

365:21

map 33:16,17,19

33:20,21 34:1,6

margin 255:14

319:10,20 321:6,8

321:15

margins 258:21

317:2

Mark 118:1

markedly 144:12

154:5

market 106:13

marketed 106:14

Markov 45:7

marks 414:9

Marshall 153:9

Mary 3:11 11:11

11:20 37:3 72:7

Mary's 36:18

mass 85:5 203:18

massive 34:8

121:22 260:9

262:6

match 393:1

material 134:7

183:12 271:9

materials 345:22

maternal 7:12

mathematical 36:3

mathematically

67:20

Mathematics 8:19

matrix 14:14 226:3

matter 96:15

100:21 102:2,4

236:2 293:18

297:20 299:11

304:18 318:4

323:21 376:1

377:8 425:17

maturation 191:2,9

191:22

maturity 191:7

Maumee 39:11

max 292:7,8 302:11

maxima 259:7

260:5 272:2 273:7

273:8 274:4

maximize 39:5

maximum 12:2

13:10,16 14:6,7,9

14:10 16:20 17:6

19:12 20:7 21:1

24:16 28:1 40:17

41:15 47:21 57:18

58:1,8 61:22

64:21 80:7 177:19

179:17 238:2

271:21 273:17,19

279:17 281:10

291:20 295:1

301:8,11,21 309:1

309:15,15 332:15

333:1

maximums 63:17

362:7

McCoy 349:15

MCL 259:18

264:15 316:5

322:10 326:21

327:12 328:17

329:1 333:5 335:2

362:22 363:3,8

MCLG 259:18

MCLs 326:19

mean 15:21 16:6,7

19:4,7 20:2,15,16

29:17 32:3 38:13

43:22 44:15 45:19

47:22 59:12 61:21

63:14 66:20 68:13

70:8 81:4,12 84:5

117:6 129:8 158:9

159:16 161:8

190:16 201:19

204:11 227:12,12

227:16 228:11

231:13 234:17

235:3 262:20

267:17,19 268:11

286:21 288:22,22

295:3,4,11,11,19

296:7 304:17,21

307:16 308:11

314:22 315:4

321:13 337:6,16

368:8 400:19

404:13,18 409:5

410:9,22 415:17

416:21 417:18,19

423:5

meaning 158:13

meaningful 139:13

182:14

means 74:15

173:13 227:8

303:14 313:12

337:19,21 360:12

405:11

meant 80:12

380:10,11

measure 58:11

89:16 185:6

190:13 203:13,19

204:4,6,9 205:22

208:4 210:13

218:5,6 221:1,13

221:21 225:18

232:14 233:4,4

239:18 240:8

243:19 247:3,20

300:21

measured 38:18

47:21 58:15 185:7

185:13 190:16

199:4 209:12

212:6 216:4,5

219:6,7 228:14

231:6 242:12,14

247:21 271:22

281:6 336:17

measurement

39:15 85:10 236:1

291:3

measurements

58:12 89:19,20

167:12 218:3

226:10 265:16

268:21 269:2

275:7 311:8

measuring 203:11

210:8,10 225:7,11

mechanical 104:16

mechanism 136:15

145:8 156:11,22

159:15,17 168:16

192:21,22 220:1,2

220:4 223:21

224:1 343:21

348:10 349:7

401:2 405:19

mechanisms

137:11,20 151:17

151:18 155:7

194:3 220:8,13

249:11 256:2

350:12,14 391:17

mechanistic 141:2

318:16 344:13

media 167:15

180:11 201:3

222:9,15

median 20:17 44:1

44:15 45:19

332:14,20

mediated 195:19

240:21

mediating 174:20

mediator 252:16

Medical 6:21

193:20

Medicine 6:8,17

193:21

medium 13:5 19:5

28:13 148:6

329:13

medium-term

32:22

meet 108:19 263:3

335:1 343:4 370:5

372:8
202-234-4433

Neal R. Gross & Co., Inc.

Page 457

meeting 4:6 5:3,6

141:3,6 154:19

182:6 183:15

257:15 324:3

357:3 366:12,20

383:17 419:13

meets 81:17,18

mega 277:14,16

member 5:17,21

6:4,9,14 139:19

276:19 325:12,19

370:14 373:22

members 1:19 2:1

10:5 97:4,20

99:11 139:22

141:5 325:12

326:3,12 327:15

328:11 329:12

367:15 373:6

374:6

membership 97:22

membrane 176:11

176:21

memories 135:21

men 359:19

Mendez 310:11

313:8 384:7,8

404:20 405:21

407:5

menopause 148:1

154:13 161:9,11

menstrual 218:12

mention 91:4

238:21 281:20

mentioned 34:15

131:5,18 177:14

181:13,14 199:16

241:22 242:16

245:18 274:21

275:17 292:5

312:22 319:13

332:11 335:7

348:4 357:1

369:12 374:14

421:14

mentioning 303:16

mercapturate

166:22

mercury 347:2

mere 118:13

merely 280:5

merit 158:13

message 48:21

166:7 167:6,12,22

172:21 317:1

messages 223:16

messenger 165:11

165:17

Messner 3:15

11:16 35:20,21

52:1 53:12 54:10

58:3 59:3,18,22

60:7,12 61:6,10

61:15 62:10,18,22

63:2 65:21 67:6

met 349:12 369:1

meta 155:21

355:10

metabolism 163:10

164:13 177:13

178:2

metabolite 167:16

180:10

metabolites 163:20

164:3,4,18 166:2

166:6,13,18 167:8

168:4,5,11 170:13

171:1 172:12

173:9 178:15

metabolize 165:20

metabolized

175:10,19,19

metabolizing

164:10

metal 396:12

metamorphosis

350:4

meta-analysis

349:18

method 63:21 64:5

112:11 227:15

330:17,18

methodological

218:4

methodologies

229:2

methodology

236:16

methods 3:10 9:13

11:13 12:12 13:13

74:9 79:20 109:8

169:18 236:12

255:5 295:22

336:12 349:22

370:16 420:22

methomyl 352:13

metric 96:2

metrics 189:6

mgs 189:17,20

190:15,22 192:3

235:14 255:17,18

MHC 254:22

mice 157:7,9,10

212:13,19 229:8

239:15 242:1

388:12

Michael 35:20

Michigan 9:19

microbiologists

419:21

micromolar 165:14

165:14 168:2,22

169:1,6,14 170:1

173:6

micromolars

169:10

mid 95:3 284:12

342:18

middle 15:3 19:5,5

20:1,19 39:15

48:10,14,20

187:21

Midwest 81:20

97:9 266:1 306:17

307:6 312:1

329:14 330:9

Midwestern 330:2

mike 3:15 11:16

24:5 160:14

287:13

miles 275:17

276:10

milieu 149:3

milk 187:2

milligram 206:18

209:21 212:2

230:1 241:7

253:15 309:6

310:7 311:10

312:20 313:7,15

415:8,10

milligrams 185:2

195:12 201:4,14

202:16 206:15

207:13,16 209:9

211:7 213:22

239:16 241:4

242:4,10 314:1

320:10,19 402:3,7

402:13 414:21

millimolar 173:12

million 277:15

357:14

millionfold 315:3

millions 49:16,16

268:5

mimic 38:15

mind 23:5 32:21

33:5,15 57:9

71:12 167:1

169:15 176:17

259:8 405:3,5

406:3 413:19

minds 70:19

mineralocorticoids

205:13

minimize 300:2

minimizes 101:7

minimum 101:19

Minnesota 6:21

119:22

minor 25:21 33:14

minority 423:21

minute 274:22

296:8 318:3

369:12

minutes 35:8 53:11

135:4 163:3

169:19 200:9

201:12 203:6

207:10 208:11

211:11 214:4

215:16 258:7

324:22 366:11

369:7,10,11,13

384:2,6

mirror 326:13

misinterpret

233:22

missed 106:19

113:5 221:10

290:5,5

missing 23:15 26:7

26:12 58:13,14

64:1 67:19 68:1

mission 62:5

Mississippi 6:8

118:2 298:21

Missouri 98:5

275:1,10 292:19

298:20

mistake 123:8

mitigated 334:14

mitochondria

170:7 205:1

mix 106:16 358:22

412:12

mixed 247:16

mixture 125:18

289:9

mixtures 86:14

356:13 365:7

ml 209:22 210:1,2

227:6,13

MOA 388:5 407:12

422:10

MOAs 406:6

mode 92:20 107:9

107:12 135:15

136:13 137:10

141:19,21 142:2

148:18 150:8,19

150:19 152:2,3,7

152:14 158:1,15

317:6 318:15
202-234-4433

Neal R. Gross & Co., Inc.

Page 458

384:14 385:3,17

389:6,13 391:10

391:17,20 393:5

393:11 398:15

400:18 403:13

404:4,22 405:22

406:12 407:7,17

408:3 412:6

413:11,14 414:7

422:5

model 36:5,8,19,20

37:16 40:17,18,19

40:21 41:1,9,12

41:15,16 42:11

43:3,20 44:7,20

44:22 45:2,10

46:11,21 50:6

52:14,15,16,17

53:11,14,15 54:9

55:9 57:15,19

58:13,18,21 59:6

59:16 60:8,16,20

61:1,16 62:9

65:20 66:4,20

67:20 68:12 73:8

90:19 91:11

159:10,11 166:20

180:19 196:13,14

257:16 298:8

376:19

modeled 31:11

53:13

modeling 24:6 36:2

36:16 51:13 80:21

129:4 182:15

281:22 425:6

models 43:7 48:22

50:5,8 51:3 53:21

55:15 63:8 73:1,9

76:5 91:11 282:5

283:14 316:19,21

352:6 421:12

model's 36:21

159:8

moderate 79:16

modern 129:14

379:5

modes 148:19

152:16 388:22

389:3 406:1

modest 166:3

modification

156:17 194:9

220:8

modified 197:6

modulates 390:8

moisture 101:12

molar 173:13

moldboard 100:22

molecular 6:13

339:19 419:1

molecule 226:2

moment 274:9

money 130:16

monitor 363:16

409:21

monitored 56:12

56:21 362:1,18

monitoring 1:8 5:9

21:6,10 34:15,16

36:12,13 44:20

45:3 50:20 70:19

71:14 72:4,21

73:6,9 74:22

75:12,17 76:6,7

77:6 78:21 79:2

82:14,14 85:22

87:9,21 89:4,8,16

90:8,10,20 94:18

119:3 121:2

258:15,17 259:14

260:3,7 262:17,19

263:12,21,22

264:10 266:20

280:18 282:17

306:18 311:22

315:17 316:3

318:20 319:3

325:18 326:6,7

328:2,5 329:12

330:7,20,22 331:6

331:9 332:6 333:9

333:20 334:2,3,6

335:20 336:6

337:9 340:7 361:9

361:11 363:15,22

364:10 410:13

monograph 344:7

Monte 45:8

month 118:10

330:12

monthly 330:12

months 93:2,2

144:16 185:15

Montreal 9:11

morning 4:4,14 5:2

5:5 10:9 12:1

95:19 117:7

324:14 339:1,9

366:6 425:12

Morseth 185:11

310:9

Mosquin 269:4,7

279:14 290:11,11

294:11,12 295:13

295:18

mother 128:21

187:3 347:3

mothers 357:16

mouse 140:8

157:15 159:3,4

181:21 229:20

move 10:3 33:11

103:4 162:21

232:10 234:4

255:7 258:20

263:15 384:5

406:5,13

moved 263:16

284:18

movement 176:22

moves 103:6

269:17 410:20

moving 37:17

38:19 58:22 87:10

88:13,16,22 90:19

258:7

mRNA 197:22

mulch 101:20

102:7

multi 134:17

multiple 117:16

261:17 349:9

351:2

multiplied 320:11

multiplier 68:10

multiply 273:22

multi-year 273:4

mutagenic 389:13

mutagenicity 399:9

mutually 349:9

351:2

M.D 2:17

N

N 184:1,1,1

naive 283:21

name 4:4,20,21

97:4 99:12 105:13

193:19 258:2

339:14 373:6

417:21

name's 35:20

nanomolar 165:13

narrative 422:4,15

narratives 408:11

narrow 18:3

237:10 351:7

nasal-anal 233:4

national 5:15,20

7:2,7 8:10 34:13

56:1 339:7,15

native 156:19

natural 99:19

100:6 102:12,14

238:22 242:16

naturally 380:8

nature 77:18 78:17

128:21 129:6

294:21 391:15

401:12

NAWQA 56:6

261:18 268:13

NCI 94:10 411:7

near 59:16 182:12

238:2 351:7

nearly 120:13

293:1 315:2 353:9

Nebraska 362:9

necessarily 68:10

162:16 220:17

224:7 226:6 231:1

291:19 410:22

necessary 101:3

250:8 334:7

necessity 103:12

need 4:18 23:1,14

27:21 68:10,14

72:4 73:22 76:14

77:11,13,20 79:20

80:6,16 83:3

86:11,20 87:10

88:12,16 91:8

92:16 93:14 94:20

114:11,16 125:5

126:3 127:14

130:12,14 148:20

183:6,11 218:17

236:9 243:14

257:21 282:14

293:8 295:5

316:21 317:7

327:11 334:13,20

380:2 414:8

417:22 418:20

needed 283:7

333:20 334:5

336:21 417:7

needing 361:3

needs 71:12 88:10

130:2 220:5 334:7

369:16 377:20

negates 393:9

negative 37:9,13

45:16 46:18,22

48:5,8,12 50:15

50:22 140:10,16

140:17,19 149:11

150:2,15 157:8,14

166:10 167:4,20

218:15,21 219:8

219:15

negatively 213:11

negatives 47:16

49:9,14 66:9
202-234-4433

Neal R. Gross & Co., Inc.

Page 459

negotiated 363:14

neither 170:20

393:9

Nelson 3:18 11:22

11:22 22:3 29:9

31:18 53:21 55:17

83:11

Nelson's 93:9

neonatal 347:15

nervous 168:12

257:11 346:14

387:2

nervousness 100:2

nested 22:9

network 36:2,4,15

36:19 37:16 42:19

50:5,6 51:3,22

53:9 96:19 97:15

97:15,22 105:14

133:13 336:3

networks 3:14

11:18 51:19 55:8

68:6

neuradrenergic

233:15,15

neural 3:13 11:17

36:2,4,15,19

37:16 42:19 50:4

50:6 51:3,19 55:8

220:14 257:9,13

336:3

neurobiologists

304:4 315:15

neurobiology 7:10

304:10 305:1

neuroendocrinol...

283:20

neuroendocrinol...

7:12 154:14

neuroendrocine

7:14

neurological 257:7

Neurology 352:22

neuron 195:17

197:15

neuronal 196:20

198:11

neurons 196:15,17

196:20 197:1,6,9

197:13,14,19

199:4,10 217:15

217:16 230:17,18

230:19,21

neurotoxicity

127:5

never 93:7 122:6

286:3

nevertheless

189:22 403:9

new 95:5 106:11

109:13,14 111:12

111:18,18 116:2

116:16,16 125:18

125:20 130:7

136:1 137:5 138:6

151:7 152:22

243:8 255:19

266:1 323:15

328:18 369:22

385:10 386:16,21

387:13 398:9

399:3,6,10 400:18

409:14,21

newborn 358:12

newer 127:16

news 120:15

nice 40:20 41:15

51:7,19 67:20,22

155:16 221:6

223:14 237:5

244:2,10

nicely 240:11 253:2

nicer 41:13

night 366:21

nighttime 250:13

NIH 237:17 353:8

nine 271:17 276:9

285:19 362:5

ninefold 225:16

nineties 121:6

130:20 284:13

326:20

nitrate 88:5

NK 245:11 251:18

node 38:1,2 40:6,8

40:10 41:2,3,4,6

52:15

nodes 38:9 52:7,9

52:21 53:2,16

NOEL 253:7

255:17 313:1

NOELs 255:15

313:1

noise 251:2 396:14

non 183:3

non-cancer 137:13

184:6,11

non-detects 331:20

363:5

non-drinking

261:8,9 262:7

non-existent 148:7

non-genotoxic

389:6

non-profit 325:6

339:16

non-specific 238:3

243:12 397:19

norepinephrine

390:6,8,10

normal 50:1 64:9

118:17 199:8

201:11 391:2

normally 5:11

12:21 149:10

214:6 390:8,17

north 6:13 107:21

108:1 109:20

110:7 135:18

136:4 163:1

Northwestern 7:11

218:11

notably 194:20

notches 15:6

note 179:17 268:3

276:1

noted 104:17

330:18 359:8

notes 54:1 365:1

notice 143:7 149:11

166:5

noting 335:16,21

notion 129:16,19

346:2 373:17

November 184:13

no-detect 67:14

no-effect 192:2

306:12 309:6

311:10 312:20

315:1,3,10 318:11

318:18

no-level 313:5

NPA 170:18

NRDC 116:4

404:15

NTP 396:18 397:4

nuance 192:10

nucleus 196:21

number 14:22 18:2

18:13 26:6,6 34:5

39:3,3 46:6 68:8,9

75:14 85:19,20

119:5,8 121:4

124:10,14 141:8

149:6 150:3,11

169:8 171:7

179:15 186:8

194:6 198:5

200:12 201:21

203:8 205:3

209:15 213:10

221:22 225:10

229:13 240:12

253:12 254:20

278:16 281:13

290:16 295:1

303:22 305:22

309:10 320:19

336:21 351:10

354:13 356:17

371:21 372:10

374:14 377:21

381:6,12,13

411:10 412:14

numbered 392:21

numbers 15:1,12

119:6 158:22

163:22 234:13

251:12 278:8

281:17 299:7

322:15 335:7

359:2 364:16

393:1 421:3

numerous 348:16

nutrient 102:1

nutrients 104:20

Nu-may 2:14 8:20

N.W 1:17

O

O 184:1,1,1

Obama 122:4

obesity 154:3

objective 98:9

objectives 335:1

obligation 378:5

obliterate 195:13

196:1

obliterated 231:9

obliteration 229:22

232:1

obscured 122:6

obscuring 122:19

observation 163:4

163:8

observations 26:2

26:4 136:8,20

290:16

observe 192:1

observed 273:8

274:3 301:8,14,21

355:21 371:11

obtained 204:2

obvious 223:20,21

224:1 225:1

329:17 347:13

393:7

obviously 72:8

100:3 113:6

114:11 116:18

162:1 180:17

188:16 224:1

255:19 268:18

314:19 340:17

occasion 178:18
202-234-4433

Neal R. Gross & Co., Inc.

