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arXiv 2025年

作者： Vaidya, Anurag Zhang, Andrew Jaume, Guillaume Song, Andrew H. Ding, Tong Wagner, Sophia J. Lu, Ming Y. Doucet, Paul Robertson, Harry Almagro-Pérez, Cristina Chen, Richard J. ElHarouni, Dina Ayoub, Georges Bossi, Connor Ligon, Keith L. Gerber, Georg Le, Long Phi Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Health Sciences and Technology Harvard-MIT CambridgeMA United States Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States Helmholtz Munich – German Research Center for Environment and Health Munich Germany School of Computation Information and Technology TUM Munich Germany CambridgeMA United States Sydney Precision Data Science Center The University of Sydney CamperdownNSW Australia Department of Oncologic Pathology Dana-Farber Cancer Institute BostonMA02215 United States Department of Pathology Boston Children’s Hospital BostonMA02115 United States Harvard Data Science Initiative Harvard University CambridgeMA United States

Foundation models are reshaping computational pathology by enabling transfer learning, where models pre-trained on vast datasets can be adapted for downstream diagnostic, prognostic, and therapeutic response tasks. Despite these advances, foundation models are still limited in their ability to encode the entire gigapixel whole-slide images without additional training and often lack complementary multimodal data. Here, we introduce THREADS, a slide-level foundation model capable of generating universal representations of whole-slide images of any size. THREADS was pretrained using a multimodal learning approach on a diverse cohort of 47,171 hematoxylin and eosin (H&E)-stained tissue sections, paired with corresponding genomic and transcriptomic profiles—the largest such paired dataset to be used for foundation model development to date. This unique training paradigm enables THREADS to capture the tissue’s underlying molecular composition, yielding powerful representations applicable to a wide array of downstream tasks. In extensive benchmarking across 54 oncology tasks, including clinical subtyping, grading, mutation prediction, immunohistochemistry status determination, treatment response prediction and survival prediction THREADS outperformed all baselines while demonstrating remarkable generalizability and label efficiency. It is particularly well-suited for predicting rare events, further emphasizing its clinical utility. We intend to make the model publicly available for the broader community. © 2025, CC BY-NC-ND.

关键词： Transfer learning

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Spaceborne Lidar and Stereogrammetry data Fusion to Predict Aboveground Biomass in Tropical Forests

Spaceborne Lidar and Stereogrammetry Data Fusion to Predict ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Rodrigo V. Leite William Wagner Margaret Wooten Monique B. Schlickmann Carlos A. Silva Cibele H. Do Amaral Diogo N. Cosenza Carlos M.M.E. Torres Ameni Mkaouar Shashank Bhushan David Shean Paul Montesano Douglas C. Morton Christopher S.R. Neigh Goddard Space Flight Center NASA Postdoctoral Program Fellow Greenbelt MD Biospheric Sciences Laboratory Code 618 NASA Goddard Space Flight Center Greenbelt MD USA Forest Biometrics Artificial Intelligence and Remote Sensing Laboratory (Silva lab) School of Forest Fisheries and Geomatics Sciences University of Florida Gainesville FL USA Earth Lab Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder Boulder CO USA Environmental Data Science Innovation & Inclusion Lab (ESIIL) University of Colorado Boulder Boulder CO USA Department of Forest Engineering Federal University of Viçosa Viçosa MG Brazil Department of Civil and Environmental Engineering University of Washington Seattle WA USA

