Virus-PLoc: a fusion classifier for predicting the subcellular localization of viral proteins within host and virus-infected cells

Viruses can reproduce their progenies only within a host cell, and their actions depend both on its destructive tendencies toward a specific host cell and on environmental conditions. Therefore, knowledge of the subcellular localization of viral proteins in a host cell or virus-infected cell is very useful for in-depth studying of their functions and mechanisms as well as designing antiviral drugs. An analysis on the Swiss-Prot database (version 50.0, released on May 30, 2006) indicates that only 23.5% of viral protein entries are annotated for their subcellular locations in this regard. As for the gene ontology database, the corresponding percentage is 23.8%. Such a gap calls for the development of high throughput tools for timely annotating the localization of viral proteins within host and virus-infected cells. In this article, a predictor called "Virus-PLoc" has been developed that is featured by fusing many basic classifiers with each engineered according to the K-nearest neighbor rule. The overall jackknife success rate obtained by Virus-PLoc in identifying the subcellular compartments of viral proteins was 80% for a benchmark dataset in which none of proteins has more than 25% sequence identity to any other in a same location site. Virus-PLoc will be freely available as a web-server at http://202.120.37.186/bioinf/virus for the public usage. Furthermore, Virus-PLoc has been used to provide large-scale predictions of all viral protein entries in Swiss-Prot database that do not have subcellular location annotations or are annotated as being uncertain. The results thus obtained have been deposited in a downloadable file prepared with Microsoft Excel and named "Tab_Virus-PLoc.xls." This file is available at the same website and will be updated twice a year to include the new entries of viral proteins and reflect the continuous development of Virus-PLoc.     

南方主要鲜食葡萄生长结实特性与温度关系的研究

对南方鲜食葡萄品种生长结实特性与温度关系的研究表明,各品种春梢生长与气温,尤其是有效积温的关系符合Y=aX³+bX²+cX三次方程.依据拟合方程得:生长速率最快的是巨峰,当有效积温达190.68℃时,温度每增加10℃新梢生长0.43433cm;生长速率最慢且对温度不敏感的是蜜香宝,有效积温高达367.19℃时,新梢生长速率仅为0.17150cm.对果实经济性状综合评价和品种优良特性的模糊评判表明,值得推广的品种有巨峰、京亚、藤稔、蜜香宝、龙宝;限制发展的品种有紫珍香;淘汰早生高墨、黑丰.     

青海湖裸鲤体腔寄生蠕虫群落研究

青海湖裸鲤体腔为三种寄生蠕虫寄生,即裂头绦虫裂头蚴,舌状绦虫裂头蚴和对盲囊缄虫的三期幼虫,这几种蠕虫不同大小的青海湖裸鲤中数量差异很大,将其看作一个组分群落,并根据其发生数量进行模糊聚类和极点排序,结果将其分为二个亚组分群落,即体长小于１５０ｍｍ的青海湖裸鲤体腔蠕虫亚组分群落和体长大于１５０ｍｍ的个体的体腔蠕虫亚组分群落。这与宿主的行为和食性改变有密切关系,舌状绦虫与另两种蠕虫间有显著负协调关系。     

模糊C－均值聚类和TWINSPAN分类的比较研究

以英国威尔士北部Ｓｎｏｗｄｏｎｉａ山地Ａｂｅｒ山谷植被为例,对模糊ｃ－均值聚类和ＴＷＩＮＳＰＡＮ分类进行了应用和比较研究。两种方法的结果一致。模糊ｃ－均值聚类结果给出样地和植被类型间的隶属程度,在一定程度上优于ＴＷＩＮＳＰＡＮ。     

异源蛋白质相互作用数据整合算法的进展

蛋白质相互作用在生物学过程和细胞功能行使中起核心作用。高通量技术的应用结合计算机预测方法的发展,使得直接和间接来源的蛋白质相互作用数据得到了大规模的增加。如何系统地整合这些数据并从中提取有用的信息是一项挑战,这也促使了许多整合算法应运而生。本文综述了八种整合蛋白质相互作用数据源的方法：投票、支持向量机、朴素贝叶斯、逻辑斯蒂回归、决策树、随机森林、基于随机森林的k-近邻法以及混合属性分类等方法。     

Establishment of a five‐enzalutamide‐resistance‐related‐gene‐based classifier for recurrence‐free survival predicting of prostate cancer

To identify prostate cancer (PCa) patients with a high risk of recurrence is critical before delivering adjuvant treatment. We developed a classifier based on the Enzalutamide treatment resistance‐related genes to assist the currently available staging system in predicting the recurrence‐free survival (RFS) prognosis of PCa patients. We overlapped the DEGs from two datasets to obtain a more convincing Enzalutamide‐resistance‐related‐gene (ERRG) cluster. The five‐ERRG‐based classifier obtained good predictive values in both the training and validation cohorts. The classifier precisely predicted RFS of patients in four cohorts, independent of patient age, pathological tumour stage, Gleason score and PSA levels. The classifier and the clinicopathological factors were combined to construct a nomogram, which had an increased predictive accuracy than that of each variable alone. Besides, we also compared the differences between high‐ and low‐risk subgroups and found their differences were enriched in cancer progression‐related pathways. The five‐ERRG‐based classifier is a practical and reliable predictor, which adds value to the existing staging system for predicting the RFS prognosis of PCa after radical prostatectomy, enabling physicians to make more informed treatment decisions concerning adjuvant therapy.     

FAHP ranking and selection of pretreatment module for membrane separation processes in textile cluster

Recent development in membrane manufacturing and extensive application of membranes in effluent treatment has opened up a new water resource. The effluent pretreatment module plays a critical role in membrane performance. Appropriate selection of conventional and advanced pretreatment modules in membrane separation processes (MSP) is significant to the success of zero effluent discharge (ZED). This study addresses performance assessment of eight conventional and advanced pretreatment modules implemented for wastewater management in a textile cluster in South India. The comparative pollutant reduction, capital, operation and maintenance (OM) cost of pretreatment modules are discussed. The ranking and interdependence of the pretreatment modules were analyzed through fuzzy analytical hierarchy process (FAHP) with MATLAB software.The pretreatment module IV ranked third with a composite weight of 15.46%. The integrated study of performance assessment and FAHP resulted in an optimum pretreatment module IV comprising the sequence of chemical precipitation, bio-oxidation processes (activated sludge processes) followed by chemical precipitation, to achieve the ZED. This study provides a techno-economically feasible solution for selection of an effective pretreatment module for MSP in the textile cluster.     

植物栽培区划的模糊数学模型－－以油橄榄（Olea europaea L.）为例

本文根据植物引种栽培工作的特点与经验,提出了一个多层次结构综合评判的模糊数学模型。以油橄榄为例,运用该模型计算结果作出了湖北省油橄榄宜林地区划图。对于不同植物,只需要修改其中评判因素及权重分配值进行计算。我们认为,模糊数学的综合评判方法为植物引种栽培区域的选择提供了一种有效的工具。     

模糊综合评判在血液流变学指标归类判定中的应用研究

首次将模糊综合评判方法,应用到在脑血管疾病诊断中颇为重要的血液流变学指标的自动归类判别中．本文引入了模糊隶属函数,并在此基础上形成单因素评价矩阵．还讨论了模糊综合评判的失效问题,给出了失效后的处理方法．实验结果表明,提出的方法是快速而有效的．