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arXiv 2024年

作者： Zhou, Songchi Song, Ge Sun, Haoqi Zhang, Deyun Leng, Yue Westover, M. Brandon Hong, Shenda National Institute of Health Data Science Peking University China Department of Bioinformatics and Biostatistics Shanghai Jiao Tong University China Department of Neurology Beth Israel Deaconess Medical Center Harvard Medical School United States HeartVoice Medical Technology Department of Psychiatry and Behavioral Sciences University of California San Francisco United States

Traditional sleep staging categorizes sleep and wakefulness into five coarse-grained classes, overlooking subtle variations within each stage. It provides limited information about the duration of arousal and may hinder research on sleep fragmentation and relevant sleep disorders. To address this issue, we propose a deep learning method for automatic and scalable annotation of continuous sleep depth index (SDI) using existing discrete sleep staging labels. Our approach was validated using polysomnography from over 10,000 recordings across four large-scale cohorts. The results showcased a strong correlation between the decrease in sleep depth index and the increase in duration of arousal. Specific case studies indicated that the sleep depth index captured more nuanced sleep structures than conventional sleep staging. Gaussian mixture models based on the digital biomarkers extracted from the sleep depth index identified two subtypes of sleep, where participants in the disturbed sleep group had a higher prevalence of sleep apnea, insomnia, poor subjective sleep quality, hypertension, and cardiovascular disease. The disturbed subtype was associated with a 42% (hazard ratio 1.42, 95% CI 1.24-1.62) increased risk of mortality and a 29% (hazard ratio 1.29, 95% CI 1.00-1.67) increased risk of fatal cardiovascular disease. Our study underscores the utility of the proposed method for continuous sleep depth annotation, which could reveal more detailed information about the sleep structure and yield novel digital biomarkers for routine clinical use in sleep medicine. Copyright © 2024, The Authors. All rights reserved.

关键词： Sleep research

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Managing Pandemics with Health Informatics: Successes and Challenges

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Yearbook of Medical Informatics 2021年第1期30卷 17-25页

作者： Basit, Mujeeb A. Lehmann, Christoph U. Medford, Richard J. Clinical Informatics Center Medical Center TX UT Southwestern Dallas United States Department of Internal Medicine Medical Center TX UT Southwestern Dallas United States Departments of Pediatrics Population & Data Sciences Bioinformatics Medical Center TX UT Southwestern Dallas United States

INTRODUCTION: The novel COVID-19 pandemic struck the world unprepared. This keynote outlines challenges and successes using data to inform providers, government officials, hospitals, and patients in a pandemic. METHODS: The authors outline the data required to manage a novel pandemic including their potential uses by governments, public health organizations, and individuals. RESULTS: An extensive discussion on data quality and on obstacles to collecting data is followed by examples of successes in clinical care, contact tracing, and forecasting. Generic local forecast model development is reviewed followed by ethical consideration around pandemic data. We leave the reader with thoughts on the next inevitable outbreak and lessons learned from the COVID-19 pandemic. CONCLUSION: COVID-19 must be a lesson for the future to direct us to better planning and preparing to manage the next pandemic with health informatics. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://***/licenses/by-nc-nd/4.

关键词： 2019 novel coronavirus disease communicable diseases COVID-19 COVID-19 testing mass immunization medical informatics pandemics public reporting of healthcare data SARS-COV-2

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The prognosis of clinical stage IIIa non-small cell lung cancer in Taiwan

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Cancer Medicine 2023年第16期12卷 17087-17097页

作者： Cheng, Ya-Fu Huang, Jing-Yang Lin, Ching-Hsiung Wang, Bing-Yen Division of Thoracic Surgery Department of Surgery Changhua Christian Hospital Changhua Taiwan Institute of Medicine Chung Shan Medical University Taichung Taiwan Center for Health Data Science Chung Shan Medical University Hospital Taichung Taiwan Department of Recreation and Holistic Wellness MingDao University Changhua Taiwan Department of Internal Medicine Division of Chest Medicine Changhua Christian Hospital Changhua Taiwan Institute of Genomics and Bioinformatics National Chung Hsing University Taichung Taiwan Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan

Lung cancer is the leading cause of cancer death. The treatment of stage IIIa remained the most controversial of all stages of non-small cell lung cancer (NSCLC). We reported on the heterogenicity and current treatment strategies of stage IIIa NSCLC in Taiwan. This study is a retrospective analysis using data from the Taiwan Society of Cancer Registry between January 2010 and December 2018. 4232 patients with stage IIIa NSCLC were included. Based on cell type, the best 5-year OS (40.40%) occurred among adenocarcinoma victims. The heterogenicity of T1N2 had the best 5-year OS (47.62%), followed by T4N0 (39.82%), and the others. Patients who underwent operations had better 5-year OS (over 50%) than those who did not (less than 30%). Segmentectomy (75.28%) and lobectomy (54.06%) showed better 5-year OS than other surgical methods (less than 50%). In multivariable analysis, young age, female, lower Charlson Comorbidity Index score, adenocarcinoma cell type, well differentiated, T1N2/T4N0 heterogenicity, treatment with operation, and segmentectomy/lobectomy/bilobectomy were significant factors. In conclusions, the heterogenicity of T1N2 had the best outcomes followed by T4N0. Patients received surgical treatment revealed much better outcomes than those did not. As always, multimodal therapies with individualized treatment tend to provide better survival outcomes. © 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

关键词： heterogenicity non-small cell lung cancer outcomes stage IIIa Taiwan

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HOW MANY DIRECTIONS DETERMINE A SHAPE AND OTHER SUFFICIENCY RESULTS FOR TWO TOPOLOGICAL TRANSFORMS

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Transactions of the American Mathematical Society Series B 2022年第32期9卷 1006-1043页

作者： Curry, Justin Mukherjee, Sayan Turner, Katharine Department of Mathematics and Statistics University at Albany SUNY Albany 12222 NY United States Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University Durham 27708 NC United States Center for Scalable Data Analytics and Artificial Intelligence Universität Leipzig Germany Max Planck Institute for Mathematics in the Natural Sciences Leipzig Germany Mathematical Sciences Institute Australian National University Canberra Australia

In this paper we consider two topological transforms that are pop-ular in applied topology: the Persistent Homology Transform and the Euler Characteristic Transform. Both of these transforms are of interest for their mathematical properties as well as their applications to science and engineer-ing, because they provide a way of summarizing shapes in a topological, yet quantitative, way. Both transforms take a shape, viewed as a tame subset M of R^d, and associates to each direction v ∈ S^d−1 a shape summary obtained by scanning M in the direction v. These shape summaries are either persistence diagrams or piecewise constant integer-valued functions called Euler curves. By using an inversion theorem of Schapira, we show that both transforms are injective on the space of shapes, i.e. each shape has a unique transform. Moreover, we prove that these transforms determine continuous maps from the sphere to the space of persistence diagrams, equipped with any Wasserstein p-distance, or the space of Euler curves, equipped with certain L^p norms. By making use of a stratified space structure on the sphere, induced by hyperplane divisions, we prove additional uniqueness results in terms of distributions on the space of Euler curves. Finally, our main result proves that any shape in a certain uncountable space of PL embedded shapes with plausible geometric bounds can be uniquely determined using only finitely many directions. © 2022 by the author(s) under Creative Commons Attribution 3.0 License (CC BY 3.0).

关键词： Euler calculus persistent homology statistical shape analysis

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Enhancing Interoperability of Digital Twin in the Maintenance phase of Lifecycle

Enhancing Interoperability of Digital Twin in the Maintenanc...

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International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Dedy Ariansyah Bens Pardamean Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

In today's digitalization era, asset management has evolved following the rapid adoption of information systems and digital technologies. In the maintenance phase of the asset, Digital Twin (DT) emerges as an effective approach to improve failure recognition and structural damage prediction through DT-driven Prognostic and Health Monitoring (PHM). As a result, DT allows asset manager to proactively plan maintenance strategy to improve the availability of the assets. However, to reduce asset downtime in a cost-effective and time efficient manner, DT for maintenance phase needs to be integrated with other software systems that manage the provision of maintenance service and spare part such as computerized maintenance management system (CMMS) or Enterprise Resource Planning (ERP). Nevertheless, many of the developed DT in PHM were not equipped with semantic data model and hence, lacks interoperability with other systems (e.g., other DT or non-DT system) and limits the effective use of the DT. To qualify as an effective DT, it needs a semantical-enabled interface to connect with data and models from various domain to enable DT-driven decision-making. This paper leverages a rule-based ontology reasoning model to enhance DT interoperability with other maintenance software systems. The results depicts that the developed model paves a way for an effective use of DT-driven system in asset maintenance management.

