In recent years,the development of Bimodal Neural Probes has brought unprecedented breakthroughs to Brain-Computer Interfaces(BCI)and neuroscience research.Traditional neural probes can only record either the brain...In recent years,the development of Bimodal Neural Probes has brought unprecedented breakthroughs to Brain-Computer Interfaces(BCI)and neuroscience research.Traditional neural probes can only record either the brain's electrical signals or the chemical signals of neurotransmitters,but bimodal probes enable the simultaneous acquisition and integration of both,allowing scientists to better understand neuronal interactions.This technology's core innovation lies in integrating Microelectrode Arrays(MEA)and Microfluidic Channels,ensuring high temporal and spatial alignment for more accurate neural signal decoding.This study explores the applications of bimodal neural probes in learning enhancement,motor recovery for paralyzed patients,Parkinson’s disease treatment,and epilepsy prediction,demonstrating their potential in neurological disease diagnosis,BCI optimization,and human-machine interaction through experimental case studies.Additionally,we analyze how key material innovations,such as graphene,polymer PI/PDMS,and PEDOT,enhance the probe's sensitivity,flexibility,and long-term stability while proposing future technological optimizations.Despite their promising prospects,widespread application faces challenges related to ethics,safety,and cost,which we address by proposing key feasibility recommendations,including establishing neural data security regulations to prevent misuse,reducing manufacturing costs through mass production and material optimization,conducting long-term biocompatibility testing to ensure stability,and developing fair-use guidelines to prevent social inequalities.Ultimately,this study highlights the significant contributions of bimodal neural probes to BCI technology and neuroscience while emphasizing the importance of responsible technological advancement.With improvements in manufacturing processes and increasing clinical applications,we believe bimodal neural probes will revolutionize human-brain interactions,profoundly impacting medicine,neurorehabilitation,and artificial intelligence.展开更多
Investigating the bioaccessibility of harmful inorganic elements in soil is crucial for understanding their behavior in the environment and accurately assessing the environmental risks associated with soil.Traditional...Investigating the bioaccessibility of harmful inorganic elements in soil is crucial for understanding their behavior in the environment and accurately assessing the environmental risks associated with soil.Traditional batch experimental methods and linear models,however,are time-consuming and often fall short in precisely quantifying bioaccessibility.In this study,using 937 data points gathered from 56 journal articles,we developed machine learning models for three harmful inorganic elements,namely,Cd,Pb,and As.After thorough analysis,the model optimized through a boosting ensemble strategy demonstrated the best performance,with an average R2 of 0.95 and an RMSE of 0.25.We further employed SHAP values in conjunction with quantitative analysis to identify the key features that influence bioaccessibility.By utilizing the developed integrated models,we carried out predictions for 3002 data points across China,clarifying the bioaccessibility of cadmium(Cd),lead(Pb),and arsenic(As)in the soils of various sites and constructed a comprehensive spatial distribution map of China using the inverse distance weighting(IDW)interpolation method.Based on these findings,we further derived the soil environmental standards for metallurgical sites in China.Our observations from the collected data indicate a reduction in the number of sites exceeding the standard levels for Cd,Pb,and As in mining/smelting sites from 5,58,and 14 to 1,24,and 7,respectively.This research offers a precise and scientific approach for cross-regional risk assessment at the continental scale and lays a solid foundation for soil environmental management.展开更多
文摘In recent years,the development of Bimodal Neural Probes has brought unprecedented breakthroughs to Brain-Computer Interfaces(BCI)and neuroscience research.Traditional neural probes can only record either the brain's electrical signals or the chemical signals of neurotransmitters,but bimodal probes enable the simultaneous acquisition and integration of both,allowing scientists to better understand neuronal interactions.This technology's core innovation lies in integrating Microelectrode Arrays(MEA)and Microfluidic Channels,ensuring high temporal and spatial alignment for more accurate neural signal decoding.This study explores the applications of bimodal neural probes in learning enhancement,motor recovery for paralyzed patients,Parkinson’s disease treatment,and epilepsy prediction,demonstrating their potential in neurological disease diagnosis,BCI optimization,and human-machine interaction through experimental case studies.Additionally,we analyze how key material innovations,such as graphene,polymer PI/PDMS,and PEDOT,enhance the probe's sensitivity,flexibility,and long-term stability while proposing future technological optimizations.Despite their promising prospects,widespread application faces challenges related to ethics,safety,and cost,which we address by proposing key feasibility recommendations,including establishing neural data security regulations to prevent misuse,reducing manufacturing costs through mass production and material optimization,conducting long-term biocompatibility testing to ensure stability,and developing fair-use guidelines to prevent social inequalities.Ultimately,this study highlights the significant contributions of bimodal neural probes to BCI technology and neuroscience while emphasizing the importance of responsible technological advancement.With improvements in manufacturing processes and increasing clinical applications,we believe bimodal neural probes will revolutionize human-brain interactions,profoundly impacting medicine,neurorehabilitation,and artificial intelligence.
基金supported by the National Key Research and Development Program of China(2019YFC1804604).
文摘Investigating the bioaccessibility of harmful inorganic elements in soil is crucial for understanding their behavior in the environment and accurately assessing the environmental risks associated with soil.Traditional batch experimental methods and linear models,however,are time-consuming and often fall short in precisely quantifying bioaccessibility.In this study,using 937 data points gathered from 56 journal articles,we developed machine learning models for three harmful inorganic elements,namely,Cd,Pb,and As.After thorough analysis,the model optimized through a boosting ensemble strategy demonstrated the best performance,with an average R2 of 0.95 and an RMSE of 0.25.We further employed SHAP values in conjunction with quantitative analysis to identify the key features that influence bioaccessibility.By utilizing the developed integrated models,we carried out predictions for 3002 data points across China,clarifying the bioaccessibility of cadmium(Cd),lead(Pb),and arsenic(As)in the soils of various sites and constructed a comprehensive spatial distribution map of China using the inverse distance weighting(IDW)interpolation method.Based on these findings,we further derived the soil environmental standards for metallurgical sites in China.Our observations from the collected data indicate a reduction in the number of sites exceeding the standard levels for Cd,Pb,and As in mining/smelting sites from 5,58,and 14 to 1,24,and 7,respectively.This research offers a precise and scientific approach for cross-regional risk assessment at the continental scale and lays a solid foundation for soil environmental management.
