群落排表分类的两种数学方法

本文描述两种执行群落排表分类的数学方法:X²分类法和信息熵分类法,并以德国西北部草地数据为例进行了应用和分析。结果表明这两个方法都是有效的群落排表分类方法,它们所排的群落表可直接地反映群落类型和种类组成之间的关系,体现了Braun-Blanquet传统排表法的特点。     

Backbone entropy of loops as a measure of their flexibility: Application to a Ras protein simulated by molecular dynamics

The flexibility of surface loops plays an important role in protein–protein and protein–peptide recognition; it is commonly studied by Molecular Dynamics or Monte Carlo simulations. We propose to measure the relative backbone flexibility of loops by the difference in their backbone conformational entropies, which are calculated here with the local states (LS) method of Meirovitch. Thus, one can compare the entropies of loops of the same protein or, under certain simulation conditions, of different proteins. These loops should be equal in size but can differ in their sequence of amino acids residues. This methodology is applied successfully to three segments of 10 residues of a Ras protein simulated by the stochastic boundary molecular dynamics procedure. For the first time estimates of backbone entropy differences are obtained, and their correlation with B factors is pointed out; for example, the segments which consist of residues 60–65 and 112–117 have average B factors of 67 and 18 Ų, respectively, and entropy difference T ΔS = 5.4 ± 0.1 kcal/mol at T = 300 K. In a large number of recent publications the entropy due to the fast motions (on the ps-ns time scale) of N–H and C–H vectors has been obtained from their order parameter, measured in nuclear magnetic resonance spin relaxation experiments. This enables one to estimate differences in the entropy of protein segments due to folding–unfolding transitions, for example. However, the vectors are assumed to be independent, and the effect of the neglected correlations is unknown; our method is expected to become an important tool for assessing this approximation. The present calculations, obtained with the LS method, suggest that the errors involved in experimental entropy differences might not be large; however, this should be verified in each case. Potential applications of entropy calculations to rational drug design are discussed. Proteins 29:127–140, 1997. © 1997 Wiley-Liss, Inc.     

基于激光雷达数据的物种分布模拟: 以美国加州内华达山脉南部区域食鱼貂分布模拟为例

生态位模型通过拟合物种分布与环境变量之间的关系提供物种空间分布预测, 在生物多样性研究中有广泛应用。激光雷达(LiDAR)是一种新兴的主动遥感技术, 已被大量应用于森林三维结构信息的提取, 但其在物种分布模拟的应用研究比较缺乏。本研究以美国加州内华达山脉南部地区的食鱼貂(Martes pennanti)的分布模拟为例, 探索LiDAR技术在物种分布模拟中的有效性。生态位模型采用5种传统多类分类器, 包括神经网络、广义线性模型、广义可加模型、最大熵模型和多元自适应回归样条模型, 并使用正样本-背景学习(presence and background learning, PBL)算法进行模型校正; 同时对这5种模型使用加权平均进行模型集成, 作为第6个模型。此外, 一类最大熵模型也被用于模拟该物种的空间分布。模型的连续输出和二值输出分别使用AUC (area under the receiver operating characteristic curve)以及基于正样本-背景数据的评价指标F_pb进行评价。结果表明, 仅考虑气候因子(温度和降水)时, 7个模型的AUC和F_pb平均值分别为0.779和1.077; 当考虑LiDAR变量(冠层容重、枝下高、叶面积指数、高程、坡度等)后, AUC和F_pb分别为0.800和1.106。该研究表明, LiDAR数据能够提高食鱼貂空间分布的预测精度, 在物种分布模拟方面存在一定的应用价值。     

基于灰熵关联的平均住院日影响因素研究

目的:分析医疗指标与平均住院日的关联程度,为医院有效缩短平均住院日提供参考依据。方法:建立基于灰熵优化的加权灰色关联度模型,对影响平均住院日的医疗指标进行重要程度的分析。结果:根据关联程度分析,可知影响平均住院日的医疗指标重要程度依次为开放床位数、床位周转次数、治疗有效率、床位使用率、年门诊量、住院手术人次和出院人数。结论:基于灰熵优化的加权灰色关联分析方法可以有效分析医疗指标对平均住院日的影响程度,提高医疗服务质量。     

