Understanding the neural underpinning of human gait and balance is one of the most pertinent challenges for 21st-century translational neuroscience due to the profound impact that falls and mobility disturbances have ...Understanding the neural underpinning of human gait and balance is one of the most pertinent challenges for 21st-century translational neuroscience due to the profound impact that falls and mobility disturbances have on our aging population.Posture and gait control does not happen automatically,as previously believed,but rather requires continuous involvement of central nervous mechanisms.To effectively exert control over the body,the brain must integrate multiple streams of sensory information,including visual,vestibular,and somatosensory signals.The mechanisms which underpin the integration of these multisensory signals are the principal topic of the present work.Existing multisensory integration theories focus on how failure of cognitive processes thought to be involved in multisensory integration leads to falls in older adults.Insufficient emphasis,however,has been placed on specific contributions of individual sensory modalities to multisensory integration processes and cross-modal interactions that occur between the sensory modalities in relation to gait and balance.In the present work,we review the contributions of somatosensory,visual,and vestibular modalities,along with their multisensory intersections to gait and balance in older adults and patients with Parkinson’s disease.We also review evidence of vestibular contributions to multisensory temporal binding windows,previously shown to be highly pertinent to fall risk in older adults.Lastly,we relate multisensory vestibular mechanisms to potential neural substrates,both at the level of neurobiology(concerning positron emission tomography imaging)and at the level of electrophysiology(concerning electroencephalography).We hope that this integrative review,drawing influence across multiple subdisciplines of neuroscience,paves the way for novel research directions and therapeutic neuromodulatory approaches,to improve the lives of older adults and patients with neurodegenerative diseases.展开更多
INTRODUCTIONIt has been reported in many studies that electro-acupuncture(EA)can positively regulate erythrocyticimmunity and T-lymphocytic subgroups.Nevertheless,its mechanism remains to be explored.In thepresent stu...INTRODUCTIONIt has been reported in many studies that electro-acupuncture(EA)can positively regulate erythrocyticimmunity and T-lymphocytic subgroups.Nevertheless,its mechanism remains to be explored.In thepresent study,a multi-group,multi-stepped and multi-indexed observation was conducted on the effects of EA展开更多
The electro-hydraulic servo system was studied to cancel the amplitude attenuation and phase delay of its sinusoidal response,by developing a network using normalized least-mean-square (LMS) adaptive filtering algorit...The electro-hydraulic servo system was studied to cancel the amplitude attenuation and phase delay of its sinusoidal response,by developing a network using normalized least-mean-square (LMS) adaptive filtering algorithm.The command input was corrected by weights to generate the desired input for the algorithm,and the feedback was brought into the feedback correction,whose output was the weighted feedback.The weights of the normalized LMS adaptive filtering algorithm were updated on-line according to the estimation error between the desired input and the weighted feedback.Thus,the updated weights were copied to the input correction.The estimation error was forced to zero by the normalized LMS adaptive filtering algorithm such that the weighted feedback was equal to the desired input,making the feedback track the command.The above concept was used as a basis for the development of amplitude phase control.The method has good real-time performance without estimating the system model.The simulation and experiment results show that the proposed amplitude phase control can efficiently cancel the amplitude attenuation and phase delay with high precision.展开更多
Building an automatic seizure onset prediction model based on multi-channel electroencephalography (EEG) signals has been a hot topic in computer science and neuroscience field for a long time. In this research, we co...Building an automatic seizure onset prediction model based on multi-channel electroencephalography (EEG) signals has been a hot topic in computer science and neuroscience field for a long time. In this research, we collect EEG data from different epilepsy patients and EEG devices and reconstruct and combine the EEG signals using an innovative electric field encephalography (EFEG) method, which establishes a virtual electric field vector, enabling extraction of electric field components and increasing detection accuracy compared to the conventional method. We extract a number of important features from the reconstructed signals and pass them through an ensemble model based on support vector machine (SVM), Random Forest (RF), and deep neural network (DNN) classifiers. By applying this EFEG channel combination method, we can achieve the highest detection accuracy at 87% which is 6% to 17% higher than the conventional channel averaging combination method. Meanwhile, to reduce the potential overfitting problem caused by DNN models on a small dataset and limited training patient, we ensemble the DNN model with two “weaker” classifiers to ensure the best performance in model transferring for different patients. Based on these methods, we can achieve the highest detection accuracy at 82% on a new patient using a different EEG device. Thus, we believe our method has good potential to be applied on different commercial and clinical devices.展开更多
