微生物在水环境污染物降解中的应用

每年有大量来自工业、农业、养殖业和城市污水处理厂的废水被排入到水环境中,因此,地球上的水环境面临大量来自生活废水、工农业废水、非法排放的废水及其它废水的污染物质(如抗生素、杀虫剂,除草剂、烃等)的严重挑战,特别是近年来随着集约化养殖的发展,废水污染问题日益突出,并且随着分析手段的进步,能够检测到被排入水环境中的化学污染物质也越来越多,这些化学污染物对水环境中的生物产生有害影响.但是,微生物在污染控制上具有许多重要的作用.因此,本文对微生物在水环境污染物降解中的应用进行了评论.结果表明微生物主要是应用在水产养殖水中,而在其它的水体系(如河、湖、海)的应用较少.     

Effects of environmental stressors on stem cells

Environmental toxicants are ubiquitous,and many are known to cause harmful health effects.However,much of what we know or think we know concerning the targets and long-term effects of exposure to environmental stressors is sadly lacking.Toxicant exposure may have health effects that are currently mischaracterized or at least mechanistically incompletely understood.While much of the recent excitement about stem cells(SCs)focuses on their potential as therapeutic agents,they also offer a valuable resource to give us insight into the mechanisms and risks of toxicant effects.Not only as a response to the increasing ethical pressure to reduce animal testing,SC studies allow us valuable insight into the true effects of human exposure to environmental stressors under controlled conditions.We present a review of the history of publications on the effects of environmental stressors on SCs,followed by a consolidation of the literature over the past five years on a subset of key environmental stressors of importance to human health and their effects on both embryonic and tissue SCs.The review will make constructive suggestions as to areas of toxicant research where further studies are needed,as well as making indications of the potential utility for advancing knowledge and directing research on environmental toxicology.     

中国栅蛛属2新种记述(蜘蛛目:栅蛛科)

本文记了分别采自云南高黎贡山的栅蛛科栅蛛属Hahnia 2新种:垭口栅蛛,新种S.yakouensis sp.nov.和肾形栅蛛,新种S.reniformis sp.nov..垭口栅蛛后眼列前曲,交媾腔大,扁圆形,交媾孔1个,位于交媾腔下缘,交媾管粗,呈"人"字形下行分成2支再向两侧扭曲.纳精囊有一肓管斜向上伸出,鉴于上述特征而与Hahnia mridulae Tikader,1970不同.肾形栅蛛交媾孔2个,位于生殖厣腹面中央,纳精囊1对,大,肾形,插入器始于生殖球左下方,鉴于上述特征而与Hahnia xinjiangensis Wang et Liang,1989不同. Abstract： The present paper deals with two new species of the genus Hahnia collected from the Gaoligong Mountains Region of Yunnan Province, China: Hahnia yakouensis sp. nov., Hahnia reniformis sp. nov..     

注重发挥博士后科研生力军作用

中国医科大学博士后科研流动站始建1995年。目前学校设有基础医学、临床医学和生物学3个博士后科研流动站。但由于每年国家财力有限,资助名额很少,在有限条件下建立、健全博士后制度,加强对博士后人员的培养,调动博士后人员的积极性是一项重要的研究课题。     

走进内蒙

2015年8月1日,我们每一个人收拾好行囊,整装待发,在邓涛老师带领下踏上了去往内蒙古的征程,拉开了探索灰色生命之旅的序幕. 队伍由两辆车、8人组成,我们早晨7点从中国科学院古脊椎动物与古人类研究所出发,披着清晨的阳光,摆脱城市的拥挤,缓缓地“逃离”了北京城.直到抵达张北之后我们的车子才能像矫健的马儿一样驰骋在广袤的公路上,沿途没有了城市的喧闹,没有了交通的堵塞,伴随我们的只有那一眼望去蓝蓝的天空、随风摆动的小草,以及那迷人的风景,偶尔打开车窗,一股凉爽的清风扑面而来,沁人心脾.经过一路的颠簸,我们在傍晚时刻到达锡林浩特,当地文物保护站的同志为我们安顿好了一切.     

