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arXiv 2021年

作者： Bleher, Michael Hahn, Lukas Neumann, Maximilian Patiño-Galindo, Juan Ángel Carrière, Mathieu Bauer, Ulrich Rabadán, Raúl Ott, Andreas Mathematics Department Karlsruhe Institute of Technology Karlsruhe Germany Institute for Mathematics Heidelberg University Heidelberg Germany Program for Mathematical Genomics Department of Systems Biology Columbia University New YorkNY United States Department of Microbiology Icahn School of Medicine at Mount Sinai New YorkNY United States DataShape Centre Inria d’Université Côte d’Azur Biot France TUM Department of Mathematics Munich Data Science Institute Munich Germany

MSC Codes 62R40, 55N31, 68U05, 68T09, 92-08, 92C60, 92D15The COVID-19 pandemic has initiated an unprecedented worldwide effort to characterize its evolution through the mapping of mutations of the coronavirus SARS-CoV-2. The early identification of mutations that could confer adaptive advantages to the virus, such as higher infectivity or immune evasion, is of paramount importance. However, the large number of currently available genomes precludes the efficient use of phylogeny-based methods. Here we present CoVtRec, a fast and scalable Topological data Analysis approach for the surveillance of emerging adaptive mutations in large genomic datasets. Our method overcomes limitations of state-of-the-art phylogeny-based approaches by quantifying the potential adaptiveness of mutations merely by their topological footprint in the genome alignment, without resorting to the reconstruction of a single optimal phylogenetic tree. Analyzing millions of SARS-CoV-2 genomes from GISAID, we find a correlation between topological signals and adaptation to the human host. By leveraging the stratification by time in sequence data, our method enables the high-resolution longitudinal analysis of topological signals of adaptation. We characterize the convergent evolution of the coronavirus throughout the whole pandemic to date, report on emerging potentially adaptive mutations, and pinpoint mutations in Variants of Concern that are likely associated with positive selection. Our approach can improve the surveillance of mutations of concern and guide experimental studies. © 2021, CC BY-NC-ND.

关键词： Coronavirus

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Fast sequential clustering in riemannian manifolds for dynamic and time-series-annotated multilayer networks

TechRxiv

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TechRxiv 2020年

作者： Ye, Cong Slavakis, Konstantinos Nakuci, Johan Muldoon, Sarah F. Medaglia, John NY14260 United States State University of New York at Buffalo China The Neuroscience Program UB SUNY United States The Department of Mathematics and the Computational and Data-Enabled Science and Engineering Program UB SUNY United States The Department of Psychology Drexel University PA19104 United States The Perelman School of Medicine University of Pennsylvania PA19104 United States

This work exploits Riemannian manifolds to build a sequential-clustering framework able to address a wide variety of clustering tasks in dynamic multilayer (brain) networks via the information extracted from their nodal time-series. The discussion follows a bottom-up path, starting from feature extraction from time-series and reaching up to Riemannian manifolds (feature spaces) to address clustering tasks such as state clustering, community detection (a.k.a. network-topology identification), and subnetwork-sequence tracking. Kernel autoregressive-moving-average modeling and kernel (partial) correlations serve as case studies of generating features in the Riemannian manifolds of Grassmann and positive-(semi)definite matrices, respectively. Feature point-clouds form clusters which are viewed as submanifolds according to Riemannian multi-manifold modeling. A novel sequential-clustering scheme of Riemannian features is also established: feature points are first sampled in a non-random way to reveal the underlying geometric information, and, then, a fast sequential-clustering scheme is brought forth that takes advantage of Riemannian distances and the angular information of tangent spaces. By virtue of the landmark points and the sequential processing of the Riemannian features, the computational complexity of the framework is rendered free from the length of the available time-series data. The effectiveness and computational efficiency of the proposed framework are validated by extensive numerical tests against several state-of-the-art manifold-learning and brain-network-clustering schemes on synthetic as well as real functional-magnetic-resonance-imaging (fMRI) and electroencephalogram (EEG) data. © 2020, CC BY.

