Neuroscience informatics最新文献

{"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":"2025-06-01","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":"Analysis and development of clinically recorded dysarthric speech corpus for patients affected with various stroke conditions","authors":"Oindrila Banerjee ,&nbsp;K.V.N. Sita Mahalakshmi ,&nbsp;M.V.S. Jyothi ,&nbsp;D. Govind ,&nbsp;U.K. Rakesh ,&nbsp;A. Rajeev ,&nbsp;K. Samudravijaya ,&nbsp;Akhilesh Kumar Dubey ,&nbsp;Suryakanth V. Gangashetty","doi":"10.1016/j.neuri.2025.100198","DOIUrl":"10.1016/j.neuri.2025.100198","url":null,"abstract":"<div><div>The manuscript presents the work related to the development of a dysarthric speech corpus for various types of stroke conditions. The corpus consists of speech recorded from 50 stroke patients and 50 healthy controls in clinical environments. Severity of stroke for each patient has been assessed by the clinician based on the National Institute of Health Stroke Scale. The text read by patients and healthy controls comprises (a) five sustained vowels, (b) three words consisting of the plosive consonant and vowels, and (c) 10 phonetically rich sentences in Telugu language. A discriminative analysis is carried out using conventional Mel Frequency Cepstral Coefficients and Convolutional Neural Networks to quantify the perceptual variations in dysarthric speech of stroke patients and healthy controls. Vowels and word utterances of the speech corpus exhibited better class discrimination characteristics compared to sentences for text dependent and speaker independent scenarios.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100198"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","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":"2025-06-01","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":"Feature fusion based deep learning model for Alzheimer's neurological disorder classification","authors":"Arhath Kumar ,&nbsp;S. Pradeep ,&nbsp;Kumud Arora ,&nbsp;G. Sreeram ,&nbsp;A. Pankajam ,&nbsp;Trupti Patil ,&nbsp;Aradhana Sahu","doi":"10.1016/j.neuri.2025.100196","DOIUrl":"10.1016/j.neuri.2025.100196","url":null,"abstract":"<div><div>Alzheimer's disease (AD) is a severe brain disorder that can cause degradation of brain tissue and memory loss. Owing to Alzheimer's disease's high cost, a number of deep learning-based models have been put out to accurately identify the illness. This study introduces a new way to classify Alzheimer's disease using deep learning and combining different types of features. The 3D lightweight MBANet developed in this research has less parameters and can concentrate on more discriminative deep structures than conventional artificial neural networks like CNN, according to experimental data. We first create a Multi-Branch Attention Network (MBANet) to gather detailed features of the hippocampus from large sets of data. A new method is created to capture texture features in the hippocampus. It uses two techniques: multi-Tree Wavelet Transform (MTWT) and Gray Length Matrix (GLM). This method works in three dimensions and at different scales. Also, standard methods are used to measure the size and shape of the hippocampus. A mixed feature fusion network is created to simplify and combine data from the hippocampus, helping to classify Alzheimer's disease more effectively. Tests on the EADC-ADNI dataset show that the proposed method for classifying Alzheimer's disease achieves an accuracy of 93.39%, a <span><math><msub><mrow><mi>F</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>-score of 93.10%, and an AUC of 93.21%. The test results show that the proposed method for classifying Alzheimer's disease is effective and better than traditional methods.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100196"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","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":"2025-06-01","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":"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":"2025-06-01","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}

{"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":"2025-06-01","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":"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":"2025-06-01","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":"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":"2025-06-01","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":"Neuroscience-informed nomogram model for early prediction of cognitive impairment in Parkinson's disease","authors":"Sudharshan Putha ,&nbsp;Swaroop Reddy Gayam ,&nbsp;Bhavani Prasad Kasaraneni ,&nbsp;Krishna Kanth Kondapaka ,&nbsp;Sateesh Kumar Nallamala ,&nbsp;Praveen Thuniki","doi":"10.1016/j.neuri.2025.100189","DOIUrl":"10.1016/j.neuri.2025.100189","url":null,"abstract":"<div><div>Cognitive impairment is a common non-motor symptom of Parkinson's disease (PD), significantly affecting patients' quality of life and posing challenges for clinical management. Early prediction of cognitive decline in PD is critical for timely diagnosis and intervention. However, the interplay of multivariate factors such as age, gender, and disease duration complicate early prediction. To address the multifactorial nature of cognitive impairment in PD, this study proposes a neuroscience-informed nomogram model constructed using multivariate logistic regression. The least absolute shrinkage and selection operator (LASSO) algorithm was applied to identify highly correlated clinical variables influencing cognitive function. Subsequently, these variables were integrated into a visualized nomogram model to facilitate early prediction of cognitive impairment (CI) risk. Performance evaluation of the model demonstrated high accuracy, consistency, and clinical applicability, significantly enhancing diagnostic efficiency for neurologists. Furthermore, the model provides visual comparisons of patient distributions across different predictor values, enabling personalized risk assessments. According to experimental analysis and verification, the model demonstrated outstanding prediction with a region under the ROC curve of 0.872 on the original training set and 0.870 on the validation set. Because the anticipated and observed probabilities were so consistent, the model was able to forecast the patient's likelihood of cognitive impairment.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100189"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}