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An altruistic resource-sharing mechanism for synchronization: The energy-speed-accuracy tradeoff.
Pub Date : 2025-02-04
Dongliang Zhang, Yuansheng Cao, Qi Ouyang, Yuhai Tu

Synchronization among a group of active agents is ubiquitous in nature. Although synchronization based on direct interactions between agents described by the Kuramoto model is well understood, the other general mechanism based on indirect interactions among agents sharing limited resources are less known. Here, we propose a minimal thermodynamically consistent model for the altruistic resource-sharing (ARS) mechanism wherein resources are needed for individual agent to advance but a more advanced agent has a lower competence to obtain resources. We show that while differential competence in ARS mechanism provides a negative feedback leading to synchronization it also breaks detailed balance and thus requires additional energy dissipation besides the cost of driving individual agents. By solving the model analytically, our study reveals a general tradeoff relation between the total energy dissipation rate and the two key performance measures of the system: average speed and synchronization accuracy. For a fixed dissipation rate, there is a distinct speed-accuracy Pareto front traversed by the scarcity of resources: scarcer resources lead to slower speed but more accurate synchronization. Increasing energy dissipation eases this tradeoff by pushing the speed-accuracy Pareto front outwards. The connections of our work to realistic biological systems such as the KaiABC system in cyanobacterial circadian clock and other theoretical results based on thermodynamic uncertainty relation are also discussed.

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引用次数: 0
When are Diffusion Priors Helpful in Sparse Reconstruction? A Study with Sparse-view CT.
Pub Date : 2025-02-04
Matt Y Cheung, Sophia Zorek, Tucker J Netherton, Laurence E Court, Sadeer Al-Kindi, Ashok Veeraraghavan, Guha Balakrishnan

Diffusion models demonstrate state-of-the-art performance on image generation, and are gaining traction for sparse medical image reconstruction tasks. However, compared to classical reconstruction algorithms relying on simple analytical priors, diffusion models have the dangerous property of producing realistic looking results even when incorrect, particularly with few observations. We investigate the utility of diffusion models as priors for image reconstruction by varying the number of observations and comparing their performance to classical priors (sparse and Tikhonov regularization) using pixel-based, structural, and downstream metrics. We make comparisons on low-dose chest wall computed tomography (CT) for fat mass quantification. First, we find that classical priors are superior to diffusion priors when the number of projections is "sufficient". Second, we find that diffusion priors can capture a large amount of detail with very few observations, significantly outperforming classical priors. However, they fall short of capturing all details, even with many observations. Finally, we find that the performance of diffusion priors plateau after extremely few (≈10-15) projections. Ultimately, our work highlights potential issues with diffusion-based sparse reconstruction and underscores the importance of further investigation, particularly in high-stakes clinical settings.

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引用次数: 0
An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer's disease. 用于解码阿尔茨海默病的可解释生成式多模态神经成像基因组学框架。
Pub Date : 2025-02-04
Giorgio Dolci, Federica Cruciani, Md Abdur Rahaman, Anees Abrol, Jiayu Chen, Zening Fu, Ilaria Boscolo Galazzo, Gloria Menegaz, Vince D Calhoun

textbf{Objective:} Alzheimer's disease (AD) is the most prevalent form of dementia worldwide, encompassing a prodromal stage known as Mild Cognitive Impairment (MCI), where patients may either progress to AD or remain stable. The objective of the work was to capture structural and functional modulations of brain structure and function relying on multimodal MRI data and Single Nucleotide Polymorphisms, also in case of missing views, with the twofold goal of classifying AD patients versus healthy controls and detecting MCI converters. % in two distinct tasks, dealing with also missing data. textbf{Approach:} We propose a multimodal DL-based classification framework where a generative module employing Cycle Generative Adversarial Networks was introduced in the latent space for imputing missing data (a common issue of multimodal approaches). Explainable AI method was then used to extract input features' relevance allowing for post-hoc validation and enhancing the interpretability of the learned representations. textbf{Main results:} Experimental results on two tasks, AD detection and MCI conversion, showed that our framework reached competitive performance in the state-of-the-art with an accuracy of $0.926pm0.02$ and $0.711pm0.01$ in the two tasks, respectively. The interpretability analysis revealed gray matter modulations in cortical and subcortical brain areas typically associated with AD. Moreover, impairments in sensory-motor and visual resting state networks along the disease continuum, as well as genetic mutations defining biological processes linked to endocytosis, amyloid-beta, and cholesterol, were identified. textbf{Significance:} Our integrative and interpretable DL approach shows promising performance for AD detection and MCI prediction while shedding light on important biological insights.