例谈2007年高考"现代生物科技专题"

通过现代生物科技专题及相关高考试题的介绍与分析,以开拓学生视野,增强科技意识,激发学生探索生命奥妙和热爱生物科学的情感,为进一步学习现代生物学奠定基础.同时,借助现代生物科技专题作试题背景,具有能力的开放性和情境的新颖性,也有利于考查和培养学生独立获取新知识、收集和处理信息的能力.所选试题涉及现代生物科技前沿的领域有:基因工程、细胞工程、蛋白质工程、癌症和克隆技术等,均体现出对现代生物技术发展的关注.     

Formidable females redux:male social integration into female networks and the value of dynamic multilayer networks

The development of multilayer network techniques is a boon for researchers who wish to understand how different interaction layers might influence each other,and how these in turn might influence group dynamics.Here,we investigate how integration between male and female grooming and aggression interaction networks influences male power trajectories in vervet monkeys Chlorocebus pygerythrus.Our previous analyses of this phenomenon used a monolayer approach,and our aim here is to extend these analyses using a dynamic multilayer approach.To do so,we constructed a temporal series of male and female interaction layers.We then used a multivariate multilevel autoregression model to compare cross-lagged associations between a male's centrality in the female grooming layer and changes in male Elo ratings.Our results confirmed our original findings:changes in male centrality within the female grooming network were weakly but positively tied to changes in their Elo ratings.However,the multilayer network approach offered additional insights into this social process,identifying how changes in a male's centrality cascade through the other network layers.This dynamic view indicates that the changes in Elo ratings are likely to be short-lived,but that male centrality within the female network had a much stronger impact throughout the multilayer network as a whole,especially on reducing intermale aggression(i.e.,aggression directed by males toward other males).We suggest that multilayer social network approaches can take advantage of increased amounts of social data that are more commonly collected these days,using a variety of methods.Such data are inherently multilevel and multilayered,and thus offer the ability to quantify more precisely the dynamics of animal social behaviors.     

Plant infection by two different viruses induce contrasting changes of vectors fitness and behavior

Insect-vectored plant viruses can induce changes in plant phenotypes,thus influencing plant-vector interactions in a way that may promote their dispersal according to their mode of transmission (i.e.,circulative vs.noncirculative).This indirect vector manipulation requires host-virus-vector coevolution and would thus be effective solely in very specific plant-virus-vector species associations.Some studies suggest this manipulation may depend on multiple factors relative to various intrinsic characteristics of vectors such as transmission efficiency.In anintegrative study,we tested the effects of infection of the Brassicaceae Camelina sativa with the noncirculative Cauliflower mosaic virus (CaMV)or the circulative Turnip yellows virus (TuYV)on the host-plant colonization of two aphid species differing in their virus transmission efficiency:the polyphagous Myzus persicae,efficient vector of both viruses,and the Brassicaceae specialist Brevicoryne brassicae,poor vector of TuYV and efficient vector of CaMV.Results confirmed the important role of virus mode of transmission as plant-mediated effects of CaMV on the two aphid species induced negative alterations of feeding behavior (i.e.,decreased phloem sap ingestion)and performance that were both conducive for virus fitness by promoting dispersion after a rapid acquisition.In addition,virus transmission efficiency may also play a role in vector manipulation by viruses as only the responses of the efficient vector to plant-mediated effects of TuYV,that is,enhanced feeding behavior and performances,were favorable to their acquisition and further dispersal.Altogether,this work demonstrated that vector transmission efficiency also has to be considered when studying the mechanisms underlying vector manipulation by viruses.Our results also re- inforce the idea that vector manipulation requires coevolution between plant,virus and vector.     

