Neuroscience informatics最新文献

{"title":"Unlocking transcranial FUS-EEG feature fusion for non-invasive sleep staging in next-gen clinical applications","authors":"Suneet Gupta ,&nbsp;Praveen Gupta ,&nbsp;Bechoo Lal ,&nbsp;Aniruddha Deka ,&nbsp;Hirakjyoti Sarma ,&nbsp;Sheifali Gupta","doi":"10.1016/j.neuri.2025.100209","DOIUrl":"10.1016/j.neuri.2025.100209","url":null,"abstract":"<div><div>Accurate and non-invasive sleep staging is essential for evaluating sleep quality and diagnosing neurological and sleep disorders. Addressing the variations in electroencephalogram (EEG) and electrooculogram (EOG) signals across different sleep stages, this study introduces a transcranial focused ultrasound (tFUS) based multimodal feature fusion deep learning model (MFDL) for automated sleep staging. The proposed framework integrates two one-dimensional convolutional neural networks (1D-CNNs) to extract sleep-relevant features from EEG and EOG signals, followed by an adaptive feature fusion module that dynamically assigns weights based on feature significance. By enhancing discriminative features and suppressing irrelevant ones, the model generates a robust multimodal representation of sleep information. Furthermore, a bidirectional long short-term memory (Bi-LSTM) network captures temporal dependencies in sleep stage transitions, improving classification accuracy. The effectiveness of MFDL is validated on the publicly available Sleep-EDF dataset, achieving 94.1% accuracy, 88.2% Kappa coefficient, and 81.9% MF1 score. Notably, the recall rates for the challenging N1 and REM sleep stages are significantly enhanced to 64.6% and 93.5%, respectively. These results highlight the potential of MFDL in enhancing tFUS-based neuromodulation by providing precise, data-driven sleep state monitoring, paving the way for advanced non-invasive brain stimulation technologies in next-gen clinical applications.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100209"},"PeriodicalIF":0.0,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069541","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}

{"title":"Enhancing seizure detection with hybrid XGBoost and recurrent neural networks","authors":"Santushti Santosh Betgeri ,&nbsp;Madhu Shukla ,&nbsp;Dinesh Kumar ,&nbsp;Surbhi B. Khan ,&nbsp;Muhammad Attique Khan ,&nbsp;Nora A. Alkhaldi","doi":"10.1016/j.neuri.2025.100206","DOIUrl":"10.1016/j.neuri.2025.100206","url":null,"abstract":"<div><div>Epileptic seizures are sudden and unpredictable, posing serious health risks and significantly affecting the quality of life of patients. An accurate and timely prediction system can help mitigate these risks by enabling preventive measures and improving patient safety. This study investigates machine learning and deep learning algorithms for seizure prediction, comparing their effectiveness on a large EEG dataset of epileptic patients. Signal processing techniques were applied to enhance data quality, and all models were trained on the same dataset for binary classification. Sixteen models were evaluated, including traditional classifiers such as Logistic Regression, K-Nearest Neighbors, Decision Trees, ensemble methods that include Random Forest, Gradient Boosting, and advanced techniques such as Extreme Gradient Boosting, Support Vector Machines, Gated Recurrent Units, and Long Short-Term Memory networks. Performance was assessed using multiple evaluation metrics on both training and validation datasets. While simpler models showed varied accuracy, ensemble and deep learning models performed significantly better, with hybrid approaches demonstrating strong generalization. Results show that whereas ensemble and deep learning models far exceeded simpler models, their accuracy varied. AUC of 0.995 and accuracy of 98.2% on validation data and 0.994 AUC with 96.8% accuracy on test data were obtained by the proposed hybrid Model integrating XGBoost with RNN-based architectures (LSTM and GRU). High recall (96.2%) shown by the Model guarantees minimal false negatives and is important for clinical uses. Furthermore, EEG signal preprocessing methods improved data quality, raising classification accuracy. This Model can be implemented for real-time monitoring using wearable devices, enabling continuous patient observation and remote healthcare applications.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100206"},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143916826","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}

