Reaching across multiple fields of focus,spanning from periodontistry to gastroenterology to neurobiology to behavior,interest in the influence of the microbiome in human physiology and pathology has risen over the pa...Reaching across multiple fields of focus,spanning from periodontistry to gastroenterology to neurobiology to behavior,interest in the influence of the microbiome in human physiology and pathology has risen over the past few decades.Microbiota co-exist in and on humans forming an evolutionarily symbiotic biological unit,a halobiont,in which disruptions in the relationship can occur through genomic alterations and mutations[1],The human microbiome consists of bacteria,viruses,fungi,and protozoans that contribute 450 times more genes to this relationship and slightly outnumber human host cells[2,3].The bacteria in the gastrointestinal(GI)tract are of the most interest and exist within five phyla:Bacteroidetes,Firmicutes,Pro-teobacteria,Actinobacteria,and Verrucomicrobi&.Within the Verrucomicrobia an interesting bacterium has emerged.展开更多
The autonomic nervous system(ANS)integrates the involuntary physiological activities of visceral organs that are vital for survival.In particular,the ANS controls heart rate,blood pressure,breathing,gastrointestinal c...The autonomic nervous system(ANS)integrates the involuntary physiological activities of visceral organs that are vital for survival.In particular,the ANS controls heart rate,blood pressure,breathing,gastrointestinal contraction and secretion,and electrolyte and fluid homeostasis.Many studies have focused on understanding the neural mechanisms of autonomic dysfunction or neuropathy in pathophysiological states including hypertension,heart failure.展开更多
Algicidal bacteria have been frequently isolated from algal blooming areas.However,knowledge regarding the microbial communities coexisting with microalgae and their potential application in preventing harmful algal b...Algicidal bacteria have been frequently isolated from algal blooming areas.However,knowledge regarding the microbial communities coexisting with microalgae and their potential application in preventing harmful algal blooms(HABs)is limited.In this study,we investigated the composition of the microbial community coexisting with harmful alga Karenia mikimotoi and its responses to algal control via nutrient stimulation or by adding algicidal strain in microcosms.The microorganisms inhabiting the K.mikimotoi culture consisted of 24 identifi ed phyla,including dominant Proteobacteria(relative abundance 76.24%±7.28%)and Bacteroidetes(22.67%±8.32%).Rhodobacteraceae,Phaeodactylibacter,and Maritimibacter predominated during the algal cultivation.Both the added nutrient and fermentation broth of algicidal strain Pseudoalteromonas QF1 caused a massive death of K.mikimotoi and substantial changes in the coexisting microbial community,in which Rhodobacteraceae and Phaeodactylibacter signifi cantly decreased,while Halomonas and Alteromonas increased.Core operational taxonomic units(OTUs)analysis indicated that 13 OTUs belonging to Rhodobacteraceae,Maritimibacter,Marivita,Nisaea,Phaeodactylibacter,Citreicella,Halomonas,Alteromonas,Marinobacter,Muricauda,and Pseudoalteromonas dominated the changes of the microbial communities observed in the K.mikimotoi culture with or without treatments.Collectively,this study indicated that microbial community inhabiting K.mikimotoi culture includes potential algicidal bacteria,and improves our knowledge about microbial community succession during biocontrol of K.mikimotoi via nutrient stimulation or by adding isolated algicidal strains.展开更多
In the economic new normal,production of hybrid rice seed in foreign countries is the necessity for reducing farmland area occupied by seed production,for ensuring national grain security,realizing cost reducing and q...In the economic new normal,production of hybrid rice seed in foreign countries is the necessity for reducing farmland area occupied by seed production,for ensuring national grain security,realizing cost reducing and quality improving of hybrid rice seed,strengthening competitive power at international market,guiding seed industry of China to go out,building transnational seed groups with core competitive power,establishing close relationship with developing countries,promoting the construction of One Belt One Road strategy,serving overall situation of diplomacy,and setting up excellent international image. It is feasible to produce hybrid seed in foreign countries considering( i) high overall national strength of China,( ii) rapid and healthy growth of China's seed industry and increasingly mature hybrid rice seed production technologies,( iii) excellent climatic conditions of foreign host countries of seed production,and( iv) low land and labor price of foreign host countries of seed production. However,there are social and policy risks,technology and trade barrier risks,market,production,and other risks for production of hybrid rice seed in foreign countries. In view of these,it came up with recommendations,including allowing delivering parent seeds of hybrid rice to foreign countries,allowing delivering hybrid rice seed to China,solving the problem of " opening in protection,and protection in opening",and formulating a package of support policies.展开更多
