The symptoms of autism spectrum disorder（ASD） have been hypothesized to be caused by changes in brain connectivity. From the clinical perspective, the‘‘disconnectivity＇＇ hypothesis has been used to explain chara...The symptoms of autism spectrum disorder（ASD） have been hypothesized to be caused by changes in brain connectivity. From the clinical perspective, the‘‘disconnectivity＇＇ hypothesis has been used to explain characteristic impairments in ‘‘socio-emotional＇＇ function.Therefore, in this study we compared the facial emotional recognition（FER） feature and the integrity of socialemotional-related white-matter tracts between children and adolescents with high-functioning ASD（HFA） and their typically developing（TD） counterparts. The correlation between the two factors was explored to find out if impairment of the white-matter tracts is the neural basis of social-emotional disorders. Compared with the TD group,FER was significantly impaired and the fractional anisotropy value of the right cingulate fasciculus was increased in the HFA group（P ／ 0.01）. In conclusion, the FER function of children and adolescents with HFA was impaired and the microstructure of the cingulate fasciculus had abnormalities.展开更多
The new multiple （G＇/G）-expansion method is proposed in this paper to seek the exact double traveling wave solutions of nonlinear partial differential equations. With the aid of symbolic computation, this new metho...The new multiple （G＇/G）-expansion method is proposed in this paper to seek the exact double traveling wave solutions of nonlinear partial differential equations. With the aid of symbolic computation, this new method is applied to construct double traveling wave solutions of the coupled nonlinear Klein-Gordon equations and the coupled SchrSdinger-Boussinesq equation. As a result, abundant double traveling wave solutions including double hyperbolic tangent function solutions, double tangent function solutions, double rational solutions, and a series of complexiton solutions of these two equations are obtained via this new method. The new multiple ＇ （G＇/G-expanslon method not only gets new exact solutions of equations directly and effectively, but also expands the scope of the solution. This new method has a very wide range of application for the study of nonlinear partial differential equations.展开更多
Wireless mesh network is a new emerging field with its potential applications in extremely unpredictable and dynamic environments.However,it is particularly vulnerable due to its features of open medium,dynamic changi...Wireless mesh network is a new emerging field with its potential applications in extremely unpredictable and dynamic environments.However,it is particularly vulnerable due to its features of open medium,dynamic changing topology, cooperative routing algorithms.The article surveys the state of the art in security for wireless mesh networks.Firstly,we analyze various possible threats to security in wireless mesh networks.Secondly,we introduce some representative solutions to these threats,including solutions to the problems of key management,secure network routing,and intrusion detection.We also provide a comparison and discussion of their respective merits and drawbacks,and propose some improvements for these drawbacks.Finally,we also discuss the remaining challenges in the area.展开更多
A sensitive approach for the qualitative detection of DNA-binding protein on the microarray was developed. DNA complexes in which a partial duplex region is formed from a biotin-primer and a circle single strand DNA ...A sensitive approach for the qualitative detection of DNA-binding protein on the microarray was developed. DNA complexes in which a partial duplex region is formed from a biotin-primer and a circle single strand DNA （ssDNA） were spotted on a microarray. The endonuclease recognition site （ERS） and the DNA-binding sites （DBS） were arranged side by side within the duplex region. The working principle of the detection system is described as follows： when the DNA-binding protein capture the DBS, the endonuclease could not attach to the ERS, and the immobilized primer in the DNA complex could be extended along the circle ssDNA by rolling circle amplification （RCA）. When no protein protects the DBS, the ERS could be attacked by the endonuclease and subsequently no rolling circle amplification occurs. Thereby we can detect the sequence specific DNA-binding activity with high-sensitivity due to the signal amplification of RCA.展开更多
The flexibility of dynamic community structure is adopted to analyze the depressive resting-state functional magnetic resonance imaging( rf MRI) signals in order to improve the accuracy of evaluating depression treatm...The flexibility of dynamic community structure is adopted to analyze the depressive resting-state functional magnetic resonance imaging( rf MRI) signals in order to improve the accuracy of evaluating depression treatment. The rf MRI signals of each brain network were obtained by the independent component correlation algorithm( ICA). Dynamic functional connections were computed with sliding windows and L1 norm. Then, the connections were used to calculate the dynamic community structure via the community-detection algorithm. The result of structure's community assignment has the general character with the brain activity changing over time. The flexibility index is one of traits of dynamic community structure, meaning the number of times a region changes. In this study, 16 patients who achieved clinical remission joined the experiment and were scanned before and after treatment. Pair permutation tests compare the difference of six brain networks' flexibility between pre-therapy and posttreatment. The results showthat the distribution of the flexibility values declines in a default network and cognitive control network between pre-therapy and post-treatment patients with statistical difference. Therefore, flexibility is a suitable approach to accurately evaluate the depression treatment effect.展开更多
Mobile ad hoc networks are often deployed in environments where the nodes of the networks are unattended and have little or no physical protection against tampering. The nodes of mobile ad hoc networks are thus suscep...Mobile ad hoc networks are often deployed in environments where the nodes of the networks are unattended and have little or no physical protection against tampering. The nodes of mobile ad hoc networks are thus susceptible to compromise. The networks are particularly vulnerable to denial of service （DOS） attacks launched through compromised aodes or intruders. In this paper, we investigated the effects of flooding attacks in network simulation 2 （NS-2） and measured the packet delivery ratio and packet delay under different flooding frequencies and different numbers of attack nodes. Simulation results show that with the increase the flooding frequencies and the numbers of attack nodes, network performance drops. But when the frequency of flooding attacks is greater than a value, the performance decrease gets smooth. Meanwhile the packet delay firstly increases and then declines to a value of stability at the end.展开更多