Page 460

occasional 166:15

166:17

occasionally

166:16

occur 30:16 39:22

42:2 54:5 75:4

120:7 127:2 129:1

200:21 201:12

211:2 293:12

305:15 317:7

359:5,9 381:21

391:18

occurred 121:5

122:16 155:1

188:18 198:20

347:15

occurrence 36:6,10

37:20 38:7,11,15

44:5 45:1,13,17

46:1,4,20,22 47:2

47:4 50:9,10,12

51:8 58:6 269:22

345:14

occurrences 288:3

occurring 40:9

164:13 180:13

230:20 266:8

298:13 386:6

occurs 105:2 168:6

195:10 199:9

208:10 346:16

October 35:5

odd 41:18

odds 155:9 351:21

352:13,15,17

353:4,17,21 354:4

odor 329:2

offense 91:10

offer 92:2 93:15

office 5:16 35:21

308:18 309:22

322:13 382:21

official 2:22 4:6

309:19

offspring 249:4

off-patent 98:17

oftentimes 230:15

298:11

Oh 355:8 372:22

Ohio 9:15

okay 11:9 14:22

18:16 19:14 25:16

25:19 28:5 33:13

33:18,19 39:13,19

40:3 41:17 43:20

45:4 67:7 69:10

80:4 83:3 93:18

96:17 131:12

158:4 162:20

177:10 182:4

192:18 193:15,16

193:18 202:1

213:6 215:13

219:12,13,22

220:11,15,19

221:16,20 222:5

223:9,11 226:3

227:11,18 230:8

230:15 232:18

233:2 234:22

237:1 250:2,6

255:6 258:1,11

270:21 284:4

287:12 303:3,9

311:13 319:3

324:1 338:15,18

344:3 363:21

366:9 367:7 393:2

394:9 400:14

415:15

old 100:22 130:6

249:20

older 186:12,18

381:13,14

oligo 161:17

oligomenorrhea

162:10

Ominous 344:14

onboard 224:10

once 16:2 66:20

72:17 74:7 90:7

335:18 344:15

378:16 382:17

383:11

oncogenic 413:15

ones 22:15,17

30:14 31:11 88:5

129:20 138:4

239:2 252:18

286:3,4 288:14

364:2 408:8 420:6

one's 409:16

one-day 80:7

241:18 259:12

307:14 308:21

311:5

one-page 267:8

one-week 28:1

ongoing 138:8

139:1 175:1

187:20 194:2

276:15 387:11

394:6 400:2

online 134:7

onset 191:2,9 231:7

386:6,14 388:14

onward 192:12

open 49:3 119:15

155:14 176:6

341:2 404:8

opening 3:6 185:8

187:9 191:8,12,22

operate 99:17

398:16 412:7

operating 70:3,14

operation 190:1

operative 136:15

385:5,17

opinion 316:18

399:11 401:9

OPP 12:11,18 13:9

13:13

opportunities

374:3

opportunity 30:20

105:21 164:7

237:2 259:5

323:15 325:3,16

339:3 356:16

378:17

opposed 116:17

161:21 228:9

326:6 333:3

379:17 381:1

optimistic 373:17

optimize 39:4

option 333:17

options 93:16

ORD 389:6

order 15:10 77:21

110:1 222:4

268:19 287:18

298:16 305:11

324:8 328:12

352:7

organ 238:18

346:13

organic 102:2,4

299:11,11

organization

139:20 343:5

organizations

97:21 119:19

organized 28:19

organizes 28:14

organochlorine

353:2 354:10

organochlorines

351:15

orientation 134:21

origin 208:6

original 102:22

286:10 288:4,7

393:5 398:5

originally 210:11

249:9

ought 93:4 166:16

267:2 382:5

outcome 98:8

236:4

outcomes 257:22

349:5,20 353:5

357:4

outlet 108:3

output 101:14

251:10

outside 110:13

outweigh 70:5

ovarian 147:6,11

161:22 402:21

ovariectomized

140:15 159:17

190:9 195:22

211:9 402:19

ovariectomy

246:12,16,19

ovaries 390:18

ovary 148:12 153:7

overall 45:16 46:3

58:1 93:19 242:18

305:15 315:13

400:10 414:6

422:1,3,15

overestimate 17:8

overlaps 129:12

overlay 33:20

overview 140:6

155:16 237:5

over-applying

129:11

ovulate 161:20

162:11

ovulation 390:19

390:21

ovulatory 161:18

O'Byrne 2:13 7:20

7:20 128:5,6,6

229:5,19 230:9

231:2,11,19 232:8

o'clock 323:19

P

package 150:5

367:2

packaged 356:13

packet 134:6 137:1

138:4

page 319:9 337:12

359:2 389:20,21

392:3 423:2

pages 340:17

367:19,19

paid 364:7

pairs 173:15 174:6

panel 1:4,15 5:12
202-234-4433

Neal R. Gross & Co., Inc.

Page 461

5:13,17,22 6:5,10

6:14,19 9:22 35:5

83:15 86:10 87:20

94:3 99:11 100:3

105:5 106:1 113:9

113:14 119:12

124:8 127:1,17

134:7 141:5

156:14 168:19

197:8 206:12

258:12 261:3

270:1 271:1 272:9

276:19 282:10

283:17 297:16

338:9 339:9

345:15 357:2

363:11 364:9,19

365:8 366:15,17

367:2,15 368:2

372:19 373:6

375:4 383:11,15

383:21 384:11

387:22 404:9

408:14 425:10

panelists 379:9

panels 127:17

345:8,9,20

panel's 183:11

paper 11:11 12:11

26:7 53:12 74:4

76:12 158:5,20

159:9 255:11

278:10,17 385:12

386:10,16 392:8

392:10 398:17

papers 137:2,3

158:18 160:2,2

366:8 395:15

419:10

parallel 376:2,10

412:11

parallels 332:10

parameter 37:6

38:13,17 39:6

44:9 45:7,11

67:16 242:14,17

parameters 38:16

38:22,22 40:5,8

41:1,12 43:8,17

43:21 51:5 55:3

65:22 66:2 67:12

67:12 68:8,9,11

129:7 237:7

238:20 239:5

240:15 241:1

242:12 245:1,3

246:14,14 247:4,8

247:11,13 252:12

252:20 253:19

254:21 255:20

pardon 100:2

park 149:22 404:1

422:2 423:5,11,14

Parkinson's 350:18

351:1,13,18 352:8

352:20 353:3,10

353:11 354:1,11

355:3

part 11:8 27:9

39:15 57:2 75:3

77:2 80:8,15

83:11 86:21 95:20

105:3 114:14,14

114:15 120:8,8

123:4 134:18

141:10 148:17

163:18 165:8

168:16 220:1,7

232:22 258:9,14

263:11,20 309:1

315:7 327:8,9,18

332:6,21 355:19

355:20 361:19,19

362:5,6,7,9,19

365:2 413:15

parted 424:19

participated 113:3

participation

114:19 119:17

327:8

particular 31:6

36:4 38:8,16 44:7

44:19 53:3 54:22

56:14 60:14 66:12

66:13 68:7 71:2

75:6 77:17 138:10

185:7 189:2

192:20 230:8

234:16 242:1

244:11 252:17

291:2 305:15

309:5 310:6

313:16,20 349:7

350:1 357:10

374:1,20 375:19

376:18 389:21

395:17 412:1

particularly 52:11

70:15,21 74:4

176:15 186:14

204:10 211:5

212:6 213:15

228:11 237:11

254:22 261:17

329:13 376:17

381:7 388:19

401:16

particulate 100:21

parties 124:5

390:16

partitioning 292:20

partly 364:20

403:18

parts 18:6,7 22:6,6

61:12 62:4 132:22

133:2 144:15

183:8 259:11,17

260:1 264:14

269:10,18 278:2,4

278:6 309:16

311:2 313:12,21

314:3 318:1

326:21 332:2,22

333:1 335:3,8

337:2

pass 28:10 99:8

338:22 363:12

passed 109:5

340:16 376:6

419:14

passive 196:2

passively 217:17

Pastoor 255:14

258:5,20 259:13

281:19 283:16

303:2,7,10 319:16

320:2,5 321:18

322:9,16 323:6

patents 270:14

path 44:18

pathogens 121:19

Pathologists 225:9

pathway 95:13

172:16 386:19

393:6,8 394:4

patience 256:8

323:13

patient 425:11

patiently 298:3

pattern 45:13

46:20 47:2 71:7,8

71:12 147:20

200:1 240:4

245:17 252:19

306:3

patterns 11:19

23:21 36:9 50:10

50:10,12,17,17,18

51:8 57:4 64:14

78:18 87:1,3,4

194:5

Paul 258:2 290:11

292:5 302:4

pause 44:8 303:6

pay 48:5 333:19

pbbs 322:15

PBS 169:4

PCOS 153:11,14

153:15,17,20

154:2,5,7,10,13

154:16 155:2,12

160:18 161:13

PDE 173:14 175:21

PDEs 179:2 181:1

PDE3 179:3

PDE4 174:12

PDE4D 174:9,12

PDE4s 174:19

PDE8 174:9,14

peak 15:4,7,9 17:1

18:3 24:13 26:8

26:12 30:14 38:7

38:10 52:12,20

53:3,7 54:13

80:16,22 181:20

190:16 191:5

199:2,6 201:21

211:18 215:6

230:12,22 260:14

264:2 268:12,16

273:8 282:13

288:6 294:18

295:6 296:8

299:21

peaks 17:22 18:13

19:2,6,6,10 21:22

22:4,7 26:5 28:13

28:13,20 53:1

54:5,14,14 68:22

201:22 204:14

211:11 253:8

259:7 260:5 290:5

293:12 294:14,21

300:12 306:11

362:5,18,20 363:7

peak's 30:16

peer 116:4

pellet 190:10

PENELOPE 2:4

Penny 9:2

people 90:10 96:10

98:14,16 117:8

123:22 133:7

253:19 265:9

299:7 300:7 317:8

330:10 380:5

410:10 411:9,10

416:12 419:8

percent 16:2,3,5,8

16:11,12,14 17:3

17:3,4 19:8,8,9,21

19:22 20:1,6,6,8

20:21 43:12 48:11

48:12 61:16,19

62:11 102:16,18
202-234-4433

Neal R. Gross & Co., Inc.

Page 462

102:21 109:3,12

111:8,11 145:2,4

145:5 146:3,9,21

147:1 163:20

174:13,14 176:20

177:4,5,19 179:19

181:5 197:9

209:17 235:1,9

273:16,19 274:1

275:21 292:6,9

301:10,20,21

302:10,12 309:8

319:18 320:12,13

320:22 322:4,7,10

325:13 331:7,21

332:13 352:19

354:7,13 362:13

362:16 363:19

401:18 414:19,22

415:1,1,7,7,9,11

416:22

percentage 21:1

29:21

percentages 25:3

percentile 37:6

46:10,12 60:5,8,9

60:14,15,17

303:20,21 307:17

307:18 308:5

314:16,19 315:11

316:14 332:15,21

354:2,4,7

percentiles 12:8

13:8 14:9 44:1

45:20

perception 396:9

397:16 398:4

percolates 34:9

perfect 40:12

perform 21:10,14

21:16 49:2 76:5

performance 12:6

13:14 16:15,22

19:2,3,18 36:11

36:21 37:14

260:15 273:21

279:14,17 281:2

281:10 283:8

291:18 292:5,13

293:2 297:4

performances

297:7

performed 212:11

295:20 302:7

performing 19:22

perinatal 127:4

period 10:9 31:6

61:8 76:21,22

85:3,17 88:18

107:20 108:20

109:1,11 111:10

144:3 194:12

200:9 211:16

214:5 215:22

218:14 239:17

240:13 244:4

247:2 266:10

267:16 268:6

270:18 374:4

383:14 421:1

periodic 56:2

periodically 162:10

periods 32:15

41:19 59:11 76:19

83:22 85:19 127:4

182:10 208:18

274:7,12 280:13

peri-fused 201:2

permanent 5:17,22

6:5,10,14 94:3

347:11,20

permit 254:15

282:1

peroxide 170:19

persistence 147:21

persistency 148:2

persistent 144:6,9

144:21 397:2

persists 165:18

person 62:2 128:7

404:15

personally 66:3

115:1 416:6

persons 353:19,20

perspective 325:3

378:17

perspectives 91:21

92:1,12 93:16

155:21

pertain 141:13

pertaining 156:10

401:4

pertains 135:16

138:13

pertinent 178:15

perturb 386:19

perturbations

385:21

per-fed 233:2

pesticide 6:9 8:10

8:22 11:18 70:1

77:18 78:19 79:15

115:11 117:6

118:17 120:6

309:22 328:6

333:10 352:6

353:12 356:12

360:11 381:17

pesticides 9:8 78:6

104:8,11,21

351:13,20 352:5

352:10 353:19,20

356:8,14 357:17

358:2,9 359:1

Pfizer 237:20 243:7

pharmaceuticals

358:5

pharmacokinetic

178:11 181:13,16

181:19

pharmacokinetics

9:12,17

pharmacy 9:15,16

phenomenon 142:7

184:15 235:5

347:16 392:1

philosophy 66:15

phosphodiesterase

171:17 172:5,13

173:3,4,7,17

179:14,19

phosphodiestera...

171:6,10 172:19

174:2,5,16 179:9

179:16,21

phosphodiestera...

171:19

phosphodiestrases

179:1

phosphorylation

172:16

photomicrograms

168:21

phrase 266:21

physical 84:5

168:14 254:4

physically 83:17

84:12

physiological

180:18 200:2

210:3 216:9 227:5

physiologist 7:17

8:1

Physiology 7:10

Ph.D 1:17,18,20,21

1:21,22,22,23 2:2

2:2,3,4,5,6,7,8,9

2:10,12,13,14,15

2:16,17,18

Pia 348:13

pick 27:22 61:3

83:18 134:2

236:15

picked 174:5

picking 234:20

picogram 227:6,7,9

227:13

picograms 204:12

209:21 210:1,2

picture 57:16

217:19

piece 82:7 192:17

193:6 210:21

269:6 406:7 411:4

piecemealed

229:12

pieces 414:5

pituitary 153:19,21

181:3,5,10 195:17

195:19 196:8

384:18 385:20

387:2 388:16

389:10 390:13

392:12

pituitary-adrenal

194:21

pituitary-gonadal

200:18 213:18

PKA 172:15

place 34:6 77:13

113:6 131:19

181:12 304:2

306:20 381:9

placed 223:3

placental 165:6

places 56:11

227:18

plan 74:1 90:20

180:21 245:22

293:7 334:22

364:1

planned 246:7

planning 104:15

249:3

plans 36:12

plant 164:4 277:20

278:4,13,19

300:12

planted 107:5

planting 100:16

316:7

plants 361:21,22

380:1,6

plant's 300:11

plasma 205:22

209:5 248:11

plausibility 143:2

plausible 152:16

play 31:22 47:16

83:13 216:15

326:11 360:22

385:22 393:8

404:5 411:16

415:4

Plaza 1:16
202-234-4433

Neal R. Gross & Co., Inc.

Page 463

please 4:20 96:11

100:2 140:20

141:14,15 142:5

143:3,17 144:14

144:22 145:8,18

146:8,16 148:8,17

149:5,16 150:9

151:21 152:6

154:17 256:17

350:20 385:16

387:12

pleasure 139:18

303:12

plot 51:21 241:5

301:9,11

plots 42:7 124:17

plow 100:22

plus 43:14 67:11

284:7

point 21:3 22:19

27:3,18 32:9

46:14,15 59:12

67:1,16,18 68:21

72:2 75:2 83:18

84:19 88:9 91:22

92:4 96:7 117:1,7

126:17 133:11,21

151:20 155:14

164:6 165:2

166:12 167:7

169:13 176:1

182:21 186:22

188:3 189:7

190:20 191:11

209:8 217:8 234:4

236:1,22 242:2

267:20 275:14

276:18 280:1

282:9 287:22,22

289:14 296:15,18

296:19 301:20

304:3 320:14

327:4 331:11

334:18 335:12

344:4 348:11,15

354:17 358:14

367:17,22 368:3

383:8,13 397:13

403:20 404:8

407:6 409:10

421:14 425:8

pointed 74:3

284:21

pointing 23:19

points 31:9 67:13

68:11,17 76:18

90:2,16 95:21

98:19 137:13

156:2 184:11

186:2 216:18

236:14 238:6

239:10 254:4

258:18 259:1

268:20 288:12

289:4,15 300:17

301:10 305:12,19

305:21 307:1,3,3

310:11 348:17

386:9 404:14

poison 346:2

policy 117:8 342:12

343:9 374:5,7

polite 365:18

political 116:6

120:9

politically 122:17

politician 122:20

politics 122:7

pollution 103:22

polycarbonate

396:15

polycystic 153:7

pool 266:22

pooled 351:6

poor 343:9 357:4

359:19

poorer 420:7

poorest 129:21

pop 230:18 341:2

popping 282:9

popular 125:20

266:2

population 23:8

63:9 116:12

230:21 268:21

269:18 270:8,10

270:20 285:18,20

291:3 295:7,22

408:2

populations 344:19

Portier 1:18,21 5:3

5:4,14 8:6 10:1

24:8 25:20 26:22

30:4,5 32:6 33:7

33:10 35:4,14,18

51:16 53:17 57:13

57:14 58:20 59:5

59:20 60:3,10,21

61:7,14,20 62:16

62:20 63:1,4 65:8

65:9 67:9,10 69:5

79:22 83:8 86:3

90:22 93:18 96:6

96:17 124:7 125:2

126:19 128:4

131:1 133:10

135:9 155:13

157:3 160:9,13

162:20 176:5

178:19 180:1

182:18 183:10

184:3 193:8,16

218:8 221:3

223:12 226:12

229:4 232:9 234:3

249:1 250:11

251:13 252:21

254:7 255:6 256:9

283:17 285:4

290:1 297:14

301:1 302:19

303:15 319:5

323:3,7,17 324:1

324:10,21 338:8

338:11,16 339:2,6

364:18 366:10

367:12,20 368:5

369:1 372:19

373:2 379:8 383:3

387:18 394:9

398:6 400:14

404:7 405:6,10,11

406:16 407:14

408:5,18 410:6,15

411:19 413:7

416:16,17 418:15

419:19 421:5

423:16 425:7

portion 251:21

Portsmouth 9:7

Portuguese 351:16

positive 37:9 45:17

47:6,17 48:4,7,12

50:13,22 140:15

149:12 218:15

220:2,4 245:13

412:4

positives 37:12

47:15 49:9,14

66:9

possibilities 235:15

possibility 158:7,14

233:18 403:3,16

413:13 414:8

416:5

possible 27:15,19

60:22 82:11 180:4

253:10 265:21

333:12,18 335:13

336:9 352:12

355:1 388:22

389:12 394:3

401:2 403:22

possibly 52:4 82:17

343:19 350:15

352:4,5 394:7

424:14

post 116:3,10

126:11 187:3

posting 116:8

postulate 141:18

185:19

postulated 141:21

184:14 389:7,19

post-emergence

104:15

post-lactationally

187:17

potent 173:8

potential 88:4

120:6 137:11

139:3 140:3

148:19 149:8

150:10 155:5

158:8,15 171:5

202:10 220:5

327:16 328:7

329:11 331:22

333:10 334:2

336:22 337:9

341:17 345:1

346:9 350:22

351:19 388:9

389:4 397:15

403:4 406:1,6

413:16,22

potentially 52:12

161:20 162:12

218:17 350:14

361:2

potentiate 171:2

potently 215:2

pounds 103:17

268:5

powdered 328:16

power 267:1

274:14 354:17

416:19 417:15

powered 234:12

349:10 351:3

418:13

powerful 229:21

ppb 269:12 278:14

286:4,5

PPM 144:18

practical 21:19

23:11 94:16 346:7

383:1

practice 30:17

133:16

practices 101:4,6

102:11 103:10

104:5,13 105:18

371:10

practicing 418:21
202-234-4433

Neal R. Gross & Co., Inc.

