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arXiv 2025年

作者： Paruchuri, Aanish Thrasher, Carl Hart, A.J. Macfarlane, Robert Jayaraman, Arthi Science in Data Science Program University of Delaware NewarkDE19713 United States Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Chemical and Biomolecular Engineering University of Delaware 150 Academy St NewarkDE19713 United States Department of Materials Science and Engineering University of Delaware NewarkDE19713 United States Data Science Institute University of Delaware NewarkDE19713 United States

Nanoparticle superlattices consisting of ordered arrangements of nanoparticles exhibit unique optical, magnetic, and electronic properties arising from nanoparticle characteristics as well as their collective behaviors. Understanding how processing conditions influence the nanoscale arrangement and microstructure is critical for engineering materials with desired macroscopic properties. Microstructural features such as grain boundaries, lattice defects, and pores significantly affect these properties but are challenging to quantify using traditional manual analyses as they are labor-intensive and prone to errors. In this work, we present a machine learning workflow for automating grain segmentation in scanning electron microscopy (SEM) images of nanoparticle superlattices. This workflow integrates signal processing techniques, such as Radon transforms, with unsupervised learning methods like agglomerative hierarchical clustering to identify and segment grains without requiring manually annotated data. In the workflow we transform the raw pixel data into explainable numerical representation of superlattice orientations for clustering. Benchmarking results demonstrate the workflow's robustness against noisy images and edge cases, with a processing speed of four images per minute on standard computational hardware. This efficiency makes the workflow scalable to large datasets and makes it a valuable tool for integrating data-driven models into decision-making processes for material design and analysis. For example, one can use this workflow to quantify grain size distributions at varying processing conditions like temperature and pressure and using that knowledge adjust processing conditions to achieve desired superlattice orientations and grain sizes. © 2025, CC BY.

关键词： Grain boundaries

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Assessing high-order effects in feature importance via predictability decomposition

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Physical Review E 2025年第3期111卷 L033301-L033301页

作者： Marlis Ontivero-Ortega Luca Faes Jesus M. Cortes Daniele Marinazzo Sebastiano Stramaglia Dipartimento Interateneo di Fisica Università degli Studi di Bari Aldo Moro and INFN Sezione di Bari 70126 Bari Italy Dipartimento di Ingegneria Università di Palermo 90128 Palermo Italy Faculty of Technical Sciences University of Novi Sad 21000 Novi Sad Serbia Biocruces-Bizkaia Health Research Institute 48903 Barakaldo Spain Biomedical Research Doctorate Program University of the Basque Country 48940 Leioa Spain Department of Cell Biology and Histology University of the Basque Country 48940 Leioa Spain IKERBASQUE Basque Foundation for Science 48009 Bilbao Spain Department of Data Analysis Ghent University 9000 Ghent Belgium

Building on recent advances in describing redundancy and synergy in multivariate interactions among random variables, we propose an approach to quantify cooperative effects in feature importance, a key technique in explainable artificial intelligence. Specifically, we introduce an adaptive version of the widely used metric Leave One Covariate Out (LOCO), designed to disentangle high-order effects involving a particular input feature in regression problems. LOCO measures the reduction in prediction error when the feature of interest is added to the set of features used in regression. Unlike the standard approach that computes LOCO using all available features, our method identifies the subsets of features that maximize and minimize LOCO. This results in a decomposition of LOCO into a two-body component and higher-order components (redundant and synergistic), while also identifying the features that contribute to these high-order effects in conjunction with the driving feature. We demonstrate the effectiveness of the proposed method in a benchmark dataset related to wine quality and to proton versus pion discrimination using simulated detector measurements generated by GEANT.