阿尔茨海默病(AD)是最常见的痴呆症,患者的认知能力会逐渐下降。阿兹海默病的连续过程包括一个被称为轻度认知功能障碍(MCI)的前驱阶段,在这一阶段,患者既可能发展为阿兹海默病,也可能保持稳定。在这项研究中,我们利用结构性和功能性核磁共振成像来研究疾病引起的灰质和功能性网络连接变化。此外,考虑到注意力缺失症具有很强的遗传因素,我们还引入了 SNPs 作为第三通道。鉴于输入的多样性,遗漏一种或多种模式是多模态方法的典型问题。因此,我们提出了一种新颖的基于深度学习的分类框架,其中的生成模块采用了循环 GAN,以弥补潜在空间中的缺失数据。此外,我们还采用了一种可解释的人工智能方法--集成梯度(Integrated Gradients)来提取输入特征的相关性,从而增强我们对所学表征的理解。我们完成了两项关键任务:注意力缺失检测和 MCI 转换预测。实验结果表明,我们的模型在CN/AD分类中达到了SOA,平均测试准确率为0.926/pm0.02$。在 MCI 任务中,我们使用预先训练好的 CN/AD 模型达到了 0.711/pm0.01 美元的平均预测准确率。可解释性分析表明,皮层和皮层下脑区的灰质发生了明显的改变,而这些区域众所周知与注意力缺失症有关。此外,感觉-运动和视觉静息态网络连接沿疾病连续性的损伤,以及定义与淀粉样蛋白-β和胆固醇形成清除和调控相关的生物过程的 SNPs 突变,也被确定为影响所取得成绩的因素。总之,我们的综合深度学习方法在揭示重要的生物学见解的同时,也显示出了对注意力缺失症检测和 MCI 预测的前景。
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引用次数: 0
Time-resolved diamond magnetic microscopy of superparamagnetic iron-oxide nanoparticles. 超顺磁性氧化铁纳米粒子的时间分辨金刚石磁性显微镜。
Pub Date : 2025-02-03
B A Richards, N Ristoff, J Smits, A Jeronimo Perez, I Fescenko, M D Aiello, F Hubert, Y Silani, N Mosavian, M Saleh Ziabari, A Berzins, J T Damron, P Kehayias, D Egbebunmi, J E Shield, D L Huber, A M Mounce, M P Lilly, T Karaulanov, A Jarmola, A Laraoui, V M Acosta

Superparamagnetic iron-oxide nanoparticles (SPIONs) are promising probes for biomedical imaging, but the heterogeneity of their magnetic properties is difficult to characterize with existing methods. Here, we perform widefield imaging of the stray magnetic fields produced by hundreds of isolated ~30-nm SPIONs using a magnetic microscope based on nitrogen-vacancy centers in diamond. By analyzing the SPION magnetic field patterns as a function of applied magnetic field, we observe substantial field-dependent transverse magnetization components that are typically obscured with ensemble characterization methods. We find negligible hysteresis in each of the three magnetization components for nearly all SPIONs in our sample. Most SPIONs exhibit a sharp Langevin saturation curve, enumerated by a characteristic polarizing applied field, B_c. The B_c distribution is highly asymmetric, with a standard deviation (1.4 mT) that is larger than the median (0.6 mT). Using time-resolved magnetic microscopy, we directly record SPION N'eel relaxation, after switching off a 31 mT applied field, with a temporal resolution of ~60 ms that is limited by the ring-down time of the electromagnet coils. For small bias fields B_{hold}=1.5-3.5 mT, we observe a broad range of SPION N'eel relaxation times--from milliseconds to seconds--that are consistent with an exponential dependence on B_{hold}. Our time-resolved diamond magnetic microscopy study reveals rich SPION sample heterogeneity and may be extended to other fundamental studies of nanomagnetism.