Page 464

precedent 374:6

precipitating

317:14

precipitation 64:14

precise 129:8

150:22

precision 38:17

precursor 204:20

204:20

predecessor 122:10

predict 52:10

predictable 242:19

predictions 66:13

271:22

predictive 244:21

predictor 64:12

predictors 248:13

predominant

147:20 167:8

168:5 205:17,20

predominantly

149:11 164:12

prefer 373:14

preferable 283:14

preference 375:2

pregnancy 186:7

pregnant 347:3

357:17

preliminary 23:18

25:15 139:4 348:9

387:12 398:10

399:15

premature 385:3

premier 342:8

prenatal 357:6

prep 182:6

prepackaged

106:15

preparation 172:6

179:14

preparing 100:15

presence 122:1

205:6 231:6

377:14

present 1:19 2:1,21

15:1 121:10,20,20

150:5 163:21

174:7 213:1

255:14,16 325:3

354:6 389:18

presentation 3:8,13

5:10 10:10 11:12

31:8 33:12 53:20

72:8 74:13 96:22

133:20 134:18

135:5 153:12

162:21 223:15

235:18 237:4

260:19 262:9

286:15 302:22

335:10 341:1

374:13 377:6

389:1 392:22

400:17

presentations 4:10

96:19 262:4

350:19 393:15

425:11

presentation's

36:17

presented 11:10

14:11 26:6 28:12

76:12 79:8 135:20

165:3 322:15

340:9 388:8 401:3

401:7,14 420:17

presenters 133:19

134:19 381:6

presenting 12:14

240:18

preserve 45:12

president 373:7

President-elect

122:4

presiding 1:18

presumably 132:21

186:9 356:4

396:13

presumed 185:9

191:20

presumption 346:4

pretty 20:12 58:17

58:17 64:6 67:21

68:18 71:18 94:16

125:7 221:9 228:8

228:10 288:15,20

298:22 338:4,22

367:13,21

prevail 114:2

prevailing 396:22

prevalent 179:4

prevention 232:3

previous 42:14,17

43:11,11 57:6

92:18 122:18

123:17 154:21

170:13 214:5

335:9 374:22

385:13

previously 192:5

238:7 332:11

pre-birth 344:22

347:1

pre-ovulatory

202:7

price 48:4

primacy 336:5

primarily 110:7

151:4 306:17

325:8 328:9 389:5

primary 114:12

198:1 249:15

primate 178:4

primates 160:21

164:1 177:15

prime 414:18

primer 173:15

174:6

principal 147:2

303:11

principles 333:13

prior 53:22 54:1,18

198:21 221:14

266:21,22 280:16

296:2

private 9:3

probabilities 37:12

47:11,15 49:5,13

probability 24:12

37:4 47:5 62:7

probably 11:2

13:11 29:2 55:11

55:12 56:17 57:5

63:14,15 68:22

77:3 97:9 120:21

126:4 127:13,21

130:3 133:7

145:11 168:15

176:20 182:21

222:12 235:4

246:10 251:5

252:15 261:15

287:5 296:9 342:8

344:4 354:3 384:1

problem 27:5

63:22 64:10 87:21

89:2 103:22 104:1

299:16 363:10

367:14 413:21

problems 86:19

125:21 413:19

procedure 296:21

297:5

proceed 31:17

135:2 383:15

proceeding 372:16

process 82:9 83:12

85:11 87:8,12

89:7 98:13 116:14

116:22 118:6,7,17

118:19 123:14,20

124:6 163:18

189:14 246:9

281:19 318:11

326:18 327:10,19

334:5 406:15

processes 180:18

193:2

produce 43:16 56:1

101:15 197:21

produced 100:13

179:17 225:15

244:3 260:9 381:8

Producers 97:8

producing 63:9

64:6 98:16 112:20

171:17

product 98:15,17

98:18 99:1 106:5

106:16,19 113:18

114:9,13 130:2

132:6 133:16

134:13 213:7

258:3 374:1

production 100:12

101:16 104:5

136:14 170:9

241:20 349:1

350:7 392:13

products 356:12

360:20 373:11

376:8 381:3,12

382:10

professor 6:2,7,16

9:10,16 193:19

profile 30:9,9 32:15

32:16,17 54:3,6

59:2 85:4 120:13

273:4,9,12,18

291:12 292:8

296:5,11 306:4

308:7

profiles 12:9,18

21:21 32:18 66:17

258:18 272:22

281:4 291:21

profiling 180:10

profound 126:5

progesterone

147:16 203:14

206:1 208:5,5,7

208:10,13,20

215:5,15 392:14

progestins 205:11

program 5:21 8:11

20:13 90:14

136:17 138:1

140:22 259:14

260:3 261:18,21

262:3,14,14,17,19

262:21,22 263:9

263:11,12,16,21

263:22 264:9,10

265:5 271:14

276:5 284:7
202-234-4433

Neal R. Gross & Co., Inc.

Page 465

310:20 312:1

316:4,6 319:3

327:9 339:17

344:7 361:11

363:15,22

programming 63:3

programs 39:3

56:1 98:22 257:3

260:7 261:6 276:2

276:15 279:19

280:4,12 284:18

289:16 306:18

309:22 315:17

340:7 361:9

382:22

progress 108:4

245:5 248:14

329:4

progression 399:11

progressively

185:3

project 124:1

237:19 243:6

prolactin 147:9

148:2,5 203:14

208:16,21 216:4

398:22

prolonged 142:3,16

147:17

prominent 407:22

promise 303:2

promoter 404:11

413:2 421:21

424:5,9,16,19

promoter/initiator

424:18

promotes 142:14

promoting 403:4

promotion 160:1,6

160:7 399:11

403:8,12 408:6

413:15 414:2

425:3

promotional 158:7

158:14 401:2,2

403:3 413:17,18

416:9,9

prompting 390:18

prone 277:4

proof 342:21

346:16

propaganda

120:20

propargite 352:17

proper 178:12

properly 245:15

353:6

properties 21:21

104:4

proposes 342:13

proposing 342:2

pros 36:14,22

51:12 82:13

prospective 354:15

prostate 115:4

protect 100:8

101:11 121:15

360:5

protecting 170:20

343:10

protection 1:1 8:21

97:1 110:15

335:16 359:11

373:11 376:5,8,17

381:3 382:10

protective 101:21

168:16

protects 334:9

protein 196:16,18

205:2 240:17,18

proteins 179:10

protocol 254:10

364:21

prove 189:22

provide 25:8 36:14

44:12,14 58:7

77:12 79:3 101:21

104:18 117:20

188:21 248:14

254:16 271:9

277:2 351:6

354:22 365:19

366:2,4 379:6

383:10 399:4,12

408:13

provided 39:12

41:10,11 55:20,21

119:7 134:6 339:8

344:10 361:13

362:12 365:14

366:17 369:16

370:22 372:12

374:1 378:14

provides 99:2

261:2 282:18

390:18

providing 188:20

377:12 419:11

provisions 376:16

provocative 160:2

Pruett 138:16,22

202:19 212:11

234:5 236:5 237:1

249:5,16 250:21

251:7,21 253:10

254:18

pseudopregnancy

147:22

pubertal 127:5

puberty 386:6,14

public 3:20 4:13

10:2,12,16,18,21

96:10 323:10

324:4,7 334:9

335:15 342:11

343:9,10 344:5,14

364:13 368:12

372:11,12 373:3

374:4 378:2 379:3

383:9,14

publication 109:20

109:21 137:5

publications 138:3

141:8

publicly 366:21

publish 364:10,12

published 102:13

108:2,5 159:22

342:5 349:13,16

352:22 365:21

386:17

publishing 137:6

pull 160:13 264:18

367:13

pulled 389:19

pulling 148:8

pulls 307:11

pulsatile 185:10

186:10 191:20

192:22 199:15,19

200:1 201:8 202:5

214:2,17 217:6

218:22 219:6

221:1

pulse 137:19

153:17,18 199:22

200:9,16 214:13

214:14

pulses 200:12,14,20

201:11,15 214:7,8

216:12 219:5,20

223:8 232:2

pump 277:10,13,15

279:2 298:6,7,16

299:1,10

pumping 298:10,12

pups 187:2,4,16

193:12

purpose 64:19 90:8

171:16 186:3

purposes 73:4

180:20

Pursuit 391:16

purview 412:14

push 11:2

put 13:20 14:18

37:10 53:10 58:4

59:9 75:9 105:8

114:18,18 130:2

150:8 153:11

160:5 164:10

175:18 193:5

201:7 277:12

297:1 308:12,16

311:16,19 317:7

342:21 363:4

365:20 373:13

381:11 413:20

putting 189:12

415:18 424:15

put-together 414:7

pyramid 263:14

264:22

P-R-O-C-E-E-D-...

4:1

p.m 183:17 184:2

323:21,22 425:16

P450 205:4

Q

qualifying 111:4

qualities 282:7

quality 8:11 41:11

56:1 72:20 105:16

359:19 369:8,15

370:9 372:3,9

376:5,17 418:21

quanta 199:20

quantitative 64:6

238:5

quarter 11:3 68:17

165:12

quarterlies 363:8

quarterly 13:19

14:18 90:12 327:1

335:4

quartile 353:16

quartiles 353:15

question 27:10

33:15 51:17,20

53:20 55:16 57:14

72:5 73:18 78:5

79:19 80:9 81:18

81:22 89:9 91:6

92:10 94:20 117:1

119:15 126:22

128:12 136:5

137:18 138:11

139:2 153:6 158:4

161:6 168:7

172:11 178:22

180:4 181:1,12

182:20 186:16

192:19 195:18

213:6,13 214:21
202-234-4433

Neal R. Gross & Co., Inc.

Page 466

222:8 228:21

229:21 231:12

233:11 234:7

236:12,16 256:11

256:13 267:3

270:1,21 272:16

274:12 276:19

283:19,21 284:5

293:4 294:8 295:4

297:15,16 298:3,5

301:2,6 304:12

310:22 314:6

321:2 341:15

345:9,18 348:8

350:16 356:15,16

361:6 364:21

365:3 368:17,18

383:21 384:12

387:9,19 394:2

399:1 404:10

414:9 417:10

421:15 425:8,13

questionable

421:20

questioning 234:11

questions 3:22

10:22 24:7,8 28:8

31:1 32:1 33:8

57:16 69:17,22

77:19 78:14 79:6

79:12 94:16

118:11 124:8

134:20 135:6,10

135:14 139:14

155:15 157:4

164:8 173:16

176:6 193:4 194:6

194:13,18 218:7

221:6,9 232:10

234:6 238:10

248:22 261:3

269:5 270:22

272:21 279:16

280:19 281:22

282:19 283:15,18

289:11 295:17

319:4,5,8 325:10

334:20,22 338:7,9

340:19,20 341:2

344:1 364:19

372:4,14,18,20

379:9 384:10

386:15 420:11,11

425:14

question's 224:22

409:13

quick 4:10 193:4

193:10 234:6

256:10,13

quickly 125:21

126:7 200:4,7

215:14 249:8

301:4

Quinn 383:5

quite 43:4 53:4,15

68:10 114:8 116:1

159:8 177:7 186:8

231:19 238:1

241:11 242:21

244:14,18,21

246:4 262:4

266:17 293:21,21

305:22 314:19

382:15

quote 25:22 154:18

369:13 370:19

371:19

quote-unquote

396:2 415:19

R

R 1:21 184:1

radar 27:13,14

radiolabeled

177:12

Radiology 6:18

Rain 100:19

rainfall 33:20 34:3

54:16 102:16

128:18 265:19

279:2 329:22

raise 92:2 276:18

277:22

raised 153:6

355:17 371:14

404:14

raises 420:11

raising 393:22

Ralph 143:4

194:13

rambled 131:4

ran 15:13 18:15

223:4 263:17

random 14:5 42:3

43:15 46:6

range 14:9,10

27:11 56:16 66:17

67:4,15 93:16

147:1 169:3

170:11 173:13

227:6,7,9,13

279:19 280:8

281:12 332:17,20

332:21 333:1

344:18,21 351:3

352:1 375:11

382:9 402:2

ranged 13:19

ranges 182:15

279:7 332:14

ranging 13:11

169:19 279:8

rank 111:16

rapid 211:10

rapidly 133:4

175:10 199:13

211:18

rarely 346:8

rat 140:18 142:11

143:21 144:7

146:5,13,18,18,22

147:3,7,15,22

148:1 152:9 154:2

181:21 186:13

195:5 196:13,14

198:8 205:17,21

229:20 249:20

385:4 394:20,21

395:6,18 397:6,17

rate 47:1 48:4,6,7,8

106:18 114:6

130:19 205:3

rates 47:9 50:22

108:17 126:11

178:2 396:7,17

397:7,8,11

rating 422:3

ratio 207:4 278:18

292:7 351:22

352:13,15,17

353:17,21 354:4

ratios 147:17

273:10 279:11

353:4

rats 49:22 142:10

142:16 143:9

144:15 155:9

171:14 185:14

196:1 202:12

208:1 212:13,20

215:18 217:11

227:4 229:8,15

243:6 384:19

385:1,22 388:11

388:12 389:11

396:18,20 397:5

398:20 401:12

402:1,19,19

413:12

raw 121:3 264:5

265:7 267:19

271:12 275:21

276:3 277:19

278:3,8,18 285:15

292:22 327:7

362:15,18 377:9

378:14

reach 191:11

206:18 209:11

246:4

reached 135:22

152:22 326:2

393:10

reaches 235:8

reaction 374:18

reactive-oxygen

170:9

read 51:11 113:10

120:15 169:2

340:16,20 342:14

345:5 355:8,9

383:7,20 388:2

407:2

readily 144:19

reading 293:9

384:9 421:15

423:11,19

readjusted 10:8

reads 297:16

ready 93:19 96:13

237:1 400:21

404:2

real 73:2 81:19

93:5,6 193:10

201:18 291:15

292:18 299:18,19

306:14 324:20

361:4

realistic 51:8 66:5

66:10 291:10,16

reality 64:19 65:2,5

81:18 126:1

realize 90:11

260:22 278:20

realized 26:16

realizing 27:4

67:11

really 17:12 27:7

32:21 49:19,19

54:10,13 55:4,9

55:11 56:7 63:12

64:13,15 65:11,19

66:7 69:1 72:22

73:3 80:6 81:16

82:18 87:7 88:12

88:18 96:2 109:16

125:12,19 133:11

159:12,16 160:18

173:10 176:8

182:5 201:20

219:14 224:12

225:19 226:4

234:10 236:1

237:21 238:7

240:3 241:19
202-234-4433

Neal R. Gross & Co., Inc.

Page 467

266:16 275:5

280:7 286:14

292:2,4,14 293:3

293:7 296:19

304:20 308:11

316:19 325:1

337:16 338:3

339:11 341:6

343:8 356:22

375:18,22,22

404:13,18 406:9

409:17 411:16,16

412:22 417:7

420:8 421:13

realm 65:17

reason 24:18 25:13

58:4 83:20 106:7

223:21 237:22

263:14 277:22

299:9 346:6

reasonable 21:11

66:21 125:4 231:3

349:10 351:4

424:3

reasoning 398:1,18

reasons 32:4 57:20

109:22 161:14,18

257:17 268:3

310:18 346:8

381:22

reassessing 321:4

recall 184:13

recapitulation

185:21

recaptured 20:2

receive 383:4

received 365:4

receiving 187:2

Recio 286:19

reciprocal 274:1

recognize 185:16

326:8

recognized 153:11

395:2

recognizes 363:20

recommend 25:8

107:7 283:9

335:12 364:9

410:21 423:5

recommendation

385:7 407:18

409:7 417:16

423:14

recommendations

333:7 361:5,8

363:11 409:9

410:19 416:17

recommended

345:10

recommending

383:6 410:17

422:13

reconsider 384:4

422:3

reconsideration

94:12

reconvene 183:14

183:16 323:18

record 96:15

182:19 323:21

365:2 372:4,7

373:13 377:16

recorded 4:21

records 264:17

recover 199:7

247:7

recovered 199:13

recovery 188:4

198:15

red 46:10 144:5,5

358:15 359:2

360:7,18 388:7

reduce 128:9 173:7

195:4 209:10

231:7 360:1,2

reduced 130:19

147:18,19 148:4

153:14 166:5

174:12,14 197:13

200:13 201:15,22

230:13 348:3

357:6,7 384:15

reduces 85:20

101:12 215:6

reducing 102:5

104:11 174:15

reduction 167:6

179:19 235:1,9

381:20,20

Reed 2:14 8:20,20

157:3,4,18 158:1

158:4 400:15,16

408:8,9 413:7,8

417:18 422:22

423:3 424:1,2

reevaluate 407:19

reevaluated 409:6

reevaluation 1:6

4:7 5:6 94:4

333:12

refer 258:14

reference 267:10

271:8 318:22

referenced 364:22

375:7

references 359:3

referencing 392:8

referred 184:19

310:9,11 398:19

referring 234:16

313:8 344:8

refers 165:4

refining 318:11

reflected 125:15

reflects 268:8

refreshing 135:21

Regal 2:15 6:20,20

124:8,9 226:13

229:4,6 249:1,2

249:13,19 250:2,6

regard 32:3 72:6

84:15 114:2

156:18 177:13

200:22 238:11

283:22 310:21

315:16 374:5

375:18,20 376:3

376:13,20 377:16

377:22 378:4,8,10

378:11 381:19

389:3 393:10

regarding 113:7

122:14 194:2,7

204:5 283:21

284:1 334:13

348:10 399:7

404:15

regardless 113:13

regards 296:18

regime 283:2

regimes 281:11

regions 110:13

111:8

register 358:17

registered 106:13

108:14,17 132:8

registrant 328:5

329:3 331:12

363:15 364:7

registrants 98:12

328:2 333:19

registrant's 55:20

registration 118:17

118:18 358:16

regression 34:11

68:12 351:19

regular 90:9

regulated 90:13

121:10 122:8

263:1

regulating 98:14

191:21 217:17

regulation 194:9

255:16 336:18

345:21

regulations 120:5

332:8

regulators 117:21

119:18

regulatory 114:2

123:7 177:12

205:2 220:8

262:22 326:18

327:21 368:13,22

370:1 372:2

reinforced 374:18

reiterate 95:19

237:8

related 28:6,8

42:16 43:10 64:11

87:17 146:2

159:22 161:13

162:13 220:13

240:7 296:6,12,13

296:16 348:17

405:19

relates 100:1 178:8

184:11

relating 186:5,9

231:12 345:11

relation 296:14

relations 288:5

relationship 95:15

138:19 145:16

146:7,11 244:18

245:8 248:15

399:20 400:9

relationships

142:20,21 245:3

relative 137:12

139:2 166:4 174:8

181:8 182:14

184:15 186:18

290:16 308:12,16

312:13 319:17

322:11 352:20

relatively 106:18

124:22 163:6,13

288:15 324:6

332:1

release 147:9

162:13 185:10

186:10 191:21

201:1,8 332:5

384:15 390:8,11

390:14 392:6,11

392:16

released 118:12

199:18,21 200:3

releasing 384:16

relevance 136:8

148:9 152:2 280:2

280:7 282:3 389:2

393:12 399:2

400:22
202-234-4433

Neal R. Gross & Co., Inc.