关键词： Regression analysis

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Genomic insights into novel extremotolerant bacteria isolated from the NASA Phoenix mission spacecraft assembly cleanrooms

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Microbiome 2025年第1期13卷 1-27页

作者： Schultz, Júnia Jamil, Tahira Sengupta, Pratyay Sivabalan, Shobhan Karthick Muthamilselvi Rawat, Anamika Patel, Niketan Krishnamurthi, Srinivasan Alam, Intikhab Singh, Nitin K. Raman, Karthik Rosado, Alexandre Soares Venkateswaran, Kasthuri Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology Thuwal Makkah 23955 Saudi Arabia Department of Biotechnology Bhupat and Jyoti Mehta School of Biosciences Indian Institute of Technology Madras Tamil Nadu Chennai 600036 India Center for Integrative Biology and Systems Medicine (IBSE) Indian Institute of Technology Madras Tamil Nadu Chennai 600036 India Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI) Indian Institute of Technology Madras Tamil Nadu Chennai 600036 India Microbial Type Culture Collection and Gene Bank (MTCC) Institute of Microbial Technology Chandigarh 160036 India NASA Jet Propulsion Laboratory California Institute of Technology Pasadena CA United States Department of Data Science and AI Wadhwani School of Data Science and AI Indian Institute of Technology Madras Tamil Nadu Chennai 600036 India Bioscience Program Biological and Environmental Science and Engineering (BESE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Makkah 23955 Saudi Arabia

Background: Human-designed oligotrophic environments, such as cleanrooms, harbor unique microbial communities shaped by selective pressures like temperature, humidity, nutrient availability, cleaning reagents, and radiation. Maintaining the biological cleanliness of NASA’s mission-associated cleanrooms, where spacecraft are assembled and tested, is critical for planetary protection. Even with stringent controls such as regulated airflow, temperature management, and rigorous cleaning, resilient microorganisms can persist in these environments, posing potential risks for space missions. Results: During the Phoenix spacecraft mission, genomes of 215 bacterial isolates were sequenced and based on overall genome-related indices, 53 strains belonging to 26 novel species were recognized. Metagenome mapping indicated less than 0.1% of the reads associated with novel species, suggesting their rarity. Genes responsible for biofilm formation, such as BolA (COG0271) and CvpA (COG1286), were predominantly found in proteobacterial members but were absent in other non-spore-forming and spore-forming species. YqgA (COG1811) was detected in most spore-forming members but was absent in Paenibacillus and non-spore-forming species. Cell fate regulators, COG1774 (YaaT), COG3679 (YlbF, YheA/YmcA), and COG4550 (YmcA, YheA/YmcA), controlling sporulation, competence, and biofilm development processes, were observed in all spore-formers but were missing in non-spore-forming species. COG analyses further revealed resistance-conferring proteins in all spore-formers (n = 13 species) and eight actinobacterial species, responsible for enhanced membrane transport and signaling under radiation (COG3253), transcription regulation under radiation stress (COG1108), and DNA repair and stress responses (COG2318). Additional functional analysis revealed that Agrococcus phoenicis, Microbacterium canaveralium, and Microbacterium jpeli contained biosynthetic gene clusters (BGCs) for ε-poly-L-lysine, benefic

关键词： BGCs Cleanroom Extreme environment Novel species Phoenix mission Phylogenomics

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Inkjet-printed colorimetric indicators from the peels of red pitaya and Kyoho grape for the detection of food spoilage gases

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Current Research in Food science 2025年 10卷 101064页

作者： Gong, Tianxi Luo, Xiaoyu Food Science and Technology Program Beijing Normal–Hong Kong Baptist University Guangdong Zhuhai 519087 China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science Beijing Normal–Hong Kong Baptist University Guangdong Zhuhai 519087 China

The development of intelligent packaging materials can be alternative means to monitor food freshness and showcase real-time quality decline during storage. Colorimetric indicators are commonly employed as such examples due to their capability to exhibit chemically-responsive color changes triggered by the in-package headspace change during food deterioration. Different food-grade indicator dyes or pigments were investigated in literature, but one challenge was to incorporate the colorant into packaging materials with easy manipulation of indicator compositions. In this study, betanin extracted from red pitaya peel and anthocyanin extracted from Kyoho grape peel were developed into inks and subsequently printed as colorimetric indicators through piezoelectric inkjet printing. The formulated ink solution containing indicator compounds exhibited Newtonian fluid characteristic and was confirmed as printable fluid. The developed indicators showed detectable color responses upon the exposure of increasing levels of different food volatile gases including acetic acid, butyric acid, trimethylamine, ammonia and hexanoic acid. For each gas analyte present at low and high doses, the indicator's color responses can be differentiated by conducting the principal component analysis and hierarchical cluster analysis, with high sensitivity. A preliminary study on seafood freshness monitoring was conducted and visible color changes were observed during shrimp deterioration. © 2025 The Authors

