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Targeting TrkB–PSD-95 coupling to mitigate neurological disorders

Neural Regeneration Research 2025年第3期20卷 715-724页

作者： Xin Yang yu-wen Alvin huang John Marshall Department of Molecular Biology Cell Biology and BiochemistryBrown UniversityProvidenceRIUSA Department of Neurology Warren Alpert Medical School of Brown UniversityProvidenceRIUSA Center for Translational Neuroscience Robert J.and Nancy D.Carney Institute for Brain Science and Brown Institute for Translational ScienceBrown UniversityProvidenceRIUSA

Tropomyosin receptor kinase B(TrkB)signaling plays a pivotal role in dendritic growth and dendritic spine formation to promote learning and *** activity-dependent release of brain-derived neurotrophic factor at synapses binds to pre-or postsynaptic TrkB resulting in the strengthening of synapses,reflected by long-term ***,the association of postsynaptic density protein-95 with TrkB enhances phospholipase Cγ-Ca^(2+)/calmodulin-dependent protein kinaseⅡand phosphatidylinositol 3-kinase-mechanistic target of rapamycin signaling required for long-term *** this review,we discuss TrkB-postsynaptic density protein-95 coupling as a promising strategy to magnify brain-derived neurotrophic factor signaling towards the development of novel therapeutics for specific neurological disorders.A reduction of TrkB signaling has been observed in neurodegenerative disorders,such as Alzheimer's disease and Huntington's disease,and enhancement of postsynaptic density protein-95 association with TrkB signaling could mitigate the observed deficiency of neuronal connectivity in schizophrenia and *** with brain-derived neurotrophic factor is problematic,due to poor pharmacokinetics,low brain penetration,and side effects resulting from activation of the p75 neurotrophin receptor or the truncated TrkB.T1 *** TrkB agonists and antibodies that activate TrkB are being intensively investigated,they cannot distinguish the multiple human TrkB splicing isoforms or cell type-specific *** TrkB–postsynaptic density protein-95 coupling provides an alternative approach to specifically boost TrkB signaling at localized synaptic sites versus global stimulation that risks many adverse side effects.

关键词： Angelman syndrome autism brain-derived neurotrophic factor depression neurodegenerative disorder neurodevelopmental disorder postsynaptic density protein-95 synaptic plasticity TrkB

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Analysis of alkaline phosphatase and γ-glutamyltransferase after radiofrequency ablation of primary liver cancer: A retrospective study

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World Journal of Gastrointestinal Surgery 2024年第9期16卷 2860-2869页

作者： wen-yu huang Sheng Zheng Dan Zhu Ying-Lang Zeng Juan Yang Xue-Li Zeng Pei Liu Shun-Ling Zhang Ming yuan Zhi-Xia Wang Department of Tumor and Vascular Interventional Therapy Xiamen Humanity HospitalXiamen 361000Fujian ProvinceChina Department of Gastroenterology The Third People's Hospital of Yunnan ProvinceKunming 650011Yunnan ProvinceChina Medical Imaging Center Qian Wei Hospital of Jilin ProvinceChangchun 130000Jilin ProvinceChina Department of Science and Education The Third People's Hospital of Yunnan ProvinceKunming 650011Yunnan ProvinceChina Graduate School of Clinical Medicine Dali UniversityDali 671000Yunnan ProvinceChina Department of Hepatobiliary Surgery Peking University Shenzhen HospitalShenzhen 518036Guangdong ProvinceChina Department of Oncology The Affiliated Shuyang Hospital of Xuzhou Medical UniversitySuqian 223600Jiangsu ProvinceChina