超顺磁性氧化铁纳米粒子(SPIONs)是一种很有前景的生物医学成像探针,但现有方法难以表征其磁性能的异质性。在这里,我们利用基于金刚石中氮空位中心的磁显微镜,对数百个孤立的 ~30-nm SPIONs 产生的杂散磁场进行了宽场成像。通过分析 SPION 磁场模式与外加磁场的函数关系,我们观察到了大量与磁场相关的横向磁化成分,而这些成分通常会被集合表征方法所掩盖。我们发现样品中几乎所有 SPION 的三个磁化分量中的磁滞都可以忽略不计。大多数 SPIONs 都表现出一条尖锐的朗格文饱和曲线,并通过一个特征极化外加磁场 B_c 加以列举。B_c 分布高度不对称,标准偏差(1.4 mT)大于中值(0.6 mT)。利用时间分辨磁显微镜,我们直接记录了关闭 31 mT 外加磁场后 SPION N'eel 的弛豫,时间分辨率约为 60 毫秒,这受到电磁线圈环形下降时间的限制。对于 B_{hold}=1.5-3.5 mT 的小偏置磁场,我们观察到范围广泛的硅核弛豫时间--从毫秒到秒--与 B_{hold} 的指数依赖关系一致。我们的时间分辨金刚石磁显微镜研究揭示了丰富的SPION样品异质性,并可扩展到纳米磁性的其他基础研究中。
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引用次数: 0
Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration.
Pub Date : 2025-02-01
Mojtaba Safari, Zach Eidex, Chih-Wei Chang, Richard L J Qiu, Xiaofeng Yang

Magnetic resonance imaging (MRI) is a non-invasive imaging modality and provides comprehensive anatomical and functional insights into the human body. However, its long acquisition times can lead to patient discomfort, motion artifacts, and limiting real-time applications. To address these challenges, strategies such as parallel imaging have been applied, which utilize multiple receiver coils to speed up the data acquisition process. Additionally, compressed sensing (CS) is a method that facilitates image reconstruction from sparse data, significantly reducing image acquisition time by minimizing the amount of data collection needed. Recently, deep learning (DL) has emerged as a powerful tool for improving MRI reconstruction. It has been integrated with parallel imaging and CS principles to achieve faster and more accurate MRI reconstructions. This review comprehensively examines DL-based techniques for MRI reconstruction. We categorize and discuss various DL-based methods, including end-to-end approaches, unrolled optimization, and federated learning, highlighting their potential benefits. Our systematic review highlights significant contributions and underscores the potential of DL in MRI reconstruction. Additionally, we summarize key results and trends in DL-based MRI reconstruction, including quantitative metrics, the dataset, acceleration factors, and the progress of and research interest in DL techniques over time. Finally, we discuss potential future directions and the importance of DL-based MRI reconstruction in advancing medical imaging. To facilitate further research in this area, we provide a GitHub repository that includes up-to-date DL-based MRI reconstruction publications and public datasets-https://github.com/mosaf/Awesome-DL-based-CS-MRI.

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引用次数: 0
Assessing Sensitivity of Brain-to-Scalp Blood Flows in Laser Speckle Imaging by Occluding the Superficial Temporal Artery.
Pub Date : 2025-01-31
Yu Xi Huang, Simon Mahler, Maya Dickson, Aidin Abedi, Yu Tung Lo, Patrick D Lyden, Jonathan Russin, Charles Liu, Changhuei Yang

Cerebral blood flow is a critical metric for cerebrovascular monitoring, with applications in stroke detection, brain injury evaluation, aging, and neurological disorders. Non-invasively measuring cerebral blood dynamics is challenging due to the scalp and skull, which obstruct direct brain access and contain their own blood dynamics that must be isolated. We developed an aggregated seven-channel speckle contrast optical spectroscopy system to measure blood flow and blood volume non-invasively. Each channel, with distinct source-to-detector distance, targeted different depths to detect scalp and brain blood dynamics separately. By briefly occluding the superficial temporal artery, which supplies blood only to the scalp, we isolated surface blood dynamics from brain signals. Results on 20 subjects show that scalp-sensitive channels experienced significant reductions in blood dynamics during occlusion, while brain-sensitive channels experienced minimal changes. This provides experimental evidence of brain-to-scalp sensitivity in optical measurements, highlighting optimal configuration for preferentially probing brain signals non-invasively.

