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Human Anatomy & Physiology Laboratory Manual, Cat Version (12th Edition)

Human Anatomy & Physiology Laboratory Manual, Cat Version (12th Edition) PDF

Author: Elaine N. Marieb and Lori A. Smith

Publisher: Pearson


Publish Date: December 1, 2019

ISBN-10: 0321971353

Pages: 992

File Type: PDF

Language: English

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Book Preface

Two hundred years ago, science was largely a plaything of  wealthy patrons, but today’s world is dominated by science and its technology. Whether or not we believe that  such domination is desirable, we all have a responsibility to  try to understand the goals and methods of science that have  seeded this knowledge and technological explosion.

The biosciences are very special and exciting because  they open the doors to an understanding of all the wondrous  workings of living things. A course in human anatomy and  physiology (a subdivision of bioscience) provides such insights in relation to your own body. Although some experience in scientific studies is helpful when beginning a study of  anatomy and physiology, perhaps the single most important  prerequisite is curiosity.

Gaining an understanding of science is a little like becoming acquainted with another person. Even though a written description can provide a good deal of information about  the person, you can never really know another unless there  is personal contact. And so it is with science—if you are to  know it well, you must deal with it intimately.

The laboratory is the setting for “intimate contact” with  science. It is where scientists test their ideas (do research),  the essential purpose of which is to provide a basis from  which predictions about scientific phenomena can be made.  Likewise, it will be the site of your “intimate contact” with  the subject of human anatomy and physiology as you are  introduced to the methods and instruments used in biological  research.

For many students, human anatomy and physiology  is an introductory-level course; and their scientific background exists, at best, as a dim memory. If this is your  predicament, this prologue may be just what you need to  fill in a few gaps and to get you started on the right track  before your actual laboratory experiences begin. So—let’s  get to it!

The Scientific Method
Science would quickly stagnate if new knowledge were  not continually derived from and added to it. The approach  scientists commonly use when they investigate various aspects of their respective disciplines is called the scientific  method. This method is not a single rigorous technique that  must be followed in a lockstep manner. It is nothing more or  less than a logical, practical, and reliable way of approaching and solving problems of every kind—scientific or otherwise to gain knowledge. It includes five major steps.

Step 1: Observation of Phenomena
The crucial first step involves observation of some phenomenon of interest. In other words, before a scientist can investigate anything, he or she must decide on a problem or focus  for the investigation. In most college laboratory experiments,  the problem or focus has been decided for you. However, to  illustrate this important step, we will assume that you want  to investigate the true nature of apples, particularly green apples. In such a case you would begin your studies by making  a number of different observations concerning apples.

Step 2: Statement of the Hypothesis
Once you have decided on a focus of concern, the next step is  to design a significant question to be answered. Such a question is usually posed in the form of a hypothesis, an unproven  conclusion that attempts to explain some phenomenon. (At its crudest level, a hypothesis can be considered to be a  “guess” or an intuitive hunch that tentatively explains some  observation.) Generally, scientists do not restrict themselves  to a single hypothesis; instead, they usually pose several and  then test each one systematically.

We will assume that to accomplish step 1, you go to the  supermarket and randomly select apples from several bins.  When you later eat the apples, you find that the green apples are  sour but that the red and yellow apples are sweet. From this  observation, you might conclude (hypothesize) that “green  apples are sour.” This statement would represent your current  understanding of green apples. You might also reasonably  predict that if you were to buy more apples, any green ones  you buy will be sour. Thus, you would have gone beyond your  initial observation that “these” green apples are sour to the  prediction that “all” green apples are sour.

Any good hypothesis must meet several criteria. First,  it must be testable. This characteristic is far more important  than its being correct. The test data may or may not support  the hypothesis, or new information may require that the hypothesis be modified. Clearly the accuracy of a prediction  in any scientific study depends on the accuracy of the initial  information on which it is based.

In our example, no great harm will come from an inaccurate prediction—that is, were we to find that some green  apples are sweet. However, in some cases human life may  depend on the accuracy of the prediction. For that and other  reasons: (1) Repeated testing of scientific ideas is important,  particularly because scientists working on the same problem  do not always agree in their conclusions. (2) Careful observation is essential, even at the very outset of a study, because  conclusions drawn from scientific tests are only as accurate as the information on which they are based.

A second criterion is that, even though hypotheses are  guesses of a sort, they must be based on measurable, describable facts. No mysticism can be theorized. We cannot conjure  up, to support our hypothesis, forces that have not been shown  to exist. For example, as scientists, we cannot say that the  tooth fairy took Johnny’s tooth unless we can prove that the  tooth fairy exists!

Third, a hypothesis must not be anthropomorphic. Human beings tend to anthropomorphize—that is, to relate all  experiences to human experience. Whereas we could state  that bears instinctively protect their young, it would be anthropomorphic to say that bears love their young, because love is a human emotional response. Thus, the initial hypothesis must be stated without interpretation.

