Communication ecology of webbing clothes moth: 4. Identification of male- and female-produced pheromones

Summary. We investigated the hypothesis that aggregation signals produced by male webbing clothes moths (WCM), Tineola bisselliella (Hum.) (Lepidoptera: Tineidae), and close-range male attractant signals produced by females have a pheromonal basis, at least in part. Gas chromatographic-electroantennographic detection (GC-EAD) and GC-mass spectrometric analyses of bioactive methanolic extracts of male WCM disclosed three candidate pheromone components: hexadecanoic acid methyl ester (16:Ester), (Z)-9-hexadecenoic acid methyl ester (Z9—16:Ester), and octadecanoic acid methyl ester (18:Ester). In bioassay experiments in a large Plexiglas™ arena, a blend of synthetic 16:Ester plus Z9—16:Ester was attractive to male and virgin (but not mated) female WCM; the 18:Ester was inactive. GC-EAD analyses of pheromone gland extracts from female WCM revealed (E,Z)-2,13-octadecadienal (E2Z13—18:Ald) and (E,Z)-2,13-octadecadienol (E2Z13—18:OH) as candidate sex pheromone components. In arena bioassay experiments, 1—5 female equivalents of synthetic E2Z13—18:Ald (0.2 ng) and E2Z13—18:OH (0.1 ng) were more attractive to male WCM than were two virgin female WCM. We anticipate that the combination of aggregation and sex pheromones, male-produced sonic aggregation signals, and habitat-derived semiochemicals will be highly effective in attracting male and female WCM to commercial traps. Received 12 January 2001; accepted 8 June 2001.     

基于光纤分布式测温的污水管道入流识别方法研究

针对排水管网物探检测需断水操作和无法对管道内部实时探查的问题,建立了一种基于光纤分布式测温的污水管道入流诊断方法,实现了无干扰条件下污水管道污水和雨水入流实时监控.采用感温光缆对选择的试验管段开展了时空高精度观测(空间精度1m、时间精度1min),获得了超过118万个管道水温数据.基于解析出的管道水温时空图像,提出了基于水温温差背景噪声值的入流定位方法;确定出无外部入流影响时管道水温空间和时间温差的背景噪声值分别约为±0.2℃和±0.5℃.采用水温图像降噪处理方法,自动识别出污水管道的旱天污水接入和雨天雨水接入事件,与现场调查结果一致.因此,该方法对污水管道入流的动态识别定位具有可靠性.     

基于视频识别的直接作业环节安全管控

概述了石化行业现场直接作业环节的安全管控现状,分析了现场管控过程中存在的问题。考虑现场安全管理和监督监控的实际需要,设计了石化行业直接作业现场智能化管控系统,并深入研究作业现场违章行为图像识别的关键技术。现场应用表明,系统充分体现了日常管理、实时监控、智能报警、统计分析等方面的便利性和有效性,各类违章行为图像识别准确率达到80%以上,具有一定的推广价值。     

荧光法快速测定浮游植物以及应用于梅梁湖预警监测可行性探讨

YSI 6600-V2多功能水质自动监测仪采用荧光原理快速测定水体中浮游植物蓝绿藻的密度,梅梁湖夏季水华高发期时,湖体中的浮游植物几乎全部是蓝绿藻。传统的浮游植物监测前处理方面就需要24~48 h,最终要数天后才有镜检结果,导致浮游植物数据滞后,不能很好的应用到预警监测中。通过比对相关性验证,保证荧光法数据的可信度,提高工作效率,为预警监测提供有力的支持。     

