International journal of neural systems最新文献_第2页

Unified Multi-Class Electroencephalogram Artifact Recognition Using Machine Learning Classifiers. 基于机器学习分类器的统一多类脑电图伪迹识别。

IF 6.4

International journal of neural systems Pub Date : 2027-01-01 Epub Date: 2026-07-22 DOI: 10.1142/S0129065727500018

Fernando Moncada Martins, Ramón Suárez, José R Villar, Víctor M González

{"title":"Unified Multi-Class Electroencephalogram Artifact Recognition Using Machine Learning Classifiers.","authors":"Fernando Moncada Martins, Ramón Suárez, José R Villar, Víctor M González","doi":"10.1142/S0129065727500018","DOIUrl":"10.1142/S0129065727500018","url":null,"abstract":"Artifacts are noisy signals that commonly contaminate electroencephalographic (EEG) recordings, mixing with underlying brain activity and degrading the quality of neurophysiological data. Previous research on epileptic Anomaly Detection has shown that this approach is also sensitive to unlabelled artifacts, often leading to an increased False Positive rate. While most methods focus on detecting or removing a single type of artifact, this work proposes a unified multi-class framework to classify several artifact types alongside normal and pathological brain activity within a recording using a single, simple Machine Learning classifier. A set of spectral, temporal and statistical features commonly associated with different artifact types is extracted. KNN and XGBoost classifiers are trained and evaluated under a cross-validation scheme. The results demonstrate strong performance for both models, achieving approximately 90% sensitivity across all classes while maintaining a 100% specificity. These findings highlight the effectiveness of a unified and interpretable approach for multi-class EEG artifact classification.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2750001"},"PeriodicalIF":6.4,"publicationDate":"2027-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148537909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Toward Efficient and Generalizable Text Dataset Distillation via a Dual-Agent Large Language Model Framework. 基于双代理大型语言模型框架的高效可泛化文本数据集蒸馏。

IF 6.4

International journal of neural systems Pub Date : 2027-01-01 Epub Date: 2026-07-15 DOI: 10.1142/S0129065727500031

Junhai Zhou, Zhongfeng Wang, Meiqi Wang

{"title":"Toward Efficient and Generalizable Text Dataset Distillation via a Dual-Agent Large Language Model Framework.","authors":"Junhai Zhou, Zhongfeng Wang, Meiqi Wang","doi":"10.1142/S0129065727500031","DOIUrl":"10.1142/S0129065727500031","url":null,"abstract":"Large-scale datasets impose substantial training costs on machine learning models. Dataset distillation addresses this issue by synthesizing compact datasets that can achieve performance comparable to that of the original data. However, text dataset distillation remains challenging: the discrete nature of text renders traditional gradient-matching methods ineffective, while embedding optimization approaches are often inefficient and exhibit limited generalization. To address these challenges, this paper proposes an LLM-native distillation framework based on dual-agent collaboration. Our framework decomposes the distillation process into two stages: selection and improvement. The Selector identifies high-quality samples through multi-dimensional scoring, while the Improver enhances data density and clarity under semantic consistency constraints and a candidate generation mechanism. The entire pipeline is automated through a self-iterative cycle of generation and selection, and leverages scoring signals to drive agent self-reward iteration, thereby avoiding differentiable optimization and trajectory matching. Experiments show that starting from a random subsampling, using only 2.5% of original data and only three iterations, the distilled dataset enables Llama-2-7B, Mistral-7B, and Qwen2.5-7B to match full-dataset performance on MMLU and Winogrande. It also improves training stability, convergence efficiency, and cross-model generalization, especially among architecturally similar models, while maintaining a competitive distillation cost of 143 GPU h. Overall, this study provides a practical solution for efficient, automated, and general-purpose text dataset distillation.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2750003"},"PeriodicalIF":6.4,"publicationDate":"2027-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Molecular Architecture of Temporal Variability Network Dysfunction in Bipolar Disorder. 双相情感障碍中时间变异性网络功能障碍的分子结构。

