The human brain is known to have six cholinergic nudei （Selden et al., 1998; Nieuwenhuys et al., 2008）. The cerebral cortex obtains cholinergic innervation mainly from the basalis nucleus of Meynert （Ch 4） in the ...The human brain is known to have six cholinergic nudei （Selden et al., 1998; Nieuwenhuys et al., 2008）. The cerebral cortex obtains cholinergic innervation mainly from the basalis nucleus of Meynert （Ch 4） in the bas- al forebrain through the medial and lateral cholinergic pathways （Selden et al., 1998; Mesulam et al., 1983）. The cingulum, the neural fiber bundle connecting the basal forebrain and the medial temporal lobe, contains the medial cholinergic pathway （Selden et al., 1998; Hong and Jang, 2010）.展开更多
Neural organoids and confocal microscopy have the potential to play an important role in microconnectome research to understand neural patterns.We present PLayer,a plug-and-play embedded neural system,which demonstrat...Neural organoids and confocal microscopy have the potential to play an important role in microconnectome research to understand neural patterns.We present PLayer,a plug-and-play embedded neural system,which demonstrates the utilization of sparse confocal microscopy layers to interpolate continuous axial resolution.With an embedded system focused on neural network pruning,image scaling,and post-processing,PLayer achieves high-performance metrics with an average structural similarity index of 0.9217 and a peak signal-to-noise ratio of 27.75 dB,all within 20 s.This represents a significant time saving of 85.71%with simplified image processing.By harnessing statistical map estimation in interpolation and incorporating the Vision Transformer–based Restorer,PLayer ensures 2D layer consistency while mitigating heavy computational dependence.As such,PLayer can reconstruct 3D neural organoid confocal data continuously under limited computational power for the wide acceptance of fundamental connectomics and pattern-related research with embedded devices.展开更多
The concept of the brain cognitive reserve is derived from the well-acknowledged notion that the degree of brain damage does not always match the severity of clinical symptoms and neurological/cognitive outcomes.It ha...The concept of the brain cognitive reserve is derived from the well-acknowledged notion that the degree of brain damage does not always match the severity of clinical symptoms and neurological/cognitive outcomes.It has been suggested that the size of the brain(brain reserve) and the extent of neural connections acquired through life(neural reserve) set a threshold beyond which noticeable impairments occur.In contrast,cognitive reserve refers to the brain's ability to adapt and reo rganize stru cturally and functionally to resist damage and maintain function,including neural reserve and brain maintenance,resilience,and compensation(Verkhratsky and Zorec,2024).展开更多
Many animal studies have reported on the neural connectivity of the vestibular nuclei（VN）.However,little is reported on the structural neural connectivity of the VN in the human brain.In this study,we attempted to i...Many animal studies have reported on the neural connectivity of the vestibular nuclei（VN）.However,little is reported on the structural neural connectivity of the VN in the human brain.In this study,we attempted to investigate the structural neural connectivity of the VN in 37 healthy subjects using diffusion tensor tractography.A seed region of interest was placed on the isolated VN using probabilistic diffusion tensor tractography.Connectivity was defined as the incidence of connection between the VN and each brain region.The VN showed 100% connectivity with the cerebellum,thalamus,oculomotor nucleus,trochlear nucleus,abducens nucleus,and reticular formation,irrespective of thresholds.At the threshold of 5 streamlines,the VN showed connectivity with the primary motor cortex（95.9%）,primary somatosensory cortex（90.5%）,premotor cortex（87.8%）,hypothalamus（86.5%）,posterior parietal cortex（75.7%）,lateral prefrontal cortex（70.3%）,ventromedial prefrontal cortex（51.4%）,and orbitofrontal cortex（40.5%）,respectively.These results suggest that the VN showed high connectivity with the cerebellum,thalamus,oculomotor nucleus,trochlear nucleus,abducens nucleus,and reticular formation,which are the brain regions related to the functions of the VN,including equilibrium,control of eye movements,conscious perception of movement,and spatial orientation.展开更多
