Bayesian estimation of movement and survival probabilities from capture-recapture data

The Arnason–Schwarz model is usually used for estimatingsurvival and movement probabilities of animal populations fromcapture-recapture data. The missing data structure of this capture-recapturemodel is exhibited and summarised via a directed graph representation.Taking advantage of this structure we implement a Gibbs samplingalgorithm from which Bayesian estimates and credible intervalsfor survival and movement probabilities are derived. Convergenceof the algorithm is proved using a duality principle. We illustrateour approach through a real example.     

利用改进的3DMax算法重构染色体3D结构

近年来,随着高通量染色体构象捕获(Hi-C)等技术的发展和高通量测序成本的降低,全基因组交互作用的数据量快速增长,交互作用图谱分辨率不断提高,促使染色体和基因组三维结构建模的研究取得了很大进展,已经提出了几种从染色体构象捕捉数据中构建单个染色体或整个基因组结构的方法。文中通过对在 Hi-C 数据基础上对染色体三维结构重建的相关文献进行分析,总结了重建染色体三维空间结构的经典算法3DMax的原理,并且提出了一种新的随机梯度上升算法:XNadam,是Nadam优化方法的一个变体,将其应用于3DMax算法中,以便提高3DMax算法的性能,从而用于预测染色体三维结构。     

等位基因多态性群体遗传结构的多元非线性分析方法

长期以来,对于多维基因多态性数据的多元统计分析,如计算遗传距离时昕用的聚类分析、分析群体遗传结构时所用的主成分分析、因子分析和典型相关分析等,一直应用为无约束条件数据而设计的经典多元线性分析方法,并没有注意基因多态性数据的“闭合效应”所带来的问题。从分析基因多态性数据的分布和结构特征入手,文中指出了基因多态性分布具有“闭合数据”的特点,分析了由于“闭合效应”的影响,经典多元线性方法用于群体遗传结构分析昕面临的困难。根据成分数据统计分析的理论和方法,提出了基因多态性群体遗传结构的多元非线性分析基本方法。并以主成分分析为例,通过实例比较和分析了经典线性主成分分析和“对数比”非线性主成分分析的结果,证明“对数比”非线性主成分分析方法是研究基因多态性群体遗传结构的良好方法,具有特异、灵敏等优点,其结果符合群体遗传学规律。     

Selection of Statistical Thresholds in Graphical Models

Reconstruction of gene regulatory networks based on experimental data usually relies on statistical evidence, necessitating the choice of a statistical threshold which defines a significant biological effect. Approaches to this problem found in the literature range from rigorous multiple testing procedures to ad hoc P-value cut-off points. However, when the data implies graphical structure, it should be possible to exploit this feature in the threshold selection process. In this article we propose a procedure based on this principle. Using coding theory we devise a measure of graphical structure, for example, highly connected nodes or chain structure. The measure for a particular graph can be compared to that of a random graph and structure inferred on that basis. By varying the statistical threshold the maximum deviation from random structure can be estimated, and the threshold is then chosen on that basis. A global test for graph structure follows naturally.     

An antibiotic from Penicillium sp. covering the cocoon of the leaf-rolling moth, Dactylioglypha tonica

An antibiotic-producing Penicillium sp. strain was isolated from cocoons of the leaf-rolling moth, Dactylioglypha tonica. An antibacterial compound was isolated from the cultured broth, and the chemical structure of the principle was determined by spectroscopic data to be a derivative of isocoumarincarboxylate.     

Neutral theory of molecular evolution

DNA sequence data are generally interpreted as favouring Kimura's neutral theory but not without dissent and often with a great deal of controversy with respect to molecular clocks, DNA polymorphism, adaptive evolution, and gene genealogy. Although the theory serves as a guiding principle, many issues concerning mutation, recombination, and selection remain unsettled. Of particular importance is the need for more knowledge about the function and structure of molecules.     

