Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are...Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.展开更多
In recent years,neonicotinoids(NEOs)and organophosphate esters(OPEs)have been widely used as substitutes for traditional pesticides and brominated fame-retardants,respectively.Previous studies have shown that those co...In recent years,neonicotinoids(NEOs)and organophosphate esters(OPEs)have been widely used as substitutes for traditional pesticides and brominated fame-retardants,respectively.Previous studies have shown that those compounds can be frequently detected in environmental and human samples,are able to penetrate the placental barrier,and are toxic to animals.Thus,it is reasonable to speculate that NEOs and OPEs may have potential adverse effects in humans,especially during development.We employed a human embryonic stem cell differentiation-and liver S9 fraction metabolism-based fast screening model to assess the potential embryonic toxicity of those two types of chemicals.We show that four NEO and five OPE prototypes targeted mostly ectoderm specification,as neural ectoderm and neural crest genes were down-regulated,and surface ectoderm and placode markers up-regulated.Human liver S9 fraction's treatment could generally reduce the effects of the chemicals,except in a few specific instances,indicating the liver may detoxify NEOs and OPEs.Our findings suggest that NEOs and OPEs interfere with human early embryonic development.展开更多
The consistency of the cell has a significant impact on battery capacity,endurance,overall performance,safety,and service life extension.However,it is challenging to identify cells with high consistency and no loss of...The consistency of the cell has a significant impact on battery capacity,endurance,overall performance,safety,and service life extension.However,it is challenging to identify cells with high consistency and no loss of battery energy.This paper presents a cell screening algorithm that integrates genetic and numerical differentiation techniques.Initially,a mathematical model for battery consistency is established,and a multi-step charging strategy is proposed to satisfy the demands of fast charging technology.Subsequently,the genetic algorithm simulates biological evolution to efficiently search for superior cell combinations within a short time while evaluating capacity,voltage consistency,and charge/discharge efficiency.Finally,through experimental validation and comparative analysis with similar algorithms,our proposed method demonstrates notable advantages in terms of both search efficiency and performance.展开更多
基金supported by the Ministry of Science and Technology of China,No.2020AAA0109605(to XL)Meizhou Major Scientific and Technological Innovation PlatformsProjects of Guangdong Provincial Science & Technology Plan Projects,No.2019A0102005(to HW).
文摘Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.
基金supported by the Ministry of Science and Technology of the People’s Republic of China (No.2020YFA0907500)the National Natural Science Foundation of China (Nos.22150710514,22021003,and 22106174)+1 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences (No.XDPB200202)the Postdoc Science Foundation of China (No.2021M693322)。
文摘In recent years,neonicotinoids(NEOs)and organophosphate esters(OPEs)have been widely used as substitutes for traditional pesticides and brominated fame-retardants,respectively.Previous studies have shown that those compounds can be frequently detected in environmental and human samples,are able to penetrate the placental barrier,and are toxic to animals.Thus,it is reasonable to speculate that NEOs and OPEs may have potential adverse effects in humans,especially during development.We employed a human embryonic stem cell differentiation-and liver S9 fraction metabolism-based fast screening model to assess the potential embryonic toxicity of those two types of chemicals.We show that four NEO and five OPE prototypes targeted mostly ectoderm specification,as neural ectoderm and neural crest genes were down-regulated,and surface ectoderm and placode markers up-regulated.Human liver S9 fraction's treatment could generally reduce the effects of the chemicals,except in a few specific instances,indicating the liver may detoxify NEOs and OPEs.Our findings suggest that NEOs and OPEs interfere with human early embryonic development.
文摘The consistency of the cell has a significant impact on battery capacity,endurance,overall performance,safety,and service life extension.However,it is challenging to identify cells with high consistency and no loss of battery energy.This paper presents a cell screening algorithm that integrates genetic and numerical differentiation techniques.Initially,a mathematical model for battery consistency is established,and a multi-step charging strategy is proposed to satisfy the demands of fast charging technology.Subsequently,the genetic algorithm simulates biological evolution to efficiently search for superior cell combinations within a short time while evaluating capacity,voltage consistency,and charge/discharge efficiency.Finally,through experimental validation and comparative analysis with similar algorithms,our proposed method demonstrates notable advantages in terms of both search efficiency and performance.
