1977-78 Annual E&port

Rk-00612

Section 3.1

Notes to Figure 9:

MST1 is the maximally specific rule version.

MGVl and MGV2 are maximally general rule versions.

Only the rule patterns (left hand sides) are shown above.

Al.1 rules shown predict the same action: the appearance of
a peak associated with atom  9" in the range 14.0 to 14.7
ppn. downfield from 'IBIS.

   The version space represented in Figure 9 above contains
several hundred rule versions: the three versions shown plus all
versions between these in the general-to-specific ordering.
However,  it can be represented simply by the two maximally
general versions, MGVl and MGV2, and the single maximally
specific version, MSVl.  The single most  specific version
contains every node and node attribute constraint consistent with
all positive training instances. In this program the classes of
positive and -negative training instances are sets of molecules
for which the indicated spectral peak does and does not appear.
Thus, any rule version more  specific specific than MSVl cannot
match every positive instance.  Two general versions are required
in this case since neither  is "above" the other in the general-
to-specific partial ordering. Any rule more general than either
MGVY. or MGV2 will match some negative instance. Furthermore, any
rule which is between these general and specific boundaries of
the version space will match all current positive instances (by
virtue of being more general than MSVl), and will match no
current negative instances (by virtue of being more specific than
MGVl or MGV2).

3.1.3.3   Version Spaces and Hule Learning

   Rather than select a single best rule  version, the
candidate elimination algorithm represents the space of all
plausible rule versions, eliminating from consideration only
those versions   found to   conflict with  observed training
instances.  Thus, the candidate elimination approach separates
the deductive step of determining which rule versions are
plausible, from the  inductive step of selecting a current-best-
hypothesis.  The algorithm  is assured of finding all correct
versions of the rule after all training data has been presented
without the need to backtrack to reconsider previous training
data or decisions.

   In this example,  RULEGEN was used to generate a set of
plausible rules characterizing the CM.H spectra of a set of

76


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training molecules.  For each rule, the associated evidence was
given to a the candidate elimination routine which formed the
version  space for this evidence set. Subsequent data may be
analyzed to modify the version space in a manner guaranteed to be
consistent with the original data.

    The candidate  elimination algorithm operates   on the
maximally general and maximally swific sets representing the
version space.  The set of maximally general rule versions (MGV)
is initialized to a single rule consisting of the most general
possible rule subgraph (a single atom graph with no constrained
node attributes) , and the predicted shift range determined by
RIJLEGEN.  Ihe set of maximally specific versions (MSV) is
initialized to a rule which contains as its subgraph the entire
molecule associated with the first observed positive instance.
The initial version space  represented by these extremal sets
therefore contains all rules which match the first positive
training  instance  (the most general possible rule, the very
specific rule, and all intermediate rules).

   The training instances are  then considered one at a time.
Each training instance  is used to eliminate from the version
space those rule versions which conflict with that instance.
This is always accomplished by shifting the maximally specific
and maximally general boundaries of the version space toward each
other as shown in Figure 10.

-e--w
l
I m0re t
  specific I
f    I
I    I
/    I
   I
I    more
I generdl *
             -

  --              I--

Most Specific Versions  I

positive /
instances I   i
         *     I
       T
negative I     f
instances 1
       I     I
Most General Versions   I
                 I
                    ----

Figure 10. Effect of Positive and Negative
Training Instances on Version Space Boundaries

   Positive training instances force elements of MSV to become
more general, whereas negative training instances  force elements
of MGV to become more specific.  The maximally specific set can,

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Section 3.1

of course,  never be replaced by a more specific set  (nor the
maximally general set by a more general one) since by definition,
any version outside the current version space boundaries is
inconsistent with previous training data.  'The action taken by
the candidate elimination algorithm in updating the extremal sets
is given below.

   For negative training instances , each element of NGV which
matches the instance must be replaced by a set of minimally more
specific versions  which do not match the instance.   These new
versions are obtained by adding constraints taken from elements
in MSV in order to ensure that they remain more general than some
MSV, and thus remain consistent with previous positive instances.
Furthermore,  each element of MSV which matches the negative
training instance must be eliminated from the set (since it is
already maximally~ specific, it cannot be replaced by a more
specific version).

   For positive  training instances,  any elements from NSV
which do not match the new instance are replaced by a set of
minimally more general elements which do match the instance. In
order to ensure that these more general versions do not match
past negative training instances , any which are not more specific
than at least one element of MGV are eliminated.   Elements from
MGV which do not match the positive instance are eliminated.

   After processing each training instance, the new maximally
general and maximally specific  sets will bound the space of all
rules consistent with the observed data.

3.1.4   Current Status and Future Work

   The incremental learning ability for Meta-DENDRAL depicted
above in Figure 8 is almost fully implemented, but as yet remains
untested.  Wutines for defining  and modifying rule version
spaces are implementd,  as well as the ability to filter out
training data explained by a rule set. The major unimplemented
portion of the incremental learning scheme is the process for
merging new rules  into the evolving rule set.  The chief issue
here is deciding when and how to chose among or merge new rules
which are similar to existing rules. We expect to complete
implementation and initial testing  of the incremental learning
ability during 1978.

   Among issues associated with the version space approach
which we expect  to explore during the current grant period are
the following:

  1) Intelligent selection of new training
data from exznination of partial results.

2) Applying

chemical

plausibility

78


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RR-00612              Section 3.1

information to select a 'Ibest" rule version from
among those contained in the version space.

  3) The extension of current methods for
dealing more completely with noisy and ambiguous
training data.

  4) The use of version spaces  for merging
similar rules.

3.2   New Capability lb I3nphasize Discrimjnatory Fewer

   One important intended use of rules formed by Meta-DENDRrlL
is the prediction of  mass spectra for use   in structure
elucidation: Predicted spectra for a set of candidate structures
are compared by computer  with themass spectrumobserved  for an
unknown compound,  and on this basis the candidates are ranked
according the likelihood of their identity with the unknown. The
ability of rules,  in this context, to differentiate correctly
among candidate hypotheses is called   their  "discriminatory
power."  Since the selection criteria previously used by Meta-
DENDRAL during  the various stages  of rule formation did not
necessarily correlate with high discriminatory power,  it was
decided to provide  the program with the option of directly
emphasizing discriminatory power during rule formation,  in order
to maximize the usefulness of the resulting rules for purposes of
structure elucidation.

