Particle Erosion Resistance of Bionic Samples Inspired from Skin Structure of Desert Lizard, Laudakin stoliczkana

In order to improve the particle erosion resistance of engineering surfaces,this paper proposed a bionic sample which is inspired from the skin structure of desert lizard,Laudakin stoliczkana.The bionic sample consists of a hard shell (aluminum) and a soft core (silicone rubber) which form a two-layer composite structure.The sand blast tests indicated that the bionic sample has better particle erosion resistance.In steady erosion period,the weight loss per unit time of the bionic sample is about 10％ smaller than the contrast sample.The anti-erosion mechanism of the bionic sample was studied by single particle impact test.The results show that,after the impact,the kinetic energy of the particle is reduced by 56.5％ on the bionic sample which is higher than that on the contrast sample (31.2％).That means the bionic sample can partly convert the kinetic energy of the particle into the deformation energy of the silicone rubber layer,thus the erosion is reduced.     

The late Neandertal supraorbital fossils from Vindija Cave,Croatia: a biased sample?

The late Neandertal sample from Vindija (Croatia) has been described as transitional between the earlier Central European Neandertals from Krapina (Croatia) and modern humans. However, the morphological differences indicating this transition may rather be the result of different sex and/or age compositions between the samples. This study tests the hypothesis that the metric differences between the Krapina and Vindija supraorbital samples are due to sampling bias. We focus upon the supraorbital region because past studies have posited this region as particularly indicative of the Vindija sample's transitional nature. Furthermore, the supraorbital region varies significantly with both age and sex.We analyzed four chords and two derived indices of supraorbital torus form as defined by Smith & Ranyard (1980, Am. J. phys. Anthrop.93, pp. 589-610). For each variable, we analyzed relative sample bias of the Krapina and Vindija samples using three sampling methods. In order to test the hypothesis that the Vindija sample contains an over-representation of females and/or young while the Krapina sample is normal or also female/young biased, we determined the probability of drawing a sample of the same size as and with a mean equal to or less than Vindija's from a Krapina-based population. In order to test the hypothesis that the Vindija sample is female/young biased while the Krapina sample is male/old biased, we determined the probability of drawing a sample of the same size as and with a mean equal or less than Vindija's from a generated population whose mean is halfway between Krapina's and Vindija's. Finally, in order to test the hypothesis that the Vindija sample is normal while the Krapina sample contains an over-representation of males and/or old, we determined the probability of drawing a sample of the same size as and with a mean equal to or greater than Krapina's from a Vindija-based population. Unless we assume that the Vindija sample is female/young and the Krapina sample is male/old biased, our results falsify the hypothesis that the metric differences between the Krapina and Vindija samples are due to sample bias.     

Simple, defensible sample sizes based on cost efficiency

Summary .   The conventional approach of choosing sample size to provide 80% or greater power ignores the cost implications of different sample size choices. Costs, however, are often impossible for investigators and funders to ignore in actual practice. Here, we propose and justify a new approach for choosing sample size based on cost efficiency, the ratio of a study's projected scientific and/or practical value to its total cost. By showing that a study's projected value exhibits diminishing marginal returns as a function of increasing sample size for a wide variety of definitions of study value, we are able to develop two simple choices that can be defended as more cost efficient than any larger sample size. The first is to choose the sample size that minimizes the average cost per subject. The second is to choose sample size to minimize total cost divided by the square root of sample size. This latter method is theoretically more justifiable for innovative studies, but also performs reasonably well and has some justification in other cases. For example, if projected study value is assumed to be proportional to power at a specific alternative and total cost is a linear function of sample size, then this approach is guaranteed either to produce more than 90% power or to be more cost efficient than any sample size that does. These methods are easy to implement, based on reliable inputs, and well justified, so they should be regarded as acceptable alternatives to current conventional approaches.     

