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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Kelvin Julian Kuncahyo Setyo Nugroho Rudi Nirwantono Bens Pardamean Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

This systematic review provides a comprehensive overview of the methods used to integrate genomic and clinical data in cancer prediction. The review includes 19 studies across various cancers, including breast, colorectal, melanoma, lung, pancreatic, and thyroid. The studies employed different methods to combine genomic and clinical data, including weighted polygenic risk scores, genetic and non-genetic risk scores, and different machine learning algorithms. The results show significant improvements in model prediction performance accuracy across multiple studies. The review highlights the potential benefits of integrating genetic and phenotypic information to improve disease risk prediction models and inform personalized healthcare strategies.

关键词： Accuracy Machine learning algorithms Genomics Data integration Medical services Predictive models Data models Bioinformatics Cancer Systematic literature review

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Spectral learning of shared dynamics between generalized-linear processes 24

Spectral learning of shared dynamics between generalized-lin...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Lucine L. Oganesian Omid G. Sani Maryam M. Shanechi Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA Ming Hsieh Department of Electrical and Computer Engineering Thomas Lord Department of Computer Science Alfred E. Mann Department of Biomedical Engineering Neuroscience Graduate Program University of Southern California Los Angeles CA

ISBN: (纸本)9798331314385

Generalized-linear dynamical models (GLDMs) remain a widely-used framework within neuroscience for modeling time-series data, such as neural spiking activity or categorical decision outcomes. Whereas the standard usage of GLDMs is to model a single data source, certain applications require jointly modeling two generalized-linear time-series sources while also dissociating their shared and private dynamics. Most existing GLDM variants and their associated learning algorithms do not support this capability. Here we address this challenge by developing a multi-step analytical subspace identification algorithm for learning a GLDM that explicitly models shared vs. private dynamics within two generalized-linear time-series. In simulations, we demonstrate our algorithm's ability to dissociate and model the dynamics within two time-series sources while being agnostic to their respective observation distributions. In neural data, we consider two specific applications of our algorithm for modeling discrete population spiking activity with respect to a secondary time-series. In both synthetic and real data, GLDMs learned with our algorithm more accurately decoded one time-series from the other using lower-dimensional latent states, as compared to models identified using existing GLDM learning algorithms.

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Sample Complexity of Kernel-Based Q-Learning

arXiv

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arXiv 2023年

作者： Yeh, Sing-Yuan Chang, Fu-Chieh Yueh, Chang-Wei Wu, Pei-Yuan Bernacchia, Alberto Vakili, Sattar MediaTek Research Graduate Program of Data Science National Taiwan University Academia Sinica Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan

Modern reinforcement learning (RL) often faces an enormous state-action space. Existing analytical results are typically for settings with a small number of state-actions, or simple models such as linearly modeled Q-functions. To derive statistically efficient RL policies handling large state-action spaces, with more general Q-functions, some recent works have considered nonlinear function approximation using kernel ridge regression. In this work, we derive sample complexities for kernel based Q-learning when a generative model exists. We propose a nonparametric Q-learning algorithm which finds an ǫ-optimal policy in an arbitrarily large scale discounted MDP. The sample complexity of the proposed algorithm is order optimal with respect to ǫ and the complexity of the kernel (in terms of its information gain). To the best of our knowledge, this is the first result showing a finite sample complexity under such a general model. © 2023, CC BY.

关键词： Reinforcement learning

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Molecular architecture of mammalian pyruvate dehydrogenase complex

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Protein & Cell 2025年第1期16卷 72-78页

作者： Maofei Chen Yutong Song Sensen Zhang Yitang Zhang Xudong Chen Minghui Zhang Meng Han Xin Gao Sai Li Maojun Yang Tsinghua-Peking Center for Life Sciences Beijing Frontier Research Center for Biological StructureSchool of Life SciencesTsinghua UniversityBeijing 100084China Ministry of Education Key Laboratory of Protein Science Tsinghua UniversityBeijing 100084China State Key Laboratory of Membrane Biology Tsinghua UniversityBeijing 100084China Department of Biochemistry and Molecular Biology School of Basic Medical SciencesShenzhen University Health Science CenterShenzhen 518060China Protein Research Technology Center Protein Chemistry and Omics PlatformSchool of Life SciencesTsinghua UniversityBeijing 100084China Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Kingdom of Saudi Arabia Cryo-EM Facility Center Southern University of Science and TechnologyShenzhen 518055China Beijing Life Science Academy Beijing 102209China