Page 468

relevancy 136:20

relevant 31:16 82:2

87:20 152:15

262:1 275:11

278:12 282:2

283:13 286:18

288:3 319:2 344:1

345:11 349:8

356:19 361:4

386:3 407:9 415:5

424:6

reliability 369:15

reliable 165:22

346:9

relied 154:20

relies 294:16

reluctant 257:19

relying 65:18

remain 125:6

215:21 330:3,4

remainder 20:12

remained 109:6

remaining 10:10

324:4

remains 45:22 46:2

106:3 111:12,20

112:1,19 393:18

398:22 399:2

remarkable 266:17

remarks 3:6 183:1

377:7

remember 159:2

214:5 215:1

216:19 276:5

279:22 317:13

362:4

remind 389:22

reminded 133:15

reminder 4:19

removal 232:14

remove 214:15

removed 201:6

reopen 94:20

repeat 22:15 47:9

145:12 256:17

419:6,8

repeatability

417:12,14

repeated 418:12

repeatedly 173:6

replacement 14:4

replica 250:4

replicate 64:1

replication 371:22

report 25:7 102:13

107:20 108:2,4

109:7,22 110:6

113:10 116:4,5

170:7 171:12

188:22 202:22

203:15 206:22

209:14,18 246:1

267:12 269:3

286:17 297:6

339:21,22 340:9

345:10 361:13

364:11 366:8,16

366:19 367:14,15

423:21,22

reported 14:10

113:8 171:14,21

172:4 175:6 192:5

216:6 248:5

253:19 268:13

278:2,14 349:19

351:11,17 352:3

352:22 353:9,22

354:9 357:15

395:11 397:9

reporters 120:16

reports 116:16

120:15 225:8,21

350:9 352:6

354:21 364:21

represent 15:7

97:19 111:7 130:5

144:6 177:18

296:1 379:1

representative 13:3

17:22 169:21

253:7 278:17

280:5

representatives

292:17

represented 16:18

133:14 375:4

representing 51:8

325:8 381:7

represents 18:2

143:22 373:8

reproducibility

370:7,10,20 371:2

371:4 372:5

reproducible

371:12

reproductive 7:3,4

7:12,14 8:1

142:12,15 144:20

147:11 148:13,14

150:14 154:12

156:10 157:16

346:14 385:4

386:2,19 391:15

reproductive/dev...

386:1

Republication

122:11

request 92:9

require 21:4 170:6

280:14 333:9

335:17 370:9,18

required 49:18

90:15 94:18 330:6

332:8 342:21

requirement 128:8

328:6 376:4

requirements

50:21 104:9 328:5

337:10 359:10

370:2,5,7 372:3,9

requires 39:7 62:12

requiring 119:3

328:1

reregistration

97:18 116:9

resample 14:5

301:15

resamples 16:7

resampling 287:17

rescale 311:15

research 7:8,17

9:11,21 92:20,22

104:7 107:20

108:2 109:7,22

110:6 119:4

121:13 124:17

136:1,17,22 138:1

138:6,17,18

149:21 151:7,11

153:1 160:4

235:22 255:19

256:2 342:7

349:16,22 391:12

420:10

researchers 349:14

359:16 389:6

resemble 154:13

resembles 305:2

reservoir 29:13

33:21 72:12

277:14,16,21

300:4 330:3

reservoirs 278:22

298:17 330:2

residuals 42:4

residue 101:8

103:5 270:13

278:2

residues 264:20

266:8 285:1,2

resist 397:22

resistance 107:3,13

125:22 130:15

resistant 107:11

179:21

resolution 92:17

293:19,20

resolve 286:22

resolved 92:17

resource 261:2

275:4 375:9

381:10 382:3

resources 24:3

99:19 100:7

102:13,14 339:8

339:15 375:3

381:11,18 417:9

respect 92:18 156:8

223:17,19 253:3

374:11

respectfully 92:9

respond 68:6 80:11

156:8 196:3

340:18

respondents

409:13

responds 165:7

213:4

response 33:9 50:1

54:3 97:16 108:22

118:14 142:20

143:15 145:16

146:7,11 150:20

152:11 154:22

157:8 166:3

167:16 173:21

196:5 198:14

199:22 207:12,21

208:8,16,21

210:16 211:4,15

211:17 212:8

229:15 236:8

238:4,5 240:4,5

240:20,21 243:13

243:15,17,20

245:8,17 247:18

248:15,20 252:7

252:11 253:21

254:1 291:7

371:18 372:21

395:5,20 401:14

402:10 414:16

415:12,20 421:13

425:6

responses 138:21

186:5 212:7 224:2

243:12 397:17

responsible 134:12

215:11 390:13

responsive 164:20

responsiveness

184:15

rest 19:17 89:9

177:19 262:8

323:4,10
202-234-4433

Neal R. Gross & Co., Inc.

Page 469

restatement 120:19

restating 329:17

restraint 241:10

243:1 252:3,10,13

254:5

restrict 120:5

restricted 233:7

360:10

restricting 53:2

result 23:6,15

40:16 45:16 100:8

102:9 104:3

116:11 142:3

145:15 155:1

162:5 168:14

192:7 197:21

198:10,14 201:18

205:5 206:10

209:17 226:5

287:2 347:6

384:19 390:10

392:16 396:7

resulted 172:18

resulting 102:15

352:18 392:5

results 17:11,13

18:19 23:17 52:16

53:9 139:4 142:16

143:2 148:14

173:2 190:4 196:7

201:3 206:5,7,11

209:3 210:22

211:22 214:6,9

215:3 234:10

245:22 251:17

281:7 316:9

337:14 344:11

371:11 388:10

395:19 411:17

412:4

resumed 96:16

323:22

rethink 95:21

406:14

return 96:8 183:8

returns 16:6 19:4,7

20:2,3,16 118:4

revealed 388:10

391:18

revenue 330:13

331:7

reversability 188:6

reversal 156:12

232:13

reversed 217:1

review 1:7 2:1 5:7

95:4 97:17 108:22

113:5,6 114:20

116:4,20 117:18

118:10,18,22

119:5 125:11

127:8 141:19

151:2 155:20

268:14 332:7

345:18 369:21

372:13 387:10

397:6,20 401:8

415:15 419:9,14

reviewed 128:2

156:15 284:14

342:9 343:12

345:8 357:8

366:15 367:21

381:5 401:1

reviewing 419:22

reviews 120:1

123:9 127:15

149:19 414:13

revised 399:21

411:22

re-registration

327:19

re-review 118:15

RfD 319:14

RIA 227:15

RIAs 227:17

rich 102:2

Richard 2:2,6 8:12

9:6 28:11 96:20

105:13 177:2

ridge 101:18

right 22:7 29:8

30:11,19 37:19

44:8 46:11,16

57:18 58:2 59:9

59:11,15,18 61:6

61:14 65:19,20

67:5,15 69:11,13

70:14 74:18 76:20

77:11 82:1 88:13

88:13,17 91:20

151:7 157:18

197:8 214:3 219:9

222:14,20 223:1

226:6,15 229:10

249:16,22 251:8

261:1 289:7,18,22

295:13,18 302:6

302:22 303:3,7

307:16 308:6

319:12 320:7

321:19 322:6,9

323:6 328:1

366:17,18 381:9

414:12 416:19,19

417:16,17 423:3

right-hand 15:19

19:11

right-of-ways

331:15

rigor 416:1,7

rise 205:11,18

212:18

rises 15:4 208:22

risk 8:22,22 9:5,13

23:2 81:11 82:7

94:21 95:6,9

141:10 151:22

152:8 182:9,16

186:3 258:9

281:18 305:3,3,5

319:19 333:17

351:7,19 352:20

353:10,14 355:3

357:15 361:1

363:14,20 369:14

369:22 370:4,11

371:14 372:7

377:11,13 381:20

384:13 387:16

388:5 391:6

393:12 394:15

404:2 408:10

410:11 417:18

418:10 419:17

424:17 425:4

risks 334:14,14

356:20 378:8

River 39:11 275:10

362:8

rivers 103:22

261:11 287:3

Riverside 6:4

RNA 165:11,17

RNAs 172:21

road 69:15 81:17

81:18 304:14

375:12

roadmap 87:7

Roberge 172:4

Roberson 324:11

324:13,19 325:1

338:10,14,18

339:4 364:6

Robert 2:5 137:15

robot's 417:21

robust 75:1,12

117:14,19 231:1

411:9,14,18

robustness 382:17

rodent 178:4 195:6

210:2 388:9

395:22

RODENTICIDE

1:4

rodents 164:1

177:7,13,15 248:4

254:1

Rohr 349:14

355:10

role 98:11 297:21

385:22 394:18

roll 55:1 124:3

rolling 31:5 76:19

96:1 259:20

271:21 272:2

288:12 307:19

312:12 315:12

rollings 292:10

romantic 129:18

room 10:4 80:4

81:6 92:19 123:4

123:5 133:8

286:12 287:10

324:3 382:16

rooms 304:1

Rooney 247:12

roughly 178:1

263:1 284:15

round 137:8 330:4

route 212:15 321:1

375:19

routes 396:6

routinely 107:7

356:10

routines 39:7

row 41:21

Rowe 247:12

RTI 269:4 290:11

304:7

rubber 69:15 81:16

81:18 304:14

Ruby 2:14 8:20

Ruby's 418:19

rule 47:13 49:2,8

400:22

ruled 406:2

rules 336:10

365:20 371:7

ruling 368:6

413:13

Rumsfeld 27:6

run 14:8 16:13

20:13 187:6

210:18 286:11

418:22 425:13

running 13:16

33:22 145:3 233:2

249:12 259:19

262:21

runoff 84:1 101:13

102:16 104:11

264:3 265:17

266:8 271:16

277:11 284:1
202-234-4433

Neal R. Gross & Co., Inc.

Page 470

288:16 299:10,20

329:20

runs 16:7,13 17:6

20:6 25:2

RUP 360:10

rural 260:2 284:9

359:19

R2D2 417:21

S

S 184:1,1,1

sacrifice 203:5

sacrifices 187:21

safe 90:9 114:16

121:10,11,12,21

122:1 259:10

262:13 317:2

325:15 376:4,16

379:6 382:21

safer 346:5

safest 114:21

safety 99:2 117:16

117:17,20 120:13

127:7 134:12

140:22 238:2

258:3,21 259:6

310:14,15,17,17

311:11 312:3

319:20 383:1

sagittal 223:2

salamander 355:17

sale 130:4

saline 254:17

sample 22:20 23:4

47:22 63:7 64:7

73:6 79:4 83:18

84:13 85:16

145:12 190:12

200:7 228:3 244:1

262:1 268:19

292:7 293:11,15

293:20 294:14,18

294:21 301:13,14

301:20 302:11

330:16 331:1,3

337:20,22 362:3

371:22 382:1

401:16 415:1,5

417:15

sampled 12:21

13:22 15:15 16:10

18:20 19:15,16

265:21 273:7,17

273:22 288:20

291:21

samplers 84:21

samples 15:8,21

21:15 23:4 45:8

49:16,21 71:16

83:16 85:19

166:14 214:4

225:10 231:8

243:22 245:21

259:11 263:6,7

264:1,2,5,6,8

265:7,8 266:10,14

266:14,15 271:16

271:20 272:3,4

273:7 274:17

275:3,10 276:13

280:14 282:6

284:6,22 287:19

298:12 301:22

302:16 315:19

316:2 327:1,7

331:4,19,22 332:3

332:13 335:4

336:21 362:14,16

sampling 11:14

12:2,7,17 13:14

13:17,19,20 14:3

14:15,17 16:1,3,4

16:9,15 17:16

18:10 19:20 20:10

21:5,9,13,16

23:16,19,22 24:3

30:21,21 49:20

64:20 65:7 70:6

71:10 74:13,19

75:1,13,18 76:1,3

76:17 77:20 78:2

78:22 80:14,17

81:19 82:6 83:18

83:22 84:19,20

85:14 86:12,19

88:10,20,22,22

91:4,16,18,19

92:6 167:7 200:12

260:15,16 263:5

265:4,12 266:1

269:19 271:1,15

272:6,17 273:4,18

274:13 279:7,13

279:18 280:10,17

280:22 281:1,4,9

281:11,14 282:1

283:7 284:2 288:6

293:1,6 296:6,10

298:11,15 308:1

327:3 334:11,22

336:9,13 337:17

379:19 382:3

San 7:16

Sanderson 164:20

167:13 171:21

sane 128:7

Santa 8:18

SAP 1:4,19 5:5

35:8 70:16 77:3

94:5 105:7 106:1

113:3 119:1,5

122:18 123:19

135:20 137:18

139:19 140:1

153:8 154:18

156:20 280:19

325:2 333:7 369:9

369:11,20 374:12

374:19 375:18

376:21 377:8,12

378:5 379:1

382:18 385:6

388:6 406:8 409:3

SAPs 113:7,11

123:4,6,12,17,17

375:1

Sass 339:7,8,10,14

364:22 365:18

366:6 375:7

Sass's 374:13

sat 94:2 318:2

satellites 129:10

satisfied 393:16

satisfies 49:8

satisfy 50:21

satisfying 415:13

save 222:4 297:15

savings 86:2

saw 18:1 24:10

29:5 33:17 34:3

69:3 130:20

167:11 224:16

231:22 240:4

249:14 286:10

317:13 390:10

saying 74:14 81:5

89:14 158:13

160:18 226:16

227:14 259:9

298:9 301:18

306:7 321:14,20

324:14 373:15

388:4 405:17

414:3 415:15

417:5 422:17

423:4,7,9,10

says 11:2 91:8

393:17

scaffold 171:16

scale 34:13 35:2

45:14 60:4 61:5

95:8 110:12 130:1

178:7 210:4

317:21

scales 293:15,16

scaling 46:9

scenarios 91:17

schedule 272:3

scheduled 118:18

schedules 13:18

schema 189:2

schematic 157:10

scheme 21:9

158:16 291:22

Schlenk 1:23 6:1,1

176:7,8 226:12,14

226:22 227:11,21

228:4,8

Schlenk's 180:4

school 6:21 100:22

science 2:1 9:22

105:8 107:21

108:1 109:20

116:16 117:8

118:3,20 122:1

163:2 333:14

345:11 383:1

Sciences 6:3 7:3,22

193:20

science-based

376:14 377:20,21

scientific 1:4 91:20

92:1 93:4 114:1

114:20 117:18

119:11 120:10

121:13 325:7

342:11 343:1,1

345:7 346:1,16

355:5 357:2

363:11 364:9

377:18 378:22

382:17 384:10

419:14

scientifically 82:22

115:19

scientist 105:16

303:11 339:15

418:22 420:8

scientists 107:7

123:2

scope 89:11 365:8

Scott 368:12,20

373:3

scrambled 175:2

scratching 232:5

screamed 286:11

screen 89:1 143:13

screening 73:3

150:11 330:21

scrutinized 369:17

SD 394:20,21 395:5

397:5,6 398:20

SDWA 262:16,21

263:9 265:15

266:3 269:9,15,19
202-234-4433

Neal R. Gross & Co., Inc.

