IF 34.3 1区医学

Lancet Global Health Pub Date : 2027-04-14 DOI: 10.1016/j.langlo.2026.103970

Dvora Joseph Davey

引用次数: 0

Enhancing Neural Encoding of Natural Scenes through Hierarchical Integration of Saliency and Semantic Context. 通过显著性和语义上下文的层次整合增强自然场景的神经编码。

IF 6.4

International journal of neural systems Pub Date : 2027-04-01 Epub Date: 2026-08-29 DOI: 10.1142/S0129065727500201

Sizhuo Wang, Fan Qin, Quan Pan, Chang Liu, Hongjia Zhu, Wenbo Li, Hongmei Yan, Wei Huang

{"title":"Enhancing Neural Encoding of Natural Scenes through Hierarchical Integration of Saliency and Semantic Context.","authors":"Sizhuo Wang, Fan Qin, Quan Pan, Chang Liu, Hongjia Zhu, Wenbo Li, Hongmei Yan, Wei Huang","doi":"10.1142/S0129065727500201","DOIUrl":"10.1142/S0129065727500201","url":null,"abstract":"Understanding how the human brain encodes complex natural scenes remains a central problem in computational neuroscience and artificial intelligence. Existing visual encoding models often rely on a single dominant feature representation and may insufficiently characterize how saliency-guided spatial information and high-level semantic context jointly contribute to cortical response prediction. To address this issue, this study proposes a saliency-guided multimodal visual encoding model, termed SMG-MVEM, to predict voxel-wise cortical responses to natural scene stimuli. The model integrates image features, saliency cues, and text-derived semantic representations through a hierarchical fusion architecture, followed by a Transformer-based brain mapper. Experiments on the Natural Scenes Dataset (NSD) show that SMG-MVEM improves prediction performance over representative neural encoding baselines and internal control variants, with the average PCC increasing from [Formula: see text] for the best-performing baseline to [Formula: see text]. Regional analyses further show that saliency contributed more strongly to early visual areas, whereas semantic features provided greater benefits in higher-order regions. Representational analyses also suggest that the model-predicted responses preserved aspects of hierarchical and category-related organization across the visual cortex. These findings indicate that structured integration of saliency and semantic context can improve cortical response prediction and provide interpretable representational patterns for natural vision.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2750020"},"PeriodicalIF":6.4,"publicationDate":"2027-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148868491","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

Exploiting Phase-Sliding Oscillations for Robust Asynchronous Brain-Computer Interface Control. 基于相位滑动振荡的鲁棒异步脑机接口控制。

IF 6.4

International journal of neural systems Pub Date : 2027-04-01 Epub Date: 2026-10-09 DOI: 10.1142/S0129065727500225

Jiaxin Li, Shuhuan Wen, Jincheng Hu, Qing Wang, Bin Zhang, Qingshu Zhou, Jing Zhao

{"title":"Exploiting Phase-Sliding Oscillations for Robust Asynchronous Brain-Computer Interface Control.","authors":"Jiaxin Li, Shuhuan Wen, Jincheng Hu, Qing Wang, Bin Zhang, Qingshu Zhou, Jing Zhao","doi":"10.1142/S0129065727500225","DOIUrl":"10.1142/S0129065727500225","url":null,"abstract":"Asynchronous steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) show great potential for real-world control, yet their performance is often impaired by phase shifts between ongoing electroencephalogram (EEG) signals and fixed-phase templates. In this study, we observed and validated a phase-sliding oscillation (PSO) phenomenon: when a fixed-phase template is used, phase-sliding trajectories derived from sliding-window sequences with different initial phases exhibit stable periodic oscillations locked to the stimulus frequency. Based on this finding, we propose a PSO-based asynchronous classification (PSO-AC) method. Specifically, EEG signals are reconstructed into sequences with varying initial phases and transformed into stable time-frequency representations via the wavelet synchrosqueezing transform to extract phase-decoupled features. A probabilistic temporal model is then used to characterize temporal dynamics and distinguish control from noncontrol states. Offline experiments (n = 22) showed that PSO-AC achieved a mean control/noncontrol accuracy of 94.2% with a 2[Formula: see text]s data length, outperforming a state-of-the-art baseline. It also maintained superior performance in nine-class classification under different time delays. In online robotic-arm experiments, PSO-AC enabled more stable command triggering and higher control efficiency, reducing the command cost per successful trial from 4.30 to 1.14.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2750022"},"PeriodicalIF":6.4,"publicationDate":"2027-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149139965","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