IF 6.4

International journal of neural systems Pub Date : 2027-01-01 Epub Date: 2026-08-05 DOI: 10.1142/S0129065727500043

Lin Jiang, Jiayu Ye, Yan Zhu, Shuqi Ma, Dezhong Yao, Zhen Yuan, Xuntai Ma, Peng Xu, Debo Dong, Fali Li

{"title":"Molecular Architecture of Temporal Variability Network Dysfunction in Bipolar Disorder.","authors":"Lin Jiang, Jiayu Ye, Yan Zhu, Shuqi Ma, Dezhong Yao, Zhen Yuan, Xuntai Ma, Peng Xu, Debo Dong, Fali Li","doi":"10.1142/S0129065727500043","DOIUrl":"10.1142/S0129065727500043","url":null,"abstract":"Characterized by recurrent fluctuations in mood states, bipolar disorder (BD) is widely conceptualized as a disconnection syndrome associated with dysregulated brain dynamics. Nevertheless, the molecular mechanisms underlying this aberrant connectivity dynamics in BD remain elusive. Using resting-state electroencephalography (EEG) data from BD patients and healthy controls, this study first delineated the characteristic alterations in temporal variability of functional connectivity in BD and further elucidated their underlying molecular mechanisms and clinical relevance. Current findings revealed significantly reduced temporal variability within large-scale brain subnetworks, most notably in the dorsal attention, somatomotor, and visual networks. Importantly, these neurodynamic signatures effectively predicted the symptom severity in individuals with BD. Moreover, the spatial patterns of these dynamic alterations are associated with the expression of BD risk genes enriched in synaptic function and metabolic pathways, as well as with the spatial organizations of various neurotransmitter receptors, including CB1, mGluR5, H3, and MOR. Collectively, these results provide evidence for a multiscale pathophysiological framework that links genetic susceptibility and chemoarchitectural alterations to dynamic brain network instability, ultimately underpinning the core clinical manifestations in BD.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2750004"},"PeriodicalIF":6.4,"publicationDate":"2027-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148674897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparative Screening of Alzheimer's Disease, Lewy Body Dementia, and Frontotemporal Dementia Using miRNA and Machine Learning. 使用miRNA和机器学习对阿尔茨海默病、路易体痴呆和额颞叶痴呆进行比较筛查。

IF 6.4

International journal of neural systems Pub Date : 2027-01-01 Epub Date: 2026-07-27 DOI: 10.1142/S012906572750002X

Benjamin Luo, Valentina L Kouznetsova, Igor F Tsigelny

{"title":"Comparative Screening of Alzheimer's Disease, Lewy Body Dementia, and Frontotemporal Dementia Using miRNA and Machine Learning.","authors":"Benjamin Luo, Valentina L Kouznetsova, Igor F Tsigelny","doi":"10.1142/S012906572750002X","DOIUrl":"10.1142/S012906572750002X","url":null,"abstract":"Current dementia diagnostic methods can be costly, invasive, or limited in their ability to distinguish between disorders with overlapping clinical symptoms. Dysregulated microRNAs (miRNAs) have emerged as promising noninvasive biomarkers for neurodegenerative disease, but individual miRNA changes alone may not capture the complex molecular patterns needed for accurate disease classification. Machine learning provides a way to integrate multiple layers of miRNA-derived information and identify disease-specific biomarker signatures. In this study, we developed machine learning models to classify dysregulated miRNAs associated with Alzheimer's disease dementia (AD), Lewy body dementia (LBD), and frontotemporal dementia (FTD). Each miRNA was represented using sequence-derived descriptors, predicted gene targets, and KEGG pathway features. The highest-performing models trained on AD, LBD, and FTD achieved 10-fold cross-validation accuracies of 90.6%, 92.9%, and 100%, respectively. When evaluated on independent datasets, the AD, LBD, and FTD models achieved accuracies of 88.9%, 77.8%, and 90.9%, respectively. Cross-disease testing showed reduced performance when models were applied across dementia types, suggesting partially disease-specific miRNA patterns while also indicating overlap among the molecular signatures of AD, LBD, and FTD. These results suggest that machine learning-based integration of miRNA sequence, target-gene, and pathway information can improve the identification of dementia-associated biomarker signatures and may support the future development of noninvasive diagnostic tools for dementia.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2750002"},"PeriodicalIF":6.4,"publicationDate":"2027-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148595362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pyramid Vision Transformer-Enhanced Conformer Network for Epileptic Seizure Recognition Using Multichannel EEG Signals. 金字塔视觉变压器增强的共形网络用于多通道脑电图信号的癫痫发作识别。

