Direct protein-protein interaction network for insecticide resistance based on subcellular localization analysis in Drosophila melanogaster

Abstract

In present study, we constructed the direct protein-protein interaction network of insecticide resistance based on subcellular localization analysis. Totally 177 of 528 resistance proteins were identified and they were located in 11 subcellular localizations. We further analyzed topological properties of the network and the biological characteristics of resistance proteins, such as k-core, neighborhood connectivity, instability index and aliphatic index. They can be used to predict the key proteins and potential mechanisms from macro-perspective. The problem of resistance has not been solved fundamentally, because the development of new insecticides can’t keep pace with the development speed of resistance, and the lack of understanding of molecular mechanism of resistance. As the further analysis to reduce data noise, we constructed the direct protein-protein interaction network of insecticide resistance based on subcellular localization analysis. The interaction between proteins located at the same subcellular location belongs to direct interactions, thus eliminating indirect interaction. Totally 177 of 528 resistance proteins were identified and they were located in 11 subcellular localizations. We further analyzed topological properties of the network and the biological characteristics of resistance proteins, such as k-core, neighborhood connectivity, instability index and aliphatic index. They can be used to predict the hub proteins and potential mechanisms from macro-perspective. This is the first study to explore the insecticide resistance molecular mechanism of Drosophila melanogaster based on subcellular localization analysis. It can provide the bioinformatics foundation for further understanding the mechanisms of insecticide resistance. It also provides a reference for the study of molecular mechanism of insecticide resistance of other insects.     

Identifying key needs for the integration of social–ecological outcomes in arctic wildlife monitoring

For effective monitoring in social–ecological systems to meet needs for biodiversity, science, and humans, desired outcomes must be clearly defined and routes from direct to derived outcomes understood. The Arctic is undergoing rapid climatic, ecological, social, and economic changes and requires effective wildlife monitoring to meet diverse stakeholder needs. To identify stakeholder priorities concerning desired outcomes of arctic wildlife monitoring, we conducted in-depth interviews with 29 arctic scientists, policy and decision makers, and representatives of indigenous organizations and nongovernmental organizations. Using qualitative content analysis, we identified and defined desired outcomes and documented links between outcomes. Using network analysis, we investigated the structure of perceived links between desired outcomes. We identified 18 desired outcomes from monitoring and classified them as either driven by monitoring information, monitoring process, or a combination of both. Highly cited outcomes were make decisions, conserve, detect change, disseminate, and secure food. These reflect key foci of arctic monitoring. Infrequently cited outcomes (e.g., govern) were emerging themes. Three modules comprised our outcome network. The modularity highlighted the low strength of perceived links between outcomes that were primarily information driven or more derived (e.g., detect change, make decisions, conserve, or secure food) and outcomes that were primarily process driven or more derived (e.g., cooperate, learn, educate). The outcomes expand monitoring community and disseminate created connections between these modules. Key desired outcomes are widely applicable to social–ecological systems within and outside the Arctic, particularly those with wildlife subsistence economies. Attributes and motivations associated with outcomes can guide development of integrated monitoring goals for biodiversity conservation and human needs. Our results demonstrated the disconnect between information- and process-driven goals and how expansion of the monitoring community and improved integration of monitoring stakeholders will help connect information- and process-derived outcomes for effective ecosystem stewardship.     

基于链路预测的安全隐患管理研究*

为探寻安全隐患的发生规律，揭示安全隐患的演化特点，为管理人员提供建议和参考。以潞安集团司马煤业有限公司2009—2015年安全隐患记录为数据源，建立无向加权安全隐患共词网络，利用链路预测技术，寻找安全隐患的内在联系及发展趋势。结果表明:相比于共同邻居、资源分配等基于局部信息的相似性指标，资源分配指标能够更好地预测安全隐患各个关键词之间的变化联系；通过分析关键词共现关系，证实预测的准确性，预测出部分未来存在但当前未产生的节点关系。安全管理人员可基于此方法采取有针对性的预防措施，减少相关隐患的发生。     

Quantitative sensitivity and reliability analysis of sensor networks for well kick detection based on dynamic Bayesian networks and Markov chain

Kick is considered as an early warning sign to the blowout that is among the most undesired and feared accidents during drilling operations. Kick detection system is commonly used to timely identify the occurrence of a kick. The method commonly used for kick detection relies on the proper selection of monitoring indicators. A kick detection system should not only have very high accuracy but also maintain reliable over a long time. Different from the existing studies focusing on improving the detection accuracy, this paper presents a frame emphasizing on quantitatively analyzing and enhancing the reliability of the kick detection sensor networks. The dynamic Bayesian network (DBN) for the sensor networks is established that employs Markov chain to obtain the reliability degradation of measurement sensors over time. The proposed method is applied and evaluated by case studies to conduct reliability and sensitivity analysis for kick detection sensor networks. The reliability analysis results demonstrate that the proposed method can quantitatively analyze the reliability of a kick detection sensor networks consisting of various sensors over given time periods. The sensitivity analysis results indicate that the proposed method is effective in identifying the critical sensors that have the greatest effect on the reliability of one certain kick detection system. Based on the analysis results, optimized logical combination of sensors of a kick detection system can be achieved. An improved sensor network for the unreliable case was proposed and evaluated.     