Dual-Track Interactive-Fusion Directed Graph Neural Network for Entity Identification in Information Propagation. 信息传播中实体识别的双轨交互融合有向图神经网络。

IF 6.4

International journal of neural systems Pub Date : 2027-04-01 Epub Date: 2026-08-31 DOI: 10.1142/S0129065727500171

Xuna Wang, Qingmei Tan

{"title":"Dual-Track Interactive-Fusion Directed Graph Neural Network for Entity Identification in Information Propagation.","authors":"Xuna Wang, Qingmei Tan","doi":"10.1142/S0129065727500171","DOIUrl":"10.1142/S0129065727500171","url":null,"abstract":"This study addresses the entity identification problem in information propagation networks and proposes a novel dual-track interactive-fusion directed graph neural network (GNN). The method characterizes node features from two complementary perspectives: static topology and dynamic path dependency. On the one hand, it considers the relative position of nodes in directed network path sequences and enhances the model's ability to represent directed structural relations through static-track encoding. On the other hand, dynamic-track encoding is used to learn the sequential associations among nodes in network path sequences. Based on this, a bidirectional cross-track interactive fusion mechanism is introduced to facilitate the interaction, fusion, and collaborative enhancement of latent information between the static and dynamic tracks, thereby improving node representations in directed graphs. Experiments on the Cora, Reddit, PubMed, and Ogbn-arxiv datasets show that, compared with the best baseline, the proposed dual-track interactive-fusion method improves the F1-score by 1.83%, 3.32%, 3.19%, and 2.61%, respectively. The corresponding reduction rates in the remaining performance gap are 5.93%, 11.98%, 13.98%, and 8.55%, respectively. This study provides a new theoretical perspective and technical approach for handling complex dynamic directed graphs and has important application value in social media analysis, public opinion monitoring, and network security.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2750017"},"PeriodicalIF":6.4,"publicationDate":"2027-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148868481","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

Neural Stability Enhancement Through Dynamic Lipschitz Constraints in Generative Adversarial Networks. 生成对抗网络中基于动态Lipschitz约束的神经稳定性增强。

IF 6.4

International journal of neural systems Pub Date : 2027-04-01 Epub Date: 2026-09-08 DOI: 10.1142/S0129065727500195

Juexin Zhang, Ying Weng, Xueping Zhao, Xiangjian He

{"title":"Neural Stability Enhancement Through Dynamic Lipschitz Constraints in Generative Adversarial Networks.","authors":"Juexin Zhang, Ying Weng, Xueping Zhao, Xiangjian He","doi":"10.1142/S0129065727500195","DOIUrl":"10.1142/S0129065727500195","url":null,"abstract":"Generative adversarial networks (GANs) are widely used for generative modeling in neural networks, yet their practical use is hindered by training instability and mode collapse. Existing stabilization approaches predominantly rely on enforcing fixed 1-Lipschitz constraints on the discriminator through techniques such as gradient penalty, spectral normalization, and gradient normalization. However, these methods fail to account for the dynamic nature of neural network training, where the appropriate Lipschitz constant target may vary with evolving system dynamics. This work challenges the conventional fixed-constraint paradigm by formalizing how the effective constraint strength and a bound-implied Lipschitz constant target can depend on architecture and training state. We provide theoretical analysis to motivate adaptive Lipschitz constraint (ALC) control and introduce ALC, a modular mechanism that adjusts the Lipschitz constant target rather than introducing another standalone regularization term. Experiments across multiple stable GAN architectures show improvements in generation quality and training stability in several evaluated settings. Our contributions advance the understanding of neural system stability in adversarial learning and provide a practical approach to ALC control in adversarial objectives.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2750019"},"PeriodicalIF":6.4,"publicationDate":"2027-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148922366","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