   This addition to Meta-DENDRAL has now been designed and
implemented. The general method employed by the the new option is
as follows.  Observed mass spectra of the training molecules are
analyzed prior to rule generation to determine how diagnostic the
various observed peaks are,  within the training set,  of the
molecules that show them.  This information is then used during
rule formation to compute  a measure of discriminatory- power for
emerging rules.  This measure is used, in combination with other
criteria,  to guide the search during rule generation, and to
control the modification and selection of rules during  the later
phases of processing.

   Preliminary testing  of this new rule formation scheme on
the monoketoandrostanes produced  rules of considerably greater
discriminatory power within that family than had been produced in
earlier work with Meta-DEJADRAL, even though the training set used
Was  only   half as  large   as that   used   earlier.   This
"discrimination option",  now integrated with the new template-
processing capability, is currently being further tested on a
group of aromatic esters to determine whether the rules formed
are consistent with what is known about the fragmentation modes
of those molecules,  and whether the rules have significant
discriminatory power outside the training set used to form them.

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Section 3.3

3.3   Improved Ranking Capability

   The program used within the Meta-DENDfWL framework to rank
candidate structures has been improved in several ways.

  A) The program now summarizes its own results and pcints
the sumnacies, thus eliminating much tedious manual analysis that
previously was necessary.  This makes possible a much more
systematic and extensive investigation of scoring functions and
their behavior than was previously possible.

   E3j A large number  of new scoring functions have been made
available, many of them specially designed for use with rules
formed under the "disccinination option."

   c1 A new ranking method has been implemented as an option,
with an eye toward improving the application of scoring functions
in canking.  This new method eliminates duplicate explanations of
peaks (which were previously permitted4 in a principled way. The
new method may be easier  to justify theoretically, and yielded
generally better ranking results than did the old method in tests
performed with monoketoandrostanes.  Further tests ace planned
with aromatic esters and marine stecols.

3.4   Data Selection Program

   It is a cormnonplace  of methodology that good inductive
generalizations depend on variety in the data set. This is no
less true in the context of rule formation by Neta-DENDRAL.
Whether the goal is to discover rules of high generality or high
discriminatory power,  one's chances of achieving this goal
[appear to] increase with increasing variety of training
instances.  This suggests that it would be useful to have a data
selection program that would select the subset of the potential
training molecules which has 'the greatest variety,  in some
appropriate and well-defined sense.

   A pceliminacy   version of such   a pcogca   has been
implemented, and experiments with it will soon be underway. The
method employed has two steps: A.4  Construction of an index of
all the   structurally   different   possible  fragmentation
environments permitted in the molecules of the set of potential
training molecules (PT( by the "half order theocy" of imass
spectral fragmentation.  B.3 Construction of an n-sized subset of
PT that contains nearly the largest number of different permitted
fragmentation environments possible for a set of that size.

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Section 3.5

3.5   Feedback Loops

3.5.1   Filtering with F&spect to Existing Ffules

   The RULEGEN program is capable of accepting previously
defined rules as a means of filtering the evidence obtained from
INTSUM before the evidence is used for rule formation. As well as
providing a convenient and natural feedback mechanism for the
p9ra.w  this facility also allows rules obtained from other
sources to be used to reduce the space which the program must
examine to find rules for a given set of data. In this manner,
the program is able to  focus attention on evidence which is not
already explained by any of the rules which it is given.

   A problem with this approach arises from the fact that the
spectral evidence may often be the result of more than one
fragmentation.  Yet the filtering mechanism assumes that any
evidence which supports a  rule is completely accounted for by
that rule.  Tests are in progress to determine the limitations of
this approach.

3.6   Prcgramimprovements

3.6-l   Defining Ruleswith EDITSTRUZ

   In addition to the programs which produce rules from the
spectral data, other programs have been developed to allow a user
to define a set of rules manually.  Dike the rules produced by
RULEGENandRULJZMOD,  these are rules of structure fragmentation
which are expressed in terms of molecular subgraph descriptions.
The programs for manual rule definition provide a simple yet
useful language for the description of these rules. A principle
part of .thi.s language is the EDITSTRUC language, developed for
CCXGEN.  This allows us to take advantage of the advanced
structure manipulation  capabilities which are a part of the
EDITSTRUC package.

   The ability to create rules manually should be particularly
useful in conjunction  with the rule filtering mechanism of
RULEGEN mentioned previously.  This provides the chemist with a
natural means of describing obvious rules which the program can
eliminate from consideration before focusing on the remaining
unexplained evidence.

3.6.2   Stability Rules in INT!XM and RULE=

   The programs have been generalized to allow the analysis of
the mass spectral data from the point of view of determining

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Section 3.6

rules about stable bonds, i.e.,  lack of fragmentation in a
molecule as well as fragmentation. Just as peaks are evidence of
fragmentation in a structure, absence of peaks is evidence that
certain fragmentations have not occurred.

   Ihe programs are now capable of examining the original data
from either point of view and proposing rules of behavior of the
molecules from that point of view. Further work remains to be
done to carry this generality through the processing performed in
RULEMOD and then in conducting experiments to determine the
usefulness of stability analysis.

3.6.3   Expand&l Template @ace

   Originally, the subgraph descriptions in the rules produced
by the RDLEGEN program were  restricted by requiring that the
internal connection patterns  of the subgraphs had   to be
completely specified.  In other mrds, for each of the interior
nodes in the subgraph, the complete set of neighbors had to be
specified. This restriction  excluded rule forms which seemed to
be both plausible and desirable, so the program was changed to
eliminate the restriction.

   In terms of the mechanism used by the program to search the
space I  implementation   of this  change meant  removing the
restriction on the subgraph matching templates that the neighbors
property be required at all but the outer levels of a template.
This allows the program  to find rules in which the internal
connection patterns of the chemical subgraphs are only partially
specified.

   For example; it is now possible to express a rule such as
"break any bond which is 2 bonds away from an oxygen atom". Such
a rule could not be expressed previously without identifying
whether the nodes between the oxygen atom and the break were
secondary, tertiary, or guaternary.

3.6.4   Small LISP and Program Efficiency

   Increased size and complexity of the Meta-DENDRAL software
has resulted in increasing efforts aimed at making the programs
more efficient and understandable. All the programs which are
part of the meta-DENDF!AL system are now capable of running in the
environment of "small LISP". This makes considerably more memory
space  available to the chemist for the data structures, thus
making possible the  solution of significantly larger problems
than were possible in the standard LISP environment.

