Cluster of Parasite Infections by the Spatial Scan Analysis in Korea

This study was performed to find out the clusters with high parasite infection risk to discuss the geographical pattern. Clusters were detected using SatScan software, which is a statistical spatial scan program using Kulldorff’s scan statistic. Information on the parasitic infection cases in Korea 2011–2019 were collected from the Korea Centers for Disease Control and Prevention. Clusters of Ascaris lumbricoides infection were detected in Jeollabuk-do, and T. trichiura in Ulsan, Busan, and Gyeongsangnam-do. C. sinensis clusters were detected in Ulsan, Daegu, Busan, Gyeongsangnam-do, and Gyeongsangbuk-do. Clusters of intestinal trematodes were detected in Ulsan, Busan, and Gyeongsangnam-do. P. westermani cluster was found in Jeollabuk-do. E. vermicularis clusters were distributed in Gangwon-do, Jeju-do, Daegu, Daejeon, and Gwangju. This clustering information can be referred for surveillance and control on the parasitic infection outbreak in the infection-prone areas.     

Irregular Shaped Small Nodule Detection Using a Robust Scan Statistic

Statistics in Biosciences - The spatial scan statistics based on the Poisson and binomial models are the most common methods to detect spatial clusters in disease surveillance. These models rely on...     

Spatial Cluster Detection for Weighted Outcomes Using Cumulative Geographic Residuals

Summary Spatial cluster detection is an important methodology for identifying regions with excessive numbers of adverse health events without making strong model assumptions on the underlying spatial dependence structure. Previous work has focused on point or individual‐level outcome data and few advances have been made when the outcome data are reported at an aggregated level, for example, at the county‐ or census‐tract level. This article proposes a new class of spatial cluster detection methods for point or aggregate data, comprising of continuous, binary, and count data. Compared with the existing spatial cluster detection methods it has the following advantages. First, it readily incorporates region‐specific weights, for example, based on a region's population or a region's outcome variance, which is the key for aggregate data. Second, the established general framework allows for area‐level and individual‐level covariate adjustment. A simulation study is conducted to evaluate the performance of the method. The proposed method is then applied to assess spatial clustering of high Body Mass Index in a health maintenance organization population in the Seattle, Washington, USA area.     

Identifying Signatures of Natural Selection in Tibetan and Andean Populations Using Dense Genome Scan Data

High-altitude hypoxia (reduced inspired oxygen tension due to decreased barometric pressure) exerts severe physiological stress on the human body. Two high-altitude regions where humans have lived for millennia are the Andean Altiplano and the Tibetan Plateau. Populations living in these regions exhibit unique circulatory, respiratory, and hematological adaptations to life at high altitude. Although these responses have been well characterized physiologically, their underlying genetic basis remains unknown. We performed a genome scan to identify genes showing evidence of adaptation to hypoxia. We looked across each chromosome to identify genomic regions with previously unknown function with respect to altitude phenotypes. In addition, groups of genes functioning in oxygen metabolism and sensing were examined to test the hypothesis that particular pathways have been involved in genetic adaptation to altitude. Applying four population genetic statistics commonly used for detecting signatures of natural selection, we identified selection-nominated candidate genes and gene regions in these two populations (Andeans and Tibetans) separately. The Tibetan and Andean patterns of genetic adaptation are largely distinct from one another, with both populations showing evidence of positive natural selection in different genes or gene regions. Interestingly, one gene previously known to be important in cellular oxygen sensing, EGLN1 (also known as PHD2), shows evidence of positive selection in both Tibetans and Andeans. However, the pattern of variation for this gene differs between the two populations. Our results indicate that several key HIF-regulatory and targeted genes are responsible for adaptation to high altitude in Andeans and Tibetans, and several different chromosomal regions are implicated in the putative response to selection. These data suggest a genetic role in high-altitude adaption and provide a basis for future genotype/phenotype association studies necessary to confirm the role of selection-nominated candidate genes and gene regions in adaptation to altitude.     