A quadratic correlation between long‐term mean group size and group density in a cooperatively breeding passerine

Both mean group size (MGS) and mean group density (MGD) are critical indices to characterize a population of cooperatively breeding birds. When a population reaches its carrying capacity, both long‐term MGS and long‐term MGD will remain relatively stable. However, there has been little study of how these two variables relate. The Masked laughingthrush Garrulax perspicillatus is a cooperatively breeding bird living in fragmented habitats. During 2010 and 2012‐2016, we used song playback to observe and confirm the group sizes and territory ranges of the birds and the data of bird presence to determine habitat suitability. By grouping the nearest territories according to their geographical coordinates, we divided the whole study area into 12 subareas and the whole population into 12 subpopulations. Then, we calculated both MGS and MGD for different time durations for each subpopulation. Finally, using MGD as independent variable and MGS as the dependent variable, we explored the correlations between MGS and MGD by fitting quadratic functions and modeling quadratic regression. Both MGS and MGD were averaged for different time durations and were cross‐related. Our results show that the MGS for more than 2 years significantly correlated with MGD for more than 3 years in a reverse parabolic shape, differing from that of short‐term effects. Our findings suggest that long‐term MGD is a better predictor of long‐term habitat quality and that long‐term MGS is determined by long‐term habitat quality in Masked Laughingthrushes. Based on above findings, we can infer that: (1) Long‐term habitat quality determines the long‐term MGS, but it sets no prerequisite for the status and source of group members; (2) Long‐term MGS in certain populations is adapted to the corresponding level of long‐term habitat quality, it facilitates us to predict the helper effects on current or future survival or reproduction in different situations. These findings and inferences are both helpful for us to understand the evolution of cooperative breeding.     

From food‐dependent statistics to metabolic parameters,a practical guide to the use of dynamic energy budget theory

The standard model of the dynamic energy budget theory for metabolic organisation has variables and parameters that can be quantified using indirect methods only. We present new methods (and software) to extract food‐independent parameter values of the energy budget from food‐dependent quantities that are easy to observe, and so facilitate the practical application of the theory to enhance predictability and extrapolation. A natural sequence of 10 steps is discussed to obtain some compound parameters first, then the primary parameters, then the composition parameters and finally the thermodynamic parameters; this sequence matches a sequence of required data of increasing complexity which is discussed in detail. Many applications do not require knowledge of all parameters, and we discuss methods to extrapolate parameters from one species to another. The conversion of mass, volume and energy measures of biomass is discussed; these conversions are not trivial because biomass can change in chemical composition in particular ways thanks to different forms of homeostasis. We solve problems like “What would be the ultimate reproduction rate and the von Bertalanffy growth rate at a specific food level, given that we have measured these statistics at abundant food?” and “What would be the maximum incubation time, given the parameters of the von Bertalanffy growth curve?”. We propose a new non‐destructive method for quantifying the chemical potential and entropy of living reserve and structure, that can potentially change our ideas on the thermodynamic properties of life. We illustrate the methods using data on daphnids and molluscs.     

基于小波包熵的运动意识任务分类研究

提出了以小波包熵作为脑电特征向量的左右手运动意识任务分类方法,对被测试者想象左右手运动时的脑电小波包熵动态变化情况及分析窗口长度的选择进行了研究.结果表明,小波包熵能很好地反映左右手运动想象的脑电特征变化,用线性判别式算法对脑电特征进行识别,分类正确率达到92.14%.由于小波包熵的计算比较简单,稳定性好,识别率高,为大脑运动意识任务的分类提供了新思路.     

Thermodynamics of Agricultural Sustainability: The Case of US Maize Agriculture

This paper considers the local, field-scale sustainability of a productive industrial maize agrosystem that has replaced a fertile grassland ecosystem.