In modern wireless communication systems,the accurate acquisition of channel state information(CSI)is critical to the performance of beamforming,non-orthogonal multiple access(NOMA),etc.However,with the application of...In modern wireless communication systems,the accurate acquisition of channel state information(CSI)is critical to the performance of beamforming,non-orthogonal multiple access(NOMA),etc.However,with the application of massive MIMO in 5G,the number of antennas increases by hundreds or even thousands times,which leads to excessive feedback overhead and poses a huge challenge to the conventional channel state information feedback scheme.In this paper,by using deep learning technology,we develop a system framework for CSI feedback based on fully connected feedforward neural networks(FCFNN),named CF-FCFNN.Through learning the training set composed of CSI,CF-FCFNN is able to recover the original CSI from the compressed CSI more accurately compared with the existing method based on deep learning without increasing the algorithm complexity.展开更多
Fully connected neural networks(FCNNs)have been developed for the closure of subgrid-scale(SGS)stress and SGS heat flux in large-eddy simulations of compressible turbulent channel flow.The FCNNbased SGS model trained ...Fully connected neural networks(FCNNs)have been developed for the closure of subgrid-scale(SGS)stress and SGS heat flux in large-eddy simulations of compressible turbulent channel flow.The FCNNbased SGS model trained using data with Mach number Ma=3.0 and Reynolds number Re=3000 was applied to situations with different Mach numbers and Reynolds numbers.The input variables of the neural network model were the filtered velocity gradients and temperature gradients at a single spatial grid point.The a priori test showed that the FCNN model had a correlation coefficient larger than 0.91 and a relative error smaller than 0.43,with much better reconstructions of SGS unclosed terms than the dynamic Smagorinsky model(DSM).In a posteriori test,the behavior of the FCNN model was marginally better than that of the DSM in predicting the mean velocity profiles,mean temperature profiles,turbulent intensities,total Reynolds stress,total Reynolds heat flux,and mean SGS flux of kinetic energy,and outperformed the Smagorinsky model.展开更多
Signals from lumbar primary afferent fibers are important for modulating locomotion of the hind-limbs.However,silver impregnation techniques,autoradiography,wheat germ agglutinin-horseradish peroxidase and cholera tox...Signals from lumbar primary afferent fibers are important for modulating locomotion of the hind-limbs.However,silver impregnation techniques,autoradiography,wheat germ agglutinin-horseradish peroxidase and cholera toxin B subunit-horseradish peroxidase cannot image the central projections and connections of the dorsal root in detail.Thus,we injected 3-k Da Texas red-dextran amine into the proximal trunks of L4 dorsal roots in adult rats.Confocal microscopy results revealed that numerous labeled arborizations and varicosities extended to the dorsal horn from T12–S4,to Clarke＇s column from T10–L2,and to the ventral horn from L1–5.The labeled varicosities at the L4 cord level were very dense,particularly in laminae I–Ⅲ,and the density decreased gradually in more rostral and caudal segments.In addition,they were predominately distributed in laminae I–IV,moderately in laminae V–VⅡ and sparsely in laminae VⅢ–X.Furthermore,direct contacts of lumbar afferent fibers with propriospinal neurons were widespread in gray matter.In conclusion,the projection and connection patterns of L4 afferents were illustrated in detail by Texas red-dextran amine-dorsal root tracing.展开更多
The fornix is involved in the transfer of information on episodic memory as a part of the Papez circuit. Diffusion tensor imaging enables to estimate the neural connectivity of the fornix. The anterior fornical body h...The fornix is involved in the transfer of information on episodic memory as a part of the Papez circuit. Diffusion tensor imaging enables to estimate the neural connectivity of the fornix. The anterior fornical body has high connectivity with the anterior commissure, and brain areas rele- vant to cholinergic nuclei （septal forebrain region and brainstem） and memory function （medial temporal lobe）. In the normal subjects, by contrast, the posterior fornical body has connectivity with the cerebral cortex and brainstem through the splenium of the corpus callosum. We believe that knowledge of the neural connectivity of the fornix would be helpful in investigation of the neural network associated with memory and recovery mechanisms following injury of the fornix.展开更多