A novel framework is proposed to obtain physiologically meaningful features for Alzheimer's disease(AD)classification based on sparse functional connectivity and non-negative matrix factorization.Specifically,the ...A novel framework is proposed to obtain physiologically meaningful features for Alzheimer's disease(AD)classification based on sparse functional connectivity and non-negative matrix factorization.Specifically,the non-negative adaptive sparse representation(NASR)method is applied to compute the sparse functional connectivity among brain regions based on functional magnetic resonance imaging(fMRI)data for feature extraction.Afterwards,the sparse non-negative matrix factorization(sNMF)method is adopted for dimensionality reduction to obtain low-dimensional features with straightforward physical meaning.The experimental results show that the proposed framework outperforms the competing frameworks in terms of classification accuracy,sensitivity and specificity.Furthermore,three sub-networks,including the default mode network,the basal ganglia-thalamus-limbic network and the temporal-insular network,are found to have notable differences between the AD patients and the healthy subjects.The proposed framework can effectively identify AD patients and has potentials for extending the understanding of the pathological changes of AD.展开更多
Background Tetralogy of Fallot(TOF)is the most common cyanotic congenital heart disease.Children with TOF would be confronted with neurological impairment across their lifetime.Our study aimed to identify the risk fac...Background Tetralogy of Fallot(TOF)is the most common cyanotic congenital heart disease.Children with TOF would be confronted with neurological impairment across their lifetime.Our study aimed to identify the risk factors for cerebral morphology changes and cognition in postoperative preschool-aged children with TOF.Methods We used mass spectrometry(MS)technology to assess the levels of serum metabolites,Wechsler preschool and primary scale of intelligence-Fourth edition(WPPSI-Ⅳ)index scores to evaluate neurodevelopmental levels and multimodal magnetic resonance imaging(MRI)to detect cortical morphological changes.Results Multiple linear regression showed that preoperative levels of serum cortisone were positively correlated with the gyrification index of the left inferior parietal gyrus in children with TOF and negatively related to their lower visual spaces index and nonverbal index.Meanwhile,preoperative SpO_(2) was negatively correlated with levels of serum cortisone after adjusting for all covariates.Furthermore,after intervening levels of cortisone in chronic hypoxic model mice,total brain volumes were reduced at both postnatal(P)11.5 and P30 days.Conclusions Our results suggest that preoperative serum cortisone levels could be used as a biomarker of neurodevelopmental impairment in children with TOF.Our study findings emphasized that preoperative levels of cortisone could influence cerebral development and cognition abilities in children with TOF.展开更多
Wireless Mesh Networks is vulnerable to attacks due to the open medium, dynamically changing network topology, cooperative algorithms, Lack of centralized monitoring and management point. The traditional way of protec...Wireless Mesh Networks is vulnerable to attacks due to the open medium, dynamically changing network topology, cooperative algorithms, Lack of centralized monitoring and management point. The traditional way of protecting networks with firewalls and encryption software is no longer suffi- cient and effective for those features. In this paper, we propose a distributed intrusion detection ap- proach based on timed automata. A cluster-based detection scheme is presented, where periodically a node is elected as the monitor node for a cluster. These monitor nodes can not only make local intrusion detection decisions, but also cooperatively take part in global intrusion detection. And then we con- struct the Finite State Machine （FSM） by the way of manually abstracting the correct behaviors of the node according to the routing protocol of Dynamic Source Routing （DSR）. The monitor nodes can verify every node＇s behavior by the Finite State Ma- chine （FSM）, and validly detect real-time attacks without signatures of intrusion or trained data.Compared with the architecture where each node is its own IDS agent, our approach is much more efficient while maintaining the same level of effectiveness. Finally, we evaluate the intrusion detection method through simulation experiments.展开更多
Determining the binding sites of the transcription factor is important for understanding of transcriptional regulation. Transcription factor c-Jun plays an important role in cell growth, differentiation and developmen...Determining the binding sites of the transcription factor is important for understanding of transcriptional regulation. Transcription factor c-Jun plays an important role in cell growth, differentiation and development, but the binding sites and the target genes are not clearly defined in the whole human genome. In this study, we performed a ChIP-Seq experiment to identify c-Jun binding site in the human genome. Forty-eight binding sites were selected to process further evaluation by dsDNA microarray assay. We identified 283 c-Jun binding sites in K562 cells. Data analysis showed that 48.8% binding sites located within 100 kb of the upstream of the annotated genes, 28.6% binding sites comprised consensus TRE/CRE motif （5′-TGAC/GTCA-3′, 5′-TGACGTCA-3′） and variant sequences. Forty-two out of the selected 48 binding sites were found to bind the c-Jun homodimer in dsDNA microarray analysis. Data analysis also showed that 1569 genes are located in the neighborhood of the 283 binding sites and 191 genes in the neighborhood of the 42 binding sites validated by dsDNA microarray. We consulted 38 c-Jun target genes in previous studies and 16 among these 38 genes were also detected in this study. The identification of c-Jun binding sites and potential target genes in the genome scale may improve our fundamental understanding in the molecular mechanisms underlying the transcription regulation related to c-Jun.展开更多
Background One of the most critical issues in human-computer interaction applications is recognizing human emotions based on speech.In recent years,the challenging problem of cross-corpus speech emotion recognition(SE...Background One of the most critical issues in human-computer interaction applications is recognizing human emotions based on speech.In recent years,the challenging problem of cross-corpus speech emotion recognition(SER)has generated extensive research.Nevertheless,the domain discrepancy between training data and testing data remains a major challenge to achieving improved system performance.Methods This paper introduces a novel multi-scale discrepancy adversarial(MSDA)network for conducting multiple timescales domain adaptation for cross-corpus SER,i.e.,integrating domain discriminators of hierarchical levels into the emotion recognition framework to mitigate the gap between the source and target domains.Specifically,we extract two kinds of speech features,i.e.,handcraft features and deep features,from three timescales of global,local,and hybrid levels.In each timescale,the domain discriminator and the feature extrator compete against each other to learn features that minimize the discrepancy between the two domains by fooling the discriminator.Results Extensive experiments on cross-corpus and cross-language SER were conducted on a combination dataset that combines one Chinese dataset and two English datasets commonly used in SER.The MSDA is affected by the strong discriminate power provided by the adversarial process,where three discriminators are working in tandem with an emotion classifier.Accordingly,the MSDA achieves the best performance over all other baseline methods.Conclusions The proposed architecture was tested on a combination of one Chinese and two English datasets.The experimental results demonstrate the superiority of our powerful discriminative model for solving cross-corpus SER.展开更多