IF 6.4

International journal of neural systems Pub Date : 2026-12-30 Epub Date: 2026-06-13 DOI: 10.1142/S0129065726500504

Weiguang Dong, Jian Lian, Xinyu Wang, Mingxin Dong

{"title":"Pyramid Vision Transformer-Enhanced Conformer Network for Epileptic Seizure Recognition Using Multichannel EEG Signals.","authors":"Weiguang Dong, Jian Lian, Xinyu Wang, Mingxin Dong","doi":"10.1142/S0129065726500504","DOIUrl":"10.1142/S0129065726500504","url":null,"abstract":"The classification of electroencephalogram (EEG) signals plays an important role in neuroscience research and clinical diagnosis of epileptic seizures. This work aims to solve EEG data classification tasks by using the Children's Hospital Boston and Massachusetts Institute of Technology (CHB-MIT) dataset, and proposes an improved Conformer structure strengthened with the Pyramid Vision Transformer (PVT). The major purpose of this study is to obtain higher classification precision and extract more distinguishable features from EEG signals. An enhanced Conformer-based framework is presented for EEG classification tasks. The PVT-Enhanced Conformer integrates the advantages of Convolutional Neural Networks (CNNs) and PVT. Specifically, the PVT module is inserted into the Conformer to optimize the original self-attention mechanism. In addition, the model is trained with cross-entropy as the loss function and the Adaptive Momentum (Adam) optimization algorithm for parameter updating. Experimental results on the CHB-MIT dataset show that the proposed model performs better than existing state-of-the-art models in three key indicators: classification accuracy (99.23%), specificity (99.61%), and sensitivity (98.11%). Furthermore, ablation experiments are conducted to evaluate the contribution of each component, which verifies the effectiveness of both PVT integration and the feature fusion strategy adopted in this work. This research provides an improved Conformer architecture for EEG data classification based on the CHB-MIT dataset. The results show the potential of the proposed method, which is expected to promote the development of artificial intelligence and biomedical engineering cases. Cross-dataset validation on Bonn and Siena datasets further confirms the method's generalization capability. Future work may focus on further refining the proposed approach and exploring its application in the diagnosis of other neurological diseases.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2650050"},"PeriodicalIF":6.4,"publicationDate":"2026-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148229769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Discrete Wavelet Convolution for Learnable Time-Frequency Representation with Application to Seizure Prediction. 离散小波卷积可学习时频表示及其在癫痫发作预测中的应用。

IF 6.4

International journal of neural systems Pub Date : 2026-12-30 Epub Date: 2026-06-24 DOI: 10.1142/S0129065726500498

