Empowering high seas governance with satellite vessel tracking data

Between 1950 and 1989, marine fisheries catch in the open‐ocean and deep‐sea beyond 200 nautical miles from shore increased by a factor of more than 10. While high seas catches have since plateaued, fishing effort continues to increase linearly. The combination of increasing effort and illegal, unreported and unregulated (IUU) fishing has led to overfishing of target stocks and declines in biodiversity. To improve management, there have been numerous calls to increase monitoring, control and surveillance (MCS). However, MCS has been unevenly implemented, undermining efforts to sustainably use high seas and straddling stocks and protect associated species and ecosystems. The United Nations General Assembly is currently negotiating a new international treaty for the conservation and sustainable use of biodiversity beyond national jurisdiction (BBNJ). The new treaty offers an excellent opportunity to address discrepancies in how MCS is applied across regional fisheries management organizations (RFMOs). This paper identifies ways that automatic identification system (AIS) data can inform MCS on the high seas and thereby enhance conservation and management of biodiversity beyond national jurisdictions. AIS data can be used to (i) identify gaps in governance to underpin the importance of a holistic scope for the new agreement; (ii) monitor area‐based management tools; and (iii) increase the capacity of countries and RFMOs to manage via the technology transfer. Any new BBNJ treaty should emphasize MCS and the role of electronic monitoring including the use of AIS data, as well as government–industry–civil society partnerships to ensure critically important technology transfer and capacity building.     

构建舆情监测信息系统是网络舆情监测的必由之路

网络舆情错综复杂,现有的手工方式已无法满足互联网舆情实时监测与分析的要求,构建网络舆情监测信息系统是及时了解民意、掌握热点、发现与化解社会问题的必由之路;网络舆情监测系统就是运用信息自动发现与搜集、智能识别与处理等多种技术,能够满足指定主题自动发现与跟踪、舆情统计分析与热点趋势预测、正面言论回复与积极舆论引导等功能要求,是网络信息预警的重要组成部分。     

Methods to detect a vian influenza virus for food safety surveillance

Avian influenza(AI),caused by the influenza A virus,has been a global concern for public health. AI outbreaks not only impact the p oultry production,but also give rise to a risk in food safety caused by viral contamination of poultry products in the food supply chain. Distinctions in AI outbreak between strains H5N1 and H7N9 indicate that early detection of the AI virus in poultry is crucial for the effective warning and control of AI to ensure food safety. Therefore,t he establishment of a poultry surveillance system for food safety by early detection is urgent and critical. In this article,methods to detect AI virus,including current methods recommended by the World Health Organization(WHO) and the World Organisation for Animal Health(Office International des Epizooties,OIE) and novel techniques not commonly used or commercialized are reviewed and evaluated for feasibility of use in the poultry surveillance system. Conventional methods usually applied for the purpose of AI diagnosis face some practical challenges to establishing a comprehensive poultry surveillance program in the poultry supply chain. Diverse development of new technologies can meet the specific requirements of AI virus detection in various stages or scenarios throughout the poultry supply chain where onsite,rapid and ultrasensitive methods are emphasized. Sy stematic approaches or integrated methods ought to be employed according to the application scenarios at every stage of the poultry supply chain to prevent AI outbreaks.     

Detection and Control of a Nosocomial Outbreak Caused by Salmonella Newport at a Large Animal Hospital