{"title":"Neuroimaging informatics framework for analyzing rare brain metastasis patterns in pleural mesothelioma using hybrid PET CT","authors":"Sumit Kumar Agrawal ,&nbsp;Indra Prakash Dubey ,&nbsp;Anoop Kumar Nair ,&nbsp;Anurag Jain ,&nbsp;Abhishek Mahato ,&nbsp;Rajeev Kumar","doi":"10.1016/j.neuri.2025.100207","DOIUrl":"10.1016/j.neuri.2025.100207","url":null,"abstract":"<div><div>A rare and hostile cancer mostly affecting the lungs, pleural mesothelioma has an exceedingly unusual but clinically relevant propagation to the brain. Their unusual appearance and low frequency make early diagnosis and accurate characterization of such uncommon brain metastases a diagnostic difficulty. The present research presents a neuroimaging informatics system using hybrid Positron Emission Tomography–Computed Tomography (PET-CT) imaging to examine and explain uncommon brain metastasis patterns in pleural mesothelioma patients. Our methodology combines sophisticated neuroinformatics technologies with AI-driven image processing algorithms to improve hybrid PET-CT scans' spatial and metabolic resolution. While a radiomics pipeline drives out quantitative characteristics like texture, intensity, and shape descriptors, a deep learning (DL)-based segmentation algorithm finds abnormal metabolic activity suggestive of metastatic lesions. Unsupervised clustering and anomaly detection resources help to examine these characteristics and find rare metastatic developments. To assist thorough case analysis, a clinical informatics layer links imaging results with patient demographics, histopathology data, and treatment history. Validated using retrospective PET-CT data from mesothelioma patients with verified brain involvement, the approach shows increased sensitivity and specificity in finding mysterious metastatic foci. This work emphasizes the need for hybrid imaging modalities in monitoring uncommon oncologic events and provides insightful analysis of the brain spread paths of pleural mesothelioma by providing a strong, AI-enhanced neuroimaging framework. The suggested method helps with early identification, and individualized treatment planning helps to clarify metastatic behavior in typical thoracic cancers.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100207"},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923562","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}

{"title":"Optimizing transcranial focused ultrasound parameters: A methodological advancement in non-invasive brain stimulation for next-gen clinical applications","authors":"Sachin Gupta ,&nbsp;Mustafa Mudhafar ,&nbsp;Yogini Dilip Borole ,&nbsp;V. Mahalakshmi ,&nbsp;Janjhyam Venkata Naga Ramesh ,&nbsp;Muhammad Attique Khan","doi":"10.1016/j.neuri.2025.100204","DOIUrl":"10.1016/j.neuri.2025.100204","url":null,"abstract":"<div><div><strong>Background:</strong> Transcranial-focused ultrasound (FUS), a non-invasive neuromodulation method, is gaining popularity for treating neurological and psychiatric disorders. However, changing stimulation settings for precise brain targeting remains challenging.</div><div><strong>Methods:</strong> Existing techniques have spatial resolution, skull acoustic transmission, and parameter selection issues that reduce clinical efficacy. These problems reduce tFUS application repeatability and safety. To address these challenges, this research proposes a novel computational-experimental strategy that combines advanced computational modeling (IACM) with in vivo validation. The proposed design uses subject-specific skull acoustic simulations, Deep Learning (DL)-based parameter optimization, and real-time feedback to increase stimulation accuracy and efficacy.</div><div><strong>Results</strong>: The recommended approach allows customized transcutaneous electrical nerve stimulation (tFUS) by modifying frequency, intensity, and targeting. Neuromodulation becomes better while staying safe. It should be adaptable enough for research and clinical usage to create neurostimulation precision medicine.</div><div><strong>Comparative analysis:</strong> The study shows that the proposed framework improves spatial precision, skull transmission effect variability, and neuromodulation efficacy compared to existing methods.</div><div><strong>Conclusion:</strong> This approach enables the development next-generation non-invasive brain stimulation devices with more therapeutic uses. Non-invasive brain stimulation (NIBS) technologies, including tFUS, TMS, and tDCS, may now accurately affect neurological and psychiatric diseases. However, these approaches are susceptible to inter-subject variability, poor targeting, and skull deformities. Artificial intelligence-driven real-time optimization frameworks like the Integrating Advanced Computational Modeling (IACM) framework are needed to overcome these constraints.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100204"},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923559","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}

{"title":"MindLift: AI-powered mental health assessment for students","authors":"Shanky Goyal ,&nbsp;RishiRaj Dutta ,&nbsp;Saurabh Dev ,&nbsp;Kola Narasimha Raju ,&nbsp;Mohammed Wasim Bhatt","doi":"10.1016/j.neuri.2025.100208","DOIUrl":"10.1016/j.neuri.2025.100208","url":null,"abstract":"<div><div>This study introduces MindLift, a student-specific AI-powered mental health assessment and intervention platform. The goal of this research is to create a real-time, multimodal system that can assess mental health through the use of behavioral pattern tracking, audio tone analysis, facial expression recognition, and text sentiment interpretation. By integrating convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based natural language processing (NLP) models, MindLift provides a comprehensive emotional analysis. Through evidence-based techniques like Cognitive Behavioral Therapy (CBT), an intelligent chatbot built into the system provides individualized mental health support. Responses and interventions are customized using important parameters like sentiment polarity, mood detection, and behavioral abnormalities. MindLift emphasizes ethical AI deployment, with strong safeguards for privacy, consent, and fairness. Preliminary studies show a notable increase in student engagement, emotional control, and willingness to seek help. Future developments include deeper personalization, wearable device integration, and wider deployment across educational institutions. The system is evaluated using metrics including accuracy, precision, recall, and F1-score across several modalities.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100208"},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935135","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}

