陕西长武玉米降水指数保险设计

天气指数保险能有效规避传统农业保险的弊端。本研究基于陕西长武长序列产量数据和气象数据,采用统计分析的方法进行种植面积、产量、单产、趋势产量、相对气象产量以及玉米生育期需供水的分析;采用基于损失的风险评估方法评价种植风险;采用基于Logistic模型的费率厘定方法进行纯费率厘定;根据降水指数和减产率的相关性,设计降水指数;采用投影寻踪的方法设计降水指数赔付方案。结果表明:长武玉米种植面积、产量、单产、趋势产量以及相对气象产量都呈现出增加趋势;生育期平均需水650 mm,降水434mm,亏缺216 mm;从4月下旬到8月下旬,玉米呈现出水分亏缺的状态,7月中旬抽雄期水分亏缺最多,为33.65 mm;9月出现微弱的水分盈余;1994、1995和1997年为减产年,减产率分别为14%、20%和44%,相应降水距平分别为-42.44%、-47.72%和-29.71%;玉米种植轻、中、重和巨灾的发生概率分别是7.49%、3.40%、1.43%和1%;玉米降水指数保险纯费率为2.92%;生育期内旬降水(阈值为40 mm)累积值指定为玉米降水指数,并以降水指数为变量进行分段赔付。本研究剖析了长武玉米种植状况和干旱灾害,并设计了玉米的降水指数保险产品,为长武玉米农业保险提供了新的选择,对解决农业保险目前的困境具有重要的意义。     

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。     

川滇生态屏障区景观生态风险评价及影响因素

探究重点生态功能区景观生态风险时空演变特征,揭示人类活动对景观生态风险恶化的潜在影响,对防范和化解景观生态风险、促进区域可持续发展具有重要意义。以2000、2010、2020年土地覆被数据为基础,运用ArcGIS 10.8和Fragstats 4.2等软件,基于景观格局指数和脆弱度指数构建景观生态风险评价模型,借助空间分析方法,揭示川滇生态屏障区景观生态风险时空演变特征,采用地理探测器测度不同区域范围的自然和社会经济因素对景观生态风险的影响。结果表明: 2000—2020年间,研究区人造地表、水体、灌木地面积总体呈上升趋势,林地、耕地、草地、湿地、裸地、冰川永久积雪面积总体呈下降趋势,景观类型转移以林地、草地、耕地间转移为主,其中,耕地转人造地表最显著;景观生态风险平均值有所下降,景观生态风险等级分布与景观类型变化及转移特征较为一致;研究区整体上以低生态风险、较低生态风险和较高生态风险等级为主;景观生态风险的时空演化特征总体受高程、降水等自然因素影响,经济社会及区域可达性对局部风险恶化区的影响较为突出。     

呼吸道感染患者多重耐药菌肺炎克雷伯菌的耐药及危险因素分析

摘要 目的：探讨与分析呼吸道感染患者多重耐药菌肺炎克雷伯菌的耐药及危险因素。方法：选择2015年1月到2020年2月本院诊治的呼吸道感染患者65例作为研究对象,收集患者的临床样本进行细菌分离与耐药分析,调查患者的临床资料并进行危险因素分析。结果：在呼吸道感染患者65例中,分离出多重耐药菌肺炎克雷伯菌32株,占比49.2 %,其中下呼吸道、上呼吸道、灌洗液、血液标本分别占50.0 %、9.4 %、25.0 %、6.3 %。32株多重耐药菌肺炎克雷伯菌对头孢曲松、头孢呋辛、氨苄西林、头孢吡肟、头孢噻肟的耐药率分别为71.9 %、87.5 %、96.9 %、84.4 %、81.3 %,对阿米卡星、头孢替坦、左氧氟沙星、亚胺培南、环丙沙星的敏感率分别为59.4 %、68.8 %、81.3 %、75.0 %、81.3 %。非条件 Logistic回归分析显示血型A型、碳青霉烯类抗菌药物使用、引流、机械通气、糖尿病等为导致多重耐药菌肺炎克雷伯菌感染的独立危险因素(P<0.05)。结论：多重耐药菌肺炎克雷伯菌感染在呼吸道感染患者中比较常见,对头孢呋辛、氨苄西林的耐药率比较高,对左氧氟沙星、环丙沙星的敏感率比较高,血型A型、碳青霉烯类抗菌药物使用、引流、机械通气、糖尿病等为导致多重耐药菌肺炎克雷伯菌感染的独立危险因素。     

