天山中部地区北山羊食性及其季节变化

本研究采用粪便显微组织分析法,调查了天山中部地区北山羊(Capra sibirica)的食物组成及季节变化。结果表明,在北山羊的粪便显微装片中共鉴定出14科37种(属)植物。禾本科为北山羊主要食物,夏季和冬季分别占食谱的54.30%和59.34%。莎草科、豆科、蔷薇科以及菊科也是北山羊较为重要的食物。其中高山早熟禾(Poa alpina)、嵩草属(Kobresia spp.)和针茅属(Stipa spp.)物种均为夏季和冬季采食较多的植物。卡方检验结果表明,北山羊的食物组成在夏季和冬季间有明显变化,蔷薇科和菊科在冬季食谱中所占的比例显著低于夏季(蔷薇科,Pearson χ~2=13.737,df=1,P0.01;菊科,Pearson χ~2=4.784,df=1,P0.05),而对禾本科的采食比例冬季显著高于夏季(禾本科,Pearson χ~2=4.319,df=1,P0.05)。种属水平上北山羊夏季与冬季的采食食物种类的比例具有极显著差异(Pearson χ~2=89.495,df=36,P0.01)。上述研究结果表明,天山中部地区的北山羊属于泛食性物种,且食性有明显的季节变化。     

小样本情况下基因效应分析中的统计检验

基因效应分析已被广泛地应用于植物遗传学研究和植物育种工作中^[3]。根据中心极限定理,在大样本情况下,可用正态分布对基因效应和尺度测验统计量进行显著性检验。当样本容量较小时,正态分布就不能用于上述显著性检验。一些作者^[1,3,4,7]认为,在小样本情况下应当用t-统计量来进行上述检验,但实际上由于各世代间的总体方差不等,通常的t-统计量不能用于这种检验。     

常用的生物统计方法及其R语言实现

统计分析是科学研究中一个极其重要的环节。本文以昆虫学研究为实例,利用模拟数据,总结了14种常用的生物统计方法及其R语言实现,重点强调了如何根据科学问题和样本数据的具体情形选取合适的统计方法。这些统计方法包括可用于均值比较分析的符号检验、Wilcoxon符号秩检验、t-检验、Wilcoxon秩和检验、Kruskal-Wallis检验、Nemenyi检验、Tukey检验、Friedman检验、单因素方差分析、重复测量方差分析和可用于相关性分析的卡方检验、Fisher精确检验、Spearman秩相关分析、Pearson相关分析,可为生物统计或R语言基础薄弱的昆虫学工作者提供参考。     

小样本情况下基因效应分析中的统计检验1)

基因效应分析已被广泛地应用于植物遗传学研究和植物育种工作中^[3]。根据中心极限定理，在大样本情况下，可用正态分布对基因效应和尺度测验统计量进行显著性检验。当样本容量较小时，正态分布就不能用于上述显著性检验。一些作者^[1,3,4,7]认为，在小样本情况下应当用t-统计量来进行上述检验，但实际上由于各世代间的总体方差不等，通常的t-统计量不能用于这种检验。     

漓江河岸带枫杨群落主要木本植物种间联结与相关分析

种间联结能够反映植物群落中物种间的关系和群落的稳定程度,对植被的恢复与重建、生物多样性保护等具有重要的意义和价值。为了解漓江河岸带枫杨群落的种间联结特征,基于2×2联列表,通过方差比率(VR)、χ~2检验、Pearson相关系数、Spearman秩相关系数检验及PCA排序等,对枫杨群落主要木本植物种间关联性进行定量分析和生态种组划分。结果表明:(1)乔木层、灌木层以及乔木和灌木层的总体种间关联性均呈不显著正联结,表明枫杨群落处于较稳定的阶段;(2)χ~2检验结果显示,绝大多数种对呈不显著关联,说明群落各种对间关联较弱,各物种间呈独立分布格局;(3)Pearson相关检验和Spearman秩相关检验发现各层次的显著率均较低,种间相关性不显著的种对占绝大部分,种间相关性不强;(4)χ~2检验、Pearson相关系数检验和Spearman秩相关系数检验结果虽具有一致性,但也存在一定的差异,Spearman秩相关系数检验更具灵敏性,结合3种方法使用效果更佳;(5)根据PCA排序和种间联结关系,将20个主要物种划分为4个生态种组。     