In the framework of continuum mechanics, one of possible consistent definitions of deformable permanent magnets is introduced and explored. Similar model can be used for ferroelectric substances. Based on the suggeste...In the framework of continuum mechanics, one of possible consistent definitions of deformable permanent magnets is introduced and explored. Similar model can be used for ferroelectric substances. Based on the suggested definition, we establish the key kinematic relationship for the deformable permanent magnet and suggest the simplest master system, allowing to analyze behavior of such substances.展开更多
Electromagnetic Radiation Source Identification(ERSI) is a key technology that is widely used in military and radiation management and in electromagnetic interference diagnostics.The discriminative capability of machi...Electromagnetic Radiation Source Identification(ERSI) is a key technology that is widely used in military and radiation management and in electromagnetic interference diagnostics.The discriminative capability of machine learning methods has recently been used for facilitating ERSI.This paper presents a new approach to improve ERSI by adopting support vector machines,which are proven to be effective tools in pattern classification and regression,on the basis of the spatial distribution of electromagnetic radiation sources.Spatial information is converted from 3D cubes to 1D vectors with subscripts as inputs in order to simplify the model.The model is trained with 187 500 data sets in order to enable it to identify the types of radiation source types with an accuracy of up to 99.9%.The influence of parameters(e.g.,penalty parameter,reflection and noise from the ambient environment,and the scaling method for the input data) are discussed.The proposed method has good performance in noisy and reverberant environment.It has an identification accuracy of 82.15% when the signal-to-noise ratio is 20 dB.The proposed method has better accuracy in a noisy environment than artificial neural networks.Given that each Electromagnetic(EM) source has unique spatial characteristics,this method can be used for EM source identification and EM interference diagnostics.展开更多
A series of 3-pyridinyl-6-aryl-l, 2, 4-triazolo[3, 4-b][1, 3, 4]thiadiazoles(PATT) were prepared, the structures were confirmed by IR and H NMR spectra. The results of cyclic 1 voltammetry measurements imply that all ...A series of 3-pyridinyl-6-aryl-l, 2, 4-triazolo[3, 4-b][1, 3, 4]thiadiazoles(PATT) were prepared, the structures were confirmed by IR and H NMR spectra. The results of cyclic 1 voltammetry measurements imply that all these compounds have a higher electron affinity (EA) than 2-(4-biphenyl)-5-(4-tert-butyl phenyl)-1, 3, 4-oxadiazole (PBD) which implies that PATT could be acting as better electron acceptors than widely used electron transporting material PBD.展开更多
The strategy of incorporating polymers into MXene-based functional materials has been widely used to improve their mechanical properties,however with inevitable sacrifice of their electrical conductivity and electroma...The strategy of incorporating polymers into MXene-based functional materials has been widely used to improve their mechanical properties,however with inevitable sacrifice of their electrical conductivity and electromagnetic interference(EMI)shielding performance.This study demonstrates a facile yet efficient layering structure design to prepare the highly robust and conductive double-layer Janus films comprised of independent aramid nanofiber(ANF)and Ti3C2Tx MXene/poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate)(PEDOT:PSS)layers.The ANF layer serves to provide good mechanical stability,whilst the MXene/PEDOT:PSS layer ensures excellent electrical conductivity.Doping PEDOT:PSS into the MXene layer enhances the interfacial bonding strength between the MXene and ANF layers and improves the hydrophobicity and water/oxidation resistance of MXene layer.The resultant ANF/MXene-PEDOT:PSS Janus film with a conductive layer thickness of 4.4μm was shown to display low sheet resistance(2.18Ω/sq),good EMI shielding effectiveness(EMI SE of 48.1 dB),high mechanical strength(155.9 MPa),and overall toughness(19.4 MJ/m^(3)).Moreover,the excellent electrical conductivity and light absorption capacity of the MXene-PEDOT:PSS conductive layer mean that these Janus films display multi-source driven heating functions,producing excellent Joule heating(382℃ at 4 V)and photothermal conversion(59.6℃ at 100 mW/m^(2))properties.展开更多