Oxygen uptake plays a crucial role in the evaluation of endurance performance during exercise and is extensively utilized for metabolic assessment. This study records the oxygen uptake during the exercise phase (i.e.,...Oxygen uptake plays a crucial role in the evaluation of endurance performance during exercise and is extensively utilized for metabolic assessment. This study records the oxygen uptake during the exercise phase (i.e., ascending or descending) of the stair exercise, utilizing an experimental dataset that includes ten participants and covers various exercise periods. Based on the designed experiment protocol, a non-parametric modeling method with kernel-based regularization is generally applied to estimate the oxygen uptake changes during the switching stairs exercise, which closely resembles daily life activities. The modeling results indicate the effectiveness of the non-parametric modeling approach when compared to fixed-order models in terms of accuracy, stability, and compatibility. The influence of exercise duration on estimated fitness reveals that the model of the phase-oxygen uptake system is not time-invariant related to respiratory metabolism regulation and muscle fatigue. Consequently, it allows us to study the humans’ conversion mechanism at different metabolic rates and facilitates the standardization and development of exercise prescriptions.展开更多
Background The use of micro-expression recognition to recognize human emotions is one of the most critical challenges in human-computer interaction applications. In recent years, cross-database micro-expression recogn...Background The use of micro-expression recognition to recognize human emotions is one of the most critical challenges in human-computer interaction applications. In recent years, cross-database micro-expression recognition(CDMER) has emerged as a significant challenge in micro-expression recognition and analysis. Because the training and testing data in CDMER come from different micro-expression databases, CDMER is more challenging than conventional micro-expression recognition. Methods In this paper, an adaptive spatio-temporal attention neural network(ASTANN) using an attention mechanism is presented to address this challenge. To this end, the micro-expression databases SMIC and CASME II are first preprocessed using an optical flow approach,which extracts motion information among video frames that represent discriminative features of micro-expression.After preprocessing, a novel adaptive framework with a spatiotemporal attention module was designed to assign spatial and temporal weights to enhance the most discriminative features. The deep neural network then extracts the cross-domain feature, in which the second-order statistics of the sample features in the source domain are aligned with those in the target domain by minimizing the correlation alignment(CORAL) loss such that the source and target databases share similar distributions. Results To evaluate the performance of ASTANN, experiments were conducted based on the SMIC and CASME II databases under the standard experimental evaluation protocol of CDMER. The experimental results demonstrate that ASTANN outperformed other methods in relevant crossdatabase tasks. Conclusions Extensive experiments were conducted on benchmark tasks, and the results show that ASTANN has superior performance compared with other approaches. This demonstrates the superiority of our method in solving the CDMER problem.展开更多
Nowadays,problem student is a common phenomenon exiting in primary and secondary school,Yemen.However,current unknown is why problem students appear and how to make an effective intervention way for reducing the probl...Nowadays,problem student is a common phenomenon exiting in primary and secondary school,Yemen.However,current unknown is why problem students appear and how to make an effective intervention way for reducing the problem behaviors.Although there have been many researches about problem student's problem behaviors researches on causes,developmental mechanism and educational strategies of problem students mainly stay on the theoretical research stage.This study aimed to investigate main reason for the formation of problem students and then make an effective intervention way and examine the invention effect.Firstly,combining existing research literature and teaching experience,this study sums up that problem behavior of problem students is mainly learning problem,interpersonal problem and willpower problem.Secondly,this study selected ten"problem students"of primary school as the participants.They are simply classified into three categories in accordance with the severity of the problem behaviors of problem students.I summarized various causes and affecting factors of problem behaviors through observing their performances and behaviors,and conduct in-depth interviews and explorations throughout the whole research process.Thirdly,on the basis of kind and severity of problems,various flexibly methods were used for the intervention study of problem behaviors.After that,I conducted interviews with the study cases,parents and teachers once again and made tracked records for the intervention effect.The intervention achieves marked improvement.For example,most students greatly improve their behaviors in class.Finally,I reflect the research process itself,considering that the case study is necessary method for the issue of"problem students".The use of qualitative research method is more conducive to enter the inner world of the studying cases,and better understand their true conditions as well.And on this basis,it is easier to find a breakthrough in the problem to improve the"problem students"and make a closer link between theoretical research and practical application.展开更多
As mobile devices and sensor technology advance,their role in communication becomes increasingly indispensable.Micro-expression recognition,an invaluable non-verbal communication method,has been extensively studied in...As mobile devices and sensor technology advance,their role in communication becomes increasingly indispensable.Micro-expression recognition,an invaluable non-verbal communication method,has been extensively studied in human-computer interaction,sentiment analysis,and security fields.However,the sensitivity and privacy implications of micro-expression data pose significant challenges for centralized machine learning methods,raising concerns about serious privacy leakage and data sharing.To address these limitations,we investigate a federated learning scheme tailored specifically for this task.Our approach prioritizes user privacy by employing federated optimization techniques,enabling the aggregation of clients’knowledge in an encrypted space without compromising data privacy.By integrating established micro-expression recognition methods into our framework,we demonstrate that our approach not only ensures robust data protection but also maintains high recognition performance comparable to non-privacy-preserving mechanisms.To our knowledge,this marks the first application of federated learning to the micro-expression recognition task.展开更多
