Eigenbehaviors: identifying structure in routine

Longitudinal behavioral data generally contains a significant amount of structure. In this work, we identify the structure inherent in daily behavior with models that can accurately analyze, predict, and cluster multimodal data from individuals and communities within the social network of a population. We represent this behavioral structure by the principal components of the complete behavioral dataset, a set of characteristic vectors we have termed eigenbehaviors. In our model, an individual’s behavior over a specific day can be approximated by a weighted sum of his or her primary eigenbehaviors. When these weights are calculated halfway through a day, they can be used to predict the day’s remaining behaviors with 79% accuracy for our test subjects. Additionally, we demonstrate the potential for this dimensionality reduction technique to infer community affiliations within the subjects’ social network by clustering individuals into a “behavior space” spanned by a set of their aggregate eigenbehaviors. These behavior spaces make it possible to determine the behavioral similarity between both individuals and groups, enabling 96% classification accuracy of community affiliations within the population-level social network. Additionally, the distance between individuals in the behavior space can be used as an estimate for relational ties such as friendship, suggesting strong behavioral homophily amongst the subjects. This approach capitalizes on the large amount of rich data previously captured during the Reality Mining study from mobile phones continuously logging location, proximate phones, and communication of 100 subjects at MIT over the course of 9 months. As wearable sensors continue to generate these types of rich, longitudinal datasets, dimensionality reduction techniques such as eigenbehaviors will play an increasingly important role in behavioral research. This contribution is part of the special issue “Social Networks: new perspectives” (Guest Editors: J. Krause, D. Lusseau and R. James). An erratum to this article can be found at     

水环境中有机污染物对藻类毒理性的探讨

近年来,环境中的有机污染物对生态环境的破坏日益加剧,并严重威胁到了人类的生存和发展。藻类作为初级生产者,是整个生态系统物质循环和能量流动的基础,因此研究水环境中有机污染物对藻类的毒理性,对于环境污染的监测具有深远的意义。就有机污染物对藻类的毒性机理、藻类对有机污染物的敏感度等问题的研究现状进行综述,并对藻类在环境监测方面的应用前景进行展望。     

4-Ethylphenol and 4-ethylguaiacol in wines: Estimating non-microbial sourced contributions and toxicological considerations

Analyses of commercially available wines suggested non-Brettanomyces sources of 4-ethylphenol and 4-ethylguaiacol. Grapes, enological additions, exposure to plastics, and oak-barrel aging were potential inputs considered. Investigations of whole grape bunch samples from two major red wine Vitis vinifera cultivars (L. cv. Cabernet Franc and Pinot Noir), a commercial mannoprotein additive, and three commercial enological tannin additions indicated they are not likely significant sources of these compounds. Studies on 15 commercial oak barrelled red wines from six Vitis vinifera cultivars (L. cv. Cabernet Franc, Cabernet Sauvignon, Dunkelfelder, Merlot, Pinot Meunier, and Pinot Noir), and a review of volatile phenol extraction from toasted oak wood, suggested that oak-aging may produce concentrations of up to 50 μg L^?1 4-ethylphenol and 4-ethylguaiacol. Thus, following potential Brettanomyces-sourced aroma impacts in wine using 4-ethylphenol and/or 4-ethylguaiacol concentrations as proxies should only be considered reliable at analyte levels > 100 μg L^?1. A review of worldwide 4-ethylphenol and 4-ethylguaiacol concentrations in wine, consumption patterns, and available toxicological data also suggested that levels of 4-ethylphenol being observed in wines worldwide do not warrant concerns about acute or long-term effects. While little is known about the toxicology of 4-ethylguaiacol, it is unlikely that elevated concentrations will pose any health-related concerns.     

Accident data for the Semantic Web

This paper describes concerns about the documentation, dissemination and use of lessons learned from mishap investigations, impediments posed by current practices, and opportunities for improvement. Lessons are presently developed, documented and stored primarily in narrative form and relational databases, and disseminated in many forms and media, including the Internet. Current practices pose many impediments to maximized development, dissemination and use. Investigation process research and new data concepts behind the Semantic Web, exploited elsewhere, offer potential opportunities to overcome these impediments. To exploit these opportunities, formation of a working group to develop an improved Semantic Web-friendly mishap investigation lessons learning system is proposed. An example illustrating an alternative approach is described to support a reasonable expectation that an alternative lessons learning system could be developed.     