Prototype-Guided Test-Time Adaptation for Real-Time EMG-Based Gesture Recognition. 基于实时肌电图的手势识别的原型引导测试时间自适应。

IF 6.4

International journal of neural systems Pub Date : 2027-04-01 Epub Date: 2026-10-10 DOI: 10.1142/S0129065727500249

Yingtong Ma, Yalin Wang, Chenyun Dai, Yao Guo

{"title":"Prototype-Guided Test-Time Adaptation for Real-Time EMG-Based Gesture Recognition.","authors":"Yingtong Ma, Yalin Wang, Chenyun Dai, Yao Guo","doi":"10.1142/S0129065727500249","DOIUrl":"10.1142/S0129065727500249","url":null,"abstract":"Surface electromyography (sEMG) signals facilitate intuitive human-machine interaction and are widely employed in applications such as prosthetics and rehabilitation. However, sEMG signal distributions are highly sensitive to variations in electrode placement, user physiology, and session conditions, which significantly hinder model generalization in real-world settings. To address this, we propose EMG-based structure-aware test-time adaptive refinement (EMG-STAR), a test-time adaptation (TTA) framework tailored for online deployment without access to target data during training. EMG-STAR improves model stability by fusing historical and batch-wise normalization statistics from both source and target domains, effectively alleviating domain shifts. Furthermore, it refines predictions using a prototype-assisted feature clustering method that enforces consistency among semantically similar target samples. To further enhance reliability, the framework jointly leverages prediction confidence and distributional alignment to filter out noisy pseudo-labels during adaptation. Comprehensive evaluations conducted on two public datasets, Hyser, and NinaPro-DB2, demonstrate that EMG-STAR consistently outperforms ten existing TTA methods. Interpretability analysis using Grad-CAM reveals that EMG-STAR predominantly attends to physiologically meaningful muscle activation regions, suggesting that the model captures neuromuscular structures rather than transient signal noise.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2750024"},"PeriodicalIF":6.4,"publicationDate":"2027-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149148937","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

Robust and Efficient Neural Architecture Design via Implicit Euler Explicitization. 基于隐式欧拉显化的鲁棒高效神经结构设计。

IF 6.4

International journal of neural systems Pub Date : 2027-03-01 Epub Date: 2026-08-19 DOI: 10.1142/S0129065727500146

Yue Huang, Jianwei Zhang, Lei Zhang, Yan Wang, Jie Wu, Zhanji Shi, Rui Gao, Yilin Guo, Chang Liu, Chaobang Gao

{"title":"Robust and Efficient Neural Architecture Design via Implicit Euler Explicitization.","authors":"Yue Huang, Jianwei Zhang, Lei Zhang, Yan Wang, Jie Wu, Zhanji Shi, Rui Gao, Yilin Guo, Chang Liu, Chaobang Gao","doi":"10.1142/S0129065727500146","DOIUrl":"10.1142/S0129065727500146","url":null,"abstract":"Modeling deep neural networks as continuous-time dynamical systems has emerged as a powerful approach for analyzing their evolution and robustness. By leveraging the superior numerical stability of implicit methods, several studies have proposed the implicit Euler-based architecture, which significantly enhances both robustness and generalization under adversarial attack. However, existing methods require solving implicit equations iteratively, which substantially increases the computational cost. To overcome these limitations, this paper proposes an explicit reformulation of the implicit Euler method, termed Implicit-to-EXplicit ResNet (IEX-ResNet). IEX-ResNet preserves the inherent numerical stability of implicit methods while eliminating the need for iterative solvers, thereby enabling the efficient training of robust neural models. Theoretical analysis demonstrates that IEX-ResNet, under local linearization and spectral radius constraint, achieves Lipschitz-bounded perturbation propagation and maintains a stable approximation of the underlying implicit dynamics. Experimental results show that, compared to conventional implicit Euler architectures, IEX-ResNet achieves improved robustness across diverse challenging settings and maintains competitive robustness while reducing training time by nearly 50% under standard evaluation. Moreover, IEX-ResNet provides practical benefits in a real-world document image restoration task. These results suggest that implicit Euler explicitization is a practical route toward efficient robust neural architecture design.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2750014"},"PeriodicalIF":6.4,"publicationDate":"2027-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803605","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