山西五鹿山自然保护区暴马丁香群落木本植物种间联结性分析

通过方差分析,χ²检验、Pearson相关系数和Spearman秩相关系数检验,采用2×2列联表对五鹿山暴马丁香林中频度较高的34个木本植物优势种,561个种对间的关联性进行了研究.分析结果表明: 34个优势种种间总体关联性呈现不显著负关联,表明该群落处于不稳定的顶级状态.χ²检验结果中,有216个种对呈正相关,345个种对呈负相关,正负关联比为0.626; Pearson相关系数检验结果为: 149个种对呈正相关,412个种对呈负相关,正负关联比为0.362; Spearman秩相关系数检验结果为: 206个种对呈正相关,365个种对呈负相关,正负关联比为0.564; Spearman秩相关系数比Pearson相关系数的检验方法灵敏度更高.     

旅游干扰对历山亚高山草甸优势种群种间相关性的影响

基于2×2列联表,应用方差比率法、χ2检验、Pearson相关系数检验及Spearm an秩相关系数检验等数量分析方法研究了旅游干扰对山西历山舜王坪亚高山草甸种间关联程度的影响。结果表明:(1)不同旅游距离带的联结指数(VR)均大于1,但统计量W均界于χ2分布的上下临界值之间。群落总体呈正关联,但联结性较弱,种的分布相对独立。(2)随着距游径距离的增加,4条不同旅游距离带的联结指数(VR)呈下降趋势;正负关联比有所增加,2χ检验显著率、Pearson相关检验显著率和Spearm an秩相关检验显著率都明显下降,说明旅游干扰对舜王坪亚高山草甸种间关联程度产生了一定的影响,距离游径越远,种间关联越趋于随机性,但这种影响尚未引起种间联结性的改变。(3)χ2检验与Pearson相关系数检验、Spearm an秩相关系数检验结果用来刻划种间相关性具有一定的相似之处,但仍存在一定差异,结合使用效果更好。与Pearson相关检验相比,Spearm an秩相关检验更具灵敏性。4)本研究结果支持随着群落演替进程的发展,群落种群总体向着无关联发展的观点。     

山西太岳山连翘群落优势种种间关系

基于2×2列联表,采用方差比率法、x2检验、Pearson相关系数和Spearman秩相关数检验等数量分析方法对山西太岳山连翘群落中25个优势种、300个种对间的种间关联性进行定量研究.方差分析表明,25个优势种群的总体种间关联性呈不显著关联,种的分布相对独立.x2检验有138个种对呈正相关,150对种对呈负相关,正负关联比为0.92;Pearson相关系数检验有100个种对呈正相关,199对种对呈负相关,正负关联比例为0.5;Spearman秩相关系数检验121个种对呈正相关,179个种对呈负相关,正负关联比例为0.67.与x2检验相比,Pearson相关分析和Spearman秩相关分析具有较高的灵敏度.根据这25个优势种群对环境的适应方式和主导生态因素结合PCA排序,将25个优势种分为4个生态种组.     

病虫发生程度预报质量评估方法

介绍了对病虫发生程度预报质量进行评估的统计检验方法———McNemar检验和Kappa检验,以及这2种检验方法的原理、假设检验和统计学意义。结合2个实例来说明2种统计检验方法在病虫发生程度预报质量评估中的应用,分析结果表明McNemar检验、Kappa检验能对病虫发生程度预测质量给出更加有效、准确的评估。     

