ArXiv Pub Date : 2024-12-09

Thorsten Prüstel, Martin Meier-Schellersheim

引用次数: 0

Predictive Strategies for the Control of Complex Motor Skills: Recent Insights into Individual and Joint Actions. 复杂运动技能控制的预测策略：对个人和联合行动的最新见解。

ArXiv Pub Date : 2024-12-05

Marta Russo, Antonella Maselli, Dagmar Sternad, Giovanni Pezzulo

引用次数: 0

Brain Morphology Normative modelling platform for abnormality and Centile estimation: Brain MoNoCle. 用于异常和百分位估计的脑形态规范建模平台：Brain MoNoCle.

ArXiv Pub Date : 2024-12-05

Bethany Little, Nida Alyas, Alexander Surtees, Gavin P Winston, John S Duncan, David A Cousins, John-Paul Taylor, Peter Taylor, Karoline Leiberg, Yujiang Wang

{"title":"Brain Morphology Normative modelling platform for abnormality and Centile estimation: Brain MoNoCle.","authors":"Bethany Little, Nida Alyas, Alexander Surtees, Gavin P Winston, John S Duncan, David A Cousins, John-Paul Taylor, Peter Taylor, Karoline Leiberg, Yujiang Wang","doi":"","DOIUrl":"","url":null,"abstract":"Normative models of brain structure estimate the effects of covariates such as age and sex using large samples of healthy controls. These models can then be applied to e.g. smaller clinical cohorts to distinguish disease effects from other covariates. However, these advanced statistical modelling approaches can be difficult to access, and processing large healthy cohorts is computationally demanding. Thus, accessible platforms with pre-trained normative models are needed. We present such a platform for brain morphology analysis as an open-source web application (https://cnnplab.shinyapps.io/BrainMoNoCle/), with six key features: (i) user-friendly web interface, (ii) individual and group outputs, (iii) multi-site analysis, (iv) regional and whole-brain analysis, (v) integration with existing tools, and (vi) featuring multiple morphology metrics. Using a diverse sample of 3,276 healthy controls across 21 sites, we pre-trained normative models on various metrics. We validated the models with a small sample of individuals with bipolar disorder, showing outputs that aligned closely with existing literature only after applying our normative modelling. Using a cohort of people with temporal lobe epilepsy, we showed that individual-level abnormalities were in line with seizure lateralisation. Finally, with the ability to investigate multiple morphology measures in the same framework, we found that biological covariates are better explained in specific morphology measures, and for applications, only some measures are sensitive to the disease process. Our platform offers a comprehensive framework to analyse brain morphology in clinical and research settings. Validations confirm the superiority of normative models and the advantage of investigating a range of brain morphology metrics together.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11177948/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332710","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

Motion-Guided Deep Image Prior for Cardiac MRI. 运动引导的心脏MRI深度图像先验。

ArXiv Pub Date : 2024-12-05

Marc Vornehm, Chong Chen, Muhammad Ahmad Sultan, Syed Murtaza Arshad, Yuchi Han, Florian Knoll, Rizwan Ahmad

{"title":"Motion-Guided Deep Image Prior for Cardiac MRI.","authors":"Marc Vornehm, Chong Chen, Muhammad Ahmad Sultan, Syed Murtaza Arshad, Yuchi Han, Florian Knoll, Rizwan Ahmad","doi":"","DOIUrl":"","url":null,"abstract":"Cardiovascular magnetic resonance imaging is a powerful diagnostic tool for assessing cardiac structure and function. Traditional breath-held imaging protocols, however, pose challenges for patients with arrhythmias or limited breath-holding capacity. We introduce Motion-Guided Deep Image prior (M-DIP), a novel unsupervised reconstruction framework for accelerated real-time cardiac MRI. M-DIP employs a spatial dictionary to synthesize a time-dependent template image, which is further refined using time-dependent deformation fields that model cardiac and respiratory motion. Unlike prior DIP-based methods, M-DIP simultaneously captures physiological motion and frame-to-frame content variations, making it applicable to a wide range of dynamic applications. We validate M-DIP using simulated MRXCAT cine phantom data as well as free-breathing real-time cine and single-shot late gadolinium enhancement data from clinical patients. Comparative analyses against state-of-the-art supervised and unsupervised approaches demonstrate M-DIP's performance and versatility. M-DIP achieved better image quality metrics on phantom data, as well as higher reader scores for in-vivo patient data.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11643223/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142829986","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

Photon-Counting CT in Cancer Radiotherapy: Technological Advances and Clinical Benefits. 癌症放疗中的光子计数 CT：技术进步与临床效益。

ArXiv Pub Date : 2024-12-04

Keyur D Shah, Jun Zhou, Justin Roper, Anees Dhabaan, Hania Al-Hallaq, Amir Pourmorteza, Xiaofeng Yang