Page 471

284:6 309:14

se 407:4

sea 98:2,2

seas 98:3

season 13:22 19:17

20:11 71:16 264:3

266:7 271:17

279:18 316:7

seasonal 13:21

23:22 87:1 358:9

Seasonally 21:13

seasons 101:22

second 5:12 32:9

38:6 40:6 41:4

47:22 55:16 94:19

122:17 145:19

150:9 159:8

219:22 229:7

264:9 268:15

274:10 296:18

303:5 343:21

372:7 390:7

secondary 168:9

195:1 217:4

secondly 180:14

secreted 208:7

secretion 148:2

153:15,16 194:19

197:16 199:16

200:5,8,22 202:6

214:2 215:7,16

218:2,22 219:7

221:1 390:6

secretions 215:11

217:7

secretory 194:4

section 76:11 135:4

137:9,14 138:10

183:1 385:11

sections 184:8

257:14 386:9

sediment 103:19

104:2 277:12

299:10 380:11

sedimentation

300:3

see 4:12 14:17 15:8

15:13 16:7,21

19:18 22:11 24:22

25:18 31:5 35:8

40:19 41:5 45:6,9

46:8 47:11 56:9

58:21 64:17 69:3

69:4 70:5,6 73:18

76:15 77:19 78:12

109:5,9 113:14

134:1 144:4 145:2

145:11,21 146:9

146:19,20 149:13

152:3 153:13

162:15 165:2,3,5

165:7,14 166:3,16

167:17 169:4,10

170:4,5,8,12,15

187:20 188:5

190:13 191:16

194:22 195:8

196:9 197:7,13,16

198:21 199:2

200:6,11,14 201:9

201:14,18 205:5,8

206:14,17 207:9

207:11,18 208:9

208:12 210:7

211:8,19 212:16

214:11,13,15

218:1 219:11

225:2 228:1,2,17

230:15,19 232:2

233:5 236:6

239:14,19 240:9

241:3,8,12,14,19

244:14,18 245:14

252:12,19 253:16

265:20,22 266:4,5

266:9 267:10,15

268:4 269:8,16

277:6 291:18

293:11 302:8

305:22 308:3,20

309:14 311:21

313:4 320:18

332:17 338:11

341:3 364:15

386:15 395:15

396:16 398:3

399:6 405:12,17

408:8 409:21

413:4,22 414:5

415:22 416:6

421:14,18 423:9

seed 46:7

seeing 34:19

144:12 151:19

159:18 169:14

228:12 234:7

341:10 392:18

414:22

seeking 24:4

seen 22:13 89:18

114:6 145:11

195:3 207:15

215:12 216:16

238:12 245:4

251:17 252:20

257:8 258:13

260:17 267:21

271:4 315:4

329:18 335:6

395:21 397:17

412:22 420:16

segue 235:17

select 55:18

selected 49:3

189:17 239:1

352:10

selection 53:11

selective 210:7

selectively 266:9

Selvage 2:16 8:2,2

10:2 180:22,22

221:4,5,16,20

222:2,5,7,15,20

223:1,10 233:10

233:10 251:14,15

semi 32:22

semi-chronic

334:15

send 69:8

senesce 397:1

senescence 142:12

142:15 144:9,20

146:19 147:2,12

148:13 154:12

157:16

senior 134:10

258:3

sense 45:5 84:7

94:1 128:9 281:18

291:5 305:6 418:9

423:17

sensitive 186:14,18

210:9 220:22

241:1 242:13

330:21 407:21

sensitivity 153:19

153:21 185:18

192:20 195:19

196:8 209:16,19

209:20 219:15

225:20 394:21

395:19

sensitized 60:2

sensor 354:5

sentence 154:21

separate 34:15

88:3 94:13 119:1

185:19 188:12

218:18 291:4

292:21 326:7

411:5 424:4

separately 34:22

110:19

September 77:4

94:8 124:4

sequence 134:22

series 18:12,12

50:8 66:16 140:11

168:11 171:14

202:8 206:8 212:9

242:14 286:3,12

seriously 378:6

serum 203:19

353:1 390:20

serve 97:14 325:13

376:21

serves 330:9

service 102:14

328:14,20 329:19

330:8 334:4

378:21

serving 4:5 379:2

session 1:18,21

4:14 5:4 8:6 10:1

24:8 25:20 26:22

30:4 32:6 33:7,10

35:4,14,18 51:16

53:17 57:13 58:20

59:5,20 60:3,10

60:21 61:7,14,20

62:16,20 63:1,4

65:8 67:9 69:5

79:22 83:8 86:3

90:22 93:18 96:6

96:17 119:6 124:7

125:2 126:19

128:4 131:1

133:10 135:9

155:13 157:3

160:9,13 162:20

176:5 178:19

180:1 182:18

183:10 184:3

193:8,16 200:13

218:8 221:3

223:12 226:12

229:4 232:9 234:3

249:1 250:11

251:13 252:21

254:7 255:6 256:9

283:17 285:4

287:10 290:1

297:14 301:1

302:19 319:5

323:3,7,17 324:1

324:10,21 338:8

338:11,16 339:2,6

364:18 366:10

367:12,20 368:5

372:19 373:2

379:8 383:3

387:18 394:9

398:6 400:14

404:7 405:6,10

406:16 407:14
202-234-4433

Neal R. Gross & Co., Inc.

Page 472

408:5,18 410:6,15

411:19 413:7

416:16 418:15

419:19 421:5

423:16 425:7,9

set 39:12 47:19

53:13 55:18 56:14

57:8,10 58:10

63:13 65:18 66:12

67:13 68:14 71:14

74:12,18,20 75:1

165:9 172:10

175:1 186:2

190:11 264:11

265:6 271:18,20

275:13,19 279:4

279:10,21 288:4

289:8 291:15,16

300:13 308:13

319:11,13 332:16

335:17,19 337:4

340:11 409:4

411:13 414:8

sets 26:1 37:20 64:5

64:7 75:12 76:1

167:21 262:12

274:8,16 280:20

281:6,8 282:3

286:6 290:15

292:20 308:17

324:6 402:17

setting 237:4

393:20

settles 123:2

settling 277:14

seven 15:15 16:3,10

17:18 20:11 26:4

76:3 80:8 133:19

134:1 207:18

229:18 279:7,18

326:15 335:11

356:10

seventh 208:2,13

seven-day 16:19,20

17:1 26:3 88:16

273:15,18 281:11

283:2 292:11

308:21

seven-year 312:8,9

severe 63:22 115:9

severely 362:2

sex 140:14 142:2

156:12 224:1,5

225:4 351:18

388:13

sexual 191:2,7,9,21

sham 213:20

214:10

shape 21:17,21

42:9 45:12 46:1

52:20 53:1,3 72:8

76:7 282:13

share 105:22

107:17 138:6

139:4 323:15

378:17,18

shared 113:16

sharp 26:5 28:13

sharper 28:20

shed 403:14

she'll 99:8

shift 57:21,21 59:1

112:17,18,22

shifted 230:14,15

shifts 143:12

shoot 222:11 421:4

short 27:20 79:16

119:13 137:14

163:13 165:12

166:11 178:7,21

182:10 184:18

186:2 254:6

255:18 267:8

283:4 292:15

293:4,15,16

312:22 313:5

324:6,20 347:18

387:22

shortening 424:11

shorter 17:18 21:4

71:20 77:1,5 95:7

178:6 186:1,11

272:17 274:12

311:20 335:5

339:13

shorter-term 288:3

shortly 137:7

212:12 246:19

short-term 27:16

52:13 53:6 187:19

288:15 389:15

shoulder 328:12

show 15:2,20 16:12

17:21 36:5,7

74:18 121:2

151:13 164:14

167:18 168:6

174:1 189:13

195:21 200:19,21

201:16 206:5

210:5 212:3

216:10 229:15

230:2 232:5

237:19 252:2

258:16 271:2

283:6 284:19

292:12 293:5

361:20

showed 142:10

143:4,18 170:13

171:18 172:12

173:20 179:13

194:5 200:20

228:15 229:17,22

231:8 252:19

253:1,14 311:14

319:10 321:7

351:19 361:17

371:17

showing 50:6 146:4

172:17 195:4

206:11 216:7

298:8 316:10

359:14 395:16

421:18

shown 22:3 144:22

148:22 163:15,19

164:4,20 165:10

168:21 169:6,20

184:22 191:4

197:22 201:3

209:3 245:2

254:21 312:1

337:11 344:16

347:11 348:3

355:12

shows 21:8 39:9

46:5 47:14 48:9

145:16 198:2

265:13,14 287:2

307:9 379:22

392:21

side 84:18 86:10

195:8 205:4 255:8

256:6 278:21

298:17 301:5

303:16,19 327:2

331:10 423:2,3

Sielken 272:19,19

291:6,7 296:4

302:2,4,5,17

304:7 305:12

sign 119:11 241:22

272:12

signal 172:14

390:18

signaling 173:1

signed 111:15

significance 151:9

206:19 209:11

234:20 235:7

395:7 403:11

415:3

significant 100:9

102:6 111:15

114:6 122:13

128:18 136:10

166:18 192:13,16

206:16 230:3

234:11 242:8,9

263:18 308:4

331:8 351:12

352:14,16,18

353:18 354:2,3,11

356:2,8 357:5

358:11 359:21

396:13 417:2

significantly

241:16 268:6

silt 100:20

simazine 86:21

89:19 326:18

similar 13:13 20:17

21:7 56:9 97:11

109:12 110:2

111:11 120:12

132:6,18 152:22

155:8 159:18

163:22 167:16

185:22 211:2

213:4 241:11

243:2 244:19

245:3 247:14

257:4 265:6

268:11,13 274:5

281:7 319:9 353:5

397:12

similarly 47:1

209:12 240:2

242:21 246:2

254:4

Simpkins 134:4

136:3,17 151:15

161:3 162:22,22

176:17 177:14

179:6 181:7 304:5

317:11

simple 27:18 73:19

73:20 79:19 88:4

128:12 188:15

242:5

simpler 101:5

336:14

simplest 86:20

simplify 78:16

300:15

simplistic 73:10

simply 18:11 45:11

45:14 114:18,18

116:7 130:21

161:17 164:8

318:7 338:4

396:15 397:22

simulate 11:18

36:8 46:20,21
202-234-4433

Neal R. Gross & Co., Inc.

Page 473

47:3 50:10,11,14

50:16,18 58:5

60:16 64:19 65:1

simulated 271:20

279:6,9,11 288:1

302:7

simulation 30:11

60:17 62:13 279:5

302:16

simulations 46:6

49:1

single 13:8,11 14:7

14:18 15:14,14,17

16:18 17:9,19

18:3 98:8 140:14

140:14,14 171:12

184:20 212:17

215:16,20 239:10

239:16 330:15

single-day 17:1

306:11

sips 85:15

sir 156:6 157:2

182:17 183:9

siRNA 174:10

175:3

siRNAs 174:12,13

sister 149:22

sit 134:3 304:4,5

site 62:6 83:17

84:13 104:22

140:14 174:3

290:20 294:13

295:8,9 337:8

sites 75:6,17 140:10

140:16,18,19

265:1 266:19

267:19 276:14,16

288:21 289:5

294:10,17,18,19

294:20 296:2

396:8

site's 294:22

site-by-site 285:12

sitting 425:10

situ 196:17 198:6

situation 81:19

178:2

six 10:11 15:10

93:2 144:16

185:15 194:13

244:1 248:11

250:18 264:18

271:17 276:7

332:9 363:2 391:4

six-hour 244:1

six-month 144:3

185:12

six-year 332:7

size 28:14,20 29:13

33:22 34:4 72:14

72:15 169:8 231:7

371:22 401:16

411:15 415:1,5

417:15

sizes 22:20 23:4

29:20 268:19

skin 359:6,8

skinny 19:2

Slaughter 368:13

368:20,21 372:22

sleep 340:22

sleeves 124:3

slices 201:2 223:3

306:7

slide 19:14 57:15

93:21 140:5 143:4

143:19,22 149:13

149:14 150:17

153:6,12 154:17

157:5 163:16,20

185:21 186:5,15

188:19 189:1

190:4,18 191:6

192:10,18 197:22

198:2 200:20

226:16 227:12

244:7 285:7

287:13 298:7

317:12,18,19

319:8,9 320:7

slides 143:18 149:6

155:17 169:6

170:14 183:3

212:10 267:4

302:22 303:4

324:16

slight 161:21

slightly 119:8

249:21 401:8

402:15 414:11

416:4

slopes 241:15

slotted 340:19

slower 29:7

slowly 15:5 175:19

230:18

small 49:13 60:4

99:14 129:16

166:17 172:21

199:22 226:2

228:9 229:20

276:7,9,9,21

277:4 329:13

330:8 401:16

415:1 421:3

smaller 28:20 30:3

68:8 82:14 284:10

299:3 346:3

smallest 129:20

smart 23:19

smarter 72:17

smearing 191:8

smears 143:20

smooth 42:19

300:13

smoothing 52:14

63:19 65:11

100:16 290:6

snapshot 95:3

230:7

Social 9:20

society 5:15 100:9

107:21 108:2

109:21 129:16

soil 100:6,14,18,19

101:1,5,7,11,22

102:1,4,10,11,15

102:17 103:5,7,17

103:18 104:3,4

sold 356:13

Soldin 210:12

solid 123:13 294:1

solubility 169:13

169:18 176:21

181:15 182:1

317:15,20,22

solubilize 169:12

soluble 380:14

382:12

solution 169:15

317:14

solutions 169:5

somebody 275:17

313:19

something's 187:18

420:19

somewhat 15:5

113:8 128:21

129:14 131:8

159:7 235:21

268:11 396:16

soon 69:1 167:9

328:17 333:12,18

335:13

sorghum 97:8,10

sorry 12:19 28:17

45:8 160:15

177:21 256:12

278:10 310:7

323:2 349:2 384:5

sort 26:13 32:22

37:9 40:2 41:6,11

42:3,9 48:9 49:12

52:19 53:5 54:11

55:1 63:2 68:15

84:1 85:2 95:3

163:4 177:6 259:5

264:5 272:10

274:18,20 279:5

293:2 300:9 327:2

328:19 340:3

356:15 413:16

416:15

sorts 55:15 382:19

sound 114:1 118:20

sounds 27:3

source 38:10 54:22

107:22 108:5

177:5 270:6

271:19 272:13

280:14 282:11

286:1 299:4

319:18 320:20

321:5,12 322:11

328:2 329:5,16

sources 26:5 54:5

55:22 56:5 172:7

177:11 262:11

298:10 331:22

333:4 375:15

sourcing 270:2

South 349:14

space 290:8 293:19

361:7

span 119:3 146:21

spanned 354:5

spanning 41:5

sparser 63:21

spatial 23:20 70:20

77:16 282:20

spatially 70:2,5

71:5

speak 36:1 138:16

151:6 237:2 406:5

speaker 135:17

339:7

speakers 11:8

134:22 348:5

speaking 91:15

185:12

speaks 55:1

special 90:14 97:17

108:22 113:4,6

118:21 125:11

187:18

specialist 7:4

specialty 7:17

species 142:1

156:19 163:12

170:9 172:22

173:16 174:10

218:13 229:10

349:21 350:11

specific 84:20 87:9
202-234-4433

Neal R. Gross & Co., Inc.

Page 474

87:17,19 105:1

127:9 142:2

178:14 179:7,8

210:9 224:9

247:17 308:20

309:21 310:5

336:6,17 337:1

374:4 375:13,16

377:22 395:18

404:16 422:5

specifically 128:1

156:11 185:6

234:2 352:8

specificity 142:22

225:19

specified 77:22

spectrometry

203:19

spend 10:9,13

193:22 194:11

384:2

spending 235:19

375:10

spent 130:16

194:17 276:6

304:1 323:13

419:20

sperm 349:1 350:7

359:19

spikes 358:9

spiking 363:1

spills 359:5

spite 129:4,4

splashes 359:6

spleen 240:17

251:18,20

spoke 145:10

146:19 287:11

sponsored 237:18

sporadic 166:11

spot 48:10,19 49:7

Sprague 140:11,13

140:17 142:10,11

142:15 143:8

144:7 145:19

146:1,12,17,20

147:3,14,18,21

148:3,6 150:21

152:9 155:8 157:6

157:17 185:14

186:13 202:12

207:22 215:18

243:6 384:19,22

385:4 388:11,17

397:17 413:12

spray 128:20

sprayer 129:10

130:3

spreadsheet 338:1

spring 330:2

336:22 358:1,5

square 275:16

276:10

St 290:18

stack 111:5

staff 297:18 325:19

338:2 373:13

stage 237:4 368:16

stages 127:3,11

346:18 347:19

376:18 411:1

staggering 105:9

stakeholder 119:16

stakeholders

118:11

stance 395:14

stand 303:8

standard 68:12

120:10 159:9

285:1 328:13,19

337:7,17

standards 204:13

210:6 332:9

standpoint 85:2

307:4 374:8

standpoints 117:2

stands 146:20

star 37:10

start 5:18 10:21

14:5 24:11 34:17

51:19 56:13,18

57:7 67:18 68:1

69:2,20 71:9 74:6

97:1 112:16

144:11 169:14

232:11 235:3,8

261:6 301:7 306:7

308:7 316:11

324:14,17 338:20

384:9 387:18

388:4 421:3

425:13

started 4:3 72:21

74:5,14 94:1

99:20 110:4 141:3

151:3 187:4 233:3

341:20

starting 17:7 68:2

74:12 144:4

starts 144:9 146:20

state 4:20 6:8,13

8:3 9:15 119:22

129:3 332:16,18

336:4 343:1 352:6

369:13 371:7

397:2

stated 118:16

statement 127:6

183:6,7 288:10

309:17 393:17

423:20

statements 156:8

states 33:16 97:20

98:4 102:15

103:21 110:5

111:7 132:9

262:15 263:21

265:3 332:6

378:20

static 270:6,12

272:5 280:14

statistic 37:5 48:1

statistical 83:12

84:3 85:2 159:6

235:5,6 236:16

297:3 325:17

326:3 354:17

415:2 420:22

statistically 166:15

192:13 351:12

352:14,16,18

353:18 354:11

359:21 417:2

statistician 9:20

35:21 326:10

statisticians 45:5

69:6 91:10 315:20

416:18 421:8

statistics 5:14 8:15

8:19 25:18 29:6

37:4 43:22 111:14

191:4 304:8 305:1

415:3 419:2,22

status 149:14

statutory 309:13

309:17

stay 175:14,20

steadily 102:12

121:4

steeper 268:11

step 25:4 150:18

195:14 314:13

328:1 391:8

stepwise 291:11

376:14

steroid 204:4,17,19

204:21 205:12

208:6 225:22

steroidogenesis

387:6

steroidogenic

205:1

steroids 204:13

205:10

Steve 6:15 9:18

90:15 91:15

138:15 202:19

210:12

Steven 1:17,20 2:8

steward 99:1

stewards 103:1

stewardship 100:1

104:8,18,21 105:2

114:7 121:6 268:9

329:5

stick 338:2

stimulate 390:21

stimulation 195:9

stochastic 28:9

265:18

Stoker 185:5

stop 11:2 51:14

133:11,21 193:3

storage 277:16,21

store 315:21

stored 85:19

story 120:18

straight 170:10

236:14

straightforward

58:17

strain 142:2 389:10

395:18

strains 144:13

388:12

strange 397:21

strategies 12:2,7

13:15 14:15 17:15

21:14,16 23:20

36:11 44:21 45:3

50:20 63:7 64:8

336:6

strategy 13:21 14:3

49:2,3 83:21

334:11 335:20

stratified 264:2

271:16

stratify 30:8

stream 29:14 66:13

277:4 298:21

streams 56:11

75:14 104:1

261:11 276:7

Street 1:17

strength 142:22

399:12 413:3

strengthened 153:2

stress 7:18 8:3

208:20 224:2

238:3 241:10

243:12,13,14,17

243:20 252:3,7,16

253:20 254:1,5

395:20 396:3,9

397:19
202-234-4433

Neal R. Gross & Co., Inc.