Objective:To investigate the regulatory effects of two traditional mineral medicines(TMMs),Gypsum Fibrosum(Shigao,GF)and Terra Flava Usta(Zaoxintu,TFU),on gut-beneficial bacteria in mice,and preliminarily explore thei...Objective:To investigate the regulatory effects of two traditional mineral medicines(TMMs),Gypsum Fibrosum(Shigao,GF)and Terra Flava Usta(Zaoxintu,TFU),on gut-beneficial bacteria in mice,and preliminarily explore their mechanisms of action.Methods:Mice were randomly divided into 3 groups(n=10 per group):the control group(standard diet),the GF group(diet supplemented with 2%GF),and the TFU group(diet supplemented with 2%TFU).After 4-week intervention,16S rRNA gene sequencing was used to analyze the changes in the gut microbiota(GM).Scanning electron microscopy,in combination with coumarin A tetramethyl rhodamine conjugate and Hoechst stainings,was used to observe the bacteria and biofilm formation.Results:Principal coordinate analysis revealed that GF and TFU significantly altered the GM composition in mice.Further analysis revealed that GF and TFU affected different types of gut bacteria,suggesting that different TMMs may selectively modulate specific bacterial populations.For certain bacteria,such as Faecalibaculum and lleibacterium,both GF and TFU exhibited growth-promoting effects,implying that they may be sensitive to TMMs and that different TMMs can increase their abundance through their respective mechanisms.Notably,Lactobacillus reuteri,a widely recognized and used probiotic,was significantly enriched in the GF group.Random forest analysis identified lleibacterium valens as a potential indicator bacterium for TMMs'impact on GM.Further mechanistic studies showed that gut bacteria formed biofilm structures on the TFU surface.Conclusions:This study provides new insights into the interaction between TMMs and GM.As safe and effective natural clays,GF and TFU hold promise as potential candidates for prebiotic development.展开更多
Emotion recognition based on electroencephalography(EEG)has a wide range of applications and has great potential value,so it has received increasing attention from academia and industry in recent years.Meanwhile,multi...Emotion recognition based on electroencephalography(EEG)has a wide range of applications and has great potential value,so it has received increasing attention from academia and industry in recent years.Meanwhile,multiple kernel learning(MKL)has also been favored by researchers for its data-driven convenience and high accuracy.However,there is little research on MKL in EEG-based emotion recognition.Therefore,this paper is dedicated to exploring the application of MKL methods in the field of EEG emotion recognition and promoting the application of MKL methods in EEG emotion recognition.Thus,we proposed a support vector machine(SVM)classifier based on the MKL algorithm EasyMKL to investigate the feasibility of MKL algorithms in EEG-based emotion recognition problems.We designed two data partition methods,random division to verify the validity of the MKL method and sequential division to simulate practical applications.Then,tri-categorization experiments were performed for neutral,negative and positive emotions based on a commonly used dataset,the Shanghai Jiao Tong University emotional EEG dataset(SEED).The average classification accuracies for random division and sequential division were 92.25%and 74.37%,respectively,which shows better classification performance than the traditional single kernel SVM.The final results show that the MKL method is obviously effective,and the application of MKL in EEG emotion recognition is worthy of further study.Through the analysis of the experimental results,we discovered that the simple mathematical operations of the features on the symmetrical electrodes could not effectively integrate the spatial information of the EEG signals to obtain better performance.It is also confirmed that higher frequency band information is more correlated with emotional state and contributes more to emotion recognition.In summary,this paper explores research on MKL methods in the field of EEG emotion recognition and provides a new way of thinking for EEG-based emotion recognition research.展开更多
We proposed two whispered speech enhancement methods based on asymmetric cost functions in this paper to deal with the amplification and attenuation distortions of whispered speech distinctively.The modified Itakura-S...We proposed two whispered speech enhancement methods based on asymmetric cost functions in this paper to deal with the amplification and attenuation distortions of whispered speech distinctively.The modified Itakura-Saito（MIS）distance function provides more penalties to speech amplification distortion,whereas the Kullback-Leibler（KL）divergence function gives more penalties to speech attenuation distortion.The experimental results show that the MIS function based method achieves significant improvement of intelligibility in contrast to the conventional speech enhancement algorithms when the signal-to-noise ratio（SNR）falls below-6 dB,whereas the KL function based one achieves the similar result as the minimum mean square error（MMSE）speech enhancement method.The results show that the effects of the amplification and attenuation distortions on the intelligibility of the enhanced whisper are different,where larger attenuation distortion may result in better intelligibility of speech with low SNR.However,the attenuation distortion has small effects on intelligibility of speech with high SNR.展开更多
Meiotic recombination occurs preferentially at certain regions in the genome referred to as hot spots which are important for generating genetic diversity and proper segregation of chromosomes during meiosis.Although ...Meiotic recombination occurs preferentially at certain regions in the genome referred to as hot spots which are important for generating genetic diversity and proper segregation of chromosomes during meiosis.Although observations concerning individual hotspots have given clues as to the mechanism of recombination initiation,the nature and causes of recombination rate variation in the genome are still little known.A rational solution is to estimate and rank recombination rates along the genome.Therefore,it is a high demand for a database that deposits and integrates those data to provide a systematical repository of genome-wide recombination rates.Homologous recombination hotspots database is a web-based database of meiotic recombination rates,which comprises enormous data and information of human,mouse,rat,D.melanogaster,C.elegans and yeast.Users can query the database in several alternative ways.The database stores various details for every sequence,such as chromosome number,hyperlinks to the respective reference,and the sequence in FASTA format.展开更多
基金supported by The National Key Research and Development Program of China (2016YFC1306200)the National Natural Science Foundation of China (91132750)+1 种基金Major Projects of the National Social Science Foundation of China (14ZDB161)the Key Research and Development Program of Jiangsu Province, China (BE2016616)
文摘The symptoms of autism spectrum disorder（ASD） have been hypothesized to be caused by changes in brain connectivity. From the clinical perspective, the‘‘disconnectivity＇＇ hypothesis has been used to explain characteristic impairments in ‘‘socio-emotional＇＇ function.Therefore, in this study we compared the facial emotional recognition（FER） feature and the integrity of socialemotional-related white-matter tracts between children and adolescents with high-functioning ASD（HFA） and their typically developing（TD） counterparts. The correlation between the two factors was explored to find out if impairment of the white-matter tracts is the neural basis of social-emotional disorders. Compared with the TD group,FER was significantly impaired and the fractional anisotropy value of the right cingulate fasciculus was increased in the HFA group（P ／ 0.01）. In conclusion, the FER function of children and adolescents with HFA was impaired and the microstructure of the cingulate fasciculus had abnormalities.