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引用次数: 0
Origin of yield stress and mechanical plasticity in model biological tissues. 生物组织中屈服应力和机械塑性的起源。
Pub Date : 2025-01-31
Anh Q Nguyen, Junxiang Huang, Dapeng Bi

During development and under normal physiological conditions, biological tissues are continuously subjected to substantial mechanical stresses. In response to large deformations cells in a tissue must undergo multicellular rearrangements in order to maintain integrity and robustness. However, how these events are connected in time and space remains unknown. Here, using computational and theoretical modeling, we studied the mechanical plasticity of epithelial monolayers under large deformations. Our results demonstrate that the jamming-unjamming (solid-fluid) transition in tissues can vary significantly depending on the degree of deformation, implying that tissues are highly unconventional materials. Using analytical modeling, we elucidate the origins of this behavior. We also demonstrate how a tissue accommodates large deformations through a collective series of rearrangements, which behave similarly to avalanches in non-living materials. We find that these tissue avalanches are governed by stress redistribution and the spatial distribution of vulnerable spots. Finally, we propose a simple and experimentally accessible framework to predict avalanches and infer tissue mechanical stress based on static images.

在发育过程中和正常生理条件下,生物组织会持续承受巨大的机械应力。为了应对巨大的变形,组织中的细胞必须进行多细胞重排,以保持完整性和稳健性。然而,这些事件在时间和空间上是如何联系在一起的仍是未知数。在这里,我们利用计算和理论建模研究了上皮单层在大变形下的机械可塑性。我们的研究结果表明,组织中的 "干扰"-"非干扰"(固体-流体)转变会随着变形程度的不同而发生显著变化,这意味着组织是一种非常规材料。通过分析建模,我们阐明了这种行为的起源。我们还展示了组织如何通过一系列集体重排来适应大变形,其行为类似于非生命材料中的雪崩。我们发现,这些 "组织雪崩 "受应力再分布和脆弱点空间分布的支配。最后,我们提出了一个简单且易于实验的框架,用于预测雪崩并根据静态图像推断组织的机械应力。
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引用次数: 0
Evaluating the Efficacy and Safety of Stereotactic Arrhythmia Radioablation in Ventricular Tachycardia: A Comprehensive Systematic Review and Meta-Analysis.
Pub Date : 2025-01-31
Keyur D Shah, Chih-Wei Chang, Sibo Tian, Pretesh Patel, Richard Qiu, Justin Roper, Jun Zhou, Zhen Tian, Xiaofeng Yang

Purpose: Stereotactic arrhythmia radioablation (STAR) has emerged as a promising non-invasive treatment for refractory ventricular tachycardia (VT), offering a novel alternative for patients who are poor candidates for catheter ablation. This systematic review and meta-analysis evaluates the safety, efficacy, and technical aspects of STAR across preclinical studies, case reports, case series, and clinical trials.

Methods and materials: A systematic review identified 80 studies published between 2015 and 2024, including 12 preclinical studies, 47 case reports, 15 case series, and 6 clinical trials. Data on patient demographics, treatment parameters, and clinical outcomes were extracted. Meta-analyses were performed for pooled mortality rates, VT burden reduction, and acute toxicities, with subgroup analyses exploring cardiomyopathy type, age, left ventricular ejection fraction (LVEF), and treatment modality.

Results: The pooled 6- and 12-month mortality rates were 16% (95% CI: 11-21%) and 32% (95% CI: 26-39%), respectively. VT burden reduction at 6 months was 75% (95% CI: 73-77%), with significant heterogeneity (I2 = 98.8%). Grade 3+ acute toxicities were observed in 7% (95% CI: 4-11%), with pneumonitis being the most common. Subgroup analyses showed comparable outcomes between LINAC- and CyberKnife-based treatments, with minor differences based on patient characteristics and cardiomyopathy type.

