指示值方法及其在昆虫中的应用

指示值(IndVal)方法于1997年由Dufrêne和Legendre提出,是生物指示方法论研究中最有意义的进步,在国外被广泛运用于生态指示物研究,然而在国内却没有得到应有的重视。本文介绍了如何运用R语言软件进行IndVal值的计算,IndVal方法在指示昆虫研究中的应用,以及运用该方法进行昆虫指示物种的判断和检验。旨在普及该方法的运用,推动我国的生物指示研究。     

Diagnosis of damage to Norway spruce (Picea abies) through biochemical criteria

Bioindication can be carried out at different hierarchical levels, eg. cell, organism, and ecosystem. While the monitoring of damage by visible criteria (e.g. loss of needles) is connected with the organism as a whole, the monitoring of damage by biochemical indicators is above all connected with cell metabolism.
The degree of vitality of a tree can be ascertained through the integration of a number of biochemical parameters. Furthermore, a differential diagnosis of a particular stress pattern can be carried out because of the feedback pattern of several biochemical indicators. In order to describe and interpret biochemical or physiological changes that have been caused by a number of factors, multivariate statistical methods are being used more frequently. Apart from cluster and discriminant analysis, it is especially factor analysis which provides a helpful tool when dealing with problems in the field of environmental analysis. Factor analysis can be used for an integrating as well as a differentiating assessment.
Within the framework of forest damage research, numerous changes at the level of cell metabolism have been detected to which a bioindicative character can be attached. A number of physiological and biochemical parameters with bioindicative character concerning Norway spruce are presented.     

溶酶体贮积症和其他罕见疾病适应证治疗药物的相关交易情况

介绍汤森路透Cortellis 竞争情报分析数据库报道的市场上高额的溶酶体贮积症和其他罕见疾病治疗药物的合作协议以及该领域某些重要的有前景的候选治疗药物。     

坡向差异对广东古兜山自然保护区蕨类植物多样性的生态影响

为揭示坡向差异对蕨类植物多样性的生态影响,作者在广东新会古兜山自然保护区海拔1,000 m以下的中低山植被东、南、西、北4个坡向,分别选取10个5 m×5 m的样方进行调查,分析蕨类植物群落多样性和种类组成的差异.结果表明:(1)东坡(半阴坡)与北坡(阴坡)物种多样性最丰富,而西坡(半阳坡)和南坡(阳坡)相对贫乏;(2)4个坡向的相似性系数均很低(不超过0.5),其中南坡(阳坡)与北坡(阴坡)间种类组成差异最大;(3)反映热量差异的区系地理性质分析显示,热带性质蕨类植物在阳坡占优势,而非热带性蕨类在阴坡占优势,南坡、西坡、东坡、北坡的热带成分比例依次下降;(4)反映光照条件的植物耐荫程度分析显示,阴性种类占优势,阳性和耐荫性种类较少,其阴性蕨类植物的比例依南坡、西坡、东坡、北坡顺序增加;阳性蕨类植物比例最高的坡面是西坡,而最低的是东坡.南坡的比例稍高于北坡.研究结果显示坡向差异对蕨类植物物种及其生态习性的多样性分布格局具有比较明显的影响,蕨类植物多样性可以作为环境和气候变化的一个较好的指示物种.     

Diversity and ecology of algae from the Lower Jordan River,Israel

Abstract

The Lower Jordan River stretches from the outlet of Lake Kinneret till the Dead Sea and is one of the most polluted rivers in Israel. In order to estimate the aquatic ecosystem of the northern part of the Lower Jordan River we used algal communities as biological indicators in the rainy and dry seasons of 2005 and 2007. We found 152 species of algae and cyanoprokaryotes from seven taxonomic divisions, which were mostly dominated by diatoms accompanied by the greens, blue‐greens and euglenoids. The xanthophycean species Heterococcus viridis, a red freshwater alga Audouinella hermannii, and a cyanobacterium Microcrocis marina were found for the first time in Israel. In the ecological analysis we revealed groups of freshwater algae according to pH, salinity, and saprobity as well as temperature, streaming and oxygenation. Quantitative information on the relationship between species and environmental variables was obtained using CCA analysis, emphasizing the major seasonal parameters. The extent of association of species with the environmental variables vector allowed us to determine the bio‐sensors and bio‐indicators, which may be relevant for monitoring of pollutants in the Lower Jordan River. The combination of bio‐indicational methods and statistics were effective in the determination of the main factors influencing algal diversity, as well as in revealing the indicators or bio‐sensing species for the most important environmental variables.     

A Bayesian platform trial design to simultaneously evaluate multiple drugs in multiple indications with mixed endpoints

In the era of targeted therapies and immunotherapies, the traditional drug development paradigm of testing one drug at a time in one indication has become increasingly inefficient. Motivated by a real-world application, we propose a master-protocol–based Bayesian platform trial design with mixed endpoints (PDME) to simultaneously evaluate multiple drugs in multiple indications, where different subsets of efficacy measures (eg, objective response and landmark progression-free survival) may be used by different indications as single or multiple endpoints. We propose a Bayesian hierarchical model to accommodate mixed endpoints and reflect the trial structure of indications that are nested within treatments. We develop a two-stage approach that first clusters the indications into homogeneous subgroups and then applies the Bayesian hierarchical model to each subgroup to achieve precision information borrowing. Patients are enrolled in a group-sequential way and adaptively assigned to treatments according to their efficacy estimates. At each interim analysis, the posterior probabilities that the treatment effect exceeds prespecified clinically relevant thresholds are used to drop ineffective treatments and “graduate” effective treatments. Simulations show that the PDME design has desirable operating characteristics compared to existing method.     

GLP-1及其受体激动剂Exendin-4临床适应症的扩展

GLP-1及其受体激动剂Exendin-4是治疗糖尿病的一种理想药物,是近年来新的研究热点之一。近年发现,该类药物可从多个生理角度发挥功能,揭示其临床适应症可能有进一步的扩展空间。对GLP-1及Exendin-4的各种已知和潜在的临床适应症进行了概述,这些适应症除了各型糖尿病外,还包括肥胖症、神经系统和心脏的疾病以及其他各种潜在适应症。     

PREDICT: a method for inferring novel drug indications with application to personalized medicine

Inferring potential drug indications, for either novel or approved drugs, is a key step in drug development. Previous computational methods in this domain have focused on either drug repositioning or matching drug and disease gene expression profiles. Here, we present a novel method for the large‐scale prediction of drug indications (PREDICT) that can handle both approved drugs and novel molecules. Our method is based on the observation that similar drugs are indicated for similar diseases, and utilizes multiple drug–drug and disease–disease similarity measures for the prediction task. On cross‐validation, it obtains high specificity and sensitivity (AUC=0.9) in predicting drug indications, surpassing existing methods. We validate our predictions by their overlap with drug indications that are currently under clinical trials, and by their agreement with tissue‐specific expression information on the drug targets. We further show that disease‐specific genetic signatures can be used to accurately predict drug indications for new diseases (AUC=0.92). This lays the computational foundation for future personalized drug treatments, where gene expression signatures from individual patients would replace the disease‐specific signatures.