A review on the factors affecting mite growth in stored grain commodities

A thorough review of the literature has identified the key factors and interactions that affect the growth of mite pests on stored grain commodities. Although many factors influence mite growth, the change and combinations of the physical conditions (temperature, relative humidity and/or moisture content) during the storage period are likely to have the greatest impact, with biological factors (e.g. predators and commodity) playing an important role. There is limited information on the effects of climate change, light, species interactions, local density dependant factors, spread of mycotoxins and action thresholds for mites. A greater understanding of these factors may identify alternative control techniques. The ability to predict mite population dynamics over a range of environmental conditions, both physical and biological, is essential in providing an early warning of mite infestations, advising when appropriate control measures are required and for evaluating control measures. This information may provide a useful aid in predicting and preventing mite population development as part of a risk based decision support system.     

检验危急值预警防控系统的设立与运行

阐述医院检验危急值预警防控系统的设立及运行模式,通过科学遴选预警项目、构建信息化平台、建立临床反馈机制等措施,优化系统流程设计。分析评价预警防控系统的运行效果,指出系统紧贴临床实际,缩短检验结果的周转时间,有效降低医疗风险,对提高医疗质量和管理水平起到重要的促进作用。     

Spatiotemporal evolution for early warning of ecological carrying capacity during the urbanization process in the Dongting Lake area,China

Early warning of ecological carrying capacity (ECC) is essential for the urbanization process. It provides information on the deterioration of the urban ecological environment and potential hazards and promotes sustainable development. However, research on early warning of ECC in the urbanization process has been limited and mainly focused on a single aspect of ECC, a comprehensive early warning system based on the three perspectives of ecological elasticity (EE), resource carrying (RC), and environmental carrying (EC) is still lacking. Additionally, previous studies did not effectively predict the future evolution trends of ECC and failed to reveal the spatiotemporal evolution characteristics. To address these shortcomings, in this study, we propose a comprehensive early warning indicator of ECC incorporating EE, RC, and EC. The state space technique and back-propagation (BP) neural network model were applied to analyze the spatiotemporal evolution of the warning state in the Dongting Lake area. The results showed that the ECC index decreased from 0.5383 to 0.5364 in 2001–2020, and the risk status was in the range of medium risk. This result was consistent with an analysis of the three subsystems, which indicated that urbanization exceeded the ECC. The early warning index of ECC is predicted to be 0.6257 in 2025. The risk status is predicted to be low risk. From 2001 to 2020, the risk status of counties were significantly different. Most counties in the central area had a low-risk status, and peripheral counties had a medium-risk status. The counties with the most serious risk status were mostly distributed along the edge of the area, in places with higher urbanization rates. Neither the spatial pattern nor the risk status is predicted to change substantially before 2025. Therefore, some targeted measures should be taken to improve the ECC and mitigate the risk.     

小麦倒春寒研究现状与进展

由于全球气候变暖,近年来小麦低温灾害事件频发,尤其是拔节-孕穗期的倒春寒灾害已成为制约小麦产量和品质的重要因素之一。本文综述了小麦倒春寒灾害的发生特点(鉴定与分级、时空特征),倒春寒对小麦生理特性(叶片、茎秆、穗部、根系)和产量、质量的影响,总结了抗倒春寒小麦育种、倒春寒危害的分子生物学机制及灾害的监测预警与风险评估等方面的研究进展,并从小麦抗倒春寒遗传基础、倒春寒危害小麦评价体系和防控技术体系等方面进行了展望,以期为抗倒春寒小麦品种的遗传改良和栽培调控新措施的建立提供理论依据。     

国际预警体系研究进展及其对国内生态环境预警研究的启示

从1990年联合国"国际减灾10年"倡导构建灾害预警系统以来,国际预警研究迄今已有30年历程。通过回溯国际预警研究发展历程,解析预警体系要素构成框架,探究各类缓慢性、突发性灾害与风险研究进展,探讨不同社会参与主体在预警体系中的职能,指出未来若干年,我国国内生态环境预警研究亟需强化"以人为本"的研究理念,突破"区域综合性生态环境评估"粗放化、模糊化处理研究区各类生态环境风险的传统窠臼,对不同类型生态环境问题开展精细化探索,同时重视风险受众对预警信息接收、接受和响应能力,并与相关机构和组织建立跨地域、跨行业的信息技术交流与协作机制,让利益相关者更广泛地参与预警体系建设,从而接轨国际研究,使研究成果可为精准化生态环境应急管理工作实践提供强有力支撑。     