The ``hitchhiking Effect'''' Revisited

The number of selectively neutral polymorphic sites in a random sample of genes can be affected by ancestral selectively favored substitutions at linked loci. The degree to which this happens depends on when in the history of the sample the selected substitutions happen, the strength of selection and the amount of crossing over between the sampled locus and the loci at which the selected substitutions occur. This phenomenon is commonly called hitchhiking. Using the coalescent process for a random sample of genes from a selectively neutral locus that is linked to a locus at which selection is taking place, a stochastic, finite population model is developed that describes the steady state effect of hitchhiking on the distribution of the number of selectively neutral polymorphic sites in a random sample. A prediction of the model is that, in regions of low crossing over, strongly selected substitutions in the history of the sample can substantially reduce the number of polymorphic sites in a random sample of genes from that expected under a neutral model.     

Accounting for variability in sample size estimation with applications to nonadherence and estimation of variance and effect size

We consider sample size calculations for testing differences in means between two samples and allowing for different variances in the two groups. Typically, the power functions depend on the sample size and a set of parameters assumed known, and the sample size needed to obtain a prespecified power is calculated. Here, we account for two sources of variability: we allow the sample size in the power function to be a stochastic variable, and we consider estimating the parameters from preliminary data. An example of the first source of variability is nonadherence (noncompliance). We assume that the proportion of subjects who will adhere to their treatment regimen is not known before the study, but that the proportion is a stochastic variable with a known distribution. Under this assumption, we develop simple closed form sample size calculations based on asymptotic normality. The second source of variability is in parameter estimates that are estimated from prior data. For example, we account for variability in estimating the variance of the normal response from existing data which are assumed to have the same variance as the study for which we are calculating the sample size. We show that we can account for the variability of the variance estimate by simply using a slightly larger nominal power in the usual sample size calculation, which we call the calibrated power. We show that the calculation of the calibrated power depends only on the sample size of the existing data, and we give a table of calibrated power by sample size. Further, we consider the calculation of the sample size in the rarer situation where we account for the variability in estimating the standardized effect size from some existing data. This latter situation, as well as several of the previous ones, is motivated by sample size calculations for a Phase II trial of a malaria vaccine candidate.     

Sample size requirements for addressing the population genetic issues of forensic use of DNA typing.

DNA typing offers a unique opportunity to identify individuals for medical and forensic purposes. Probabilistic inference regarding the chance occurrence of a match between the DNA type of an evidentiary sample and that of an accused suspect, however, requires reliable estimation of genotype and allele frequencies in the population. Although population-based data on DNA typing at several hypervariable loci are being accumulated at various laboratories, a rigorous treatment of the sample size needed for such purposes has not been made from population genetic considerations. It is shown here that the loci that are potentially most useful for forensic identification of individuals have the intrinsic property that they involve a large number of segregating alleles, and a great majority of these alleles are rare. As a consequence, because of the large number of possible genotypes at the hypervariable loci that offer the maximum potential for individualization, the sample size needed to observe all possible genotypes in a sample is large. In fact, the size is so large that even if such a huge number of individuals could be sampled, it could not be guaranteed that such a sample was drawn from a single homogeneous population. Therefore adequate estimation of genotypic probabilities must be based on allele frequencies, and the sample size needed to represent all possible alleles is far more reasonable. Further economization of sample size is possible if one wants to have representation of only the frequent alleles in the sample, so that the rare allele frequencies can be approximated by an upper bound for forensic applications.     

Estimating the probability that the sample mean is within a desired fraction of the standard deviation of the true mean

The use of small sample sizes in human and primate evolutionary research is commonplace. Estimating how well small samples represent the underlying population, however, is not commonplace. Because the accuracy of determinations of taxonomy, phylogeny, and evolutionary process are dependant upon how well the study sample represents the population of interest, characterizing the uncertainty, or potential error, associated with analyses of small sample sizes is essential. We present a method for estimating the probability that the sample mean is within a desired fraction of the standard deviation of the true mean using small (n < 10) or very small (n ≤ 5) sample sizes. This method can be used by researchers to determine post hoc the probability that their sample is a meaningful approximation of the population parameter. We tested the method using a large craniometric data set commonly used by researchers in the field. Given our results, we suggest that sample estimates of the population mean can be reasonable and meaningful even when based on small, and perhaps even very small, sample sizes.     