肺癌根治术后并发症危险因素分析及其生活质量变化研究

摘要 目的：探讨肺癌根治术后并发症的危险因素,并分析其生活质量的变化。方法：纳入我院2018年9月～2020年7月收治的行肺癌根治术患者104例,对患者的临床资料进行回顾性分析。根据患者术后3个月的并发症发生情况,分成并发症组（n=32）和非并发症组（n=72）,分析肺癌根治术后并发症发生的影响因素,利用癌症病人生活质量测定量表（QLQ-C30）评估患者术前及术后3个月的生活质量。结果：肺癌根治术后32例出现并发症,其中切口感染6例,肺炎7例,肺不张9例,心律失常6例,脓胸4例。并发症组年龄≥60岁、烟龄≥10年、传统开胸术、慢性阻塞性肺疾病史人数占比高于非并发症组（P＜0.05）。多因素Logistic回归分析显示,年龄≥60岁（OR=2.978,95%CI：1.415-6.267）、烟龄≥10年（OR=3.847,95%CI：1.869-7.918）、传统开胸术（OR=3.065,95%CI：1.544-6.084）、慢性阻塞性肺疾病史（OR=2.848,95%CI：1.481-5.477）是肺癌根治术后患者发生并发症的危险因素（P＜0.05）。非并发症组术后3个月的总体生活质量量表、角色功能、躯体功能、情绪功能、社会功能评分高于术前与并发症组,且恶心呕吐、疲乏、疼痛、便秘、呼吸困难、食欲下降、睡眠障碍、腹泻评分较术前与并发症组明显降低（P＜0.05）。结论：肺癌根治术后并发症的发生主要与患者年龄、烟龄、手术方式、慢性阻塞性肺疾病史有关,且并发症对患者术后生活质量影响较大。     

维生素A、E水平与儿童反复呼吸道感染的相关性和危险因素研究

摘要 目的：研究维生素A（VA）、维生素E（VE）水平与儿童反复呼吸道感染（RRI）的相关性和危险因素。方法：选择本院2017年1月~2019年10月诊治的318例RRI患儿,同期选择285例健康体检儿童作为对照组,比较各组VA、VE表达情况,并分析儿童RRI发生的危险因素。结果：RRI组VA及VE水平低于对照组,VA及VE缺乏率高于对照组,差异有统计学意义（P<0.05）。1~3岁患儿VA及VE水平高于3~6岁组且高于>6岁组（P<0.05）。对照组和RRI组性别、出生时体重比较无统计学差异（P>0.05）;RRI组自然分娩、母乳喂养率低于对照组,偏食、过敏体质、抗生素滥用、人均居住面积<13 m²、母亲文化高中及以下率高于对照组（P<0.05）。多因素Logistic回归分析显示,早产、偏食、过敏体质、被动吸烟、抗生素滥用、人均居住面积<13 m²是RRI发生的独立危险因素,VA、VE、自然分娩、母乳喂养、母亲文化程度是RRI发生的保护因素。结论：RRI儿童VA、VE水平较低,且RRI的发生和多种危险因素相关,临床应针对性的进行预防干预。     

6种复配杀虫剂对七星瓢虫的毒力测定及风险评估

采用药膜法分别测定了10％甲维盐·茚虫威悬浮剂、12％甲维盐·虫螨腈悬浮剂、12％虫螨腈·虱螨脲悬浮剂、14％呋虫胺·螺虫乙酯悬浮剂、22％吡虫啉·螺虫乙酯悬浮剂和6％吡虫啉·高效氯氟氰菊酯悬浮剂6种复配杀虫剂对七星瓢虫Coccinella septempunctata Linnaeus幼虫急性毒性,并进行了初级风险评估.结果 显示,6种药剂对七星瓢虫48 h的LR5o(半致死用量,Median lethal rate)分别为0.812、2.255、4.082、22.735、6.755和0.00467 g a.i/hm2.在农田内暴露场景下,6种复配杀虫剂对七星瓢虫风险均不可接受;在农田外暴露场景下,仅有14％呋虫胺·螺虫乙酯悬浮剂和22％吡虫啉·螺虫乙酯悬浮剂对七星瓢虫风险可接受,其它均不可接受.结果 表明在田间最大推荐用量下,6种药剂对七星瓢虫的初级风险评价均存在高风险.     

