The average inner from cluster members to their centroidshow how dense a cluster is packed. Formally, deviations are calculated similarlyto Euclidian distances and to standard deviations in conventional statistics. Squaring the deviations and multiplying each with their cluster size (number of members in the cluster) shows the inner SSQ of each cluster:
Why check the electrodes? Because you want your gel to run from where you loaded the wells to the bottom of the gel, if it goes the wrong way you will run your product into the buffer. You need the negative electrode on the loaded wells side and a positive electrode on side without wells; that way your product, which is negatively, charged moves towards the positive end. Running a gel takes about 40 – 60 minutes.
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Impressive as this may be, it is difficult to illuminateexperimental practice in biology on the basis of Bayesian results alone(an exception will be discussed in ). The reason is that notonly the prior probabilities, but also the likelihoodsP(e|h) often have a subjective character(Schaffner 1993, p. 239f.). Likelihoods express how likely a hypothesismakes a certain empirical finding. While it is possible to calculatethis value for certain statistical hypotheses, for the mechanisticmodels that are typical of experimental biology, these values can onlybe guessed. Thus, they are in some respects like the priorprobabilities of the hypotheses themselves. But this would makehypothesis testing in biology a much more subjective affair thanappears to be the case, at least in the judgment of many scientists. Anepistemology of biological experimentation should at the very least beable to provide a rationale for the actual judgments of scientists.
Define a buffer and explain how a buffer works
This is the titration curve for the carbonic-acid-bicarbonate buffer. Note that the pH of the blood (7.4) lies outside the region of greatest buffering capacity (green).
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Why is the buffering capacity of the carbonic-acid-bicarbonate buffer highest when the pH is close to the pK value, but lower at normal blood pH? The answer to this question lies in the shape of the titration curve for the buffer, which is shown in Figure 4, below.
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The slope of the curve is flattest where the pH is equal to the pK value (6.1) for the buffer. Here, the buffering capacity is greatest because a shift in the relative concentrations of bicarbonate and carbon dioxide produces only a small change in the pH of the solution. However, at pH values higher than 7.1, the slope of the curve is much higher. Here, a shift in the relative concentrations of bicarbonate and carbon dioxide produces a large change in the pH of the solution. Hence, at the physiological blood pH of 7.4, other organs must help to control the amounts of HCO3- and CO2 in the blood to keep the pH relatively constant, as described above.
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Other buffers perform a more minor role than the carbonic-acid-bicarbonate buffer in regulating the pH of the blood. The phosphate buffer consists of phosphoric acid (H3PO4) in equilibrium with dihydrogen phosphate ion (H2PO4-) and H+. The pK for the phosphate buffer is 6.8, which allows this buffer to function within its optimal buffering range at physiological pH. The phosphate buffer only plays a minor role in the blood, however, because H3PO4 and H2PO4- are found in very low concentration in the blood. Hemoglobin also acts as a pH buffer in the blood. Recall from the from Chem 151 that hemoglobin protein can reversibly bind either H+ (to the protein) or O2 (to the Fe of the heme group), but that when one of these substances is bound, the other is released (as explained by the Bohr effect). During exercise, hemoglobin helps to control the pH of the blood by binding some of the excess protons that are generated in the muscles. At the same time, molecular oxygen is released for use by the muscles.
Why are buffers important to living organisms
The transcension hypothesis proposes that a universal process of evolutionary development guides all sufficiently advanced civilizations into what may be called "inner space," a computationally optimal domain of increasingly dense, productive, miniaturized, and efficient scales of space, time, energy, and matter, and eventually, to a black-hole-like destination.