Reverse Sample Genome Probing, a New Technique for Identification of Bacteria in Environmental Samples by DNA Hybridization, and Its Application to the Identification of Sulfate-Reducing Bacteria in Oil Field Samples

A novel method for the identification of bacteria in environmental samples by DNA hybridization is presented. It is based on the fact that, even within a genus, the genomes of different bacteria may have little overall sequence homology. This allows the use of the labeled genomic DNA of a given bacterium (referred to as a “standard”) to probe for its presence and that of bacteria with highly homologous genomes in total DNA obtained from an environmental sample. Alternatively, total DNA extracted from the sample can be labeled and used to probe filters on which denatured chromosomal DNA from relevant bacterial standards has been spotted. The latter technique is referred to as reverse sample genome probing, since it is the reverse of the usual practice of deriving probes from reference bacteria for analyzing a DNA sample. Reverse sample genome probing allows identification of bacteria in a sample in a single step once a master filter with suitable standards has been developed. Application of reverse sample genome probing to the identification of sulfate-reducing bacteria in 31 samples obtained primarily from oil fields in the province of Alberta has indicated that there are at least 20 genotypically different sulfate-reducing bacteria in these samples.     

On-line biological sample cleanup for electrospray mass spectrometry using sol-gel columns

Using a slight overpressure, a urine sample is loaded onto a monolithic photopolymerized sol-gel column that has been derivatized with hydrophobic carbon chains and then the complex urine matrix is washed with aqueous solution. A buffer containing organic solvent is used to elute the adsorbed peptides by an applied voltage and the sample is then introduced into a mass spectrometer by sheath flow electrospray. The importance of desalting this type of sample is demonstrated by an experiment that shows that the signal intensity of a test solution with neurotensin, sprayed directly into the mass spectrometer, decreased from 4.5x10(4) cps to no detectible signal when just 10% urine is added to the sample solution. We suggest that this procedure may find general application for desalting biological samples prior to mass spectrometric analysis.     

Estimation of allele frequencies in ethnically heterogeneous populations

A method for reconstructing allele frequencies characteristic of an original ethnically homogeneous population before the start of migration processes is described. Information on both the ethnic group studied and offspring of interethnic marriages is used to estimate the allele frequencies. This makes it possible to increase the informativeness of the sample, which, in the case of ethnic heterogeneity, depends not only on allele frequencies and the total sample size, but also on the ethnic structure of the sample. The problem of estimating allele frequency in an ethnically heterogeneous sample has been solved analytically for diallelic loci. It has been demonstrated that, if offspring of interethnic marriages with the same degree of outbreeding is added to a sample of the ethnic group studied, the sample informativeness does not change. To utilize the information contained in the phenotypes of the offspring of interethnic marriages, representatives of the population from which migration occurs should be included into the sample. The size of the sample ensuring the preassigned accuracy of estimation is minimized at a certain ratio between the numbers of the offspring of interethnic marriages and the "immigrants." To analyze polyallelic loci, a software package has been developed that allows estimating allele frequencies, determining the errors of these estimates, and planning the sample ensuring the preassigned accuracy of estimation. The package is available free at http://mga.bionet.bionet.nsc.ru/PopMixed/PopMixed.html.     