关键词： Ontologies Software systems data models Enterprise resource planning Digital twins Prognostics and health management Interoperability

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Adapting Segment Anything Model for Ulcer Segmentation in Inflammatory Bowel Disease

Adapting Segment Anything Model for Ulcer Segmentation in In...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Lingrui Cai Ryan W. Stidham Emily Wittrup Kayvan Najarian Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor Michigan Department of Internal Medicine Division of Gastroenterology Michigan Medicine Ann Arbor Michigan Michigan Institute for Data Science University of Michigan Ann Arbor Michigan Max Harry Weil Institute for Critical Care Research and Innovation University of Michigan Ann Arbor Michigan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Inflammatory Bowel Disease (IBD) is a widespread gastrointestinal disorder characterized by inflammation and ulceration, affecting millions of individuals. Mucosal injury assessment through colonoscopy plays a crucial role in determining disease severity and therapeutic efficacy in IBD. This study draws inspiration from the Segment Anything Model (SAM) framework and tailors it for ulcer segmentation using a Parameter-Efficient Transfer Learning (PETL) approach and incorporating binary cross-entropy loss with boundary loss. Comparative assessments against state-of-the-art UNet-based convolutional neural network models underscore SAM’s superior performance, especially when faced with limited training samples. Our ulcer segmentation models demonstrate Dice, IoU, and 95%HD scores of 61.92±0.69, 75.18±0.38, and 10.07±0.05 in the colon dataset, and 55.60±0.82, 77.00±0.67, and 7.74±0.23 in the ileum dataset, respectively. This study highlights the potential of our approach to significantly enhance the efficiency and accuracy of IBD diagnosis and assessment through cutting-edge computer vision techniques.

关键词： Inflammatory bowel disease Training Adaptation models Accuracy Computational modeling Transfer learning Colonoscopy Transformers Colon Convolutional neural networks

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Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review 2

Trends, Opportunities, and Challenges in Detecting Depressiv...

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2nd International Conference on Computer System, Information Technology, and Electrical Engineering, COSITE 2023

作者： Elwirehardja, Gregorius Natanael Isnan, Mahmud Perbangsa, Anzaludin Samsinga Muchtar, Kahlil Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mahmud Isnan Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350343069

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches. © 2023 IEEE.

关键词： Machine learning

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Energy guided diffusion for generating neurally exciting images 23

Energy guided diffusion for generating neurally exciting ima...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Paweł A. Pierzchlewicz Konstantin F. Willeke Arne F. Nix Pavithra Elumalai Kelli Restivo Tori Shinn Cate Nealley Gabrielle Rodriguez Saumil Patel Katrin Franke Andreas S. Tolias Fabian H. Sinz Institute for Bioinformatics and Medical Informatics Tübingen University Tübingen Germany and Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Tübingen Germany aand Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Department of Neuroscience Baylor College of Medicine Houston TX and Center for Neuroscience and Artificial Intelligence Baylor College of Medicine Houston TX Department of Neuroscience Baylor College of Medicine Houston TX and Center for Neuroscience and Artificial Intelligence Baylor College of Medicine Houston TX and Department of Electrical and Computer Engineering Rice University Houston TX Institute for Bioinformatics and Medical Informatics Tübingen University Tübingen Germany and Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany and Department of Neuroscience Baylor College of Medicine Houston TX and Center for Neuroscience and Artificial Intelligence Baylor College of Medicine Houston TX

In recent years, most exciting inputs (MEIs) synthesized from encoding models of neuronal activity have become an established method for studying tuning properties of biological and artificial visual systems. However, as we move up the visual hierarchy, the complexity of neuronal computations increases. Consequently, it becomes more challenging to model neuronal activity, requiring more complex models. In this study, we introduce a novel readout architecture inspired by the mechanism of visual attention. This new architecture, which we call attention readout, together with a data-driven convolutional core outperforms previous task-driven models in predicting the activity of neurons in macaque area V4. However, as our predictive network becomes deeper and more complex, synthesizing MEIs via straightforward gradient ascent (GA) can struggle to produce qualitatively good results and overfit to idiosyncrasies of a more complex model, potentially decreasing the MEI's model-to-brain transferability. To solve this problem, we propose a diffusion-based method for generating MEIs via Energy Guidance (EGG). We show that for models of macaque V4, EGG generates single neuron MEIs that generalize better across varying model architectures than the state-of-the-art GA, while at the same time reducing computational costs by a factor of 4.7x, facilitating experimentally challenging closed-loop experiments. Furthermore, EGG diffusion can be used to generate other neurally exciting images, like most exciting naturalistic images that are on par with a selection of highly activating natural images, or image reconstructions that generalize better across architectures. Finally, EGG is simple to implement, requires no retraining of the diffusion model, and can easily be generalized to provide other characterizations of the visual system, such as invariances. Thus, EGG provides a general and flexible framework to study the coding properties of the visual system in the context of natural ima

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Identify Non-mutational p53 Functional Deficiency in Human Cancers

