毒死蜱低剂量重复染毒致学习记忆功能障碍及CREB信号途径的抑制(Repeated Exposures to Low-Level Chlorpyrifos Result in Learning Memory Impairment and Inhibition of the CREB Signal System)

毒死蜱(CPF)是我国推荐使用的低毒有机磷农药之一,目前在全世界广泛应用.选择不引起动物出现全身系统毒性的CPF剂量(1、5、10mg·kg-1)对大鼠连续染毒4周,评价CPF对动物的学习记忆功能和CREB信号通路的影响.结果显示:CPF连续染毒后动物出现记忆能力的损伤,随着染毒剂量的增加,海马、皮质和纹状体CREB及ERKI/Ⅱ磷酸化蛋白表达逐渐减少,这表明ERK/CREB信号通路可能是参与毒死蜱低剂量重复暴露致学习记忆功能改变的重要途径.     

基于机器学习和卫星遥感的PM2.5/10 空间连续分布反演方法研究

在过去的几十年里,快速的经济发展以及工业化、城镇化进程加速使得中国的资源环境承担的压力不断加大。作为影响空气质量的首要污染物,PM_2.5和PM₁₀（记为PM_2.5/10）直接影响着广大人民群众的身体健康。因此,针对PM_2.5/10浓度进行遥感反演研究,对环境监测和控制改善全国空气环境质量具有重要的意义。近些年来,随着对近地面PM_2.5/10浓度研究的不断深入,基于遥感影像数据进行PM_2.5/10浓度的反演方法也日益增多。本文利用Google Earth engine（GEE）平台获取了海量的Landsat 8 OLI遥感影像数据,并结合气象信息、空间特征等参数,采用机器学习中常用的多层映射反向传播神经网络构建了波段反射率与PM_2.5/10浓度之间的反演模型,以获得PM_2.5/10在研究区域的连续分布。为了提高基础PM_2.5/10反演模型的反演精度,还从影响因素和前溯时间两个维度出发,探寻了模型的最优化输入参数组合,并最终实现了对PM_2.5/10浓度的精准反演。以北京市地区为例,模型的PM_2.5和PM₁₀的反演精度R²分别达到0.814和0.796,均方根误差RMSE分别为19.21 μg?m^?3和28.31 μg?m^?3。鉴于该反演结果具有较高的准确性和可靠性,本文所建立的方法模型为研究PM_2.5/10在空间上的连续分布特征提供了新的思路和方法,具有较为重要的科研意义与广泛的应用价值。     

毫米波近炸探测器超宽带电磁脉冲防护加固措施研究

目的 研究毫米波引信近炸探测器超宽带(Ultra-wideband,UWB)电磁脉冲防护加固措施,提高引信的电磁防护能力。方法 利用超宽带辐照试验平台,开展辐照试验,通过测试,确定受损探测器的损伤部位,明确辐照效应机理,并提出针对性加固措施。结果 超宽带电磁脉冲可使探测器出现死机或硬损伤的现象,受试探测器的效应场强阈值在50~80 kV/m。死机可在重启后恢复,而硬损伤为不可恢复现象。结论 毫米波引信近炸探测器对超宽带电磁脉冲较为敏感,锁相环回路是探测器的敏感部位。探测器暴露在辐照场下的射频电路和天线窗口是超宽带能量的主要耦合通道。采取改进元器件布局、更换器件和屏蔽线等加固措施后,探测器超宽带的防护能力有较大提升,证明了加固措施的有效性。     

Environmental policy evaluation in the EU: between learning,accountability, and political opportunities?

ABSTRACT

Policy evaluation has grown significantly in the EU environmental sector since the 1990s. In identifying and exploring the putative drivers behind its rise – a desire to learn, a quest for greater accountability, and a wish to manipulate political opportunity structures – new ground is broken by examining how and why the existing literatures on these drivers have largely studied them in isolation. The complementarities and potential tensions between the three drivers are then addressed in order to advance existing research, drawing on emerging empirical examples in climate policy, a very dynamic area of evaluation activity in the EU. The conclusions suggest that future studies should explore the interactions between the three drivers to open up new and exciting research opportunities in order to comprehend contemporary environmental policy and politics in the EU.     

Designing Systematic Conservation Assessments that Promote Effective Implementation: Best Practice from South Africa

Abstract:  Systematic conservation assessment and conservation planning are two distinct fields of conservation science often confused as one and the same. Systematic conservation assessment is the technical, often computer-based, identification of priority areas for conservation. Conservation planning is composed of a systematic conservation assessment coupled with processes for development of an implementation strategy and stakeholder collaboration. The peer-reviewed conservation biology literature abounds with studies analyzing the performance of assessments (e.g., area-selection techniques). This information alone, however, can never deliver effective conservation action; it informs conservation planning. Examples of how to translate systematic assessment outputs into knowledge and then use them for "doing" conservation are rare. South Africa has received generous international and domestic funding for regional conservation planning since the mid-1990s. We reviewed eight South African conservation planning processes and identified key ingredients of best practice for undertaking systematic conservation assessments in a way that facilitates implementing conservation action. These key ingredients include the design of conservation planning processes, skills for conservation assessment teams, collaboration with stakeholders, and interpretation and mainstreaming of products (e.g., maps) for stakeholders. Social learning institutions are critical to the successful operationalization of assessments within broader conservation planning processes and should include not only conservation planners but also diverse interest groups, including rural landowners, politicians, and government employees.     