Accurate prediction of the rate of penetration(ROP)is significant for drilling optimization.While the intelligent ROP prediction model based on fully connected neural networks(FNN)outperforms traditional ROP equations...Accurate prediction of the rate of penetration(ROP)is significant for drilling optimization.While the intelligent ROP prediction model based on fully connected neural networks(FNN)outperforms traditional ROP equations and machine learning algorithms,its lack of interpretability undermines its credibility.This study proposes a novel interpretation and characterization method for the FNN ROP prediction model using the Rectified Linear Unit(ReLU)activation function.By leveraging the derivative of the ReLU function,the FNN function calculation process is transformed into vector operations.The FNN model is linearly characterized through further simplification,enabling its interpretation and analysis.The proposed method is applied in ROP prediction scenarios using drilling data from three vertical wells in the Tarim Oilfield.The results demonstrate that the FNN ROP prediction model with ReLU as the activation function performs exceptionally well.The relative activation frequency curve of hidden layer neurons aids in analyzing the overfitting of the FNN ROP model and determining drilling data similarity.In the well sections with similar drilling data,averaging the weight parameters enables linear characterization of the FNN ROP prediction model,leading to the establishment of a corresponding linear representation equation.Furthermore,the quantitative analysis of each feature's influence on ROP facilitates the proposal of drilling parameter optimization schemes for the current well section.The established linear characterization equation exhibits high precision,strong stability,and adaptability through the application and validation across multiple well sections.展开更多
The storage layer within the Moxizhuang Oilfield in the Junggar Basin develops various types of interlayer barriers with significant differences in morphology and scale of development. In response to the issues of int...The storage layer within the Moxizhuang Oilfield in the Junggar Basin develops various types of interlayer barriers with significant differences in morphology and scale of development. In response to the issues of interlayer barriers affecting the formation of oil and gas reservoirs and controlling oil-water distribution, this study proposes precise classification and quantitative identification of interlayer barriers in the study area based on a fully connected neural network combined with grey relational analysis. Taking the second member of the Sangonghe Formation (J1S22) in the Moxizhuang Oilfield as an example, combined with previous research, this study statistically analyzes the lithology and logging response characteristics of three types of interlayer barriers in the study area. Based on differences in composition, lithology, and genesis, interlayer barrier types are classified. Sensitive logging data such as natural gamma, acoustic time difference, and resistivity are selected through crossover plots. Grey relational analysis is used to calculate comprehensive discrimination indicators for interlayer barriers. Combined with the fully connected neural network method, an interlayer barrier identification model is established, and model training is conducted to verify the accuracy of interlayer barrier identification. The results indicate that the interlayer barrier identification model based on a fully connected neural network can rapidly and accurately identify interlayer barriers and their types. Its application in the second member of the Sangonghe Formation in the Moxizhuang Oilfield in the Junggar Basin has proven that the identification results of this method for interlayer barriers have a conformity rate exceeding 90% with core data, demonstrating excellent performance in interlayer barrier identification and proving the effectiveness of the model for interlayer barrier identification and prediction in this area. The research conclusions can provide theoretical guidance and technical reference for the identification and evaluation of interlayer barriers in the second member of the Sangonghe Formation in the Moxizhuang Oilfield in the Junggar Basin.展开更多
Attention deficit hyperactivity disorder（ADHD） is a pervasive psychiatric disorder that affects both children and adults. Adult male and female patients with ADHD are differentially affected, but few studies have ex...Attention deficit hyperactivity disorder（ADHD） is a pervasive psychiatric disorder that affects both children and adults. Adult male and female patients with ADHD are differentially affected, but few studies have explored the differences. The purpose of this study was to quantify differences between adult male and female patients with ADHD based on neuroimaging and connectivity analysis. Resting-state functional magnetic resonance imaging scans were obtained and preprocessed in 82 patients. Group-wise differences between male and female patients were quantified using degree centrality for different brain regions. The medial-, middle-, and inferior-frontal gyrus, superior parietal lobule, precuneus, supramarginal gyrus, superior- and middle-temporal gyrus, middle occipital gyrus, and cuneus were identified as regions with significant group-wise differences. The identified regions were correlated with clinical scores reflecting depression and anxiety and significant correlations were found. Adult ADHD patients exhibit different levels of depression and anxiety depending on sex, and our study provides insight into how changes in brain circuitry might differentially impact male and female ADHD patients.展开更多
