JPRS ID: 9938 USSR REPORT LIFE SCIENCES BIOMEDICAL AND BEHAVIORAL SCIENCES
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JPRS L/9938
25 Aug~st 1981
USSR Re ort
p
LIFE SCIENCES
BIOMEDICAL AND BEHAVIORAL SCIENCES
(FOUO 10/81)
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JPRS L/9938
25 August 1981
USS R REPO~T
LIFE $CIENCES
BIOMEDICAL AND BEHAVIORAL SCIENCES '
(FOVO lo/s1)
CONTENTS
BIOCHEMISTRY
Algorithms in Biometrics 1
BIOTECHNOLOGY
Site-Specific Nature of Recombination in Escherichia
Coli K-12 and Possibility of Elimination of 'Hot'
Sites 7
Chapter 4. Culture of Microalgae as the Regenerating
Element in a Biological Life Support System for Man 12
ENVIRONMENT
Current Problems of Z~ogeography 35
Diversity Factors in Mathematical Ecology and Population
Genetics 40
PHYSIOLOGY
Fundamentals of Comparative Physiology of Sensory Systems--
A Textbook 43
Temperature Compensation and Behavioral Homeostasis 48
Biorhythms and Work 50
Time Environment and Biological Rhythms 53
Chapter 2. The Gravitational Sensory System 56
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Reproduction of 'Passive' Avoidance in Rats With Administration of 64
Pharmacological Agents
� Group Relations Among Animals: Some Aspects of Population
Physiology in Pharmacol~gy and Toxicology 70
PSYCHOLOGY
Invariant Method of Identifying the Emotional State of a Group of
Speakers an the Basis of Their Speech 73
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BIOCHEMISTRY
UDC: 578.087.1
ALGORITHMS IN BIOMETRICS
Moscow ALGORITMY BIOMETRII in Russian 1980 (signed to press 19 Nov 80) pp 2-4, 11-13,
87-88
[Annotation, foreword by Prof B. V. Gnedenko of Moscow State University, academician
of the Ukrainian Academy of Sciences, introduction and table of contents from book
"Algorithms in Biometrics", by Nikolay Aleksandrovich Plokhinskiy, 2d edition,
revised and enlarged, edited by Prof B. V. Gnedenko, Moscow Society of Naturalists,
Izdatel'stvo Moskovskogo universiteta, 4900 copies, 150 pages, illustrated]
[Text] The second edition contains 60 algorithms (there were 29 in the first) and
explains how they are used. The algorithms are referable to five areas of modern
statistical biology [biometrics]: summary characteristics of tags, theory of repre-
sentativeness, variance analysis, mathematical models in biology, informative
indicators in biology. The second edition of algorithms is intended for saecialists
in biology, university students and instructors, as well as industrial workers in
the field of agriculture.
Foreword
Problems related to the content and nature of mathematical education of biologists
have never been as important as today. The exceptional complexity of biological
phenomena, on the one hand, and comprehensive penetration of physicechemical and
technical methods of research, on the other, as well as the turn to investigation
of microbiological and global processes, inevitably lead to the necessity of
studying mathematical methods in biology. We must have broader ideas about the
capabilities of modern mathematics, and there must be joint participation, on a
regular basis, of biologists and mathematicians in solving major and pressing
biological problems.
How can a mathematician know what he must give primarily to a biologist with res-
pect to mathematical information if he does not have even a superficial idea about
the tasks of biological science? How can a biologist demand an explanation about
some parts of mathematics or other of mathematicians, if he has no conception of .
its capabilities?
The mathematical education of biologists should be based on profoundly comprehended
and interpreted needs of biological science. Courses of mathematics at biology
faculties should be placined on expressly this basis. This also would implement
entirely the remarkable thesis of V. I. Lenin, to the effect that human cognition
is proceeding from vivid contemplation to abstract thought, and from the latter to
practice.
1
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We must admit that the first and final phases of this scheme for acquirfng knowledge
have disappeared from mathematics courses for biologists, and only the bare middle
part remains. As a result, a biologist does not see the link between mathematical con-
cepts and the problems, to the solution of which his life is dedicated.
The existing literature on mathematics for biologists is extremely spare in our
country, while the need for it is enormous. Unfortundtely, the existing textbooks
are very abstract or mediocre.
In my opinion, the mathematical education of biologists should help in the following
tasks:
1. It should give them an idea about the meaning of mathematical ,3~~proaches ~hat
could serve to learn about quantitative patterns in biological phenumena.
2. It should teach them the fundamentals of processing experimental biolo;ical data.
3. It should teach them not to be afraid of mathematically formulated articles
dealing with biology and of having a critical attitude toward the premises and
mathematical system [structure] used in them.
4. It should give them an idea about the principles involved in building mathematical
models of biological processes.
5. It should convince them of the benefits of professional collaboration with mathe-
maticians on a regular basis, since they are capable of becoming interested in
studying biological phenomena. This program does not by any means imply that
a biologist is to change into a mathematician, and its purpose is to arm the biolo-
gist with mathematical methods.
