北京松山自然保护区森林群落物种多样性及其神经网络预测

物种多样性是群落结构和功能复杂性的一种度量,物种多样性的空间分布格局受许多环境因子的影响.运用多样性指数,多层感知器网络,分析了松山保护区森林群落物种多样性与群落类型、结构和生境之间的关系.结果表明:(1)大果榆+山杨混交林、油松+青杨混交林物种丰富度、多样性和均匀度均较高,而大果榆林、华北落叶松林的各项指数值均较低.Patrick指数和Shannon-Weiner指数在森林群落中均表现为草本层＞灌木层＞乔木层;Pielou指数在榆林中表现为草本层＞乔木层＞灌木层,而在其他森林群落中表现为灌木层＞草本层＞乔木层.(2)功能层物种多样性在海拔梯度上的变化趋势不同,在乔木层,丰富度、多样性和均匀度随海拔的升高逐渐降低;在灌木层,丰富度、多样性和均匀度均呈比较明显的单峰曲线变化趋势;在草本层,丰富度和多样性随海拔的升高都呈下降趋势,而在草本层,均匀度变化不大.(3)用多层感知器网络预测功能层多样性效果很好,结果发现坡向对乔木层和灌木层物种多样性的影响最大,而海拔高度对草本层物种多样性的影响最大.     

L-缬氨酸发酵培养基的神经网络建模与遗传算法优化

运用神经网络对L-缬氨酸发酵培养基组成进行建模,在神经网络模型的基础上采用遗传算法对培养基组成进行优化,得到最佳发酵培养基组成.结果表明,运用神经网络并结合遗传算法是一种行之有效的优化方法.按最佳发酵培养基组成进行发酵实验64h,可在发酵液中积累L-缬氨酸28.5g/L.     

Poly (l-lysine) based semi-interpenetrating polymer network as pH-responsive hydrogel for controlled release of a model protein drug streptokinase

With the aim of developing a pH-sensitive controlled drug release system, a poly (L-lysine) (PLL) based cationic semi-interpenetrating polymer network (semi-IPN) has been synthesized. This cationic hydrogel was designed to swell at lower pH and de-swell at higher pH and therefore be applicable for achieving regulated drug release at a specific pH range. In addition to the pH sensitivity, this hydrogel was anticipated to interact with an ionic drug, providing another means to regulate the release rate of ionic drugs. This semi-IPN hydrogel was prepared using a free-radical polymerization method and by crosslinking of the polyethylene glycol (PEG)-methacrylate polymer through the PLL network. The two polymers were penetrated with each other via interpolymer complexation to yield the semi-IPN structures. The PLL hydrogel thus prepared showed dynamic swelling/de-swelling behavior in response to pH change, and such a behavior was influenced by both the concentrations of PLL and PEG-methacrylate. Drug release from this semi-IPN hydrogel was also investigated using a model protein drug, streptokinase. Streptokinase release was found to be dependent on its ionic interaction with the PLL backbones as well as on the swelling of the semi-IPN hydrogel. These results suggest that a PLL semi-IPN hydrogel could potentially be used as a drug delivery platform to modulate drug release by pH-sensitivity and ionic interaction.     

人类蛋白质相互作用组

高通量酵母双杂交与免疫亲和纯化技术的快速发展和日臻成熟,使得在蛋白质组水平上大规模地研究蛋白质之间的相互作用成为可能。目前,人类蛋白质互作网络在细胞、组织、器官乃至整个个体水平的研究已经陆续展开。蛋白质互作网络中蛋白质数量也由少数几个向整个蛋白质组扩展。同时,功能、疾病、生态等相关的蛋白质互作网络研究也取得了一定的成果。然而,人类的蛋白质互作网络研究正面临着一些问题和挑战。本文综述了人类蛋白质互作网络的研究方法、研究进展以及面临的挑战,同时指出了人类蛋白质互作网络研究的方向和目标。     

细菌群体感应信号网络及其应用

群体感应（Quorum sensing,QS）是一种细菌细胞与细胞间的通讯系统,即细菌通过分泌扩散性小分子信号感知细菌群体的密度,从而引起一组特定基因在转录水平协调表达。大量研究已表明,群体感应系统控制细菌多种生理行为和过程,以及与真核宿主（寄主）的互作。参与群体感应调控的信号分子多种多样,QS系统所调控的功能也具有多样性,甚至菌株专化性。通过聚焦同一细菌中由多个QS系统组成的信号网络,综合评述了不同QS系统之间如何相互作用全局调控基因表达,以及QS系统如何通过与其它全局调控系统整合精细调节细菌的社会行为以及环境适应性及其应用前景。     

生物网络研究进展述评

生物网络是一类典型的复杂适应性系统,包含了许多个体的多层次的各种相互作用和关系,在过去的十年里,利用复杂网络理论对生物网络进行研究引起了人们的注意并获得了快速发展.本文首先从从度分布、聚类系数及鲁棒性等角度对现阶段生物网络性质的研究进行了简要介绍,后进一步对生物网络的聚类算法及主要建模理论做出了概括.今后的研究趋势在于如何建立合理的生物网络模型,以深入研究生物网络的各种性质.     