BACKGROUND Changes in alkaline phosphatase(ALP)andγ-glutamyltransferase(GGT)levels in patients with primary liver cancer(PLC)after radiofrequency ablation(RFA).Hepatocellular carcinoma is a malignant tumor with high incidence *** a common local treatment,RFA has attracted much attention for its efficacy and influence on liver *** To investigate the effect of serum ALP and GGT levels on the prognosis of patients with PLC treated by *** The preoperative clinical data of 165 patients who were pathologically or clinically diagnosed with PLC and who received RFA in our hospital between October 2018 and June 2023 were *** chi-square test was used to compare the data between *** Kaplan-Meier method and Cox regression were used to analyze the associ-ations between serum ALP and GGT levels and overall survival,progression-free survival(PFS)and clinical characteristics of patients before *** The 1-year survival rates of patients with normal(≤135 U/L)and abnormal(>135 U/L)serum ALP before treatment were 91%and 79%,respectively;the 2-year survival rates were 90%and 68%,respectively;and the 5-year survival rates were 35%and 18%,*** difference between the two groups was statistically significant(P=0.01).Before treatment,the 1-year survival rates of patients with normal serum GGT levels(≤45 U/L)and abnormal serum GGT levels(>45 U/L)were 95%and 87%,the 2-year survival rates were 85%and 71%,and the 5-year survival rates were 37%and 21%,*** difference between the two groups was statist-ically significant(P<0.001).Serum ALP[hazard ratio(HR)=1.766,95%confidence interval(95%CI):1.068-2.921,P=0.027]and GGT(HR=2.312,95%CI:1.367-3.912,P=0.002)is closely related to the overall survival of PLC patients after RF ablation and is an independent prognostic *** 1-year PFS rates were 72%and 50%,the 2-year PFS rates were 52%and 21%,and the 5-year PFS rates were 14%and 3%,*** difference between the

关键词： Alkaline phosphatase γ-glutamyltransferase Radiofrequency ablation Primary liver cancer Retrospective study

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Dual-domain and Multiscale Fusion Deep Neural Network for PPG Biometric Recognition

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Machine Intelligence Research 2023年第5期20卷 707-715页

作者： Chun-Ying Liu Gong-Ping Yang yu-wen huang Fu-Xian huang School of computer Heze UniversityHeze 274015China School of Software Shandong UniversityJinan 250101China

Photoplethysmography(PPG)biometrics have received considerable *** deep learning has achieved good performance for PPG biometrics,several challenges remain open:1)How to effectively extract the feature fusion representation from time and frequency PPG signals.2)How to effectively capture a series of PPG signal transition information.3)How to extract timevarying information from one-dimensional time-frequency sequential *** address these challenges,we propose a dual-domain and multiscale fusion deep neural network(DMFDNN)for PPG biometric *** DMFDNN is mainly composed of a two-branch deep learning framework for PPG biometrics,which can learn the time-varying and multiscale discriminative features from the time and frequency ***,we design a multiscale extraction module to capture transition information,which consists of multiple convolution layers with different receptive fields for capturing multiscale transition *** addition,the dual-domain attention module is proposed to strengthen the domain of greater contributions from time-domain and frequency-domain data for PPG *** on the four datasets demonstrate that DMFDNN outperforms the state-of-the-art methods for PPG biometrics.

关键词： Photoplethysmography(PPG)signal biometric recognition multiple scale deep neural network dual-domain attention.

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Quantitative analysis of plasma HBV DNA for early evaluation of the response to transcatheter arterial embolization for HBV-related hepatocellular carcinoma

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World Journal of Gastroenterology 2005年第39期11卷 6193-6196页

作者： Ying-wen Su yu-wen huang Sheng-Hsuan Chen Chin-yuan Tzen Division of Hematology-Oncology Department of Internal Medicine Mackay Memorial Hospital Taipei Taiwan China Department of Medical Research Mackay Memorial Hospital Taipei Taiwan China Department of Gastroenterology Taipei Medical University Hospital Taipei Taiwan China Department of Medical Research Department of Pathology Mackay Memorial Hospital Taipei Taiwan China

AIM： To assesse changes in plasma HBV DNA after TAE in HBV-related HCC and correlate the levels with the pattern of lipiodol accumulation on CT. METHODS： Between April and June 2001, 14 patients with HBV-associated HCC who underwent TAE for inoperable or recurrent tumor were studied. Levels of plasma HBV DNA were measured by real-time quantitative PCR daily for five consecutive days after TAE. More than twofold elevation of circulating HBV DNA was considered as a definite elevation. Abdominal CT was performed 1-2 mo after TAE for the measurement of lipiodol retention. RESULTS： Circulating HBV DNA in 10 out of 13 patients was elevated after TAE, except for one patient whose plasma HBV DNA was undetectable before and after TAE. In group Ⅰ patients （n = 6）, the HBV DNA elevation persisted for more than 2 d, while in group Ⅱ （n = 7）, the HBV DNA elevation only appeared for i d or did not reach a definite elevation. There were no significant differences in age or tumor size between the two groups. Patients in group Ⅰ had significantly better lipiodol retention （79.31±28.79%） on subsequent abdominal CT than group Ⅱ （18.43±10.61%）（P = 0.02）. CONCLUSION： Patients with durable HBV DNA elevation for more than 2 d correlated with good lipiodol retention measured 1 mo later, while others associated with poor lipiodol retention. Thus, circulating HBV DNA may be an early indicator of the success or failure of TAE.