Page 475

stressor 208:22

224:6,8

stressors 240:13

244:12,16,19

251:22 254:5

stretched 341:11

341:13

strike 181:18

strikes 115:5

striking 146:10

string 41:20

strips 104:14,14

strong 171:22

395:21 411:18

412:6

strongly 149:17

struck 374:13

378:19 380:5

structure 45:22

55:4 67:18 68:7

84:8

structures 336:9

347:7

struggled 32:10

struggling 37:2

stuck 392:19

421:13

studies 1:8 5:8 8:10

87:17,22 107:22

108:18 109:17

110:3,5,8,16,17

110:19,21,22

111:4,9 118:9

119:5 136:19

138:7 139:1,7,9

149:7,12 150:1,3

150:12,14 157:13

158:6 160:7,8

164:11,15 168:12

168:19 175:1

176:18 177:12

178:4,4 179:11

181:13,16 182:7

182:14 184:18

185:2 186:1 192:6

194:2 198:10

202:9 206:6,8

210:22 212:10

213:10 216:7

221:11 222:8

224:17 229:14

233:1 237:16,17

237:22,22 242:6

242:15 246:4

247:12,22 248:14

249:14,21 250:5

253:11,12 257:8

257:10 284:8

319:1 344:21

347:12 349:10,16

349:19,20 350:3

350:11 351:3,11

354:22 356:18

357:19 359:17

365:5,13 366:14

366:14 368:2

369:16,19,20

370:3,4 372:6,8

372:11,13 385:11

387:14 394:6,13

395:22 396:11,14

396:18,19,19

397:4,5,6,10

398:9 399:3,10

400:1,3 401:1,9

402:15 403:6,8

413:3,17,18 414:4

414:10 416:10,13

420:1,4,16 421:18

422:2,14,18

study 8:3 34:15,16

44:20 50:1 79:2

80:2 94:11 108:16

139:5 141:8 144:3

144:11,17 158:10

176:18 182:6

185:6,7,11,12

186:16,20 187:20

189:21 192:3

198:17 211:3

212:1 213:19

214:10 215:4

229:12 234:13

242:1 245:5 246:7

246:11 249:10,12

250:9 256:13,18

310:8,9,10 331:19

332:1 352:2,11,21

353:8,22 354:15

354:17 355:10,16

356:5 357:5,13

371:9,21 401:10

401:14,19 402:11

402:11 411:8,9

414:15 416:19

417:4,6 418:7,8

419:6,8 420:5

421:1

studying 168:12

stuff 59:6 69:14

127:16 183:4

222:11 277:13

307:11

subgroup 187:13

187:14 188:4

189:5

subgroups 191:14

191:16

subject 255:19

329:14

submit 139:7

228:21 282:10

submitted 110:5

119:5 137:2

225:10 326:14

373:12

subsection 135:7

subset 21:22

270:11

subsets 270:4

271:19,19

subside 54:6

substances 395:2

substantial 84:11

84:14 117:10

209:15 317:3

346:15

substitute 131:19

subtypes 181:9

sub-chronic 334:15

success 55:13

288:11

succinate 170:17

succinctly 337:10

sucking 183:11

sudden 294:2

suffered 115:9

suffice 238:13

310:22

sufficient 258:17

283:3 316:10

343:14 399:12

417:9

suggest 48:14

91:15 122:19

123:19 196:7

199:11 219:3

281:9 282:4,8

283:12 337:15

345:19 385:12

402:17 403:9,21

412:12 413:5

419:15 421:22

suggested 156:1

233:19 344:13

356:5

suggesting 156:5

174:19 197:14

207:21 213:2

214:17 215:9

252:14 408:1

412:15

suggestion 25:5

158:3 159:14

299:16

suggestions 79:10

182:8 345:19

suggestive 355:1

suggests 95:6

156:16 200:15

208:18 210:15

216:6,14 350:12

suitable 193:7

272:11 280:16

sum 260:20 266:11

355:9,11

summarize 17:11

215:13 248:2

269:2 373:14

388:3

summarized

337:11

summarizing

136:21 137:22

285:13,14

summary 3:18

23:17 43:21 50:4

69:9 93:19 255:10

267:8 271:8

340:12,13,13

summed 348:13

357:10

summer 116:4

175:6

summing 60:13,19

superb 261:22

superfusion 201:7

223:4

supervises 261:19

supplement 90:9

supplemental

80:21 342:4

supplied 116:3

supplies 284:2

308:1 314:9 315:5

supply 259:9 277:3

277:17 299:7

300:10 379:7

383:2

support 280:11

375:5 387:15

400:18 413:22

supported 95:8

387:7 389:13

394:20

supporting 122:1

supports 395:14

suppose 54:20

365:14

suppress 160:20

suppressed 156:2

187:12

suppressing 394:19

suppression 148:10

160:16,22 162:13
202-234-4433

Neal R. Gross & Co., Inc.

Page 476

179:17 184:22

189:15 190:15

191:17,19 194:4

254:3 395:12

sure 29:2,17 50:7

51:11 66:19 70:11

81:1 83:13 84:9

111:1 119:12

127:15 135:12

161:5 175:3 181:5

181:11 189:18

219:2,4 227:21

230:6 245:14

319:12 320:3

321:6,10 348:13

401:15 402:9

404:3 406:19

411:17 423:22

surface 78:18

103:5,20 104:2,12

116:19,19 261:5,7

262:11 263:3

284:1,18,19,22

285:2,3 327:17

331:17 337:18

360:12 379:17,20

379:20 380:19

surge 142:9 145:9

145:13 147:4,13

148:10 184:21

187:12 188:3

189:15 190:3,9

191:17,18 192:21

195:5,13 197:1,2

197:3,9,12 198:18

199:3,7,12 202:7

212:8 214:20

215:1,2,4,10

216:13 217:7

220:1,4 230:1,12

230:13 231:6,7,14

232:4 384:18

394:19 395:13

surges 147:10

231:3

surprised 166:14

192:7 231:16

234:10 395:15

409:18

surprising 106:5

126:2

surprisingly

109:16

surrounds 309:18

389:21

survey 8:9,13 112:7

survival 348:2,3

Susan 2:2 7:15 35:4

susceptibility

344:18 347:22

397:18

susceptible 146:4

suspect 52:2,15

53:8 338:16

sweet 48:10,19

Swenberg 135:18

139:16,17 157:12

157:21 158:2,17

161:2 182:12

185:12

switch 218:14

323:9 324:15

synapses 347:5

syndrome 153:7

synergistically

355:13

Syngenta 82:1

84:10 85:21 94:19

97:1 110:14,19,21

127:21 133:19,19

134:6,11,18

136:16 138:17

156:7 237:16

245:5 258:3,4

262:16,18 263:12

285:11 303:11

327:8 340:6,6

381:8 397:10

420:17

Syngenta's 256:1

synthesis 198:3

255:3

synthesized 171:15

synthesizing

204:21

synthetic 12:15

196:3 299:11

system 7:14,14

12:19 23:1,2,2,14

33:4 54:7 66:7

67:3 72:11 75:20

78:4 81:20 89:15

90:12 117:13

130:9 137:21

138:13,21 176:2

180:5 184:16

187:11 192:20

198:11,14 201:1

201:20 220:21

224:11 233:16

236:8 238:17

246:6,13 248:7,9

248:13 251:3

257:11 259:15

272:22 273:9

275:1,9,11 277:18

278:9 279:19

286:5 288:19

289:13 298:8,20

299:4 301:9,12,15

301:22 327:5

329:10 330:9,13

331:5,8 346:13,14

348:19 350:2

355:7,15 356:4

364:15

systems 13:2,4,4,6

13:18 15:4 17:13

21:22 22:9,12,14

23:8 24:2 54:3

55:19 56:3,7,10

56:15,16,20 71:4

71:15 74:5 75:7

75:10 86:16

101:16 103:13

180:19 236:3,8

262:10 263:3,9,15

263:19 264:12,19

265:3 269:11,16

269:21 270:3,5

271:13 272:6,12

273:14 276:21

280:6,13 282:10

282:12 284:9

285:16 289:12,17

289:21 298:5

301:7,17,19 311:9

329:8,13 337:18

337:21 340:4

346:13,14,17

357:10 362:14,17

362:20 378:15

382:8,15

system's 277:2

387:2

T

T 2:13 184:1

240:19,21 247:17

247:17

table 16:18 18:16

22:4 36:14,22

91:5 134:3 255:8

256:7 269:1,5

271:11 272:20

297:1 303:17,19

326:1 377:3

383:20 423:4

tables 14:12 15:12

340:3

tags 418:18

tails 59:7

take 10:14,19 30:10

33:19 34:12 35:2

37:22 38:4 45:10

46:19 56:22 57:11

60:6 70:13 71:11

75:11,22 76:14

83:1 85:15 86:20

93:1 95:12 96:7

115:4,17 116:15

117:4 127:14

164:22 182:17

193:5 195:22

202:20 243:9,21

245:20 259:4

269:14 300:20

301:7 304:22

305:8,9,11,18

306:10,15 307:9

307:14 311:14

312:5 314:22

319:3 323:4,18

324:5 327:7 360:4

361:6 374:3 380:7

380:10,12 402:9

414:4 416:8

419:16 422:19

taken 13:3 116:5

120:20 123:15,17

195:6 201:10,13

202:18 204:21

211:22 222:22

239:8 264:6

274:17 276:12

298:12 307:1

327:3 332:3

361:11 362:13

378:6 389:18

takes 64:21 65:22

130:8 410:19

take-home 223:15

talk 11:16 80:7

99:22 139:22

184:13 189:3

259:5 260:4,6

287:5 325:1 330:5

369:7

talked 12:5 60:21

86:8 95:18 151:5

288:1 411:21

talking 24:5 31:14

35:10 42:21 44:19

61:18 65:12 81:1

87:18,22 92:5,19

135:3 161:11

162:12 246:15

258:6,8,21 261:10

295:9 296:20

297:19 315:2

317:11 338:20

400:19

tandem 203:18

tangent 233:12

Tanguay 156:9
202-234-4433

Neal R. Gross & Co., Inc.

Page 477

tank 106:16

tap 304:16 363:9

target 23:20 87:9

175:4

targeted 21:13

289:11 391:13

targets 150:22

151:5

task 106:1 254:12

taste 329:1

team 351:17

technical 51:20

222:7 325:7

419:12

techniques 178:14

291:4

technologies 83:12

85:10 109:14

111:13,19

technology 85:13

130:5

teeth 121:16

tell 43:8 61:11,15

70:12 129:3 202:9

203:10 206:6

217:19 231:9

237:15 274:11

telling 194:1 364:3

tells 417:2 420:6,9

temporal 23:21

71:7,8,11 77:16

77:17 78:17 95:7

95:17 142:21

282:20 373:16

399:19

temporality 95:11

temporally 70:3,5

152:13

ten 61:12 62:7

109:5,8 168:2

233:21 235:2

263:21 265:3

276:13 310:7

tend 68:8 75:4

182:13 186:3

270:13 299:21

tendency 395:21

tenfold 228:10

310:16,17 415:16

tens 22:22

tenuation 230:3

ten-day 292:11

ten-year 107:19

terbuthylazine

120:14 131:21

132:7

Teresa 2:9 7:9

term 17:19 27:21

33:1 37:19 39:20

45:12 53:5 255:18

255:18 271:6

311:20 312:6,22

313:1,5,6 314:1

315:8 335:6

termed 412:20

terminated 109:21

termination 190:21

199:6

terms 21:2 23:3,13

35:1 41:10 49:20

55:22 73:13 83:3

86:18 91:16,20

93:14,16 124:10

197:20 202:5

209:21 210:7

216:15 217:13

219:6 226:16

233:20 239:9

241:1,6 245:8

248:19 295:21

296:3 300:10

371:8 381:4

416:13

terraces 104:14

terribly 59:19

66:11 192:7

test 48:1 64:7

111:16 188:5

225:10 226:8

291:12 292:3,8,18

370:17,21 371:4

371:15

tested 198:19,22

238:2 296:21

344:16,21 349:8

351:1,8

testes 348:22

testimony 364:22

testing 217:14

314:10 346:6

357:20 381:1,5

testosterone 136:11

203:22 205:16

206:2 209:6

224:10,18 225:12

tests 291:18

Texas 136:4 163:1

text 389:17,20

TGAC 159:3

thank 5:3 10:1

11:21 30:3 53:19

91:2 97:3 99:10

105:5,12 124:6,7

133:17 139:17

155:12,17 176:4,5

182:4 183:8 184:7

194:13 218:10

220:19 221:2

223:11 229:2

236:18,22 237:1,3

256:7 257:22

258:1 294:12

302:4 323:12,15

325:2 339:10

364:17 368:11

372:17 373:1,2,5

378:16 379:7,8

380:21 383:3

398:5 404:7 425:9

425:15

thanks 25:19 96:13

99:10 113:2

118:22 321:3

theory 24:18

Theresa 218:10

thin 19:2 338:22

thing 22:19 26:21

28:1 40:15 41:18

44:8 46:3 60:5

66:22 67:10 74:16

84:17 89:4,14

106:13 112:6

141:15,15 144:17

153:5 179:16

184:12 208:9

229:7 246:22

259:8 266:4 267:5

268:15 278:20

300:17 305:17

306:14 318:9

321:20 324:19

358:14 364:1

388:5 393:1

410:10,12

things 18:17 29:15

34:20 44:2,11,14

45:9 52:19 54:15

54:19 61:21 63:19

64:11 67:5 74:2

76:13 77:7 78:9

86:6 87:5 92:21

95:21 102:8

126:22 127:20

128:2,14 129:22

143:6 157:10

175:5 181:18

217:12 224:4,18

236:1 243:10

284:3 290:7,8

293:7,10 296:5

300:15 303:18

305:7 343:7

354:19 355:11

365:6 397:21

418:22

think 5:1 10:2 14:1

14:20 18:7 21:2,8

22:3 23:5,21

24:20,21 27:13

28:7,8,22 29:12

29:14 31:20,21

32:4,9,11,20 33:4

37:1 38:20 39:11

39:16 42:18 48:16

51:12,18 52:1,5

54:16 55:5,12

58:3,9 64:4 65:3

65:14 68:16 69:6

70:9,22 71:17

74:2 76:13 78:5

79:21 80:10,13,18

81:12 84:17 85:7

85:8,11,21 86:7,8

91:19 92:3,11

93:11,19 95:10,12

95:15 96:5,7

113:12,16 119:8

123:13,21 126:9

128:7 129:7

132:22 133:6,10

143:4,17 155:13

155:19 156:19

157:21 159:15

161:12 162:21

163:2 164:15

165:8 166:17

167:20 176:19

177:3,8 178:15

182:11 183:5

194:5 202:2 204:4

206:12 220:12

221:20 223:22

228:9 231:14

233:9,14 234:3,19

236:6 238:9 244:5

246:17 249:5

254:12 255:7

256:10 257:3

260:18 265:10

266:11,12 270:15

272:8,10 275:2

276:1 282:14

285:16,18 287:4

288:9 289:14

293:3 294:5 295:6

295:8 297:9,15

298:18 299:15,19

301:6 302:14

304:2,12,15

310:21 316:18,22

317:4 318:13

320:6 321:19

323:3 328:3,4

336:2 337:12

338:4 339:22
202-234-4433

Neal R. Gross & Co., Inc.

Page 478

340:17,22 341:22

343:8 349:12

354:19 355:7

356:17,22 357:3

357:11,22 358:10

358:10 363:21

364:8 365:19

366:11,16,18

367:5,12 368:1,5

375:22 376:10,19

379:13,16 381:8

382:14,22 383:13

383:22 392:3

394:3,6 399:18

401:17 405:16

406:18 407:10

408:16,21 411:11

412:13 413:2,10

416:13 417:7

419:18 420:3

421:1,14 422:7,7

422:10 423:12,16

423:18 424:3,17

425:7

thinking 64:18

67:10 85:7 87:6

216:22 222:10

280:21 317:10

379:13,19 382:19

416:18

third 40:8 41:3

55:2

thirds 135:11 420:4

Thirty 271:15

thorough 141:19

178:6

thoroughly 288:20

349:8 367:21

374:21

thought 63:3 67:1

93:22 181:4

187:10 234:7

257:9,20 272:8

295:14 318:2,3

404:14 423:7

thoughts 86:6

176:14 374:5

thousand 319:15

321:11

thousandfold 309:7

310:13 311:1,11

312:3

thousands 22:21,22

49:22

three 10:17 12:18

17:19 30:19 53:2

59:14 67:11 76:2

111:3 136:18,22

137:1 138:2 140:8

148:21 165:15

169:18 170:16

184:20 191:14

195:11 202:14

206:9 207:2

239:21 240:2

247:7 255:5

262:12 271:3

273:5 286:4,5

309:1,16 326:21

329:9 337:2 362:6

383:16 390:12

392:13 393:4

411:1

threefold 310:19

310:20

three-day 14:7

17:10 18:20 19:15

19:16 32:13

292:10

three-hour 214:4

three-month 88:17

three-node 40:18

40:21 41:1 52:13

52:17

three-year 111:9

threshold 61:2 62:2

343:3 425:2

thresholds 259:2

throwing 25:13

thumb 366:7

Thurman 3:18

11:7,9 25:6 28:3

29:9,10 31:18,19

34:2,11 35:12,16

56:6 69:8,10

80:10 81:9 82:20

84:9,22 85:12

89:13 90:21 267:4

Thursday 338:20

thymus 243:10

tidier 189:20

tie 34:22

tied 125:12

ties 237:12

tiger 355:16

tight 10:6

tightly 52:18

till 101:18,18,19,19

102:22 103:9,14

183:13

tillage 100:14,17,19

101:3,4,6,18

102:3,10 103:3,10

103:13 104:13

107:1 112:5,8,11

112:15,16 126:17

tilling 101:10

Tim 303:10

time 6:19 10:13

11:5,15,16 13:13

16:2,4,5 20:6,19

20:20 21:1 27:11

35:18 39:22 40:9

41:5,19 50:8 52:6

59:11 61:2,4,8,12

61:16 84:19 85:3

85:15,17 93:6

94:12 95:3,14

102:19 105:10

108:14 109:1,10

111:9 114:4

116:19 119:13

123:22 130:8

135:22 136:12

143:12 144:3

150:5 159:5

173:20 178:7,7,12

181:22 182:10

185:8 186:11

187:3,7,7,10

188:9 189:10

194:1,12,16

199:20 208:18

209:8 211:16

215:22 217:9

221:19 230:8,15

230:17 231:5

234:18 235:19

236:14 239:10

240:13 241:13

242:21 244:4

245:1 247:2,5,9

248:12 250:14,14

250:17,19 254:13

255:16 264:3

265:13 267:13,17

274:6 288:20

290:8 293:15,16

293:19 297:17

298:10 299:16,18

299:19 304:1

315:22 323:13

331:14 334:8

335:10,15,19

345:5 346:17

350:4 353:21

356:10 358:13

368:3 371:3

372:12 375:10

381:21 391:13

397:21 400:5

404:3 406:10,11

406:13 417:10,17

419:5,20

times 14:4 15:20

43:13,14 46:15

47:4,8 94:5 116:2

133:14 156:15

158:19 166:11

169:19 170:14

190:14 250:22

251:1 294:21

417:20 418:2,20

timing 71:10 265:5

265:18 346:20

tired 338:13

tissue 168:13

222:21

tissues 179:5 181:9

204:22 205:7

titrated 253:6

today 11:4 12:14

43:15 94:15 99:17

99:22 115:21

123:5 125:11,17

126:3,8,10 129:8

134:14,22 138:5

139:21 142:8

156:21 187:14

188:19 189:4

193:22 194:7,11

194:16 202:22

231:8 237:3

238:11 244:8

296:13 303:12

318:22 339:5

355:8 366:11

368:1,18,21 377:7

378:19 381:6

393:15 394:7

395:11

today's 43:12,16

101:2 425:9

token 255:7

told 94:5 113:10

120:17 369:4

tolerant 107:4

109:15 111:19

tolerate 48:11

49:11

tolerated 238:2

Tomatis 344:3

tomorrow 93:1

296:14 338:19

339:1 366:6

425:12

tonight 216:19

tool 64:6 103:8

106:3 107:2

112:19 229:22

338:4 374:2

toolbox 114:16,22

tools 114:16,21

379:5

top 17:5,6 19:1
202-234-4433

Neal R. Gross & Co., Inc.

