A growing body of evidence suggests that the gut microbiota contributes to the development of neurodegenerative diseases via the microbiota-gut-brain axis.As a contributing factor,microbiota dysbiosis always occurs in...A growing body of evidence suggests that the gut microbiota contributes to the development of neurodegenerative diseases via the microbiota-gut-brain axis.As a contributing factor,microbiota dysbiosis always occurs in pathological changes of neurodegenerative diseases,such as Alzheimer’s disease,Parkinson’s disease,and amyotrophic lateral sclerosis.High-throughput sequencing technology has helped to reveal that the bidirectional communication between the central nervous system and the enteric nervous system is facilitated by the microbiota’s diverse microorganisms,and for both neuroimmune and neuroendocrine systems.Here,we summarize the bioinformatics analysis and wet-biology validation for the gut metagenomics in neurodegenerative diseases,with an emphasis on multi-omics studies and the gut virome.The pathogen-associated signaling biomarkers for identifying brain disorders and potential therapeutic targets are also elucidated.Finally,we discuss the role of diet,prebiotics,probiotics,postbiotics and exercise interventions in remodeling the microbiome and reducing the symptoms of neurodegenerative diseases.展开更多
Understanding the persistence of antibody in convalescent COVID-19 patients may help to answer the current major concerns such as the risk of reinfection,the protection period of vaccination and the possibility of bui...Understanding the persistence of antibody in convalescent COVID-19 patients may help to answer the current major concerns such as the risk of reinfection,the protection period of vaccination and the possibility of building an active herd immunity.This retrospective cohort study included 172 COVID-19 patients who were hospitalized in Wuhan.A total of404 serum samples were obtained over six months from hospitalization to convalescence.Antibodies in the specimens were quantitatively analyzed by the capture chemiluminescence immunoassays(CLIA).All patients were positive for the anti-SARS-Co V-2 Ig M/Ig G at the onset of COVID-19 symptoms,and the Ig G antibody persisted in all the patients during the convalescence.However,only approximately 25%of patients can detect the Ig M antibodies,Ig M against N protein(NIg M)and receptor binding domain of S protein(RBD-Ig M)at the 27 th week.The titers of Ig M,N-Ig M and RBD-Ig M reduced to 16.7%,17.6%and 15.2%of their peak values respectively.In contrast,the titers of Ig G,N-Ig G and RBD-Ig G peaked at 4–5 th week and reduced to 85.9%,62.6%and 87.2%of their peak values respectively at the end of observation.Dynamic behavior of antibodies and their correlation in age,gender and severity groups were investigated.In general,the COVID-19 antibody was sustained at high levels for over six months in most of the convalescent patients.Only a few patients with antibody reducing to an undetectable level which needs further attention.The humoral immune response against SARS-Co V-2 infection in COVID-19 patients exhibits a typical dynamic of acquired immunity.展开更多
Withthe rapiddevelopment of deep learning,the size of data sets anddeepneuralnetworks(DNNs)models are also booming.As a result,the intolerable long time for models’training or inference with conventional strategies c...Withthe rapiddevelopment of deep learning,the size of data sets anddeepneuralnetworks(DNNs)models are also booming.As a result,the intolerable long time for models’training or inference with conventional strategies can not meet the satisfaction of modern tasks gradually.Moreover,devices stay idle in the scenario of edge computing(EC),which presents a waste of resources since they can share the pressure of the busy devices but they do not.To address the problem,the strategy leveraging distributed processing has been applied to load computation tasks from a single processor to a group of devices,which results in the acceleration of training or inference of DNN models and promotes the high utilization of devices in edge computing.Compared with existing papers,this paper presents an enlightening and novel review of applying distributed processing with data and model parallelism to improve deep learning tasks in edge computing.Considering the practicalities,commonly used lightweight models in a distributed system are introduced as well.As the key technique,the parallel strategy will be described in detail.Then some typical applications of distributed processing will be analyzed.Finally,the challenges of distributed processing with edge computing will be described.展开更多