{"title":"Photon-Counting CT in Cancer Radiotherapy: Technological Advances and Clinical Benefits.","authors":"Keyur D Shah, Jun Zhou, Justin Roper, Anees Dhabaan, Hania Al-Hallaq, Amir Pourmorteza, Xiaofeng Yang","doi":"","DOIUrl":"","url":null,"abstract":"Photon-counting computed tomography (PCCT) marks a significant advancement over conventional energy-integrating detector (EID) CT systems. This review highlights PCCT's superior spatial and contrast resolution, reduced radiation dose, and multi-energy imaging capabilities, which address key challenges in radiotherapy, such as accurate tumor delineation, precise dose calculation, and treatment response monitoring. PCCT's improved anatomical clarity enhances tumor targeting while minimizing damage to surrounding healthy tissues. Additionally, metal artifact reduction (MAR) and quantitative imaging capabilities optimize workflows, enabling adaptive radiotherapy and radiomics-driven personalized treatment. Emerging clinical applications in brachytherapy and radiopharmaceutical therapy (RPT) show promising outcomes, although challenges like high costs and limited software integration remain. With advancements in artificial intelligence (AI) and dedicated radiotherapy packages, PCCT is poised to transform precision, safety, and efficacy in cancer radiotherapy, marking it as a pivotal technology for future clinical practice.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11581100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142689966","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

mdCATH: A Large-Scale MD Dataset for Data-Driven Computational Biophysics. mdCATH：用于数据驱动计算生物物理学的大规模 MD 数据集。

ArXiv Pub Date : 2024-12-03

Antonio Mirarchi, Toni Giorgino, Gianni De Fabritiis

引用次数: 0

Chemomechanical regulation of growing tissues from a thermodynamically-consistent framework and its application to tumor spheroid growth. 热力学一致框架下生长组织的化学机械调控及其在肿瘤球体生长中的应用

ArXiv Pub Date : 2024-12-03

Nonthakorn Olaranont, Chaozhen Wei, John Lowengrub, Min Wu

引用次数: 0

Time-heterogeneity of the Förster Radius from Dipole Orientational Dynamics Impacts Single-Molecule FRET Experiments. 来自偶极子方向动力学的福斯特半径时间异质性解释了观测到的动态偏移。

ArXiv Pub Date : 2024-12-03

David Frost, Keisha Cook, Hugo Sanabria

{"title":"Time-heterogeneity of the Förster Radius from Dipole Orientational Dynamics Impacts Single-Molecule FRET Experiments.","authors":"David Frost, Keisha Cook, Hugo Sanabria","doi":"","DOIUrl":"","url":null,"abstract":"Förster resonance energy transfer (FRET) is a quantum mechanical phenomenon involving the non-radiative transfer of energy between coupled electric dipoles. Due to the strong dependence of FRET on the distance between the dipoles, it is frequently used as a \"molecular ruler\" in biology, chemistry, and physics. This is done by placing dipolar molecules called dyes on molecules of interest. In time-resolved confocal single-molecule FRET (smFRET) experiments, the joint distribution of the FRET efficiency and the donor fluorescence lifetime can reveal underlying molecular conformational dynamics via deviation from their theoretical Förster relationship. This deviation is referred to as a dynamic shift. Quantifying the dynamic shift caused by the motion of the fluorescent dyes is essential to decoupling the dynamics of the studied molecules and the dyes. We develop novel Langevin models for the dye linker dynamics, including rotational dynamics, based on first principle physics and proper dye linker chemistry to match accessible volumes predicted by molecular dynamics simulations. By simulating the dyes' stochastic translational and rotational dynamics, we show that the observed dynamic shift can largely be attributed to the mutual orientational dynamics of the electric dipole moments associated with the dyes, not their accessible volume. Our models provide the most up-to-date and accurate estimation of FRET.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11065046/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140872698","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

New Graphs at the braingraph.org Website for Studying the Aging Brain Circuitry. braingraph.org网站上研究大脑回路老化的新图表。

ArXiv Pub Date : 2024-12-02

Balint Varga, Vince Grolmusz

引用次数: 0

Multi-Scale Representation Learning for Protein Fitness Prediction. 蛋白质适宜性预测的多尺度表征学习

ArXiv Pub Date : 2024-12-02

Zuobai Zhang, Pascal Notin, Yining Huang, Aurélie Lozano, Vijil Chenthamarakshan, Debora Marks, Payel Das, Jian Tang

{"title":"Multi-Scale Representation Learning for Protein Fitness Prediction.","authors":"Zuobai Zhang, Pascal Notin, Yining Huang, Aurélie Lozano, Vijil Chenthamarakshan, Debora Marks, Payel Das, Jian Tang","doi":"","DOIUrl":"","url":null,"abstract":"Designing novel functional proteins crucially depends on accurately modeling their fitness landscape. Given the limited availability of functional annotations from wet-lab experiments, previous methods have primarily relied on self-supervised models trained on vast, unlabeled protein sequence or structure datasets. While initial protein representation learning studies solely focused on either sequence or structural features, recent hybrid architectures have sought to merge these modalities to harness their respective strengths. However, these sequence-structure models have so far achieved only incremental improvements when compared to the leading sequence-only approaches, highlighting unresolved challenges effectively leveraging these modalities together. Moreover, the function of certain proteins is highly dependent on the granular aspects of their surface topology, which have been overlooked by prior models. To address these limitations, we introduce the Sequence-Structure-Surface Fitness (S3F) model - a novel multimodal representation learning framework that integrates protein features across several scales. Our approach combines sequence representations from a protein language model with Geometric Vector Perceptron networks encoding protein backbone and detailed surface topology. The proposed method achieves state-of-the-art fitness prediction on the ProteinGym benchmark encompassing 217 substitution deep mutational scanning assays, and provides insights into the determinants of protein function. Our code is at https://github.com/DeepGraphLearning/S3F.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11643228/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830494","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

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