This paper tries to present another theoretical view in the study of population geography by applying the principle of artificial neural network.It is our view that the approach to population geography study is of two...This paper tries to present another theoretical view in the study of population geography by applying the principle of artificial neural network.It is our view that the approach to population geography study is of two kinds so far: the synthetic analysis and An2 synthetic analysis.展开更多
In this work,we demonstrate aπ-phase-shifted tilted fiber Bragg grating(π-PSTFBG)-based sensor for measuring the refractive index(RI)of NaCl solutions,achieving a real-time and online measurement system by employing...In this work,we demonstrate aπ-phase-shifted tilted fiber Bragg grating(π-PSTFBG)-based sensor for measuring the refractive index(RI)of NaCl solutions,achieving a real-time and online measurement system by employing a densely connected convolutional neural network(D-CNN)model to demodulate the full spectrum.The proposedπ-PSTFBG sensor is prepared by using the advanced fiber grating inscription system based on a two-beam interferometry method,which could introduce deeper features of dip-splitting for all the lossy dips in the spectrum,giving the possibility of fully measuring the change of RI.This enhanced feature gives relatively higher prediction accuracy(R^(2) of 99.67%)using the well-trained D-CNN model compared with the results achieved by pure TFBG or that with a gold coating.As a further demonstration from a practical view,a prototype integrated with the proposed D-CNN algorithm is developed to conduct RI measurement of NaCl solutions in real time using aπ-PSTFBG-based RI sensor.The results show that the proposed real-time demodulation system is capable of measuring RI with an average error of 1.6×10^(-4)RIU in a short response time of<1 s.The demonstrated spectral demodulation approach powered by deep learning shows great potential in real-time analysis for chemical solutions and point-of-care medical testing based on RI changes,especially for the portable requirements.展开更多
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Educa-tion,Science and Technology,No.2012R1A1A4A01001873
文摘The human brain is known to have six cholinergic nudei （Selden et al., 1998; Nieuwenhuys et al., 2008）. The cerebral cortex obtains cholinergic innervation mainly from the basalis nucleus of Meynert （Ch 4） in the bas- al forebrain through the medial and lateral cholinergic pathways （Selden et al., 1998; Mesulam et al., 1983）. The cingulum, the neural fiber bundle connecting the basal forebrain and the medial temporal lobe, contains the medial cholinergic pathway （Selden et al., 1998; Hong and Jang, 2010）.
基金supported by the National Key R&D Program of China(Grant No.2021YFA1001000)the National Natural Science Foundation of China(Grant Nos.82111530212,U23A20282,and 61971255)+2 种基金the Natural Science Founda-tion of Guangdong Province(Grant No.2021B1515020092)the Shenzhen Bay Laboratory Fund(Grant No.SZBL2020090501014)the Shenzhen Science,Technology and Innovation Commission(Grant Nos.KJZD20231023094659002,JCYJ20220530142809022,and WDZC20220811170401001).
文摘Neural organoids and confocal microscopy have the potential to play an important role in microconnectome research to understand neural patterns.We present PLayer,a plug-and-play embedded neural system,which demonstrates the utilization of sparse confocal microscopy layers to interpolate continuous axial resolution.With an embedded system focused on neural network pruning,image scaling,and post-processing,PLayer achieves high-performance metrics with an average structural similarity index of 0.9217 and a peak signal-to-noise ratio of 27.75 dB,all within 20 s.This represents a significant time saving of 85.71%with simplified image processing.By harnessing statistical map estimation in interpolation and incorporating the Vision Transformer–based Restorer,PLayer ensures 2D layer consistency while mitigating heavy computational dependence.As such,PLayer can reconstruct 3D neural organoid confocal data continuously under limited computational power for the wide acceptance of fundamental connectomics and pattern-related research with embedded devices.