Stem cell therapy applied for digestive anastomosis: Current state and future perspectives

BACKGROUND Digestive tract resections are usually followed by an anastomosis.Anastomotic leakage,normally due to failed healing,is the most feared complication in digestive surgery because it is associated with high morbidity and mortality.Despite technical and technological advances and focused research,its rates have remained almost unchanged the last decades.In the last two decades,stem cells(SCs)have been shown to enhance healing in animal and human studies;hence,SCs have emerged since 2008 as an alternative to improve anastomoses outcomes.AIM To summarise the published knowledge of SC utilisation as a preventative tool for hollow digestive viscera anastomotic or suture leaks.METHODS PubMed,Science Direct,Scopus and Cochrane searches were performed using the key words“anastomosis”,“colorectal/colonic anastomoses”,“anastomotic leak”,“stem cells”,“progenitor cells”,“cellular therapy”and“cell therapy”in order to identify relevant articles published in English and Spanish during the years of 2000 to 2021.Studies employing SCs,performing digestive anastomoses in hollow viscera or digestive perforation sutures and monitoring healing were finally included.Reference lists from the selected articles were reviewed to identify additional pertinent articles.METHODS Given the great variability in the study designs,anastomotic models,interventions(SCs,doses and vehicles)and outcome measures,performing a reliable meta-analysis was considered impossible,so we present the studies,their results and limitations.RESULTS Eighteen preclinical studies and three review papers were identified;no clinical studies have been published and there are no registered clinical trials.Experimental studies,mainly in rat and porcine models and occasionally in very adverse conditions such as ischaemia or colitis,have been demonstrated SCs as safe and have shown some encouraging morphological,functional and even clinical results.Mesenchymal SCs are mostly employed,and delivery routes are mainly local injections and cell sheets followed by biosutures(sutures coated by SCs)or purely topical.As potential weaknesses,animal models need to be improved to make them more comparable and equivalent to clinical practice,and the SC isolation processes need to be standardised.There is notable heterogeneity in the studies,making them difficult to compare.Further investigations are needed to establish the indications,the administration system,potential adjuvants,the final efficacy and to confirm safety and exclude definitively oncological concerns.CONCLUSION The future role of SC therapy to induce healing processes in digestive anastomoses/sutures still needs to be determined and seems to be currently far from clinical use.     

Increased monoamine oxidase activity and imidazoline binding sites in insulin-resistant adipocytes from obese Zucker rats

BACKGROUND Despite overt insulin resistance,adipocytes of genetically obese Zucker rats accumulate the excess of calorie intake in the form of lipids.AIM To investigate whether factors can replace or reinforce insulin lipogenic action by exploring glucose uptake activation by hydrogen peroxide,since it is produced by monoamine oxidase(MAO)and semicarbazide-sensitive amine oxidase(SSAO)in adipocytes.METHODS 3H-2-deoxyglucose uptake(2-DG)was determined in adipocytes from obese and lean rats in response to insulin or MAO and SSAO substrates such as tyramine and benzylamine.14C-tyramine oxidation and binding of imidazolinic radioligands[3H-Idazoxan,3H-(2-benzofuranyl)-2-imidazoline]were studied in adipocytes,the liver,and muscle.The influence of in vivo administration of tyramine+vanadium on glucose handling was assessed in lean and obese rats.RESULTS 2-DG uptake and lipogenesis stimulation by insulin were dampened in adipocytes from obese rats,when compared to their lean littermates.Tyramine and benzylamine activation of hexose uptake was vanadate-dependent and was also limited,while MAO was increased and SSAO decreased.These changes were adipocyte-specific and accompanied by a greater number of imidazoline I2 binding sites in the obese rat,when compared to the lean.In vitro,tyramine precluded the binding to I2 sites,while in vivo,its administration together with vanadium lowered fasting plasma levels of glucose and triacylglycerols in obese CONCLUSION The adipocytes from obese Zucker rats exhibit increased MAO activity and imidazoline binding site number.However,probably as a consequence of SSAO down-regulation,the glucose transport stimulation by tyramine is decreased as much as that of insulin in these insulin-resistant adipocytes.The adipocyte amine oxidases deserve more studies with respect to their putative contribution to the management of glucose and lipid handling.     