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Quantifying aboveground biomass (AGB) in tropical forests is a challenging but necessary task to support actions to preserve and restore these ecosystems. Recent methods have leveraged the integration of NASA’s spaceborne lidar GEDI with imaging sensors to improve AGB predictions. However, the use of high-resolution (<1 m) stereo images from spaceborne sensors still needs to be explored within these frameworks. The objective of this study was to predict AGB in a tropical forest patch by combining stereo images and GEDI data. A digital surface model (Stereo-DSM) was generated by stereophotogrammetric processing of high-resolution stereo pairs collected from a spaceborne sensor. A canopy height model (Stereo-CHM) was then derived by subtracting an airborne lidar-derived digital terrain model from the Stereo-DSM. Descriptive statistics were calculated from the Stereo-CHM to be used as predictors in the model to predict AGB. We also calculated vegetation indices from the Harmonized Landsat-Sentinel and Sentinel-1 images to compare their relative importance in the model and assess their relationship to the Stereo-CHM metrics. Finally, we trained a Random Forest model using stereogrammetry - derived, multispectral and SAR metrics as features and GEDI’s footprint-level AGB product as a reference. The model to predict AGB yielded performance metrics of r = 0.63, RMSE = 25.38 Mg/ha, and MD = 2.1 Mg/ha. The metrics from the Stereo-CHM were ranked as the most important to the model. This is an indication that these metrics can add important information related to canopy structure to inform GEDI-based models to predict AGB. It is still necessary to evaluate these results considering a variation in canopy cover, topography, and understory vegetation. The findings are important to support advances on the integration of large footprint spaceborne lidar and images with sub-meter spatial resolution to characterize vegetation in tropical forests.

关键词： Measurement Image sensors Forests Laser radar Atmospheric modeling Vegetation mapping Predictive models

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Factors that affect the technological transition of firms toward the industry 4.0 technologies

arXiv

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arXiv 2022年

作者： Kim, Seung Hwan Jeon, Jeong hwan Aridi, Anwar Jun, Bogang Technology Management Economics and Policy Program Seoul National University Seoul Korea Republic of Department of Electrical Engineering Ulsan National Institute of Science and Technology Ulsan Korea Republic of Graduate School of Artificial Intelligence Ulsan National Institute of Science and Technology Ulsan Korea Republic of Finance Competitiveness and Innovation Global Practice Europe and Central Asia Unit The World Bank Group Department of Economics Inha University Korea Republic of Department of Data Science Inha University Korea Republic of Research Center for Small Businesses Ecosystem Inha University Korea Republic of

This research aims to identify factors that affect the technological transition of firms toward industry 4.0 technologies (I4Ts) focusing on firm capabilities and policy impact using relatedness and complexity measures. For the analysis, a unique dataset of Korean manufacturing firms’ patent and their financial and market information was used. Following the Principle of Relatedness, which is a recently shaped empirical principle in the field of economic complexity, economic geography, and regional studies, we build a technology space and then trace each firm’s footprint on the space. Using the technology space of firms, we can identify firms that successfully develop a new industry 4.0 technology and examine whether their accumulated capabilities in their previous technology domains positively affect their technological diversification and which factors play a critical role in their transition towards industry 4.0. In addition, by combining data on whether the firms received government support on R&D activities, we further analyzed the role of government policy in supporting firms’ knowledge activity in new industry 4.0 technologies. We found that firms with higher related technologies and more government support are more likely to enter new I4Ts. We expect our research to inform policymakers who aim to diversify firms’ technological capabilities into I4Ts. © 2022, CC BY.

关键词： Industry 4.0

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Finite-size effects on the convergence time in continuous-opinion dynamics

arXiv

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arXiv 2021年

作者： Jo, Hang-Hyun Masuda, Naoki Department of Physics The Catholic University of Korea Bucheon14662 Korea Republic of Department of Mathematics State University of New York at Buffalo 14260-2900 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo 14260-5030 United States

We study finite-size effects on the convergence time in a continuous-opinion dynamics model. In the model, each individual's opinion is represented by a real number on a finite interval, e.g., [0;1], and a uniformly randomly chosen individual updates its opinion by partially mimicking the opinion of a uniformly randomly chosen neighbor. We numerically find that the characteristic time to the convergence increases as the system size increases according to a particular functional form in the case of lattice networks. In contrast, unless the individuals perfectly copy the opinion of their neighbors in each opinion updating, the convergence time is approximately independent of the system size in the case of regular random graphs, uncorrelated scale-free networks, and complete graphs. We also provide a mean-field analysis of the model to understand the case of the complete graph. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph theory