{"title":"A MATLAB-based tool for converting fNIRS time-series data to Homer3-compatible formats","authors":"Chao Wang ,&nbsp;Xiaojun Cheng ,&nbsp;Shichao Liu","doi":"10.1016/j.neuri.2025.100205","DOIUrl":"10.1016/j.neuri.2025.100205","url":null,"abstract":"<div><div>Functional Near-Infrared Spectroscopy (fNIRS) is increasingly used in cognitive neuroscience and clinical research, yet preprocessing raw time-series data remains challenging. We introduce a lightweight MATLAB tool to automate the conversion of fNIRS data into Homer3-compatible “*.nirs” format. Our solution targets non-SNIRF raw data and offers a standardized, user-friendly method to streamline fNIRS data preparation. This Technical Note describes the tool's design, workflow, and potential improvements for future development.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100205"},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906484","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}

{"title":"Deep learning-based multi-brain capsule network for Next-Gen Clinical Emotion recognition using EEG signals","authors":"Ritu Dahiya ,&nbsp;Mamatha G ,&nbsp;Shila Sumol Jawale ,&nbsp;Santanu Das ,&nbsp;Sagar Choudhary ,&nbsp;Vinod Motiram Rathod ,&nbsp;Bhawna Janghel Rajput","doi":"10.1016/j.neuri.2025.100203","DOIUrl":"10.1016/j.neuri.2025.100203","url":null,"abstract":"<div><div>Deep learning techniques are crucial for next-generation clinical applications, particularly in Next-Gen Clinical Emotion recognition. To enhance classification accuracy, we propose an Attention mechanism based Capsule Network Model (At-CapNet) for Multi-Brain Region. EEG-tNIRS signals were collected using Next-Gen Clinical Emotion-inducing visual stimuli to construct the TYUT3.0 dataset, from which EEG and tNIRS features were extracted and mapped into matrices. A multi-brain region attention mechanism was applied to integrate EEG and tNIRS features, assigning different weights to features from distinct brain regions to obtain high-quality primary capsules. Additionally, a capsule network module was introduced to optimize the number of capsules entering the dynamic routing mechanism, improving computational efficiency. Experimental validation on the TYUT3.0 Next-Gen Clinical Emotion dataset demonstrates that integrating EEG and tNIRS improves recognition accuracy by 1.53% and 14.35% compared to single-modality signals. Moreover, the At-CapNet model achieves an average accuracy improvement of 4.98% over the original CapsNet model and outperforms existing CapsNet-based Next-Gen Clinical Emotion recognition models by 1% to 5%. This research contributes to the advancement of non-invasive neurotechnology for precise Next-Gen Clinical Emotion recognition, with potential implications for next-generation clinical diagnostics and interventions.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100203"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143916824","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}

{"title":"Integrating brain-inspired computation with big-data analytics for advanced diagnostics in neuroradiology","authors":"Senthil Kumar ,&nbsp;J. Ramprasath ,&nbsp;V. Kalpana ,&nbsp;Manikandan Rajagopal ,&nbsp;Maheswaran S ,&nbsp;Rupesh Gupta","doi":"10.1016/j.neuri.2025.100202","DOIUrl":"10.1016/j.neuri.2025.100202","url":null,"abstract":"<div><h3>Introduction</h3><div>Neuroradiology encounters considerable difficulties owing to imaging data's intricacy and high-dimensional characteristics. Conventional diagnostic techniques often encounter challenges regarding precision and scalability, resulting in delays and possible misinterpretations. This paper presents the Big-Data Analytics-based Diagnostics (BDA-D) framework, a revolutionary method using computational models derived from neural architectures and sophisticated analytics to tackle these difficulties.</div></div><div><h3>Methods</h3><div>The BDA-D architecture utilizes data mining, pattern recognition, and machine learning to glean useful neuroanatomical characteristics from massive datasets. By simulating human thought processes, this method speeds up clinical decision-making and improves diagnostic accuracy. To evaluate the effectiveness of the framework, it is put to the test in a clinical environment.</div></div><div><h3>Results and Discussion</h3><div>Diagnostic precision, processing speed, and dependability are all enhanced by experimental validation. By detecting even the most minute neuroanatomical changes, BDA-D allows for more accurate diagnosis than traditional approaches. Based on the results, neuroradiologists may improve their practices by using cutting-edge computational methods to close the gap between data-driven analytics and their practical use in the clinic. BDA-D discovers important patterns from high-dimensional neuroimaging data through biologically inspired neural networks, reaching a remarkable diagnosis accuracy of 97.18%. Its 95.42% increase in processing speed allows rapid study of important disorders such as strokes and neurodegenerative diseases. BDA-D reduces inter-observer variability with a dependable value of 94.96%, increasing clinical confidence in AI-assisted diagnosis.</div></div><div><h3>Conclusion</h3><div>A revolutionary change in neurodiagnostics, the BDA-D framework improves efficiency and reliability. This method has the potential to completely transform neuroradiology by combining big-data analytics with sophisticated computer models. It will allow for more rapid and precise diagnosis.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100202"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942951","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}