关键词： Anthocyanin Betacyanin Colorimetric indicator Fruit waste Intelligent packaging

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The Impact of Natural External Forcing on Ocean Heat Uptake Efficiency Since the 1980s

arXiv

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arXiv 2025年

作者： Shi, Jia-Rui Zanna, Laure Adcroft, Alistair Courant Institute of Mathematical Sciences New York University New YorkNY United States Center for Data Science New York University New YorkNY United States Princeton University Atmospheric and Oceanic Sciences Program Princeton United States

We investigate the temporal evolution of ocean heat uptake efficiency (OHUE) using observations and large ensemble model simulations. OHUE, defined as the ratio of the rate in ocean heat uptake to changes in global mean surface temperature anomalies, has exhibited significant variability over recent decades. We found a relatively low OHUE in the late 1980s, a peak around 2000, and a subsequent decline. A key finding is the significant influence of natural external forcing, mainly volcanic eruptions, which causes an abrupt decline in OHUE followed by a gradual recovery. The 1991 Mount Pinatubo eruption, a major volcanic event of the 20th century, had a lasting impact on OHUE. This study emphasizes the contribution of mid-latitudes to global OHUE changes. Our findings underscore the importance of considering natural external forcing in understanding climate dynamics and suggest conducting idealized experiments to quantify the potential effects of future volcanic eruptions on OHUE. © 2025, CC BY.

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Genetic keys to microbial gut colonization

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Cell Host & Microbe 2025年第5期33卷 605-607页

作者： Karthik Anantharaman Cody Martin Department of Bacteriology University of Wisconsin-Madison Madison WI USA Department of Integrative Biology University of Wisconsin-Madison Madison WI USA Department of Data Science and AI Indian Institute of Technology Madras Chennai TN India Microbiology Doctoral Training Program University of Wisconsin-Madison Madison WI USA

What enables certain microbes to successfully colonize the mammalian gut? In a recent issue of Cell, Liu et al. present a microbial cross-species genomic framework that identifies conserved genetic determinants of gut residency. This study provides a roadmap for developing more effective microbiome-based therapeutics, advancing our understanding of host-microbe interactions.

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KNU-DTI: KNowledge United Drug-Target Interaction prediction

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Computers in Biology and Medicine 2025年 189卷 109927-109927页

作者： Heo, Ryong Lee, Dahyeon Kim, Byung Ju Seo, Sangmin Park, Sanghyun Park, Chihyun Interdisciplinary Graduate Program in Medical Bigdata Convergence Kangwon National University Gangwon-do Chuncheon-si24341 Korea Republic of Department of Data Science Kangwon National University Korea Republic of UBLBio Corporation Yeongtong-ro 237 Gyeonggi-do Suwon16679 Korea Republic of Department of Computer Science Yonsei University Yonsei-ro 50 Seodaemun-gu Seoul03722 Korea Republic of Department of Computer Science and Engineering Kangwon National University Korea Republic of