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Genomics, Proteomics & bioinformatics 2024年第5期22卷 85-98页

作者： Qianpeng Li Yang Zhang Sicheng Luo Zhang Zhang Ann L.Oberg David E.Kozono Hua Lu Jann N.Sarkaria Lina Ma Liguo Wang National Genomics Data Center China National Center for BioinformationBeijing 100101China Beijing Institute of Genomics Chinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China Division of Computational Biology Mayo Clinic College of Medicine and ScienceRochesterMN 55905USA Department of Radiation Oncology Dana-Farber Cancer Institute and Brigham and Women's HospitalBostonMA 02215USA Department of Biochemistry&Molecular Biology and Tulane Cancer Center Tulane University School of MedicineNew OrleansLA 70112USA Department of Radiation Oncology Mayo Clinic College of Medicine and ScienceRochesterMN 55905USA Department of Biochemistry and Molecular Biology Mayo Clinic College of Medicine and ScienceRochesterMN 55905USA Bioinformatics and Computational Biology Graduate Program University of Minnesota RochesterRochesterMN 55904USA

An accurate assessment of p53's functional statuses is critical for cancer genomic ***,there is a significant challenge in identifying tumors with non-mutational p53 inactivation which is not detectable through DNA *** undetected cases are often misclassified as p53-normal,leading to inaccurate prognosis and downstream association *** address this issue,we built the support vector machine(SVM)models to systematically reassess p53's functional statuses in TP53 wild-type(TP53^(WT))tumors from multiple The Cancer Genome Atlas(TCGA)***-validation demonstrated the good performance of the SVM models with a mean area under the receiver operating characteristic curve(AUROC)of 0.9822,precision of 0.9747,and recall of *** study revealed that a significant proportion(87%-99%)of TP53^(WT) tumors actually had compromised p53 *** analyses uncovered that these genetically intact but functionally impaired(termed as predictively reduced function of p53 or TP53^(WT)-pRF)tumors exhibited genomic and pathophysiologic features akin to TP53-mutant tumors:heightened genomic instability and elevated levels of ***,patients with TP53^(WT)-pRF tumors experienced significantly shortened overall survival or progression-free survival compared to those with predictively normal function of p53(TP53^(WT)-pN)tumors,and these patients also displayed increased sensitivity to platinum-based chemotherapy and radiation therapy.

关键词： Cancer Composite expression DNA mutation Machine learning p53 deficiency

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Unveiling the effects of micro and nano plastics in embryonic development

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Toxicology Reports 2025年 14卷 101954页

作者： Nair, Sanjay R. Nihad, Muhammad Shenoy P, Sudheer Gupta, Sebanti Bose, Bipasha Stem Cells and Regenerative Medicine Centre Yenepoya Research Centre Yenepoya (Deemed to be University) University Road Deralakatte Karnataka Mangalore 575018 India Division of Data Analytics Bioinformatics and Structural Biology Yenepoya Research Centre Yenepoya (Deemed to be University) University Road Deralakatte Karnataka Mangalore 575018 India

The improper disposal and degradation of plastics causes the formation and spread of micro and nano-sized plastic particles in the ecosystem. The widespread presence of these micro and nanoplastics leads to their accumulation in the biotic and abiotic components of the environment, thereby affecting the cellular and metabolic functions of organisms. Despite being classified as xenobiotic agents, information about their sources and exposure related to reproductive health is limited. Micro and nano plastic exposure during early developmental stages can cause abnormal embryonic development. It can trigger neurotoxicity and inflammatory responses as well in the developing embryo. In embryonic development, a comprehensive study of their role in pluripotency, gastrulation, and multi-differentiation potential is scarce. Due to ethical concerns associated with the direct use of human embryos, pluripotent cells and its 3D in vitro models (with cell lines) are an alternative source for effective research. Thus, the 3D Embryoid body (EB) model provides a platform for conducting embryotoxicity and multi-differentiation potential research. Pluripotent stem cells such as embryonic and induced pluripotent stem cells derived embryoid bodies (EBs) serve as a robust 3D in vitro model that mimics characteristics similar to that of human embryos. Thus, the 3D EB model provides a platform for conducting embryotoxicity and multi-differentiation potential research. Accordingly, this review discusses the significance of 3D in vitro models in conducting effective embryotoxicity research. Further, we also evaluated the possible sources/routes of microplastic generation and analyzed their surface chemistry and cytotoxic effects reported till date. © 2025 The Authors

关键词： 3D in vitro models Cell-lineage specification Embryoid bodies Embryotoxicity Micro and nano plastics PLuripotent stem cells

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