基金Supported by the National Natural Science Foundation of China(Grant Nos.11202106、61201444)the Specialized Research Fund for the Doctoral Program of Higher Education(Grant No.20123228120005)+4 种基金the Jiangsu Information and Communication Engineering Preponderant Discipline Platformthe Natural Science Foundation of Jiangsu Province(Grant No.BK20131005)the Jiangsu Qing Lan Projectthe Natural Sciences Fundation of the Universities of Jiangsu Province(Grant No.13KJB170016)the Fundamental Research Funds for the Southeast University(Grant No.CDLS-2016-03)
文摘The new multiple （G＇/G）-expansion method is proposed in this paper to seek the exact double traveling wave solutions of nonlinear partial differential equations. With the aid of symbolic computation, this new method is applied to construct double traveling wave solutions of the coupled nonlinear Klein-Gordon equations and the coupled SchrSdinger-Boussinesq equation. As a result, abundant double traveling wave solutions including double hyperbolic tangent function solutions, double tangent function solutions, double rational solutions, and a series of complexiton solutions of these two equations are obtained via this new method. The new multiple ＇ （G＇/G-expanslon method not only gets new exact solutions of equations directly and effectively, but also expands the scope of the solution. This new method has a very wide range of application for the study of nonlinear partial differential equations.
基金Project supported by the Shanghai Minicipal Natural Science Foundation(Grant No09ZR1414900)the National High Technology Development 863 Program of China(Grant No2006AA01Z436,No2007AA01Z452,No2009AA01Z118)
文摘Wireless mesh network is a new emerging field with its potential applications in extremely unpredictable and dynamic environments.However,it is particularly vulnerable due to its features of open medium,dynamic changing topology, cooperative routing algorithms.The article surveys the state of the art in security for wireless mesh networks.Firstly,we analyze various possible threats to security in wireless mesh networks.Secondly,we introduce some representative solutions to these threats,including solutions to the problems of key management,secure network routing,and intrusion detection.We also provide a comparison and discussion of their respective merits and drawbacks,and propose some improvements for these drawbacks.Finally,we also discuss the remaining challenges in the area.
基金supported by the National Natural Science Foundation of China(Nos.60501010,60701008 and 60771024)
文摘A sensitive approach for the qualitative detection of DNA-binding protein on the microarray was developed. DNA complexes in which a partial duplex region is formed from a biotin-primer and a circle single strand DNA （ssDNA） were spotted on a microarray. The endonuclease recognition site （ERS） and the DNA-binding sites （DBS） were arranged side by side within the duplex region. The working principle of the detection system is described as follows： when the DNA-binding protein capture the DBS, the endonuclease could not attach to the ERS, and the immobilized primer in the DNA complex could be extended along the circle ssDNA by rolling circle amplification （RCA）. When no protein protects the DBS, the ERS could be attacked by the endonuclease and subsequently no rolling circle amplification occurs. Thereby we can detect the sequence specific DNA-binding activity with high-sensitivity due to the signal amplification of RCA.
基金The National High Technology Research and Development Program of China(863 program)(No.2015AA020509)the National Natural Science Foundation of China(No.81571639,81371522,61372032)
文摘The flexibility of dynamic community structure is adopted to analyze the depressive resting-state functional magnetic resonance imaging( rf MRI) signals in order to improve the accuracy of evaluating depression treatment. The rf MRI signals of each brain network were obtained by the independent component correlation algorithm( ICA). Dynamic functional connections were computed with sliding windows and L1 norm. Then, the connections were used to calculate the dynamic community structure via the community-detection algorithm. The result of structure's community assignment has the general character with the brain activity changing over time. The flexibility index is one of traits of dynamic community structure, meaning the number of times a region changes. In this study, 16 patients who achieved clinical remission joined the experiment and were scanned before and after treatment. Pair permutation tests compare the difference of six brain networks' flexibility between pre-therapy and posttreatment. The results showthat the distribution of the flexibility values declines in a default network and cognitive control network between pre-therapy and post-treatment patients with statistical difference. Therefore, flexibility is a suitable approach to accurately evaluate the depression treatment effect.
基金the Shanghai Municipal Natural Science Foundation (No.09ZR1414900)the National High Technology Research and Development Program (863) of China (Nos.2006AA01Z436,2007AA01Z452 and 2009AA01Z118)
文摘Mobile ad hoc networks are often deployed in environments where the nodes of the networks are unattended and have little or no physical protection against tampering. The nodes of mobile ad hoc networks are thus susceptible to compromise. The networks are particularly vulnerable to denial of service （DOS） attacks launched through compromised aodes or intruders. In this paper, we investigated the effects of flooding attacks in network simulation 2 （NS-2） and measured the packet delivery ratio and packet delay under different flooding frequencies and different numbers of attack nodes. Simulation results show that with the increase the flooding frequencies and the numbers of attack nodes, network performance drops. But when the frequency of flooding attacks is greater than a value, the performance decrease gets smooth. Meanwhile the packet delay firstly increases and then declines to a value of stability at the end.