A field experiment characterizing variable detection rates during plant surveys

Surveys aimed at finding threatened and invasive species can be challenging due to individual rarity and low and variable individual detection rates. Detection rate in plant surveys typically varies due to differences among observers, among the individual plants being surveyed (targets), and across background environments. Interactions among these 3 components may occur but are rarely estimated due to limited replication and control during data collection. We conducted an experiment to investigate sources of variation in detection of 2 Pilosella species that are invasive and sparsely distributed in the Alpine National Park, Australia. These species are superficially similar in appearance to other yellow-flowered plants occurring in this landscape. We controlled the presence and color of flowers on target Pilosella plants and controlled their placement in plots, which were selected for their variation in cover of non-target yellow flowers and dominant vegetation type. Observers mimicked Pilosella surveys in the plots and reported 1 categorical and 4 quantitative indicators of their survey experience level. We applied survival analysis to detection data to model the influence of both controlled and uncontrolled variables on detection rate. Orange- and yellow-flowering Pilosella in grass- and heath-dominated vegetation were detected at a higher rate than nonflowering Pilosella. However, this detection gain diminished as the cover of other co-occurring yellow-flowering species increased. Recent experience with Pilosella surveys improved detection rate. Detection experiments are a direct and accessible means of understanding detection processes and interpreting survey data for threatened and invasive species. Our detection findings have been used for survey planning and can inform progress toward eradication. Interaction of target and background characteristics determined detection rate, which enhanced predictions in the Pilosella eradication program and demonstrated the difficulty of transferring detection findings into untested environments.     

A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

• A spectral machine learning approach is proposed for predicting mixed antibiotic. • Pretreatment is far simpler than traditional detection methods. • Performance of the model is compared in different influencing factors. • Spectral machine learning is promising in the detection of complex substances. Antibiotics are widely used in medicine and animal husbandry. However, due to the resistance of antibiotics to degradation, large amounts of antibiotics enter the environment, posing a potential risk to the ecosystem and public health. Therefore, the detection of antibiotics in the environment is necessary. Nevertheless, conventional detection methods usually involve complex pretreatment techniques and expensive instrumentation, which impose considerable time and economic costs. In this paper, we proposed a method for the fast detection of mixed antibiotics based on simplified pretreatment using spectral machine learning. With the help of a modified spectrometer, a large number of characteristic images were generated to map antibiotic information. The relationship between characteristic images and antibiotic concentrations was established by machine learning model. The coefficient of determination and root mean squared error were used to evaluate the prediction performance of the machine learning model. The results show that a well-trained machine learning model can accurately predict multiple antibiotic concentrations simultaneously with almost no pretreatment. The results from this study have some referential value for promoting the development of environmental detection technologies and digital environmental management strategies.     

基于无人机的大气细颗粒物与臭氧污染探测与溯源研究进展

近年来,中国大气细颗粒物(PM_2.5)污染的治理已取得阶段性成效,但臭氧(O₃)污染快速上升,实现PM_2.5和O₃协同控制的基础与关键是针对大气污染物的精细化探测和污染溯源.随着无人机技术和传感器技术的迅速发展,基于无人机平台的大气污染探测可以有效获得近地层的PM_2.5和O₃结构特征,并结合计算机算法对大气污染事件进行精准溯源,具有高时效性、高灵活性和高时空分辨率的特征,有助于研究人员了解区域污染物的分布、变化以及来源,为大气复合污染的协同控制提供科学依据.通过回顾传统的大气污染探测方法,总结了污染探测领域常用的无人机飞行平台类型和探测仪器,归纳了基于无人机的PM_2.5和O₃污染探测应用与相关溯源算法,并展望了无人机大气探测的未来研究方向.     

饮用水快速砂滤池优势微生物群落的代谢功能解析

快速砂滤池广泛应用于饮用水处理中,其净水效能一直被认为是物理化学作用,而对滤池表面附着微生物的净水作用仍不明晰.为了解析滤池中微生物的群落构成和功能特征,研究对国内8个城市的11座饮用水快滤池的进出水和滤料进行采样分析.进出水水质分析结果表明经过滤池处理,溶解性有机碳(DOC)有少量去除,氨氮(NH_4~+-N)显著降低,硝酸盐氮(NO_3~--N)显著增加,总氮(TN)未发生明显变化.利用宏基因组技术获得了滤池中微生物群落的构成和功能信息,滤池优势菌属(相对丰度占前10%)共14种,包括两类氨氧化细菌Nitrospira和Nitrosomonas.对优势菌属的功能基因信息进行分析,发现优势微生物菌群具有更高的碳水化合物、氮、硫和异生物质代谢功能丰度. Aeromonas的碳水化合物代谢基因相对丰度最高,Bradyrhizobium的氮、硫及异生物质代谢基因的相对丰度最高,说明这两种菌是影响饮用水水质的重要菌属.通过评价各个优势菌属对异生物质的代谢潜能,发现Bradyrhizobium、Sphingomonas、Methyloglobulus、Sphingopyxis和Klebsiella是饮用水快速砂滤池中降解微量有机污染物的关键菌.