柯尔克孜族成人的体型

探讨新疆柯尔克孜族成人体型特点及其规律,并分析该族群在不同地区(南、北疆)体型的差异性。应用人体测量法,在知情同意原则的基础上,2017年7月至2018年8月,随机抽取新疆南疆和北疆地区柯尔克孜族成人作为调查对象,共计1270名(男551名,女719名)。按年龄、性别、地区分组,对所有调查对象的身高、体质量等10项指标进行了测量,并应用Heath-Carter体型评价法进行体型判定。研究发现,南疆地区柯尔克孜族成人体型具有性别间的差异;南、北疆柯尔克孜族成人体型存在地区间的差异;从体型上看柯尔克孜族与汉族较相似。     

Changes in somatotypes of European males between 17 and 24 years

In order to examine the possible changes in somatotype of young males, 14 boys who had been somatotyped annually from 11 to 18 years by the Health-Carter anthropometric rating method were re-somatotyped at 24 years. The analysis focused on years 17, 18 and 24. Discriminant analysis, somatotype dispersion indices and distances, and the I-index were used to analyze the data. The findings showed a significant shift in somatotype from ectomorphmesomorph towards endo-mesomorphy between 17 and 24 years. This shift was greater than that observed by Zuk ('58), and was in the same direction. It is hypothesized that the somatotypes of males become more endo-mesomorphic from 17 years to the mid-twenties.     

Assessing the affinities of fossils using canonical variates and generalized distances

Canonical variate analysis and generalized distances are commonly used multivariate statistical techniques for assessing the comparative morphology of living and fossil primates. Some common pitfalls of these methods when used to analyze fossil specimens are: (1) ignoring the possibility that a fossil belongs to a group other than one of the predefined reference samples (i.e., restricted versus unrestricted approaches to classification), (2) misinterpreting probabilities of group membership (i.e., posterior versus typicality probabilities), and (3) failing to understand how sample sizes influence multivariate ordinations in trying to effectively illustrate the morphometric affinities of a fossil (i.e., weighted versus unweighted analyses with fossils entered indirectly or directly into the analysis). To better understand canonincal variate analysis and generalized distances, the workings of these methods are portrayed graphically as a series of rotations and rescaling of data plotted in a multivariate data space. The influence of sample sizes and the importance of higher-numberer canonical variates are emphasized. Simple examples taken from the literature illustrate how the different approaches to including a fosil in canonical variate analysis may affect the multivariate results upon which physical anthropologists base their interpretations of the fossil's morphology.     

运用体型方法研究中国学生 (山西) 的体格发育

采用体型方法研究人体体格特征,并与遗传、生理、运动、健康状况、营养方式等相结合,具有一定的意义。本文运用Heath-Carter人体测量体型方法研究中国学生的体型,通过对山西地区485名普通大中专男女学生及130名体育系男女学生的10个人体测量项目的体型分析,结果表明：男女学生的体型存在显著性差异;16—22岁体型发生一定的变化,且男女变化规律不同;坚持体育锻炼的体育系学生的体型与普通系学生的体型存在显著性差异,体育锻炼对体型的影响有性别之分;中国学生与国外学生比较,体型存在差异,男性表现比较明显。     

Somatotype of the individuals with lower extremity amputation and its association with cardiovascular risk

Anthropometric somatotyping is one of the methods to describe the shape of the human body, which shows some associations with an individual's health and disease condition, especially with cardiovascular diseases (CVD). Individuals with lower extremity amputation (LEA) are known to be more vulnerable to the cardiovascular risk. The objectives of the present study are to report the somatotype of the individuals having lower extremity amputation, to study the possible variation in somatotype between two groups of amputated individuals, and to study the association between cardiovascular disease risk factor and somatotype components among individuals with locomotor disability. 102 adult male individuals with unilateral lower-extremity amputation residing in Calcutta and adjoining areas were investigated. The anthropometric data for somatotyping and data on cardiovascular risk traits (such as body mass index, blood pressure measurements, blood lipids) have been collected. The somatotyping technique of Carter & Heath (1990) has been followed. The result shows high mean values of endomorphy and mesomorphy components and a low mean value of the ectomorphy component among the amputated individuals having cardiovascular risks. The results of both discriminant analysis and logistic regression analysis show a significant relationship between somatotype components and CVD risk among the individuals with LEA. The findings of the present study support the findings of similar studies conducted on the normal population. Diagnosis of CVD risk condition through somatotyping can be utilized in prevention/treatment management for the individuals with LEA.     