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Section 3.6

3.6.5   Help Facilities

    As  the programs   have increased in  complexity and
usefulness, we have had to face problems of documentation and
explanation of the programs to its users.  Text explanations of
the various aspects of the programs must be provided, and kept up
to date, to allow others to use the system. It is also important
that the text descriptions of the programs be available to the
programs themselves  to be used during program execution to
provide on-line guidance  to the user concerning the use of the
programs.

   Text descriptions   of the  programs must  be closely
associated with the programs themselves to insure that program
changes are  reflected accurately in changes in the text which
describes them. Yet text explanations must be incorporated into
the programs so as not to take up space which should be available
during program execution  to be used for producing results.
Attempt has been made  to resolve these sometimes conflicting
goals through the useof  the cormnent facilities of LISP, and
through  the generation  of programs and   conventions for
prograrrrning which allow program documentation and explanations to
be incorporated into the programs as comments in the appropriate
places.  There are then programs which have access to this
information to produce  documents and on-line explanations about
the programs.

4   COLLAMRATIVERFSEAPCH

    4.1    CCNGEN Users
Dr. Peter Gund of Merck, Sharpe and bhme Laboratories contacted
us for a current CONGEN manual and Guest login information. He
now feels that he has analytical problems which would lend
themselves well to checking with CONGEN.

    Professor Richard E.  Moore of the University of Hawaii
visited Stanford and was provided with a CaGEN demonstration on
a problem relating to his own marine sterol work.   We discussed
system 'access and Tymnet node availability with him. He plans to
return in the near future with another problem, and then consider
the possibility of requesting access.

    Dr.  Jean-Claude Braekman of the University of Brussels
travels across Brussels to use a terminal at the offices of the
Belgium Chemical Society, in order to access CONGEN on SUMEX.
Dr. Braekman uses the mail facilities to remain in contact with
Prof. Djerassi's research group.

83


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Section 4.1

    Dr.  Martin Huber,  a postdoctoral  fellow in Professor
Wipke's SECS group has been starting work in an area which was
related to the graph theoretic basis for CONGEN.  In an effort to
encourage  cross-fertilization or  ideas, we  encouraged and
arranged a meeting between him and several of the DENDRAL project
members.  The resulting discussion, at the least, provided Dr.
Huber with suggestions and information for  further study.
Likewise, DKWDRAL   was able to   obtain a better   idea of
similarities in research interests between the two groups. We
are currently  pursuing several  problems in  graph theory
concerning analysis of molecular  structures.  These problems
arose directly from this meeting and concurrent discussions with
Prof. Wipke.

   During the special symposium at the San Francisco ACS
meeting in the fall of 1976 which Ms. Suzanne Johnson  helped to
organize and chair, members of the DENIXAL group provided on-line
demonstrations of CONGEN during the "hands-on" session.   At this
time Professor Kurt Mislow of Princeton University expressed
interest in using the program.  Later, we provided him with Guest
access information and answers to his questions concerning
terminals and other useful programs available to chemists on
various commercial networks.   As a result of this effort,
Professor Mislow has used CCS%EN and has been considering its use
as a teaching aid.  He wrote us this past spring to enquire
whether Guest access to CCNGEN might be possible for his friend
Professor Weiss, head  of the Department of  Chemistry at
Northeastern University.  'We subsequently provided Professor
Weiss with the information necessary to access CCXGEN on a trial
basis.

   In November 1976, Dr. Stan Lang of Lederle I&s' Infectious
Disease &search Section, requested access to CCXEN. After
being providing with the appropriate information and initial
help, he encouraged Dr. Leon Goldman to request access also, and
to request information on  obtaining a copy of the teletype DPAW
program used to draw CONGEN structures on teletypes.   Arecent
phone conversation with Dr. Babu Venkataraghavan, a new member of
the research group at Lederle, indicated that the TTY DRAW'
program was being used quite successfully. Also interested in
the  possibility of  support for  graphics  terminals, Dr.
Venkataraghavan called to discuss the problem in terms of
Qmrigraph,  which they already have on their PDP-10.   We have
exported a complete copy of all the DRAW program files, including
ample data files, to Dr. Venkataraghavan and are currently in
contact with him on implementation questions.

   A further example of cooperation between DENDRAL and
Professor Wipke's group  concerns the sharing  of graphics
programs.  DE%DRAL obtained the Fortran sources for programs
created by the SECS group to do molecular modelling and structure
display on the DEC GI40.  Wanting to interface these programs to
CONGEN, but not wanting to limit CCRGEN graphics to one terminal

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Section 4.1

type, DENDPAL personnel modified the program to use the Qnnigraph
graphics package  available on SDMEX.  Glenn Ouchi of the SECS
project,  has become familiar with the relationship of the
graphics in CONGEN to the Bodeller's graphics. SECS has become
aware of the desirability of supporting additional terminal types
for graphics output,   and will be  investigating Qnnigraph
applications to this area.

   One of the students who used CONGEIN in Prof. !3jerassi's
molecular structure elucidation course introduced the program to
a graduate  student of Professor E.J.   Eisenbraun's (Oklahoma
State University).  Professor Eisenbraun is a well known marine
natural products   chemist- FIe  has requested   Guest access
information, and appropriate materials were provided in spring of
1977. Professor Eisenbraun subsequently visited Stanford and got
a personal demonstration of COJ3GEN.

   We have been in contact with Dr. Karl Kuhlman, a chemist
and PROPHET user at SRI International.  We have arranged for a
group of DE'NDF&L chemists to get together with the SRI group for
exchange demonstrations:  CCNGEN for PPOPHET, and discussion of
similar problem areas with visiting PROPHET representatives.

    Dr.  David Pensak  of Dupcnt in  Wilmington, Delaware
originally started out as a Ct3JGEY Guest user.  In return, he
contributed a good deal of knowledge concerning evaluation and
use of molecular modelling programs. At the current time he is
beginning to a build a research group in computer applications in
chemistry, and views SDMEX/DENDR& somewhat as a resource from
which to obtain knowledge of hardware, software and people.

   Dr. Milton Levenberg of Abbott Laboratories first expressed
interest in CONGEN at an ACS meeting two years ago.  He was given
an account and appropriate information at that time.  He had used
CNNIGRAPH to develop a program to display and plot mass spectra,
which he gladly provided to us- That program now provides a means
for chemists to obtain a plot of their spectra which have been
obtained on mass spectrometers which are not yet equipped with
automatic computer output.

