Penalized classification for optimal statistical selection of markers from high-throughput genotyping: application in sheep breeds

The identification of individuals’ breed of origin has several practical applications in livestock and is useful in different biological contexts such as conservation genetics, breeding and authentication of animal products. In this paper, penalized multinomial regression was applied to identify the minimum number of single nucleotide polymorphisms (SNPs) from high-throughput genotyping data for individual assignment to dairy sheep breeds reared in Sicily. The combined use of penalized multinomial regression and stability selection reduced the number of SNPs required to 48. A final validation step on an independent population was carried out obtaining 100% correctly classified individuals. The results using independent analysis, such as admixture, F_st, principal component analysis and random forest, confirmed the ability of these methods in selecting distinctive markers. The identified SNPs may constitute a starting point for the development of a SNP based identification test as a tool for breed assignment and traceability of animal products.     

基于微波遥感技术探测森林地表土壤含水率

森林地表土壤含水率是森林生态系统中的重要参数,使用微波遥感技术快速准确地估算区域尺度上的森林地表土壤含水率,对于森林生态系统研究具有重要的现实意义.本文利用TDR-300土壤含水率速测仪测得黑龙江大兴安岭地区塔河林业局盘古林场内120块样地的森林地表土壤含水率作为因变量,利用C波段全极化SAR数据的极化分解参数作为自变量,构造多元线性回归统计模型和BP神经网络模型,定量估测森林地表土壤含水率,通过模型反演获得区域尺度上森林地表土壤含水率的空间分布.结果表明: 多元线性回归统计模型的精度为86.0%,均方差根误差(RMSE)为3.0%;BP神经网络模型的精度为89.4%,RMSE为2.7%.说明利用BP神经网络模型定量估测森林地表土壤含水率优于多元线性回归模型,将全极化SAR数据通过BP神经网络模型进行仿真,最终得到研究区域的森林地表土壤含水率空间分布图.     

The best of both worlds: Phylogenetic eigenvector regression and mapping

Eigenfunction analyses have been widely used to model patterns of autocorrelation in time, space and phylogeny. In a phylogenetic context, Diniz-Filho et al. (1998) proposed what they called Phylogenetic Eigenvector Regression (PVR), in which pairwise phylogenetic distances among species are submitted to a Principal Coordinate Analysis, and eigenvectors are then used as explanatory variables in regression, correlation or ANOVAs. More recently, a new approach called Phylogenetic Eigenvector Mapping (PEM) was proposed, with the main advantage of explicitly incorporating a model-based warping in phylogenetic distance in which an Ornstein-Uhlenbeck (O-U) process is fitted to data before eigenvector extraction. Here we compared PVR and PEM in respect to estimated phylogenetic signal, correlated evolution under alternative evolutionary models and phylogenetic imputation, using simulated data. Despite similarity between the two approaches, PEM has a slightly higher prediction ability and is more general than the original PVR. Even so, in a conceptual sense, PEM may provide a technique in the best of both worlds, combining the flexibility of data-driven and empirical eigenfunction analyses and the sounding insights provided by evolutionary models well known in comparative analyses.     

Genomic and Functional Diversity of Bacteria Isolated from Hot Springs in Odisha,India

This study focused on culture-dependent survey of important bacterial community diversity of hot springs of Odisha. India. Molecular and cultural techniques were employed for assessing and exploiting the genetic and functional variability among the isolates obtained from three alkaline and mesophilic hot springs. A total of 48 isolates belonging to family Bacillaceae, Paenibacillaceae, Planococcaceae, Pseudomonadaceae and Enterobacteriaceae were identified. Majority of the bacterial isolates were affiliated with the genus Bacillus. Morphologically all the isolated bacteria were either Gram-positive spore-forming rods, or Gram-negative rods. The optimum temperature for growth of the isolates varied between 37°C to 50°C. The functional diversity revealed that many of the predominant and scarce isolates produced a variety of extracellular enzymes such as amylase, cellulase, lipase, phosphatase and protease, and genus Bacillus dominated for extracellular enzymatic activity. We employed two molecular markers to characterize the isolates. The hsp60 universal target sequence was found to be more discriminatory than 16s rRNA gene sequences. The cultivable bacterial community structure that colonized in the investigated thermal springs did not reveal much overlapping. Our results indicate that bacteria in the geothermal environment are metabolically active and cultivable populations may have great potential in biotechnology.     