ESPRIT-Tree: hierarchical clustering analysis of millions of 16S rRNA pyrosequences in quasilinear computational time

Taxonomy-independent analysis plays an essential role in microbial community analysis. Hierarchical clustering is one of the most widely employed approaches to finding operational taxonomic units, the basis for many downstream analyses. Most existing algorithms have quadratic space and computational complexities, and thus can be used only for small or medium-scale problems. We propose a new online learning-based algorithm that simultaneously addresses the space and computational issues of prior work. The basic idea is to partition a sequence space into a set of subspaces using a partition tree constructed using a pseudometric, then recursively refine a clustering structure in these subspaces. The technique relies on new methods for fast closest-pair searching and efficient dynamic insertion and deletion of tree nodes. To avoid exhaustive computation of pairwise distances between clusters, we represent each cluster of sequences as a probabilistic sequence, and define a set of operations to align these probabilistic sequences and compute genetic distances between them. We present analyses of space and computational complexity, and demonstrate the effectiveness of our new algorithm using a human gut microbiota data set with over one million sequences. The new algorithm exhibits a quasilinear time and space complexity comparable to greedy heuristic clustering algorithms, while achieving a similar accuracy to the standard hierarchical clustering algorithm.     

Dealing with virtual aggregation – a new index for analysing heterogeneous point patterns

Point pattern analyses such as the estimation of Ripley's K‐function or the pair‐correlation function g are commonly used in ecology to characterise ecological patterns in space. However, a major disadvantage of these methods is their missing ability to deal with spatial heterogeneity. A heterogeneous intensity of points causes a systematic bias in estimates of the K‐ and g‐functions, a phenomenon termed “virtual aggregation” in the recent literature. To address this problem, we derive a new index, called K2‐index, as an extension of existing point pattern characteristics. The K2‐index has a heuristic interpretation as an approximation to the first derivative of the g‐function. We estimate the K‐, g‐ and K2‐functions for six different types of simulated point patterns and show that the K2‐index may provide important information on point patterns that the other methods fail to detect. The results indicate that particularly the small‐scale distributions of points are better represented by the K2‐index. This might be important for testing hypotheses on ecological processes, because most of these processes, such as direct neighbour interactions, occur very locally. When applied to empirical patterns of molehill distribution, the results of the K2‐analysis show regularity up to distances between 0.1 and 0.4 m in most of the study areas, and aggregation of molehills up to distances between 0.2 and 1.1 m. The type and scale of these deviations from randomness agree with a priori expectations on the hill‐building behaviour of moles. In contrast, the estimated g‐functions merely indicate aggregation at the full range from 0 to 7 m (or even above). Considering the advantages and disadvantages of the different methods, we suggest that the K2‐index should be used as a complement to existing approaches, particularly for point patterns generated by processes that act on more than one scale.     

Can patterns of spatial autocorrelation reveal population processes? An analysis with the fire salamander

Spatial autocorrelation (SAC) is often observed in species distribution data, and can be caused by exogenous, autocorrelated factors determining species distribution, or by endogenous population processes determining clustering such as dispersal. However, it remains debated whether SAC patterns can actually reveal endogenous processes. We reviewed studies measuring dispersal of the salamander Salamandra salamandra, to formulate a priori hypotheses on the scale at which dispersal is expected to determine population distribution. We then tested the hypotheses by analysing SAC in distribution data, and evaluating whether controlling for the effect of environmental variables can reveal endogenous processes. We surveyed 565 streams to obtain species distribution data; we also recorded landscape and microhabitat features known to affect the species. We used multiple approaches to tease apart endogenous and exogenous SAC: the analysis of residuals of logistic regression models considering different environmental variables; the analysis of eigenvectors extracted by several implementations of spatial eigenvector mapping. In capture–mark–recapture studies, 98% of individuals moved 500 m or less. Both species distribution and environmental features were strongly autocorrelated. The residuals of logistic regression relating species to environmental variables were autocorrelated at distances up to 500 m; analyses considering different sets of environmental variables, or assuming non‐linear species habitat relationships, yielded identical results. The results of spatial eigenvector mapping strongly depended on the matrix of distances used. Nevertheless, the eigenvectors of models with best fit were autocorrelated at distances up to 200–500 m. The concordance between multiple approaches suggests that 500 m is the scale at which dispersal connects breeding localities, increasing probability of occurrence. If exogenous variables are correctly identified, the analysis of SAC can provide important insights on endogenous population processes, such as the flow of individuals. SAC analysis can also provide important information for conservation, as the existence of metapopulations or population networks is essential for long term persistence of amphibians.     