Page 479

20:5 33:21 37:17

51:3 103:5 128:3

223:5 240:10

263:13 264:21

267:10 268:3

269:8 293:7 308:5

313:4 316:5,9,16

topic 136:2 194:14

236:6 262:9 384:3

topics 135:2,13,13

total 86:22 89:16

89:20 90:5 113:7

125:7 260:1

266:11 268:4

269:17 330:13

331:2 362:16

totally 88:11 285:8

tox 81:11 132:17

348:1

toxic 346:3,4

355:14

toxicities 178:16

toxicity 95:13

170:3,4,12,21

171:2 257:14

346:9 349:7

350:12

toxicologic 138:11

184:9 186:2

toxicological 7:7

87:17 259:1

314:10 333:15

338:21

toxicologist 5:21

6:9 7:8 9:8

303:10 312:17

380:16

toxicologists 31:22

toxicology 5:20

6:13 9:4,10 92:5

119:2 134:11

137:17 150:14

256:4 258:7

305:10 319:1

339:19 341:6

385:9,11

track 90:7 139:12

252:13

tract 7:4

trade 373:8

tradeoff 80:2,5

tradeoffs 80:18

traditional 150:13

traditionally 110:5

tragedies 115:13

trained 419:2

training 326:11

transcript 4:22

198:1

transcription

176:13

transfected 175:2

transferred 204:22

transformed 163:5

transgene 159:1

transgenic 158:20

159:3 196:13

401:11 402:19

414:18

transient 199:13

346:19

transition 137:9

183:2 184:5

translate 95:16

translational

235:22

transparency

141:17 333:14

372:15

transparent 119:16

transport 83:19

transportation

130:8

transporting 84:13

treat 200:10 203:4

treated 165:12

185:15 186:22

187:15,17 190:6

190:19 192:12

195:22 197:17

201:4,13 202:14

221:19 402:19

treatment 185:17

187:19,19 188:2

188:10 189:14

190:3,8 191:3

192:2 198:16,20

199:6,9 203:8

207:8 208:3 209:4

213:21 215:5,12

215:17 217:2

223:18 229:14

234:14 277:20

327:6 328:10,15

328:21 329:6

333:4 380:1,6

395:1 396:3

401:22

treatments 108:7

108:11,18 109:1,3

109:11,17 111:5

treatment-related

235:4

trees 360:15

tremendous 117:17

trend 209:7 235:10

267:16,22 268:7

268:11,13 285:7

285:10 381:19

trending 192:15

260:12 267:22

trends 29:11

260:12 267:6

272:14 283:1

trials 108:6

Triangle 149:21

triazine 86:22

96:19 97:15

105:14 116:10

120:12 131:19

133:13 164:2,3

175:18 327:13

triazines 132:19

171:5 176:3

326:19

tricks 88:21

tried 78:15 112:11

222:13 340:21

trifling 118:5

trigger 47:10 90:16

triggered 316:3

trihalomethanes

121:17

trips 85:20 101:10

trouble 343:7

true 12:8,9,16

13:15 14:11 15:13

15:16 16:8 20:22

26:1 29:22 37:5,6

44:22 63:9 64:2

66:6 70:9 112:11

219:17 274:16

278:11,18 279:9

279:11 281:6

282:4 288:13

291:9 292:8 294:7

305:5 321:18

400:16 401:10

truly 275:4 282:17

trust 135:5

truth 26:14 63:11

64:1

try 52:6 55:5,9 67:2

69:12 74:14,15

105:19 110:11

136:19 139:13

163:4 185:19

186:16 188:12

194:6 210:21

237:8 299:13

303:13 365:15

372:17 374:3

413:9

trying 52:8,12 66:3

67:7,17 73:7 77:7

226:9 274:2

286:22 289:1

295:6,7 304:11

314:5 320:1

327:10 419:21

tube 226:5

TUESDAY 1:13

tumor 140:14

150:20 164:19

165:6 388:11

391:20 393:5,11

395:5 396:1 397:7

397:11 399:11

402:20 403:12

424:14

tumorigenesis

400:9

tumors 142:1

143:10 152:8

154:10 157:8

343:22 384:22

385:21 388:19

389:1,10 391:7

394:22 396:7,20

398:20 401:5

402:18,22

tune 314:20

turf 360:15

turn 5:2 11:6,20

43:7 99:5 105:11

123:11 137:15

138:10 139:15

161:2 171:4

273:20 283:16

384:20 387:3

409:18 410:1

turned 88:12

137:18

turning 62:8

281:21

turns 174:4 238:9

240:22

Twain 118:1

twice 342:10

343:12 383:12

twist 123:11 277:1

twisted 122:6

twisting 122:19

two 5:5 10:4,18

11:10 12:12 16:4

16:11 17:21,22

18:4 19:11 30:19

33:6 36:18 39:17

43:8,11,14,17

46:5,13,15 54:14

67:17 70:17 77:19

78:16 91:3 94:15

94:16,22 107:19

108:13 118:22

122:14 134:18
202-234-4433

Neal R. Gross & Co., Inc.

Page 480

135:10,13 139:7

145:19 149:5,19

157:4 158:6,18

165:15,17 166:1

166:20 167:17

173:5 174:1,7

183:8 186:20

194:18 198:19

199:5 211:6

214:12 218:18

220:13 229:1,9

232:9 233:6

234:18 239:2

246:4 249:14

255:12 257:16

260:6 261:7

267:18 272:15

273:5 278:7 281:1

281:9 285:17

290:12 295:16,19

296:4 298:18

308:17 310:18

313:19 322:21,21

324:6 326:7 339:9

339:12,20 355:17

356:5 368:15

372:3,14 381:14

388:3 390:16

392:10 393:4

396:12 400:22

401:9 402:14,17

403:6,11 411:1

413:17,18 414:4,5

414:10 416:9,13

418:22 420:3,18

twofold 353:9

two-fold 166:7

two-node 53:14

two-pronged 294:6

two-step 334:5

two-thirds 118:16

270:7 362:19

type 28:1 32:2

51:13 54:6 65:1

78:5 140:14

161:19 165:20

195:3 211:14

286:2 299:3

329:22 365:5

388:11

types 18:1 52:22

67:5 149:9 173:19

174:6 175:17

186:1 257:20

261:7

typical 18:11

104:10 235:6

243:11,11 247:20

310:15

typically 124:17

246:15 263:5,22

276:9 277:11

t-test 111:15

T.V 326:11

U

ubiquitous 331:16

Ueda 158:6 401:4

ugly 37:16 43:3

ultimate 77:15

374:8

ultimately 77:9

89:21 200:10

295:22 328:11

379:4

unable 343:13

344:9

unaffected 190:21

191:3

unaware 275:8

uncertain 250:10

uncertainties

150:18 310:19,20

401:7,11 416:21

uncertainty 45:6

72:1 73:15 80:20

82:3,4 93:11

95:22 246:3

281:15 282:2

283:7 309:7 311:2

319:14 320:11

321:11,16,22

322:1,18

unchanged 154:4

398:22

unclear 349:5

393:18,22

underestimate 25:1

underestimates

57:19

underestimating

16:14 19:8,21

24:16 26:14

underestimation

20:4

underlie 194:4

197:15

underlining 238:20

underlying 150:20

152:7

underpin 313:9

underpowered

417:3

understand 38:12

40:22 51:5 80:9

82:11 83:14,15

86:10,11 98:15,18

113:15,19 115:1

115:13 136:19

158:9 161:6 178:1

192:15 228:4

249:17 258:10

260:13 283:1

290:3 294:2

297:19 299:1,9

300:7 318:12,17

320:4 336:22

379:16 406:20

418:20 419:2,22

understanding

49:1,4 132:14,17

140:3 159:6 275:5

275:19 280:6

287:16 299:5

307:5 356:19

374:11,15 375:17

382:8 386:18

391:14 406:20

407:17

understands 98:10

understood 150:22

154:15,15

undertaken 376:3

undertook 168:11

under-powered

420:10

unexplained

161:18

unfinished 378:14

unfortunate 375:8

unfortunately

232:16 345:15

354:12 392:22

397:3

unheard 129:14

Union 120:5 127:7

127:10

unique 54:7 104:10

115:5 235:21

261:2 282:7

395:19

unit 425:4

United 33:16 132:9

378:20

unity 351:7

universe 265:21

universities 108:3

110:10 111:6

university 6:3,8,14

6:16,20 7:11,16

8:3,15,17 9:7,11

9:16,19 57:10

75:13 99:18

110:15,21 136:4

153:10 163:1

193:21 210:13

218:11 349:14

unknown 151:11

291:20

unlock 236:10

unnecessary 282:6

316:20

unrealistic 86:15

unreasonable

385:18

unsafe 358:20

360:18 361:2

unsurprising

270:14

unsurprisingly

270:17

unusual 144:7

159:7,8 181:19

211:14

unusually 119:3

update 95:4 339:21

340:10 362:12

409:20

updated 361:13

updates 364:12

uphill 279:3

upper 12:8 13:8

upside 409:18

410:1

upstream 276:21

uptake 180:8

upwards 294:7

411:9,11

urge 397:22

urinary 244:9

248:10,16

urine 163:21

243:22 244:1,20

245:20 246:18

250:12,16 251:6

251:10 359:15,19

360:3

usage 128:10

use 3:8 11:13 34:3

36:1 37:4 39:4

44:19 45:2 48:22

50:19 51:19 52:7

52:21 56:4 57:4

58:13 60:7 64:5

69:15 70:21 71:16

71:22 72:14,15

74:9,21 76:2,10

76:10,15 77:5

87:4 89:1 103:13

105:22 107:8

108:14 113:22

114:3,6 120:6,12

120:12 122:8,9

125:6,9,11 130:11

130:12,13 131:10
202-234-4433

Neal R. Gross & Co., Inc.

Page 481

151:21 159:9

174:3 203:12

204:3 222:16

225:18 227:19

249:21 259:6

260:20 261:1

262:13 268:4,9

280:15 283:9,13

284:11 299:18

312:16 313:17

333:8,15 334:16

336:5 338:3 352:6

353:12 356:15

359:10 360:10

369:21 370:3

371:16,22 372:6

useful 21:19,20

85:6,8 91:12,14

221:21 239:5

246:10,17 281:13

336:8

user 372:15

users 333:19

381:17 411:11

uses 58:21 254:15

290:13 331:16

358:21 379:4

USGS 70:11 84:9

268:12 331:19,22

356:9

usually 30:18 52:7

106:14,17 108:12

162:2 186:1 224:9

321:15

utero 256:14,19

utilities 325:9,11

325:12,20 327:7

328:14,21 329:7

329:19 331:12

333:21 334:4

335:21 336:4,7

363:4

utility 98:10 126:2

328:11

utilize 128:17

utilized 110:13

U.K 9:7 119:20

131:6,10

U.S 1:1 8:8,12

102:11 111:6

116:11 152:18

325:14 345:22

357:15 384:8

V

vaginal 143:20

185:8 187:9 191:8

191:12,22

validate 280:21

validation 370:15

370:17 371:15

validity 377:18

valuable 112:19

275:4

value 13:10 14:11

15:14,14,17 16:9

20:22 24:16,18

37:6 39:9,20 42:2

43:9 47:11,17,18

47:19,20,21 48:3

48:6,13 63:7

66:19 78:12 98:10

98:15,18 103:1

113:14 114:10

173:5 224:15

239:13 253:13,14

269:10 273:22

278:11,11,14

291:1 301:8,11

306:6 308:5 309:5

310:6 311:4,9

313:22 315:4,9

336:17 337:5

values 12:8 13:11

13:16 14:9,11

16:18 18:5 20:19

21:6 23:15 37:18

38:13,16,18 39:6

39:10,15 40:14

41:20 44:9 45:11

45:19 46:7 58:14

59:22 60:6 67:19

88:14 209:18

239:20 240:9

241:14,17 243:3

243:21 244:20

260:14 268:12

270:11 271:22

279:9,12,18

281:11 282:22

308:17 311:17,19

311:20 312:7,13

313:8,9 314:22

316:12 322:10

336:18,19

variability 30:9

50:16,17 52:22

55:3,10 70:4,6,20

93:11 251:12

275:6 291:17

329:15 337:20,22

370:20 371:4

variable 30:13

281:22 299:4

variables 64:12,22

variation 225:14

228:7,9

variations 12:12

varies 70:2 371:2

variety 104:19

164:18 172:7

389:15

various 56:5 77:21

179:2 181:8

272:17 274:6

286:6

vary 275:16

varying 75:14,14

76:18

vegetative 104:14

vehicle 203:5

221:19 222:12

253:12

verbal 326:4,13

verify 195:15

version 116:3

189:2 416:5

versus 14:15 29:14

30:18 124:11

153:14 187:19

188:15 226:10

241:13 242:21

248:12 289:13

vertical 46:9

vertically 302:15

Veterinary 6:7,17

viability 170:5

view 295:20 331:11

377:17

vindicated 123:6

violates 365:22

violation 332:11

violations 329:8

viral 356:3

Virginia 9:4 153:10

virtually 19:13

149:9 373:9

visit 83:17

visiting 84:12

visualize 196:16

visually 33:15

vitro 1:8 5:8 136:7

136:20 149:7

151:10,18,19

168:3,4,18 169:3

176:2,18 216:6,7

222:19 387:14

vitro-in 175:8

vivo 151:14 163:9

164:8 168:6 175:8

175:11 201:1

222:8 387:14

400:22

VMP 262:18 266:4

266:9 269:12,16

270:8 274:14

281:3 290:19

VOICE 300:16

voiced 272:9

volume 54:4

103:21 105:7

251:6 382:3

voluntary 262:17

263:12,16,20,22

265:5

volunteers 378:21

Vroom 368:15

373:4,5,7 380:21

vulnerability 188:9

188:15,17

vulnerable 24:2

34:20 289:12,17

289:20 332:4

337:19

W

waiting 25:5 298:2

411:17

walk 141:12

wall 349:3 358:12

419:4

want 4:9 20:18

24:19 27:14 29:10

43:5 45:6 48:14

49:13 61:3,11

62:6 66:17 69:1

74:2,15 79:11,12

82:22 83:5 89:13

92:15 105:5

113:13 133:11,11

142:5,18 168:17

180:14 182:13,22

184:12 194:13

202:1 204:3

212:10 216:18

217:22 218:13

233:22 235:17

237:3 259:21

271:10 275:5,6,15

277:12 293:14,19

297:11 298:1

300:12 317:5

318:9 325:2 326:9

327:3 330:5

333:22 342:14

348:11,12,15

358:14 365:15

367:4 368:17

379:17 404:9

405:7 406:19

421:9 425:9

wanted 30:5 31:19

39:21,22 42:11

47:8 69:4 99:22

111:1 160:11
202-234-4433

Neal R. Gross & Co., Inc.

Page 482

164:22 167:2

185:18 189:18,22

256:12 287:5

295:15 304:9

321:5,10 369:3

371:16

wants 4:12 372:6

warning 259:6

Washington 1:17

339:18

wasn't 10:6 58:18

74:7 111:1 130:22

223:20 240:6

285:8 350:6

water 1:8 5:8 8:10

11:8 12:19 26:4

33:4,22 35:22

39:13 54:3,4,5,7

54:22 55:19 56:1

56:3,7,10,10,15

62:3 67:3 70:2

71:2,4,15 72:11

72:12 74:5,8 75:7

75:10,20 78:4,8

78:19 81:20 83:16

83:18 85:14 89:15

89:22 90:4,10

94:18 95:18 101:1

101:11 103:20

104:2,12 105:16

117:4,13 119:2

121:3,11,12,15,18

121:20 122:2

129:3 131:7,14

134:20 258:15

259:9,10,16,21

260:2 261:6,8,8,8

261:9,10,12 262:7

262:10,10,11,14

263:2,3,9,10,15

263:19 264:5,8,12

264:19 265:2,8

268:17 269:11,16

269:20 270:2,5,6

270:11,12 271:13

271:13,19 272:6

272:11,13,22,22

273:9,14 275:1,6

275:7,9 276:3,3

276:20,20 277:2

277:18,19 278:3,5

278:8,9,18 279:1

279:2,19 280:1,8

280:13,14 281:16

282:10,12 283:10

284:1,2,6,8,9,14

284:18,19,22

285:2,3,15,15,16

286:1,5 288:19

298:5,20,22 299:4

299:4 300:18,19

300:20,21 301:7,9

301:12,14,17,19

301:22 304:20

306:16 307:6,7,22

308:9,13,19 309:8

310:1,20 311:5,9

311:22 312:11

313:14 314:9

315:5,16,19 316:8

317:2,15,20,22

318:7,19 319:22

320:12,22 321:4

321:12 322:13

324:12 325:4,8,9

325:10,13,15,18

326:5 327:6,7,17

328:3,7,14,20

329:8,19 330:8,12

330:19 331:5,10

332:1,6,7,9 333:3

333:4,10,18,21

334:1,4 335:21

336:4 337:5,18

340:4,5 352:3,12

355:18 356:11

357:10 358:2,6,8

360:12 361:10

362:4,11,14,17,18

362:21 363:9

364:15 373:19

375:16 376:4,16

377:10,15 378:10

378:13,14,15

379:17,18,20,21

380:13,19,20

381:1,2 382:1,8

382:21,21 383:1

waters 56:8 329:16

331:17 361:14

watershed 34:4

75:4 277:7 278:22

330:1 340:5 361:9

361:9 362:8

watersheds 57:5

275:15 276:9,22

279:21 280:17

361:12,16 362:1,4

363:13,20

waterways 100:21

103:19 357:18

water's 38:10

water-soluble

380:12

water/static 271:18

wave 230:19

way 13:12 20:13,18

23:17 24:19 27:22

28:12 36:20,20

37:13 39:18 40:21

42:13,15 45:1

52:9 53:22 58:4

82:4 99:2 100:14

100:15 110:3

118:16 141:2

160:6 169:20

171:11 187:1

216:22 219:10

227:8 230:14

234:14 256:21

264:11 282:12,18

295:20 300:10

305:6 311:6

312:17 313:3

342:12 364:12

382:6 397:16

412:20 414:12

417:3 420:15

424:19,22

ways 29:15 36:18

72:10,16 97:11

126:11 243:19

297:22

weakly 175:12

weather 128:14

website 116:8

364:14

websites 110:9

weed 98:21 101:5

103:8,11 105:15

107:10,13,21

108:1 109:20

112:9 125:14,21

130:20

weeds 100:17 103:6

106:19 107:11

112:12 124:15,16

125:1 130:21

week 18:8 78:15

87:18 105:6 118:8

367:6 379:14

weekly 12:22 15:8

21:9 71:15 74:12

74:17,17,19 77:6

122:4 316:6

weeks 15:6,11 16:4

16:11 18:4 39:17

61:8 62:4,8,8

189:16 190:7

213:5 221:8 247:7

274:22 303:22

355:17

weigh 106:2

weight 59:10 94:9

103:20 114:1

115:19 149:16

150:6 154:1

206:11,13,16,22

207:2,3,4,4 233:1

235:8,12 294:22

313:19 322:19

357:7,7 389:14

400:10 411:16

415:19 422:1,1,3

422:15

weighted 267:17

274:7 337:6

weighting 291:1,1

294:16

Welcome 5:5

welcoming 182:8

wells 284:3,10,10

well-being 236:2

well-described

173:4

went 29:20 35:6

39:9,18 74:21

96:15 109:6

154:20 195:14

221:9 291:21

292:18 296:8

314:13 317:17

323:21 353:14

388:6

weren't 58:15

192:7

we'll 5:17 10:14,16

10:18,19,20,21

11:2,2,22 12:9

24:3 69:8 75:15

76:2 77:3 91:22

93:7 96:7,8 97:1

124:3 133:21

135:11 136:21

139:4 141:11

142:8,19 155:14

162:21 176:6

178:18 182:16

183:12,13,14

184:4 188:3 194:6

228:22,22 229:2

232:10 234:4

236:19 256:7

258:20 261:21

262:13 297:17

318:17 325:21

368:6 406:14

409:20 410:14

423:22

we're 5:10,12 10:3

10:8 11:9 17:7

19:20 20:19 21:19

22:2 23:3,6 25:5

27:17 28:4 29:15

31:17 32:12 34:18
202-234-4433

Neal R. Gross & Co., Inc.