   When Kent Morrill was a graduate student in chemistry he
developed an interest in  C3BJGEN and various of the Meta-DENDRRL
programs.  When he left recently for a job with Tennessee
Eastman, he requested Tymnet login information to take with him.
As a result of his interest, Dr. Gary Santee of Eastman Kodak in
Rochester requested  information for Guest access to C(Z%3ZNN.
Kodak inay also be in the process of forming  a computer
applications in chemistry group, and once again, we seem to be
viewed as a potential information resource  in this type of
effort.

    Dr.  Gretchen Schwenzer was a postdoctoral fellow with
DENDRALl.  When she left Stanford for a job at Monsanto, it was

85


1977-78 Annual F&port

RR-00612              Section 4.1

with the idea of taking part in helping to develop a computer
applications  in chemistry group.   She tooviews SraiIEX asan
information and know-how resource.  To that end, we have had
several phone calls and terminal  links from her concerning
graphics, terminals, modelling programs and text editors. She is
interested in  obtaining  several  copies   of documentation
preparation programs either developed or supported at SUPIJZX.

   Dr. Robert Shapiro of New York University came to visit
Stanford in September of 1977 to learn to use CCNGEN. He spent a
week in residence to discuss structure elucidation problems
relating to nucleic acids and their interactions with other
substances.  We are also pursuing ideas on the automated analysis
of W spectra of such compounds, based on empirical rules derived
from study of known systems.

   In November of 1976, Dr.  Henry Stoklosa of Ciba-Geigy
approached one of the members of the DENDRAL project for trial
use of INTSDM.  During a subsequent' visit to Stanford, we
introduced him to C@GEN and its use.  We have been keeping him
up to date on  recent developments because he indicated that
CCNGEN is beginning  to have more and more use to him in the
analytical task of evaluating additive bonding in polymeric
materials.

    Dr.  Geza Szonyi  of Polaroid corporation was one of the
original persons to enquire about SUMEX/CCNGEN access as a result
of the  "invitations for use"  which were included as a part of
early journal articles.  He has recently requested trial access
to mGEN.  Phone conversations indicate that his group is
evaluating computer  systems which will offer them the greatest
latitude in applying computers to their work in various fields of
chemistry and related data management.  Once again, DENDFJALis
viewed as a potential knowledge source.

   Drs. D. Williams and R. McGrew from the Midland, Michigan
site of Dow Chemical came to visit Stanford and receive an
introduction to CONGEN.  They were given a CONGEN demonstration,
and as a result, requested a copy of the teletype DRAW portion of
the program,  which we sent to them. This brings to five the
number of sites which are now using the teletype DRAW program in
some fashion,  Also included are: Lederle Labs in New York, (Dr.
Babu Venkataraghavan); Dept. of Computer Science at SUNY, (Dr.
Dave Larson); Dept. of Chemistry, Arizona State Univ., (Prof.
Morton Munk); Dept. of Chemistry, Niyagi Institute,  (Prof.
Hidetsugu Abe): and Cambridge University, (Neil Gray).

4.2   MarineNatural Products

86


1977-78 Annual Reprt

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Section 4.2

4.2.1   Mass Spectral File Search System

   An attempt was made  to obtain mass spectra for all marine
sterols reported in the literature  (Appendix A).  The old mass
spectral files were scanned and pertinent sterol mass spectra
were digitized (a file of non marine  sterol mass spectra were
also acquired from the older files as a supplement to  themarine
file) (see Appendix B.  Marine sterol researchers were requested
to send samples of specific sterols which they reported or sterol
mixtures known to contain the requested sterol (see Appendix B.
In a few  cases sterols were isolated from crude extracts of
organisms known to contain the sterols.  The high resolution X-
MS spectra of the available sterols were recorded using a Hewlett
Packard 7610A gas chromatograph equipped with a 10' X 2 IIITI "U"
shaped column (3 per cent Foly S-179 on gas chrom Q or 3 per cent
OV-17 on gas chrom Q (column temp. 260 degrees C) and interfaced
with a Varian Mat 711 double focussing  mass spectrometer
(equipped with a Watson-Biemann dual stage separator, an all
glass  inlet system and a  PDP-11/145 computer   for data
acquisition).   High resolution   spectra were   recorded for
subsequent fragmentation  analysis by the application of date
interpretation and summary programs, e.g.,. INTSUM,  and to
facilitate handling of   the data for construction   of the
searchable files.  Within the framework of the available data
acquisition and reduction systems, the rapid analysis scheme has
been tested, and the advantages and limitations are the subject
of the following section.

   The spectra of 52 marine sterols were compiled in a
computer searchable format.  The spectra, which are essential to
have available for careful comparison following the search
report,  have been plotted, and the plausible or established
interpretations of the higher molecular m/e peaks have been
indicated on the spectra.  Spectral  interpretations have been
coded in Fig. 8 in a series of 32 symbols which have been
appropriately marked on the spectra of each sterol in Appendix C
tiich is the file of marine sterol spectra constructed in our
laboratory.  Attached is a list of investigators who reported and
received copies of   this file.  This summary  of proposed
fragmentation  rules is acting  as a preliminary guide  in the
INTSUM evaluation.

   The S-H program was used to match every spectrum in the
file (Appendix C) to every other spectrum to gain an indication
of how all the spectra rank to one another in terms of the
similarity index described previously (Table V). A rank of 999
indicates a psitive identification:  therefore,  each spectrum
when compared against itself results in a rank of 999. Ranking
values below 500 indicate positive nonidentity  and are not
recorded.  Ranking values approaching 750 indicate a possible
match is not ranking higher due  to variations  in spectrometer
operating conditions.  Table V displays a numbar of interesting
results.  First,  several separate sterols rank at the identity

87


1977-78 Annual Report      RR-00612

Section 4.2

rank, that is, they have mass spectra which are similar enough to
be basically indistinguishable:

   Sterols 15 and 18 Appendix A: this indicates that
mass spectrometry cannot distinguish between slightly
different side chain alkylation patterns in some cases.
This agrees with the similar evaluations   in the
literature.

    Sterols 68 and 71:  this indicates that mass
spectrometry cannot distinguish between side chain
double bond geometrical isomers (E and Z) in this case.

   Sterols 90 and 80: these are again sterols with
slightly different patterns of side chain alkylation.

See pp. 88a-c for Table V.

88


Table V.