文摘The concept of the brain cognitive reserve is derived from the well-acknowledged notion that the degree of brain damage does not always match the severity of clinical symptoms and neurological/cognitive outcomes.It has been suggested that the size of the brain(brain reserve) and the extent of neural connections acquired through life(neural reserve) set a threshold beyond which noticeable impairments occur.In contrast,cognitive reserve refers to the brain's ability to adapt and reo rganize stru cturally and functionally to resist damage and maintain function,including neural reserve and brain maintenance,resilience,and compensation(Verkhratsky and Zorec,2024).
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2015R1D1A4A01020385)
文摘Many animal studies have reported on the neural connectivity of the vestibular nuclei（VN）.However,little is reported on the structural neural connectivity of the VN in the human brain.In this study,we attempted to investigate the structural neural connectivity of the VN in 37 healthy subjects using diffusion tensor tractography.A seed region of interest was placed on the isolated VN using probabilistic diffusion tensor tractography.Connectivity was defined as the incidence of connection between the VN and each brain region.The VN showed 100% connectivity with the cerebellum,thalamus,oculomotor nucleus,trochlear nucleus,abducens nucleus,and reticular formation,irrespective of thresholds.At the threshold of 5 streamlines,the VN showed connectivity with the primary motor cortex（95.9%）,primary somatosensory cortex（90.5%）,premotor cortex（87.8%）,hypothalamus（86.5%）,posterior parietal cortex（75.7%）,lateral prefrontal cortex（70.3%）,ventromedial prefrontal cortex（51.4%）,and orbitofrontal cortex（40.5%）,respectively.These results suggest that the VN showed high connectivity with the cerebellum,thalamus,oculomotor nucleus,trochlear nucleus,abducens nucleus,and reticular formation,which are the brain regions related to the functions of the VN,including equilibrium,control of eye movements,conscious perception of movement,and spatial orientation.
基金This work was supported by the Key Research and Development Project of Shaanxi Province under Grant no.2019ZDLGY07-07.
文摘In modern wireless communication systems,the accurate acquisition of channel state information(CSI)is critical to the performance of beamforming,non-orthogonal multiple access(NOMA),etc.However,with the application of massive MIMO in 5G,the number of antennas increases by hundreds or even thousands times,which leads to excessive feedback overhead and poses a huge challenge to the conventional channel state information feedback scheme.In this paper,by using deep learning technology,we develop a system framework for CSI feedback based on fully connected feedforward neural networks(FCFNN),named CF-FCFNN.Through learning the training set composed of CSI,CF-FCFNN is able to recover the original CSI from the compressed CSI more accurately compared with the existing method based on deep learning without increasing the algorithm complexity.
基金Financial support provided by the National Natural Science Foundation of China(Grant Nos.11702042 and 91952104)。
文摘Fully connected neural networks(FCNNs)have been developed for the closure of subgrid-scale(SGS)stress and SGS heat flux in large-eddy simulations of compressible turbulent channel flow.The FCNNbased SGS model trained using data with Mach number Ma=3.0 and Reynolds number Re=3000 was applied to situations with different Mach numbers and Reynolds numbers.The input variables of the neural network model were the filtered velocity gradients and temperature gradients at a single spatial grid point.The a priori test showed that the FCNN model had a correlation coefficient larger than 0.91 and a relative error smaller than 0.43,with much better reconstructions of SGS unclosed terms than the dynamic Smagorinsky model(DSM).In a posteriori test,the behavior of the FCNN model was marginally better than that of the DSM in predicting the mean velocity profiles,mean temperature profiles,turbulent intensities,total Reynolds stress,total Reynolds heat flux,and mean SGS flux of kinetic energy,and outperformed the Smagorinsky model.