九连山常绿阔叶林乔木优势种群的种间关联性分析

九连山国家级自然保护区分布着原生的典型常绿阔叶林, 且已形成不同的优势群落, 目前尚不清楚群落乔木层中优势物种之间的作用关系。采用2×2联列表, 通过方差分析, χ²检验、Pearson相关系数和Spearman秩相关系数检验, 对九连山常绿阔叶林乔木层中重要值较高的28个优势种群、378个种对间的关联性进行定量研究。方差分析表明: 28个优势种群的总体种间关联性呈显著的正关联, 反映该群落处于较稳定的顶极阶段。不同检验结果表明: χ²检验结果有140个种对呈正相关, 238个种对呈负相关, 正负关联比为0.588; Pearson相关系数检验有104个种对呈正相关, 274个种对呈负相关, 正负关联比为0.380; Spearman秩相关系数检验有144个种对呈正相关, 234个种对呈负相关, 正负关联比为0.615; 与Pearson相关系数检验方法相比, Spearman秩相关系数检验具有较高的灵敏度。378个种对中, 绝大多数种对的联结关系未达到显著水平, 种对间的独立性相对较强, 这种种间联结的松散性可能与群落目前的发展阶段及物种本身的生态学特性有关, 各群落正处于稳定的顶极阶段。根据28个优势种群对环境的适应方式和主导生态因素, 可将它们划分为阳生植物和阴生植物两大生态种组。     

Managing data locally to answer questions globally: The role of collaborative science in ecology

Ecologists are increasingly asking large‐scale and/or broad‐scope questions that require vast datasets. In response, various top‐down efforts and incentives have been implemented to encourage data sharing and integration. However, despite general consensus on the critical need for more open ecological data, several roadblocks still discourage compliance and participation in these projects; as a result, ecological data remain largely unavailable. Grassroots initiatives (i.e. efforts initiated and led by cohesive groups of scientists focused on specific goals) have thus far been overlooked as a powerful means to meet these challenges. These bottom‐up collaborative data integration projects can play a crucial role in making high quality datasets available because they tackle the heterogeneity of ecological data at a scale where it is still manageable, all the while offering the support and structure to do so. These initiatives foster best practices in data management and provide tangible rewards to researchers who choose to invest time in sound data stewardship. By maintaining proximity between data generators and data users, grassroots initiatives improve data interpretation and ensure high‐quality data integration while providing fair acknowledgement to data generators. We encourage researchers to formalize existing collaborations and to engage in local activities that improve the availability and distribution of ecological data. By fostering communication and interaction among scientists, we are convinced that grassroots initiatives can significantly support the development of global‐scale data repositories. In doing so, these projects help address important ecological questions and support policy decisions.     

Invited review: Big Data in precision dairy farming

Insight into current scientific applications of Big Data in the precision dairy farming area may help us to understand the inflated expectations around Big Data. The objective of this invited review paper is to give that scientific background and determine whether Big Data has overcome the peak of inflated expectations. A conceptual model was created, and a literature search in Scopus resulted in 1442 scientific peer reviewed papers. After thorough screening on relevance and classification by the authors, 142 papers remained for further analysis. The area of precision dairy farming (with classes in the primary chain (dairy farm, feed, breed, health, food, retail, consumer) and levels for object of interest (animal, farm, network)), the Big Data-V area (with categories on Volume, Velocity, Variety and other V’s) and the data analytics area (with categories in analysis methods (supervised learning, unsupervised learning, semi-supervised classification, reinforcement learning) and data characteristics (time-series, streaming, sequence, graph, spatial, multimedia)) were analysed. The animal sublevel, with 83% of the papers, exceeds the farm sublevel and network sublevel. Within the animal sublevel, topics within the dairy farm level prevailed with 58% over the health level (33%). Within the Big Data category, the Volume category was most favoured with 59% of the papers, followed by 37% of papers that included the Variety category. None of the papers included the Velocity category. Supervised learning, representing 87% of the papers, exceeds unsupervised learning (12%). Within supervised learning, 64% of the papers dealt with classification issues and exceeds the regression methods (36%). Time-series were used in 61% of the papers and were mostly dealing with animal-based farm data. Multimedia data appeared in a greater number of recent papers. Based on these results, it can be concluded that Big Data is a relevant topic of research within the precision dairy farming area, but that the full potential of Big Data in this precision dairy farming area is not utilised yet. However, the present authors expect the full potential of Big Data, within the precision dairy farming area, will be reached when multiple Big Data characteristics (Volume, Variety and other V’s) and sources (animal, groups, farms and chain parts) are used simultaneously, adding value to operational and strategic decision.     

Reliability of reliability coefficients in the estimation of asymmetry

Although promising to provide insight into the interaction between genotype and environment, investigations into fluctuating asymmetry suffer from a lack of standardization in the reporting of measurement error. In the present paper we show, using both anthropometric and odonto-metric data, that the use of the reliability coefficient calculated for a bilateral measurement provides no indication of the reliability of the corresponding asymmetry estimate, because reliability of asymmetry depends on the relationship between measurement error and the difference between sides. Thus, we suggest that future investigations either provide reliability coefficients for asymmetry estimates specifically, or use methods that account for measurement error. © 1995 Wiley-Liss, Inc.     