Rethinking patch size and isolation effects: the habitat amount hypothesis

I challenge (1) the assumption that habitat patches are natural units of measurement for species richness, and (2) the assumption of distinct effects of habitat patch size and isolation on species richness. I propose a simpler view of the relationship between habitat distribution and species richness, the ‘habitat amount hypothesis’, and I suggest ways of testing it. The habitat amount hypothesis posits that, for habitat patches in a matrix of non‐habitat, the patch size effect and the patch isolation effect are driven mainly by a single underlying process, the sample area effect. The hypothesis predicts that species richness in equal‐sized sample sites should increase with the total amount of habitat in the ‘local landscape’ of the sample site, where the local landscape is the area within an appropriate distance of the sample site. It also predicts that species richness in a sample site is independent of the area of the particular patch in which the sample site is located (its ‘local patch’), except insofar as the area of that patch contributes to the amount of habitat in the local landscape of the sample site. The habitat amount hypothesis replaces two predictor variables, patch size and isolation, with a single predictor variable, habitat amount, when species richness is analysed for equal‐sized sample sites rather than for unequal‐sized habitat patches. Studies to test the hypothesis should ensure that ‘habitat’ is correctly defined, and the spatial extent of the local landscape is appropriate, for the species group under consideration. If supported, the habitat amount hypothesis would mean that to predict the relationship between habitat distribution and species richness: (1) distinguishing between patch‐scale and landscape‐scale habitat effects is unnecessary; (2) distinguishing between patch size effects and patch isolation effects is unnecessary; (3) considering habitat configuration independent of habitat amount is unnecessary; and (4) delineating discrete habitat patches is unnecessary.     

Automated agar plate streaker: a linear plater on Society for Biomolecular Sciences standard plates

Several protocols for bacterial isolation and techniques for aerobic plate counting rely on the use of a spiral plater to deposit concentration gradients of microbial suspensions onto a circular agar plate to isolate colony growth. The advantage of applying a gradient of concentrations across the agar surface is that the original microbiological sample can be applied at a single concentration rather than as multiple serial dilutions. The spiral plater gradually dilutes the sample across a compact area and therefore saves time preparing dilutions and multiple agar plates. Commercial spiral platers are not automated and require manual sample loading. Dispensing of the sample volume and rate of gradients are often very limited in range. Furthermore, the spiral sample application cannot be used with rectangular microplates. Another limitation of commercial spiral platers is that they are useful only for dilute, filtered suspensions and cannot plate suspensions of coarse organic particles therefore precluding the use of many kinds of microorganism-containing substrata. An automated agar plate spreader capable of processing 99 rectangular microplates in unattended mode is described. This novel instrument is capable of dispensing discrete volumes of sample in a linear pattern. It can be programmed to dispense a sample suspense at a uniform application rate or across a decreasing concentration gradient.     

Sample preparation issues for tissue imaging by imaging MS

Imaging MS is a powerful technique that combines the chemical and spatial analysis of surfaces. It allows spatial localization of multiple different compounds that are recorded in parallel without the need of a label. It is currently one of the rapidly developing techniques in the proteomics toolbox. Different complementary imaging MS methods, i.e. MALDI and secondary ion MS imaging for direct tissue analysis, can be applied on exactly the same tissue sample. This allows the identification of small molecules, peptides and proteins present on the same sample surface. Sample preparation is crucial to obtain high quality, reliable and reproducible complementary molecular images. It is essential to optimize the conditions for each step in the sample preparation protocol, ranging from sample collection and storage to surface modification. In this article, we review and discuss the importance of correct sample treatment in case of MALDI and secondary ion MS imaging experiments and describe the experimental requirements for optimal sample preparation.     

On Use of Distinct Respondents in Randomized Response Surveys

It is well known for direct response surveys (DR), where the responses are obtained from the respondents directly, that the sample mean, based on distinct units of a simple random sample selected with replacement (SRSWR) method, is more efficient than the sample mean based on all the units including repetition. In this paper, it is shown that a linear unbiased estimator based on distinct units is inadmissible for estimating a finite population mean when the sample is selected by an arbitrary with replacement (WR) sampling scheme and the responses are obtained independently by some RR technique. Efficiencies for a few linear unbiased estimators are compared under SRSWR sampling.     

Critical sample sizes for determining the statistical significance of mutation frequencies

Based on the assumption that the numbers of mutations observed in an untreated and treated sample of individuals are binomial random variables, a method is presented to compute the probability of observing a specific number of mutations as a function of the sample sizes and the number of mutations in the untreated control sample. Knowledge of the true mutation frequencies is not required. The formalism is then used to compute critical sample sizes for testing hypotheses concerning mutation frequencies in the two populations.     