LIBRARY SEARCH REPORT FOR EXPERIp!NT SEARCHING
  52 SPECTRA IN 1MARINE AGAINST THEMSELVES

STEROL

-13

10

14
-ii-

16

17

18

23

25

26

27

999

99
599
547
554

999
G--
999

_.. .
399
508

?99

299
376
555
504

999

999
888
612
510

099

999

999
745
594
547

999
631
642
581

999
647

990
593
581
5e0

399
629


Table V.
(cont. 1

Qab

STEROL                          STEROLS MATCHED

44

-ii

60

61

-ii

68

999  114    Cl     0 398 (24S)-24-NETHYLC~OLEST~~5,2S~O~E~~-3B~TA-
660   79  999       ij  398 24-~~THYLC~OLEST~~5,22e-DTENIJHETA-oL
635   75  999       $d  398
516   64           ~ALP~AI~~~~ETHYLCYOLESTAI~,~~EIJ~ETA-U~
          0      0  412 ~2~S)-24-ET~iYtC~OLESrA-5,25-DIE~J-3~~TA.O

                  -
999  ?nG  999       0  398
    58            24-f~FTHYLC#oLESTA-5,2~~2~)-~I~h-3BETA-~L
547     999       R  398
521   61            3~-~ET~iYtCHOLESlA-5,22~~O~E~~3~ETA~OL
       990       U  426 GORGCST-5-EN-3nEtA-UL
527  39  999       0  426  r2dZ)-2daPROPYLJnENEt~O~-E~TA-5-F~~3~ETA-

9991132 999

                  .-
999   86  999        1.j  481; 5AI PtlAc24-~ETWYiCHQLEST-7-EN-3~ElA-~L
666   55  999       c"  414  5ALPrA.24-FTwYLCwotEsT-7-fN-39Eth-oL
529   45  999      C'I  386 SALPrn~C~OLES7-7-E~~~3RET,~-O~.

994  Sif  999       (:  612 S~LPYA~24-~7~YLC~OLES;a-7r22~-OI~N-3~ET~
524   62    0       k?  412  230NOR-GGRGOST-S-EN-30ETA-OL.
51    59  999       ~1  413 ~~-~~T~YLC~~ILESTA-S,~?E-O~E~~~~~ET~-~L
52    44  99s       u  412 23,24-OI~ETtiYLC~~LESTA~5,22~@~~l~-3RETb-~

I                                                                           -


Table V.

(cont.)

STEROL                            STEROLS MATCHED

t-
75

77
1
78

79

82
t-
86

88

I--
96
l-


1977-78 Annual Report      RR-00612             Section 4.2

   An important limitation of the file search system is then
its inability to distinguish between variations in side chain
alkylation.  These various side chain alkylation patterns are
very important with respect to biosynthetic processs.  Since
these sterols have different retention indices, this limitation
has been overcome by searching a file of retention indices as
well as mass spectra.   A computer program  for accurately
calculating  retention indices has been developed by William
Yeager,  Department of Genetics, Stanford University, and is
applicable to the rapid analysis sequence.  Michael Kohraman has
prepared a file of carefully measured retention indices from
samples used to compile the mass  spectral file;  (Table VI)
therefore, the limitations concerning identification of isomeric
side chain alkylation patterns have been reduced.

See p. 8ga for Table VI.

89


TABLE Yf

RETENTION INDICES OF STEROLS OF SP2250

, 3047  4
1 3282  4 3268   10 3317

1       I       I

" -.
z Y- I   ,  ----
<-   3442              I         I          I          I          I          I
I m
b        336 1
L.         4' 3334 24 3383 271'3402  ~$3245  zs/ 3290  3oj        31 3322

ry  I' 7l378 14.                 2         2  3219   2           I          1
       I  23l     241      I      I      1      I      I    1

I        33     I     I     I     I     I
34           34     371          I

3497 35i'3480 4.4 3492 4xi 3292 4a    I     I
      !    !    !

                                     4      I        47      I
             3538 .49 `3474  '         5x
uB*                       I        c7

           I
5 LLl. I          I
       - ~s9l          I         I -         , d2-v --,          I
                      I       I     I     I     I

3489 7q

I                    I       I       I       I       I       I      1


1977-78 Annual Report      RR-00612

Section 4.2

    second,  some sterols have very  distinctive mass spectra
with respect to  the other spectra in the file, and no other
spectrum ranks above 500 (for 17 spectra): however,  the majority
of spectra do show some  similarities to other spectra  in the
file,  i-e, have a cross rank > 500 with another  sterol mass
spectrum in the file.  It is interesting that sterols which are
saturated match only with other saturated sterols,  sterols with
one nuclear unsaturation match only with other sterols with one
nuclear unsaturation, sterols with 2 nuclear  unsaturations match
only sterols with 2 nuclear unsaturations, and sterols with one
nuclear and one side chain unsaturation (or ring junction) match
only sterols possessing that property.  The empirical ranking
algorithm described previously has detected the number and
general positions of unsaturation in the sterols.   Therefore, if
a new sterol is detected by the file search procedures then the
general structural properties of the new sterol (number of
nuclear and side chain double bonds) may be indicated by the
structures of the sterols with which it is ranked even though the
ranking values are very low.

   The real utility of the search system will be in rapidly
sorting a tremendous quantity of experimental data in an effort
to reveal the sterols of novel structure. This is of tremendous
utility because marine  sterol mixtures are generally complex,
containing  over 40 sterols in some cases.   However,  once the
sterol of novel structure is pointed out, then a careful analysis
of the mass spectral fragmentation  in terms of known processes
must proceed.  Rules generated via INTSW, etc. analyses of the
extensive marine sterol high resolution mass spectral files will
help greatly by providing firm guidelines for the structural
evaluations of the previously unencountered sterols.

4.2.2   Researchers Receiving Marine Sterol Data

Dr. J. 8. Heather
The Upjohn Company
Chemical Process,
Rsch & Development
Kalamazoo, Mich.

Dr. Steven C. Welch
Dept of Chemistry
University of Houston
Houston, Texas 77004

Dr. Richard M. Wing
Univ of California
Riverside, Ca.

90


1977-78 Annual Report

RR-00612

Prof. Paul J. Scheuer
University of Hawaii
2545 The Mall
Dept of Chemistry
Honolulu, Hawaii

Dr. Yuzura Shimizu
Univ of Rhode Island
College of Phar.macy
53 Foqarty
Kingston, R-1.