The increasing integration of intermittent renewable energy sources(RESs)poses great challenges to active distribution networks(ADNs),such as frequent voltage fluctuations.This paper proposes a novel ADN strategy base...The increasing integration of intermittent renewable energy sources(RESs)poses great challenges to active distribution networks(ADNs),such as frequent voltage fluctuations.This paper proposes a novel ADN strategy based on multiagent deep reinforcement learning(MADRL),which harnesses the regulating function of switch state transitions for the realtime voltage regulation and loss minimization.After deploying the calculated optimal switch topologies,the distribution network operator will dynamically adjust the distributed energy resources(DERs)to enhance the operation performance of ADNs based on the policies trained by the MADRL algorithm.Owing to the model-free characteristics and the generalization of deep reinforcement learning,the proposed strategy can still achieve optimization objectives even when applied to similar but unseen environments.Additionally,integrating parameter sharing(PS)and prioritized experience replay(PER)mechanisms substantially improves the strategic performance and scalability.This framework has been tested on modified IEEE 33-bus,IEEE 118-bus,and three-phase unbalanced 123-bus systems.The results demonstrate the significant real-time regulation capabilities of the proposed strategy.展开更多
Most learning-based low-light image enhancement methods typically suffer from two problems.First,they require a large amount of paired data for training,which are difficult to acquire in most cases.Second,in the proce...Most learning-based low-light image enhancement methods typically suffer from two problems.First,they require a large amount of paired data for training,which are difficult to acquire in most cases.Second,in the process of enhancement,image noise is difficult to be removed and may even be amplified.In other words,performing denoising and illumination enhancement at the same time is difficult.As an alternative to supervised learning strategies that use a large amount of paired data,as presented in previous work,this paper presents an mixed-attention guided generative adversarial network called MAGAN for low-light image enhancement in a fully unsupervised fashion.We introduce a mixed-attention module layer,which can model the relationship between each pixel and feature of the image.In this way,our network can enhance a low-light image and remove its noise simultaneously.In addition,we conduct extensive experiments on paired and no-reference datasets to show the superiority of our method in enhancing low-light images.展开更多
Heavy-duty diesel vehicles are important sources of urban nitrogen oxides(NOx)in actual applications for environmental compliance,emitting more than 80%of NOx and more than 90%of particulate matter(PM)in total vehicle...Heavy-duty diesel vehicles are important sources of urban nitrogen oxides(NOx)in actual applications for environmental compliance,emitting more than 80%of NOx and more than 90%of particulate matter(PM)in total vehicle emissions.The detection and control of heavy-duty diesel emissions are critical for protecting public health.Currently,vehicles on the road must be regularly tested,every six months or once a year,to filter out high-emission mobile sources at vehicle inspection stations.However,it is difficult to effectively screen high-emission vehicles in time with a long interval between annual inspections,and the fixed threshold cannot adapt to the dynamic changes of vehicle driving conditions.An on-board diagnostic device(OBD)is installed inside the vehicle and can record the vehicle’s emission data in real time.In this paper,we propose a temporal optimization long short-term memory(LSTM)and adaptive dynamic threshold approach to identify heavy-duty high-emitters by using OBD data,which can continuously track and record the emission status in real time.First,a temporal optimization LSTM emission prediction model is established to solve the attention bias discrepancy problem on time steps that is caused by the large number of OBD data streams in practice.Then,the concentration prediction error sequence is detected and distinguished from the anomalous emission contexts using flexible criteria,calculated by an adaptive dynamic threshold with changing driving conditions.Finally,a similarity metric strategy for the time series is introduced to correct some pseudo anomalous results.Experiments on three real OBD time-series emission datasets demonstrate that our method can achieve high accuracy anomalous emission identification.展开更多
基金This insight was supported by Michigan Technological University Portage Health Foundation,America Heart Association(16PRE27780121)National Natural Science Foundation of China(31871150).