A mathematical framework for protein structure comparison

Comparison of protein structures is important for revealing the evolutionary relationship among proteins, predicting protein functions and predicting protein structures. Many methods have been developed in the past to align two or multiple protein structures. Despite the importance of this problem, rigorous mathematical or statistical frameworks have seldom been pursued for general protein structure comparison. One notable issue in this field is that with many different distances used to measure the similarity between protein structures, none of them are proper distances when protein structures of different sequences are compared. Statistical approaches based on those non-proper distances or similarity scores as random variables are thus not mathematically rigorous. In this work, we develop a mathematical framework for protein structure comparison by treating protein structures as three-dimensional curves. Using an elastic Riemannian metric on spaces of curves, geodesic distance, a proper distance on spaces of curves, can be computed for any two protein structures. In this framework, protein structures can be treated as random variables on the shape manifold, and means and covariance can be computed for populations of protein structures. Furthermore, these moments can be used to build Gaussian-type probability distributions of protein structures for use in hypothesis testing. The covariance of a population of protein structures can reveal the population-specific variations and be helpful in improving structure classification. With curves representing protein structures, the matching is performed using elastic shape analysis of curves, which can effectively model conformational changes and insertions/deletions. We show that our method performs comparably with commonly used methods in protein structure classification on a large manually annotated data set.     

Inferring landscape effects on dispersal from genetic distances: how far can we go?

Functional connectivity affects demography and gene dynamics in fragmented populations. Besides species-specific dispersal ability, the connectivity between local populations is affected by the landscape elements encountered during dispersal. Documenting these effects is thus a central issue for the conservation and management of fragmented populations. In this study, we compare the power and accuracy of three methods (partial correlations, regressions and Approximate Bayesian Computations) that use genetic distances to infer the effect of landscape upon dispersal. We use stochastic individual-based simulations of fragmented populations surrounded by landscape elements that differ in their permeability to dispersal. The power and accuracy of all three methods are good when there is a strong contrast between the permeability of different landscape elements. The power and accuracy can be further improved by restricting analyses to adjacent pairs of populations. Landscape elements that strongly impede dispersal are the easiest to identify. However, power and accuracy decrease drastically when landscape complexity increases and the contrast between the permeability of landscape elements decreases. We provide guidelines for future studies and underline the needs to evaluate or develop approaches that are more powerful.     

Statistical and physiological distinction of constitution types

Most conventional human health and function evaluation methods are based on a traditional notion that all the population characteristics follow the Gaussian distribution law with the parameters M and s forming the basis of the norm conception. But some known facts contradict this idea that requires checking the statistical homogeneity of population characteristics. Analysis of statistical distribution and central tendencies for simple measured indices in population and somatotypes samples proved an idea of natural population distinctions by a broad set of morpho-functional features (by means of 23-D matrix cluster analysis for different indices) and provided the scientific grounds to use a constitutional approach in human sciences and physical education as well. Gaussian distribution law was found within somatotype groups permitting the use of its parameters for norm evaluation. In practice for somatotype determination the relative girth body dimensions (normalized by body height) were proved to be preferable.     

Spatial analysis of two-species interactions

Summary In this paper, I present and discuss some methods for the analysis of univariate and bivariate spatial point pattern data. Examples of such data in ecology include x-y coordinates of organisms in mapped field plots. I illustrate the methods with analyses of data from mapped field plots on Mount St. Helens, Washington state, USA. The statistical methods I emphasize are graphical methods that rely on analysis of distances between organisms. Hypothesis testing for methods like these is easily done using Monte Carlo methods, which I also discuss. For both univariate and bivariate analyses, I find that second-order methods such as K-function plots are often preferable to first-order methods (i.e., QQ-plots). However, for multivariate analyses, these second-order methods are more sensitive to small sample sizes than first-order analyses.     

Mantel test in population genetics

The comparison of genetic divergence or genetic distances, estimated by pairwise F_ST and related statistics, with geographical distances by Mantel test is one of the most popular approaches to evaluate spatial processes driving population structure. There have been, however, recent criticisms and discussions on the statistical performance of the Mantel test. Simultaneously, alternative frameworks for data analyses are being proposed. Here, we review the Mantel test and its variations, including Mantel correlograms and partial correlations and regressions. For illustrative purposes, we studied spatial genetic divergence among 25 populations of Dipteryx alata (“Baru”), a tree species endemic to the Cerrado, the Brazilian savannas, based on 8 microsatellite loci. We also applied alternative methods to analyze spatial patterns in this dataset, especially a multivariate generalization of Spatial Eigenfunction Analysis based on redundancy analysis. The different approaches resulted in similar estimates of the magnitude of spatial structure in the genetic data. Furthermore, the results were expected based on previous knowledge of the ecological and evolutionary processes underlying genetic variation in this species. Our review shows that a careful application and interpretation of Mantel tests, especially Mantel correlograms, can overcome some potential statistical problems and provide a simple and useful tool for multivariate analysis of spatial patterns of genetic divergence.     