Immigrant bacteria located on leaf surfaces are important to the health of plants as well as to people who consume fresh fruits and vegetables. However, the spatial distribution and organization of these immigrant bac...Immigrant bacteria located on leaf surfaces are important to the health of plants as well as to people who consume fresh fruits and vegetables. However, the spatial distribution and organization of these immigrant bacteria on leaf surfaces are still poorly understood. To examine the spatial organization of these strains, two bacterial strains on tobacco leaves： （1） an indigenous strain, Pseudomonas stutzeri Nov. Y2011 labeled with green fluorescent protein, and （2） an immigrant strain Pantoea agglomerans labeled with cyan fluorescent protein isolated from pear, were studied. Under moist conditions, P. agglomerans cells quickly disappeared from direct observation by laser- scanning confocal microscopy, although elution results indicated that large amounts of live cells were still present on the leaves. Following exposure to desiccation stress, particles of cyan fluorescent protein-labeled P. agglomerans were visible within cracked aggregates of P. stutzeri Nov. Y2011. Detailed observation of sectioned aggregates showed that colonies of immigrant P. agglomerans were embedded within aggregates of P. stutzeri Nov. Y2011. Furthermore, carbon-resource partitioning studies suggested that these two species could coexist without significant nutritional competition. This is the first observation of an immigrant bacterium embedding within aggregates of indigenous bacteria on leaves to evade harsh conditions in the phyllosphere.展开更多
The field of weather forecasting makes extensive use of radar big data to extract information about precipitation, storms, lightning, and other weather phenomena to aid in the prediction and monitoring of weather chan...The field of weather forecasting makes extensive use of radar big data to extract information about precipitation, storms, lightning, and other weather phenomena to aid in the prediction and monitoring of weather changes. To improve the quality of radar data, machine learning and fuzzy logic algorithms are often used to identify and classify non-meteorological clutter in weather data. However, these methods often require dozens of texture features as inputs and need to manually adjust the thresholds to cope with different clutter types, which leads to significant time costs. In this paper, we propose a multi-scale weighted connected UNet to address these challenges by combining the channel attention feature fusion module and the UNet structure model. The task of recognizing non-meteorological clutter is regarded as a semantic segmentation problem, which eliminates the need to manually set thresholds for clutter pixel-level classification. Additionally, the channel-focused feature fusion mechanism is able to analyze the deep latent features of the input parameters and suppress the useless features, so that only six polarization parameters are required as inputs. Furthermore, the model incorporates full-scale deep supervision to improve the edge segmentation accuracy of clutter and meteorological echoes. Experiments confirm that our proposed model outperforms the compared models in clutter identification with Critical Success Index (CSI) of 0.808.展开更多
Digital twinning and edge computing are attractive solutions to support computing-intensive and servicesensitive Internet of Vehicles applications.Most of the existing Internet of Vehicles service offloading solutions...Digital twinning and edge computing are attractive solutions to support computing-intensive and servicesensitive Internet of Vehicles applications.Most of the existing Internet of Vehicles service offloading solutions only consider edge–cloud collaboration,but the collaboration between small cell eNodeB(SCeNB)should not be ignored.Service delays far lower than offloading tasks to the cloud can be obtained through reasonable collaborative computing between nodes.The proposed framework realizes