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Unraveling the genetic mechanisms of UV radiation resistance in Bacillus through biofilm formation, sporulation, and carotenoid production

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Genomics 2025年第4期117卷 111066页

作者： Shrestha, Prasansah Kim, Byeollee Han, So-Ra Lee, Hyun Oh, Tae-Jin Department of Life Science and Biochemical Engineering SunMoon University Asan 31460 South Korea Bio Big Data-based Chungnam Smart Clean Research Leader Training Program SunMoon University Asan 31460 South Korea Genome-based BioIT Convergence Institute Asan 31460 South Korea Division of Computer Science and Engineering SunMoon University Asan 31460 South Korea Department of Pharmaceutical Engineering and Biotechnology SunMoon University Asan 31460 South Korea

Bacillus species are Gram-positive bacteria that are rod-shaped, endospore-forming, and aerobic or facultatively anaerobic. With over 300 recognized species, Bacillus subtilis stands out as a well-studied model organism. The genus's various species exhibit a wide range of physiological capabilities, allowing them to thrive in diverse environmental conditions. Each cell produces a single endospore, which is highly resistant to heat, cold, radiation, desiccation, and disinfectants. Among Bacillus strains, those capable of producing spores, biofilms, and carotenoids demonstrate significant resilience to UV light. This review examines the genes involved in spore formation, biofilm development, and carotenoid synthesis, emphasizing their roles in UV radiation survival. We explore the interconnections between these processes and their combined contribution to UV resistance, focusing on the underlying genetic mechanisms. These insights will benefit researchers studying the genetic basis of UV radiation resistance in Bacillus species. Importance: Bacteria employ adaptive strategies in extreme environments through rapid changes in gene expression, altering their phenotype for survival. Bacillus species, for example, defend against UV radiation by making spores, creating biofilms, and producing pigments. During sporulation, sigma factors (σ^F, σ^E, σ^G, and σ^K) regulate gene expression to adapt to environmental shifts. It has been found that the spores of some species may contain pigments that strongly absorb UV radiation, playing a crucial role in spore UV resistance. UV light penetrates biofilm matrices minimally, mainly affecting surface cells, which produce compounds like mycosporine-like amino acids and carotenoids to shield against UV damage. © 2025 The Authors

关键词： Bacillus Biofilm Carotenoid Spore UV-radiation resistance

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Peptide-Coated Bacteriorhodopsin-Based Photoelectric Biosensor for Detecting Rheumatoid Arthritis

Biosensors

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Biosensors 2023年第10期13卷 929-929页

作者： Chen, Hsiu-Mei Tsai, Yi-Hsuan Hsu, Chien-Yi Wang, Yong-Yi Hsieh, Cheng-En Chen, Jin-Hua Chang, Yu-Sheng Lin, Ching-Yu Department of Chemical Engineering National Taiwan University of Science and Technology Taipei10607 Taiwan Department of Family Medicine Wan Fang Hospital Taipei Medical University Taipei11696 Taiwan Division of Cardiology and Cardiovascular Research Center Department of Internal Medicine Taipei Medical University Hospital Taipei11031 Taiwan Division of Cardiology Department of Internal Medicine School of Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Taipei Heart Institute Taipei Medical University Taipei11031 Taiwan Graduate Institute of Data Science College of Management Taipei Medical University Taipei11031 Taiwan Office of Data Science Taipei Medical University Taipei11031 Taiwan Division of Allergy Immunology and Rheumatology Department of Internal Medicine Shuang Ho Hospital Taipei Medical University New Taipei City23561 Taiwan Division of Allergy Immunology and Rheumatology Department of Internal Medicine School of Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan School of Medical Laboratory Science and Biotechnology College of Medical Science and Technology Taipei Medical University Taipei11031 Taiwan Ph.D. Program in Medical Biotechnology College of Medical Science and Technology Taipei Medical University Taipei11031 Taiwan