Multivariate paired data analysis: multilevel PLSDA versus OPLSDA

Metabolomics data obtained from (human) nutritional intervention studies can have a rather complex structure that depends on the underlying experimental design. In this paper we discuss the complex structure in data caused by a cross-over designed experiment. In such a design, each subject in the study population acts as his or her own control and makes the data paired. For a single univariate response a paired t-test or repeated measures ANOVA can be used to test the differences between the paired observations. The same principle holds for multivariate data. In the current paper we compare a method that exploits the paired data structure in cross-over multivariate data (multilevel PLSDA) with a method that is often used by default but that ignores the paired structure (OPLSDA). The results from both methods have been evaluated in a small simulated example as well as in a genuine data set from a cross-over designed nutritional metabolomics study. It is shown that exploiting the paired data structure underlying the cross-over design considerably improves the power and the interpretability of the multivariate solution. Furthermore, the multilevel approach provides complementary information about (I) the diversity and abundance of the treatment effects within the different (subsets of) subjects across the study population, and (II) the intrinsic differences between these study subjects.     

基于深度学习的蛋白质建模与设计

随着蛋白质序列及结构数据的大量累积,在获得了大量描述性信息之后如何有效利用海量数据,从已有数据中高效提取信息并且应用到下游任务当中就成为了研究者亟待解决的问题。蛋白质的设计可使新蛋白的研发不再受限于实验条件,这对药物靶点预测、新药研发和材料设计等领域具有重要意义。深度学习作为一种高效的数据特征提取方法,可以通过它对蛋白质数据进行建模,进而加入先验信息对蛋白质进行设计。故此基于深度学习的蛋白质设计就成为一个具有广阔前景的研究领域。文中主要阐述基于深度学习的蛋白质序列与结构数据的建模和设计方法。详述该方法的策略、原理、适用范围、应用实例。讨论了深度学习方法在本领域的应用前景及局限性,以期为相关研究提供参考。     

A minimal view of single-particle imaging with X-ray lasers

The ability to serially interrogate single biomolecules with femtosecond X-ray pulses from free-electron lasers has ushered in the possibility of determining the three-dimensional structure of biomolecules without crystallization. However, the complexity of imaging a sample''s structure from very many of its noisy and incomplete diffraction data can be daunting. In this review, we introduce a simple analogue of this imaging workflow, use it to describe a structure reconstruction algorithm based on the expectation maximization principle, and consider the effects of extraneous noise. Such a minimal model can aid experiment and algorithm design in future studies.     

fastruct: model‐based clustering made faster

Bayesian model‐based clustering programs have gained increased popularity in studies of population structure since the publication of the software structure . These programs are generally acknowledged as performing well, but their running‐time may be prohibitive. fastruct is a non‐Bayesian implementation of the classical model with no‐admixture uncorrelated allele frequencies. This new program relies on the expectation–maximization principle, and produces assignment rivalling other model‐based clustering programs. In addition, it can be manyfold faster than Bayesian implementations. The software consists of a command‐line engine, which is suitable for batch analysis of data, and a graphical interface, which is convenient for exploring data.     

Bilayer structure of dihexadecyl phosphate

The X-ray scattering data of dihexadecyl phosphate (DHP) were analyzed by the distance distribution function directly obtained from the scattered intensity by Fourier transformation with no prior assumption about size and shape. The detergent in aqueous solutions is a random distribution of lamellae with a thickness of 58 Å and consistent with a hollow vesicular structure of an outer diameter of 340 Å with some overall size heterogeneity. The electron density profile perpendicular to the lamellar plane indicates a bilayer as the underlying structural principle.     

De novo design of foldable proteins with smooth folding funnel: automated negative design and experimental verification

De novo sequence design of foldable proteins provides a way of investigating principles of protein architecture. We performed fully automated sequence design for a target structure having a three-helix bundle topology and synthesized the designed sequences. Our design principle is different from the conventional approach, in that instead of optimizing interactions within the target structure, we design the global shape of the protein folding funnel. This includes automated implementation of negative design by explicitly requiring higher free energy of the denatured state. The designed sequences do not have significant similarity to those of any natural proteins. The NMR and CD spectroscopic data indicated that one designed sequence has a well-defined three-dimensional structure as well as alpha-helical content consistent with the target.     

