ArXiv最新文献 - Book学术

MassSpecGym: A benchmark for the discovery and identification of molecules. MassSpecGym：发现和识别分子的基准。

ArXiv Pub Date : 2025-02-14

Roman Bushuiev, Anton Bushuiev, Niek F de Jonge, Adamo Young, Fleming Kretschmer, Raman Samusevich, Janne Heirman, Fei Wang, Luke Zhang, Kai Dührkop, Marcus Ludwig, Nils A Haupt, Apurva Kalia, Corinna Brungs, Robin Schmid, Russell Greiner, Bo Wang, David S Wishart, Li-Ping Liu, Juho Rousu, Wout Bittremieux, Hannes Rost, Tytus D Mak, Soha Hassoun, Florian Huber, Justin J J van der Hooft, Michael A Stravs, Sebastian Böcker, Josef Sivic, Tomáš Pluskal

{"title":"MassSpecGym: A benchmark for the discovery and identification of molecules.","authors":"Roman Bushuiev, Anton Bushuiev, Niek F de Jonge, Adamo Young, Fleming Kretschmer, Raman Samusevich, Janne Heirman, Fei Wang, Luke Zhang, Kai Dührkop, Marcus Ludwig, Nils A Haupt, Apurva Kalia, Corinna Brungs, Robin Schmid, Russell Greiner, Bo Wang, David S Wishart, Li-Ping Liu, Juho Rousu, Wout Bittremieux, Hannes Rost, Tytus D Mak, Soha Hassoun, Florian Huber, Justin J J van der Hooft, Michael A Stravs, Sebastian Böcker, Josef Sivic, Tomáš Pluskal","doi":"","DOIUrl":"","url":null,"abstract":"The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym -- the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://github.com/pluskal-lab/MassSpecGym.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11581121/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142689948","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}

引用次数: 0

The shape of the brain's connections is predictive of cognitive performance: an explainable machine learning study.

ArXiv Pub Date : 2025-02-14

Yui Lo, Yuqian Chen, Dongnan Liu, Wan Liu, Leo Zekelman, Jarrett Rushmore, Fan Zhang, Yogesh Rathi, Nikos Makris, Alexandra J Golby, Weidong Cai, Lauren J O'Donnell

{"title":"The shape of the brain's connections is predictive of cognitive performance: an explainable machine learning study.","authors":"Yui Lo, Yuqian Chen, Dongnan Liu, Wan Liu, Leo Zekelman, Jarrett Rushmore, Fan Zhang, Yogesh Rathi, Nikos Makris, Alexandra J Golby, Weidong Cai, Lauren J O'Donnell","doi":"","DOIUrl":"","url":null,"abstract":"The shape of the brain's white matter connections is relatively unexplored in diffusion MRI tractography analysis. While it is known that tract shape varies in populations and across the human lifespan, it is unknown if the variability in dMRI tractography-derived shape may relate to the brain's functional variability across individuals. This work explores the potential of leveraging tractography fiber cluster shape measures to predict subject-specific cognitive performance. We implement machine learning models to predict individual cognitive performance scores. We study a large-scale database from the HCP-YA study. We apply an atlas-based fiber cluster parcellation to the dMRI tractography of each individual. We compute 15 shape, microstructure, and connectivity features for each fiber cluster. Using these features as input, we train a total of 210 models to predict 7 different NIH Toolbox cognitive performance assessments. We apply an explainable AI technique, SHAP, to assess the importance of each fiber cluster for prediction. Our results demonstrate that shape measures are predictive of individual cognitive performance. The studied shape measures, such as irregularity, diameter, total surface area, volume, and branch volume, are as effective for prediction as microstructure and connectivity measures. The overall best-performing feature is a shape feature, irregularity, which describes how different a cluster's shape is from an idealized cylinder. Further interpretation using SHAP values suggest that fiber clusters with features highly predictive of cognitive ability are widespread throughout the brain, including fiber clusters from the superficial association, deep association, cerebellar, striatal, and projection pathways. This study demonstrates the strong potential of shape descriptors to enhance the study of the brain's white matter and its relationship to cognitive function.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143485026","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}

引用次数: 0

In-Silico Investigation of 3D Quantitative Angiography for Internal Carotid Aneurysms Using Biplane Imaging and 3D Vascular Geometry Constraints.

