Environmental temperature is a major factor affecting animal performance in South China. With global warming, heat stress will become more and more serious. This paper reviewed the effects of heat stress on metabolism...Environmental temperature is a major factor affecting animal performance in South China. With global warming, heat stress will become more and more serious. This paper reviewed the effects of heat stress on metabolism of proteins, glucose, fat and energy in skeletal muscle and related mechanisms so as to provide theoretical guidance for alleviating heat stress and improving production performance of animal suffering from heat stress.展开更多
Oxidative stress is a major factor affecting animal health and production performance. This paper briefly introduced the signaling pathways(i.e. NF-κB signaling pathway, MAPK, AP-1 and PGC-1α) of oxidative stress an...Oxidative stress is a major factor affecting animal health and production performance. This paper briefly introduced the signaling pathways(i.e. NF-κB signaling pathway, MAPK, AP-1 and PGC-1α) of oxidative stress and the main genes regulating the signals of oxidative stress in skeletal muscle, providing a theoretical basis for reducing oxidative stress damage.展开更多
The overproduction of free radicals is the main reason for the peroxidation damage, metabolic disturbance and antioxidant imbalance in animals. This paper summarized the types and generation mechanism of intracellular...The overproduction of free radicals is the main reason for the peroxidation damage, metabolic disturbance and antioxidant imbalance in animals. This paper summarized the types and generation mechanism of intracellular free radicals and clarified the sources of free radicals in skeletal muscles and digestive tracts of animals.展开更多
Introduction:Foodborne diseases are a growing public health problem and have caused a large burden of disease in China.This study analyzed epidemiological characteristics of foodborne diseases in China in 2020 to prov...Introduction:Foodborne diseases are a growing public health problem and have caused a large burden of disease in China.This study analyzed epidemiological characteristics of foodborne diseases in China in 2020 to provide a scientific basis for prevention and control measures.Methods:Data were collected from 30 of 31 provincial-level administrative divisions(PLADs)in the mainland of China,excluding Xizang(Tibet)Autonomous Region,via the National Foodborne Disease Outbreaks Surveillance System・The number and proportion of outbreaks,illnesses,hospitalizations,deaths by setting,pathogen-food category pairs and etiology were calculated.Results:In 2020,7,073 foodborne disease outbreaks were reported,resulting in 37,454 illnesses and 143 deaths.Among the identified pathogens,microbial pathogens were the most common confirmed etiology,accounting for 41.7%of illnesses.Poisonous mushrooms caused the largest proportion of outbreaks(58.0%)and deaths(57.6%).For venues where foodborne disease outbreaks occur,household had the highest number of outbreaks(4,140)and deaths(128),and catering service locations caused the largest proportion of illnesses(59.9%).Outbreaks occurring between June and September accounted for 62.8%of total outbreaks.Conclusions:Foodborne disease outbreaks mainly occurred in households・Microbial pathogens remained the top cause of outbreak-associated illnesses.Poisonous mushrooms were ranked the top cause of deaths in private homes in China.The supervision and management of food safety and health education should be strengthened to reduce the burden of foodborne diseases・Publicity should be increased to reduce the incidence of mushroom poisonings in families,and supervision and management of food should be strengthened to reduce microbial contamination.展开更多