Dear Editor,Pyruvate dehydrogenase complex(PDHc) is a large multienzyme assembly(Mr = 4–10 million Daltons) consisting of three essential components: pyruvate dehydrogenase(E1p), dihydrolipoyl transacetylase(E2p), and dihydrolipoyl dehydrogenase(E3). These three enzymes perform distinct functions sequentially to catalyze the oxidative decarboxylation of pyruvate with formation of nicotinamide adenine dinucleotide(NADH) and acetyl-coenzyme A(Patel and Roche, 1990).

关键词： sequentially oxidative Dalton

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AnisoGNN: graph neural networks generalizing to anisotropic properties of polycrystals

arXiv

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arXiv 2024年

作者： Hu, Guangyu Latypov, Marat I. Department of Materials Science and Engineering University of Arizona TucsonAZ85721 United States Graduate Interdisciplinary Program in Applied Mathematics University of Arizona TucsonAZ85721 United States

We present AnisoGNNs - graph neural networks (GNNs) that generalize predictions of anisotropic properties of polycrystals in arbitrary testing directions without the need in excessive training data. To this end, we develop GNNs with a physics-inspired combination of node attributes and aggregation function. We demonstrate the excellent generalization capabilities of AnisoGNNs in predicting anisotropic elastic and inelastic properties of two alloys. © 2024, CC BY.

关键词： Anisotropy

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The dependence in microcystin removal with powdered activated carbon on variant properties, carbon properties, and dissolved organic matter

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Chemosphere 2024年 351卷 141205页

作者： Huang, Yuzhou Lenhart, John J. Environmental Science Graduate Program The Ohio State University United States Department of Civil Environmental and Geodetic Engineering The Ohio State University United States

Powdered activated carbon (PAC) is commonly used by water treatment plants to remove harmful cyanotoxins such as microcystins (MCs) produced during seasonal harmful algal blooms. MC removal by PAC depends upon the properties of the PAC, the properties of the MC variant, and the presence and properties of dissolved organic matter (DOM). To identify which of these factors has the greatest impact on the removal of MC by PAC, we evaluated the removal of four different MC variants (MC-LR, MC-LA, MC-RR and desmethylated MC-RR) by three different PAC types (wood-based, coal-blend and coal-based). The role of DOM properties was evaluated using DOM isolated from two different sources, a terrestrial source (Suwannee River Fulvic Acid, SRFA) and a microbial source (Grand Lake St Marys DOM, GLSM). The results of adsorption experiments conducted over a period of 72 h demonstrated the wood-based PAC, which had the highest surface area and mesopore volume of the PAC tested, had the highest adsorption rate and capacity for all four MC variants. Of the variants studied, neutrally charged MC-RR was adsorbed more rapidly and to a greater extent on all of the PAC types than were the other variants. Although MC-LA and MC-LR had the greatest hydrophobicity, their negative charges resulted in their being adsorbed the least. As expected, DOM inhibited microcystin adsorption to PAC. The degree of inhibition, however, did not significantly vary for the two DOM types evaluated, indicating the properties of the DOM on MC adsorption to PAC was less important than the PAC properties or MC variant properties. Overall, PAC properties were a more important factor in MC removal than were the MC properties or DOM conditions. © 2024 Elsevier Ltd

关键词： Adsorption DOM Kinetic Mesopores Microcystin PAC

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Big data in healthcare:Conceptual network structure,key challenges and opportunities

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Digital Communications and Networks 2023年第4期9卷 856-868页

作者： Leonardo B.Furstenaua Pedro Leivas Michele Kremer Sott Michael S.Dohan Jose Ricardo Lopez-Robles Manuel J.Cobo Nicola Luigi Bragazzi Kim-Kwang Raymond Choo Post-Graduate Program in Industrial Engineering Federal University of Rio Grande Do SulPorto Alegre90035-190Brazil University of Santa Cruz Do Sul Av.Independencia 2293Santa Cruz Do SulRSBrazil Business School Unisinos UniversityAv.Dr.Nilo Peçanha 160091330-002Porto AlegreBrazil Faculty of Business Administration Lakehead UniversityThunder BayOntarioCanada Autonomous University of Zacatecas Av.Ramon Lopez Velarde Col.CentroZacatecas98000Mexico Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain York University ON M3J 1P3Canada Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249USA

