基于不同分析和取样方法研究褪黑激素对育成期母貂行为分配的影响

本文基于不同分析和取样方法研究褪黑激素对育成期母貂行为分配的影响.从貂场中随机选取当年出生的6只未埋植褪黑激素、6只埋植褪黑激素的美国短毛黑母貂.采用高清摄像头分别在埋植褪黑激素后的第30天(Ⅰ期)、第60天(Ⅱ期)、第75天(Ⅲ期)记录所有水貂的行为.埋植褪黑激素对水貂行为分配的影响,混合线性模型(mixed lin...     

Predicting the subcellular localization of human proteins using machine learning and exploratory data analysis

Identifying the subcellular localization of proteins is particularly helpful in the functional annotation of gene products. In this study, we use Machine Learning and Exploratory Data Analysis （EDA） techniques to examine and characterize amino acid sequences of human proteins localized in nine cellular compartments. A dataset of 3,749 protein sequences representing human proteins was extracted from the SWISS-PROT database. Feature vectors were created to capture specific amino acid sequence characteristics. Relative to a Support Vector Machine, a Multi-layer Perceptron, and a Naive Bayes classifier, the C4.5 Decision Tree algorithm was the most consistent performer across all nine compartments in reliably predicting the subcellular localization of proteins based on their amino acid sequences （average Precision=0.88; average Sensitivity=0.86）. Furthermore, EDA graphics characterized essential features of proteins in each compartment. As examples, proteins localized to the plasma membrane had higher proportions of hydrophobic amino acids; cytoplasmic proteins had higher proportions of neutral amino acids; and mitochondrial proteins had higher proportions of neutral amino acids and lower proportions of polar amino acids. These data showed that the C4.5 classifier and EDA tools can be effective for characterizing and predicting the subcellular localization of human proteins based on their amino acid sequences.     

Growth performance,haematological assessment and chemical composition of Nile tilapia,Oreochromis niloticus (Linnaeus, 1758) fed different levels of Aloe vera extract as feed additives in a closed aquaculture system

A 105-day experimental trial was conducted to assess different levels of dietary Aleo vera extract supplementation on water quality parameters, proximate composition, growth performance and haematological parameters of fry Oreochromis niloticus. Four different percentages of dietary leaf extract powder of Aleo vera (ALE) with a basal feed, designated as, i.e., T0 (Control group; without ALE), T1 (1% ALE), T2 (2% ALE), and T3 (3% ALE). Fish fry was reared in concrete tanks (7.0 m, 1.6 m, 1.0: L, W, H; water volume 11.2 m3/tank), with an average initial weight 4.04 ± 0.03 g/ fry, and each treatment was triplicated. Fry was randomly distributed at a stocking rate of 450 individuals/ tanks. The water quality parameters revealed that temperature, pH, salinity, dissolved oxygen (DO) and nitrates were found in a promising range as given by FAO/WHO limits. However, the record values obtained for Electric Conductivity (EC), Total dissolved solids (TDS), and alkalinities were not found in all tanks' suitable range according to FAO/WHO limits. The results revealed a significant impact of different percentages of dietary ALE supplementation on fry's body composition and haematological parameters. Moreover, the final body weight, final body length, average daily weight gain (g), net weight gain (g) and specific growth rate (%) were significantly higher (p < 0.05) in T1 and T2 compared with T0 and T3 treatments. The poorest feed conversion ratio was recorded in the T2 group compared with other treatments. Thus, the current study provides information about the nutritional quality of Nile tilapia culturing in Pakistan.     

拽线法:一个构建系统发育树的新算法

这篇文章要讨论的拽线法（DL）是贪婪算法的一种。和Fitch—Margoliash（FM）一样,DL也是基于距离矩阵构建系统发育树,但是和FM算法相比,DL具有低复杂度、较高的容错性和准确度高的优点。当存在误差时,DL算法只是加大了不在同一个父节点下的基因序列的距离,但能够准确的判断序列的亲缘关系,进而得到完美的进化树拓扑结构;相比之下,FM算法让各个基因序列间的距离均摊了这种误差,从而有可能将本应该具有相同父节点的基因序列分到不同的分支。     