关键词： Hepatocellular carcinoma Transcatheter arterial embolization HBV DNA

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Incremental Multi-Label Learning with Active Queries

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Journal of Computer Science & Technology 2020年第2期35卷 234-246页

作者： Sheng-Jun huang Guo-Xiang Li wen-yu huang Shao-yuan Li College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China Key Laboratory of Pattern Analysis and Machine Intelligence Ministry of Industry and Information Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing University Nanjing 210023China

In multi-label learning,it is rather expensive to label instances since they are simultaneously associated with multiple ***,active learning,which reduces the labeling cost by actively querying the labels of the most valuable data,becomes particularly important for multi-label learning.A good multi-label active learning algorithm usually consists of two crucial elements:a reasonable criterion to evaluate the gain of querying the label for an instance,and an effective classification model,based on whose prediction the criterion can be accurately *** this paper,we first introduce an effective multi-label classification model by combining label ranking with threshold learning,which is incrementally trained to avoid retraining from scratch after every *** on this model,we then propose to exploit both uncertainty and diversity in the instance space as well as the label space,and actively query the instance-label pairs which can improve the classification model *** experiments on 20 datasets demonstrate the superiority of the proposed approach to state-of-the-art methods.

关键词： active learning multi-label learning uncertainty diversity

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Multi-Scale Deep Cascade Bi-Forest for Electrocardiogram Biometric Recognition

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Journal of Computer Science & Technology 2021年第3期36卷 617-632页

作者： yu-wen huang Gong-Ping Yang Kui-Kui Wang Hai-Ying Liu Yi-Long Yin School of Software Shandong UniversityJinan 250101China School of Computer Heze UniversityHeze 274015China Department of Computer Engineering Changji UniversityChangji 831100China

Electrocardiogram(ECG)biometric recognition has emerged as a hot research topic in the past *** some promising results have been reported,especially using sparse representation learning(SRL)and deep neural network,robust identification for small-scale data is still a *** address this issue,we integrate SRL into a deep cascade model,and propose a multi-scale deep cascade bi-forest(MDCBF)model for ECG biometric *** design the bi-forest based feature generator by fusing L1-norm sparsity and L2-norm collaborative representation to efficiently deal with *** we propose a deep cascade framework,which includes multi-scale signal coding and deep cascade *** the former,we design an adaptive weighted pooling operation,which can fully explore the discriminative information of segments with low *** deep cascade coding,we propose level-wise class coding without backpropagation to mine more discriminative *** experiments are conducted on four small-scale ECG databases,and the results demonstrate that the proposed method performs competitively with state-of-the-art methods.

关键词： electrocardiogram(ECG)biometric recognition small-scale data deep cascade bi-forest multi-scale division sparse representation learning

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Role of epithelial-to-mesenchymal transition in the pulmonary fi brosis induced by paraquat in rats

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World Journal of Emergency Medicine 2021年第3期12卷 214-220页

作者： Jian-hua Yi Zhao-cai Zhang Mei-bian Zhang Xin He Hao-ran Lin Hai-wen huang Hai-bin Dai yu-wen huang Emergency Department the Second Affi liated Hospital of Zhejiang University School of MedicineHangzhou 310009China Scientifi c Research Department the Second Affi liated Hospital of Zhejiang University School of MedicineHangzhou 310009China Zhejiang Provincial Center for Disease Control and Prevention Hangzhou 301051China Shangyu People’s Hospital Shaoxing 312300China Pharmacy Department the Second Affi liated Hospital of Zhejiang University School of MedicineHangzhou 310009China

BACKGROUND:This study aims to explore the characteristics of the epithelial-to-mesenchymal transition(EMT)process and its underlying molecular mechanisms in the period of paraquat(PQ)-induced pulmonary fi brosis(PF).METHODS:Picrosirius red staining and collagen volume fraction were utilized to evaluate the pathological changes of PQ-induced PF in ***,Western blot,and real-time reverse transcriptase-polymerase chain reaction(RT-PCR)were used to measure the protein and gene expression of EMT markers,EMT-associated transcription factors,and regulators of EMT-related pathways,***:The collagen deposition in the alveolar septum and increased PF markers were characteristics of pathological changes in PQ-induced PF,reached a peak on day 14 after PQ poisoning,and then decreased on day *** protein and gene expression of the fibrosis marker,EMT markers,transcription factors,and regulators of EMT-related signaling pathways signifi cantly increased at diff erent time points after PQ poisoning compared with corresponding controls(P0.05).CONCLUSIONS:EMT process plays a role in the PQ-induced PF,in which most PF and EMT markers have a peak phenomenon,and its underlying molecular mechanisms might be determined by further studies.