Compensation-Balanced Recurrent Neural Architecture Based on Nonlinear Spiking Neural Membrane Systems. 基于非线性脉冲神经膜系统的补偿平衡递归神经结构。

IF 6.4

International journal of neural systems Pub Date : 2027-03-01 Epub Date: 2026-08-31 DOI: 10.1142/S0129065727500213

Jun Fu, Hong Peng, Bing Li, Ziyin Zhou

{"title":"Compensation-Balanced Recurrent Neural Architecture Based on Nonlinear Spiking Neural Membrane Systems.","authors":"Jun Fu, Hong Peng, Bing Li, Ziyin Zhou","doi":"10.1142/S0129065727500213","DOIUrl":"10.1142/S0129065727500213","url":null,"abstract":"Nonlinear spiking neural P (NSNP) systems offer a biologically inspired framework for modeling nonlinear temporal dynamics via spike consumption and generation. Existing NSNP-based recurrent architectures, such as the long short-term memory model inspired from spiking neural P systems (LSTM-SNP), primarily rely on implicit gating mechanisms and lack explicit state correction during recurrent propagation, which can lead to unstable state evolution and excessive information decay in long-term temporal modeling. To address this limitation, a compensation-balanced LSTM-SNP (CBLSTM-SNP) architecture is proposed. The model introduces a compensation-driven regulation mechanism, comprising a regulation branch and an adaptive compensation branch, to dynamically adjust hidden state evolution. From a nonlinear dynamical systems perspective, CBLSTM-SNP establishes a balanced recurrent state transition integrating memory preservation, nonlinear spike processing, and adaptive state restoration. Theoretical analysis confirms the boundedness and Lipschitz continuity of the proposed recurrent dynamics. Empirical evaluations on five benchmark time series datasets demonstrate that CBLSTM-SNP produces smoother hidden state trajectories and achieves comparable forecasting performance compared with several representative recurrent and hybrid models.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2750021"},"PeriodicalIF":6.4,"publicationDate":"2027-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148868510","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

Sulfur-doped graphitic carbon nitride/cerium tungstate nanocomposites for ultrasensitive dopamine sensing in mouse striatal tissue. 硫掺杂石墨氮化碳/钨酸铈纳米复合材料在小鼠纹状体组织中的超灵敏多巴胺传感。

IF 6.7 1区化学

Talanta Pub Date : 2027-03-01 Epub Date: 2026-10-17 DOI: 10.1016/j.talanta.2026.130799

Hyeon-Geun Jang, Ramalingam Manikandan, Sung Min Moon, Uma Aruljothi Mugunthan, JeongIn Lee, Geon Woo Kang, Jinyong Park, Jang-Hee Yoon, Jaewon Lee, Seung-Cheol Chang