我国医疗服务数据中心发展现状

医疗服务数据中心能够通过对医疗数据的采集、存储、维护和分析,在评价和提升患者安全、助力医疗质量管理、为患者就医提供导向、推动生物银行的建设发展等方面发挥着非常重要的作用。尽管相比发达国家,我国的国家医疗服务数据中心的建设起步较晚,但已在指导医疗服务和服务医疗管理方面取得了一定的成绩。     

From information to understanding: the role of model organism databases in comparative and functional genomics

Data integration is key to functional and comparative genomics because integration allows diverse data types to be evaluated in new contexts. To achieve data integration in a scalable and sensible way, semantic standards are needed, both for naming things (standardized nomenclatures, use of key words) and also for knowledge representation. The Mouse Genome Informatics database and other model organism databases help to close the gap between information and understanding of biological processes because these resources enforce well-defined nomenclature and knowledge representation standards. Model organism databases have a critical role to play in ensuring that diverse kinds of data, especially genome-scale data sets and information, remain useful to the biological community in the long-term. The efforts of model organism database groups ensure not only that organism-specific data are integrated, curated and accessible but also that the information is structured in such a way that comparison of biological knowledge across model organisms is facilitated.     

Partial least squares for functional joint models with applications to the Alzheimer's disease neuroimaging initiative study

Many biomedical studies have identified important imaging biomarkers that are associated with both repeated clinical measures and a survival outcome. The functional joint model (FJM) framework, proposed by Li and Luo in 2017, investigates the association between repeated clinical measures and survival data, while adjusting for both high-dimensional images and low-dimensional covariates based on the functional principal component analysis (FPCA). In this paper, we propose a novel algorithm for the estimation of FJM based on the functional partial least squares (FPLS). Our numerical studies demonstrate that, compared to FPCA, the proposed FPLS algorithm can yield more accurate and robust estimation and prediction performance in many important scenarios. We apply the proposed FPLS algorithm to a neuroimaging study. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.     

代谢组学信息获取与数据预处理瓶颈问题探讨

经过近15 年的快速发展,代谢组学已逐步成熟并渗透到生命科学和医学研究领域的各个层面。随着认识的不断深入,各种影响代谢组学研究的细节因素和技术瓶颈逐步被揭示。从生物样品采集与制备、原始数据采集、数据预处理和方法学验证等方面系统探讨了影响代谢组学研究的主要瓶颈问题,以期推动代谢组学研究的精细化和规范化发展。     

The principal airborne and allergenic pollen species in the Netherlands

Summary A list of the principal airborne and allergenic pollen species in the Netherlands was compiled on the basis of the pollen lists of Leiden and Helmond, the Leiden pollen calendar, the hour-square frequencies of the species in question in the Netherlands and the degree of allergenicity of the extent known. Twenty-two trees and shrubs, 9 herbs and 32 grasses were selected.     

FAIR research data management as community approach in bioengineering

Research data management (RDM) requires standards, policies, and guidelines. Findable, accessible, interoperable, and reusable (FAIR) data management is critical for sustainable research. Therefore, collaborative approaches for managing FAIR-structured data are becoming increasingly important for long-term, sustainable RDM. However, they are rather hesitantly applied in bioengineering. One of the reasons may be found in the interdisciplinary character of the research field. In addition, bioengineering as application of principles of biology and tools of process engineering, often have to meet different criteria. In consequence, RDM is complicated by the fact that researchers from different scientific institutions must meet the criteria of their home institution, which can lead to additional conflicts. Therefore, centrally provided general repositories implementing a collaborative approach that enables data storage from the outset In a biotechnology research network with over 20 tandem projects, it was demonstrated how FAIR-RDM can be implemented through a collaborative approach and the use of a data structure. In addition, the importance of a structure within a repository was demonstrated to keep biotechnology research data available throughout the entire data lifecycle. Furthermore, the biotechnology research network highlighted the importance of a structure within a repository to keep research data available throughout the entire data lifecycle.