Somatotype in 6-11-year-old Italian and Estonian schoolchildren

The study of somatotypes can contribute to the understanding of variability in human body build. The aim of this study was to compare the somatotypes of Italian and Estonian schoolchildren in order to evaluate factors that might lead to variability in somatotypes. The sample consisted of 762 Italian and 366 Estonian children aged 6-11 years. They were somatotyped by the Heath-Carter anthropometric method. Data on organised extra-curricular physical activity and hours of weekly training were also collected. One-way ANOVA was used to evaluate country-related variations of somatotype in each age/sex group, while factorial ANOVA was used to test the influence of country and organised physical activity on the variability of the anthropometric characteristics and somatotype components.There are significant differences in mean somatotypes between the Italian and Estonian children in many age classes and a different constitutional trend in children from the two different countries is observed. The Italian children are more endomorphic and less mesomorphic and ectomorphic than the Estonian children. On the other hand, it emerges from factorial ANOVA, that the somatotype components do not present significant variations related to organised physical activity and to the interaction between the country of origin and sport practice. Moreover, the results of the forward stepwise discriminant analyses show that mesomorphy is the best discriminator between the two countries, followed by ectomorphy.Our findings suggest that the observed differences between Italian and Estonian children could be related mainly to country rather than to the practice of organised physical activity in the two countries.     

Computational methods for Gene Orthology inference

Accurate inference of orthologous genes is a pre-requisite for most comparative genomics studies, and is also important for functional annotation of new genomes. Identification of orthologous gene sets typically involves phylogenetic tree analysis, heuristic algorithms based on sequence conservation, synteny analysis, or some combination of these approaches. The most direct tree-based methods typically rely on the comparison of an individual gene tree with a species tree. Once the two trees are accurately constructed, orthologs are straightforwardly identified by the definition of orthology as those homologs that are related by speciation, rather than gene duplication, at their most recent point of origin. Although ideal for the purpose of orthology identification in principle, phylogenetic trees are computationally expensive to construct for large numbers of genes and genomes, and they often contain errors, especially at large evolutionary distances. Moreover, in many organisms, in particular prokaryotes and viruses, evolution does not appear to have followed a simple 'tree-like' mode, which makes conventional tree reconciliation inapplicable. Other, heuristic methods identify probable orthologs as the closest homologous pairs or groups of genes in a set of organisms. These approaches are faster and easier to automate than tree-based methods, with efficient implementations provided by graph-theoretical algorithms enabling comparisons of thousands of genomes. Comparisons of these two approaches show that, despite conceptual differences, they produce similar sets of orthologs, especially at short evolutionary distances. Synteny also can aid in identification of orthologs. Often, tree-based, sequence similarity- and synteny-based approaches can be combined into flexible hybrid methods.     

Incomplete distance matrices,supertrees and bat phylogeny

In this paper, we evaluate the relative performance of competing approaches for estimating phylogenies from incomplete distance matrices. The direct approach proceeds with phylogenetic reconstruction while ignoring missing cells, whereas the indirect approach proceeds by estimating the missing distances prior to phylogenetic analysis. Two distinct indirect procedures based on the ultrametric inequality and the four-point condition are further compared. Using simulations, we show that more reliable results are obtained when such indirect methods are used. Expectedly, the phylogenies become less accurate as the percentage of missing cells increases, but combining different estimation methods greatly improves the accuracy. An application to bat phylogeny confirms the results obtained in the simulation study and illustrates the effect of missing distances in the construction of supertrees.     

Multiple regression on distance matrices: a multivariate spatial analysis tool

I explore the use of multiple regression on distance matrices (MRM), an extension of partial Mantel analysis, in spatial analysis of ecological data. MRM involves a multiple regression of a response matrix on any number of explanatory matrices, where each matrix contains distances or similarities (in terms of ecological, spatial, or other attributes) between all pair-wise combinations of n objects (sample units); tests of statistical significance are performed by permutation. The method is flexible in terms of the types of data that may be analyzed (counts, presence–absence, continuous, categorical) and the shapes of response curves. MRM offers several advantages over traditional partial Mantel analysis: (1) separating environmental distances into distinct distance matrices allows inferences to be made at the level of individual variables; (2) nonparametric or nonlinear multiple regression methods may be employed; and (3) spatial autocorrelation may be quantified and tested at different spatial scales using a series of lag matrices, each representing a geographic distance class. The MRM lag matrices model may be parameterized to yield very similar inferences regarding spatial autocorrelation as the Mantel correlogram. Unlike the correlogram, however, the lag matrices model may also include environmental distance matrices, so that spatial patterns in species abundance distances (community similarity) may be quantified while controlling for the environmental similarity between sites. Examples of spatial analyses with MRM are presented.     

