ArXiv最新文献

{"title":"Geometry of the cumulant series in neuroimaging.","authors":"Santiago Coelho, Filip Szczepankiewicz, Els Fieremans, Dmitry S Novikov","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Water diffusion gives rise to micrometer-scale sensitivity of diffusion MRI (dMRI) to cellular-level tissue structure. The advent of precision medicine and quantitative imaging hinges on revealing the information content of dMRI, and providing its parsimonious basis- and hardware-independent \"fingerprint\". Here we reveal the geometry of a multi-dimensional dMRI signal, classify all 21 invariants of diffusion and covariance tensors in terms of irreducible representations of the group of rotations, and relate them to tissue properties. Previously studied dMRI contrasts are expressed via 7 invariants, while the remaining 14 provide novel complementary information. We design acquisitions based on icosahedral vertices guaranteeing minimal number of measurements to determine 3-4 most used invariants in only 1-2 minutes for the whole brain. Representing dMRI signals via scalar invariant maps with definite symmetries will underpin machine learning classifiers of brain pathology, development, and aging, while fast protocols will enable translation of advanced dMRI into clinical practice.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11398539/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142303253","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":"Instability of a fluctuating biomimetic membrane driven by an applied uniform DC electric field.","authors":"Zongxin Yu, Shuozhen Zhao, Michael J Miksis, Petia M Vlahovska","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The linear stability of a lipid membrane under a DC electric field, applied perpendicularly to the interface, is investigated in the electrokinetic framework, taking account to the dynamics of the Debye layers formed near the membrane. The perturbed charge in the Debye layer redistributes and destabilizes the membrane via electrical surface stress interior and exterior to the membrane. The instability is suppressed as the difference in the electrolyte concentration of the solutions separated by the membrane increases, due to a weakened base state electric field near the membrane. This result contrasts with the destabilizing effect predicted using the leaky dielectric model in cases of asymmetric conductivity. We attribute this difference to the varying assumptions about the perturbation amplitude relative to the Debye length, which result in different regimes of validity for the linear stability analysis within these two frameworks.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11875285/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143545079","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":"Diffuse-charge dynamics across a capacitive interface in a DC electric field.","authors":"Shuozhen Zhao, Bhavya Balu, Zongxin Yu, Michael J Miksis, Petia M Vlahovska","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Cells and cellular organelles are encapsulated by nanometrically thin membranes whose main component is a lipid bilayer. In the presence of electric fields, the ion-impermeable lipid bilayer acts as a capacitor and supports a potential difference across the membrane. We analyze the charging dynamics of a planar membrane separating bulk solutions with different electrolyte concentrations upon the application of an applied uniform DC electric field. The membrane is modeled as a zero-thickness capacitive interface. The evolution of the electric potential and ions distributions in the bulk are solved for using the Poisson-Nernst-Planck (PNP) equations. Asymptotic solutions are derived in the limit of thin Debye layers and weak fields (compared to the thermal electric potential).</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11875280/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544788","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":"Time-series attribution maps with regularized contrastive learning.","authors":"Steffen Schneider, Rodrigo González Laiz, Anastasiia Filippova, Markus Frey, Mackenzie Weygandt Mathis","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Gradient-based attribution methods aim to explain decisions of deep learning models but so far lack identifiability guarantees. Here, we propose a method to generate attribution maps with identifiability guarantees by developing a regularized contrastive learning algorithm trained on time-series data plus a new attribution method called Inverted Neuron Gradient (collectively named xCEBRA). We show theoretically that xCEBRA has favorable properties for identifying the Jacobian matrix of the data generating process. Empirically, we demonstrate robust approximation of zero vs. non-zero entries in the ground-truth attribution map on synthetic datasets, and significant improvements across previous attribution methods based on feature ablation, Shapley values, and other gradient-based methods. Our work constitutes a first example of identifiable inference of time-series attribution maps and opens avenues to a better understanding of time-series data, such as for neural dynamics and decision-processes within neural networks.