Score distribution

Figure 7.4. The significance level of an identification result can be determined once the distribution of scores for false identification results is known. Score distributions for true results can vary between experiments and are typically unknown, in contrast with the distribution of scores for false identification results, which can be derived by various methods (see text for details). A score that is in a region with little overlap with the distribution for false results yields a good significance level (the gray area is small).

Alignment scores generated from the comparison of a repeat profile with a database of randomized sequences are derived with Searchwise (Birney et al., 1996), which uses a Smith-Waterman comparison (Smith and Waterman, 1981). A number n of score distributions for the 1st (optimal), 2nd (first suboptimal), and up to the wth highest scores of the profile compared with randomized sequences are fitted to n EVDs. Parameters are obtained for each fit that allow the transformation of alignment scores for the top n (sub)optimal alignments into values. Since these E values are dependent on the repeat number, they are sensitive to the number of true-positive repeats in a sequence. 

For the blend uniformity application, the primary concern is overall heterogeneity within the sample. This is directly indicated by the width of the distribution and is quantitatively measured as the percent standard deviation relative to the mean (%SD). The %SD value obtained from the PLS scores distribution from each of the six imaged tablets is noted on the histograms in Figure 8.15(A-F). The value of %STD consistently decreases as the blending quality of the sample improves. 

Here, the situation is almost reversed only a minority of the mutants (1.1%) exhibit a combined z-score that indicates destabilization (Figure 11.9 and 11.10 for a comparison of the z-score distributions). This result is in good agreement with our prior knowledge about immunoglobulin structures the amino acids of the epitope are supposed to be primarily selected for binding the antigen, rather than for their contribution to structural stability. 

Tig. 11.10. Z-score distribution for two randomized regions of an Fd fragment (PDB file laqk, chain H). White bars residues 100, 104—106 (part of antigen binding region), black bars residues 153, 180, 182, 184 (in loop of C-terminal half of the chain). The arrow marks the z-score of tire wild type. 

Mean acceptability scores and score distributions were almost identical for the irradiated and nonirradiated samples. The mean score for the irradiated sample was 4.36 and for the nonirradiated sample 4.38 (4.0 = neither like nor dislike and 5.0 = like slightly). Six panel members indicated no preference, 35 panelists preferred the irradiated sample, and 37 preferred the nonirradiated sample. Fifty-three percent of the panelists scored the two samples one point or less apart. No effect was observed on the relative scores owing to the order of sample presentation, although average scores for both samples were 0.7 point higher when the irradiation sample was presented first. The distribution of scores is shown in Table X. 

Figure 6. Decidability coefficient d vs. the number of sorted iris features. Best d was obtained for 324 bits. The corresponding genuine and impostor score distributions are presented in the inset.

Four dice of similar appearance are provided for a game of chance. Three of the dice are fair, and one is loaded their score distributions are as follows ... 

Bess. TL and Harvey. R.l (2002) Bimodal Score Distributions and the Myers-Briggs Type Indicator Fact or Artifact. J Pers Assess 78 176-186. 

It is important to understand the statistical limitations of this external means of quality assessment when gauging the competence of a laboratory. Typically, the results of a chemical analysis of a sample will have a normal distribution. This means that the majority of results will be centred on a mean value but, inevitably, some results will lie at the extremes of the distribution. The statistics of a normal distribution mean that about 95% of the data points will lie within a z score between —2 and -1-2. Performance in a PT, therefore, is considered satisfactory if a participant s z score lies within this range. It follows that if a participant s z score lies outside this range, that is, z > 2, there is about a 1 in 20 chance that their result is in fact an acceptable result from the extreme of the distribution. If a participant s z score is greater than 3 ( z > 3), the probability that then-result is actually acceptable is only about 1 in 300. A typical PT z-score distribution is illustrated in Figure 10.5. 

On the other hand, the scores of the rest of the students (group B) also follow the log-normal distribution with mean 480 and standard deviation 15 so the parameters are xb = 6.1733 and a = 9.7609 x 10 for this group. Then, by using the law of total probability, the score distribution of all students is given by ... 

Figure 2.3 shows the probability distribution of the scores for top students (solid line) and ordinary students (dashed line). Figure 2.4 shows the unconditional score distribution for all students. 

Performance on intelligence tests of 6-12-year-olds treated with Lipiodol scarcely deviates from the test performance of untreated children the means are 90.2 and 88.0, respectively. The score distributions (Figure A) too are very much alike. 

In the oldest age group no differences were found for coordination of body movements. The average scores are almost equal and the score distributions more or less follow the normal distribution. But the other two psychomotor factors (manual dexterity and speed of reaction) yield significant differences (p 1. 01) between the control group on... 

Here too, the two-topped score distribution, notable especially in the youngest groups with small numbers of children, is the result of the scores of only four children. Three of these children also had the lowest scores on the mental development test. 

It can be established, on the basis of an analysis of the score distributions, that in the age groups where mental and psychomotor retardation occurs as a consequence of iodine deficiency, usually the whole group is involved and not just part of it (question 2). Especially in the groups with larger numbers of children, where random... 

The last question posed in the survey concerns the effect of iodine prophylaxis on mental and psychomotor development. Earlier research (a.o. 39, 40, 41) did not yield unequivocal results. In the Spain-survey, the group of treated children was treated with Lipiodol between the age of four and ten, 32 months before the (second) administration of the tests. Test performance was found not to have improved as a result of the iodine treatment. The average scores of the iodine-deficient and the treated groups as well as their score distributions are nearly identical. Apparently, even certain effects of habituation or learning, which might have occurred in the second administration of the tests, did not play any role whatever. It would, at this point, be premature to assume that a treatment with Lipiodol has no positive effects whatsoever on the mental and psychomotor development of children from iodine-deficient areas or that the deficiencies involved are irreversible. Further research is certainly called for. There is still a possibility that the effects may have waned because of the long time interval of 32 months between the administration of the Lipiodol and the psychological tests. 

North Carolina statewide grade schoolchildren Reading and mathematics scores PbB measured via screening High PbB children s test scores distribution more impacted Pb more toxic at lower tail of distribution Miranda etal. (2009)... 

Fig. 15.5 Effect of energy minimization before doeking on the docking results of zanamivir. Minimization of the hydrogen positions (a) or the side ehain atom positions (b) was performed with the force field used for simulation in absence of water and ligand using 500 cycles steepest descent and 500 cycles of conjugate gradient minimization. In comparison to the corresponding results without minimization (Fig. 15.4c) especially the scoring distributions for the ligand-bound simulation is shifted towards more unfavorable poses...

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