文摘Understanding the neural underpinning of human gait and balance is one of the most pertinent challenges for 21st-century translational neuroscience due to the profound impact that falls and mobility disturbances have on our aging population.Posture and gait control does not happen automatically,as previously believed,but rather requires continuous involvement of central nervous mechanisms.To effectively exert control over the body,the brain must integrate multiple streams of sensory information,including visual,vestibular,and somatosensory signals.The mechanisms which underpin the integration of these multisensory signals are the principal topic of the present work.Existing multisensory integration theories focus on how failure of cognitive processes thought to be involved in multisensory integration leads to falls in older adults.Insufficient emphasis,however,has been placed on specific contributions of individual sensory modalities to multisensory integration processes and cross-modal interactions that occur between the sensory modalities in relation to gait and balance.In the present work,we review the contributions of somatosensory,visual,and vestibular modalities,along with their multisensory intersections to gait and balance in older adults and patients with Parkinson’s disease.We also review evidence of vestibular contributions to multisensory temporal binding windows,previously shown to be highly pertinent to fall risk in older adults.Lastly,we relate multisensory vestibular mechanisms to potential neural substrates,both at the level of neurobiology(concerning positron emission tomography imaging)and at the level of electrophysiology(concerning electroencephalography).We hope that this integrative review,drawing influence across multiple subdisciplines of neuroscience,paves the way for novel research directions and therapeutic neuromodulatory approaches,to improve the lives of older adults and patients with neurodegenerative diseases.
基金the National Natural Science Foundation of China,№39970888.
文摘INTRODUCTIONIt has been reported in many studies that electro-acupuncture(EA)can positively regulate erythrocyticimmunity and T-lymphocytic subgroups.Nevertheless,its mechanism remains to be explored.In thepresent study,a multi-group,multi-stepped and multi-indexed observation was conducted on the effects of EA
基金Project(50905037) supported by the National Natural Science Foundation of ChinaProject(20092304120014) supported by Specialized Research Fund for the Doctoral Program of Higher Education of China+2 种基金 Project(20100471021) supported by the China Postdoctoral Science Foundation Project(LBH-Q09134) supported by Heilongjiang Postdoctoral Science-Research Foundation,China Project (HEUFT09013) supported by the Foundation of Harbin Engineering University,China
文摘The electro-hydraulic servo system was studied to cancel the amplitude attenuation and phase delay of its sinusoidal response,by developing a network using normalized least-mean-square (LMS) adaptive filtering algorithm.The command input was corrected by weights to generate the desired input for the algorithm,and the feedback was brought into the feedback correction,whose output was the weighted feedback.The weights of the normalized LMS adaptive filtering algorithm were updated on-line according to the estimation error between the desired input and the weighted feedback.Thus,the updated weights were copied to the input correction.The estimation error was forced to zero by the normalized LMS adaptive filtering algorithm such that the weighted feedback was equal to the desired input,making the feedback track the command.The above concept was used as a basis for the development of amplitude phase control.The method has good real-time performance without estimating the system model.The simulation and experiment results show that the proposed amplitude phase control can efficiently cancel the amplitude attenuation and phase delay with high precision.
文摘Building an automatic seizure onset prediction model based on multi-channel electroencephalography (EEG) signals has been a hot topic in computer science and neuroscience field for a long time. In this research, we collect EEG data from different epilepsy patients and EEG devices and reconstruct and combine the EEG signals using an innovative electric field encephalography (EFEG) method, which establishes a virtual electric field vector, enabling extraction of electric field components and increasing detection accuracy compared to the conventional method. We extract a number of important features from the reconstructed signals and pass them through an ensemble model based on support vector machine (SVM), Random Forest (RF), and deep neural network (DNN) classifiers. By applying this EFEG channel combination method, we can achieve the highest detection accuracy at 87% which is 6% to 17% higher than the conventional channel averaging combination method. Meanwhile, to reduce the potential overfitting problem caused by DNN models on a small dataset and limited training patient, we ensemble the DNN model with two “weaker” classifiers to ensure the best performance in model transferring for different patients. Based on these methods, we can achieve the highest detection accuracy at 82% on a new patient using a different EEG device. Thus, we believe our method has good potential to be applied on different commercial and clinical devices.