基金The Foundation of Hygiene and Health of Jiangsu Province(No.H2018042)the National Natural Science Foundation of China(No.61773114)the Key Research and Development Plan(Industry Foresight and Common Key Technology)of Jiangsu Province(No.BE2017007-3)
文摘A novel framework is proposed to obtain physiologically meaningful features for Alzheimer's disease(AD)classification based on sparse functional connectivity and non-negative matrix factorization.Specifically,the non-negative adaptive sparse representation(NASR)method is applied to compute the sparse functional connectivity among brain regions based on functional magnetic resonance imaging(fMRI)data for feature extraction.Afterwards,the sparse non-negative matrix factorization(sNMF)method is adopted for dimensionality reduction to obtain low-dimensional features with straightforward physical meaning.The experimental results show that the proposed framework outperforms the competing frameworks in terms of classification accuracy,sensitivity and specificity.Furthermore,three sub-networks,including the default mode network,the basal ganglia-thalamus-limbic network and the temporal-insular network,are found to have notable differences between the AD patients and the healthy subjects.The proposed framework can effectively identify AD patients and has potentials for extending the understanding of the pathological changes of AD.
基金supported by the National Natural Science Foundation of China(82270310,81970265).
文摘Background Tetralogy of Fallot(TOF)is the most common cyanotic congenital heart disease.Children with TOF would be confronted with neurological impairment across their lifetime.Our study aimed to identify the risk factors for cerebral morphology changes and cognition in postoperative preschool-aged children with TOF.Methods We used mass spectrometry(MS)technology to assess the levels of serum metabolites,Wechsler preschool and primary scale of intelligence-Fourth edition(WPPSI-Ⅳ)index scores to evaluate neurodevelopmental levels and multimodal magnetic resonance imaging(MRI)to detect cortical morphological changes.Results Multiple linear regression showed that preoperative levels of serum cortisone were positively correlated with the gyrification index of the left inferior parietal gyrus in children with TOF and negatively related to their lower visual spaces index and nonverbal index.Meanwhile,preoperative SpO_(2) was negatively correlated with levels of serum cortisone after adjusting for all covariates.Furthermore,after intervening levels of cortisone in chronic hypoxic model mice,total brain volumes were reduced at both postnatal(P)11.5 and P30 days.Conclusions Our results suggest that preoperative serum cortisone levels could be used as a biomarker of neurodevelopmental impairment in children with TOF.Our study findings emphasized that preoperative levels of cortisone could influence cerebral development and cognition abilities in children with TOF.
基金Acknowledgements Project supported by the National Natural Science Foundation of China （Grant No.60932003）, the National High Technology Development 863 Program of China （Grant No.2007AA01Z452, No. 2009AA01 Z118 ）, Project supported by Shanghai Municipal Natural Science Foundation （Grant No.09ZRI414900）, National Undergraduate Innovative Test Program （091024812）.
文摘Wireless Mesh Networks is vulnerable to attacks due to the open medium, dynamically changing network topology, cooperative algorithms, Lack of centralized monitoring and management point. The traditional way of protecting networks with firewalls and encryption software is no longer suffi- cient and effective for those features. In this paper, we propose a distributed intrusion detection ap- proach based on timed automata. A cluster-based detection scheme is presented, where periodically a node is elected as the monitor node for a cluster. These monitor nodes can not only make local intrusion detection decisions, but also cooperatively take part in global intrusion detection. And then we con- struct the Finite State Machine （FSM） by the way of manually abstracting the correct behaviors of the node according to the routing protocol of Dynamic Source Routing （DSR）. The monitor nodes can verify every node＇s behavior by the Finite State Ma- chine （FSM）, and validly detect real-time attacks without signatures of intrusion or trained data.Compared with the architecture where each node is its own IDS agent, our approach is much more efficient while maintaining the same level of effectiveness. Finally, we evaluate the intrusion detection method through simulation experiments.
基金supported by the National Natural Science Foundation of China(Nos.30973375 and 30600152)
文摘Determining the binding sites of the transcription factor is important for understanding of transcriptional regulation. Transcription factor c-Jun plays an important role in cell growth, differentiation and development, but the binding sites and the target genes are not clearly defined in the whole human genome. In this study, we performed a ChIP-Seq experiment to identify c-Jun binding site in the human genome. Forty-eight binding sites were selected to process further evaluation by dsDNA microarray assay. We identified 283 c-Jun binding sites in K562 cells. Data analysis showed that 48.8% binding sites located within 100 kb of the upstream of the annotated genes, 28.6% binding sites comprised consensus TRE/CRE motif （5′-TGAC/GTCA-3′, 5′-TGACGTCA-3′） and variant sequences. Forty-two out of the selected 48 binding sites were found to bind the c-Jun homodimer in dsDNA microarray analysis. Data analysis also showed that 1569 genes are located in the neighborhood of the 283 binding sites and 191 genes in the neighborhood of the 42 binding sites validated by dsDNA microarray. We consulted 38 c-Jun target genes in previous studies and 16 among these 38 genes were also detected in this study. The identification of c-Jun binding sites and potential target genes in the genome scale may improve our fundamental understanding in the molecular mechanisms underlying the transcription regulation related to c-Jun.
基金the National Nature Science Foundation of China(U2003207,61902064)the Jiangsu Frontier Technology Basic Research Project(BK20192004).