A Scan Statistic for Binary Outcome Based on Hypergeometric Probability Model,with an Application to Detecting Spatial Clusters of Japanese Encephalitis

As a useful tool for geographical cluster detection of events, the spatial scan statistic is widely applied in many fields and plays an increasingly important role. The classic version of the spatial scan statistic for the binary outcome is developed by Kulldorff, based on the Bernoulli or the Poisson probability model. In this paper, we apply the Hypergeometric probability model to construct the likelihood function under the null hypothesis. Compared with existing methods, the likelihood function under the null hypothesis is an alternative and indirect method to identify the potential cluster, and the test statistic is the extreme value of the likelihood function. Similar with Kulldorff’s methods, we adopt Monte Carlo test for the test of significance. Both methods are applied for detecting spatial clusters of Japanese encephalitis in Sichuan province, China, in 2009, and the detected clusters are identical. Through a simulation to independent benchmark data, it is indicated that the test statistic based on the Hypergeometric model outweighs Kulldorff’s statistics for clusters of high population density or large size; otherwise Kulldorff’s statistics are superior.     

Spatial Mode Selection by the Phase Modulation of Subwavelength Plasmonic Grating

The extraordinary transmission of the subwavelength gold grating has been investigated by the rigorous coupled-wave analysis and verified by the metal–insulator–metal plasmonic waveguide method. The physical mechanisms of the extraordinary transmission are characterized as the excitation of the surface plasmon polariton modes. The subwavelength grating integrated with the distributed Bragg reflector is proposed to modulate the phase to realize spatial mode selection, which is prospected to be applied for transverse mode selection in the vertical cavity surface-emitting laser.     

Using Hierarchical Clustering and Dendrograms to Quantify the Clustering of Membrane Proteins

Cell biologists have developed methods to label membrane proteins with gold nanoparticles and then extract spatial point patterns of the gold particles from transmission electron microscopy images using image processing software. Previously, the resulting patterns were analyzed using the Hopkins statistic, which distinguishes nonclustered from modestly and highly clustered distributions, but is not designed to quantify the number or sizes of the clusters. Clusters were defined by the partitional clustering approach which required the choice of a distance. Two points from a pattern were put in the same cluster if they were closer than this distance. In this study, we present a new methodology based on hierarchical clustering to quantify clustering. An intrinsic distance is computed, which is the distance that produces the maximum number of clusters in the biological data, eliminating the need to choose a distance. To quantify the extent of clustering, we compare the clustering distance between the experimental data being analyzed with that from simulated random data. Results are then expressed as a dimensionless number, the clustering ratio that facilitates the comparison of clustering between experiments. Replacing the chosen cluster distance by the intrinsic clustering distance emphasizes densely packed clusters that are likely more important to downstream signaling events.     

A Robust Genome‐Wide Scan Statistic of the Wellcome Trust Case–Control Consortium

Summary In genome‐wide association (GWA) studies, test statistics that are efficient and robust across various genetic models are preferable, particularly for studying multiple diseases in the Wellcome Trust Case–Control Consortium ( WTCCC, 2007 , Nature 447 , 661–678). A new test statistic, the minimum of the p‐values of the trend test and Pearson's test, was considered by the WTCCC. It is referred to here as MIN2. Because the minimum of two p‐values is no longer a valid p‐value itself, the WTCCC only used it to rank single nucleotide polymorphisms (SNPs) but did not report the p‐values of the associated SNPs when MIN2 was used for ranking. Given its importance in practice, we derive the asymptotic null distribution of MIN2, study some of its analytical properties related to GWA studies, and compare it with existing methods (the trend test, Pearson's test, MAX3, and the constrained likelihood ratio test [CLRT]) by simulations across a wide range of possible genetic models: the recessive (REC), additive (ADD), multiplicative (MUL), dominant (DOM), and overdominant models. The results show that MAX3 and CLRT have greater efficiency robustness than other tests when the REC, ADD/MUL, and DOM models are possible, whereas Pearson's test and MIN2 have greater efficiency robustness if the possible genetic models also include the overdominant model. We conclude that robust tests (MAX3, MIN2, CLRT, and Pearson's test) are preferable to a single trend test for initial GWA studies. The four robust tests are applied to more than 100 SNPs associated with 11 common diseases identified by the two WTCCC GWA studies.     