Can environmental heterogeneity explain individual foraging variation in wild bottlenose dolphins (<Emphasis Type="Italic">Tursiops</Emphasis> sp.)?

Because behavioral variation within and among populations may result from ecological, social, genetic and phenotypic differences, identifying the mechanism(s) responsible is challenging. Observational studies typically examine social learning by excluding ecological and genetic factors, but this approach is insufficient for many complex behaviors associated with substantial environmental variation. Indian Ocean bottlenose dolphins (Tursiops sp.) in Shark Bay, Western Australia show individual differences in foraging tactics, including possible tool use with marine sponges and social learning may be responsible for this diversity. However, the contributions of ecological factors to the development of these foraging tactics were not previously investigated. Here, we determined the relationship between ecological variables and foraging tactics and assessed whether differences in habitat use could explain individual differences in foraging tactics. We monitored 14 survey zones to identify how foraging tactics were spatially distributed and matched behavioral data to the ecological variables within each zone. Three of four foraging tactics were significantly correlated with ecological characteristics such as seagrass biomass, water depth, presence of marine sponges and season. Further, individual differences in habitat use were associated with some tactics. However, several tactics overlapped spatially and previous findings suggest demographic and social factors also contribute to the individual variation in this population. This study illustrates the importance of environmental heterogeneity in shaping foraging diversity and shows that investigating social learning by ruling out alternative mechanisms may often be too simplistic, highlighting the need for methods incorporating the relative contributions of multiple factors.     

Application of machine learning methods to palaeoecological data

A palaeoecological study was conducted to investigate past environmental conditions and vegetation dynamics around the southwestern Ljubljana Moor. In order to find potential regularities and/or dependencies among co-existent plant species through time, different machine learning methods were applied to pollen records from the cores taken at Bistra and Ho?evarica. The data comprised relative pollen frequencies of the most common plant genera/families at particular core depths that correspond to particular ages in the Early and Mid Holocene periods. The applied methods include equation discovery and hierarchical clustering. Both methods have found plausible and explainable relationships among identified plant genera/families.     

基于机器学习方法的安全帽佩戴行为检测

施工现场作业人员是否佩戴安全帽主要依靠人工检查,存在监管效率低、时效性差等问题,为了实时自动监管作业人员是否佩戴安全帽,提出1种基于机器学习的安全帽佩戴行为检测方法。首先利用深度学习YOLOv3算法检测出现场视频中的施工人员脸部位置,根据安全帽与人脸的关系估算出安全帽潜在区域;然后对安全帽潜在区域图像进行增强处理,使用HOG（方向梯度直方图）提取样本的特征向量;再利用SVM（机器学习的支持向量机）分类器对脸部上方是否有安全帽进行判断,进而实现对施工人员安全帽佩戴行为的实时检测与预警。以某高铁站施工现场为例进行验证,研究数据表明在施工通道和塔吊作业区域,该方法可实时有效检测出工人未佩戴安全帽的行为,识别率达90%。     

Alignment of threat,effort, and perceived success in North American conservation translocations

The use of conservation translocations to mitigate human effects on biodiversity is increasing, but how these efforts are allocated remains unclear. Based on a comprehensive literature review and online author survey, we sought to determine the goals of translocation efforts, whether they focus on species and regions with high threat and likelihood of perceived success, and how success might be improved. We systematically searched the ISI Web of Knowledge and Academic Search Complete databases to determine the species and regions of conservation translocations and found 1863 articles on conservation translocations in the United States, Canada, Mexico, Central America, and Caribbean published from 1974 to 2013. We questioned 330 relevant authors to determine the motivation for translocations, how translocations were evaluated, and obstacles encountered. Conservation translocations in North America were geographically widespread (in 21 countries), increased in frequency over time for all animal classes (from 1 in 1974 to 84 in 2013), and included 279 different species. Reintroductions and reinforcements were more common in the United States than in Canada and Mexico, Central America, or the Caribbean, and their prevalence was correlated with the number of species at risk at national and state or provincial levels. Translocated species had a higher threat status at state and provincial levels than globally (International Union for Conservation of Nature Red List categorization), suggesting that translocations may have been motivated by regional priorities rather than global risk. Our survey of authors was consistent with these results; most translocations were requested, supported, or funded by government agencies and downlisting species at national or state or provincial levels was the main goal. Nonetheless, downlisting was the least reported measure of success, whereas survival and reproduction of translocated individuals were the most reported. Reported barriers to success included biological factors such as animal mortality and nonbiological factors, such as financial constraints, which were less often considered in the selection of release sites. Our review thus highlights discrepancies between project goals and evaluation criteria and between risk factors considered and obstacles encountered, indicating room to further optimize translocation projects.     

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