and maintains the simulation of collaboration between SCeNB nodes by constructing a digital twin that maintains SCeNB nodes in the central controller,thereby realizing user task offloading positions,sub-channel allocation,and computing resource allocation.Then an algorithm named AUC-AC is proposed,based on the dominant actor–critic network and the auction mechanism.In order to obtain a better command of global information,the convolutional block attention mechanism(CBAM)is used in the digital twin of each SCeNB node to observe its environment and learn strategies.Numerical results show that our experimental scheme is better than several baseline algorithms in terms of service delay.展开更多
Apoptosis is a form of programmed cell death that is essential for maintaining internal environmental stability.Disordered apoptosis can cause a variety of diseases;therefore,sensing apoptosis can provide help in stud...Apoptosis is a form of programmed cell death that is essential for maintaining internal environmental stability.Disordered apoptosis can cause a variety of diseases;therefore,sensing apoptosis can provide help in study of mechanism of the relevant diseases and drug development.It is known that caspase-3 is a key enzyme involved in apoptosis and the expression of its activity is an indication of apoptosis.Here,we present a genetically encoded switch-on m Neon Green2-based molecular biosensor.m Neon Green2 is the brightest monomeric green fluorescent protein.The substrate of caspase-3,DEVD amino acid residues,is inserted in it,while cyclized by insertion of Nostoc punctiforme Dna E intein to abolish the fluorescence(inactive state).Caspase-3-catalyzed cleavage of DEVD linearizes m Neon Green2 and rebuilds the natural barrel structure to restore the fluorescence(activated state).The characterization exhibited that the Caspase-3 biosensor has shortened response time,higher sensitivity,and prolonged functional shelf life in detection of caspase-3 amongst the existing counterparts.We also used the Caspase-3 biosensor to evaluate the effect of several drugs on the induction of apoptosis of He La and MCF-7 tumor cells and inhibition of Zika virus invasion.展开更多
This study presents high-precision analyses of stable potassium(K)isotope ratio using the recently-developed,collision-cell multi-collector inductively coupled plasma mass spectrometry(CC-MC-ICP-MS,Nu Sapphire).The ac...This study presents high-precision analyses of stable potassium(K)isotope ratio using the recently-developed,collision-cell multi-collector inductively coupled plasma mass spectrometry(CC-MC-ICP-MS,Nu Sapphire).The accuracy of our analyses is confirmed by measuring well-characterized geostandards(including rocks and seawater).Our results are consistent with literature values and a precision of 0.04‰(2SD)has been achieved based on multiple measurements of BCR-2 geostandard over a six-month period.We also evaluate factors that may lead to artificial isotope fractionations,including the mismatches in K concentration and acid molarity between samples and bracketing standards,as well as potential matrices.As the K adsorption capacity of AGW50-X8(200-400 mesh)is reduced with an increasing amount of matrix elements,less than 150µg K was loaded during the column chemistry.To evaluate the potential use of K isotopes as an archive of paleo seawater composition,δ^(41)K values of an international seawater standard(IAPSO),a Mn-nodule(NOD-P-1),and two iron formation standards(FeR-2 and FeR-4)are reported.The δ^(41)K value of IAPSO is consistent with other seawater samples reported previously,further substantiating a homogeneous K isotopic distribution in modern global oceans.The K isotopes in Mn-nodule(NOD-P-1:−0.121±0.013‰)and iron formation samples(FeR-2:−0.538±0.009‰;FeR-4:−0.401±0.008‰)seem to be an effective tracer of their formation genesis and compositional changes of ancient seawater.Our results suggest that high-precision measurements of stable K isotopes can be routinely obtained and open up a large variety of geological applications,such as continental weathering,hydrothermal circulation and alteration of oceanic crust.展开更多
Dear Editor,Acute myeloid leukemia (AML) is an aggressive form of a hematological neoplastic disorder induced by the oncogenic transformation of hematopoietic stem and myeloid progenitor cells (Bossis et al., 2014).