Dr. Maktoob Alam
University of Houston
College of Pharmacy
Dept. of Med. Chem.
and Pharmacgnosy
Houston, Texas 77004

Dr. Ron Quinn
Wche Research Inst.
P. 0. Box 255
Dee Why NSW 2099
AUSTRALIA

Section 4.2

Dr. K. Ivanetich
Dept Physiol. & Med.
Biochemistry
Medical School
Observatory, Cape
SomH AFRICA

91


1977-78 Annual Report

RR-00612

Section 4.2

5   Carbon-13 Work

   The work described in this section was accomplished in
conjunction with work on structure elucidation  and theory
formation programs (sections 2 and 4). It is presented together
here to make a more coherent presentation.

   Carbon-13 nuclear magnetic  resonance (CMR) has developed
into an important tool for the structural chemist. A natural
abundance CMR spectrum which is fully proton decoupled consists
of a number of sharp peaks which correspond to the resonance
frequencies in an appliedlogagnetic field of the various types of
carbon atoms present. A  C shift is the amount an observed peak
is   shifted   from   that of a   reference  peak,  usually
tetramethylsilane (TMS) .

   In last year's annual  report we discussed an extension of
Meta-DENDPAL which allowed the program to form rules in the
domain of CMR spectroscopy. During the past year we continued
work on this  program , and wrote a second program which applies
CMR rules to structure elucidation problems.   Pules generated
from a combined set of paraffins ?t!l  acyclic amines have been
used to successfully identify the   C NMR spectra of molecules
not in the training set data.  The introduction of a limited set
of stereochemical terms   to the rule  generation procedure
demonstrated the feasibility of extending the method to more
complicated systems.  A description of the rule formation and
structure elucidation programs is given in  [171.  Results are
presented there for  the combined set of paraffin and acyclic
amines,  as well as for a combined set of trans decalins and
monohydroxylated androstanes.

5.1   Rule Formation *suits

   A set of rules was genera !@ using a subset of the paraffin
data from Lindeman and Adams

acyclic amine data from Eggert and combined Wf.~;~~~zstw;~ th:
                   Djerassi
empirical formula
training  set for  C H20
             la er
        ii!   and C6H15N were excluded from the
              use in testing the generality of the
rules.  The rule set was tested by generating all structural
isomers with the empirical formulas C H20 (35 isomers) and C6H 5N
(39 isomers),  predicting the spec um of each isomer,
              e         ti en
ranking the predicted spectra by similarity to a known spectrum.
'Ihe rank of the predicted spectra associated with the correct
candidate structure provides  an indication of the utility and

-----

  l2 Lindeman, L.P. and J.Q. Adams, Anal. Chem,, (1971), 43,~.
1245.

l3 Dggert, H. and C. Djerassi,  J. Am@r.   them. sot.
(1973),95,p. 3710.

92


1977-78 Annual Report

RR-00612              Section 5.1

validity of the generated rules.  For the above test we used the
24 CgH2 spectra available from the work of Lindeman and Mams.
The pr ea icted spectra of the 35 structural isomers were compared
and ranked against each of these available spectra.   The results
of this ranking for CgH20
shown in Table VII.    as well as a similar test on C6Hl5N are

Empirical   Number-of   Numberof   Bank of Correct Structure
Formula   Candidates  Spectra    (Freg of Correct
                  1st  2nd ...6th.%ing)

c9 H29       35       24   20/24   3/24       l/24

'6 H15 N      39       11   8/11    2/11   l/11

Table VII.  Results of Structure Banking

5.2   Adding Stereochemistry to the Rule Language

   The work on the paraffins and acyclic amines requires only
topological descriptors in the lj anguage of   atom features.
Because af $e dependence of    C  shifts on stereochemical
features     it is necessary to  have the facility to include
stereochemical terms when they are required. Substituents placed
on systems which have static conformations such as trans decalin
and androstane with trans ring fusions can be described in
discrete terms.  The terms we selected describe the orientation
on the ring of the substituent as either axial or equatorial, and
either alpha or beta.  For instance, a substituent is beta in lo-
methyl-trans-decalin if it is on the same side of the ring as the
methyl group and alpha if on the opposite side of the ring from
the methyl group.  The rule generation program with the extension
of the language  to include these atom features was run on a
combined set of trans decalins,  lO-n&hyl-trans-decalols and
monohydroxylated androstanes with tran f6 ring fusions select
from the works of Grover and Stothers    and Eggert et. al. ti

  14 -                        -
      Grover, S.H.   and J-B-   Stothers, Can. J. Chem.
(1974),52,p. 870,
  l5 Eggert, H., C. VanAntwerp, N. Bhacca, and C. Djerassi, J.
Org. Chem.,(1976),41,p. 71.
  l6 Grover, Cp, cit.

17 Eggert, OP. cit.

93


1977-78 Annual Report      RR-00612             Section 5.2

Sixty rules were generated to cover the 249 data peaks of 17
compounds. Samples of the rules generated are shown in Figure
11. The examination of these rules will show that they are useful
for the chemist who wants to study contributions to the total
shift as well as for structure elucidation.

See p. 94a for rules.

   Figure 11.  Sample rules constructed from decalins and
hydroxy steroids with trans ring fusions. The '*I identifies the
carbon atom to which the shift is assigned.     is  in ppn
downfield from TMS.

5.3   Structure Elucidation

   IWleculx structure elucidation using CMR consists of using
a set of rules which simanarize the CMR behavior of a set of
compounds to identify other unknown compounds within that or
similar classes.  The information which the chemist must supply
to the structure elucidation program includes the empirical
formula of the unknown as well as its observed spectrum. Two
parameters may be set by the chemist to select the number of
plausible structures to be determined, and to specify the error
range in ppm which should be assigned to the rules to account for
deficiencies in the training data, experimental error, solvent
effects, etc.  From this information and its store of CMR rules,
the program assembles a  set of structures which are plausible
sources of the unknown spectrum.

   Molecular structure elucidation is accomplished by our
program by selecting a shift (peak) in the observed spectrum,
then finding the rules which are possible explanations for this
shift.  The rules selected postulate partial substructures which

94


-' Alpha Carbon Rules

I   I
    C
c\c/ \  + 70.0 d &4,(70.5
I*
OHeq

Beta Carbon Rules

*kc I
I   I
Y/"\ - 35.6<6&36.4

Gamma Carbon Rules

20& S(*),( 20.5

I   I
    C
Yc( \  __+ 71.8,<&~& 72.5

I
OHax

OH-C&
ax  I   I   __ic 67.6,<bk)s68.1
   C \ C

OH- C/"
ax  I

    C
Y /
  C

/ \F
  C

C* ,":!
I   I                                .-
c\,/c  - 16.9,<sos17.1

OH
-I

OHax

94a

    /

.,.