基于模式生物秀丽隐杆线虫的环境污染物生殖毒性研究进展

由于人类社会的发展,环境污染也越来越严重,研究环境污染物对生态及人类健康的影响极为重要。生殖系统是人和动物繁衍后代的重要系统,常见环境污染物对生殖系统的影响正引起研究人员的广泛关注。秀丽隐杆线虫具有身体透明、品系丰富和遗传背景清楚等优点,为生殖毒理学研究提供了一个便利的平台。以秀丽隐杆线虫为模型已对多种环境污染物的生殖毒性进行了评价。本文重点叙述了重金属、纳米材料、有机物等化学污染,辐射、磁场等物理污染,以及病毒等生物污染对秀丽线虫生殖系统的影响,并归纳讨论了环境污染物造成生殖功能障碍的几种主要机制,如氧化应激、DNA损伤、内质网应激以及神经损伤等,但不同类型污染物的毒性效应与其自身的物化性质紧密相关。环境污染物诱导的生殖毒性中不同响应机制之间的关联性、不同发育阶段的敏感性差异、污染物的传代效应以及有效的缓解方式的筛选等都有待进一步的开展。     

基因芯片在环境毒理学研究中的应用进展

基因芯片可以将大量的DNA信息集成到 1cm2 左右的芯片上 ,对生命信息具有大规模平行处理的能力 ,为环境毒理学研究提供了一个理想的平台。基因芯片可以精确地完成污染物对人类基因表达影响的分析 ,并对污染物进行分类与分级 ,筛选毒物靶标和确定毒性机理。本文描述了基因芯片技术、毒理学应用研究现状及应用前景。引用文献 2 9篇。     

火灾烟气毒性的定量评价方法评述

火灾烟气毒性定量评价是毒理学研究的一个新兴领域.烟气毒性与建筑材料、燃烧条件、烟气毒物在建筑物内的传播规律及暴露时间有关,这些都是传统毒理学评价方法难以解决的问题.烟气毒性定量评价的目的是通过建立数学模型,以最少的动物试验,从宏观上更加定量化、系统化地评价和预测火灾烟气毒性.本文介绍了国外火灾烟气毒性定量评价的标准、烟气定量评价的数学模型,评述了这些模型的适用范围和优缺点,分析了火灾烟气毒性定量评价的发展方向.     

稻田土壤细菌对重金属镉的氧化应激反应研究

研究重金属镉胁迫下稻田土壤大肠杆菌K12、枯草芽孢杆菌B19及Ralstonia eutropha DKC1中超氧化物歧化酶(SOD)、过氧化氢酶(CAT)和ATP酶活性等应激酶活性的短期(46 h)变化.结果表明,不同浓度镉对E.coli K12,B.subtilisB19和R.eutrophaDKC1培养不同时间的SOD、CAT和ATP酶活性均有不同程度的诱导作用,表现在前期、低浓度时激活,后期、高浓度时抑制,体现了不同微生物间的差异以及抗镉潜力的不同.3种指标间接地反映了环境中有毒有害物质的存在,且能在早期较灵敏地指示污染的影响,作为环境受到污染胁迫的细胞生化指标具有一定可行性.     

有机物的结构——活性/性质定量关系及其在环境化学和环境毒理学中的应用

有机物结构-活性定量关系研究(QSAR,Quantitative Structure-Activity Relationship)用于环境化学和环境毒理学领域取得了引人注目的成功。有机污染物在环境中的行为,尤其在生物体内的富集和致毒是环境化学和环境毒理学的主要研究方向之一。QSAR法可以从已有的大量情报中最大量地提取有关环境污染物结构—活性关系进行深入的研究。     

Variability of the Integrated Biochemical Index in Fish Exposed to Technogenic Water of Mining and Ore-Processing Works

The hazard presented to aquatic animals by technogenic waters from mining and ore processing works, with an abnormally high potassium content and alkaline pH, was studied in experiments with fish using the integrated biochemical index (IBI) allowing an aggregate estimation of changes in the animal organism. The results showed that the IBI value directly depends on the extent of technogenic pollution, as well as on the species and age of fish.