Spotlight surveys for white-tailed deer: Monitoring panacea or exercise in futility?

Many monitoring programs for white-tailed deer (Odocoileus virginianus) on both private and public lands across the United States have long relied on the use of road-based spotlight surveys for monitoring population size and trends. Research has suggested spotlight surveys are ineffective and that road-based surveys for deer are biased because of highly variable detection rates. To evaluate variability in detection rates relative to the assumption that repeated surveys along roads will provide reliable trend data for use in calculating deer density estimates, we collected 5 years of thermal-imager and spotlight survey data using a multiple-observer, closed-capture approach. Using a Huggin's closed capture model, data bootstrapping, and variance components analyses, our results suggest that density estimates for white-tailed deer generated from data collected during road-based spotlight surveys are likely not reflective of the standing deer population. Detection probabilities during individual spotlight surveys ranged from 0.00 to 0.80 (median = 0.45) across all surveys, and differed by observer, survey, management unit, and survey transect replicate. Mean spotlight detection probability (0.41) and process standard deviation (0.12) estimates indicated considerable variability across surveys, observers, transects, and years, which precludes the generation of a correction factor or use of spotlight data to evaluate long-term trends at any scale. Although recommended by many state, federal, and non-governmental agencies, our results suggest that the benefit of spotlight survey data for monitoring deer populations is limited and likely represents a waste of resources with no appreciable management information gained. © 2012 The Wildlife Society.     

De novo discovery of antibody drugs – great promise demands scrutiny

ABSTRACT

We live in an era of rapidly advancing computing capacity and algorithmic sophistication. “Big data” and “artificial intelligence”find progressively wider use in all spheres of human activity, including healthcare. A diverse array of computational technologies is being applied with increasing frequency to antibody drug research and development (R&D). Their successful applications are met with great interest due to the potential for accelerating and streamlining the antibody R&D process. While this excitement is very likely justified in the long term, it is less likely that the transition from the first use to routine practice will escape challenges that other new technologies had experienced before they began to blossom. This transition typically requires many cycles of iterative learning that rely on the deconstruction of the technology to understand its pitfalls and define vectors for optimization. The study by Vasquez et al. identifies a key obstacle to such learning: the lack of transparency regarding methodology in computational antibody design reports, which has the potential to mislead the community efforts     

香叶天竺葵快速繁殖的研究

以柠檬香的皱波天竺葵 (Pelargoniucrispum)的叶片和茎段为外植体进行试管培养 ,结果叶片和茎段可诱导形成愈伤组织。经试验筛选结果为 :培养基MS BA 0 .75mg L NAA 0 .2 0mg L最适于愈伤组织的诱导 ,也适于不定芽分化和从生芽的分化增殖 ;培养基 1 2MS NAA 0 .3mg L IAA 0 .1mg L为根系诱导及生长的最佳配方 ,生根率达 1 0 0 %。     

Long‐term ecological monitoring and institutional memories

Many important ecological management issues can only be addressed by long‐term monitoring or through studies carried out over extended periods. But such studies require institutional settings that ensure funding is sustained and that data arising from these studies are securely managed. Recent experience suggests both are difficult to achieve. This is because management agencies and research bodies are periodically restructured, especially in recent years. This has often led to long‐term work being terminated. But there is anecdotal evidence that the data collected in at least some of these studies are not always lost. Instead, it can remain stored in the back rooms of agencies or in the personal files of former staff. Such data are clearly at risk; with time fewer people remain aware of the work or of the existence of data that were collected, thereby increasing the likelihood that the information will eventually disappear. This seems a waste. Securing funds for any long‐term ecological study is always likely to be difficult, and many of these previous long‐term studies are likely to be relevant to some of our present management problems. One approach to taking advantage of these earlier studies would be to ask scientific and professional associations to survey their older members to identify relevant previous investigations. But any re‐establishment of former studies will require the creation of new institutional arrangements, more robust institutional memories and sufficient funds that are able to sustain any resurrected investigations into the future.     

How LCA studies deal with uncertainty

In recent years many workers have examined the implications of various sources of uncertainty for the reliability of Life Cycle Assessment (LCA). Indeed, the International Standardization Organization (ISO) has recognised the relevance of this work by including several cautionary statements in the ISO 14040 series of standards. However, in practice, there is a risk that the significance of these uncertainties for the results of an LCA could be overlooked as practitioners strive to complete studies on time and within budget. This paper presents the findings of a survey of LCA studies we made to determine the extent to which the problem of uncertainty had been dealt with in practice. This survey revealed that the significance of the limitations on the reliability of LCA results given in the standards has not been fully appreciated by practitioners. We conclude that the standards need to be revised to ensure that LCA studies include at least a qualitative discussion on all relevant aspects of uncertainty.     

A knowledge-driven agent-centred framework for data mining in EMG

In this paper, we present a multi-agent framework for data mining in electromyography. This application, based on a web interface, provides a set of functionalities allowing to manipulate 1000 medical cases and more than 25,000 neurological tests stored in a medical database. The aim is to extract medical information using data mining algorithms and to supply a knowledge base with pertinent information. The multi-agent platform gives the possibility to distribute the data management process between several autonomous entities. This framework provides a parallel and flexible data manipulation.     

基于Web服务的流感病毒基因组自动化翻译注释系统

随着流感病毒基因组测序数据的急剧增加,深入挖掘流感病毒基因组大数据蕴含的生物学信息成为研究热点。基于中国流感病毒流行特征数据,建设一个集自动化、一体化和信息化的序列库系统,对于实现流感病毒基因组批量快速翻译、注释、存储、查询、分析具有重要的应用价值。本课题组通过集成一系列软件和工具包,并结合自主研发的其他功能,在底层维护的2个关键的参考数据集基础上另外追加了翻译注释信息最佳匹配的精细化筛选规则,构建具有流感病毒基因组信息存储、自动化翻译、蛋白序列精准注释、同源序列比对和进化树分析等功能的自动化系统。结果显示,通过Web端输入fasta格式的流感病毒基因序列,本系统可针对参考序列片段数据集(blastdb.fasta)进行Blast同源性检索,可以鉴定流感病毒的型别(A、B或C)、亚型和基因片段(1~8片段);在此基础上,通过查询数据库底层用于翻译、注释的基因片段参考数据集,可以获得一组肽段数据集,然后通过循环调用ProSplign软件对其进行预测。结合精细化的筛选准入规则,选出与输入序列匹配最好的翻译后产物,作为该输入序列的预测蛋白,输出为gbk,asn和fasta等通用格式的文件,给出序列长度、是否全长、病毒型别、亚型、片段等信息。基于以上工作,另外自主研发了系统其他的附加功能如进化树分析展示、基因组数据存储等功能,构建成基于Web服务的流感病毒基因组自动化翻译注释系统。本研究提示,系统高度集成系列软件以及自有的注释翻译数据库文件,实现从序列存储、翻译、注释到序列分析和展示的功能,可全面满足我国高通量基因检测数据共享化、本土化、一体化、自动化的需求。     

全国大学生生命科学竞赛数据分析与展望

全国大学生生命科学竞赛至今已举办三届,赛事组织好、规模大、参与度高,对促进生命科学教育与研究具有重要作用。文中简述全国大学生生命科学竞赛的模式与现状,并基于前三届竞赛数据分地区分年度统计分析报名数据和竞赛成绩,同时结合生命科学领域的新变化新认识进行展望,以更好地推动赛事发展。