Using the revised thermodynamic approach of Svirezhev (1998 Svirezhev, Y. M. 1998. “Thermodynamic orientors: How to use Thermodynamic concepts in ecology”. In Eco Targets, Goal Functions, and Orientors, 102–122. Berlin: Springer Verlag. [Crossref] [Google Scholar], 2000 Svirezhev, Y. M. 2000. Thermodynamics and ecology. Ecological Modelling, 132: 11–22. [Crossref], [Web of Science ®] [Google Scholar]) and Steinborn and Svirezhev (2000) Steinborn, W. and Svirezhev, Y. M. 2000. Entropy as an indicator of sustainability in agro-ecosystems: North Germany case study. Ecol. Mode., 133: 247–257. [Crossref], [Web of Science ®] [Google Scholar], it is shown that currently this agrosystem is unsustainable in the U.S., with or without tilling the soil. The calculated average erosion rates of soil necessary to dissipate the entropy produced by U.S. maize agriculture, 23–45 t ha^?1 yr^?1, are bounded from above by an experimental estimate of mean soil erosion by conventional agriculture worldwide, 47 t ha^?1 yr^?1, (Montgomery, 2007 Montgomery, D. R. 2007. Soil erosion and agricultural sustainability. PNAS, 104(33): 13268–13272. [Crossref], [PubMed], [Web of Science ®] [Google Scholar]). Between 1982 and 1997, US agriculture caused an estimated 7–23 t ha^?1 yr^?1 of average erosion with the mean of 15 t ha^?1 yr^?1 (USDA-NRCS Database). The lower mean erosion rate of no till agriculture, 1.5 t ha^?1 yr^?1 (Montgomery, 2007 Montgomery, D. R. 2007. Soil erosion and agricultural sustainability. PNAS, 104(33): 13268–13272. [Crossref], [PubMed], [Web of Science ®] [Google Scholar]), necessitates the elimination of weeds and pests with field chemicals—with the ensuing chemical and biological soil degradation, and chemical runoff—to dissipate the produced entropy. The increased use of field chemicals that replace tillers is equivalent to the killing or injuring of up to 300 kg ha^?1 yr^?1 of soil flora and fauna. Additional soil degradation, not calculated here, occurs by acidification, buildup of insoluble metal compounds, and buildup of toxic residues from field chemicals. The degree of unsustainability of an average U.S. maize field is high, requiring 6–13 times more energy to reverse soil erosion and degradation, etc., than the direct energy inputs to maize agriculture. This additional energy, if spent, would not increase maize yields. The calculated “critical yield” of “organic” maize agriculture that does not use field chemicals and fossil fuels is only 30 percent lower than the average maize yield of 8.7 tons per hectare (～140 bu/acre) assumed here. This critical yield would not likely be achieved and sustained by large monocultures, but might be achieved by more balanced organic polycultures (Baum et al., 2008 Baum, A. W., Patzek, T. W., Bender, M., Renich, S. and Jackson, W. 2008. The Visible, Sustainable Farm: A Comprehensive Energy Analysis of a Midwestern Farm 1–34. Posted at petroleum.berkeley.edu/papers/Biofuels/SSF?Report3-051408.pdf [Google Scholar]).     

A statistical rescoring scheme for protein-ligand docking: Consideration of entropic effect

Computational prediction of protein-ligand binding modes provides useful information on the relationship between structure and activity needed for drug design. A statistical rescoring method that incorporates entropic effect is proposed to improve the accuracy of binding mode prediction. A probability function for two sampled conformations to belong to the same broad basin in the potential energy surface is introduced to estimate the contribution of the state represented by a sampled conformation to the configurational integral. The rescoring function is reduced to the colony energy introduced by Xiang et al. (Proc Natl Acad Sci USA 2002;99:7432-7437) when a particular functional form for the probability function is used. The scheme is applied to rescore protein-ligand complex conformations generated by AutoDock. It is demonstrated that this simple rescoring improves prediction accuracy substantially when tested on 163 protein-ligand complexes with known experimental structures. For example, the percentage of complexes for which predicted ligand conformations are within 1 A root-mean-square deviation from the native conformations is doubled from about 20% to more than 40%. Rescoring with 11 different scoring functions including AutoDock scoring functions were also tested using the ensemble of conformations generated by Wang et al. (J Med Chem 2003;46:2287-2303). Comparison with other methods that use clustering and estimation of conformational entropy is provided. Examination of the docked poses reveals that the rescoring corrects the predictions in which ligands are tightly fit into the binding pockets and have low energies, but have too little room for conformational freedom and thus have low entropy.