Oxypred: Prediction and Classification of Oxygen-Binding Proteins

This study describes a method for predicting and classifying oxygen-binding pro- teins. Firstly, support vector machine (SVM) modules were developed using amino acid composition and dipeptide composition for predicting oxygen-binding pro- teins, and achieved maximum accuracy of 85.5% and 87.8%, respectively. Sec- ondly, an SVM module was developed based on amino acid composition, classify- ing the predicted oxygen-binding proteins into six classes with accuracy of 95.8%, 97.5%, 97.5%, 96.9%, 99.4%, and 96.0% for erythrocruorin, hemerythrin, hemo- cyanin, hemoglobin, leghemoglobin, and myoglobin proteins, respectively. Finally, an SVM module was developed using dipeptide composition for classifying the oxygen-binding proteins, and achieved maximum accuracy of 96.1%, 98.7%, 98.7%, 85.6%, 99.6%, and 93.3% for the above six classes, respectively. All modules were trained and tested by five-fold cross validation. Based on the above approach, a web server Oxypred was developed for predicting and classifying oxygen-binding proteins(available from http://www.imtech.res.in/raghava/oxypred/).     

Oxypred: prediction and classification of oxygen-binding proteins

This study describes a method for predicting and classifying oxygen-binding proteins. Firstly, support vector machine (SVM) modules were developed using amino acid composition and dipeptide composition for predicting oxygen-binding proteins, and achieved maximum accuracy of 85.5% and 87.8%, respectively. Secondly, an SVM module was developed based on amino acid composition, classifying the predicted oxygen-binding proteins into six classes with accuracy of 95.8%, 97.5%, 97.5%, 96.9%, 99.4%, and 96.0% for erythrocruorin, hemerythrin, hemocyanin, hemoglobin, leghemoglobin, and myoglobin proteins, respectively. Finally, an SVM module was developed using dipeptide composition for classifying the oxygen-binding proteins, and achieved maximum accuracy of 96.1%, 98.7%, 98.7%, 85.6%, 99.6%, and 93.3% for the above six classes, respectively. All modules were trained and tested by five-fold cross validation. Based on the above approach, a web server Oxypred was developed for predicting and classifying oxygen-binding proteins (available from http://www.imtech.res.in/raghava/oxypred/).     

Prediction of neurotoxins based on their function and source

We have developed a method NTXpred for predicting neurotoxins and classifying them based on their function and origin. The dataset used in this study consists of 582 non-redundant, experimentally annotated neurotoxins obtained from Swiss-Prot. A number of modules have been developed for predicting neurotoxins using residue composition based on feed-forwarded neural network (FNN), recurrent neural network (RNN), support vector machine (SVM) and achieved maximum accuracy of 84.19%, 92.75%, 97.72% respectively. In addition, SVM modules have been developed for classifying neurotoxins based on their source (e.g., eubacteria, cnidarians, molluscs, arthropods have been and chordate) using amino acid composition and dipeptide composition and achieved maximum overall accuracy of 78.94% and 88.07% respectively. The overall accuracy increased to 92.10%, when the evolutionary information obtained from PSI-BLAST was combined with SVM module of source classification. We have also developed SVM modules for classifying neurotoxins based on functions using amino acid, dipeptide composition and achieved overall accuracy of 83.11%, 91.10% respectively. The overall accuracy of function classification improved to 95.11%, when PSI-BLAST output was combined with SVM module. All the modules developed in this study were evaluated using five-fold cross-validation technique. The NTXpred is available at www.imtech.res.in/raghava/ntxpred/ and mirror site at http://bioinformatics.uams.edu/mirror/ntxpred.     