977-78 Annual Eqort      RR-00612             Section 5.3

might be in the molecule.  These substructures are then assembled
jigsaw  puzzle fashion   to construct the   final molecule.
Constraints stemming from   both the observed  spectrum and
information associated with each rule are used to constrain the
process so that only "reasonable" structures will be considered.

   The structure elucidation program has been run on several
test cases using unknown paraffin and acyclic amine spectra with
reasonable success.  This program is described in detail in 1171.

    5.4   Geometric Distortions in Steroids

   For a given molecule, deviations between its observed 13C
p spectrum and its spectrum predicted from a set of empirical

C NMR rules is  often explained in terms  of geometric
distortions.
distortions on
field program has been used ti model geometric  distortions in
mono-hydroxy- 5 alpha, ll-alpha androstanes.
many types of slight geometric distortions on the
examined   in terms   of the   non-bonded   interactions. The
delta(al.pha) and delta(beta) effects could be characterized in a
few terms suggested by the non-bonded interactions.   The results
of this study were published in [16].

6    DATA CoLlLEcTIm AND DAm F4EnumIoN

6.1   DENDFU&GC/MSarxl MSWork

   The following is a  summary of the activities in the CC/MS
lab for the past year.  This work involves both developnent of
the Gc/M!Z Computer systems for both high and low resolution CC/MS
applications  and application of the existing system to mass
spectral analyses of compounds of biomedical importance.

A) Iow resolution m/MS: (manual mode)

   93 sterol mixtures (marine sterol extractions) for Dr.
Djerassi's group.  Identification of free sterols.

   8) High F&solution a/MS
  la N.L. Allinger, M.T. Tribble, M.A. Miller and D.H. Hertz,
J. Amer. C&m, Sot., 93, 1637 (1971).

  lg D.H.  Wertz and N-L. Allinger, Tetrahedron, 30, 1579
(1974).

95


1977-78 Annual Report

RR-00612

Section 6.1

Total sample mixtures: 86

for:  1) Dr. Djerassi            52
    2)  Genetics              25
    3) Prof. Adlercreutz, Finland 9

   1) Dr. Djerassi: all marine sterols, especially for library
purposes and thesis of Bob Carlson.

   2) Genetics:  Urine extractions, channel-black and carbon-
black, all for assistance in identification of unknown ccrinpounds
whose structures could not be elucidated by low resolution mass
spectral data coupled with library search.

   3) Prof.  Adlercreutz, Clinical Chemistry, University of
Helsinki, Finland needed quantitation of a corticosteroid. Tests
were made to find sensitivity limit with Aldosterone-TMS.   5 w
Alderstone-'IBIS was limitation.  An unknown corticosteroid with a
M+504 (low resolution spectrum) could not be identified by H. R.
GC/WS due to amount of sample availability plus   lack of
sensitivity on our  instrument.  The sample was a substance
occurring in patients who have no aldosterone, but still may have
hypertension or hypokallemia.

High resolution MS

43 samples total:

for:  1) Dr. Djerassi             29
    2) Prof. Fringuelli, Italia 8
    3) Prof. Nakano, Venezuela     6

   1) Dr. Djerassi: Structure identification of new sterols
plus terpenes.

   2) Prof.  Fringuelli, Perugia University, Perugia, Italia.
Had 8 samples of furan, thiophen, selenophen and tellurophen
derivatives for mass fragmentation studies. H. R. resolved all
isotopes of each substance (up to 8 isotopes) and gave clear
identification pattern.  He is preparing and sending us more sets
of compounds.

   3) Prof. Nakano, Instituto Venezolano, Caracas, Venezuela,
needed high resolution spectra of Oxadiazole derivatives for
fragmentation studies, and successful identification of all six
samples were possible.

Computerized MS (Incl. trials)

H, R. (R-10000) + CC/MS H.R. R-5000

Start Jan. 77.                      Total
SO 1696  to 1921   Da (*I                 225
so 1839 to la59   DOS dublication nos.      20

96


1977-78 Annual Report      RR-00612

Section 6.1

SO 1960 to 2379    RT-11                 419
SO 1883 to 1955    IDS                   72
SO 2437 to 2477    RT-11                  40
SO 1956 to 2031    DOS                   75
SO 2479  to 2481    RT-11                  2
SO 2032 to 2037    DOS                   5
SO 2516 to 2907    m-11                391

Total samples tested

1249

* DOS and KY+11 refer to the two different operating systems for
the PDP-11 computer system  During the past year we have had to
convert operating systems.

    6.2   Collaborators Bceiving the CLEANUP   and HISLIB
Programs

   Following is an alphabetical list of people who have
requested copies of the program for extracting better resolved
mass spectra from GC/MS data, described in [lo].