To understand and engineer plant metabolism, we need a comprehensive and accurate annotation of all metabolic information across plant species. As a step towards this goal, we generated genome-scale metabolic pathway ...To understand and engineer plant metabolism, we need a comprehensive and accurate annotation of all metabolic information across plant species. As a step towards this goal, we generated genome-scale metabolic pathway databases of 126 algal and plant genomes, ranging from model organisms to crops to medicinal plants(https://plantcyc.org).Of these, 104 have not been reported before.We systematically evaluated the quality of the databases, which revealed that our semi-automated validation pipeline dramatically improves the quality. We then compared the metabolic content across the 126 organisms using multiple correspondence analysis and found that Brassicaceae,Poaceae, and Chlorophyta appeared as metabolically distinct groups. To demonstrate the utility of this resource, we used recently published sorghum transcriptomics data to discover previously unreported trends of metabolism underlying drought tolerance. We also used single-cell transcriptomics data from the Arabidopsis root to infer cell typespecific metabolic pathways. This work shows the quality and quantity of our resource and demonstrates its wide-ranging utility in integrating metabolism with other areas of plant biology.展开更多
Brain-computer interface(BCI)based on Steady-State Visual Evoked Potentials(SSVEP)provides an effective method for human-computer communication.In practical application scenarios,SSVEP-BCI systems are easily interfere...Brain-computer interface(BCI)based on Steady-State Visual Evoked Potentials(SSVEP)provides an effective method for human-computer communication.In practical application scenarios,SSVEP-BCI systems are easily interfered by physiological noises such as electromyography(EMG)and electrooculography(EOG).The performance of traditional SSVEP recognition methods will degrade in such a noisy environment,which limits their real-world applications.To alleviate the interference of noise,existing works either require additional reference electrodes or are designed for removing background noise such as trend terms rather than physiological noises.In this study,we utilize adversarial training(AT)and neural networks(NNs)to construct a robust recognition method for SSVEP contaminated by physiological noise.During model training,we generate adversarial noises which are most harmful to the current model according to gradients and enforce the model to overcome them.In this way,we strengthen the robustness of the model to potential noises,such as physiological noises.In this study,we recorded a real-world speaking SSVEP dataset and simulated various noisy datasets to conducted comparison experiments on two benchmark models named EEGNet and DeepConvNet.The experimental results demonstrated that AT strategies can help the neural networks get better performance on SSVEP data contaminated by EMG and EOG.We also verified that introducing AT can slightly improve the performance of models under a cross-subject scenario.Our method can be integrated into existing deep learning methods efficiently and will contribute to the real-world applications of SSVEP.展开更多
基金Supported by Key Project of Natural Science Foundation of Hubei Province(2013CFA100)National Natural Science Foundation of China(31472117)
文摘Environmental temperature is a major factor affecting animal performance in South China. With global warming, heat stress will become more and more serious. This paper reviewed the effects of heat stress on metabolism of proteins, glucose, fat and energy in skeletal muscle and related mechanisms so as to provide theoretical guidance for alleviating heat stress and improving production performance of animal suffering from heat stress.
基金Supported by Key Project of Natural Science Foundation of Hubei Province(2013CFA100)National Natural Science Foundation of China(31472117)
文摘Oxidative stress is a major factor affecting animal health and production performance. This paper briefly introduced the signaling pathways(i.e. NF-κB signaling pathway, MAPK, AP-1 and PGC-1α) of oxidative stress and the main genes regulating the signals of oxidative stress in skeletal muscle, providing a theoretical basis for reducing oxidative stress damage.
基金Supported by Natural Science Foundation of Hubei Province(2013CFA100)National Natural Science Foundation of China(31472117)
文摘The overproduction of free radicals is the main reason for the peroxidation damage, metabolic disturbance and antioxidant imbalance in animals. This paper summarized the types and generation mechanism of intracellular free radicals and clarified the sources of free radicals in skeletal muscles and digestive tracts of animals.
基金Supported by The National Key Research and Development Program of China(Grant number 2017YFC1601502).