基金financially supported by the National Natural Science Foundation of China,No.32002235(to MT)the Science and Technology Foundation of Taian of Shandong Province,No.2020NS216(to XL)。
文摘A growing body of evidence suggests that the gut microbiota contributes to the development of neurodegenerative diseases via the microbiota-gut-brain axis.As a contributing factor,microbiota dysbiosis always occurs in pathological changes of neurodegenerative diseases,such as Alzheimer’s disease,Parkinson’s disease,and amyotrophic lateral sclerosis.High-throughput sequencing technology has helped to reveal that the bidirectional communication between the central nervous system and the enteric nervous system is facilitated by the microbiota’s diverse microorganisms,and for both neuroimmune and neuroendocrine systems.Here,we summarize the bioinformatics analysis and wet-biology validation for the gut metagenomics in neurodegenerative diseases,with an emphasis on multi-omics studies and the gut virome.The pathogen-associated signaling biomarkers for identifying brain disorders and potential therapeutic targets are also elucidated.Finally,we discuss the role of diet,prebiotics,probiotics,postbiotics and exercise interventions in remodeling the microbiome and reducing the symptoms of neurodegenerative diseases.
基金supported by the National Key Research and Development Program of China(Grant No.2020YFC0861100 and 2020YFC0000)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB29050100)+2 种基金the Key Technology Development Program of Shenzhen(Grant No.JSGG20200225153042494)the Youth Innovation Promotion Association of CAS(Grant No.2014308)the Interdisciplinary Innovation Team of CAS。
文摘Understanding the persistence of antibody in convalescent COVID-19 patients may help to answer the current major concerns such as the risk of reinfection,the protection period of vaccination and the possibility of building an active herd immunity.This retrospective cohort study included 172 COVID-19 patients who were hospitalized in Wuhan.A total of404 serum samples were obtained over six months from hospitalization to convalescence.Antibodies in the specimens were quantitatively analyzed by the capture chemiluminescence immunoassays(CLIA).All patients were positive for the anti-SARS-Co V-2 Ig M/Ig G at the onset of COVID-19 symptoms,and the Ig G antibody persisted in all the patients during the convalescence.However,only approximately 25%of patients can detect the Ig M antibodies,Ig M against N protein(NIg M)and receptor binding domain of S protein(RBD-Ig M)at the 27 th week.The titers of Ig M,N-Ig M and RBD-Ig M reduced to 16.7%,17.6%and 15.2%of their peak values respectively.In contrast,the titers of Ig G,N-Ig G and RBD-Ig G peaked at 4–5 th week and reduced to 85.9%,62.6%and 87.2%of their peak values respectively at the end of observation.Dynamic behavior of antibodies and their correlation in age,gender and severity groups were investigated.In general,the COVID-19 antibody was sustained at high levels for over six months in most of the convalescent patients.Only a few patients with antibody reducing to an undetectable level which needs further attention.The humoral immune response against SARS-Co V-2 infection in COVID-19 patients exhibits a typical dynamic of acquired immunity.
基金supported by the Natural Science Foundation of Jiangsu Province of China under Grant No.BK20211284the Financial and Science Technology Plan Project of Xinjiang Production,Construction Corps under Grant No.2020DB005the National Natural Science Foundation of China under Grant Nos.61872219,62002276 and 62177014。
文摘Withthe rapiddevelopment of deep learning,the size of data sets anddeepneuralnetworks(DNNs)models are also booming.As a result,the intolerable long time for models’training or inference with conventional strategies can not meet the satisfaction of modern tasks gradually.Moreover,devices stay idle in the scenario of edge computing(EC),which presents a waste of resources since they can share the pressure of the busy devices but they do not.To address the problem,the strategy leveraging distributed processing has been applied to load computation tasks from a single processor to a group of devices,which results in the acceleration of training or inference of DNN models and promotes the high utilization of devices in edge computing.Compared with existing papers,this paper presents an enlightening and novel review of applying distributed processing with data and model parallelism to improve deep learning tasks in edge computing.Considering the practicalities,commonly used lightweight models in a distributed system are introduced as well.As the key technique,the parallel strategy will be described in detail.Then some typical applications of distributed processing will be analyzed.Finally,the challenges of distributed processing with edge computing will be described.