世界各国应对番茄褐色皱果病毒跨境传播采取的植物检疫措施分析及启示

[目的]为了防范番茄褐色皱果病毒（Tomato brown rugose fruit virus,ToBRFV）跨境传入我国,本文对世界各国应对ToBRFV跨境传播采取的植物检疫措施进行分析,以期为我国应对ToBRFV提供参考。[方法]通过查阅各国政府和国际组织官网、文献等资料,收集世界各国应对ToBRFV的相关信息,对其开展的早期预警、出台的植物检疫政策和防控措施等进行梳理和分析。[结果]为防范ToBRFV的快速蔓延风险,各国和组织通过将其列入警示名单、向WTO提交相关通报、发表相关文章等手段开展早期预警。为防止ToBRFV的传入和扩散,各国和组织快速出台将其列为官方管制对象、制修订相关进境检疫法律法规等植物检疫政策;从产地、出口前、进口后等方面制定了严格的防控措施。[结论]早识别、早应对是国际上应对ToBRFV跨境传播的关键举措,各国的检疫措施整体严格。建议我国继续关注ToBRFV发生动态、适时调整检疫措施、提高口岸检疫能力、开展调查监测。     

Environmental DNA/RNA for pathogen and parasite detection,surveillance, and ecology

Detection of pathogens, parasites, and other symbionts in environmental samples via eDNA/eRNA (collectively eNA) is an increasingly important source of information about their occurrence and activity. There is great potential for using such detections as a proxy for infection of host organisms in connected habitats, for pathogen monitoring and surveillance, and for early warning systems for disease. However, many factors require consideration, and appropriate methods developed and verified, in order that eNA detections can be reliably interpreted and adopted for surveillance and assessment of disease risk, and potentially inclusion in international standards, such as the World Organisation for Animal Health guidelines. Disease manifestation results from host–symbiont–environment interactions between hosts, demanding a multifactorial approach to interpretation of eNA signals.     

基于植保大数据的病虫害移动智能采集新设备

为提高农作物重大病虫害发生信息自动化、智能化采集能力,全面提升监测预警水平,笔者基于大数据、人工智能和深度学习技术,研发了一款农作物病虫害移动智能采集设备——智宝,主要实现了3个方面的功能:一是病虫害发生信息自动采集上报.通过该产品进行人工拍照,可实现对田间农作物重大病虫害发生图像、发生位置、发生数量、微环境因子等数据的实时采集和上报.二是自动识别计数.基于植保大数据与人工智能技术,通过构建病虫害自动识别系统,可实现重大病虫害精准识别与分析,只要拍摄照片,即可快速、精确地识别病虫害种类,并自动计数、上报到指定的测报系统.三是自动分析判别分级.针对拍摄采集上报的重大病虫害发生信息,系统可在自动识别和计数的基础上,进一步对病虫害发生严重程度进行智能判别分级,甚至根据相关预测模型,对病虫害的发生趋势进行辅助分析预测,提出预测建议.通过2016—2019年组织多地植保机构进行试验改进,该技术产品日趋成熟,有望在未来的农作物病虫害发生信息采集和预测预报工作中推广使用.     

世界双遗产地生态安全预警体系构建及应用——以武夷山风景名胜区为例

基于遗产地内涵与标准的深入分析,提出符合世界遗产地保护目的的双遗产地生态安全概念,构建“压力-状态-调控”遗产地生态安全预警框架模型并筛选预警指标.在此基础上,以受自然和人类生态过程作用最强烈且频繁的武夷山风景名胜区为研究对象,引入可拓学中的物元模型理论对景区生态安全进行预警.结果表明： 景区生态安全预警准确率达84%;1997和2009年景区总体生态安全等级均处于Ⅰ级安全水平,即属于无警状态,但2009年生态安全系统风险增加,较1997年更趋于向更低水平的安全等级(Ⅱ)转变.2个时期处于敏感预警等级的单项预警“重警源”指标（限制因子）有所差异;1997-2009年间景区生态安全系统压力增加的同时其调控能力也在增强,但系统仍面临生态安全压力.人口密度、人口自然增长率、茶园面积指数、人均耕地面积、干旱程度、生态环保建设投入是制约景区未来（2009年之后）生态安全状态的主要限制因子.总体而言,景区生态安全状态较好（无警水平）,但就个别预警指标而言,依然存在风险,需要针对上述限制因子采取有效防控措施,维持景区生态安全的良好状态.     