基金supported by the Medical Sciences Foundation of China,No.14CXZ007
文摘Signals from lumbar primary afferent fibers are important for modulating locomotion of the hind-limbs.However,silver impregnation techniques,autoradiography,wheat germ agglutinin-horseradish peroxidase and cholera toxin B subunit-horseradish peroxidase cannot image the central projections and connections of the dorsal root in detail.Thus,we injected 3-k Da Texas red-dextran amine into the proximal trunks of L4 dorsal roots in adult rats.Confocal microscopy results revealed that numerous labeled arborizations and varicosities extended to the dorsal horn from T12–S4,to Clarke＇s column from T10–L2,and to the ventral horn from L1–5.The labeled varicosities at the L4 cord level were very dense,particularly in laminae I–Ⅲ,and the density decreased gradually in more rostral and caudal segments.In addition,they were predominately distributed in laminae I–IV,moderately in laminae V–VⅡ and sparsely in laminae VⅢ–X.Furthermore,direct contacts of lumbar afferent fibers with propriospinal neurons were widespread in gray matter.In conclusion,the projection and connection patterns of L4 afferents were illustrated in detail by Texas red-dextran amine-dorsal root tracing.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education,Science and Technology,No.2012R1A1A4A01001873
文摘The fornix is involved in the transfer of information on episodic memory as a part of the Papez circuit. Diffusion tensor imaging enables to estimate the neural connectivity of the fornix. The anterior fornical body has high connectivity with the anterior commissure, and brain areas rele- vant to cholinergic nuclei （septal forebrain region and brainstem） and memory function （medial temporal lobe）. In the normal subjects, by contrast, the posterior fornical body has connectivity with the cerebral cortex and brainstem through the splenium of the corpus callosum. We believe that knowledge of the neural connectivity of the fornix would be helpful in investigation of the neural network associated with memory and recovery mechanisms following injury of the fornix.
基金The authors greatly thanked the financial support from the National Key Research and Development Program of China(funded by National Natural Science Foundation of China,No.2019YFA0708300)the Strategic Cooperation Technology Projects of CNPC and CUPB(funded by China National Petroleum Corporation,No.ZLZX2020-03)+1 种基金the National Science Fund for Distinguished Young Scholars(funded by National Natural Science Foundation of China,No.52125401)Science Foundation of China University of Petroleum,Beijing(funded by China University of petroleum,Beijing,No.2462022SZBH002).
文摘Accurate prediction of the rate of penetration(ROP)is significant for drilling optimization.While the intelligent ROP prediction model based on fully connected neural networks(FNN)outperforms traditional ROP equations and machine learning algorithms,its lack of interpretability undermines its credibility.This study proposes a novel interpretation and characterization method for the FNN ROP prediction model using the Rectified Linear Unit(ReLU)activation function.By leveraging the derivative of the ReLU function,the FNN function calculation process is transformed into vector operations.The FNN model is linearly characterized through further simplification,enabling its interpretation and analysis.The proposed method is applied in ROP prediction scenarios using drilling data from three vertical wells in the Tarim Oilfield.The results demonstrate that the FNN ROP prediction model with ReLU as the activation function performs exceptionally well.The relative activation frequency curve of hidden layer neurons aids in analyzing the overfitting of the FNN ROP model and determining drilling data similarity.In the well sections with similar drilling data,averaging the weight parameters enables linear characterization of the FNN ROP prediction model,leading to the establishment of a corresponding linear representation equation.Furthermore,the quantitative analysis of each feature's influence on ROP facilitates the proposal of drilling parameter optimization schemes for the current well section.The established linear characterization equation exhibits high precision,strong stability,and adaptability through the application and validation across multiple well sections.