关键词： Pulmonary fibrosis Paraquat Epithelial-to-mesenchymal transition Rats

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ECG Biometrics via Enhanced Correlation and Semantic-rich Embedding

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Machine Intelligence Research 2023年第5期20卷 697-706页

作者： Kui-Kui Wang Gong-Ping Yang Lu Yang yu-wen huang Yi-Long Yin School of Software Shandong UniversityJinan 250101China School of Computer Heze UniversityHeze 274015China School of Computer Science and Technology Shandong Jianzhu UniversityJinan 250101China

Electrocardiogram(ECG)biometric recognition has gained considerable attention,and various methods have been proposed to facilitate its ***,one limitation is that the diversity of ECG signals affects the recognition *** address this issue,in this paper,we propose a novel ECG biometrics framework based on enhanced correlation and semantic-rich ***,we construct an enhanced correlation between the base feature and latent representation by using only one ***,to fully exploit the semantic information,we take both the label and pairwise similarity into consideration to reduce the influence of ECG sample ***,to solve the objective function,we propose an effective and efficient algorithm for ***,extensive experiments are conducted on two benchmark datasets,and the experimental results show the effectiveness of our framework.

关键词： Biometrics matrix factorization electrocardiogram(ECG) semantic information enhanced correlation

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Controlling underestimation bias in reinforcement learning via minmax operation

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Chinese Journal of Aeronautics 2024年第7期37卷 406-417页

作者： Fanghui huang Yixin HE yu ZHANG Xinyang DENG wen JIANG School of Electronics and Information Northwestern Polytechnical UniversityXi’an 710129China College of Information Science and Engineering Jiaxing UniversityJiaxing 314001China

Obtaining the accurate value estimation and reducing the estimation bias are the key issues in reinforcement ***,current methods that address the overestimation problem tend to introduce underestimation,which face a challenge of precise decision-making in many *** address this issue,we conduct a theoretical analysis of the underestimation bias and propose the minmax operation,which allow for flexible control of the estimation ***,we select the maximum value of each action from multiple parallel state-action networks to create a new state-action value ***,a minimum value is selected to obtain more accurate value ***,based on the minmax operation,we propose two novel algorithms by combining Deep Q-Network(DQN)and Double DQN(DDQN),named minmax-DQN and ***,we conduct theoretical analyses of the estimation bias and variance caused by our proposed minmax operation,which show that this operation significantly improves both underestimation and overestimation biases and leads to the unbiased ***,the variance is also reduced,which is helpful to improve the network training ***,we conduct numerous comparative experiments in various environments,which empirically demonstrate the superiority of our method.

关键词： Reinforcement learning Minmax operation Estimation bias Underestimation bias Variance

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Air target intention recognition and causal effect analysis combining uncertainty information reasoning and potential outcome framework

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Chinese Journal of Aeronautics 2024年第1期37卷 287-299页

作者： yu ZHANG Fanghui huang Xinyang DENG Mingda LI wen JIANG School of Electronics and Information Northwestern Polytechnical UniversityXi’an 710129China

Recognizing target intent is crucial for making decisions on the ***,the imperfect and ambiguous character of battlefield situations challenges the validity and causation analysis of classical intent recognition *** with the challenge,a target intention causal analysis paradigm is proposed by combining with an Intervention Retrieval(IR)model and a Hybrid Intention Recognition(HIR)*** target data acquired by the sensors are modelled as Basic Probability Assignments(BPAs)based on evidence theory to create uncertain ***,the HIR model is utilized to recognize intent for a tested sample from uncertain ***,the intervention operator under the evidence structure is utilized to perform attribute intervention on the tested *** retrieval is performed in the sample database based on the IR model to generate the intention distribution of the pseudo-intervention samples to analyze the causal effects of individual sample *** simulation results demonstrate that our framework successfully identifies the target intention under the evidence structure and goes further to analyze the causal impact of sample attributes on the target intention.

关键词： Causal effect analysis Hybrid intention recognition Intervention retrieval Target intention Uncertainty reasoning

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