Quantitative Methods
New submissions
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New submissions for Fri, 10 May 24
- [1] arXiv:2405.05712 [pdf, ps, other]
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Title: Characterization of the Autonomic Nervous System Activity in Females Classified According to Mood Scores During the Follicular PhaseComments: 5 pages, 2 figures, 1 table, 2024 IEEE International Conference on Robotics and Automation (ICRA 2024)Subjects: Quantitative Methods (q-bio.QM)
Many sexually mature females suffer from premenstrual syndrome (PMS), but effective coping methods for PMS are limited due to the complexity of symptoms and unclear pathogenesis. Awareness has shown promise in alleviating PMS symptoms but faces challenges in long-term recording and consistency. Our research goal is to establish a convenient and simple method to make individual female aware of their own psychological, and autonomic conditions. In previous research, we demonstrated that participants could be classified into non-PMS and PMS groups based on mood scores obtained during the follicular phase. However, the properties of neurophysiological activity in the participants classified by mood scores have not been elucidated. This study aimed to classify participants based on their scores on a mood questionnaire during the follicular phase and to evaluate their autonomic nervous system (ANS) activity using a simple device that measures pulse waves from the earlobe. Participants were grouped into Cluster I (high positive mood) and Cluster II (low mood). Cluster II participants showed reduced parasympathetic nervous system activity from the follicular to the menstrual phase, indicating potential PMS symptoms. The study demonstrates the feasibility of using mood scores to classify individuals into PMS and non-PMS groups and monitor ANS changes across menstrual phases. Despite limitations such as sample size and device variability, the findings highlight a promising avenue for convenient PMS self-monitoring.
Cross-lists for Fri, 10 May 24
- [2] arXiv:2405.05425 (cross-list from physics.bio-ph) [pdf, other]
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Title: Identifying stable communities in Hi-C data using a multifractal null modelSubjects: Biological Physics (physics.bio-ph); Molecular Networks (q-bio.MN); Quantitative Methods (q-bio.QM)
Chromosome capture techniques like Hi-C have expanded our understanding of mammalian genome 3D architecture and how it influences gene activity. To analyze Hi-C data sets, researchers increasingly treat them as DNA-contact networks and use standard community detection techniques to identify mesoscale 3D communities. However, there are considerable challenges in finding significant communities because the Hi-C networks have cross-scale interactions and are almost fully connected. This paper presents a pipeline to distil 3D communities that remain intact under experimental noise. To this end, we bootstrap an ensemble of Hi-C datasets representing noisy data and extract 3D communities that we compare with the unperturbed dataset. Notably, we extract the communities by maximizing local modularity (using the Generalized Louvain method), which considers the multifractal spectrum recently discovered in Hi-C maps. Our pipeline finds that stable communities (under noise) typically have above-average internal contact frequencies and tend to be enriched in active chromatin marks. We also find they fold into more nested cross-scale hierarchies than less stable ones. Apart from presenting how to systematically extract robust communities in Hi-C data, our paper offers new ways to generate null models that take advantage of the network's multifractal properties. We anticipate this has a broad applicability to several network applications.
- [3] arXiv:2405.05665 (cross-list from cs.LG) [pdf, other]
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Title: SubGDiff: A Subgraph Diffusion Model to Improve Molecular Representation LearningComments: 31 pagesSubjects: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
Molecular representation learning has shown great success in advancing AI-based drug discovery. The core of many recent works is based on the fact that the 3D geometric structure of molecules provides essential information about their physical and chemical characteristics. Recently, denoising diffusion probabilistic models have achieved impressive performance in 3D molecular representation learning. However, most existing molecular diffusion models treat each atom as an independent entity, overlooking the dependency among atoms within the molecular substructures. This paper introduces a novel approach that enhances molecular representation learning by incorporating substructural information within the diffusion process. We propose a novel diffusion model termed SubGDiff for involving the molecular subgraph information in diffusion. Specifically, SubGDiff adopts three vital techniques: i) subgraph prediction, ii) expectation state, and iii) k-step same subgraph diffusion, to enhance the perception of molecular substructure in the denoising network. Experimentally, extensive downstream tasks demonstrate the superior performance of our approach. The code is available at https://github.com/youjibiying/SubGDiff.
- [4] arXiv:2405.05790 (cross-list from cs.CE) [pdf, ps, other]
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Title: A Robust eLORETA Technique for Localization of Brain Sources in the Presence of Forward Model UncertaintiesSubjects: Computational Engineering, Finance, and Science (cs.CE); Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM)
In this paper, we present a robust version of the well-known exact low-resolution electromagnetic tomography (eLORETA) technique, named ReLORETA, to localize brain sources in the presence of different forward model uncertainties. Methods: We first assume that the true lead field matrix is a transformation of the existing lead field matrix distorted by uncertainties and propose an iterative approach to estimate this transformation accurately. Major sources of the forward model uncertainties, including differences in geometry, conductivity, and source space resolution between the real and simulated head models, and misaligned electrode positions, are then simulated to test the proposed method. Results: ReLORETA and eLORETA are applied to simulated focal sources in different regions of the brain and the presence of various noise levels as well as real data from a patient with focal epilepsy. The results show that ReLORETA is considerably more robust and accurate than eLORETA in all cases. Conclusion: Having successfully dealt with the forward model uncertainties, ReLORETA proved to be a promising method for real-world clinical applications. Significance: eLORETA is one of the localization techniques that could be used to study brain activity for medical applications such as determining the epileptogenic zone in patients with medically refractory epilepsy. However, the major limitation of eLORETA is sensitivity to the uncertainties in the forward model. Since this problem can substantially undermine its performance in real-world applications where the exact lead field matrix is unknown, developing a more robust method capable of dealing with these uncertainties is of significant interest.
Replacements for Fri, 10 May 24
- [5] arXiv:2309.08779 (replaced) [pdf, ps, other]
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Title: Computational framework for the generation of one-dimensional vascular models accounting for uncertainty in networks extracted from medical imagesAuthors: Michelle A Bartolo, Alyssa M Taylor-LaPole, Darsh Gandhi, Alexandria Johnson, Yaqi Li, Emma Slack, Isaiah Stevens, Zachary Turner, Justin D Weigand, Charles Puelz, Dirk Husmeier, Mette S OlufsenComments: 42 pages, 10 figuresSubjects: Tissues and Organs (q-bio.TO); Quantitative Methods (q-bio.QM)
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