{"title":"AI-driven EEG neuroscientific analysis for evaluating the influence of emotions on false memory","authors":"V. Mahalakshmi","doi":"10.1016/j.neuri.2025.100201","DOIUrl":"10.1016/j.neuri.2025.100201","url":null,"abstract":"<div><div>Investigating the brain mechanisms behind memory processing depends on an awareness of how emotions influence false memory. This study used AI-driven EEG microstate analysis to investigate how emotions affect the generation of false memories from both a temporal and a geographic perspective. Within emotional groups, AI-augmented computational models showed distinct brain processing patterns, particularly during the recall processing stage. By altering cognitive processing dynamics, these results support the hypothesis that AI-enhanced brain activity analysis can effectively mimic the influence of emotional states on the formation of false memories. This work explores emotional implications on false memory by combining artificial intelligence (AI) with EEG-based microstate analysis, therefore offering greater understanding of brain dynamics at several cognitive phases. EEG data collected under various emotional states were analyzed using AI-powered techniques to enable exact extraction of microstate templates (Microstates 1–5) for every emotional group. Phase-locked value (AI-PLV) brain functional networks were built inside microstates displaying notable temporal coverage variations. Driven by artificial intelligence, temporal and geographical analysis of EEG signals revealed different brain processing mechanisms among emotional groupings. The group with pleasant emotions showed continuous activity in prefrontal Microstates 3 and 5, therefore suggesting improved cognitive processing. Reflecting a concentration on information integration, the neutral group showed extended involvement in central-active Microstates 3 and 4. These results emphasize how artificial intelligence is helping neuroscientific research to progress by offering a strong framework for comprehending AI-driven emotional-based aberrations in memory recall.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100201"},"PeriodicalIF":0.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843617","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}

{"title":"Enhanced detection of headache presentation in unruptured brain arteriovenous malformation through combined radiologic features: A cross-sectional study","authors":"Chia-Yu Liu ,&nbsp;Chia-Feng Lu ,&nbsp;Jr-Wei Wu ,&nbsp;Yong-Sin Hu ,&nbsp;Jih-Yuan Lin ,&nbsp;Huai-Che Yang ,&nbsp;Jing-Kai Loo ,&nbsp;Feng-Chi Chang ,&nbsp;Kang-Du Liu ,&nbsp;Chung-Jung Lin","doi":"10.1016/j.neuri.2025.100200","DOIUrl":"10.1016/j.neuri.2025.100200","url":null,"abstract":"<div><h3>Background</h3><div>Although determining angioarchitecture provide qualitative insights into headache-susceptible brain arteriovenous malformation (BAVM), the potential of quantitative radiomics to detect headache in unruptured BAVM remains unclear. We developed classification models that integrate radiomic features and angioarchitecture to assist unruptured BAVM headache treatment decision-making.</div></div><div><h3>Methods</h3><div>We considered patients with unruptured BAVM who underwent magnetic resonance imaging between 2010 and 2023. 146 radiomic features were assessed. Radiomic features were delineated, and angioarchitecture was analyzed. Statistical analyses, including least absolute shrinkage and selection operator regression and logistic regression, were used to select features and develop models. Receiver operating characteristic and decision curve analyses were performed to evaluate performance.</div></div><div><h3>Results</h3><div>The clinical model based on age, sex, and parieto-occipital lesion location achieved an area under the curve (AUC) of 0.741. Adding two significant radiomic features and one angioarchitecture feature enhanced the models. The radiomic and angioarchitecture models achieved an AUC of 0.763. The combined model, with an AUC of 0.799, significantly outperformed the clinical model (<span><math><mi>P</mi><mo>=</mo><mn>0.046</mn></math></span>). Decision curve analysis indicated that the combined model performed best at threshold probabilities between 15% and 40%.</div></div><div><h3>Conclusion</h3><div>Integrating radiomic features and angioarchitecture enhances the identification of unruptured BAVM headache.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100200"},"PeriodicalIF":0.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807506","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}