Generic Scan Using AFLP Markers as a Means to Assess the Role of Directional Selection in the Divergence of Sympatric Whitefish Ecotypes

DOI:10.1093/molbev/msh153 David Campbell and     

Effective Size of Populations under Selection

Equations to approximate the effective size (N(e)) of populations under continued selection are obtained that include the possibility of partial full-sib mating and other systems such as assortative mating. The general equation for the case of equal number of sexes and constant number of breeding individuals (N) is N(e) = 4N/[2(1 - α(I)) + (S(k)(2) + 4Q(2)C(2)) (1 + α(I) + 2α(O))], where S(k)(2) is the variance of family size due to sampling without selection, C(2) is the variance of selective advantages among families (the squared coefficient of variation of the expected number of offspring per family), α(I) is the deviation from Hardy-Weinberg proportions, α(O) is the correlation between genes of male and female parents, and Q(2) is the term accounting for the cumulative effect of selection on an inherited trait. This is obtained as Q = 2/[2 - G(1 + r)], where G is the remaining proportion of genetic variance in selected individuals and r is the correlation of the expected selective values of male and female parents. The method is also extended to the general case of different numbers of male and female parents. The predictive value of the formulae is tested under a model of truncation selection with the infinitesimal model of gene effects, where C(2) and G are a function of the selection intensity, the heritability and the intraclass correlation of sibs. Under random mating r = α(I) = -1/(N - 1) and α(O) = 0. Under partial full-sib mating with an average proportion β of full-sib matings per generation, r & β and α(O) & α(I) & β/ (4 - 3β). The prediction equation is compared to other approximations based on the long-term contributions of ancestors to descendants. Finally, based on the approach followed, a system of mating (compensatory mating) is proposed to reduce rates of inbreeding without loss of response in selection programs in which selected individuals from the largest families are mated to those from the smallest families.     

Using a Phylogenetic Framework to Assess the Role of Fruit Size in Food Selection by the Andean Night Monkey (Aotus lemurinus)

International Journal of Primatology - The study of diet and food selection is foundational to understanding how primates interact with their environment. Due to the potential evolutionary...     

Racial Differences in Selection of Ideal Body Size by Adolescent Females

Cultural differences may partially account for the fact that more black women than white women are overweight in the United States. This study measured perceptions of ideal body size among 93 black and 80 white females, 14–17 years old, who were randomly selected from three public high schools in a southeastern state. The subjects' height and weight were measured along with their estimates of their mother's body size, weight control attitudes, and demographic variables . Blacks preferred a significantly larger body sue than whites when asked to select ideal body size (p=0.045). Subjects who estimated their mother's size to be larger, also selected a larger ideal body size (p=.047). Those who perceived themselves as too fat were more likely to skip meals to help control their weight, compared with those who perceived themselves as normal or too thin (p=.003). Approximately 30% of the sample was overweight or obese. There were not significant racial differences in weight or BMI . These results suggest that overweight is more acceptable among black females than among white females and may help explain why more black females are obese .     

Improving Estimations of Spatial Distribution of Soil Respiration Using the Bayesian Maximum Entropy Algorithm and Soil Temperature as Auxiliary Data