文摘Reaching across multiple fields of focus,spanning from periodontistry to gastroenterology to neurobiology to behavior,interest in the influence of the microbiome in human physiology and pathology has risen over the past few decades.Microbiota co-exist in and on humans forming an evolutionarily symbiotic biological unit,a halobiont,in which disruptions in the relationship can occur through genomic alterations and mutations[1],The human microbiome consists of bacteria,viruses,fungi,and protozoans that contribute 450 times more genes to this relationship and slightly outnumber human host cells[2,3].The bacteria in the gastrointestinal(GI)tract are of the most interest and exist within five phyla:Bacteroidetes,Firmicutes,Pro-teobacteria,Actinobacteria,and Verrucomicrobi&.Within the Verrucomicrobia an interesting bacterium has emerged.
文摘The autonomic nervous system(ANS)integrates the involuntary physiological activities of visceral organs that are vital for survival.In particular,the ANS controls heart rate,blood pressure,breathing,gastrointestinal contraction and secretion,and electrolyte and fluid homeostasis.Many studies have focused on understanding the neural mechanisms of autonomic dysfunction or neuropathy in pathophysiological states including hypertension,heart failure.
基金Supported by the National Natural Science Foundation of China(Nos.31971503,31901188)the Shandong Provincial Agricultural Fine Species Project(No.2019LZGC020)+5 种基金the Jining Key Research and Development Project of Shandong Province(No.2019ZDGH019)the Shandong Provincial Natural Science Foundation(Nos.ZR2019BB040,ZR2020MC042)the Interdisciplinary Project of Qufu Normal University(No.XKJJC201903)the Key Research and Development Project of Liaoning Province(No.2018228004)the Revitalization Talents Program of Liaoning Province(No.XLYC1907109),the Shandong Provincial Key Research and Development Project(No.2018GSF117035)the Shandong Provincial Higher Educational Science and Technology Program(No.J17KA112)。
文摘Algicidal bacteria have been frequently isolated from algal blooming areas.However,knowledge regarding the microbial communities coexisting with microalgae and their potential application in preventing harmful algal blooms(HABs)is limited.In this study,we investigated the composition of the microbial community coexisting with harmful alga Karenia mikimotoi and its responses to algal control via nutrient stimulation or by adding algicidal strain in microcosms.The microorganisms inhabiting the K.mikimotoi culture consisted of 24 identifi ed phyla,including dominant Proteobacteria(relative abundance 76.24%±7.28%)and Bacteroidetes(22.67%±8.32%).Rhodobacteraceae,Phaeodactylibacter,and Maritimibacter predominated during the algal cultivation.Both the added nutrient and fermentation broth of algicidal strain Pseudoalteromonas QF1 caused a massive death of K.mikimotoi and substantial changes in the coexisting microbial community,in which Rhodobacteraceae and Phaeodactylibacter signifi cantly decreased,while Halomonas and Alteromonas increased.Core operational taxonomic units(OTUs)analysis indicated that 13 OTUs belonging to Rhodobacteraceae,Maritimibacter,Marivita,Nisaea,Phaeodactylibacter,Citreicella,Halomonas,Alteromonas,Marinobacter,Muricauda,and Pseudoalteromonas dominated the changes of the microbial communities observed in the K.mikimotoi culture with or without treatments.Collectively,this study indicated that microbial community inhabiting K.mikimotoi culture includes potential algicidal bacteria,and improves our knowledge about microbial community succession during biocontrol of K.mikimotoi via nutrient stimulation or by adding isolated algicidal strains.
基金Supported by Project of Hubei Agricultural Science and Technology Innovation Center(2007-620-003-03-05)Open Fund Project of Hubei Collaborative Innovation Center for Grain Industry(LXT-16-03)
文摘In the economic new normal,production of hybrid rice seed in foreign countries is the necessity for reducing farmland area occupied by seed production,for ensuring national grain security,realizing cost reducing and quality improving of hybrid rice seed,strengthening competitive power at international market,guiding seed industry of China to go out,building transnational seed groups with core competitive power,establishing close relationship with developing countries,promoting the construction of One Belt One Road strategy,serving overall situation of diplomacy,and setting up excellent international image. It is feasible to produce hybrid seed in foreign countries considering( i) high overall national strength of China,( ii) rapid and healthy growth of China's seed industry and increasingly mature hybrid rice seed production technologies,( iii) excellent climatic conditions of foreign host countries of seed production,and( iv) low land and labor price of foreign host countries of seed production. However,there are social and policy risks,technology and trade barrier risks,market,production,and other risks for production of hybrid rice seed in foreign countries. In view of these,it came up with recommendations,including allowing delivering parent seeds of hybrid rice to foreign countries,allowing delivering hybrid rice seed to China,solving the problem of " opening in protection,and protection in opening",and formulating a package of support policies.