An effective early diagnosis is important for rheumatoid arthritis (RA) management. This study reveals a novel RA detection method using bacteriorhodopsin as a photoelectric transducer, a light-driven proton pump in purple membranes (PMs). It was devised by covalently conjugating a PM monolayer-coated electrode with a citrullinated-inter-alpha-trypsin inhibitor heavy chain 3 (ITIH3)542–556 peptide that recognizes the serum RA-associated autoantibodies. The direct serum coating decreased the photocurrents in the biosensor, with the reduction in the photocurrent caused by coating with an RA-patient serum that is significantly larger than that with a healthy-control serum (38.1% vs. 20.2%). The difference in the reduction in the photocurrent between those two serum groups widened after the serum-coated biosensor was further labeled with gold nanoparticle (AuNP)-conjugated anti-IgA (anti-IgA-AuNP) (53.6% vs. 30.6%). Both atomic force microscopic (AFM) and Raman analyses confirmed the sequential peptide, serum, and anti-IgA-AuNP coatings on the PM-coated substrates. The reductions in the photocurrent measured in both the serum and anti-IgA-AuNPs coating steps correlated well with the results using commercial enzyme-linked immunosorbent assay kits (Spearman rho = 0.805 and 0.787, respectively), with both a sensitivity and specificity close to 100% in both steps. It was shown that an RA diagnosis can be performed in either a single- or two-step mode using the developed biosensor. © 2023 by the authors.

关键词： Peptides

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Hybrid Intelligent-Annotation Organ Segmentation on Medical datasets

Hybrid Intelligent-Annotation Organ Segmentation on Medical ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Tao Peng Jing Zhao Yidong Gu Gongye Di Lei Zhang Jing Cai School of Future Science and Engineering Soochow University Suzhou China Department of Radiation Oncology UT Southwestern Medical Center Dallas TX USA Department of Ultrasound Tsinghua University Affiliated Beijing Tsinghua Changgung Hospital Beijing China Department of Medical Ultrasound Suzhou Municipal Hospital The Affiliated Suzhou Hospital of Nanjing Medical University Suzhou Jiangsu China Department of Ultrasonic The Affiliated Taizhou People's Hospital of Nanjing Medical University Taizhou China Graduate Program of Medical Physics and Data Science Research Center Duke Kunshan University Kunshan Jiangsu China Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong China

Ultrasound image segmentation is crucial for early disease detection and treatment planning but remains a challenging task due to the low contrast of organ boundaries and varying image quality. Current methods often require manual intervention or have limited accuracy. In this paper, we propose a novel hybrid framework that combines an automatic option polygon segment (AOPS) algorithm and a distributed- and memory-based evolution (DME) algorithm for precise ultrasound organ segmentation. Our pipeline consists of two cascaded stages: (1) a coarse segmentation step using the AOPS algorithm, which determines the number of vertices/clusters without human intervention, and (2) a refinement step using the DME algorithm to hunt for the optimal neural network, which is then used to represent a smooth, explainable mathematical expression of the organ boundary. We employ the fractional backpropagation learning network with L2 regularization (FBLN) for training and use the scaled exponential linear unit (SELU) activation function to address the vanishing gradient problem. This is a new attempt such a hybrid framework is applied to ultrasound organ segmentation tasks, and it demonstrates significant contributions in terms of accuracy, smoothness, and computational efficiency.

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Contour Detection from Ultrasound Kidney Images with A Coarse-to-Fine Approach

Contour Detection from Ultrasound Kidney Images with A Coars...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Tao Peng Yidong Gu Yanqing Xu Caishan Wang Lei Zhang Jing Cai School of Future Science and Engineering Soochow University Suzhou China Department of Radiation Oncology UT Southwestern Medical Center Dallas TX USA Department of Medical Ultrasound the Affiliated Suzhou Hospital of Nanjing Medical University Suzhou Municipal Hospital Suzhou Jiangsu China McDermott Center for Human Growth and Development University of Texas Southwestern Medical Center Dallas Texas USA Department of Ultrasound the Second Affiliated Hospital of Soochow University Suzhou China Graduate Program of Medical Physics and Data Science Research Center Duke Kunshan University Kunshan Jiangsu China Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong China