Multi-Step Polynomial Regression Method to Model and Forecast Malaria Incidence

Malaria is one of the most severe problems faced by the world even today. Understanding the causative factors such as age, sex, social factors, environmental variability etc. as well as underlying transmission dynamics of the disease is important for epidemiological research on malaria and its eradication. Thus, development of suitable modeling approach and methodology, based on the available data on the incidence of the disease and other related factors is of utmost importance. In this study, we developed a simple non-linear regression methodology in modeling and forecasting malaria incidence in Chennai city, India, and predicted future disease incidence with high confidence level. We considered three types of data to develop the regression methodology: a longer time series data of Slide Positivity Rates (SPR) of malaria; a smaller time series data (deaths due to Plasmodium vivax) of one year; and spatial data (zonal distribution of P. vivax deaths) for the city along with the climatic factors, population and previous incidence of the disease. We performed variable selection by simple correlation study, identification of the initial relationship between variables through non-linear curve fitting and used multi-step methods for induction of variables in the non-linear regression analysis along with applied Gauss-Markov models, and ANOVA for testing the prediction, validity and constructing the confidence intervals. The results execute the applicability of our method for different types of data, the autoregressive nature of forecasting, and show high prediction power for both SPR and P. vivax deaths, where the one-lag SPR values plays an influential role and proves useful for better prediction. Different climatic factors are identified as playing crucial role on shaping the disease curve. Further, disease incidence at zonal level and the effect of causative factors on different zonal clusters indicate the pattern of malaria prevalence in the city. The study also demonstrates that with excellent models of climatic forecasts readily available, using this method one can predict the disease incidence at long forecasting horizons, with high degree of efficiency and based on such technique a useful early warning system can be developed region wise or nation wise for disease prevention and control activities.     

Evaluating surveillance strategies for the early detection of low pathogenicity avian influenza infections

In recent years, the early detection of low pathogenicity avian influenza (LPAI) viruses in poultry has become increasingly important, given their potential to mutate into highly pathogenic viruses. However, evaluations of LPAI surveillance have mainly focused on prevalence and not on the ability to act as an early warning system. We used a simulation model based on data from Italian LPAI epidemics in turkeys to evaluate different surveillance strategies in terms of their performance as early warning systems. The strategies differed in terms of sample size, sampling frequency, diagnostic tests, and whether or not active surveillance (i.e., routine laboratory testing of farms) was performed, and were also tested under different epidemiological scenarios. We compared surveillance strategies by simulating within-farm outbreaks. The output measures were the proportion of infected farms that are detected and the farm reproduction number (R(h)). The first one provides an indication of the sensitivity of the surveillance system to detect within-farm infections, whereas R(h) reflects the effectiveness of outbreak detection (i.e., if detection occurs soon enough to bring an epidemic under control). Increasing the sampling frequency was the most effective means of improving the timeliness of detection (i.e., it occurs earlier), whereas increasing the sample size increased the likelihood of detection. Surveillance was only effective in preventing an epidemic if actions were taken within two days of sampling. The strategies were not affected by the quality of the diagnostic test, although performing both serological and virological assays increased the sensitivity of active surveillance. Early detection of LPAI outbreaks in turkeys can be achieved by increasing the sampling frequency for active surveillance, though very frequent sampling may not be sustainable in the long term. We suggest that, when no LPAI virus is circulating yet and there is a low risk of virus introduction, a less frequent sampling approach might be admitted, provided that the surveillance is intensified as soon as the first outbreak is detected.     

The social impacts of the heat–health watch/warning system in Phoenix,Arizona: assessing the perceived risk and response of the public

Heat is the leading weather-related killer in the United States. Although previous research suggests that social influences affect human responses to natural disaster warnings, no studies have examined the social impacts of heat or heat warnings on a population. Here, 201 surveys were distributed in Metropolitan Phoenix to determine the social impacts of the heat warning system, or more specifically, to gauge risk perception and warning response. Consistent with previous research, increased risk perception of heat results in increased response to a warning. Different social factors such as sex, race, age, and income all play an important role in determining whether or not people will respond to a warning. In particular, there is a strong sense of perceived risk to the heat among Hispanics which translates to increased response when heat warnings are issued. Based on these findings, suggestions are presented to help improve the Phoenix Heat Warning System.     