基金supported by the Knowledge Innovation Program of the Chinese Academy of Sciences (No. KSCX2YW-JS401, KSCX2-YW-G-072)the National Natural Science Foundation of China (No. 20777089, 21177145)the National High Technology Research and Development Program (863) of China (No. 2007AA06A407)
文摘Immigrant bacteria located on leaf surfaces are important to the health of plants as well as to people who consume fresh fruits and vegetables. However, the spatial distribution and organization of these immigrant bacteria on leaf surfaces are still poorly understood. To examine the spatial organization of these strains, two bacterial strains on tobacco leaves： （1） an indigenous strain, Pseudomonas stutzeri Nov. Y2011 labeled with green fluorescent protein, and （2） an immigrant strain Pantoea agglomerans labeled with cyan fluorescent protein isolated from pear, were studied. Under moist conditions, P. agglomerans cells quickly disappeared from direct observation by laser- scanning confocal microscopy, although elution results indicated that large amounts of live cells were still present on the leaves. Following exposure to desiccation stress, particles of cyan fluorescent protein-labeled P. agglomerans were visible within cracked aggregates of P. stutzeri Nov. Y2011. Detailed observation of sectioned aggregates showed that colonies of immigrant P. agglomerans were embedded within aggregates of P. stutzeri Nov. Y2011. Furthermore, carbon-resource partitioning studies suggested that these two species could coexist without significant nutritional competition. This is the first observation of an immigrant bacterium embedding within aggregates of indigenous bacteria on leaves to evade harsh conditions in the phyllosphere.
基金supported by the National Key Research and Development Program of China(International Technology Cooperation Project,No.2021YFE014400)the China Meteorological Service Association Meteorological Technology Innovation Platform General Project(No.CMSA2023MD004).
文摘The field of weather forecasting makes extensive use of radar big data to extract information about precipitation, storms, lightning, and other weather phenomena to aid in the prediction and monitoring of weather changes. To improve the quality of radar data, machine learning and fuzzy logic algorithms are often used to identify and classify non-meteorological clutter in weather data. However, these methods often require dozens of texture features as inputs and need to manually adjust the thresholds to cope with different clutter types, which leads to significant time costs. In this paper, we propose a multi-scale weighted connected UNet to address these challenges by combining the channel attention feature fusion module and the UNet structure model. The task of recognizing non-meteorological clutter is regarded as a semantic segmentation problem, which eliminates the need to manually set thresholds for clutter pixel-level classification. Additionally, the channel-focused feature fusion mechanism is able to analyze the deep latent features of the input parameters and suppress the useless features, so that only six polarization parameters are required as inputs. Furthermore, the model incorporates full-scale deep supervision to improve the edge segmentation accuracy of clutter and meteorological echoes. Experiments confirm that our proposed model outperforms the compared models in clutter identification with Critical Success Index (CSI) of 0.808.
基金This research was supported by the Natural Science Foundation of Jiangsu Province of China(No.BK20211284)the Financial and Science Technology Plan Project of Xinjiang Production and Construction Corps(No.2020DB005)+1 种基金the National Natural Science Foundation of China(No.61872219)NUIST Students’Platform for Innovation and Entrepreneurship Training Program(No.202110300569).
文摘Digital twinning and edge computing are attractive solutions to support computing-intensive and servicesensitive Internet of Vehicles applications.Most of the existing Internet of Vehicles service offloading solutions only consider edge–cloud collaboration,but the collaboration between small cell eNodeB(SCeNB)should not be ignored.Service delays far lower than offloading tasks to the cloud can be obtained through reasonable collaborative computing between nodes.The proposed framework realizes and maintains the simulation of collaboration between SCeNB nodes by constructing a digital twin that maintains SCeNB nodes in the central controller,thereby realizing user task offloading positions,sub-channel allocation,and computing resource allocation.Then an algorithm named AUC-AC is proposed,based on the dominant actor–critic network and the auction mechanism.In order to obtain a better command of global information,the convolutional block attention mechanism(CBAM)is used in the digital twin of each SCeNB node to observe its environment and learn strategies.Numerical results show that our experimental scheme is better than several baseline algorithms in terms of service delay.