哀牢山湿性常绿阔叶林木质藤本植物地上部分生物量及其对人为干扰的响应

 木质藤本植物是森林, 尤其是热带和亚热带森林中的重要组分。由于野外调查的困难, 对其生态学的研究相对较少。对哀牢山原生山地湿性常绿阔叶林和4类次生林中的藤本植物进行了调查, 利用48株藤本植物样木实测数据, 采用样本回归分析法, 选取藤本植物的不同参数作为自变量, 分别对冠层和林下两类藤本混合种生物量模型进行了拟合比较, 结合样地内长度≥50 cm的所有藤本植物的调查资料估算了各森林群落藤本植物地上部分生物量, 探讨了原生林中藤本植物地上部分生物量的组成与分布特征, 以及人为干扰对藤本植物地上部分生物量的影响。结果表明: 1)以藤本基径为自变量建立幂函数回归模型, 其相关系数较高, 具有较高的实用价值; 2)该区山地湿性常绿阔叶林中藤本植物地上部分生物量为9.82×10³ kg·hm^–2, 其中冠层藤本(基径≥1.0 cm, 长度≥5.0 m)生物量占藤本植物总生物量的99.70%, 林下藤本(基径<1.0 cm, 长度<5.0 m)的地上部分生物量很低; 3)人为干扰后林下藤本植物的生物量相对增加, 而冠层藤本植物的地上部分生物量显著减少; 经过约100年恢复演替的老龄栎类萌生林藤本植物地上部分生物量才达到接近原生林的水平。     

Reducing variability of crossvalidation for smoothing-parameter choice

One of the attractions of crossvalidation, as a tool for smoothing-parameterchoice, is its applicability to a wide variety of estimatortypes and contexts. However, its detractors comment adverselyon the relatively high variance of crossvalidatory smoothingparameters, noting that this compromises the performance ofthe estimators in which those parameters are used. We show thatthe variability can be reduced simply, significantly and reliablyby employing bootstrap aggregation or bagging. We establishthat in theory, when bagging is implemented using an adaptivelychosen resample size, the variability of crossvalidation canbe reduced by an order of magnitude. However, it is arguablymore attractive to use a simpler approach, based for exampleon half-sample bagging, which can reduce variability by approximately50%.     

基于支持向量回归的棉铃虫蛹发育历期估测

温度与发育速率关系模拟是昆虫学研究的一个重要内容, 传统基于经验风险最小的非线性参数模型(Logan模型、Lactin模型和王氏模型)存在诸多弊端。本文基于结构风险最小的改进支持向量回归（SVR）研究温度与棉铃虫Helicoverpa armigera蛹发育历期关系。结果表明: 与传统非线性模型相比, SVR模型性能优异; 基于全部92个样本, SVR模型拟合和留一法预测的决定系数R²分别为0.998和0.996, 估测的蛹期三基点温度更可信。从全部样本中依温度均匀选取部分样本实施独立预测, 当训练集为20个样本时, SVR模型独立预测的R²为0.981, 优于传统非线性模型中独立预测最佳的Lactin模型（R²=0.958）; 当训练集进一步减少到12个样本时, SVR模型的R²仅降低到0.964, 而传统非线性模型均已不适用。结果提示SVR模型在小样本情况下较传统非线性模型优势明显, 在昆虫发育历期估测建模中有应用前景。     

新基因水稻 OsLSD1 的克隆及拟南芥和水稻类 LSD1 基因家族的生物信息学分析

类 LSD1 (LSD1-like) 基因家族是一类特殊的 C2C2 型锌指蛋白基因,编码植物特有的转录因子 . 目前已经研究的 2 个成员拟南芥 LSD1 (lesions stimulating disease resistance 1) 和 LOL1 (LSD-One-Like 1) 基因均参与植物细胞程序化死亡 (programmed cell death, PCD) 的调控 . 从水稻 cDNA 文库中克隆到 1 个类 LSD1 基因,命名为 OsLSD1. 该基因长 988 bp ,包含一个 432 bp 的开放阅读框,推导的氨基酸序列 (143 个氨基酸 ) 含有 3 个内部保守的锌指结构域 . DNA 印迹结果表明 OsLSD1 基因在水稻基因组中为单拷贝,且在根、茎和叶中表达 . 借助于生物信息学分析技术,从拟南芥和水稻数据库中各识别出 5 个和 7 个 ( 包括 OsLSD1) 类 LSD1 基因 . 分析了这些类 LSD1 基因的结构,蛋白质结构域组成 . 系统进化分析表明,无论基于编码区的核苷酸或氨基酸序列都可以将这些类 LSD1 基因分为 2 类 . 虽然不存在拟南芥或水稻特有的类 LSD1 蛋白,但有些结构域是水稻所特有的,也有些基因是来源于复制事件 .