Conclusions: STAR demonstrates significant potential in reducing VT burden and improving patient outcomes. While favorable acute safety profiles and efficacy support clinical adoption, variability in treatment protocols underscores the need for standardized practices. Future studies should aim to optimize patient selection, establish robust dosimetric standards, and evaluate long-term safety.

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引用次数: 0
A Network-Driven Framework for Enhancing Gene-Disease Association Studies in Coronary Artery Disease.
Pub Date : 2025-01-31
Gutama Ibrahim Mohammad, Johan Lm Björkegren, Tom Michoel

Motivation: Over the last decade, genome-wide association studies (GWAS) have successfully identified numerous genetic variants associated with complex diseases. These associations have the potential to reveal the molecular mechanisms underlying complex diseases and lead to the identification of novel drug targets. Despite these advancements, the biological pathways and mechanisms linking genetic variants to complex diseases are still not fully understood. Most trait-associated variants reside in non-coding regions and are presumed to influence phenotypes through regulatory effects on gene expression. Yet, it is often unclear which genes they regulate and in which cell types this regulation occurs. Transcriptome-wide association studies (TWAS) aim to bridge this gap by detecting trait-associated tissue gene expression regulated by GWAS variants. However, traditional TWAS approaches frequently overlook the critical contributions of trans-regulatory effects and fail to integrate comprehensive regulatory networks. Here, we present a novel framework that leverages tissue-specific gene regulatory networks (GRNs) to integrate cis- and trans-genetic regulatory effects into the TWAS framework for complex diseases.

Results: We validate our approach using coronary artery disease (CAD), utilizing data from the STARNET project, which provides multi-tissue gene expression and genetic data from around 600 living patients with cardiovascular disease. Preliminary results demonstrate the potential of our GRN-driven framework to uncover more genes and pathways that may underlie CAD. This framework extends traditional TWAS methodologies by utilizing tissue-specific regulatory insights and advancing the understanding of complex disease genetic architecture.

Availability: https://github.com/guutama/GRN-TWAS.

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引用次数: 0
Advancing bioinformatics with large language models: components, applications and perspectives. 生物信息学中的大型语言模型:应用与前景。
Pub Date : 2025-01-31
Jiajia Liu, Mengyuan Yang, Yankai Yu, Haixia Xu, Tiangang Wang, Kang Li, Xiaobo Zhou

Large language models (LLMs) are a class of artificial intelligence models based on deep learning, which have great performance in various tasks, especially in natural language processing (NLP). Large language models typically consist of artificial neural networks with numerous parameters, trained on large amounts of unlabeled input using self-supervised or semi-supervised learning. However, their potential for solving bioinformatics problems may even exceed their proficiency in modeling human language. In this review, we will provide a comprehensive overview of the essential components of large language models (LLMs) in bioinformatics, spanning genomics, transcriptomics, proteomics, drug discovery, and single-cell analysis. Key aspects covered include tokenization methods for diverse data types, the architecture of transformer models, the core attention mechanism, and the pre-training processes underlying these models. Additionally, we will introduce currently available foundation models and highlight their downstream applications across various bioinformatics domains. Finally, drawing from our experience, we will offer practical guidance for both LLM users and developers, emphasizing strategies to optimize their use and foster further innovation in the field.

大型语言模型(LLMs)是一类基于深度学习的人工智能模型,在各种任务中,尤其是在自然语言处理(NLP)中表现出色。大型语言模型通常由具有大量参数的人工神经网络组成,通过自监督或半监督学习在大量无标记输入上进行训练。然而,这些模型在解决生物信息学问题方面的潜力甚至可能超过它们在人类语言建模方面的能力。在这篇综述中,我们将总结自然语言处理中使用的著名大型语言模型,如 BERT 和 GPT,并重点探讨大型语言模型在生物信息学中不同 omics 层面的应用,主要包括大型语言模型在基因组学、转录组学、蛋白质组学、药物发现和单细胞分析中的应用。最后,本综述总结了大型语言模型在解决生物信息学问题方面的潜力和前景。
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引用次数: 0
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