基金supported by the National Natural Science Foundation of China(21890743)National Key Research and Development Program of China(2017YFA0205500,2018YFA0902702)the Strategic Priority Research Program of the Chinese Academy of Sciences,China(XDB29050100)。
文摘Apoptosis is a form of programmed cell death that is essential for maintaining internal environmental stability.Disordered apoptosis can cause a variety of diseases;therefore,sensing apoptosis can provide help in study of mechanism of the relevant diseases and drug development.It is known that caspase-3 is a key enzyme involved in apoptosis and the expression of its activity is an indication of apoptosis.Here,we present a genetically encoded switch-on m Neon Green2-based molecular biosensor.m Neon Green2 is the brightest monomeric green fluorescent protein.The substrate of caspase-3,DEVD amino acid residues,is inserted in it,while cyclized by insertion of Nostoc punctiforme Dna E intein to abolish the fluorescence(inactive state).Caspase-3-catalyzed cleavage of DEVD linearizes m Neon Green2 and rebuilds the natural barrel structure to restore the fluorescence(activated state).The characterization exhibited that the Caspase-3 biosensor has shortened response time,higher sensitivity,and prolonged functional shelf life in detection of caspase-3 amongst the existing counterparts.We also used the Caspase-3 biosensor to evaluate the effect of several drugs on the induction of apoptosis of He La and MCF-7 tumor cells and inhibition of Zika virus invasion.
基金financially supported by the Experimental Technology Innovation Fund of the Institute of Geology and Geophysics,Chinese Academy of Sciences(Grant No.TEC 202103)the Youth Innovation Promotion Association,Chinese Academy of Sciences。
文摘This study presents high-precision analyses of stable potassium(K)isotope ratio using the recently-developed,collision-cell multi-collector inductively coupled plasma mass spectrometry(CC-MC-ICP-MS,Nu Sapphire).The accuracy of our analyses is confirmed by measuring well-characterized geostandards(including rocks and seawater).Our results are consistent with literature values and a precision of 0.04‰(2SD)has been achieved based on multiple measurements of BCR-2 geostandard over a six-month period.We also evaluate factors that may lead to artificial isotope fractionations,including the mismatches in K concentration and acid molarity between samples and bracketing standards,as well as potential matrices.As the K adsorption capacity of AGW50-X8(200-400 mesh)is reduced with an increasing amount of matrix elements,less than 150µg K was loaded during the column chemistry.To evaluate the potential use of K isotopes as an archive of paleo seawater composition,δ^(41)K values of an international seawater standard(IAPSO),a Mn-nodule(NOD-P-1),and two iron formation standards(FeR-2 and FeR-4)are reported.The δ^(41)K value of IAPSO is consistent with other seawater samples reported previously,further substantiating a homogeneous K isotopic distribution in modern global oceans.The K isotopes in Mn-nodule(NOD-P-1:−0.121±0.013‰)and iron formation samples(FeR-2:−0.538±0.009‰;FeR-4:−0.401±0.008‰)seem to be an effective tracer of their formation genesis and compositional changes of ancient seawater.Our results suggest that high-precision measurements of stable K isotopes can be routinely obtained and open up a large variety of geological applications,such as continental weathering,hydrothermal circulation and alteration of oceanic crust.
基金supported by the National Key Research and Development Program of China (2017YFA0205500 and 2018YFA0902702)the National Natural Science Foundation of China(21890743)the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB29050100)。
文摘Dear Editor,Acute myeloid leukemia (AML) is an aggressive form of a hematological neoplastic disorder induced by the oncogenic transformation of hematopoietic stem and myeloid progenitor cells (Bossis et al., 2014).