ArXiv Pub Date : 2025-02-13

Kyle A Williams, Swetadri Vasan Setlur Nagesh, Daniel R Bednarek, Stephen Rudin, Ciprian N Ionita

{"title":"In-Silico Investigation of 3D Quantitative Angiography for Internal Carotid Aneurysms Using Biplane Imaging and 3D Vascular Geometry Constraints.","authors":"Kyle A Williams, Swetadri Vasan Setlur Nagesh, Daniel R Bednarek, Stephen Rudin, Ciprian N Ionita","doi":"","DOIUrl":"","url":null,"abstract":"Quantitative angiography (QA) in two dimensions has been instrumental in assessing neurovascular contrast flow patterns, aiding disease severity and treatment outcome evaluations. However, QA requires high spatio-temporal resolution, restricting its use to digital subtraction angiography (DSA), and is prone to errors in quantification of highly 3D flow patterns. This study examines whether 3D QA information can be recovered by reconstructing four-dimensional (4D) angiography using data from standard clinical imaging protocols. Patient-specific internal carotid aneurysm models were used to generate high-fidelity computational fluid dynamics (CFD) simulations of contrast flow. The resulting 4D angiograms were used to simulate biplane DSA under clinical imaging protocols. 4D angiography was reconstructed from two views using back-projection constrained by an a priori 3D geometry. Quantitative angiographic parametric imaging (API) metrics obtained from the CFD-based 4D angiography and reconstructed 4D angiography were compared using mean square error (MSE) and mean absolute percentage error (MAPE). The reconstructed 4D datasets effectively captured 3D flow dynamics, achieving an average MSE of 0.007 across models and flow conditions. API metrics such as PH and AUC closely matched the CFD ground truth, with temporal metrics showing some variability in regions with overlapping projections. These results demonstrate the potential to recover 3D QA information using simulated 4D angiography constrained by standard clinical imaging parameters. The method provides a robust framework for evaluating and improving QA in clinical neurovascular applications, offering new insights into the dynamics of aneurysmal contrast flow.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844626/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484896","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}

引用次数: 0

A Physics-Informed Deep Learning Model for MRI Brain Motion Correction.

ArXiv Pub Date : 2025-02-13

Mojtaba Safari, Shansong Wang, Zach Eidex, Richard Qiu, Chih-Wei Chang, David S Yu, Xiaofeng Yang

{"title":"A Physics-Informed Deep Learning Model for MRI Brain Motion Correction.","authors":"Mojtaba Safari, Shansong Wang, Zach Eidex, Richard Qiu, Chih-Wei Chang, David S Yu, Xiaofeng Yang","doi":"","DOIUrl":"","url":null,"abstract":"Background: MRI is crucial for brain imaging but is highly susceptible to motion artifacts due to long acquisition times. This study introduces PI-MoCoNet, a physics-informed motion correction network that integrates spatial and k-space information to remove motion artifacts without explicit motion parameter estimation, enhancing image fidelity and diagnostic reliability.Materials and methods: PI-MoCoNet consists of a motion detection network (U-net with spatial averaging) to identify corrupted k-space lines and a motion correction network (U-net with Swin Transformer blocks) to reconstruct motion-free images. The correction is guided by three loss functions: reconstruction (L1), perceptual (LPIPS), and data consistency (Ldc). Motion artifacts were simulated via rigid phase encoding perturbations and evaluated on IXI and MR-ART datasets against Pix2Pix, CycleGAN, and U-net using PSNR, SSIM, and NMSE.Results: PI-MoCoNet significantly improved image quality. On IXI, for minor artifacts, PSNR increased from 34.15 dB to 45.95 dB, SSIM from 0.87 to 1.00, and NMSE reduced from 0.55% to 0.04%. For moderate artifacts, PSNR improved from 30.23 dB to 42.16 dB, SSIM from 0.80 to 0.99, and NMSE from 1.32% to 0.09%. For heavy artifacts, PSNR rose from 27.99 dB to 36.01 dB, SSIM from 0.75 to 0.97, and NMSE decreased from 2.21% to 0.36%. On MR-ART, PI-MoCoNet achieved PSNR gains of ~10 dB and SSIM improvements of up to 0.20, with NMSE reductions of ~6%. Ablation studies confirmed the importance of data consistency and perceptual losses, yielding a 1 dB PSNR gain and 0.17% NMSE reduction.Conclusions: PI-MoCoNet effectively mitigates motion artifacts in brain MRI, outperforming existing methods. Its ability to integrate spatial and k-space information makes it a promising tool for clinical use in motion-prone settings. Code: https://github.com/mosaf/PI-MoCoNet.git.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844622/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484838","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}

引用次数: 0

Optimizing Global Genomic Surveillance for Early Detection of Emerging SARS-CoV-2 Variants.