High-density small-volume gel loading directly from capillary tubes

A technique has been developed for high lane density loading of small-volume DNA samples in a horizontal agarose gel. This technique has been investigated with a simple hand-held tool that is made to couple to sample output from a new capillary-based sample automation system. The approach consists of piercing the gel with pressurized sample capillaries and relieving the pressure shortly before withdrawal. The pressurization prevents the capillary from aspirating the gel buffer and keeps the sample at the tip of the capillary, so that it may be sucked into the gel during withdrawal. This method is shown to be adequate for a wide range of DNA ladders and PCR-based screening. In addition to allowing smaller lanes and a higher lane density than is achievable with traditional well-forming techniques, it relaxes the need for well formation and the alignment of the sample loader with those wells, providing an easy, efficient means of loading agarose gels.     

Comparing parasite numbers between samples of hosts

The comparison of parasite numbers or intensities between different samples of hosts is a common and important question in most parasitological studies. The main question is whether the values in one sample tend to be higher (or lower) than the values of the other sample. We argue that it is more appropriate to test a null hypothesis about the probability that an individual host from one sample has a higher value than individual hosts from a second sample rather than testing hypotheses about means or medians. We present a recently proposed statistical test especially designed to test hypotheses about that probability. This novel test is more appropriate than other statistical tests, such as Student's t-test, the Mann-Whitney U-test, or a bootstrap test based on Welch's t-statistic, regularly used by parasitologists.     

Sample Size Determination for Designing a Strata-Matched Case-Control Study to Detect Multiple Risk Factors

Investigations of sample size for planning case-control studies have usually been limited to detecting a single factor. In this paper, we investigate sample size for multiple risk factors in strata-matched case-control studies. We construct an omnibus statistic for testing M different risk factors based on the jointly sufficient statistics of parameters associated with the risk factors. The statistic is non-iterative, and it reduces to the Cochran statistic when M = 1. The asymptotic power function of the test is a non-central chi-square with M degrees of freedom and the sample size required for a specific power can be obtained by the inverse relationship. We find that the equal sample allocation is optimum. A Monte Carlo experiment demonstrates that an approximate formula for calculating sample size is satisfactory in typical epidemiologic studies. An approximate sample size obtained using Bonferroni's method for multiple comparisons is much larger than that obtained using the omnibus test. Approximate sample size formulas investigated in this paper using the omnibus test, as well as the individual tests, can be useful in designing case-control studies for detecting multiple risk factors.     

Resolution in zonal rotors

We have studied “static” factors affecting resolution in zonal rotors, that is, factors that are independent of diffusion or sedimentation of the sample.For most zonal rotors under the recommended conditions, there is proportionately very little zone broadening of sample volumes larger than 10 cc. There appears to be a correlation between the height of the rotor and resolution.We show that step gradients surrounding the sample can result in zone broadening in the case of slowly sedimenting particles. We did not detect any deleterious effects from increasing the initial sample radius.     

Analysis of data from experiments using double labeling

Frequently as a result of experiments in which two isotopes are used one is left with a sequence of samples, the ratio of labeling in each sample, and the problem of analyzing the ratios. Suppose that the experiments are designed so that one expects uniform labeling except for one or two special groups of samples. The problem, then, is to find these groups. Because of the variability in the count rate from sample to sample, the variance of the ratios differs from sample to sample making statistical analysis difficult. Furthermore, there is significant serial correlation in the sample disintegrations per minute for each of the isotopes. We have found that the serial correlation in the labeling ratio is small and of questionable significance in controls but becomes significant when there is a subsequence of samples in which the labeling ratio differs from that in the remainder of the gel. We examine the analysis of variance as a test for significant deviations in the labeling ratio and suggest a method for plotting deviations of labeling ratio from the average background labeling ratio. Finally, we develop a method of estimating the mean labeling ratio from the regression of disintegrations per minute of one isotope on those of the other isotope. This provides another way of plotting deviations in labeling ratio in terms of the residuals around the line of regression.