Page 483

34:21 44:4 45:1

46:3 57:11,17

59:19 61:18 63:17

64:18 69:17,18

70:3,14 71:3,6,18

72:5,15,18 73:16

73:20 74:11 75:6

75:11,15,22 76:8

76:10,15,20 77:7

78:3,10,11 80:14

82:21 86:18 87:9

87:18 88:21 89:15

92:16,19 93:19

94:6,12,22 95:2

96:2,18,21 114:12

116:17 126:22

134:17 137:9

138:5,10,15

139:12 143:11,13

146:2 151:8,18

156:21 159:18

162:11 166:14

175:3 178:10,16

180:17 182:7,8

188:4 189:3,4,6

210:8 217:12

218:4 224:11

226:7,8 230:6,22

232:5 235:3 243:4

249:8 255:7

257:12,19 258:11

260:4 264:22

290:7 292:3 295:6

297:7 302:20

304:11 310:21

311:19 312:2

314:8,10 315:5

316:17 318:4

319:1,2 321:20

323:9,17 325:6,15

325:20 338:13

368:8,18 380:13

383:14,16,21,22

384:1,3 392:17

397:4 400:19

405:3 407:3,20

408:22 409:1

410:16,17 413:10

420:9 423:7,17,21

425:5,12,13

we've 11:5 19:12

23:10 29:11 31:20

32:4 34:14 56:17

57:2 70:18 79:9

89:18 95:18 98:19

106:21 112:4

113:3,15 114:5

137:5 168:3 231:6

232:6 235:18

237:12 239:7,15

240:15 242:12,14

244:12 250:21,22

254:18 255:2

265:20 283:6

305:7 306:22

307:1,12 308:1,2

311:16,22 315:18

316:1 324:7,22

325:11 326:17

327:17 336:15

363:18 365:4

384:4 390:15

405:17,22

what-not 91:7

whichever 218:13

whiskers 307:10

311:15,18

white 11:11 12:11

26:7 76:12 96:20

97:2,3,5 112:22

113:2 125:8

126:16 127:13

128:16 131:3,9,13

131:21 132:3,12

132:16 133:2,6

278:10,16 392:8

392:10 398:17

wholesale 118:4

wide 119:3 150:11

170:10 276:8

329:14 344:18

382:9

widely 106:6,7

widespread 331:16

wife 113:9

Wilcoxton 111:15

wildlife 101:13

349:21 361:22,22

William 2:7 9:14

Williams 2:17 7:1,1

160:10,11,15

161:10,17 162:7

178:20,21 404:11

404:12

willing 402:9

wind 100:19 101:12

window 188:8

400:6

windows 347:10

winds 102:18

winter 59:10,11

67:14

wish 105:10 135:1

354:8 377:6 408:6

417:8

wishing 36:22

383:10

Wistar 142:10

195:6,7

within-test 370:19

women 148:5,7

153:7 157:6

161:12,19 162:10

wonder 31:3 54:12

132:20 420:15

wondered 28:12

404:12

wondering 25:11

181:15 182:1

253:5 298:14

word 168:20 226:4

243:9 259:7

260:18,21 261:1

265:11

worded 409:13

words 36:12 54:2

269:19 270:12

288:7 323:8

331:20 334:16

351:9

work 12:5,15 13:7

71:22 72:22 100:7

105:17 106:10

113:17 151:3

156:15 159:21

182:6 217:14

218:17 236:7

262:2 269:6 272:1

279:15 284:12

286:18 287:2

294:3 299:8

300:11 307:4

315:19,21 318:15

343:21 365:16

380:9,17 389:5

400:12

workers 358:19,22

360:6

working 71:18

75:21 76:21

105:16 118:13

123:3 124:5

139:12 160:3

245:15 281:6

294:6 316:17

323:5 376:22

382:15 407:4

408:22 419:20

works 40:22 106:8

106:8 318:17

324:12 325:4

world 119:19 343:4

worried 63:18

112:10

worry 63:18 112:9

worrying 282:13

worst 277:11

worth 358:10 411:3

411:4,17

wouldn't 68:14

92:2 115:21

176:10 358:4,8

395:15 396:22

412:19

Wow 338:10

wrap 99:9

wrapping 70:19

282:16

wrestle 78:5,7

wrestling 72:6

write 388:1 423:22

writing 423:18

written 4:22

326:14 327:18

328:8

wrong 80:11 91:11

321:21

wrongness 73:13

wrote 118:1 344:8

X

X 306:1

Y

year 14:2 19:18

20:12 21:16 22:16

22:17,18,21,21,21

23:3,3,8,9 30:14

35:6,13,17 38:10

39:16,22 40:10

47:3 54:1,15 57:4

57:5 58:9,11

60:13,15,20 70:16

88:14 94:8 107:9

107:9,10 116:9,15

137:3 139:11,12

149:15,17 152:5

263:6 264:1,6

271:16 272:3,4

286:14 287:19

330:4,14 331:6

363:1,6 368:9

404:2

yearly 307:2

years 13:4 22:1,1

22:14 30:19 46:21

54:21 56:12,18,21

57:6,7,9 60:16

62:13,14 70:17

98:22 106:6 109:5

109:8,9,19 110:2

111:3 114:19

115:3 122:7 123:3

125:10,13 126:5,5

126:10,15 130:6

136:18,22 138:2
202-234-4433

Neal R. Gross & Co., Inc.

Page 484

160:4 196:22

224:3,3 233:21

257:17 264:18

269:19 273:1

275:3 281:5

285:19,21 286:13

288:19 291:13

294:9 305:22

327:15,17 332:10

340:11 345:14

378:6 396:12

Year's 266:2

year-to-year 55:2

55:10 56:12

291:16

Yee 137:4

yesterday 4:11

10:5 12:4 22:3

31:9 32:10 35:19

37:11 61:22 69:14

81:16 90:16 92:4

94:6 113:9 116:15

142:7 143:5

145:10 149:4

151:6 153:5

199:17 206:22

224:18 237:5

276:19 296:12,14

313:9 314:7 348:5

350:20

yesterday's 43:13

yield 65:7 107:16

111:22 124:19,22

yielded 109:2,11

111:10

yields 124:10,11

York 116:2

young 2:18 8:14,14

24:9,10 25:6,10

25:16,19 65:12,13

66:14 67:7,11

68:21 79:22 80:1

81:3 85:7 186:17

191:20 195:7

234:9,17 236:11

290:1,2 293:8

295:2,14 297:9

420:14

younger 161:12

249:21

y-axis 143:22 145:1

311:16

Z

zero 18:6 24:13

47:18 59:14 61:4

244:1 248:10

251:1,2 265:13

415:7

zoom 35:1

zooming 71:3

$

$1,200 331:6

$100 331:1

$180,000 330:14

$200 330:17

$30 112:2 330:12

$300 331:2

0

0.01 176:19

0.1 132:22 133:2

0.100 165:13

0.4 332:22

000041 320:19

004 332:2

05 278:6 342:5

09 278:4 340:1

1

1 19:22 20:1,21

120:8 165:13

169:5 186:21

189:5 191:15

202:17,21 209:4

266:6 307:16

332:20 333:1

361:19,19

1,000 320:11

322:17

1,060 364:5

1,172 363:13,16

1,400 291:13

1,500 330:10

1.0 384:12

1.1 279:22 281:12

341:15 354:4

1.17 352:1

1.2 279:20 386:15

425:13

1.28 273:22

1.4 348:8

1.6 264:14 350:16

386:15

1.61 332:20

1.67 352:14

1.8 255:18 279:20

310:7 313:6 314:1

319:12,14 320:10

322:17 355:6

1.87 352:16

1.9 353:21 356:15

1.92 352:17

1:15 183:14,14,17

184:2

10 46:12 48:11,11

62:4 155:21

165:14 168:22

210:1 227:7 228:1

255:17 306:9

307:19 311:9

313:1,5,15 322:22

10,000 47:7 60:16

10.7 18:7

10:08 96:8

10:09 96:15

10:25 96:8

10:27 96:16

100 124:22 181:5

189:17 190:15

202:16 204:12

206:14 207:16

209:9 212:2

239:15 241:4

242:10 253:15

259:11 276:10

301:19 320:21

362:7,18,20

414:22 415:1,7,9

415:11,12,12

416:22

100,000 262:6

315:18

1001 1:17

103 285:15

11 3:10 16:13 18:7

111-day 249:20

12 22:5,8,10 38:16

93:2 189:16 190:6

190:7 211:13

228:16 287:14

335:7

12,000 263:2

12-1/2 335:7

12.5 311:2 320:9

12:02 183:13

123 118:8 119:7

125 349:15

13 17:4 67:11

227:12 302:21

130 56:16

132 263:19

134 270:7

14 16:3,11 17:4

19:17 20:11 76:3

157:5 202:17,21

207:20,21 209:4,8

215:17

14th 1:17

14-day 14:20 15:16

147 362:9

15 16:12 119:6

125:10 177:4,19

227:7 266:13

308:6

15-minute 96:7

323:18

150 266:13 277:17

150,000 315:10

150-some 56:16

152 265:2

153 362:14,17

16 67:12 114:19

125:10 298:8

361:18

161,000 332:13

17 269:18 271:17

272:4 340:17

1700 230:10

18 362:1

180,000 261:14

19 354:14

1970 160:5

1970s 342:18

1971 342:18

1982 102:17

1986 108:20

1988 113:5 309:4

1991 328:18 343:18

1993 262:21 263:8

275:2

1994 113:5 134:13

149:20 267:16

268:6

1995 97:16 329:9

1996 376:6

1997 141:4 171:12

264:16

1998 39:11

1999 407:6,7

2

2 17:2 19:9,21

43:14 209:22

227:9 240:17

247:1 254:22

2B 343:19,19

2.0 386:10

2.1 280:8 361:6

2.2 354:6

2.3 353:17

2.6 279:22 280:8

281:12 351:22

20 22:6,8,9 122:3

124:19 126:5,10

126:15 130:6

211:11 227:13

244:13 309:8

319:18 320:12

322:4,7,10 327:15

327:17 335:7

355:19 361:12,16

362:3,5

20-fold 173:8

20-minute 286:15
202-234-4433

Neal R. Gross & Co., Inc.

Page 485

20-year 107:19

200 124:20 195:12

201:4,13 211:7

213:21 239:15

241:4 266:13

312:20 355:19,20

2000 119:20 135:21

137:17 149:15,17

149:20,20 150:4

151:2 152:5 153:1

153:8 154:18

185:5 275:2 345:8

385:7 388:5,6

389:18

2000s 284:13

2001 341:20

2002 171:21 284:15

371:14

2003 95:9 102:17

138:18 263:17

264:11 271:14

345:8 384:12

387:16 394:15

398:5,10 406:8

409:14

2004 119:20 158:6

172:3 401:4 403:6

2005 109:10 141:10

158:6 362:13

399:22 401:4

402:11 408:10

2006 109:22 111:3

116:9 259:17

267:16 268:6

271:14 278:1

353:9 360:7

2007 102:13 113:11

119:21 329:9

361:12

2008 102:20 111:3

119:21 122:3

278:14 361:12

362:10,13

2009 259:18 276:12

352:2 355:16

357:4,13

2010 1:13 94:5

155:21 183:16

276:16 349:13

351:16 352:21

2011 94:7 405:4

406:14 409:3,7,19

411:6

2013 118:18

202 272:21 273:11

291:13 292:3

296:20 297:1

301:6,10,10 302:7

21 14:22

212,000 284:5

22 26:6 111:7

22,500 264:7

22.7 269:12

220 355:20

227 278:2,8,11

23 278:1

23,000 314:18

236 109:17

24 111:6 169:21

251:1

24,000 357:8

24-hour 167:7

25 16:2 46:13 192:2

202:15 206:17

235:13 244:13

250 266:14

26 264:1,5,6

26,500 265:8

27 1:13 16:13

183:16 333:1

28 14:20 202:17,17

203:1 206:10

212:14,21,22

233:3 241:21

245:7

28-day 241:18

242:3,3

298 311:4 322:5

3

3 17:2 19:21 168:22

169:5 259:16

267:20 335:3

343:13 344:10

362:19,21 386:16

3D 34:1,1

3X 281:14

3.2 386:10

3.5 111:11

3.6 385:11

3:45 323:21

30 12:19 14:20

17:18 22:6,8,9

23:4 97:19 125:12

126:5,10,15 130:6

146:21 156:1

168:22 169:9,14

200:9 201:12

203:6 207:10

208:11 215:16

264:1 277:13

287:18 306:9

307:20 335:7,11

357:14

30-day 76:3

30-plus 276:15

300 103:16 170:1

239:15 241:4

275:3

31 266:6

31.25 15:18

310 111:4

33 389:20 392:3

33,000 317:22

34 275:16

340 3:22

344 140:18 145:20

157:6 388:12

35 3:14 23:4 272:3

287:18

350,000 313:21

36 67:13

365 12:22 14:6

58:12 60:13

265:13 307:2

365-day 12:17

306:3,3 308:7

37-1/2 335:8

37.5 242:4 260:1

38 43:12,13

4

4 3:6 15:21 16:8

18:2 19:8 26:6

179:3 342:16,20

363:19 412:14

4B 181:1,4

4D 181:1

4,200 325:12

4,293 111:4

4,400 263:8 269:11

4,500 14:4

4,600 331:19

4.23 332:12

4.6 109:12

4:02 323:22

4:40 384:3

40 97:20 145:3

146:21 156:1

227:13 363:16

364:5

40,000 306:22

400 144:15,18

271:13

400-plus 353:16

410,000 314:20

42 102:16

43 354:13

44 102:18 166:14

332:6

440 288:19

45 145:4 384:2,5

5

5 16:8 17:3 309:6

354:5 402:2 403:6

414:21 415:8

5,000 15:21 319:20

321:8

5,811 109:17

5-1/2 235:1

5.3 111:10

5.4 76:12 109:11

5.86 352:1

5.9 109:2

5:00 11:2

5:30 11:3

5:40 384:5,6,6

5:45 384:6

5:48 425:16

50 20:5,6 98:22

106:6 122:7

174:13 185:1

189:20 190:22

209:16 211:7

213:22 227:5

230:1 312:21

362:6 401:18

402:3,13 414:19

414:22 415:7,10

415:12 416:22

50th 315:11

50,000 263:7

268:19 316:2

325:12 411:12

500 330:9 402:7,13

5100 195:12

6

6 17:3 251:2

6,000 319:11 321:6

6,300 275:17

6.25 255:18 313:5

6.3 109:2

6.5 202:15 206:17

207:13 235:14

6.7 269:10,17

6:00 11:4

60 20:8 147:1

174:14 179:19

275:21 324:16

60,000 411:12

60-day 14:21

62 109:19

63,000 314:3

319:21

65 145:5 179:19

650 102:21

69 3:18

69,000 314:21

7

7 16:4,10 202:17,21

209:4 215:17

70 147:1 313:18

322:20,21 352:19
202-234-4433

Neal R. Gross & Co., Inc.

Page 486

72 165:13,18

169:20

73 18:6 278:14

75 197:9

75th 307:17 314:19

78 273:19 274:1,1

292:8 301:10,20

8

8 16:10 179:3 235:9

239:21

8th 118:12

8:30 1:15 425:12

8:31 4:2

80 111:8 146:2

228:2 275:21

302:12 320:13

325:13 362:13,16

367:18

81 329:9

82 367:19

85 177:3

86 351:17

9

9 19:8 235:9

90 17:18 19:12 33:1

33:2 135:4 144:1

144:2 188:2 263:1

277:16 280:12

306:9 307:20

314:21 335:11

352:19 354:7

90th 332:15,21

354:4,6

90,000 411:9

90-day 13:12 14:19

21:11 55:21 71:17

76:21 144:11

259:19 271:6,21

272:2 287:14

288:11,14 310:3,4

312:6,10,12

315:12

90-days 20:10

900 239:21 342:17

91 343:12

93 263:17

95 108:21 273:16

292:6 301:19

302:10

95th 60:9 303:20

354:2

95% 351:22

96 3:20 109:10

285:16

99 343:13,20

99th 46:10,12 60:5

60:8 270:9

99.8 331:21

99.9 19:12 303:21

99.9th 269:9,12,17

270:9 282:21

307:18 308:5

314:16 316:14