2-D DIGE analysis of UV-C radiation-responsive proteins in globe artichoke leaves

Plants respond to ultraviolet stress inducing a self-defence through the regulation of specific gene family members. The UV acclimation is the result of biochemical and physiological processes, such as enhancement of the antioxidant enzymatic system and accumulation of UV-absorbing phenolic compounds (e.g. flavonoids). Globe artichoke is an attractive species for studying the protein network involved in UV stress response, being characterized by remarkable levels of inducible antioxidants. Proteomic tools can assist the evaluation of the expression patterns of UV-responsive proteins and we applied the difference in-gel electrophoresis (DIGE) technology for monitoring the globe artichoke proteome variation at four time points following an acute UV-C exposure. A total of 145 UV-C-modulated proteins were observed and 119 were identified by LC-MS/MS using a ～144,000 customized Compositae protein database, which included about 19,000 globe artichoke unigenes. Proteins were Gene Ontology (GO) categorized, visualized on their pathways and their behaviour was discussed. A predicted protein interaction network was produced and highly connected hub-like proteins were highlighted. Most of the proteins differentially modulated were chloroplast located, involved in photosynthesis, sugar metabolisms, protein folding and abiotic stress. The identification of UV-C-responsive proteins may contribute to shed light on the molecular mechanisms underlying plant responses to UV stress.     

基于BP神经网络模型的全球森林土壤异养硝化过程N2O排放通量估算

N₂O作为重要的温室气体之一,对地球和人类都有很大的影响。为了深入探究对有机氮异养硝化作用及其产生N₂O过程的影响机制,完善全球N₂O通量估算模型,本研究采用Pearson相关性分析与广义可加模型(GAM)对全球135个样点有机氮异养硝化速率及其产生N₂O速率的影响因子进行分析,然后将主要影响因子作为BP神经网络的输入层来模拟全球森林土壤有机氮异养硝化速率及其产生N₂O速率的空间分布。结果显示,土壤pH和土壤C/N是影响有机氮异养硝化速率的主要因素,土壤C/N、土壤孔隙含水量(WFPS)以及土壤温度是影响有机氮异养硝化产生N₂O速率的主要因素。全球森林土壤异养硝化速率平均为0.4241(0.0014～0.689)μg N·g^-1·d^-1,异养硝化产生N₂O速率平均为0.2936(0.21～1.103)μg N₂O·kg^-1·d^-1     

LncRNA and mRNA integration network reconstruction reveals novel key regulators in esophageal squamous-cell carcinoma

Many experimental and computational studies have identified key protein coding genes in initiation and progression of esophageal squamous cell carcinoma (ESCC). However, the number of researches that tried to reveal the role of long non-coding RNAs (lncRNAs) in ESCC has been limited. LncRNAs are one of the important regulators of cancers which are transcribed dominantly in the genome and in various conditions. The main goal of this study was to use a systems biology approach to predict novel lncRNAs as well as protein coding genes associated with ESCC and assess their prognostic values. By using microarray expression data for mRNAs and lncRNAs from a large number of ESCC patients, we utilized “Weighted Gene Co-expression Network Analysis” (WGCNA) method to make a big coding-non-coding gene co-expression network, and discovered important functional modules. Gene set enrichment and pathway analysis revealed major biological processes and pathways involved in these modules. After selecting some protein coding genes involved in biological processes and pathways related to cancer, we used “LncTar”, a computational tool to predict potential interactions between these genes and lncRNAs. By combining interaction results with Pearson correlations, we introduced some novel lncRNAs with putative key regulatory roles in the network. Survival analysis with Kaplan-Meier estimator and Log-rank test statistic confirmed that most of the introduced genes are associated with poor prognosis in ESCC. Overall, our study reveals novel protein coding genes and lncRNAs associated with ESCC, along with their predicted interactions. Based on the promising results of survival analysis, these genes can be used as good estimators of patients' survival, or even can be analyzed further as new potential signatures or targets for the therapy of ESCC disease.