Soil respiration inherently shows strong spatial variability. It is difficult to obtain an accurate characterization of soil respiration with an insufficient number of monitoring points. However, it is expensive and cumbersome to deploy many sensors. To solve this problem, we proposed employing the Bayesian Maximum Entropy (BME) algorithm, using soil temperature as auxiliary information, to study the spatial distribution of soil respiration. The BME algorithm used the soft data (auxiliary information) effectively to improve the estimation accuracy of the spatiotemporal distribution of soil respiration. Based on the functional relationship between soil temperature and soil respiration, the BME algorithm satisfactorily integrated soil temperature data into said spatial distribution. As a means of comparison, we also applied the Ordinary Kriging (OK) and Co-Kriging (Co-OK) methods. The results indicated that the root mean squared errors (RMSEs) and absolute values of bias for both Day 1 and Day 2 were the lowest for the BME method, thus demonstrating its higher estimation accuracy. Further, we compared the performance of the BME algorithm coupled with auxiliary information, namely soil temperature data, and the OK method without auxiliary information in the same study area for 9, 21, and 37 sampled points. The results showed that the RMSEs for the BME algorithm (0.972 and 1.193) were less than those for the OK method (1.146 and 1.539) when the number of sampled points was 9 and 37, respectively. This indicates that the former method using auxiliary information could reduce the required number of sampling points for studying spatial distribution of soil respiration. Thus, the BME algorithm, coupled with soil temperature data, can not only improve the accuracy of soil respiration spatial interpolation but can also reduce the number of sampling points.     

应用资源选择函数对褐马鸡冬季夜栖地选择的研究

2006年11月至2007年1月,在陕西黄龙山腹地的北寺山林区,对褐马鸡Crossoptilon mantchuricum冬季夜栖地的选择进行了研究。共记录到34个夜宿地,以夜宿树为中心各做一个10m×10m样方,测定夜栖地海拔、坡向、坡度、坡位、夜栖树高度和胸径、乔木层盖度、乔木数量、灌木层盖度、草本盖度、离最近水源距离、离林间道路的距离、离最近林缘的距离、离最近居民点距离等参数;对照样方采用随机抽样法,在研究区域内设置同样数目的对照样方,测定同样指标。结果表明,褐马鸡冬季夜栖地选择的资源选择函数为:logit(p)=-11.537 2.245×坡度 3.452×乔木盖度 2.001×乔木数量-2.903×与最近居民点的距离。根据拟合出的资源选择函数,褐马鸡冬季对夜栖地的选择概率为P=elogit(p)/(1 elogit(p)),模型的正确预测率可达到93.8%。模型表明,冬季褐马鸡夜栖地的选择概率与坡度、乔木盖度和栖树数量呈正相关,与距居民点距离呈负相关。     

Prey Size Selection under Simultaneous Choice by the Broad-Headed Skink (Eumeces laticeps)

Diet selection among several prey types present in a dense aggregation, permitting a predator to become satiated without changing patches, may be important for predators that can eat many small prey items in a single bout. Choice in this scenario differs from that in optimal foraging models for sequential diet choice model and simultaneous choice models when travel time between patches is needed. Furthermore, satiation and depletion effects may be important in dense prey aggregations. We predicted that in dense prey aggregations, predators should eat the most profitable prey first, switching to smaller prey as larger ones become depleted and predators become satiated, and that prey below some minimum profitability should be rejected. When large numbers of prey of varying sizes were presented simultaneously, broad‐headed skinks (Eumeces laticeps) preferentially consumed large crickets, ate some medium‐sized crickets late in ingestion sequences, but ate no small crickets. Prey depletion, with selection of the currently most profitable prey type, appears to account for much of observed prey switching, and satiation may contribute. When four crickets of each of four sizes were presented, lizards ate largest first, then medium‐sized. Some then ate small crickets, but none ate very small crickets. These observations and exclusion of small crickets from the diet by many lizards when larger ones were unavailable support the predictions. In tests with three sizes of juvenile mice presented singly, the smallest were attacked at shortest latency and eaten, medium‐sized mice were attacked at greater latency but could not be subdued, and large mice were not attacked. These data suggest that as prey become too large to subdue and eat readily, profitability declines until they are excluded from the diet. Unsuccessful attacks on medium‐sized mice suggest that lizards had to learn their own capabilities with respect to a novel prey type.     