北疆牧区雪灾预警与风险评估方法

在分析北疆牧区畜牧业生产与雪灾分布特征的基础上,利用RS、GIS和地面监测资料,从草地抗灾力、家畜承灾体和积雪致灾力3个子系统中选择9个因素作为预警参评因子,用家畜死亡率作为风险评估因子,构建了一个在完全放牧状态下的牧区雪灾预警与风险评估体系和模式.采用多层次综合法和目标线性加权函数法,建立了雪灾预警分级模型、判别模型和风险评估模型,据此可对草地和家畜抵御雪灾的强度和雪灾的风险期望损失进行动态预警与评估.北疆牧区雪灾预警与风险评估模型的精度分别达85%和72%.     

Renal programming: cause for concern?

Development of the kidney can be altered in utero in response to a suboptimal environment. The intrarenal factors that have been most well characterized as being sensitive to programming events are kidney mass/nephron endowment, the renin-angiotensin system, tubular sodium handling, and the renal sympathetic nerves. Newborns that have been subjected to an adverse intrauterine environment may thus begin life at a distinct disadvantage, in terms of renal function, at a time when the kidney must take over the primary role for extracellular fluid homeostasis from the placenta. A poor beginning, causing renal programming, has been linked to increased risk of hypertension and renal disease in adulthood. However, although a cause for concern, increasingly, evidence demonstrates that renal programming is not a fait accompli in terms of future cardiovascular and renal disease. A greater understanding of postnatal renal maturation and the impact of secondary factors (genes, sex, diet, stress, and disease) on this process is required to predict which babies are at risk of increased cardiovascular and renal disease as adults and to be able to devise preventative measures.     

Model Development for Risk Assessment of Driving on Freeway under Rainy Weather Conditions

Rainy weather conditions could result in significantly negative impacts on driving on freeways. However, due to lack of enough historical data and monitoring facilities, many regions are not able to establish reliable risk assessment models to identify such impacts. Given the situation, this paper provides an alternative solution where the procedure of risk assessment is developed based on drivers’ subjective questionnaire and its performance is validated by using actual crash data. First, an ordered logit model was developed, based on questionnaire data collected from Freeway G15 in China, to estimate the relationship between drivers’ perceived risk and factors, including vehicle type, rain intensity, traffic volume, and location. Then, weighted driving risk for different conditions was obtained by the model, and further divided into four levels of early warning (specified by colors) using a rank order cluster analysis. After that, a risk matrix was established to determine which warning color should be disseminated to drivers, given a specific condition. Finally, to validate the proposed procedure, actual crash data from Freeway G15 were compared with the safety prediction based on the risk matrix. The results show that the risk matrix obtained in the study is able to predict driving risk consistent with actual safety implications, under rainy weather conditions.     

进境台湾果蔬主要病虫风险评估与早期监测预警

[目的] 定量评估进境台湾果蔬主要病虫在大陆的入侵风险,为早期监测预警提供依据。[方法] 首先,基于外来物种传入、定殖、扩散和危害的一般过程,构建了包括4个层次26个指标的进境台湾果蔬有害生物入侵综合风险评估指标体系,规范了风险指数的计算方法,评估了14种病虫随台湾果蔬入侵的综合风险;其次,利用气候生态位模型预测了它们在我国的适生区;最后,融合进境台湾果蔬到达港口、物流中转站、集散地和主要病虫的适生区的空间分布,通过空间叠加分析划定了它们在我国传入并定殖的风险区及其空间异质性。[结果] 14种被评估病虫在我国的综合风险值均大于0.7,属高风险等级。14种病虫在我国都存在广阔的适生区,但不同物种适生区的面积和空间分布存在差异。福建、广东、海南等东部和南部沿海地区是它们适生区的高度重叠区,超过95%的台湾果蔬到达港口、物流中转和集散地的集中分布区,潜在的入侵风险极高。[结论] 进境台湾果蔬贸易能够促进或加剧外来有害生物的入侵。研究进一步证实了台湾已经并将继续成为大陆外来有害生物入侵的踏板。建议进一步加强海关检疫、开展和完善监测预警等措施预防和控制有害生物入侵,为农业生物安全提供保障。     