Background: Nosocomial salmonellosis is often assumed to occur because infection control and surveillance practices are inadequate, but published evidence is lacking to support the related contention that rigorous application of these practices can impact the severity of outbreaks. Objective: Describe active surveillance, early recognition, and intensive mitigation efforts used in an effort to control an outbreak of nosocomial Salmonella enterica serotype Newport infections without hospital closure. Animals: Large animals hospitalized at a referral hospital. Methods: This prospective outbreak investigation was initiated when Salmonella Newport infections were detected among hospitalized animals by active surveillance. Data were analyzed to identify temporal and spatial patterns for epidemic spread of Salmonella in the hospital. Mitigation efforts were aggressively adjusted in response to surveillance data. Genetic relatedness of isolates was investigated by pulsed‐field gel electrophoresis. Results: Of 145 large animals sampled, 8 (5.6%) were infected with the Salmonella strain associated with this outbreak, and all but 1 shed Salmonella in the absence of or before the onset of disease. This strain was recovered from 14.2% (42/295) of environmental samples (ENV samples), indicating that widespread environmental contamination had occurred. Isolates of Salmonella Newport obtained from infected animals and the environment were genetically indistinguishable, confirming clonal dissemination. Conclusions and Clinical Importance: Active surveillance allowed early detection of nosocomial Salmonella transmission and hospital contamination. Use of aggressive interventions was followed by cessation of transmission. Active surveillance can allow earlier recognition and mitigation compared with programs by only sampling of clinically affected animals.     

广西部分地区猪群主要疫病免疫抗体监测和免疫效果分析

从广西5个地市33家养殖场随机采集免疫过猪繁殖与呼吸综合征(PRRS)、猪瘟(CSF)、猪伪狂犬病(PR)疫苗的血清样品393份,使用免疫胶体金方法对样品的抗体水平进行检测。结果显示,不同养殖规模的猪场抗体阳性率总体较高的是猪瘟,达75.06%;其次是猪伪狂犬病,为71.25%;而猪繁殖与呼吸综合征抗体最低,仅为42.24%。各种病原抗体在不同规模的猪场有较大的差别,规模猪场的猪群某些疫病的抗体未必比散养猪群高。通过对不同类型的疫苗所产生的抗体水平进行比较,结果发现,使用猪繁殖与呼吸综合征灭活苗和弱毒苗、猪瘟脾淋苗和细胞苗、猪伪狂犬病国产苗和进口苗免疫所产生的抗体水平没有明显差异。     

基于遥感图象的土地利用分类方法研究

随着遥感技术和各种地学分析模型的发展和深入,利用遥感技术实现城市用地的快速分类将具有重要的实践意义。从遥感图像的应用出发,深入研究了基于遥感影像下的城镇用地的分类方法,并对提出的分类方法进行了若干讨论。     

计算机视觉技术在智慧羊场中的研究进展

智慧羊场具有科学化、标准化管理的优点,是羊场转型发展的趋势。计算机视觉技术是人工智能的核心,可以实现非接触性、自动化、实时监测羊的个体信息。本文总结并分析了计算机视觉在羊场的个体识别方法、行为识别方法、体尺与体重估测、疾病监测的研究现状,并指出计算机视觉在智慧羊场的进一步发展趋势。 [关键词] 计算机视觉|智慧羊场|个体识别|行为识别|体尺与体重估测|疾病监测     

Australian surveillance for avian influenza viruses in wild birds between July 2005 and June 2007

Objective   To identify and gain an understanding of the influenza viruses circulating in wild birds in Australia.
Design   A total of 16,303 swabs and 3782 blood samples were collected and analysed for avian influenza (AI) viruses from 16,420 wild birds in Australia between July 2005 and June 2007. Anseriformes and Charadriiformes were primarily targeted.
Procedures   Cloacal, oropharyngeal and faecal (environmental) swabs were tested using polymerase chain reaction (PCR) for the AI type A matrix gene. Positive samples underwent virus culture and subtyping. Serum samples were analysed using a blocking enzyme-linked immunosorbent assay for influenza A virus nucleoprotein.
Results   No highly pathogenic AI viruses were identified. However, 164 PCR tests were positive for the AI type A matrix gene, 46 of which were identified to subtype. A total of five viruses were isolated, three of which had a corresponding positive PCR and subtype identification (H3N8, H4N6, H7N6). Low pathogenic AI H5 and/or H7 was present in wild birds in New South Wales, Tasmania, Victoria and Western Australia. Antibodies to influenza A were also detected in 15.0% of the birds sampled.
Conclusions   Although low pathogenic AI virus subtypes are currently circulating in Australia, their prevalence is low (1.0% positive PCR). Surveillance activities for AI in wild birds should be continued to provide further epidemiological information about circulating viruses and to identify any changes in subtype prevalence.