The mathematical works of the biologist, N. A. Plokhinskiy, are rather similar in
their initial premises to the general theses advanced in this foreword.
The textbook, "Algorithms in Biometrics," deals with the description and explanation
of computing procedures that are needed to process statistical data as they apply
to biological problems. The contents of this book are tied in with the author's
well-known books, "Biometrics," "Manual of Biometrics," "Heredity" and enlarges upon
his previously published book, "Algorithms of Biometrics" (1967).
The offered textbook is, in a certain sense, the concluding part of the many years
- N. A. Plokhinskiy worked on the use of classical methods of mathematical statistics
and probability theory in biological research.
This book meets the pressing needs of biologists, and it is an exc.ellent gift for
the numerous students and followers of N. A. Plokhinskiy.
Introduction
" An algorithm is the systematized description of a purposeful sequence of operations.
[actions].
The algorithms furnished in this guide describe the form, order and formulas that
are needed to find the most frequently used biometric parameters.
2
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Mathematical methods, which have been modified to conform with specific objects
and phenomena in life, as well as the distinctions of biological studies, are
presently used in many branches of biology.
The questions of which mathematical methods, when and in what form they should be
used, as well as the biological meaning of eventual results of calculations, are
dealt with in the theoretical part of biometrics, and they are answered in accord-
- ance with the objectives of each specific study. After these questions are
answered, we proceed to the practical use of the chosen methods, i.e., biometric
processing of primary data.
This guide has as its purpose to organize, define and simplify the techniques for
biometric calculations thatare needed to analyze the results of experiments and
observations, or when using production accounting records.
The algorithms submitted here are referable to methods t~at were chosen from the
vast armamentarium of modern mathem~tics (probability theory, mathematical statis-
tics and other branches), as being the most suitable for modern biological studies.
The major difficulty of preparing this textbook was in the choice of standardized
(at least, within the framework of one manual) terms arrd symbols. It was found
impossible to take an existing integral system, since such a system does not exist.
Many various designatic~ns for the same parameters are used in works dealing with
mathematics; there are seven different symbols to designate the arithmetic mean,
nine differenr_ symbols for the sum of the squares of central deviations, six
different terms for the concept of "reliability of difference," five different
terms to designate the main property of any group consisting of dissimilar ob~ects
with reference to the tag under study.
Such a diversity of symbols and parameters can be easily attributed to the fact
that the letters of three alphabets--Latin, Greek and Gothic--are not enough to
designate the enormous quantity of mathematical parameters, so that it is impossible
to assign a special symbol for each parameter. Mathematical schools and different
mathematicians find a solution to this situation in their own and, of course,
different ways. Some prefer to use the symbol M to refer to the mean, others
prefer the square of the mean-square deviation. Some use the symbol V for the
datum (result of primary measurement of an abject), others use the symbol x,
although it would seem that the symbol for an unknown or symbol of an argument
should not be used for values that are known from the very start of a study aud
usually not viewed as arguments, but on the contrary as a function of the arguments
studied, the.influences.
In some cases, abstract mathematical terms lead biologists into error when studying
specific phenomena. For example, designation of dissimilarity of objects in a
group by the term "variability" (which refers to a ve.ry different phenomenon in
biology) could lead to improper interpretation of the term "heritability."
In preparing this guide, we had to introduce designations that do not reflect any
specific mathematical system of terms and symbols (there is no such system), but
those that were the most suitable for biologists and corresponded rather accurately
to the biological essence of a phenomenon or parameter.
The attached table is a brief summary of the main biometric terms and symbols.
3
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Main terms and symbols used in biometrics
In this manual In works of other authors
Tag [signJ--elementary distinction of Random value, variab'le
each object on the outside, inside,
in constitution, anatomy, histology,
physiology, productivi~y
Datum (result of ineasuring sign, Value acquired by random variable,
its value, magnitude) V variant V, X, x, z~, a
Group size (number of objects in Size, volume of group n, N
group) n, N
Mean value of tag M= EV/n Mean value of random variable
General mean M M, m, a, b, S, e, x
Sample mean M
Diversity (presence of dissimilar Variability, fluctuation, dispers-!on
objects in group) and even ''scatter"
Sum of sc~uares, dispersion C= Su~ of squares of central deviations,
E(V-M) sun of squares, dispersion:
E(V-M)2, E(x-x)2, Ex2, S, SS, S0, SA,
SAQ, CQ
Variance, mean square Mean square, dispersion, deviation
2 C variance.~2, SZ, v2, E, M, MQ, ES
6 n-1
Mean square deviation, sigma Mean square deviation, standard Q, S, v
C
Q
- n-1
General sigma Q,
Sample sigma Q, S
Reliable difference (one can expect Substantial, reliable, significant, real,
the same dtfference betweer? general difference, there is a difference,
means as was found between sample "difference is reliable, i.e., real,"
means)--difference in sign, magni- samples from different general sets
tude of difference, confidence limits
(M1>MZ) ~M1>M2)
- Unreliable difference (vague results Insignificant, etc., difference. Samples
were obtained) from one general set
(M1>M2) (M1 o
n GK D~FOfom)0
and calm states, ti
D(Fom, f o)