利用1961—2016年宁夏20个气象站冬季逐月气温资料,英国气象局哈德莱Hadley中心秋季逐月海冰密集度资料,美国国家环境预报中心/国家大气研究中心(NCEP/NCAR)冬季逐月大气再分析资料,分析2016年宁夏冬季气温异常特征及其成因。结果表明:2...利用1961—2016年宁夏20个气象站冬季逐月气温资料,英国气象局哈德莱Hadley中心秋季逐月海冰密集度资料,美国国家环境预报中心/国家大气研究中心(NCEP/NCAR)冬季逐月大气再分析资料,分析2016年宁夏冬季气温异常特征及其成因。结果表明:2016年冬季,宁夏气温创1961年以来同期最高。2016年500 hPa欧亚中高纬地区纬向环流明显,乌拉尔山阻塞高压异常偏弱,中国大陆上空位势高度场异常偏强,极涡偏向大西洋欧洲区和北美大陆;东亚冬季风指数为-1. 29 m·s^(-1),为1961年以来第5低值;西伯利亚高压强度距平为-1. 5 h Pa,为2000年以来第2低值。秋季格陵兰海冰密集度偏低,导致东亚冬季风偏弱;500 h Pa位势高度场上北极、欧亚大陆和阿留申地区呈现"-+-"的波列形态,使得北极与欧亚大陆中高纬地区的位势高度差增强,中高纬西风气流偏强,纬向活动加强;海平面气压场上西伯利亚高压偏弱,不利于北极冷空气向低纬地区侵袭,使得2016年冬季宁夏气温异常偏高。展开更多
The impact of pesticides on insect pollinators has caused worldwide concern. Both global bee decline and stopping the use of pesticides may have serious consequences for food security. Automated and accurate predictio...The impact of pesticides on insect pollinators has caused worldwide concern. Both global bee decline and stopping the use of pesticides may have serious consequences for food security. Automated and accurate prediction of chemical poisoning of honey bees is a challenging task owing to a lack of understanding of chemical toxicity and introspection. Deep learning(DL) shows potential utility for general and highly variable tasks across fields. Here, we developed a new DL model of deep graph attention convolutional neural networks(GACNN) with the combination of undirected graph(UG) and attention convolutional neural networks(ACNN) to accurately classify chemical poisoning of honey bees. We used a training dataset of 720 pesticides and an external validation dataset of 90 pesticides, which is one order of magnitude larger than the previous datasets. We tested its performance in two ways: poisonous versus nonpoisonous and GACNN versus other frequently-used machine learning models. The first case represents the accuracy in identifying bee poisonous chemicals. The second represents performance advantages. The GACNN achieved ~6% higher performance for predicting toxic samples and more stable with ~7%Matthews Correlation Coefficient(MCC) higher compared to all tested models, demonstrating GACNN is capable of accurately classifying chemicals and has considerable potential in practical applications.In addition, we also summarized and evaluated the mechanisms underlying the response of honey bees to chemical exposure based on the mapping of molecular similarity. Moreover, our cloud platform(http://beetox.cn) of this model provides low-cost universal access to information, which could vitally enhance environmental risk assessment.展开更多
文摘利用1961—2016年宁夏20个气象站冬季逐月气温资料,英国气象局哈德莱Hadley中心秋季逐月海冰密集度资料,美国国家环境预报中心/国家大气研究中心(NCEP/NCAR)冬季逐月大气再分析资料,分析2016年宁夏冬季气温异常特征及其成因。结果表明:2016年冬季,宁夏气温创1961年以来同期最高。2016年500 hPa欧亚中高纬地区纬向环流明显,乌拉尔山阻塞高压异常偏弱,中国大陆上空位势高度场异常偏强,极涡偏向大西洋欧洲区和北美大陆;东亚冬季风指数为-1. 29 m·s^(-1),为1961年以来第5低值;西伯利亚高压强度距平为-1. 5 h Pa,为2000年以来第2低值。秋季格陵兰海冰密集度偏低,导致东亚冬季风偏弱;500 h Pa位势高度场上北极、欧亚大陆和阿留申地区呈现"-+-"的波列形态,使得北极与欧亚大陆中高纬地区的位势高度差增强,中高纬西风气流偏强,纬向活动加强;海平面气压场上西伯利亚高压偏弱,不利于北极冷空气向低纬地区侵袭,使得2016年冬季宁夏气温异常偏高。
基金This work was supported in part by the National Key Research and Development Program of China(2017YFD0200506)the National Natural Science Foundation of China(21837001 and 21907036).
文摘The impact of pesticides on insect pollinators has caused worldwide concern. Both global bee decline and stopping the use of pesticides may have serious consequences for food security. Automated and accurate prediction of chemical poisoning of honey bees is a challenging task owing to a lack of understanding of chemical toxicity and introspection. Deep learning(DL) shows potential utility for general and highly variable tasks across fields. Here, we developed a new DL model of deep graph attention convolutional neural networks(GACNN) with the combination of undirected graph(UG) and attention convolutional neural networks(ACNN) to accurately classify chemical poisoning of honey bees. We used a training dataset of 720 pesticides and an external validation dataset of 90 pesticides, which is one order of magnitude larger than the previous datasets. We tested its performance in two ways: poisonous versus nonpoisonous and GACNN versus other frequently-used machine learning models. The first case represents the accuracy in identifying bee poisonous chemicals. The second represents performance advantages. The GACNN achieved ~6% higher performance for predicting toxic samples and more stable with ~7%Matthews Correlation Coefficient(MCC) higher compared to all tested models, demonstrating GACNN is capable of accurately classifying chemicals and has considerable potential in practical applications.In addition, we also summarized and evaluated the mechanisms underlying the response of honey bees to chemical exposure based on the mapping of molecular similarity. Moreover, our cloud platform(http://beetox.cn) of this model provides low-cost universal access to information, which could vitally enhance environmental risk assessment.