Assessment of risk management and control measures against coronavirus disease

This article presents the COVID-19 situation and control measures taken by the Government of Pakistan. Two waves of pandemic are faced globally and similar in the study area. We have investigated the risk management decision in two phases. Primarily, strict lockdown was observed from March 2020 to July 2020 and smart lockdown was enforced from August 2020 to December 2020. It has been studied that during strict lockdown, COVID cases reduced gradually but reopening of institutes and smart lockdown strategy resulted gradual increase in confirmed cases and death rates. During first wave of COVID-19 in Pakistan, a total confirmed number of patients of COVID-19 were 263,496 till 18th of July 2020 with total deaths of 5,568 people and 204,276 recoveries, while total number of COVID-19 patients reached 555,511 till 9th of February 2021 with total deaths of 12,026 people. Province of Sindh was affected badly with total number of 251,434 COVID-19 cases followed by Punjab Province with total number of 161,347 COVID-19 till 9th of February 2020.     

Cancer mortality in a population-based cohort of American Indians – The strong heart study

BackgroundCancer mortality among American Indian (AI) people varies widely, but factors associated with cancer mortality are infrequently assessed.MethodsCancer deaths were identified from death certificate data for 3516 participants of the Strong Heart Study, a population-based cohort study of AI adults ages 45–74 years in Arizona, Oklahoma, and North and South Dakota. Cancer mortality was calculated by age, sex and region. Cox proportional hazards model was used to assess independent associations between baseline factors in 1989 and cancer death by 2010.ResultsAfter a median follow-up of 15.3 years, the cancer death rate per 1000 person-years was 6.33 (95 % CI 5.67–7.04). Cancer mortality was highest among men in North/South Dakota (8.18; 95 % CI 6.46–10.23) and lowest among women in Arizona (4.57; 95 % CI 2.87–6.92). Factors independently associated with increased cancer mortality included age, current or former smoking, waist circumference, albuminuria, urinary cadmium, and prior cancer history. Factors associated with decreased cancer mortality included Oklahoma compared to Dakota residence, higher body mass index and total cholesterol. Sex was not associated with cancer mortality. Lung cancer was the leading cause of cancer mortality overall (1.56/1000 person-years), but no lung cancer deaths occurred among Arizona participants. Mortality from unspecified cancer was relatively high (0.48/100 person-years; 95 % CI 0.32−0.71).ConclusionsRegional variation in AI cancer mortality persisted despite adjustment for individual risk factors. Mortality from unspecified cancer was high. Better understanding of regional differences in cancer mortality, and better classification of cancer deaths, will help healthcare programs address cancer in AI communities.     

The future excess fraction of cancer due to lifestyle factors in Australia

BackgroundMany cancers are caused by exposure to lifestyle, environmental, and occupational factors. Earlier studies have estimated the number of cancers occurring in a single year which are attributable to past exposures to these factors. However, there is now increasing appreciation that estimates of the future burden of cancer may be more useful for policy and prevention. We aimed to calculate the future number of cancers expected to arise as a result of exposure to 23 modifiable risk factors.MethodsWe used the future excess fraction (FEF) method to estimate the lifetime burden of cancer (2016–2098) among Australian adults who were exposed to modifiable lifestyle, environmental, and occupational risk factors in 2016. Calculations were conducted for 26 cancer sites and 78 cancer-risk factor pairings.ResultsThe cohort of 18.8 million adult Australians in 2016 will develop an estimated 7.6 million cancers during their lifetime, of which 1.8 million (24%) will be attributable to exposure to modifiable risk factors. Cancer sites with the highest number of future attributable cancers were colon and rectum (n = 717,700), lung (n = 380,400), and liver (n = 103,200). The highest number of future cancers will be attributable to exposure to tobacco smoke (n = 583,500), followed by overweight/obesity (n = 333,100) and alcohol consumption (n = 249,700).ConclusionA significant proportion of future cancers will result from recent levels of exposure to modifiable risk factors. Our results provide direct, pertinent information to help determine where preventive measures could best be targeted.