文摘Introduction:Foodborne diseases are a growing public health problem and have caused a large burden of disease in China.This study analyzed epidemiological characteristics of foodborne diseases in China in 2020 to provide a scientific basis for prevention and control measures.Methods:Data were collected from 30 of 31 provincial-level administrative divisions(PLADs)in the mainland of China,excluding Xizang(Tibet)Autonomous Region,via the National Foodborne Disease Outbreaks Surveillance System・The number and proportion of outbreaks,illnesses,hospitalizations,deaths by setting,pathogen-food category pairs and etiology were calculated.Results:In 2020,7,073 foodborne disease outbreaks were reported,resulting in 37,454 illnesses and 143 deaths.Among the identified pathogens,microbial pathogens were the most common confirmed etiology,accounting for 41.7%of illnesses.Poisonous mushrooms caused the largest proportion of outbreaks(58.0%)and deaths(57.6%).For venues where foodborne disease outbreaks occur,household had the highest number of outbreaks(4,140)and deaths(128),and catering service locations caused the largest proportion of illnesses(59.9%).Outbreaks occurring between June and September accounted for 62.8%of total outbreaks.Conclusions:Foodborne disease outbreaks mainly occurred in households・Microbial pathogens remained the top cause of outbreak-associated illnesses.Poisonous mushrooms were ranked the top cause of deaths in private homes in China.The supervision and management of food safety and health education should be strengthened to reduce the burden of foodborne diseases・Publicity should be increased to reduce the incidence of mushroom poisonings in families,and supervision and management of food should be strengthened to reduce microbial contamination.
基金This work was supported by grants from the National Science Foundation(IOS-1546838,IOS-1026003)the US Department of Energy,Office of Science,Office of Biological and Environmental Research,Genomic Science Program grant nos.DE-SC0018277,DE-SC0008769,DE-SC0020366,and DE-SC0021286.
文摘To understand and engineer plant metabolism, we need a comprehensive and accurate annotation of all metabolic information across plant species. As a step towards this goal, we generated genome-scale metabolic pathway databases of 126 algal and plant genomes, ranging from model organisms to crops to medicinal plants(https://plantcyc.org).Of these, 104 have not been reported before.We systematically evaluated the quality of the databases, which revealed that our semi-automated validation pipeline dramatically improves the quality. We then compared the metabolic content across the 126 organisms using multiple correspondence analysis and found that Brassicaceae,Poaceae, and Chlorophyta appeared as metabolically distinct groups. To demonstrate the utility of this resource, we used recently published sorghum transcriptomics data to discover previously unreported trends of metabolism underlying drought tolerance. We also used single-cell transcriptomics data from the Arabidopsis root to infer cell typespecific metabolic pathways. This work shows the quality and quantity of our resource and demonstrates its wide-ranging utility in integrating metabolism with other areas of plant biology.
基金This work was supported in part by the National Natural Science Foundation of China under Grant 61922075,Grant 32271431,and Grant 82272070in part by the Fundamental Research Funds for the Central Universities under Grant KY2100000123+1 种基金in part by the China Postdoctoral Science Foundation under Grant 2022M723055in part by the University Synergy Innovation Program of Anhui Province under Grant GXXT-2019-025.
文摘Brain-computer interface(BCI)based on Steady-State Visual Evoked Potentials(SSVEP)provides an effective method for human-computer communication.In practical application scenarios,SSVEP-BCI systems are easily interfered by physiological noises such as electromyography(EMG)and electrooculography(EOG).The performance of traditional SSVEP recognition methods will degrade in such a noisy environment,which limits their real-world applications.To alleviate the interference of noise,existing works either require additional reference electrodes or are designed for removing background noise such as trend terms rather than physiological noises.In this study,we utilize adversarial training(AT)and neural networks(NNs)to construct a robust recognition method for SSVEP contaminated by physiological noise.During model training,we generate adversarial noises which are most harmful to the current model according to gradients and enforce the model to overcome them.In this way,we strengthen the robustness of the model to potential noises,such as physiological noises.In this study,we recorded a real-world speaking SSVEP dataset and simulated various noisy datasets to conducted comparison experiments on two benchmark models named EEGNet and DeepConvNet.The experimental results demonstrated that AT strategies can help the neural networks get better performance on SSVEP data contaminated by EMG and EOG.We also verified that introducing AT can slightly improve the performance of models under a cross-subject scenario.Our method can be integrated into existing deep learning methods efficiently and will contribute to the real-world applications of SSVEP.