Big data is a concept that deals with large or complex data sets by using data analysis tools(e.g.,data mining,machine learning)to analyze information extracted from several sources *** data has attracted wide attention from academia,for example,in supporting patients and health professionals by improving the accuracy of decision-making,diagnosis and disease *** research aimed to perform a Bibliometric Performance and Network Analysis(BPNA)supported by a Scoping Review(SR)to depict the strategic themes,thematic evolution structure,main challenges and opportunities related to the concept of big data applied in the healthcare *** this goal in mind,4857 documents from the Web of science covering the period between 2009 to June 2020 were analyzed with the support of SciMAT *** bibliometric performance showed the number of publications and citations over time,scientific productivity and the geographic distribution of publications and research *** strategic diagram yielded 20 clusters and their relative importance in terms of centrality and *** thematic evolution structure presented the most important themes and how it changes over ***,we presented the main challenges and future opportunities of big data in healthcare.

关键词： Big data Healthcare digitalization Bibliometric Strategic intelligence Co-word analysis SciMAT

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Fucoxanthin induces human melanoma cytotoxicity by thwarting the JAK2/STAT3/BCL-xL signaling axis

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Environmental Toxicology 2024年第6期39卷 3356-3366页

作者： Kuo, Min-Yung Dai, Wen-Chyi Chang, Jie-Li Chang, Jo-Shu Lee, Tse-Min Chang, Chia-Che Pediatric Surgery Division Department of Surgery Tungs' Taichung MetroHarbor Hospital Taichung Taiwan Doctoral Program in Biotechnology Industrial Innovation and Management National Chung Hsing University Taichung Taiwan Taichung Municipal Taichung First Senior High School Taichung Taiwan Department of Chemical and Materials Engineering Research Center for Smart Sustainable Circular Economy Tunghai University Taichung Taiwan Department of Life Sciences National Chung Hsing University Taichung Taiwan Department of Marine Biotechnology and Resources National Sun Yat-sen University Kaohsiung Taiwan Graduate Institute of Biomedical Sciences Master Program in Precision Health Doctoral Program in Translational Medicine Rong Hsing Research Center for Translational Medicine The iEGG and Animal Biotechnology Research Center National Chung Hsing University Taichung Taiwan Department of Medical Laboratory Science and Biotechnology Asia University Taichung Taiwan Department of Medical Research China Medical University Hospital Taichung Taiwan Traditional Herbal Medicine Research Center Taipei Medical University Hospital Taipei Taiwan

Melanoma is the most lethal skin malignancy. Fucoxanthin is a marine carotenoid with significant anticancer activities. Intriguingly, Fucoxanthin's impact on human melanoma remains elusive. Signal Transducer and Activator of Transcription 3 (STAT3) represents a promising target in cancer therapy due to its persistent activation in various cancers, including melanoma. Herein, we revealed that Fucoxanthin is cytotoxic to human melanoma cell lines A2758 and A375 while showing limited cytotoxicity to normal human melanocytes. Apoptosis is a primary reason for Fucoxanthin's melanoma cytotoxicity, as the pan-caspase inhibitor z-VAD-fmk drastically abrogated Fucoxanthin-elicited clonogenicity blockage. Besides, Fucoxanthin downregulated tyrosine 705-phosphorylated STAT3 (p-STAT3 (Y705)), either inherently present in melanoma cells or inducible by interleukin 6 (IL-6) stimulation. Notably, ectopic expression of STAT3-C, a dominant-active STAT3 mutant, abolished Fucoxanthin-elicited melanoma cell apoptosis and clonogenicity inhibition, supporting the pivotal role of STAT3 blockage in Fucoxanthin's melanoma cytotoxicity. Moreover, Fucoxanthin lowered BCL-xL levels by blocking STAT3 activation, while ectopic BCL-xL expression rescued melanoma cells from Fucoxanthin-induced killing. Lastly, Fucoxanthin was found to diminish the levels of JAK2 with dual phosphorylation at tyrosine residues 1007 and 1008 in melanoma cells, suggesting that Fucoxanthin impairs STAT3 signaling by blocking JAK2 activation. Collectively, we present the first evidence that Fucoxanthin is cytotoxic selectively against human melanoma cells while sparing normal melanocytes. Mechanistically, Fucoxanthin targets the JAK2/STAT3/BCL-xL antiapoptotic axis to provoke melanoma cell death. This discovery implicates the potential application of Fucoxanthin as a chemopreventive or therapeutic strategy for melanoma management. © 2024 Wiley Periodicals LLC.