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11875283/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143545154","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":"Lattice ultrasensitivity amplifies signals in E. coli without fine-tuning.","authors":"Derek M Sherry, Isabella R Graf, Samuel J Bryant, Thierry Emonet, Benjamin B Machta","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The E. coli chemosensory lattice, consisting of receptors, kinases, and adaptor proteins, is an important test case for biochemical signal processing. Kinase output is characterized by precise adaptation to a wide range of background ligand levels and large gain in response to small relative changes in concentration. Existing models of this lattice achieve their gain through allosteric interactions between either receptors or core units of receptors and kinases. Here we introduce a model which operates through an entirely different mechanism in which receptors gate inherently far from equilibrium enzymatic reactions between neighboring kinases. Our lattice model achieves gain through a mechanism more closely related to zero-order ultrasensitivity than to allostery. Thus, we call it lattice ultrasensitivity (LU). Unlike other lattice critical models, the LU model can achieve arbitrarily high gain through time-scale separation, rather than through fine-tuning. The model also captures qualitative experimental results which are difficult to reconcile with existing models. We discuss possible implementations in the lattice's baseplate where long flexible linkers could potentially mediate interactions between neighboring core units.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11160871/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141297587","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":"A Bayesian Multivariate Spatial Point Pattern Model: Application to Oral Microbiome FISH Image Data.","authors":"Kyu Ha Lee, Brent A Coull, Suman Majumder, Patrick J La Riviere, Jessica L Mark Welch, Jacqueline R Starr","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Advances in cellular imaging technologies, especially those based on fluorescence in situ hybridization (FISH) now allow detailed visualization of the spatial organization of human or bacterial cells. Quantifying this spatial organization is crucial for understanding the function of multicellular tissues or biofilms, with implications for human health and disease. To address the need for better methods to achieve such quantification, we propose a flexible multivariate point process model that characterizes and estimates complex spatial interactions among multiple cell types. The proposed Bayesian framework is appealing due to its unified estimation process and the ability to directly quantify uncertainty in key estimates of interest, such as those of inter-type correlation and the proportion of variance due to inter-type relationships. To ensure stable and interpretable estimation, we consider shrinkage priors for coefficients associated with latent processes. Model selection and comparison are conducted by using a deviance information criterion designed for models with latent variables, effectively balancing the risk of overfitting with that of oversimplifying key quantities. Furthermore, we develop a hierarchical modeling approach to integrate multiple image-specific estimates from a given subject, allowing inference at both the global and subject-specific levels. We apply the proposed method to microbial biofilm image data from the human tongue dorsum and find that specific taxon pairs, such as Streptococcus mitis-Streptococcus salivarius and Streptococcus mitis-Veillonella, exhibit strong positive spatial correlations, while others, such as Actinomyces-Rothia, show slight negative correlations. For most of the taxa, a substantial portion of spatial variance can be attributed to inter-taxon relationships.</p>","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/PMC11875300/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544185","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":"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":"<p><p>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.</p>","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}

{"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":"<p><p>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.</p>","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}