文摘The storage layer within the Moxizhuang Oilfield in the Junggar Basin develops various types of interlayer barriers with significant differences in morphology and scale of development. In response to the issues of interlayer barriers affecting the formation of oil and gas reservoirs and controlling oil-water distribution, this study proposes precise classification and quantitative identification of interlayer barriers in the study area based on a fully connected neural network combined with grey relational analysis. Taking the second member of the Sangonghe Formation (J1S22) in the Moxizhuang Oilfield as an example, combined with previous research, this study statistically analyzes the lithology and logging response characteristics of three types of interlayer barriers in the study area. Based on differences in composition, lithology, and genesis, interlayer barrier types are classified. Sensitive logging data such as natural gamma, acoustic time difference, and resistivity are selected through crossover plots. Grey relational analysis is used to calculate comprehensive discrimination indicators for interlayer barriers. Combined with the fully connected neural network method, an interlayer barrier identification model is established, and model training is conducted to verify the accuracy of interlayer barrier identification. The results indicate that the interlayer barrier identification model based on a fully connected neural network can rapidly and accurately identify interlayer barriers and their types. Its application in the second member of the Sangonghe Formation in the Moxizhuang Oilfield in the Junggar Basin has proven that the identification results of this method for interlayer barriers have a conformity rate exceeding 90% with core data, demonstrating excellent performance in interlayer barrier identification and proving the effectiveness of the model for interlayer barrier identification and prediction in this area. The research conclusions can provide theoretical guidance and technical reference for the identification and evaluation of interlayer barriers in the second member of the Sangonghe Formation in the Moxizhuang Oilfield in the Junggar Basin.
基金supported in part by the Institute for Basic Science(to HP)No.IBS-R015-D1
文摘Attention deficit hyperactivity disorder（ADHD） is a pervasive psychiatric disorder that affects both children and adults. Adult male and female patients with ADHD are differentially affected, but few studies have explored the differences. The purpose of this study was to quantify differences between adult male and female patients with ADHD based on neuroimaging and connectivity analysis. Resting-state functional magnetic resonance imaging scans were obtained and preprocessed in 82 patients. Group-wise differences between male and female patients were quantified using degree centrality for different brain regions. The medial-, middle-, and inferior-frontal gyrus, superior parietal lobule, precuneus, supramarginal gyrus, superior- and middle-temporal gyrus, middle occipital gyrus, and cuneus were identified as regions with significant group-wise differences. The identified regions were correlated with clinical scores reflecting depression and anxiety and significant correlations were found. Adult ADHD patients exhibit different levels of depression and anxiety depending on sex, and our study provides insight into how changes in brain circuitry might differentially impact male and female ADHD patients.
文摘This paper tries to present another theoretical view in the study of population geography by applying the principle of artificial neural network.It is our view that the approach to population geography study is of two kinds so far: the synthetic analysis and An2 synthetic analysis.
基金National Natural Science Foundation of China(62275277,U2001601)Guangdong Project(2021QN02X055)+3 种基金Guangdong ST Programme(2024B0101030001)Fundamental Research Funds for the Central Universities,Sun Yat-sen University(23lgbj007)Science and Technology Planning Project of Guangzhou(2024A04J9891)Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(SML2023SP231)。
文摘In this work,we demonstrate aπ-phase-shifted tilted fiber Bragg grating(π-PSTFBG)-based sensor for measuring the refractive index(RI)of NaCl solutions,achieving a real-time and online measurement system by employing a densely connected convolutional neural network(D-CNN)model to demodulate the full spectrum.The proposedπ-PSTFBG sensor is prepared by using the advanced fiber grating inscription system based on a two-beam interferometry method,which could introduce deeper features of dip-splitting for all the lossy dips in the spectrum,giving the possibility of fully measuring the change of RI.This enhanced feature gives relatively higher prediction accuracy(R^(2) of 99.67%)using the well-trained D-CNN model compared with the results achieved by pure TFBG or that with a gold coating.As a further demonstration from a practical view,a prototype integrated with the proposed D-CNN algorithm is developed to conduct RI measurement of NaCl solutions in real time using aπ-PSTFBG-based RI sensor.The results show that the proposed real-time demodulation system is capable of measuring RI with an average error of 1.6×10^(-4)RIU in a short response time of<1 s.The demonstrated spectral demodulation approach powered by deep learning shows great potential in real-time analysis for chemical solutions and point-of-care medical testing based on RI changes,especially for the portable requirements.