基金supported by the National Natural Science Foundation of China(No.52077146)Sichuan Science and Technology Program(No.2023NSFSC1945)。
文摘The increasing integration of intermittent renewable energy sources(RESs)poses great challenges to active distribution networks(ADNs),such as frequent voltage fluctuations.This paper proposes a novel ADN strategy based on multiagent deep reinforcement learning(MADRL),which harnesses the regulating function of switch state transitions for the realtime voltage regulation and loss minimization.After deploying the calculated optimal switch topologies,the distribution network operator will dynamically adjust the distributed energy resources(DERs)to enhance the operation performance of ADNs based on the policies trained by the MADRL algorithm.Owing to the model-free characteristics and the generalization of deep reinforcement learning,the proposed strategy can still achieve optimization objectives even when applied to similar but unseen environments.Additionally,integrating parameter sharing(PS)and prioritized experience replay(PER)mechanisms substantially improves the strategic performance and scalability.This framework has been tested on modified IEEE 33-bus,IEEE 118-bus,and three-phase unbalanced 123-bus systems.The results demonstrate the significant real-time regulation capabilities of the proposed strategy.
基金supported in part by the National Natural Science Foundation of China(No.62072169)Changsha Science and Technology Research Plan(No.KQ2004005)
文摘Most learning-based low-light image enhancement methods typically suffer from two problems.First,they require a large amount of paired data for training,which are difficult to acquire in most cases.Second,in the process of enhancement,image noise is difficult to be removed and may even be amplified.In other words,performing denoising and illumination enhancement at the same time is difficult.As an alternative to supervised learning strategies that use a large amount of paired data,as presented in previous work,this paper presents an mixed-attention guided generative adversarial network called MAGAN for low-light image enhancement in a fully unsupervised fashion.We introduce a mixed-attention module layer,which can model the relationship between each pixel and feature of the image.In this way,our network can enhance a low-light image and remove its noise simultaneously.In addition,we conduct extensive experiments on paired and no-reference datasets to show the superiority of our method in enhancing low-light images.
基金Project supported by the National Natural Science Foundation of China (Nos.62033012 and 62103124)the Major Special Science and Technology Project of Anhui Province,China (No.202003a07020009)。
文摘Heavy-duty diesel vehicles are important sources of urban nitrogen oxides(NOx)in actual applications for environmental compliance,emitting more than 80%of NOx and more than 90%of particulate matter(PM)in total vehicle emissions.The detection and control of heavy-duty diesel emissions are critical for protecting public health.Currently,vehicles on the road must be regularly tested,every six months or once a year,to filter out high-emission mobile sources at vehicle inspection stations.However,it is difficult to effectively screen high-emission vehicles in time with a long interval between annual inspections,and the fixed threshold cannot adapt to the dynamic changes of vehicle driving conditions.An on-board diagnostic device(OBD)is installed inside the vehicle and can record the vehicle’s emission data in real time.In this paper,we propose a temporal optimization long short-term memory(LSTM)and adaptive dynamic threshold approach to identify heavy-duty high-emitters by using OBD data,which can continuously track and record the emission status in real time.First,a temporal optimization LSTM emission prediction model is established to solve the attention bias discrepancy problem on time steps that is caused by the large number of OBD data streams in practice.Then,the concentration prediction error sequence is detected and distinguished from the anomalous emission contexts using flexible criteria,calculated by an adaptive dynamic threshold with changing driving conditions.Finally,a similarity metric strategy for the time series is introduced to correct some pseudo anomalous results.Experiments on three real OBD time-series emission datasets demonstrate that our method can achieve high accuracy anomalous emission identification.