Weisen Lu, Haotian Li, Guoyang Liu, Yi Li, Weidong Zhou

{"title":"Discrete Wavelet Convolution for Learnable Time-Frequency Representation with Application to Seizure Prediction.","authors":"Weisen Lu, Haotian Li, Guoyang Liu, Yi Li, Weidong Zhou","doi":"10.1142/S0129065726500498","DOIUrl":"10.1142/S0129065726500498","url":null,"abstract":"Accurate and adaptive time-frequency representation is essential for analyzing nonstationary signals in critical applications, such as epileptic seizure prediction utilizing electroencephalogram (EEG) data. However, existing deep learning approaches often suffer from two major limitations: the reliance on fixed, nonadaptive feature extraction methods and the lack of model interpretability. To bridge these gaps, we propose Adaptive Discrete Wavelet Convolution (ADWConv), a general-purpose trainable multi-level wavelet decomposition front-end, with validation in an end-to-end seizure prediction framework. ADWConv parameterizes the low-frequency and high-frequency filters within each decomposition level and decouples their kernel shapes from level-specific scaling factors, enabling task-related adaptive weighting of frequency bands. This end-to-end learning is guided by a joint optimization strategy, which incorporates a regularization loss with wavelet priors to ensure the learned filters are both discriminative and physically interpretable. Extensive experiments were conducted on the public CHB-MIT database and the private SH-SDU database. On the CHB-MIT database, the ADWConv-based model attained an event-based sensitivity of 98.65%, while maintaining a false prediction rate (FPR) of only 0.014/h. Comparable results were also demonstrated on the SH-SDU database, attaining 96.53% event-based sensitivity with 0.028/h FPR. Ablation and visualization analyses confirm that learning the wavelet kernels is the primary driver of performance gains and supports task-specific adaptation of discriminative frequency bands. Overall, ADWConv improves the feature-level interpretability of a deep learning framework for seizure prediction while maintaining competitive accuracy and robustness.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2650049"},"PeriodicalIF":6.4,"publicationDate":"2026-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148304330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transformer-Based Anomaly Detection for Neurodegenerative Screening in MRI Images. 基于变压器的MRI图像神经退行性筛查异常检测。

IF 6.4

International journal of neural systems Pub Date : 2026-12-30 Epub Date: 2026-06-27 DOI: 10.1142/S0129065726500528

Enol García González, Mădălina Dicu, José R Villar, Camelia Chira

{"title":"Transformer-Based Anomaly Detection for Neurodegenerative Screening in MRI Images.","authors":"Enol García González, Mădălina Dicu, José R Villar, Camelia Chira","doi":"10.1142/S0129065726500528","DOIUrl":"10.1142/S0129065726500528","url":null,"abstract":"The automatic detection of anomalies in medical images is a significant challenge in the assisted diagnosis of neurodegenerative diseases such as Alzheimer's. This paper presents an anomaly detection model based on Transformers for the analysis of brain magnetic resonance images. The proposed architecture combines a Vision Transformer as an encoder with a memory bank module that allows modeling the distribution of healthy brains and detecting deviations through reconstruction error. The model is trained using a one-class learning approach, using only images considered normal, with the aim of learning the representation of normality and automatically flagging atypical structural patterns. To adapt volumetric studies to the architecture, a preprocessing procedure is designed that transforms three-dimensional information into a two-dimensional representation compatible with the model. The results obtained demonstrate a solid ability to characterize normality and generate reliable predictions, confirming the viability of Transformer-based architectures for unsupervised anomaly detection in neuroimaging. This approach lays the foundation for future extensions in clinical settings and other medical imaging applications.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2650052"},"PeriodicalIF":6.4,"publicationDate":"2026-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148347862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Announcement: The 2026 Hojjat Adeli Award for Outstanding Contributions in Neural Systems. 公告：2026年Hojjat Adeli神经系统杰出贡献奖。

IF 6.4

International journal of neural systems Pub Date : 2026-12-30 Epub Date: 2026-08-20 DOI: 10.1142/S0129065726820010

引用次数: 0

Latent Space Projections and Atlases, a Cautionary Tale in Deep Neuroimaging using Autoencoders. 潜在空间投影和地图集，使用自编码器的深度神经成像的警示故事。

IF 6.4

International journal of neural systems Pub Date : 2026-12-30 Epub Date: 2026-06-30 DOI: 10.1142/S012906572650053X

J M Gorriz, F Segovia, C Jimenez-Mesa, J E Arco, F J Martinez, J Ramirez, S Abulikemu, J Suckling