Support vector machine-based method for subcellular localization of human proteins using amino acid compositions, their order, and similarity search

Here we report a systematic approach for predicting subcellular localization (cytoplasm, mitochondrial, nuclear, and plasma membrane) of human proteins. First, support vector machine (SVM)-based modules for predicting subcellular localization using traditional amino acid and dipeptide (i + 1) composition achieved overall accuracy of 76.6 and 77.8%, respectively. PSI-BLAST, when carried out using a similarity-based search against a nonredundant data base of experimentally annotated proteins, yielded 73.3% accuracy. To gain further insight, a hybrid module (hybrid1) was developed based on amino acid composition, dipeptide composition, and similarity information and attained better accuracy of 84.9%. In addition, SVM modules based on a different higher order dipeptide i.e. i + 2, i + 3, and i + 4 were also constructed for the prediction of subcellular localization of human proteins, and overall accuracy of 79.7, 77.5, and 77.1% was accomplished, respectively. Furthermore, another SVM module hybrid2 was developed using traditional dipeptide (i + 1) and higher order dipeptide (i + 2, i + 3, and i + 4) compositions, which gave an overall accuracy of 81.3%. We also developed SVM module hybrid3 based on amino acid composition, traditional and higher order dipeptide compositions, and PSI-BLAST output and achieved an overall accuracy of 84.4%. A Web server HSLPred (www.imtech.res.in/raghava/hslpred/ or bioinformatics.uams.edu/raghava/hslpred/) has been designed to predict subcellular localization of human proteins using the above approaches.     

A machine learning based method for the prediction of secretory proteins using amino acid composition, their order and similarity-search

Most of the prediction methods for secretory proteins require the presence of a correct N-terminal end of the preprotein for correct classification. As large scale genome sequencing projects sometimes assign the 5'-end of genes incorrectly, many proteins are encoded without the correct N-terminus leading to incorrect prediction. In this study, a systematic attempt has been made to predict secretory proteins irrespective of presence or absence of N-terminal signal peptides (also known as classical and non-classical secreted proteins respectively), using machine-learning techniques; artificial neural network (ANN) and support vector machine (SVM). We trained and tested our methods on a dataset of 3321 secretory and 3654 non-secretory mammalian proteins using five-fold cross-validation technique. First, ANN-based modules have been developed for predicting secretory proteins using 33 physico-chemical properties, amino acid composition and dipeptide composition and achieved accuracies of 73.1%, 76.1% and 77.1%, respectively. Similarly, SVM-based modules using 33 physico-chemical properties, amino acid, and dipeptide composition have been able to achieve accuracies of 77.4%, 79.4% and 79.9%, respectively. In addition, BLAST and PSI-BLAST modules designed for predicting secretory proteins based on similarity search achieved 23.4% and 26.9% accuracy, respectively. Finally, we developed a hybrid-approach by integrating amino acid and dipeptide composition based SVM modules and PSI-BLAST module that increased the accuracy to 83.2%, which is significantly better than individual modules. We also achieved high sensitivity of 60.4% with low value of 5% false positive predictions using hybrid module. A web server SRTpred has been developed based on above study for predicting classical and non-classical secreted proteins from whole sequence of mammalian proteins, which is available from http://www.imtech.res.in/raghava/srtpred/.     

VGIchan： Prediction and Classification of Voltage-Gated Ion Channels

This study describes methods for predicting and classifying voltage-gated ion channels. Firstly, a standard support vector machine （SVM） method was developed for predicting ion channels by using amino acid composition and dipeptide composition, with an accuracy of 82.89% and 85.56%, respectively. The accuracy of this SVM method was improved from 85.56% to 89.11% when combined with PSIBLAST similarity search. Then we developed an SVM method for classifying ion channels （potassium, sodium, calcium, and chloride） by using dipeptide composition and achieved an overall accuracy of 96.89%. We further achieved a classification accuracy of 97.78% by using a hybrid method that combines dipeptidebased SVM and hidden Markov model methods. A web server VGIchan has been developed for predicting and classifying voltage-gated ion channels using the above approaches. VGIchan is freely available at www.imtech.res.in/raghava/vgichan/.     