关键词： Time series

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Improving Prostate Cancer Detection with Breast Histopathology Images 1

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15th European Congress on Digital Pathology, ECDP 2019

作者： Khan, Umair Akhtar Hasan Stürenberg, Carolin Gencoglu, Oguzhan Sandeman, Kevin Heikkinen, Timo Rannikko, Antti Mirtti, Tuomas Top Data Science Ltd. Helsinki Finland Faculty of Medicine Department of Pathology and Research Program in Systemic Oncology University of Helsinki Helsinki Finland Department of Laboratory Medicine Department of Pathology Skåne University Hospital Malmö Sweden Helsinki University Hospital Faculty of Medicine Department of Urology and Research Program in Systemic Oncology University of Helsinki Helsinki Finland

ISBN: (数字)9783030239374

ISBN: (纸本)9783030239367

Deep neural networks have introduced significant advancements in the field of machine learning-based analysis of digital pathology images including prostate tissue images. With the help of transfer learning, classification and segmentation performance of neural network models have been further increased. However, due to the absence of large, extensively annotated, publicly available prostate histopathology datasets, several previous studies employ datasets from well-studied computer vision tasks such as ImageNet dataset. In this work, we propose a transfer learning scheme from breast histopathology images to improve prostate cancer detection performance. We validate our approach on annotated prostate whole slide images by using a publicly available breast histopathology dataset as pre-training. We show that the proposed cross-cancer approach outperforms transfer learning from ImageNet dataset. © 2019, Springer Nature Switzerland AG.

关键词： Computer aided diagnosis

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Multi-label classification for multi-temporal, multi-spatial coral reef condition monitoring using vision foundation model with adapter learning

arXiv

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arXiv 2025年

作者： Shao, Xinlei Chen, Hongruixuan Zhao, Fan Magson, Kirsty Chen, Jundong Li, Peiran Wang, Jiaqi Sasaki, Jun Department of Socio-Cultural Environmental Studies Graduate School of Frontier Sciences The University of Tokyo Chiba Kashiwa277-8563 Japan Department of Complexity Science and Engineering Graduate School of Frontier Sciences The University of Tokyo Chiba Kashiwa277-8563 Japan Department of Environment Systems Graduate School of Frontier Sciences The University of Tokyo Chiba Kashiwa277-8563 Japan New Heaven Reef Conservation Program Koh Tao Surat Thani84360 Thailand Data Science and AI Innovation Research Promotion Center Shiga University Shiga Hikone522-8522 Japan Interfaculty Initiative in Information Studies The University of Tokyo Tokyo113-0033 Japan

Coral reef ecosystems provide essential ecosystem services, but face significant threats from climate change and human activities. Although advances in deep learning have enabled automatic classification of coral reef conditions, conventional deep models struggle to achieve high performance when processing complex underwater ecological images. Vision foundation models, known for their high accuracy and cross-domain generalizability, offer promising solutions. However, fine-tuning these models requires substantial computational resources and results in high carbon emissions. To address these challenges, adapter learning methods such as Low-Rank Adaptation (LoRA) have emerged as a solution. This study introduces an approach integrating the DINOv2 vision foundation model with the LoRA fine-tuning method. The approach leverages multi-temporal field images collected through underwater surveys at 15 dive sites at Koh Tao, Thailand, with all images labeled according to universal standards used in citizen science-based conservation programs. The experimental results demonstrate that the DINOv2-LoRA model achieved superior accuracy, with a match ratio of 64.77%, compared to 60.34% achieved by the best conventional model. Furthermore, incorporating LoRA reduced the trainable parameters from 1,100M to 5.91M. Transfer learning experiments conducted under different temporal and spatial settings highlight the exceptional generalizability of DINOv2-LoRA across different seasons and sites. This study is the first to explore the efficient adaptation of foundation models for multi-label classification of coral reef conditions under multi-temporal and multi-spatial settings. The proposed method advances the classification of coral reef conditions and provides a tool for monitoring, conserving, and managing coral reef ecosystems. © 2025, CC BY-NC-SA.

关键词： Biotic

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A model for Optimizing the health and economic impacts of Covid-19 under social distancing measures;A study for the number of passengers and their seating arrangements in Aircrafts

arXiv

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arXiv 2020年

作者： Ghorbani, Elaheh Molavian, Hamid Barez, Fred Department of Aviation and Technology Study San Jose State University San JoseCA95192 United States Magic Distance San JoseCA95126 United States Data Science Immersive Program Galvanize Inc. San FranciscoCA94105 United States