{"title":"Latent Space Projections and Atlases, a Cautionary Tale in Deep Neuroimaging using Autoencoders.","authors":"J M Gorriz, F Segovia, C Jimenez-Mesa, J E Arco, F J Martinez, J Ramirez, S Abulikemu, J Suckling","doi":"10.1142/S012906572650053X","DOIUrl":"10.1142/S012906572650053X","url":null,"abstract":"This study introduces a deep learning framework for the inferential exploration of latent representations in 3D brain MRI, leveraging a simple convolutional autoencoder with a hierarchical encoder and a compact latent space. Trained on segmented gray matter images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the model learns latent representations that preserve neuroanatomical structure and reflect clinical variability across cognitive status. Dimensionality reduction techniques (PCA, t-SNE, PLS, UMAP) were applied to visualize and interpret the latent space, correlating it with anatomical regions defined by the AAL atlas. As a novel contribution, the Latent-Regional Correlation Profiling (LRCP) framework, which combines statistical association and supervised discriminability to identify brain regions that encode clinically relevant latent information is proposed. Our results show that even minimal architectures capture meaningful patterns associated with progression to Alzheimer's disease. Interpretability is assessed by applying SHAP-based regression to a post-hoc model that predicts reconstruction error from atlas-based regional gray matter intensities, thereby identifying anatomically meaningful regions involved in class-specific reconstruction strategies. These findings are further validated using statistical agnostic methods, highlighting the importance of rigorous evaluation in neuroimaging. This work demonstrates the potential of autoencoders as exploratory tools for biomarker discovery and hypothesis generation in clinical neuroscience.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2650053"},"PeriodicalIF":6.4,"publicationDate":"2026-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148355278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Motor Imagery Decoding for Stroke Patients Using Data Augmentation and Transfer Learning. 使用数据增强和迁移学习增强脑卒中患者的运动图像解码。

IF 6.4

International journal of neural systems Pub Date : 2026-12-30 Epub Date: 2026-07-16 DOI: 10.1142/S0129065726500516

Fangzhou Xu, Yuan Sun, Zhixiao Lun, Pengcheng Zhao, Zhaoxia Zhang, Chao Zhang, Aoning Zhang, Chao Feng, Yang Zhang, Tzyy-Ping Jung, Jiancai Leng

{"title":"Enhancing Motor Imagery Decoding for Stroke Patients Using Data Augmentation and Transfer Learning.","authors":"Fangzhou Xu, Yuan Sun, Zhixiao Lun, Pengcheng Zhao, Zhaoxia Zhang, Chao Zhang, Aoning Zhang, Chao Feng, Yang Zhang, Tzyy-Ping Jung, Jiancai Leng","doi":"10.1142/S0129065726500516","DOIUrl":"https://doi.org/10.1142/S0129065726500516","url":null,"abstract":"This study proposes an innovative WalkBCI real-time motor imagery brain-computer interface system to solve the problems of time-consuming model calibration and low EEG decoding accuracy in stroke patients because of individual differences. WalkBCI integrates generative adversarial networks and transfer-learning techniques to generate motor imagery feature data via RM-GAN using the resting data of target subjects, which shortens the time of calibration data acquisition and reduces patient fatigue. Meanwhile, the system enhances classification performance by combining source domain data during transfer learning optimization. The system is evaluated in both offline and online experiments to assess its practicality and stability. The study results show that WalkBCI outperformed traditional methods on the stroke patient dataset. In the offline experiments, the model's accuracy, precision, recall, and F 1-score of the model increased by 3.3%, 2.6%, 3.8%, and 3.4%, respectively. In online experiments, the system maintained stable classification performance with a maximum accuracy of 72.5%, while reducing calibration time by 58%. WalkBCI effectively lowered subjects' fatigue, and the comparison experiments revealed average reductions of 7.5 points in FS-14 scores and 74.05 in frontal theta/beta values. This study offers an efficient and stable solution for real-time EEG decoding, particularly well-suited for motor-imagery tasks in stroke patients.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":"36 13","pages":"2650051"},"PeriodicalIF":6.4,"publicationDate":"2026-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0