文摘Background One of the most critical issues in human-computer interaction applications is recognizing human emotions based on speech.In recent years,the challenging problem of cross-corpus speech emotion recognition(SER)has generated extensive research.Nevertheless,the domain discrepancy between training data and testing data remains a major challenge to achieving improved system performance.Methods This paper introduces a novel multi-scale discrepancy adversarial(MSDA)network for conducting multiple timescales domain adaptation for cross-corpus SER,i.e.,integrating domain discriminators of hierarchical levels into the emotion recognition framework to mitigate the gap between the source and target domains.Specifically,we extract two kinds of speech features,i.e.,handcraft features and deep features,from three timescales of global,local,and hybrid levels.In each timescale,the domain discriminator and the feature extrator compete against each other to learn features that minimize the discrepancy between the two domains by fooling the discriminator.Results Extensive experiments on cross-corpus and cross-language SER were conducted on a combination dataset that combines one Chinese dataset and two English datasets commonly used in SER.The MSDA is affected by the strong discriminate power provided by the adversarial process,where three discriminators are working in tandem with an emotion classifier.Accordingly,the MSDA achieves the best performance over all other baseline methods.Conclusions The proposed architecture was tested on a combination of one Chinese and two English datasets.The experimental results demonstrate the superiority of our powerful discriminative model for solving cross-corpus SER.
基金supported by the National Natural Science Foundation of China(No.62103449)the Start-up Research Fund of Southeast University(RF1028623007)the Zhishan Youth Scholar Support Program of Southeast University(2242023R40044).
文摘Oxygen uptake plays a crucial role in the evaluation of endurance performance during exercise and is extensively utilized for metabolic assessment. This study records the oxygen uptake during the exercise phase (i.e., ascending or descending) of the stair exercise, utilizing an experimental dataset that includes ten participants and covers various exercise periods. Based on the designed experiment protocol, a non-parametric modeling method with kernel-based regularization is generally applied to estimate the oxygen uptake changes during the switching stairs exercise, which closely resembles daily life activities. The modeling results indicate the effectiveness of the non-parametric modeling approach when compared to fixed-order models in terms of accuracy, stability, and compatibility. The influence of exercise duration on estimated fitness reveals that the model of the phase-oxygen uptake system is not time-invariant related to respiratory metabolism regulation and muscle fatigue. Consequently, it allows us to study the humans’ conversion mechanism at different metabolic rates and facilitates the standardization and development of exercise prescriptions.
文摘Background The use of micro-expression recognition to recognize human emotions is one of the most critical challenges in human-computer interaction applications. In recent years, cross-database micro-expression recognition(CDMER) has emerged as a significant challenge in micro-expression recognition and analysis. Because the training and testing data in CDMER come from different micro-expression databases, CDMER is more challenging than conventional micro-expression recognition. Methods In this paper, an adaptive spatio-temporal attention neural network(ASTANN) using an attention mechanism is presented to address this challenge. To this end, the micro-expression databases SMIC and CASME II are first preprocessed using an optical flow approach,which extracts motion information among video frames that represent discriminative features of micro-expression.After preprocessing, a novel adaptive framework with a spatiotemporal attention module was designed to assign spatial and temporal weights to enhance the most discriminative features. The deep neural network then extracts the cross-domain feature, in which the second-order statistics of the sample features in the source domain are aligned with those in the target domain by minimizing the correlation alignment(CORAL) loss such that the source and target databases share similar distributions. Results To evaluate the performance of ASTANN, experiments were conducted based on the SMIC and CASME II databases under the standard experimental evaluation protocol of CDMER. The experimental results demonstrate that ASTANN outperformed other methods in relevant crossdatabase tasks. Conclusions Extensive experiments were conducted on benchmark tasks, and the results show that ASTANN has superior performance compared with other approaches. This demonstrates the superiority of our method in solving the CDMER problem.
文摘Nowadays,problem student is a common phenomenon exiting in primary and secondary school,Yemen.However,current unknown is why problem students appear and how to make an effective intervention way for reducing the problem behaviors.Although there have been many researches about problem student's problem behaviors researches on causes,developmental mechanism and educational strategies of problem students mainly stay on the theoretical research stage.This study aimed to investigate main reason for the formation of problem students and then make an effective intervention way and examine the invention effect.Firstly,combining existing research literature and teaching experience,this study sums up that problem behavior of problem students is mainly learning problem,interpersonal problem and willpower problem.Secondly,this study selected ten"problem students"of primary school as the participants.They are simply classified into three categories in accordance with the severity of the problem behaviors of problem students.I summarized various causes and affecting factors of problem behaviors through observing their performances and behaviors,and conduct in-depth interviews and explorations throughout the whole research process.Thirdly,on the basis of kind and severity of problems,various flexibly methods were used for the intervention study of problem behaviors.After that,I conducted interviews with the study cases,parents and teachers once again and made tracked records for the intervention effect.The intervention achieves marked improvement.For example,most students greatly improve their behaviors in class.Finally,I reflect the research process itself,considering that the case study is necessary method for the issue of"problem students".The use of qualitative research method is more conducive to enter the inner world of the studying cases,and better understand their true conditions as well.And on this basis,it is easier to find a breakthrough in the problem to improve the"problem students"and make a closer link between theoretical research and practical application.
基金supported by the Science and Technology Development Fund of Macao,China(No.0035/2023/ITP1)the National Natural Science Foundation of China(No.62076122)+2 种基金the Basic Science(Natural Science)Research Project of Higher Education Institutions in Jiangsu Province(No.24KJA520003)the 333 High-Level Talents in Jiangsu Province(2024)the Fundamental Research Funds for the Central Universities(No.2242024k30027).
文摘As mobile devices and sensor technology advance,their role in communication becomes increasingly indispensable.Micro-expression recognition,an invaluable non-verbal communication method,has been extensively studied in human-computer interaction,sentiment analysis,and security fields.However,the sensitivity and privacy implications of micro-expression data pose significant challenges for centralized machine learning methods,raising concerns about serious privacy leakage and data sharing.To address these limitations,we investigate a federated learning scheme tailored specifically for this task.Our approach prioritizes user privacy by employing federated optimization techniques,enabling the aggregation of clients’knowledge in an encrypted space without compromising data privacy.By integrating established micro-expression recognition methods into our framework,we demonstrate that our approach not only ensures robust data protection but also maintains high recognition performance comparable to non-privacy-preserving mechanisms.To our knowledge,this marks the first application of federated learning to the micro-expression recognition task.
基金Supported by Natural Science Foundation of China(No.31770540)the Key Research Program of Jiangsu Province(No.BE2018663)。
文摘Objective:To investigate the regulatory effects of two traditional mineral medicines(TMMs),Gypsum Fibrosum(Shigao,GF)and Terra Flava Usta(Zaoxintu,TFU),on gut-beneficial bacteria in mice,and preliminarily explore their mechanisms of action.Methods:Mice were randomly divided into 3 groups(n=10 per group):the control group(standard diet),the GF group(diet supplemented with 2%GF),and the TFU group(diet supplemented with 2%TFU).After 4-week intervention,16S rRNA gene sequencing was used to analyze the changes in the gut microbiota(GM).Scanning electron microscopy,in combination with coumarin A tetramethyl rhodamine conjugate and Hoechst stainings,was used to observe the bacteria and biofilm formation.Results:Principal coordinate analysis revealed that GF and TFU significantly altered the GM composition in mice.Further analysis revealed that GF and TFU affected different types of gut bacteria,suggesting that different TMMs may selectively modulate specific bacterial populations.For certain bacteria,such as Faecalibaculum and lleibacterium,both GF and TFU exhibited growth-promoting effects,implying that they may be sensitive to TMMs and that different TMMs can increase their abundance through their respective mechanisms.Notably,Lactobacillus reuteri,a widely recognized and used probiotic,was significantly enriched in the GF group.Random forest analysis identified lleibacterium valens as a potential indicator bacterium for TMMs'impact on GM.Further mechanistic studies showed that gut bacteria formed biofilm structures on the TFU surface.Conclusions:This study provides new insights into the interaction between TMMs and GM.As safe and effective natural clays,GF and TFU hold promise as potential candidates for prebiotic development.
基金supported by National Natural Science Foundation of China(No.62176054)University Synergy Innovation Program of Anhui Province,China(No.GXXT-2020-015)。
文摘Emotion recognition based on electroencephalography(EEG)has a wide range of applications and has great potential value,so it has received increasing attention from academia and industry in recent years.Meanwhile,multiple kernel learning(MKL)has also been favored by researchers for its data-driven convenience and high accuracy.However,there is little research on MKL in EEG-based emotion recognition.Therefore,this paper is dedicated to exploring the application of MKL methods in the field of EEG emotion recognition and promoting the application of MKL methods in EEG emotion recognition.Thus,we proposed a support vector machine(SVM)classifier based on the MKL algorithm EasyMKL to investigate the feasibility of MKL algorithms in EEG-based emotion recognition problems.We designed two data partition methods,random division to verify the validity of the MKL method and sequential division to simulate practical applications.Then,tri-categorization experiments were performed for neutral,negative and positive emotions based on a commonly used dataset,the Shanghai Jiao Tong University emotional EEG dataset(SEED).The average classification accuracies for random division and sequential division were 92.25%and 74.37%,respectively,which shows better classification performance than the traditional single kernel SVM.The final results show that the MKL method is obviously effective,and the application of MKL in EEG emotion recognition is worthy of further study.Through the analysis of the experimental results,we discovered that the simple mathematical operations of the features on the symmetrical electrodes could not effectively integrate the spatial information of the EEG signals to obtain better performance.It is also confirmed that higher frequency band information is more correlated with emotional state and contributes more to emotion recognition.In summary,this paper explores research on MKL methods in the field of EEG emotion recognition and provides a new way of thinking for EEG-based emotion recognition research.
基金supported by the National Natural Science Foundation of China(61301295,61273266,61231002)the Natural Science Foundation of Anhui Province(1308085QF100,1408085MF113)the Doctoral Fund of Anhui University
文摘We proposed two whispered speech enhancement methods based on asymmetric cost functions in this paper to deal with the amplification and attenuation distortions of whispered speech distinctively.The modified Itakura-Saito（MIS）distance function provides more penalties to speech amplification distortion,whereas the Kullback-Leibler（KL）divergence function gives more penalties to speech attenuation distortion.The experimental results show that the MIS function based method achieves significant improvement of intelligibility in contrast to the conventional speech enhancement algorithms when the signal-to-noise ratio（SNR）falls below-6 dB,whereas the KL function based one achieves the similar result as the minimum mean square error（MMSE）speech enhancement method.The results show that the effects of the amplification and attenuation distortions on the intelligibility of the enhanced whisper are different,where larger attenuation distortion may result in better intelligibility of speech with low SNR.However,the attenuation distortion has small effects on intelligibility of speech with high SNR.
文摘Meiotic recombination occurs preferentially at certain regions in the genome referred to as hot spots which are important for generating genetic diversity and proper segregation of chromosomes during meiosis.Although observations concerning individual hotspots have given clues as to the mechanism of recombination initiation,the nature and causes of recombination rate variation in the genome are still little known.A rational solution is to estimate and rank recombination rates along the genome.Therefore,it is a high demand for a database that deposits and integrates those data to provide a systematical repository of genome-wide recombination rates.Homologous recombination hotspots database is a web-based database of meiotic recombination rates,which comprises enormous data and information of human,mouse,rat,D.melanogaster,C.elegans and yeast.Users can query the database in several alternative ways.The database stores various details for every sequence,such as chromosome number,hyperlinks to the respective reference,and the sequence in FASTA format.