Covid-19 has had a disastrous economic impact on countries and industries as countries have gone through the lockdown process to reduce the health impact of Covid-19. As countries have begun the reopening phases, businesses have been allowed to have on-site customers but with reduced number of people on their sites to follow the social distancing measures. This has resulted in a big burden on the economic side of these businesses as their number of customers have decreased substantially. In this study we propose a model to minimize the economic impact of Covid-19 for businesses and organizations that have implemented social distancing measures as well as health impact of Covid-19 for their customers and employees. We introduce quantity Spread in which minimizing Spread gives the optimum number and arrangement of people on a given site with a social distancing measure. We apply our model to a real-world case and optimize the number of passengers and their arrangements under a social distancing measure for two different popular aircraft seat layouts using Annealing Monte Carlo technique. We obtain the optimum numbers and optimal arrangements of passengers considering both family groups and individual passengers for the social distancing measure. The obtained optimal arrangements of passengers show complex patterns with groups and individual passengers are mixed in complex and non-trivial ways. This demonstrates the necessity of using our model or its variants to find these optimal arrangements. Moreover, we show that any other arrangements of passengers with the same number of passengers is a suboptimal arrangement with higher health risks as a result of lower distance between passengers. Our model could be implemented to other social setups such as sports events, theaters, medical centers, etc. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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BOAssembler: A Bayesian Optimization Framework to Improve RNA-Seq Assembly Performance

arXiv

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arXiv 2019年

作者： Mao, Shunfu Jiang, Yihan Mathew, Edwin Basil Kannan, Sreeram Department of Electrical and Computer Engineering University of Washington Seattle United States Data Science Program University of Washington Seattle United States

Motivation: High throughput sequencing ofRNA(RNA-Seq) can provide us with millions of short fragments of RNA transcripts from a sample. How to better recover the original RNA transcripts from those fragments (RNA-Seq assembly) is still a difficult task. For example, RNA-Seq assembly tools typically require hyperparameter tuning to achieve good performance for particular datasets. This kind of tuning is usually unintuitive and time-consuming. Consequently, users often resort to default parameters, which do not guarantee consistent good performance for various datasets. Results: Here we propose BOAssembler, a framework that enables end-To-end automatic tuning of RNASeq assemblers, based on Bayesian Optimization principles. Experiments show this data-driven approach is effective to improve the overall assembly performance. The approach would be helpful for downstream (e.g. gene, protein, cell) analysis, and more broadly, for future bioinformatics benchmark studies. Copyright © 2019, The Authors. All rights reserved.

关键词： RNA

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

引用

Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Improvement of Jakarta's air quality during large scale social restriction

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IOP Conference Series: Earth and Environmental science 2021年第1期729卷

作者： R Rahutomo K Purwandari J W C Sigalingging B Pardamean Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta Indonesia 10720 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

After Large-Scale Social Restriction (PSBB) established in Jakarta, a change of air quality was indicated by the citizens. Representatives of Indonesia's Agency for Meteorology, Climatology, and Geophysics (BMKG) supported as the fact that mobility of Jakarta's citizen and commuters were reduced. With utilizing Weather data Acquisition System managed by BMKG, this research compared fluctuations of Jakarta's air quality during two periods of time: Pre-PSBB and During-PSBB. This study proved that Jakarta's air quality has positively improved compared to the previous year, although in the lower range of unhealthy level.

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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Developmental exposure of California mice to endocrine disrupting chemicals and potential effects on the microbiome-gut-brain axis at adulthood (vol 10, 10902, 2020)

引用

SCIENTIFIC REPORTS 2020年第1期10卷 1-17页

作者： Kaur, Sarabjit Sarma, Saurav J. Marshall, Brittney L. Liu, Yang Kinkade, Jessica A. Bellamy, Madison M. Mao, Jiude Helferich, William G. Schenk, A. Katrin Bivens, Nathan J. Lei, Zhentian Sumner, Lloyd W. Bowden, John A. Koelmel, Jeremy P. Joshi, Trupti Rosenfeld, Cheryl S. Christopher S Bond Life Sciences Center University of Missouri Columbia USA Biomedical Sciences University of Missouri Columbia USA MU Metabolomics Center University of Missouri Columbia USA MU Institute of Data Science and Informatics University of Missouri Columbia USA Food Science and Human Nutrition University of Illinois Urbana USA Physics Randolph College Lynchburg USA DNA Core Facility University of Missouri Columbia USA Department of Biochemistry University of Missouri Columbia USA Department of Physiological Sciences College of Veterinary Medicine University of Florida Gainesville USA Center for Environmental and Human Toxicology College of Veterinary Medicine University of Florida Gainesville USA Environmental Health Sciences Yale University New Haven USA Department of Health Management and Informatics School of Medicine University of Missouri Columbia USA Thompson Center for Autism and Neurobehavioral Disorders University of Missouri Columbia USA Genetics Area Program University of Missouri Columbia USA

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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