ArXiv Pub Date : 2025-02-13

Haogao Gu, Jifan Li, Wanying Sun, Mengting Li, Kathy Leung, Joseph T Wu, Hsiang-Yu Yuan, Maggie H Wang, Bingyi Yang, Matthew R McKay, Ning Ning, Leo L M Poon

{"title":"Optimizing Global Genomic Surveillance for Early Detection of Emerging SARS-CoV-2 Variants.","authors":"Haogao Gu, Jifan Li, Wanying Sun, Mengting Li, Kathy Leung, Joseph T Wu, Hsiang-Yu Yuan, Maggie H Wang, Bingyi Yang, Matthew R McKay, Ning Ning, Leo L M Poon","doi":"","DOIUrl":"","url":null,"abstract":"Background: Global viral threats underscore the need for effective genomic surveillance, but high costs and uneven resource distribution hamper its implementation. Targeting surveillance to international travelers in major travel hubs may offer a more efficient strategy for the early detection of SARS-CoV-2 variants.Methods: We developed and calibrated a multiple-strain metapopulation model of global SARS-CoV-2 transmission using extensive epidemiological, phylogenetic, and high-resolution air travel data. We then compared baseline surveillance with various resource-allocation approaches that prioritize travelers, focusing on Omicron BA.1/BA.2 retrospectively and on hypothetical future variants under different emergence, transmission and vaccine effectiveness scenarios.Findings: Focusing existing surveillance resources on travelers at key global hubs significantly shortened detection delays without increasing total surveillance efforts. In retrospective analyses of Omicron BA.1/BA.2, traveler-targeted approaches consistently outperformed baseline strategies, even when overall resources were reduced. Simulations indicate that focusing surveillance on key travel hubs outperform baseline practices in detecting future variants, across different possible origins, even with reduced resources. This approach also remains effective in future pandemic scenarios with varying reproductive numbers and vaccine effectiveness.Interpretation: These findings provide a quantitative, cost-effective framework for strengthening global genomic surveillance. By reallocating resources toward international travelers in select travel hubs, early detection of emerging variants can be enhanced, informing rapid public health interventions and bolstering preparedness for future pandemics.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844623/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143485023","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}

引用次数: 0

Trajectory Inference for Single Cell Omics.

ArXiv Pub Date : 2025-02-13

Alexandre Hutton, Jesse G Meyer

引用次数: 0

Normative Cerebral Perfusion Across the Lifespan.

ArXiv Pub Date : 2025-02-12

Xinglin Zeng, Yiran Li, Lin Hua, Ruoxi Lu, Lucas Lemos Franco, Peter Kochunov, Shuo Chen, John A Detre, Ze Wang

{"title":"Normative Cerebral Perfusion Across the Lifespan.","authors":"Xinglin Zeng, Yiran Li, Lin Hua, Ruoxi Lu, Lucas Lemos Franco, Peter Kochunov, Shuo Chen, John A Detre, Ze Wang","doi":"","DOIUrl":"","url":null,"abstract":"Cerebral perfusion plays a crucial role in maintaining brain function and is tightly coupled with neuronal activity. While previous studies have examined cerebral perfusion trajectories across development and aging, precise characterization of its lifespan dynamics has been limited by small sample sizes and methodological inconsistencies. In this study, we construct the first comprehensive normative model of cerebral perfusion across the human lifespan (birth to 85 years) using a large multi-site dataset of over 12,000 high-quality arterial spin labeling (ASL) MRI scans. Leveraging generalized additive models for location, scale, and shape (GAMLSS), we mapped nonlinear growth trajectories of cerebral perfusion at global, network, and regional levels. We observed a rapid postnatal increase in cerebral perfusion, peaking at approximately 7.1 years, followed by a gradual decline into adulthood. Sex differences were evident, with distinct regional maturation patterns rather than uniform differences across all brain regions. Beyond normative modeling, we quantified individual deviations from expected CBF patterns in neurodegenerative and psychiatric conditions, identifying disease-specific perfusion abnormalities across four brain disorders. Using longitudinal data, we established typical and atypical cerebral perfusion trajectories, highlighting the prognostic value of perfusion-based biomarkers for detecting disease progression. Our findings provide a robust normative framework for cerebral perfusion, facilitating precise characterization of brain health across the lifespan and enhancing the early identification of neurovascular dysfunction in clinical populations.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844630/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143485022","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}

引用次数: 0

Persistent Sheaf Laplacian Analysis of Protein Flexibility.

ArXiv Pub Date : 2025-02-12

Nicole Hayes, Xiaoqi Wei, Hongsong Feng, Ekaterina Merkurjev, Guo-Wei Wei

引用次数: 0

Data Sharing in the PRIMED Consortium: Design, implementation, and recommendations for future policymaking.