Women's Preferences for Penis Size: A New Research Method Using Selection among 3D Models

Women’s preferences for penis size may affect men’s comfort with their own bodies and may have implications for sexual health. Studies of women’s penis size preferences typically have relied on their abstract ratings or selecting amongst 2D, flaccid images. This study used haptic stimuli to allow assessment of women’s size recall accuracy for the first time, as well as examine their preferences for erect penis sizes in different relationship contexts. Women (N = 75) selected amongst 33, 3D models. Women recalled model size accurately using this method, although they made more errors with respect to penis length than circumference. Women preferred a penis of slightly larger circumference and length for one-time (length = 6.4 inches/16.3 cm, circumference = 5.0 inches/12.7 cm) versus long-term (length = 6.3 inches/16.0 cm, circumference = 4.8 inches/12.2 cm) sexual partners. These first estimates of erect penis size preferences using 3D models suggest women accurately recall size and prefer penises only slightly larger than average.     

Spatial Mark-Recapture Method in the Estimation of Crayfish Population Size

The mark-recapture method is considered for estimation of population size of slowly moving animals like crayfish. The Petersen type estimator for closed population is generalized for situations where recaptures are spatially dependent between the capture sites, and its variance approximation is derived using point processes as models for the population. The method of quadratic forms is suggested to be used as variance estimator. Finally, a trapping design is proposed where one trap at recapture is replaced by four adjacent traps. A simulation experiment is performed to explain the robusticity of the new trapping design against movements of animals.     

Spatial Clustering of the Curlin Secretion Lipoprotein Requires Curli Fiber Assembly

Gram-negative bacteria assemble functional amyloid surface fibers called curli. CsgB nucleates the major curli subunit protein, CsgA, into a self-propagating amyloid fiber on the cell surface. The CsgG lipoprotein is sufficient for curlin transport across the outer membrane and is hypothesized to be the central molecule of the curli fiber secretion and assembly complex. We tested the hypothesis that the curli secretion protein, CsgG, was restricted to certain areas of the cell to promote the interaction of CsgA and CsgB during curli assembly. Here, electron microscopic analysis of curli-producing strains showed that relatively few cells in the population contacted curli fibers and that curli emanated from spatially discrete points on the cell surface. Microscopic analysis revealed that CsgG was surface exposed and spatially clustered around curli fibers. CsgG localization to the outer membrane and exposure of the surface domain were not dependent on any other csg-encoded protein, but the clustering of CsgG required the csg-encoded proteins CsgE, CsgF, CsgA, and CsgB. CsgG formed stable oligomers in all the csg mutant strains, but these oligomers were distinct from the CsgG complexes assembled in wild-type cells. Finally, we found that efficient fiber assembly was required for the spatial clustering of CsgG. These results suggest a new model where curli fiber formation is spatially coordinated with the CsgG assembly apparatus.     

Combining Experiments and Simulations Using the Maximum Entropy Principle

A key component of computational biology is to compare the results of computer modelling with experimental measurements. Despite substantial progress in the models and algorithms used in many areas of computational biology, such comparisons sometimes reveal that the computations are not in quantitative agreement with experimental data. The principle of maximum entropy is a general procedure for constructing probability distributions in the light of new data, making it a natural tool in cases when an initial model provides results that are at odds with experiments. The number of maximum entropy applications in our field has grown steadily in recent years, in areas as diverse as sequence analysis, structural modelling, and neurobiology. In this Perspectives article, we give a broad introduction to the method, in an attempt to encourage its further adoption. The general procedure is explained in the context of a simple example, after which we proceed with a real-world application in the field of molecular simulations, where the maximum entropy procedure has recently provided new insight. Given the limited accuracy of force fields, macromolecular simulations sometimes produce results that are at not in complete and quantitative accordance with experiments. A common solution to this problem is to explicitly ensure agreement between the two by perturbing the potential energy function towards the experimental data. So far, a general consensus for how such perturbations should be implemented has been lacking. Three very recent papers have explored this problem using the maximum entropy approach, providing both new theoretical and practical insights to the problem. We highlight each of these contributions in turn and conclude with a discussion on remaining challenges.