生命机体危险因子信息特征分析

旨在生命机体与危险因子间建立生命物质信息对话平台,以物质化的信息,或信息化的物质的思维进行理解和推论,建立物质承载信息,信息通过物质的形式传递的思维模式,对生命信息中危险因子的属性、分类以及生命机体对危险因子的识别方式进行分析,从以上两个方面不同角度阐述危险因子与生命机体机制信息的相关互动关系。在分析与解读的过程中,主张建立合乎自然生成逻辑的科学概念,而摒弃一些主观是非逻辑所推导建立起来的概念,还原生命物质和生命过程的本来生物学位置及性质;主张一元化的思考方式和整体性理解生命信息安全控制机制的核心部分。分析结果:就生命信息中危险因子的属性和分类进行了分析,指出危险因子具有生命信息属性和生命安全属性两个属性,依据危险因子物质来源的不同,分外源性和内源性两类,绘出危险因子来源分类汇总图;从机体方面对危险因子识别方式不同,分为PRR识别方式和抗原(样)受体识别方式两类,并绘出危险因子识别方式分类汇总图。综合危险因子的属性、分类和生命机体对危险因子的识别方式,绘制出危险因子与生命机体机制信息的相关互动图,从保守性结构信息、生理产物信息、对机体损伤信息、代谢通路信息、对机体变应信息、精细纯外观构象信息及内在性核心信息7个方面对两者的相互关系进行了引证和分析,进一步阐释了生命信息安全控制机制。     

生物信息学软件研究的可视化分析

以Web of Science数据库为数据来源,利用Cite Space和UCINET软件对发表在Nucleic Acids Research期刊上有关生物信息学软件研究的文献做了可视化分析,揭示了该领域的研究力量、作者团队与高被引作者、知识基础、期刊分布、研究热点与前沿,为生物信息学软件的研究和发展提供必要的参考依据。     

Meta‐Analysis of Diagnostic Test Accuracy Studies with Multiple Thresholds using Survival Methods

Diagnostic tests play an important role in clinical practice. The objective of a diagnostic test accuracy study is to compare an experimental diagnostic test with a reference standard. The majority of these studies dichotomize test results into two categories: negative and positive. But often the underlying test results may be categorized into more than two, ordered, categories. This article concerns the situation where multiple studies have evaluated the same diagnostic test with the same multiple thresholds in a population of non‐diseased and diseased individuals. Recently, bivariate meta‐analysis has been proposed for the pooling of sensitivity and specificity, which are likely to be negatively correlated within studies. These ideas have been extended to the situation of diagnostic tests with multiple thresholds, leading to a multinomial model with multivariate normal between‐study variation. This approach is efficient, but computer‐intensive and its convergence is highly dependent on starting values. Moreover, monotonicity of the sensitivities/specificities for increasing thresholds is not guaranteed. Here, we propose a Poisson‐correlated gamma frailty model, previously applied to a seemingly quite different situation, meta‐analysis of paired survival curves. Since the approach is based on hazards, it guarantees monotonicity of the sensitivities/specificities for increasing thresholds. The approach is less efficient than the multinomial/normal approach. On the other hand, the Poisson‐correlated gamma frailty model makes no assumptions on the relationship between sensitivity and specificity, gives consistent results, appears to be quite robust against different between‐study variation models, and is computationally very fast and reliable with regard to the overall sensitivities/specificities.     

Diversity and correlation of specific aromatic hydrocarbon biodegradation capabilities

This work investigated the biodegradation capabilitiesof indigenous microorganisms exposed to differentcombinations of aromatic hydrocarbons. Considerablediversity was found in the catabolic specificity of 55strains. Toluene was the most commonly degradedcompound, followed by p-xylene, m-xyleneand ethylbenzene. Strains capable of degradingo-xylene and benzene, which were theleast-frequently-degraded compounds, exhibited broaderbiodegradation capabilities. Kappa statistics showeda significant correlation between the abilities todegrade toluene and ethylbenzene, p-xylene andm-xylene, and p-xylene and o-xylene. The ability to degrade naphthalene was correlated tothe ability to degrade other alkylbenzenes, but notbenzene. In addition, the inability to degradebenzene was correlated to the inability to degradeo-xylene. Factorial analysis of variance showedthat biodegradation capabilities were generallybroader when aromatic hydrocarbons were fed asmixtures than when fed separately. Beneficialsubstrate interactions included enhanced degradationof benzene, p-xylene, and naphthalene whentoluene was present, and enhanced degradation ofnaphthalene by ethylbenzene. Such heuristicrelationships may be useful to predict biodegradationpatterns when bacteria are exposed to differentaromatic hydrocarbon mixtures.     

Predictive margins with survey data

In the analysis of covariance, the display of adjusted treatment means allows one to compare mean (treatment) group outcomes controlling for different covariate distributions in the groups. Predictive margins are a generalization of adjusted treatment means to nonlinear models. The predictive margin for group r represents the average predicted response if everyone in the sample had been in group r. This paper discusses the use of predictive margins with complex survey data, where an important consideration is the choice of covariate distribution used to standardize the predictive margin. It is suggested that the textbook formula for the standard error of an adjusted treatment mean from the analysis of covariance may be inappropriate for applications involving survey data. Applications are given using data from the 1992 National Health Interview Survey (NHIS) and the Epidemiologic Followup Study to the first National Health and Nutrition Examination Survey (NHANES I).