BTXpred: prediction of bacterial toxins

This paper describes a method developed for predicting bacterial toxins from their amino acid sequences. All the modules, developed in this study, were trained and tested on a non-redundant dataset of 150 bacterial toxins that included 77 exotoxins and 73 endotoxins. Firstly, support vector machines (SVM) based modules were developed for predicting the bacterial toxins using amino acids and dipeptides composition and achieved an accuracy of 96.07% and 92.50%, respectively. Secondly, SVM based modules were developed for discriminating entotoxins and exotoxins, using amino acids and dipeptides composition and achieved an accuracy of 95.71% and 92.86%, respectively. In addition, modules have been developed for classifying the exotoxins (e.g. activate adenylate cyclase, activate guanylate cyclase, neurotoxins) using hidden Markov models (HMM), PSI-BLAST and a combination of the two and achieved overall accuracy of 95.75%, 97.87% and 100%, respectively. Based on the above study, a web server called 'BTXpred' has been developed, which is available at http://www.imtech.res.in/raghava/btxpred/. Supplementary information is available at http://www.imtech.res.in/raghava/btxpred/supplementary.html.     

Support vector machine based prediction of glutathione S-transferase proteins

Glutathione S-transferase (GST) proteins play vital role in living organism that includes detoxification of exogenous and endogenous chemicals, survivability during stress condition. This paper describes a method developed for predicting GST proteins. We have used a dataset of 107 GST and 107 non-GST proteins for training and the performance of the method was evaluated with five-fold cross-validation technique. First a SVM based method has been developed using amino acid and dipeptide composition and achieved the maximum accuracy of 91.59% and 95.79% respectively. In addition we developed a SVM based method using tripeptide composition and achieved maximum accuracy 97.66% which is better than accuracy achieved by HMM based searching (96.26%). Based on above study a web-server GSTPred has been developed (http://www.imtech.res.in/raghava/gstpred/).     

RSLpred: an integrative system for predicting subcellular localization of rice proteins combining compositional and evolutionary information

The attainment of complete map‐based sequence for rice (Oryza sativa) is clearly a major milestone for the research community. Identifying the localization of encoded proteins is the key to understanding their functional characteristics and facilitating their purification. Our proposed method, RSLpred, is an effort in this direction for genome‐scale subcellular prediction of encoded rice proteins. First, the support vector machine (SVM)‐based modules have been developed using traditional amino acid‐, dipeptide‐ (i+1) and four parts‐amino acid composition and achieved an overall accuracy of 81.43, 80.88 and 81.10%, respectively. Secondly, a similarity search‐based module has been developed using position‐specific iterated‐basic local alignment search tool and achieved 68.35% accuracy. Another module developed using evolutionary information of a protein sequence extracted from position‐specific scoring matrix achieved an accuracy of 87.10%. In this study, a large number of modules have been developed using various encoding schemes like higher‐order dipeptide composition, N‐ and C‐terminal, splitted amino acid composition and the hybrid information. In order to benchmark RSLpred, it was tested on an independent set of rice proteins where it outperformed widely used prediction methods such as TargetP, Wolf‐PSORT, PA‐SUB, Plant‐Ploc and ESLpred. To assist the plant research community, an online web tool ‘RSLpred’ has been developed for subcellular prediction of query rice proteins, which is freely accessible at http://www.imtech.res.in/raghava/rslpred.     

DPROT: prediction of disordered proteins using evolutionary information

The association of structurally disordered proteins with a number of diseases has engendered enormous interest and therefore demands a prediction method that would facilitate their expeditious study at molecular level. The present study describes the development of a computational method for predicting disordered proteins using sequence and profile compositions as input features for the training of SVM models. First, we developed the amino acid and dipeptide compositions based SVM modules which yielded sensitivities of 75.6 and 73.2% along with Matthew’s Correlation Coefficient (MCC) values of 0.75 and 0.60, respectively. In addition, the use of predicted secondary structure content (coil, sheet and helices) in the form of composition values attained a sensitivity of 76.8% and MCC value of 0.77. Finally, the training of SVM models using evolutionary information hidden in the multiple sequence alignment profile improved the prediction performance by achieving a sensitivity value of 78% and MCC of 0.78. Furthermore, when evaluated on an independent dataset of partially disordered proteins, the same SVM module provided a correct prediction rate of 86.6%. Based on the above study, a web server (“DPROT”) was developed for the prediction of disordered proteins, which is available at .