ArXiv Pub Date : 2025-02-12

Johanna L Smith, Quenna Wong, Whitney Hornsby, Matthew P Conomos, Benjamin D Heavner, Iftikhar J Kullo, Bruce M Psaty, Stephen S Rich, Bamidele Tayo, Pradeep Natarajan, Sarah C Nelson, Polygenic Risk Methods In Diverse Populations Primed Consortium Data Sharing Working Group, Polygenic Risk Methods In Diverse Populations Primed Consortium

{"title":"Data Sharing in the PRIMED Consortium: Design, implementation, and recommendations for future policymaking.","authors":"Johanna L Smith, Quenna Wong, Whitney Hornsby, Matthew P Conomos, Benjamin D Heavner, Iftikhar J Kullo, Bruce M Psaty, Stephen S Rich, Bamidele Tayo, Pradeep Natarajan, Sarah C Nelson, Polygenic Risk Methods In Diverse Populations Primed Consortium Data Sharing Working Group, Polygenic Risk Methods In Diverse Populations Primed Consortium","doi":"","DOIUrl":"","url":null,"abstract":"Sharing diverse genomic and other biomedical datasets is critical to advance scientific discoveries and their equitable translation to improve human health. However, data sharing remains challenging in the context of legacy datasets, evolving policies, multi-institutional consortium science, and international stakeholders. The NIH-funded Polygenic Risk Methods in Diverse Populations (PRIMED) Consortium was established to improve the performance of polygenic risk estimates for a broad range of health and disease outcomes with global impacts. Improving polygenic risk score performance across genetically diverse populations requires access to large, diverse cohorts. We report on the design and implementation of data sharing policies and procedures developed in PRIMED to aggregate and analyze data from multiple, heterogeneous sources while adhering to existing data sharing policies for each integrated dataset. We describe two primary data sharing mechanisms: coordinated dbGaP applications and a Consortium Data Sharing Agreement, as well as provide alternatives when individual-level data cannot be shared within the Consortium (e.g., federated analyses). We also describe technical implementation of Consortium data sharing in the NHGRI Analysis Visualization and Informatics Lab-space (AnVIL) cloud platform, to share derived individual-level data, genomic summary results, and methods workflows with appropriate permissions. As a Consortium making secondary use of pre-existing data sources, we also discuss challenges and propose solutions for release of individual- and summary-level data products to the broader scientific community. We make recommendations for ongoing and future policymaking with the goal of informing future consortia and other research activities.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844628/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484880","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}

引用次数: 0

An affordable, wearable, fiber-free pulsed-mode diffuse speckle contrast flowmetry (PM-DSCF) sensor for noninvasive measurements of deep cerebral blood flow.

ArXiv Pub Date : 2025-02-11

Chaebeom Yeo, Xuhui Liu, Mehrana Mohtasebi, Faezeh Akbari, Faraneh Fathi, Guoqiang Yu

{"title":"An affordable, wearable, fiber-free pulsed-mode diffuse speckle contrast flowmetry (PM-DSCF) sensor for noninvasive measurements of deep cerebral blood flow.","authors":"Chaebeom Yeo, Xuhui Liu, Mehrana Mohtasebi, Faezeh Akbari, Faraneh Fathi, Guoqiang Yu","doi":"","DOIUrl":"","url":null,"abstract":"Significance: Measuring cerebral blood flow (CBF) is crucial for diagnosing various cerebral diseases. An affordable, wearable, and fiber-free continuous-wave speckle contrast flowmetry (CW-DSCF) technique has been developed for continuous monitoring of CBF variations. However, its application in adult humans is limited by shallow tissue penetration.Aim: To develop an innovative pulse-mode DSCF (PM-DSCF) system for continuous monitoring of CBF variations in adult humans.Approach: The PM-DSCF utilizes an 808 nm laser diode and a small NanEye camera to capture diffuse laser speckle fluctuations caused by red blood cell movement in the brain (i.e., CBF). Operating in short-pulse mode (duty cycle < 5%), the system maximizes peak pulse light power for deeper tissue penetration, while ensuring that the average power density remains within ANSI safety standards for skin exposure. The PM-DSCF was evaluated on tissue-simulating phantoms and in adult humans.Results: The maximum effective source-detector distance increased from 15 mm (CW-DSCF) to 35 mm (PM-DSCF). The PM-DSCF successfully detected CBF variations in adult brains during head-up-tilting experiments, consistent with physiological expectations.Conclusions: Switching from CW mode to PM mode significantly increases the maximum tissue penetration depth from ~7.5 mm (CW-DSCF) to ~17.5 mm (PM-DSCF), enabling successful CBF measurements in adult humans.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844631/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484862","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}

引用次数: 0