Missing data in craniometrics: a simulation study

Craniometric measurements represent a useful tool for studying the differentiation of mammal populations. However, the fragility of skulls often leads to incomplete data matrices. Damaged specimens or incomplete sets of measurements are usually discarded prior to statistical analysis. We assessed the performance of two strategies that avoid elimination of observations: (1) pairwise deletion of missing cells, and (2) estimation of missing data using available measurements. The effect of these distinct approaches on the computation of inter-individual distances and population differentiation analyses were evaluated using craniometric measurements obtained from insular populations of deer micePeromyscus maniculatus (Wagner, 1845). In our simulations, Euclidean distances were greatly altered by pairwise deletion, whereas Gower’s distance coefficient corrected for missing data provided accurate results. Among the different estimation methods compared in this paper, the regression-based approximations weighted by coefficients of determination (r ²) outperformed the competing approaches. We further show that incomplete sets of craniometric measurements can be used to compute distance matrices, provided that an appropriate coefficient is selected. However, the application of estimation procedures provides a flexible approach that allows researchers to analyse incomplete data sets.     

内蒙古汉族青少年体型分析

运用Heath-Carter体型法,对内蒙古地区3088名（男1489,女1599）7－18岁汉族城乡青少年体型进行分析。结果表明城乡男生偏中胚层型的外胚层型体型最多,中胚层－外胚层均衡型和偏外胚层型的中胚层型 之;城乡女生偏中胚层型的内胚层型体型最多,三胚层中间型和偏内胚层型换上胚层型次之。8－13岁时,同性别同龄组城乡学生的体型差异明显,13岁以后体型接近,城乡男女学生体型的性别间差别显著。     

Principles and methods of geometric morphometrics

The basic concepts, notions and methods of geometric morphometrics (GM) are considered. This approach implies multivariate analysis of landmark coordinates located following certain rules on the surface of a morphological object. The aim of GM is to reveal differences between morphological objects by their shapes as such, the "size factor" being excluded. The GM is based on the concept of Kendall's space (KS) defined as a hypersphere with points distributed on its surface. These points are the shapes defined as aligned landmark configurations. KS is a non-Euclidian space, its metrics called Procrustes is defined by landmark configuration of a reference shape relative to which other shapes are aligned and compared. The differences among shapes are measured as Procrustes distances between respective points. For the linear methods of multivariate statistics to be applied to comparison of shapes, the respective points are projected onto the tangent plane (tangent space), the tangent point being defined by the reference. There are two principal methods of shape comparisons in GM: the Procrustes superimposition (a version of the least squares analysis) and thin-plate spline analysis. In the first case, Procrustes residuals are the outcome shape variables which remain after isometric alignment of the shapes being compared. Their summation over all landmarks yields Procrustes distances among these shapes. The Procrustes distances can be used in multivariate analyses just as the Euclidian distances. In the second case, the shapes are fitted to the references by stretching/compressing and shearing until complete identity of their landmark configurations. Eigenvectors of resulting bending energy matrix are defined as new shape variables, principal warps which yield another shape space with the origin defined by the reference. Projections of the shapes being compared onto principal warps yield partial warps, and their covariance matrix decomposition into eigenvectors yields relative warps which are similar to principal components (in particular, they are mutually orthogonal). Both partial and relative warps can be used in many multivariate statistic analyses as quantitative shape variables. Results of thin-plate spline analysis can be represented graphically by transformation grid which displays type, amount and localization of the shape differences. Basis rules of sample composition and landmark positioning to be used in GM are considered. At present, rigid (with minimal degrees of freedom) 2D morphological objects are most suitable for GM applications. It is important to recognize three type of real landmarks, and additionally semi-landmarks and "virtual" landmarks. Some procedures of thin-plate spline analysis are considered exemplified by some study cases, as well as applications of some standard multivariate methods to GM results. They make it possible to evaluate correlation between different shapes, as well as between a shape and some non-shape variables (linear measurements etc); to evaluate the differences among organisms by shape of a morphological structure; to identify landmarks which most accounted for both correlation and differences between the shapes. An annotated list of most popular softwares for GM is provided.