{"title":"Sulfur-doped graphitic carbon nitride/cerium tungstate nanocomposites for ultrasensitive dopamine sensing in mouse striatal tissue.","authors":"Hyeon-Geun Jang, Ramalingam Manikandan, Sung Min Moon, Uma Aruljothi Mugunthan, JeongIn Lee, Geon Woo Kang, Jinyong Park, Jang-Hee Yoon, Jaewon Lee, Seung-Cheol Chang","doi":"10.1016/j.talanta.2026.130799","DOIUrl":"10.1016/j.talanta.2026.130799","url":null,"abstract":"Following the COVID-19 pandemic, the demand for rapid diagnostic tools for the highly specific and sensitive detection of disease-related biomarkers has increased significantly. Hybrid nanocomposite-modified disposable screen-printed electrodes have shown remarkable potential for the electrochemical biosensing of various biomarkers. This study reports the fabrication of a hybrid nanocomposite comprising sulfur-doped graphitic carbon nitride (S-g-C3N4) nanosheets integrated with two-dimensional cerium tungstate (CeW) for highly sensitive electrochemical dopamine detection. The synthesized S-g-C3N4@CeW hybrid nanocomposite was thoroughly characterized using field-emission scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy, Fourier-transform infrared spectroscopy, and electrochemical measurements. Electroanalytical performance was evaluated using differential pulse voltammetry (DPV) under optimized experimental conditions. The fabricated electrochemical sensor exhibited a wide linear concentration range for dopamine from 0.1 to 500 μM, with a low detection limit of 1.13 nM and high sensitivity of 151.69 μA μM-1 cm-2. Finally, the fabricated sensor was used for the ex-vivo determination of dopamine in Parkinson's disease-induced mouse striatum-tissue samples, achieving good accuracy with recovery rates of 92.0 to 101.4%. This study demonstrates a promising strategy for fabricating hybrid nanocomposite-modified disposable electrochemical sensors for neurotransmitter detection in animal-disease models.","PeriodicalId":435,"journal":{"name":"Talanta","volume":"313 Pt A","pages":"130799"},"PeriodicalIF":6.7,"publicationDate":"2027-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149226809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fe-porphyrin MOF nanozyme-based colorimetric sensor assisted by machine learning for detecting antioxidants in fruit juices. 基于fe -卟啉MOF纳米酶的比色传感器，机器学习辅助检测果汁中的抗氧化剂。

IF 6.7 1区化学

Talanta Pub Date : 2027-03-01 Epub Date: 2026-10-19 DOI: 10.1016/j.talanta.2026.130805

Yijie Kong, Xinyu Feng, Xindan Su, Yanming Bi, Lu Zhang, Sha Liu

{"title":"Fe-porphyrin MOF nanozyme-based colorimetric sensor assisted by machine learning for detecting antioxidants in fruit juices.","authors":"Yijie Kong, Xinyu Feng, Xindan Su, Yanming Bi, Lu Zhang, Sha Liu","doi":"10.1016/j.talanta.2026.130805","DOIUrl":"10.1016/j.talanta.2026.130805","url":null,"abstract":"Antioxidants (AOs) are key bioactive components governing the nutritional quality and oxidative stability of fruit juices. However, their similar reducing properties make it challenging for conventional single-signal colorimetric methods to combine individual quantitative analysis with pattern recognition in complex food matrices. Herein, an Fe-porphyrin metal-organic framework (FeP-MOF) nanozyme was constructed using iron(III) meso-tetra(4-carboxyphenyl)porphyrin chloride (Fe-TCPPCl) as the functional ligand, and further integrated into a platform for high-throughput quantification and machine-learning-assisted pattern recognition. The porous MOF framework enhanced the accessibility and catalytic efficiency of Fe-porphyrin active sites. Based on AOs-mediated inhibition of the FeP-MOF/H2O2/TMB oxidation reaction, a microplate-based colorimetric sensor was developed for quantitative detection of ascorbic acid, glutathione, cysteine, gallic acid, and tannic acid, with limits of detection of 0.52-41.54 μM. Moreover, a multi-pH cross-reactive sensor array converted single absorbance signals into composition-sensitive multidimensional fingerprints. Coupled with linear discriminant analysis and hierarchical cluster analysis, the array achieved cross-validated classification accuracies of 100.0% for five antioxidants and 98.0% for five fruit-juice categories. Results also agreed well with high-performance liquid chromatography measurements. This work integrates nanozyme amplification, quantitative analysis of individual antioxidants, fingerprint sensing, and data-driven classification, providing a versatile strategy for high-throughput AOs analysis and compositional evaluation in complex food systems.","PeriodicalId":435,"journal":{"name":"Talanta","volume":"313 Pt A","pages":"130805"},"PeriodicalIF":6.7,"publicationDate":"2027-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149226812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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