Identification of Plant Resistance Gene with Random Forest

为了解决传统基于同源序列比对的抗性基因识别方法中假阳性高、无法发现新的抗性基因的问题,提出了一种利用随机森林分类器和k-MEAnS聚类降采样方法的抗性基因识别算法。针对目前研究工作中挖掘盲目性大的问题,进行两点改进:引入了随机森林分类器和188维组合特征来进行抗性基因识别,这种基于样本统计学习的方法能够有效地捕捉抗性基因内在特性;对于训练过程中存在的严重类别不平衡现象,使用基于聚类的降采样方法得到了更具代表性的训练集,进一步降低了识别误差。实验结果表明,该算法可以有效地进行抗性基因的识别工作,能够对现有实验验证数据进行准确的分类,并在反例集上也获得了较高的精度。The traditional homology sequence alignment based approaches usually have high false positive rate and consequently new resistance genes are difficult to be identified.This paper presents a resistance gene identification approach by applying random forest classifier and K-Means under-sampling method.In order to solve the aimless problem in gene-mining research,two main contributions are provided.Firstly,it introduces random forest and 188 dimension features to identify resistance genes,accordingly the sample statistic learning approach can efficiently capture the internal characteristic of resistance genes.Secondly,it selects a more representative training subset and reduces the identification errors for solving the serious imbalanced classification during the training process.The experimental results indicate that the approach can efficiently identify the resistance genes,not only precisely clas-sifying the existing experimental verified data,but also obtaining high accuracy on the negative sample dataset.国家自然科学基金(60932008;61172098;60871092;61001013);中央高校基本科研业务费专项资金(HIT.ICRST.2010022);高等学校博士学科点专项科研基金(201003446)---

不同季节垃圾填埋场周围重金属污染特征及评价

采集漳州市某生活垃圾填埋场周围冬夏两季的地表水(包括渗滤液)、土壤以及植物样品,分析9种重金属总量的季节性变化.结果表明,冬季地表水中Zn和As,土壤和植物中的Cd和As的含量均大于夏季,而夏季波动性强于冬季;夏季渗滤液中总Hg的含量明显高于冬季,其余变化不明显.地表水重金属含量均低于地表水环境质量Ⅴ类标准限值,渗滤液中所有重金属含量均低于污水排放二级标准限值.土壤中重金属Cd、As和Pb的污染最重,已达到重度污染,冬季的污染明显高于夏季.冬季植物富集能力强于夏季,细叶芒对Cd的富集能力较强,可作为重金属污染修复的先锋植物.环保公益性行业科研专项(201509054)资助~

Text mining in bioinformatics

从两个角度讨论应用于生物信息学中的文本挖掘方法。以搜索生物知识为目标,利用文本挖掘方法进行文献检索,进而构建相关数据库,如在PubMEd中挖掘蛋白质相互作用和基因疾病关系等知识。总结了可以应用文本挖掘技术的生物信息学问题,如蛋白质结构与功能的分析。探讨了文本挖掘研究者可以探索的生物信息学领域,以便更多的文本挖掘研究者可以将相关成果应用于生物信息学的研究中。Text mining methods in bioinformatics are discussed from two views.First,three problems are reviewed including searching biology knowledge,retrieving the reference by text mining method and reconstructing databases.For example,protein-protein interaction and gene-disease relationship can be mined from PubMed.Then the bioinformatics applications of text mining are concluded,such as protein structure and function prediction.At last,more methods and applications are discussed for helping text mining researchers to do more contribution in bioinformatics.国家自然科学基金项目(61001013、60932008、61001143

酶光耦合催化系统转化co2研究进展

地球上的能量主要来源于太阳光辐射,绿色植物及微生物通过光合作用吸收光能,经过光电催化过程和多酶催化过程,将CO_2转化为碳水化合物.基于仿生思想,模拟光合作用中酶光协同催化过程,构建酶光耦合系统,利用酶催化过程进行CO_2转化,利用光催化过程提供能量及电子,协调优化酶催化和光催化过程,实现CO_2高效绿色转化,可有效调节因化石燃料过度使用引起的碳循环失衡.酶光耦合催化系统构建过程简便,催化产物种类可控,为生物催化在化工、能源、环境等领域的应用提供了范例.本文从单酶催化和多酶催化角度分别介绍了两类酶催化系统转化CO_2的研究现状,从电子传递角度介绍了辅酶依赖型和辅酶非依赖型酶光耦合催化系统的研究进展.最后,对本领域发展现状和趋势进行了简要总结和展望

酶光耦合催化系统转化CO_2研究进展

地球上的能量主要来源于太阳光辐射,绿色植物及微生物通过光合作用吸收光能,经过光电催化过程和多酶催化过程,将CO_2转化为碳水化合物.基于仿生思想,模拟光合作用中酶光协同催化过程,构建酶光耦合系统,利用酶催化过程进行CO_2转化,利用光催化过程提供能量及电子,协调优化酶催化和光催化过程,实现CO_2高效绿色转化,可有效调节因化石燃料过度使用引起的碳循环失衡.酶光耦合催化系统构建过程简便,催化产物种类可控,为生物催化在化工、能源、环境等领域的应用提供了范例.本文从单酶催化和多酶催化角度分别介绍了两类酶催化系统转化CO_2的研究现状,从电子传递角度介绍了辅酶依赖型和辅酶非依赖型酶光耦合催化系统的研究进展.最后,对本领域发展现状和趋势进行了简要总结和展望

JUNO Sensitivity on Proton Decay p→νˉK+p\to \bar\nu K^+ Searches

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in $p\to \bar\nu K^+$ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar\nu K^+$ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is $9.6 \times 10^{33}$ years, competitive with the current best limits on the proton lifetime in this channel

JUNO sensitivity on proton decay p → ν K + searches*

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in the $p\to \bar{\nu} K^+$ mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar{\nu} K^+$ is 36.9% ± 4.9% with a background level of $0.2\pm 0.05({\rm syst})\pm$$0.2({\rm stat})$ events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is $9.6 \times 10^{33}$ years, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies