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SSRN 2023年

作者： Kong, Lijun Fan, Lin Liu, Hao Zhang, JiaWei Hu, Mengdi Fan, Li Zhu, Hongliang Yan, Shancheng School of Geographic and Biologic Information Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing210023 China State Key Laboratory of Bioelectronics Jiangsu Key Laboratory for Biomaterials and Devices Southeast University Nanjing210096 China Nanjing University Nanjing210093 China

Ag and Cu based nanostructures serve as advanced functional materials for biomedical applications, due to their unique properties. Here, we proposed a novel Neurotransmitter Biosensing method based on Ag-Cu composite nanozyme, synthesized through the soft film plate method. Supported by the soft film template, the Ag-Cu nanozymes were stably kept to an ultrafine 2D structure with high monodispersity, which provided a large specific surface area and sufficient binding sites, leading to controllable and improved multi-nanozyme activities over similar-sized mono-Ag and mono-Cu, and up to 4.95 times of natural enzyme-level. Beyond that, the multi-path enzymatic reaction processes catalyzed by Ag-Cu composite nanozymes were firstly theoretically discussed in detail, according to the theoretical redox potential of redox couples in the reaction systems. On this basis, the Ag-Cu nanosheets based Neurotransmitter Biosensing is successfully applied in rapid detection for glutathione and dopamine, possessing a linear range of 10~100 μM and 1~10 μM, and a detection limit of 5.066 μM and 0.288 μM, respectively, which exhibited superior performance for biomedical purposes over most commercially available products in speed and precision. © 2023, The Authors. All rights reserved.

关键词： Binary alloys

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Soft Film Plate Method Based Ultrafine Ag-Cu Nanosheets with Multi-Nanozyme Activities for Neurotransmitter Biosensing

SSRN

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SSRN 2023年

作者： Fan, Lin Kong, Lijun Liu, Hao Zhang, JiaWei Hu, Mengdi Fan, Li Zhu, Hongliang Yan, Shancheng School of Geographic and Biologic Information Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing210023 China Nanjing University Nanjing210093 China State Key Laboratory of Bioelectronics Jiangsu Key Laboratory for Biomaterials and Devices Southeast University Nanjing210096 China

Ag and Cu based nanostructures serve as advanced functional materials for biomedical applications, due to their unique properties. Here, we proposed a novel neurotransmitter biosensing method based on Ag-Cu composite nanozyme, synthesized through the soft film plate method. Supported by the soft film template, the Ag-Cu nanozymes were stably kept to an ultrafine 2D structure with high monodispersity, which provided a large specific surface area and sufficient binding sites, leading to controllable and improved multi-nanozyme activities over similar-sized mono-Ag and mono-Cu, and up to 4.95 times of natural enzyme-level. The multi-path enzymatic reaction processes catalyzed by Ag-Cu composite nanozymes were firstly theoretically discussed in detail, according to the theoretical redox potential of redox couples in the reaction systems. On this basis, the Ag-Cu nanosheets based neurotransmitter biosensing is successfully applied in rapid detection for glutathione and dopamine, possessing a linear range of 10~100 μM and 1~10 μM, and a detection limit of 3.01 μM and 0.29 μM, respectively, which exhibited superior performance for biomedical purposes over most commercially available products in speed and precision. © 2023, The Authors. All rights reserved.

关键词： Binary alloys

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Local and global structure-aware entropy regularized mean teacher model for 3d left atrium segmentation 23rd

Local and global structure-aware entropy regularized mean te...

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23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Hang, Wenlong Feng, Wei Liang, Shuang Yu, Lequan Wang, Qiong Choi, Kup-Sze Qin, Jing School of Computer Science and Technology Nanjing Tech University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Department of Radiation Oncology Stanford University StanfordCA94305 United States CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China School of Nursing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783030597092

Emerging self-ensembling methods have achieved promising semi-supervised segmentation performances on medical images through forcing consistent predictions of unannotated data under different perturbations. However, the consistency only penalizes on independent pixel-level predictions, making structure-level information of predictions not exploited in the learning procedure. In view of this, we propose a novel structure-aware entropy regularized mean teacher model to address the above limitation. Specifically, we firstly introduce the entropy minimization principle to the student network, thereby adjusting itself to produce high-confident predictions of unannotated images. Based on this, we design a local structural consistency loss to encourage the consistency of inter-voxel similarities within the same local region of predictions from teacher and student networks. To further capture local structural dependencies, we enforce the global structural consistency by matching the weighted self-information maps between two networks. In this way, our model can minimize the prediction uncertainty of unannotated images, and more importantly that it can capture local and global structural information and their complementarity. We evaluate the proposed method on a publicly available 3D left atrium MR image dataset. Experimental results demonstrate that our method achieves outstanding segmentation performances than the state-of-the-art approaches in scenes with limited annotated images. © Springer Nature Switzerland AG 2020.

关键词： Forecasting

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Threshold distribution of equal states for quantitative amplitude fluctuations

arXiv

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arXiv 2023年

作者： Yao, Wenpo Yao, Wenli Wang, Jun School of Geographic and Biologic Information Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing210023 China Ministry of Education State Key Laboratory of Hydroscience and Engineering Tsinghua University Beijing100084 China

Objective. The distribution of equal states (DES) quantifies amplitude fluctuations in biomedical signals. However, under certain conditions, such as a high resolution of data collection or special signal processing techniques, equal states may be very rare, whereupon the DES fails to measure the amplitude fluctuations. Approach. To address this problem, we develop a novel threshold DES (tDES) that measures the distribution of differential states within a threshold. To evaluate the proposed tDES, we first analyze five sets of synthetic signals generated in different frequency bands. We then analyze sleep electroencephalography (EEG) datasets taken from the public PhysioNet. Main results. Synthetic signals and detrend-filtered sleep EEGs have no neighboring equal values;however, tDES can effectively measure the amplitude fluctuations within these data. The tDES of EEG data increases significantly as the sleep stage increases, even with datasets covering very short periods, indicating decreased amplitude fluctuations in sleep EEGs. Generally speaking, the presence of more low-frequency components in a physiological series reflects smaller amplitude fluctuations and larger DES. *** tDES provides a reliable computing method for quantifying amplitude fluctuations, exhibiting the characteristics of conceptual simplicity and computational robustness. Our findings broaden the application of quantitative amplitude fluctuations and contribute to the classification of sleep stages based on EEG data. © 2023, CC0.

关键词： Electroencephalography

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Research on the MEG of depression patients based on multivariate TE partial information decomposition

Research on the MEG of depression patients based on multivar...

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作者： Zihan Chen Yunjie Fang Wei Yan Jun Wang Jin Li Fengzhen Hou Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Department of Psychiatry The Affiliated Brain Hospital of Nanjing Medical University College of Physics and Information Technology Shaanxi Normal University Key Laboratory of Biomedical Functional Materials China Pharmaceutical University

Transfer entropy（TE） has been broadly used in the field of *** this paper,the partial information decomposition algorithm is employed to decompose multivariate TE into synergistic,redundant and unique *** this work,the synergistic part is believed as more suitable as the computation *** recorded the magnetoencephalogram（MEG） data of 6 subjects with depression and 13 normal subjects under different emotional stimulations,and studied the coupling between multiple symmetric channels in the frontal area in the brain of *** experimental results show that under different emotional stimulations,normal people present significant difference from the depression patients,especially in the right frontal ***,under negative emotional stimulation,the difference in synergistic value between normal people and depression patients is *** synergistic value of depression patients has become bigger,which indicates that the brain complexity of depression patients has grown,and their brain activities have increased.

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Networked Information Interactions in Schizophrenia Magnetoencephalograms Based on Permutation Transfer Entropy

SSRN

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SSRN 2023年

作者： Wang, Qiong Yang, Xinran Yan, Wei Yu, Jiafeng Wang, Jun School of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Jiangsu Nanjing210003 China School of Geographic and Biologic Information Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Jiangsu Nanjing210023 China School of Physics and Information Engineering Jiangsu Second Normal University Jiangsu Nanjing210013 China Department of Psychiatry Affiliated Nanjing Brain Hospital Nanjing Medical University Jiangsu Nanjing210029 China Shandong Key Laboratory of Biophysics Institute of Biophysics Dezhou University Dezhou253023 China

Brain network science plays an important role in the exploration of neurological and psychiatric diseases. To explore the information interactions in the magnetoencephalogram (MEG) data of schizophrenia, we construct resting-state brain networks based on permutation transfer entropy in 17 schizophrenia patients (SCZs) and 14 healthy controls (HCs). In this process, the effects of equal values on permutation and probability distribution are particularly considered. We quantify three network features, namely, weight, complexity and nonequilibrium, to characterize the schizophrenia MEG network. The results indicate that the level of information interactions between brain regions, the inward, outward and total information flows in brain regions of SCZs are generally smaller than those of HCs. In the complexity analysis, the Shannon entropy of the information inflows and outflows in the right parietal region of SCZs exhibits significant differences, and further the Shannon entropy of the information inflows (p=0.003) and outflows (p=0.034) of the whole brain of SCZs is significantly higher than that for HCs. Additionally, SCZs have lower values of local nonequilibrium than HCs in all brain regions except the middle central and middle frontal regions, with the right parietal region having the strongest significance (p=0.015), and the whole-brain nonequilibrium of SCZs is significantly lower than that of HCs (p=0.022). The MEG network constructed based on permutation transfer entropy can be used to effectively extract the characteristics of schizophrenia, and the comparative analysis of the complexity and nonequilibrium features can expand the exploration of the pathological and physiological mechanisms of schizophrenia. © 2023, The Authors. All rights reserved.

关键词： Probability distributions

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Epileptic Left-right Brain Network Analysis Based on Kendall's Improved Synchronization Algorithm

Epileptic Left-right Brain Network Analysis Based on Kendall...

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International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Xin Zou Ting Sun Chuchu Ding Jiafei Dai Jin Li Jun Wang Fengzhen Hou Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Nanjing General Hospital of Nanjing Military Command Nanjing China College of Physics and Information Technology Shaanxi Normal University Xi’an China Key Laboratory of Biomedical Functional Materials China Pharmaceutical University Nanjing China

ISBN: (纸本)9781538657393;9781538657386

Complex networks can be regarded as an abstraction of the description of a complex system. This paper presents an improved nonlinear synchronization algorithm IRC (inverse rank correlation) based on Kendall rank correlation. The IRC coupling coefficient is asymmetric. The brain function network has directionality by constructing with the IRC coupling coefficient matrices, which is helpful to study the nonlinear dynamic behavior of the brain network. The IRC algorithm was used to construct the left and right brain functional networks based on multi-channel EEG data, and the average degree index of the regional brain function network was analyzed to study the similarities and differences of regional brain networks between epilepsy patients and normal people. The results show that the improved algorithm can significantly distinguish between epilepsy and normal left brain region functional networks, in which the complexity of the epilepsy and the normal left brain are different. The experimental data show that the analysis of IRC network will help the clinical diagnosis and analysis of epilepsy, further deepen the study of the neurological dynamics of the brain, and provide an effective tool for clinical diagnosis.

关键词： Epilepsy Correlation Electroencephalography Couplings Mathematical model Correlation coefficient Complexity theory

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Analysis of Epilepsy EEG and ECG Correlation Based on IRC Algorithm

Analysis of Epilepsy EEG and ECG Correlation Based on IRC Al...

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International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Ting Sun Xin Zou Chuchu Ding Jiafei Dai Jin Li Jun Wang Fengzhen Hou Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Nanjing General Hospital of Nanjing Military Command Nanjing China College of Physics and Information Technology Shaanxi Normal University Xi’an China Key Laboratory of Biomedical Functional Materials China Pharmaceutical University Nanjing China

ISBN: (纸本)9781538657393;9781538657386

In this paper, the correlation analysis of EEG signals and ECG signals between epileptic and normal population was carried out based on the improved synchronization algorithm IRC based on Kendall. In clinical animal experiments, it was found that when the epilepsy sample attacks, irregular heart rate changes often occur. In this experiment, an improved IRC algorithm was used to analyze the correlation between EEG signals and ECG signals to study whether the correlation between epilepsy patients was different from that of normal people. Experimental results show that using the IRC algorithm based on Kendall improvement can significantly distinguish the correlation between EEG signals and ECG signals in the left forehead and left anterior temporal regions of normal and epileptic populations, and further study on the correlation between brain and heart in epileptic patients.

关键词： Electroencephalography Electrocardiography Correlation Epilepsy Correlation coefficient Electrodes Forehead

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Gestational Diabetes Mellitus Prediction Based on Two Classification Algorithms

Gestational Diabetes Mellitus Prediction Based on Two Classi...

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International Congress on Image and Signal Processing, BioMedical engineering and Informatics (CISP-BMEI)

作者： Weiyang Zhong Shanxi Jiang Wei Wu Danhong Peng Tian Xu Jun Wang Gongdao Wang Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing PR China China Tower Corporation Limited Nantong Branch PR China Key Laboratory of Environment Medicine and Engineering Ministry of Education School of Public Health Southeast University Nanjing PR China Zhongda Hospital Southeast University NanjingSchool of Science China Pharmaceutical University Nanjing PR China

Gestational diabetes mellitus (GDM) is a disease with normal glucose tolerance before pregnancy and only diabetes during pregnancy. The discovery of GDM has a long history. The specific causes and mechanisms of its occurrence are still unclear. There is a lack of research on intelligent diagnosis of GDM. GDM has many adverse effects on pregnant women and fetuses, which is of great significance for the early diagnosis of GDM. Based on the measured data of the hospital, this paper realizes the intelligent diagnosis of GDM by using a improved KNN algorithm and a improved BP neural network.

关键词： Classification algorithms Training Neural networks Diabetes Pregnancy Genetic algorithms Sociology

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Comparative analysis of time irreversibility and amplitude irreversibility based on joint permutation

arXiv

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arXiv 2022年

作者： Yao, Wenpo Yao, Wenli Xu, Rongshuang Wang, Jun School of Geographic and Biologic Information Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing210023 China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Ministry of Education China Department of Hydraulic Engineering School of Civil Engineering Tsinghua University Beijing100084 China School of Applied Meteorology Nanjing University of Information Science & Technology Nanjing210044 China

Although time irreversibility (TIR) and amplitude irreversibility (AIR) are relevant concepts for nonequilibrium analysis, their association has received little attention. This paper conducts a systematic comparative analysis of the relationship between TIR and AIR based on statistical descriptions and numerical simulations. To simplify the quantification of TIR and AIR, the amplitude permutation and global information of the associated vector are combined to produce a joint probability estimation. Chaotic logistic, Henon, and Lorenz series are generated to evaluate TIR and AIR according to surrogate theory, and the distributions of joint permutations for these model series are measured to compare the degree of TIR and AIR. The joint permutation TIR and AIR are then used to investigate nonequilibrium pathological features in epileptic electroencephalography data. Test results suggest that epileptic brain electrical activities, particular those during the onset of seizure, manifest higher nonequilibrium characteristics. According to the statistical definitions and targeted pairs of joint permutations in the chaotic model data, TIR and AIR are fundamentally different nonequilibrium descriptors from time- and amplitude-reversibility, respectively, and thus require different forms of numerical analysis. At the same time, TIR and AIR both provide measures for fluctuation theorems associated with nonequilibrium processes, and have similar probabilistic differences in the pairs of joint permutations and consistent outcomes when used to analyze both the model series and real-world signals. Overall, comparative analysis of TIR and AIR contributes to our understanding of nonequilibrium features and broadens the scope of quantitative nonequilibrium measures. Additionally, the construction of joint permutations contributes to the development of symbolic time series analysis. Copyright © 2022, The Authors. All rights reserved.

关键词： Electroencephalography

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