97


1977-78 Annual Report      RR-00612

Section 6.2

Dr. Craig Anderson           Dr. W.K. Elkin
Gulf South Research Institute   Department of Toxicology
P.O. Box 26500             Swedish Medical Research Council
New Orleans, Louisiana 70186    Karolinska Institutet
                     S-104 01 Stockholm, Sweden
Dr. John 8. Bagger
Department of Chemistry       Dr. Paul V. Fennessey
Colorado State University     B.F. Stolinsky Psch Laboratories
Fort Collins, Colorado 80521   Department of Pediatrics
                      Univ of Colorado Medical Ctr
                      4200 East Ninth Avenue
Dr. Rod Britten            Denver, Colorado 80220
Jet Propulsion Laboratories
4800 Oak Grove Drive, 168-227   Dr. Claude Finn
Pasadena, California 91103     School of Pharmacy
                     U. C. San Francisco Medical Ctr
                    San Francisco, California 94143
Dr. Pobert D. Brown
Bristol Laboratories
P. 0. Box 657            Dr. R. Fluckiger
Syracuse, New York 13201      Balzers Aktiengesellschaft fur
                      Hochvakuumtechnik und Dunne
                          Schichten
Dr. Peter Bruck               FL-9496 Balzers
Magyar Tudomanyos Akademia     Furstentum Liechtenstein
Kozponti Kemiai Kutato Intezete
1088 Budapest Puskin u. 11-13.   Dr. A.N. Freedman
HungarY                Central Electricity
                      Research Laboratories
Dr. Lawrence Burkhard        Kelvin Avenue, Leatherhead
Water Chemistry Laboratory     Surrey, England
University of Wisconsin
660 North Park Street         Dr. Nelson M. Frew
Madison, Wisconsin 53706      Chemistry Department
                  Woods Hole Oceanographic
Dr. Richard M. Caprioli        Institution
Dr. William E. Seifert, Jr.    Woods Hole, Massachusetts 02543
Program inBiomolecular Analysis
Univ of Texas Medical School    Dr. Richard Cans
P. 0. Box 20708            Chemical Research Division
Houston, Texas 77025         Bound Brook Laboratories
                   American Cyanamid Company
Dr. Henry E. Dayringer       Bound Brook, New Jersey 08805
Mail Zone VIA
Monsanto Agricultural Research   Mrs. E.M. Gomm
800 North Lindbergh Boulevard   Department of Chemistry
St. Iouis, Missouri 63166     University of Natal
                   P-0. Box 375, Pietermaritzburg
Dr. James F. Elder          Natal, South Africa
574 Building
Analytical Laboratories      Dr. Sydney M. Cordon
Dow Chemical U.S.A.         Chemistry Division
Midland, Michigan 48640      Atomic Energy Board
                  Private Bag 256, Pretoria
                  Republic of South Africa
                       98


1977-78 Annual Report

RR-00612

Section 6.2

Dr. Richard A-Graham        Dr. H. Knoeppel
FSL                       EURA'ICM - CCR
U. S. Army Natick Laboratories   Casella Postale No. 1
Natick, Massachusetts 01760    Ispra, Italy

Dr. Donald A. Griffin
Dept of Agricultural Chemistry
Oregon State University
Corvallis, Oregon 97331

Dr. G. Knowles
Water Research Centre
Stevenage Laboratory
Elder Way, Stevenage
Hertfordshire SGl lTH, England

Dr. William Haddon
Western Regional Research Center
U.S. Department of Agriculture
800 Buchanan Street
Albany, California 94710

Dr. Thomas Knudsen
Northrop Services
Box 12313
Research Triangle Park, N.C.

Dr. P.T. Holland
Ministry of Agriculture
and Fisheries
Private Bag, Hamilton
New Zealand

Dr. Douglas W. Kuehl
Mass Spectrometry Laboratory
Environmental Rich Laboratory
6201 Congdon Boulevard
Duluth, Minnesota 55804

Dr. I-Howe
Shell Biosciences Laboratory
Sittingbourne Research Centre
Sittingbourne, Kent ME9 8AG,
England

Dr.Ake Lundin
LKB-PROW AB
Molecular Analysis Division
S-161 25 Bromma 1
Sweden

Akio Ide, Ph.D.
Ehhe University
Agricultural Chemistry Dept
Matsuyama 790, Japan

Dr. John L. MacDonald
Central Research
Ralston Purina Company
Checkerboard Square
St. Louis, Missouri 63188

Dr. J. B. Justice
Emory University
Atlanta, Georgia 30322

Dr. R.G.A.R. Maclagan
Department of Chemistry
University of Canterbury
Christchurch 1, New Zealand

Dr. Graham S. King
Department of Chemical Pathology
Queen Charlotte's Hospital
Goldhawk mad
London, ENGLAND W6 OXG

Dr. John C. Marshall
Department of Chemistry
The University of North Carolina
Chapel Hill, N.C.

Dr. Daniel R. Knapp
Department of Pharmacology
1Medical Univ of South Carolina
80 Barre Street
Charleston,South Carol ina 29401
                       99

Dr. R. A. F. Matheson
Chemistry Section
Environmental Protection Service
5151 George Street
Halifax, Nova Scotia CANADA


1977-78 Annual Report      RR-00612             Section 6.2

Dr. James A. McCloskey, Jr.    Dr. Philip W. Ryan
Professor, Biomedical Chemistry Battelle Pacific Northwest
Dept. Biopharmaceutical Sciences Laboratories, 329 Bldg.

University of Utah
Salt Lake City, Utah 84112

Dr. Ingolf Meineke
Fachbereich Chemie
Philipps Universitaet
3550 Marburg/Lahn, Lahnberge
WEST GERMANY

Dr. Roy 0. Morris
Dept. Agricultural Chemistry
Oregon State University
Corvallis, Oregon 97331

Dr. James E. Oberholtzer
Arthur D. Little, Inc.
Acorn Park
Cambridge, Massachusetts 02140

Mr. Andrew Pallos
Aerospace Corporation
P-0. Box 92957
Los Angeles, California 90009

Mr. Dan Pearce
Grange Co Sheriff-Coroner Dept
550 N. Flower Street
Santa Ana, California 92702

Dr. William R. Penrose
Newfoundland Biological Station
3 Water St. East
St. John's, Newfoundland
Ale IA1

Dr. I&ald D. Plattner
Northern Regional Research Lab.
U.S. Department of Agriculture
Peoria, Illinois 61604

Ken E+xek
Scientific Instruments Division
Hewlett-Packard Company
1601 California Avenue
Palo Alto, California 94304

Battelle Boulevard -
Richland, Washington 99352

Dr. R&ert S. Schroeder
Gulf Oil Chemicals Company
P. 0. Box 2900
Shawnee Mission, Kansas 66201

Dr. J. Scrivens
Imperial Chemical Industries
PO Box 90 Wilton
Middlesbrough Cleveland
TS68JEEngland

Dr. Walter M. Shackelford
Analytical Chemistry Branch
Environmental Rsch Laboratory
Athens, Georgia 30601

Dr. M.A. Shaw
Unilever Research
Port Sunlight Laboratory
Port Sunlight
Wirral, Merseyside L62 4XN,
England

Dr. Jacob Shen
The Standard Oil Company
4440 Warrensville Center Road
Cleveland, Ohio 44128

Dr. M. M. Siegel
FMC Corporation
Chemical Group
Box 8
Princeton, New Jersey 08540

Dr. G. P. Slater
National Rsch Council of Canada
Prairie Regional Laboratory
110 Gymnasium F&ad,
University Campus
Saskatoon, Saskatchewan CAMu34

Dr. Carroll A. Smith
Div of Chemical Oceanography
University of Miami
4600 Rickenbacker Causeway
Miami, Florida 33149

100