虚拟水战略:拯救民勤绿洲的新思路

位于甘肃石羊河下游的民绿洲，水资源极度缺乏，生产、生活和生态用水矛盾突出，出现了严重的生态危机，其中湖区的生态经济系统已呈“崩溃”之势。拯救民勤绿洲成为社会广泛关注的重大问题。“虚拟水”是20世纪90年代初Tony Allan提出的一个新概念，虚拟水战略是指贫水区域通过贸易的方式从富水地区购进水资源密集型产品来获得本区水资源安全的一种构想。本文通过对节水、调水、移民等常规方案与虚拟水战略的对比分析后，认为虚拟水战略是拯救民勤绿洲的一个全新思路，探讨了虚拟水战略实施的政策建议；提出了创新区域水资源管理体制和区域经济发展思路以及建立国家生态补偿机制的主张。     

如何优化经济欠发达地区农村客运网

经济欠发达地区农村的道路情况和客运网络的建设相对落后,同时,由于受交通不便的制约,农村经济发展速度受到很大的影响,农民出行难的问题仍然没有得到根本上的解决。本文通过对经济欠发达地区农村客运网现状的分析,找出目前造成农村客运网络落后的原因,并提出相应的对策和建议。     

中国生物质能政策变迁研究:2000—2019年

生物质柴油和生物质沼气是生物质能发展的两大着力点,两者在性质上存在较大差异,相应的政策设计和发展也各有不同。以政策网络理论、政策变迁相关理论为理论基础,通过搜集整理中央层面关于生物质柴油和生物质沼气的政策文本,采用政策计量和内容分析法,分析政策的发文时间、文本类型等文本外部属性特征,且根据政策文本内容划分了5类政策目标和13类政策工具,深入比较不同生物质能源在变迁中政策目标、政策工具和政策主体网络的差异。研究结果表明,中国生物质能的政策变迁,呈现多元化的演进动态:(1)生物质柴油与沼气政策的发文单位与发文类型在各时间段有所异同,整体来看,生物质柴油与沼气发文最多的单位为国务院和国家发展和改革委员会,不同的是生物质柴油发文数量位于第三位的是国家能源局,而生物质沼气是生态环境部和农业农村部。在发文类型方面,"通知与意见"类型构成了主要占比。(2)同一生物质的政策目标与工具在不同时期的政策变迁中,有延续性和差异性。例如,生物质柴油在各阶段始终以技术创新、能源供应连续性为主要政策目标来保证政策延续性,但在不同阶段也会关注环境可持续性、工业发展等;在政策工具上,生物质柴油即使在变迁中也都会使用财政支持,而各阶段配合使用的工具重心也不一致。(3)不同生物质能源使用的政策工具在同一时期也存在异同。如在第二阶段生物质柴油和生物质沼气所聚焦的政策目标和政策工具完全不同,在第一和第三阶段两者则存在小部分相似。(4)不同生物质能源的政策网络在各阶段表现存在异同。全国人民代表大会和国务院始终是政策网络中的重要主体,各个阶段不同生物质能发文主体的角色和协调互动情况随政策变迁而变化。最后,在研究结论的基础上提出政策目标与政策工具统筹运用、建立有效的政策网络主体协同治理机制的启示。     

沿海农田林网建设关键技术的经济学择优

对江苏沿海地区农田林网的经营类型、造林树种、林带配置进行了技术经济学分析和多目标灰色局势决策择优。结果表明：农田林网经营类型宜选择复合经营的果材兼用型林网。造林树种可选择速生丰产的杨树等用材树种和经济效益高的桃树等果树。林网配置在海堤河岸上选择用材树种，营造基干林带，筑起大网格框架；在道路沟渠上立体配置林果树种，营造主林带和副林带，构建中网格；在脱盐排水沟上添加果树辅助林带，建立小网格，形成多树种多林种合作、大中小网格配套、多道防线联防、多效益多产品产出的复合配置模式。     

应用BP神经网络预测城市需水量的探析

在对辽宁省营口市城市需水量预测中，采用BP人工神经网络方法对该市人均综合用水量进行预测。进而预测出城市需水量，取得了满意效果。从精度检验、数据类比和趋势分析等角度，论证了此种预测方法的可行性和有效性。     

昆明市环境监测信息系统的开发与完善

通过环境监测信息控制系统管理环境监测数据,可提高数据管理水平和数据处理质量、速度,可将环境监测信息尽快地转化为决策依据,有利于提高环境管理和环境污染治理决策水平.本文总结了昆明市环境监测信息系统的设计、开发要点并提出了进一步完善的展望.