{"title":"In-Silico Investigation of 3D Quantitative Angiography for Internal Carotid Aneurysms Using Biplane Imaging and 3D Vascular Geometry Constraints.","authors":"Kyle A Williams, Sv Setlur Nagesh, Daniel R Bednarek, Stephen Rudin, Ciprian Ionita","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Background: </strong>Quantitative angiography (QA) in two dimensions has been instrumental in assessing neurovascular contrast flow patterns, aiding disease severity evaluation and treatment outcome prediction using data-driven models. However, QA requires high temporal and spatial resolution, restricting its use to digital subtraction angiography (DSA).</p><p><strong>Purpose: </strong>The 2D projective nature of DSA introduces errors and noise due to the inherently three-dimensional flow dynamics. This study examines whether 3D QA information can be recovered by reconstructing four-dimensional (4D) angiography using data from standard clinical imaging protocols.</p><p><strong>Methods: </strong>Patient-specific 3D vascular geometries were used to generate high-fidelity computational fluid dynamics (CFD) simulations of contrast flow in internal carotid aneurysms. The resulting 4D angiograms, representing ground truth, were used to simulate biplane DSA under clinical imaging protocols, including projection spacing and injection timing. 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, respectively, were compared using mean square error (MSE) and mean absolute percentage error (MAPE).</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>This study highlights the feasibility of recovering 3D QA information from reconstructed 4D DSA simulated from biplane projections. 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.</p>","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}

{"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":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Magnetic resonance imaging (MRI) is an essential brain imaging tool, but its long acquisition times make it highly susceptible to motion artifacts that can degrade diagnostic quality.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;This work aims to develop and evaluate a novel physics-informed motion correction network, termed PI-MoCoNet, which leverages complementary information from both the spatial and &lt;i&gt;k&lt;/i&gt;-space domains. The primary goal is to robustly remove motion artifacts from high-resolution brain MRI images without explicit motion parameter estimation, thereby preserving image fidelity and enhancing diagnostic reliability.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Materials and methods: &lt;/strong&gt;PI-MoCoNet is designed as a dual-network framework consisting of a motion detection network and a motion correction network. The motion detection network employs a U-net architecture to identify corrupted &lt;i&gt;k&lt;/i&gt;-space lines using a spatial averaging module, thereby reducing prediction uncertainty. The correction network, inspired by recent advances in U-net architectures and incorporating Swin Transformer blocks, reconstructs motion-corrected images by leveraging three loss components: the reconstruction loss &lt;math&gt; &lt;mrow&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt; &lt;mrow&gt;&lt;msub&gt;&lt;mtext&gt;𝓛&lt;/mtext&gt; &lt;mn&gt;1&lt;/mn&gt;&lt;/msub&gt; &lt;/mrow&gt; &lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt; &lt;/mrow&gt; &lt;/math&gt; , a learned perceptual image patch similarity (LPIPS) loss, and a data consistency loss &lt;math&gt; &lt;mrow&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt; &lt;mrow&gt;&lt;msub&gt;&lt;mtext&gt;𝓛&lt;/mtext&gt; &lt;mtext&gt;dc&lt;/mtext&gt;&lt;/msub&gt; &lt;/mrow&gt; &lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt; &lt;/mrow&gt; &lt;/math&gt; that enforces fidelity in the &lt;i&gt;k&lt;/i&gt;-space domain. Realistic motion artifacts were simulated by perturbing phase encoding lines with random rigid transformations. The method was evaluated on two public datasets (IXI and MR-ART). Comparative assessments were made against baseline models, including Pix2Pix GAN, CycleGAN, and a conventional U-net, using quantitative metrics such as peak signal-to-noise ratio(PSNR), structural similarity index measure (SSIM), and normalized mean square error (NMSE).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;PI-MoCoNet demonstrated significant improvements over competing methods across all levels of motion artifacts. On the IXI dataset, for minor motion artifacts, PSNR improved from 34.15 dB in the motion-corrupted images to 45.95 dB after correction, SSIM increased from 0.87 to 1.00, and NMSE was reduced from 0.55% to 0.04%. For moderate artifacts, PSNR increased from 30.23 dB to 42.16 dB, SSIM from 0.80 to 0.99, and NMSE from 1.32% to 0.09%. In the case of heavy artifacts, PSNR improved 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 the MR-ART dataset, PSNR values increased from 23.15 dB to 33.01 dB for low artifact levels and from 21.23 dB to 31.72 dB for high artifact levels; concurrently, SSIM improved from 0.72 to 0.87 and from 0.63 to 0.83, while NMSE decreased from 10.08% to 6.24% and from 14.77% to 8.32%, respectively. An ablation study furt","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}