An Antibiotic from Penicillium sp. Covering the Cocoon of the Leaf-rolling Moth,Dactylioglypha tonica

An antibiotic-producing Penicillium sp. strain was isolated from cocoons of the leaf-rolling moth, Dactylioglypha tonica. An antibacterial compound was isolated from the cultured broth, and the chemical structure of the principle was determined by spectroscopic data to be a derivative of isocoumarincarboxylate.     

人类群体遗传结构的协方差阵主成分分析方法

目的:探讨基因频率矩阵的中心化(或均值化)协方差阵主成分分析方法在人类群体遗传结构研究中的适用性和合理性。方法:从基因频率矩阵的结构特征入手,分析中心化、均值化协方差阵主成分分析与标准化相关阵主成分分析在特征根、特征向量以及降维效果等方面的差异,并通过实例比较不同方法在解释群体遗传结构特征上合理性。结果:中心化(或均值化)协方差阵的主成分不仅反映了基因变异程度的“方差信息量权”,而且反映了基因间相互影响程度的“相关信息量权”;标准化相关阵的主成分反映的仅是“相关信息量权”,不包括“方差信息量权”。通过比较中国26个汉族人群HLA-A基因座中心化协方差阵和标准化相关阵2种主成分分析结果,证实中心化协方差阵主成分分析方法在特征根与特征向量、保留主成分的个数和对主成分的群体遗传学解释的合理性等方面均优于标准化相关阵主成分分析方法。结论:在对群体遗传结构进行主成分分析时,应使用中心化(或均值化)变换消除基因频率矩阵中量级的影响,然后在用其协方差阵提取主成分。     

A PCA-based method for ancestral informative markers selection in structured populations

Identification of population structure can help trace population histories and identify disease genes. Structured association (SA) is a commonly used approach for population structure identification and association mapping. A major issue with SA is that its performance greatly depends on the informativeness and the numbers of ancestral informative markers (AIMs). Present major AIM selection methods mostly require prior individual ancestry information, which is usually not available or uncertain in practice. To address this potential weakness, we herein develop a novel approach for AIM selection based on principle component analysis (PCA), which does not require prior ancestry information of study subjects. Our simulation and real genetic data analysis results suggest that, with equivalent AIMs, PCA-based selected AIMs can significantly increase the accuracy of inferred individual ancestries compared with traditionally randomly selected AIMs. Our method can easily be applied to whole genome data to select a set of highly informative AIMs in population structure, which can then be used to identify potential population structure and correct possible statistical biases caused by population stratification.     

General generative principles

In search of general generative principles I start from the postulate of a reality which comprises both materialistic and psychic aspects. This overall reality, described by others as the world of archetypes, is not directly accessible to sensory perception. Yet, by studying archetypical manifestations, it is possible to distinguish different structures or generative principles in it. Comparison of three models, which depict developmental processes in different disciplines, shows that they have the same basic structure. The effects of the archetypes involved can be described as four general generative principles: 1st the principle of wholeness (connection and integration of conflicting opposites), 2nd the principle of complementation and compensation (periodic change of opposite influences); 3rd the enstructuring principle (relative stability or spatial aspect of a manifest structure), and 4th the principle of gesture (change or temporal aspect of that structure).     