基于纳米孔测序技术的柑橘黄龙病检测方法建立

柑橘黄龙病(Huanglongbing)是柑橘生产上最具毁灭性的病害,及时快速地进行早期检测和诊断是防控黄龙病的关键措施之一.本文利用掌上纳米孔测序仪MinION对携带黄龙病菌Candidatus Liberibacter asiaticus的DNA样品进行测序,并利用Blast、GraphMap、minimap2以及两种bwa的比对方法将测序结果比对到黄龙病菌基因组上,比对结果均匀的比对到黄龙病菌基因组上,并未发现严重的偏倚现象,验证了其测序结果的可靠性.本技术可弥补因柑橘木虱Diaphorina citri(Kuwayama)虫体过小或损坏难以进行光学识别的不足,并可同时检测虫体是否携带有黄龙病菌,对有黄龙病发生风险但尚未有黄龙病实际发生的柑橘种植区提供实时实地的监控与预警.     

病人安全管理信息系统理论设计

由于病人安全风险因素的复杂性,人工管理不但任务繁钜而且对可能发生的医疗不良事件也难以做到及时的预警与防控。借助信息技术来提升病人安全管理绩效,是改善病人安全状况的创新性探索。本研究立足于我国病人安全管理的现实需要,通过运用数学建模技术,从三个维度、两个预警点,对医院病人安全信息进行全面、动态的收集、分析与评估,并在此基础上,实现对病人安全状况的实时监测与预警,达到降低医疗风险、减少不良事件,最大限度保障病人安全的目标。     

HRAS: a webserver for early warning of human health risk brought by aflatoxin

Most people are aware that outdoor air pollution can damage their health, but many do not know that indoor air pollution can also exhibit significant negative health effects. Fungi parasitizing in air conditioning and ventilation systems can be one of indoor air pollution sources. Aflatoxin produced by Aspergillus flavus (A. flavus) became a central focus of indoor air pollution, especially in farmer markets. Therefore we developed an early warning system, Health Risk Assessment System, to estimate the growth rate of A. flavus, predict the amount of aflatoxin and provide early warning information. Firstly, the growth of A. flavus and the production of aflatoxin under different conditions were widely obtained through a comprehensive literature review. Secondly, three mathematical models were established to predict the A. flavus colony growth rate, lag phase duration and aflatoxin content, as functions of temperature and water activity based on present studies. Finally, all the results were evaluated by the user-supplied data using PHP programming language. We utilized the web page to show the results and display warning information. The JpGraph library was used to create a dynamic line chart, refreshing the warning information dynamically in real-time. The HARS provides accurate information for early warning purposes to let us take timely steps to protect ourselves.     

What Can Be Done to Ensure the Usefulness of Epidemiologic Data for Risk Assessment Purposes?

Epidemiologic studies can play a central role in risk assessments. They are used in all risk assessment phases: hazard identification, dose-response, and exposure assessment. Epidemiologic studies have often been the first to show that a particular environmental exposure is a hazard to health. They have numerous advantages with respect to other sources of data which are used in risk assessments, the most important being that they do not require the assumption that they are generalizable to humans. For this reason, fewer and lower uncertainty factors may be appropriate in risk characterization based on epidemiologic studies. Unfortunately, epidemiologic studies have numerous problems, the most important being that the exposures are often not precisely measured. This article presents in detail the advantages of and problems with epidemiologic studies. It discusses two approaches to ensure their usefulness, biomarkers and an ordinance which requires baseline and subsequent surveillance of possible exposures and health effects from newly sited potentially polluting facilities. Biomarkers are biochemical measures of exposure, susceptibility factors, or preclinical pathological changes. Biomarkers are a way of dealing with the problems of poor measures, differential susceptibility and lack of early measures of disease occurrence that inherent in many environmental epidemiologic studies. The advantages of biomarkers is they can provide objective information on exposure days, months or even years later and evidence of pathology perhaps years earlier. The ordinance makes possible the use of a powerful epidemiologic study design, the prospective cohort study, where confounder(s) are best measured, and exposures, pathological changes, and health effects can be detected as soon as possible.