文摘In the framework of continuum mechanics, one of possible consistent definitions of deformable permanent magnets is introduced and explored. Similar model can be used for ferroelectric substances. Based on the suggested definition, we establish the key kinematic relationship for the deformable permanent magnet and suggest the simplest master system, allowing to analyze behavior of such substances.
基金supported by the National Natural Science Foundation of China under Grant No.61201024
文摘Electromagnetic Radiation Source Identification(ERSI) is a key technology that is widely used in military and radiation management and in electromagnetic interference diagnostics.The discriminative capability of machine learning methods has recently been used for facilitating ERSI.This paper presents a new approach to improve ERSI by adopting support vector machines,which are proven to be effective tools in pattern classification and regression,on the basis of the spatial distribution of electromagnetic radiation sources.Spatial information is converted from 3D cubes to 1D vectors with subscripts as inputs in order to simplify the model.The model is trained with 187 500 data sets in order to enable it to identify the types of radiation source types with an accuracy of up to 99.9%.The influence of parameters(e.g.,penalty parameter,reflection and noise from the ambient environment,and the scaling method for the input data) are discussed.The proposed method has good performance in noisy and reverberant environment.It has an identification accuracy of 82.15% when the signal-to-noise ratio is 20 dB.The proposed method has better accuracy in a noisy environment than artificial neural networks.Given that each Electromagnetic(EM) source has unique spatial characteristics,this method can be used for EM source identification and EM interference diagnostics.
文摘A series of 3-pyridinyl-6-aryl-l, 2, 4-triazolo[3, 4-b][1, 3, 4]thiadiazoles(PATT) were prepared, the structures were confirmed by IR and H NMR spectra. The results of cyclic 1 voltammetry measurements imply that all these compounds have a higher electron affinity (EA) than 2-(4-biphenyl)-5-(4-tert-butyl phenyl)-1, 3, 4-oxadiazole (PBD) which implies that PATT could be acting as better electron acceptors than widely used electron transporting material PBD.
基金support for this work by the National Key Research and Development Program of China(No.2019YFA0706802)the National Natural Science Foundation of China(Nos.51903223 and 12072325)the National Natural Science Foundation of China of Henan Province(No.222300420541).
文摘The strategy of incorporating polymers into MXene-based functional materials has been widely used to improve their mechanical properties,however with inevitable sacrifice of their electrical conductivity and electromagnetic interference(EMI)shielding performance.This study demonstrates a facile yet efficient layering structure design to prepare the highly robust and conductive double-layer Janus films comprised of independent aramid nanofiber(ANF)and Ti3C2Tx MXene/poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate)(PEDOT:PSS)layers.The ANF layer serves to provide good mechanical stability,whilst the MXene/PEDOT:PSS layer ensures excellent electrical conductivity.Doping PEDOT:PSS into the MXene layer enhances the interfacial bonding strength between the MXene and ANF layers and improves the hydrophobicity and water/oxidation resistance of MXene layer.The resultant ANF/MXene-PEDOT:PSS Janus film with a conductive layer thickness of 4.4μm was shown to display low sheet resistance(2.18Ω/sq),good EMI shielding effectiveness(EMI SE of 48.1 dB),high mechanical strength(155.9 MPa),and overall toughness(19.4 MJ/m^(3)).Moreover,the excellent electrical conductivity and light absorption capacity of the MXene-PEDOT:PSS conductive layer mean that these Janus films display multi-source driven heating functions,producing excellent Joule heating(382℃ at 4 V)and photothermal conversion(59.6℃ at 100 mW/m^(2))properties.