A parsimonious view of the parsimony principle in ecology and evolution

The idea that simplicity of explanation is important in science is as old as science itself. However, scientists often assume that parsimonious theories, hypothesis and models are more plausible than complex ones, forgetting that there is no empirical evidence to connect parsimony with credibility. The justification for the parsimony principle is strongly dependent on philosophical and statistical inference. Parsimony may have a true epistemic value in the evaluation of correlative and predictive models, as simpler models are less prone to overfitting. However, when natural mechanisms are explicitly modelled to represent the causes of biological phenomena, the application of the parsimony principle to judge the plausibility of mechanistic models would entail an unsupported belief that nature is simple. Here, we discuss the challenges we face in justifying, measuring, and assessing the trade‐off between simplicity and complexity in ecological and evolutionary studies. We conclude that invoking the parsimony principle in ecology and evolution is particularly important in model‐building programs in which models are viewed primarily as an operational tool to make predictions (an instrumentalist view) and in which data play a prominent role in deciding the structure of the model. However, theoretical advances in ecology and evolutionary biology may be derailed by the use of the parsimony principle to judge explanatory mechanistic models that are designed to understand complex natural phenomena. We advocate a parsimonious use of the parsimony principle.     

群体遗传学研究中的数据处理方法I．RAPD数据的AMOVA分析

近年来,RAPD数据和AMOVA分析广泛地应用于群体遗传学和保护遗传学研究。然而,由于RAPD标记具显性特点。加上目前进行AMOVA分析所依赖的RAPDistance软件不完善,使得对RAPD数据进行AMOVA分析时存在许多不足。本文介绍了AMOVA分析的基本过程,同时引入一个新的程序DCFA用以替代RADistance并详述了将DCFA与WINAMOVA联用,对RAPD数据进行AMOVA分析的具体步骤与注意事项,最后,以产自中国和巴西8个普通野生稻（Oryza furipogon)天然群体为例,演示了对RAPD表型数据进行AMOVA分析的过程,讨论了AMOVA分析结果在群体遗传结构上的意义。通过对AMOVA算法的分析,同时比较4种距离系数所得AMOVA结果,我们认为在进行AMOVA分析时选择NEI－LI距离和欧氏距离平方较为合适,而目前国内使用较多的JACCARD系数不适合AMOVA分析。     

A Hypothesis Test for Equality of Bayesian Network Models

Bayesian network models are commonly used to model gene expression data. Some applications require a comparison of the network structure of a set of genes between varying phenotypes. In principle, separately fit models can be directly compared, but it is difficult to assign statistical significance to any observed differences. There would therefore be an advantage to the development of a rigorous hypothesis test for homogeneity of network structure. In this paper, a generalized likelihood ratio test based on Bayesian network models is developed, with significance level estimated using permutation replications. In order to be computationally feasible, a number of algorithms are introduced. First, a method for approximating multivariate distributions due to Chow and Liu (1968) is adapted, permitting the polynomial-time calculation of a maximum likelihood Bayesian network with maximum indegree of one. Second, sequential testing principles are applied to the permutation test, allowing significant reduction of computation time while preserving reported error rates used in multiple testing. The method is applied to gene-set analysis, using two sets of experimental data, and some advantage to a pathway modelling approach to this problem is reported.     

The Isolation Principle of Clustering: Structural Characteristics and Implementation

The isolation principle rests on defining internal and external differentiation for each subset of at least two objects. Subsets with larger external than internal differentiation form isolated groups in the sense that they are internally cohesive and externally isolated. Objects that do not belong to any isolated group are termed solitary. The collection of all isolated groups and solitary objects forms a hierarchical (encaptic) structure. This ubiquitous characteristic of biological organization provides the motivation to identify universally applicable practical methods for the detection of such structure, to distinguish primary types of structure, to quantify their distinctiveness, and to simplify interpretation of structural aspects. A method implementing the isolation principle (by generating all isolated groups and solitary objects) is proven to be specified by single-linkage clustering. Basically, the absence of structure can be stated if no isolated groups exist, the condition for which is provided. Structures that allow for classifications in the sense of complete partitioning into disjoint isolated groups are characterized, and measures of distinctiveness of classification are developed. Among other primary types of structure, chaining (complete nesting) and ties (isolated groups without internal structure) are considered in more detail. Some biological examples for the interpretation of structure resulting from application of the isolation principle are outlined.