Ultrasound kidney image segmentation presents significant challenges due to missing or ambiguous boundaries. In this study, we introduce a coarse-to-refinement approach incorporating four novel aspects. Firstly, we leverage the properties of a principal curve (PC) to automatically fine-tune the curve shape and employ a neural network's learning ability to reduce model error. Secondly, a deep fusion learning network is utilized for the coarse segmentation step, incorporating a parallel architecture to enhance deep-learning performance. Thirdly, addressing the limitation of standard PC-based methods in determining the number of vertices automatically, we propose an automatic searching polygon tracking method using a mean shift clustering-based approach to replace the projection and vertex extension step in standard PC-based methods. Lastly, we develop an explainable mathematical map function for the kidney contour, as denoted by the neural network output (i.e., optimized vertices), which aligns well with the ground truth contour. We conducted various experiments to evaluate our method's performance, demonstrating its effectiveness in ultrasound kidney image segmentation.

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Estimating Effectiveness of Identifying Human Trafficking via data Envelopment Analysis

arXiv

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arXiv 2020年

作者： Dimas, Geri L. Khalkhali, Malak El Bender, Alex Maass, Kayse Lee Konrad, Renata A. Blom, Jeffrey S. Zhu, Joe Trapp, Andrew C. Data Science Program Worcester Polytechnic Institute United States Business School Worcester Polytechnic Institute United States Mechanical and Industrial Engineering Department Northeastern University United States Love Justice International United States Business School Worcester Polytechnic Institute Data Science Program Worcester Polytechnic Institute United States

Transit monitoring is a preventative approach used to identify possible cases of human trafficking prior to exploitation while an individual is in transit or before one crosses a border. Transit monitoring is often conducted by non-governmental organizations (NGOs) who train staff to identify and intercept suspicious activity. Love Justice International (LJI) is a well-established NGO that has been conducting transit monitoring for years along the Nepal-India border at multiple monitoring stations. In partnership with LJI, we developed a system that uses data envelopment analysis (DEA) to help LJI decision-makers evaluate the performance of these stations at intercepting potential human-trafficking victims given the amount of resources (e.g. staff, etc.) available and make specific operational improvement recommendations. Our model consists of 91 decision-making units (DMUs) from 7 stations over 13 quarters and considers three inputs, four outputs, and 3 homogeneity criteria. Using this model we identified efficient stations, compared rankings of station performance, and recommended strategies to improve efficiency. To the best of our knowledge, this is the first application of DEA in the anti-human trafficking domain. Copyright © 2020, The Authors. All rights reserved.

关键词： data envelopment analysis

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SADDEL: Joint speech separation and denoising model based on multitask learning

arXiv

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arXiv 2020年

作者： Wu, Yuan-Kuei Tuan, Chao-I Lee, Hung-Yi Tsao, Yu Graduate Institute of Communication Engineering National Taiwan University Data Science Degree Program National Taiwan University Research Center for Information Technology Innovation Academia Sinica

Speech data collected in real-world scenarios often encounters two issues. First, multiple sources may exist simultaneously, and the number of sources may vary with time. Second, the existence of background noise in recording is inevitable. To handle the first issue, we refer to speech separation approaches, that separate speech from an unknown number of speakers. To address the second issue, we refer to speech denoising approaches, which remove noise components and retrieve pure speech signals. Numerous deep learning based methods for speech separation and denoising have been proposed that show promising results. However, few works attempt to address the issues simultaneously, despite speech separation and denoising tasks having similar nature. In this study, we propose a joint speech separation and denoising framework based on the multitask learning criterion to tackle the two issues simultaneously. The experimental results show that the proposed framework not only performs well on both speech separation and denoising tasks, but also outperforms related methods in most conditions. Copyright © 2020, The Authors. All rights reserved.

关键词： Source separation

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