Step 3: Data Collection
Once the initial hypothesis has been stated, scientists plan  experiments that will provide data (or evidence) to support or  disprove their hypotheses—that is, they test their hypotheses.  They accumulate data by making qualitative or quantitative  observations of some sort. The observations are often aided  by the use of various types of equipment such as cameras,  microscopes, stimulators, or various electronic devices that  allow chemical and physiological measurements to be taken. Observations referred to as qualitative are those we can  make with our senses—that is, by using our vision, hearing,  or sense of taste, smell, or touch. For some quick practice  in qualitative observation, compare and contrast an orange  and an apple. (Compare means to emphasize the similarities  between two things, whereas contrast means to emphasize the differences.)

Whereas the differences between an apple and an orange are obvious, this is not always the case in biological observations. Quite often a scientist tries to detect very  subtle differences that cannot be determined by qualitative  observations; data must be derived from measurements. Such  observations based on precise measurements of one type or  another are quantitative observations. Examples of quantitative observations include careful measurements of body or  organ dimensions such as mass, size, and volume; measurement of volumes of oxygen consumed during metabolic studies; determination of the concentration of glucose in urine;  and determination of the differences in blood pressure and  pulse under conditions of rest and exercise. An apple and an  orange could be compared quantitatively by analyzing the  relative amounts of sugar and water in a given volume of fruit  flesh, the pigments and vitamins present in the apple skin and  orange peel, and so on.

A valuable part of data gathering is the use of experiments to support or disprove a hypothesis. An experiment is  a procedure designed to describe the factors in a given situation that affect one another (that is, to discover cause and  effect) under certain conditions.

Two general rules govern experimentation. The first of  these rules is that the experiment(s) should be conducted in  such a manner that every variable (any factor that might affect the outcome of the experiment) is under the control of the  experimenter. The independent variables are manipulated  by the experimenter. For example, if the goal is to determine  the effect of body temperature on breathing rate, the independent variable is body temperature. The effect observed  or value measured (in this case breathing rate) is called the  dependent or response variable. Its value “depends” on the  value chosen for the independent variable. The ideal way to  perform such an experiment is to set up and run a series of  tests that are all identical, except for one specific factor that  is varied.

One specimen (or group of specimens) is used as the  control against which all other experimental samples are  compared. The importance of the control sample cannot  be overemphasized. The control group provides the “normal standard” against which all other samples are compared  relative to the dependent variable. Taking our example one  step further, if we wanted to investigate the effects of body  temperature (the independent variable) on breathing rate (the  dependent variable), we could collect data on the breathing rate of individuals with “normal” body temperature (the implicit control group), and compare these data to breathingrate measurements obtained from groups of individuals with  higher and lower body temperatures.

The second rule governing experimentation is that valid  results require that testing be done on large numbers of subjects. It is essential to understand that it is nearly impossible  to control all possible variables in biological tests. Indeed,  there is a bit of scientific wisdom that mirrors this truth—that  is, that laboratory animals, even in the most rigidly controlled  and carefully designed experiments, “will do as they damn  well please.” Thus, stating that the testing of a drug for its  painkilling effects was successful after having tested it on  only one postoperative patient would be scientific suicide.  Large numbers of patients would have to receive the drug and  be monitored for a decrease in postoperative pain before such  a statement could have any scientific validity. Then, other researchers would have to be able to uphold those conclusions  by running similar experiments. Repeatability is an important  part of the scientific method and is the primary basis for sup port or rejection of many hypotheses. During experimentation and observation, data must be  carefully recorded. Usually, such initial, or raw, data are  recorded in table form. The table should be labeled to show  the variables investigated and the results for each sample.  At this point, accurate recording of observations is the primary concern. Later, these raw data will be reorganized and  manipulated to show more explicitly the outcome of the  experimentation.

Some of the observations that you will be asked to make  in the anatomy and physiology laboratory will require that  a drawing be made. Don’t panic! The purpose of making  drawings (in addition to providing a record) is to force you to  observe things very closely. You need not be an artist (most  biological drawings are simple outline drawings), but you do  need to be neat and as accurate as possible. It is advisable  to use a 4H pencil to do your drawings because it is easily  erased and doesn’t smudge. Before beginning to draw, you  should examine your specimen closely, studying it as though  you were going to have to draw it from memory. For example,  when looking at cells you should ask yourself questions  such as “What is their shape—the relationship of length and  width? How are they joined together?” Then decide precisely  what you are going to show and how large the drawing must  be to show the necessary detail. After making the drawing,  add labels in the margins and connect them by straight lines  (leader lines) to the structures being named.

Step 4: Manipulation and Analysis  of Data

The form of the final data varies, depending on the nature of  the data collected. Usually, the final data represent information converted from the original measured values (raw data)  to some other form. This may mean that averaging or some  other statistical treatment must be applied, or it may require  conversions from one kind of units to another. In other cases,  graphs may be needed to display the data.

Elementary Treatment of Data
Only very elementary statistical treatment of data is required  in this manual. For example, you will be expected to understand and/or compute an average (mean), percentages, and a  range wo of these statistics, the mean and the range, are use ful in describing the typical case among a large number of  samples evaluated. Let us use a simple example. We will  assume that the following heart rates (in beats/min) were  recorded during an experiment: 64, 70, 82, 94, 85, 75, 72,  78. If you put these numbers in numerical order, the range is  easily computed, because the range is the difference between  the highest and lowest numbers obtained (highest number  minus lowest number). The mean is obtained by summing the items and dividing the sum by the number of items. What is  the range and the mean for the set of numbers just provided?

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