关键词： Carotenoids

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Orchard: building large cancer phylogenies using stochastic combinatorial search

arXiv

引用

arXiv 2023年

作者： Kulman, Ethan Kuang, Rui Morris, Quaid Computational and Systems Biology Program Sloan Kettering Institute New YorkNY United States Department of Computer Science and Engineering University of Minnesota MinneapolisMN United States

Phylogenies depicting the evolutionary history of genetically heterogeneous subpopulations of cells from the same cancer, i.e., cancer phylogenies, offer valuable insights about cancer development and guide treatment strategies. Many methods exist that reconstruct cancer phylogenies using point mutations detected with bulk DNA sequencing. However, these methods become inaccurate when reconstructing phylogenies with more than 30 mutations, or, in some cases, fail to recover a phylogeny altogether. Here, we introduce Orchard, a cancer phylogeny reconstruction algorithm that is fast and accurate using up to 1000 mutations. Orchard samples without replacement from a factorized approximation of the posterior distribution over phylogenies, a novel result derived in this paper. Each factor in this approximate posterior corresponds to a conditional distribution for adding a new mutation to a partially built phylogeny. Orchard optimizes each factor sequentially, generating a sequence of incrementally larger phylogenies that ultimately culminate in a complete tree containing all mutations. Our evaluations demonstrate that Orchard outperforms state-of-the-art cancer phylogeny reconstruction methods in reconstructing more plausible phylogenies across 90 simulated cancers and 14 B-progenitor acute lymphoblastic leukemias (B-ALLs). Remarkably, Orchard accurately reconstructs cancer phylogenies using up to 1,000 mutations. Additionally, we demonstrate that the large and accurate phylogenies reconstructed by Orchard are useful for identifying patterns of somatic mutations and genetic variations among distinct cancer cell subpopulations. © 2023, CC BY.

关键词： Plant diseases

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Plugging Effect of Fine Pore Water in Opc and LC3 Paste During Accelerated Carbonation Monitored Via Single-Sided Nuclear Magnetic Resonance Spectroscopy

SSRN

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SSRN 2024年

作者： Cheng, Luge Kurihara, Ryo Ohkubo, Takahiro Kitagaki, Ryoma Suda, Yuya Teramoto, Atsushi Maruyama, Ippei Department of Architecture Graduate School of Engineering The University of Tokyo Tokyo113-8656 Japan Department of Applied Chemistry and Biotechnology Graduate School of Engineering Chiba University Chiba263-8522 Japan Division of Human Environmental Systems Graduate School of Engineering Hokkaido University Sapporo060-8628 Japan Department of Architecture Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8527 Japan Civil Engineering Program Faculty of Engineering University of the Ryukyus Okinawa903-0213 Japan Nagoya University Japan

This study focuses on the investigation of the influence of CO2 concentration on the carbonation process in cementitious paste using ordinary Portlandite cement and limestone-calcined clay cement, specifically on the water content distribution. Employing single-sided nuclear magnetic resonance spectroscopy for water profiling, we revealed that the water content of fine pores (interlayer space and gel pores) was consistent at the carbonation front under accelerated carbonation processes with CO2 concentrations of 5% and 1%, demonstrating the plugging effect where fine pore water governs the carbonation progress. This effect was absent under natural carbonation conditions because of the carbonation process was slower than drying. It was confirmed that the rate-determining mechanisms of natural and accelerated carbonation with CO2 concentrations greater than 1% were different. © 2024, The Authors. All rights reserved.

关键词： Carbonation

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