Motivation: Accurately predicting drug-target protein interactions (DTI) is a cornerstone of drug discovery, enabling the identification of potential therapeutic compounds. Sequence-based prediction models, despite their simplicity, hold great promise in extracting essential information directly from raw sequences. However, the focus in recent DTI studies has increasingly shifted toward enhancing algorithmic complexity, often at the expense of fully leveraging robust sequence representation learning methods. This shift has led to the underestimation and gradual neglect of methodologies aimed at effectively capturing discriminative features from sequences. Our work seeks to address this oversight by emphasizing the value of well-constructed sequence representation algorithms, demonstrating that even with simple interaction mapping algorithm techniques, accurate DTI models can be achieved. By prioritizing meaningful information extraction over excessive model complexity, we aim to advance the development of practical and generalizable DTI prediction frameworks. Results: We developed the KNowledge Uniting DTI model (KNU-DTI), which retrieves structural information and unites them. Protein structural properties were obtained using structural property sequence (SPS). Extended-connectivity fingerprint (ECFP) was used to estimate the structure-activity relationship in molecules. Including these two features, a total of five latent vectors were derived from protein and molecule via various neural networks and integrated by elemental-wise addition to predict binding interactions or affinity. Using four test concepts to evaluate the model, we show that the model outperforms recently published competitors. Finally, a case study indicated that our model has a competitive edge over existing docking simulations in some cases. © 2025

关键词： Prediction models

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Meta-Learning for Generalizable Connectome Modeling Across Heterogeneous Atlas Spaces

Meta-Learning for Generalizable Connectome Modeling Across H...

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IEEE International Symposium on Biomedical Imaging

作者： Qinghao Liang Brendan D. Adkinson Dustin Scheinost Department of Biomedical Engineering Yale University New Haven CT Interdepartmental Neuroscience Program Yale University New Haven CT Department of Radiology and Biomedical Imaging Yale School of Medicine New Haven CT Department of Statistics and Data Science Yale University New Haven CT

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Leveraging large-scale neuroimaging datasets to improve predictive modeling in smaller, specialized cohorts is essential for translating research findings into clinical applications. To address this challenge, we propose a meta-learning inspired approach that enhances phenotype prediction by exploiting inter-phenotype correlations across independent datasets, without requiring uniform imaging data processing. By employing mappings derived from a small, independent cohort with data processed using different atlases, we align the data in the meta-training set to the target space of the meta-testing set. This approach eliminates the need for extensive data reprocessing. Evaluation in the UK Biobank dataset shows that our framework significantly improves predictive performance on unseen phenotypes, demonstrating its potential to generalize connectome-based models to small, heterogeneous cohorts.

关键词： Metalearning Neuroimaging Phenotypes Correlation Biological system modeling Predictive models data processing Biomedical imaging

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BENCHMARKING COMMUNITY DRUG RESPONSE PREDICTION MODELS: dataSETS, MODELS, TOOLS, AND METRICS FOR CROSS-dataSET GENERALIZATION ANALYSIS

arXiv

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arXiv 2025年

作者： Partin, Alexander Vasanthakumari, Priyanka Narykov, Oleksandr Wilke, Andreas Koussa, Natasha Jones, Sara E. Zhu, Yitan Overbeek, Jamie C. Jain, Rajeev Fernando, Gayara Demini Sanchez-Villalobos, Cesar Garcia-Cardona, Cristina Mohd-Yusof, Jamaludin Chia, Nicholas Wozniak, Justin M. Ghosh, Souparno Pal, Ranadip Brettin, Thomas S. Weil, M. Ryan Stevens, Rick L. Division of Data Science and Learning Argonne National Laboratory LemontIL United States Frederick National Laboratory for Cancer Research Cancer Data Science Initiatives Cancer Research Technology Program FrederickMD United States Department of Statistics University of Nebraska–Lincoln LincolnNE United States Department of Electrical & Computer Engineering Texas Tech University LubbockTX United States Division of Computer Computational and Statistical Sciences Los Alamos National Laboratory Los AlamosNM United States Department of Computer Science The University of Chicago ChicagoIL United States

Deep learning (DL) and machine learning (ML) models have shown promise in drug response prediction (DRP), yet their ability to generalize across datasets remains an open question, raising concerns about their real-world applicability. Due to the lack of standardized benchmarking approaches, model evaluations and comparisons often rely on inconsistent datasets and evaluation criteria, making it difficult to assess true predictive capabilities. In this work, we introduce a benchmarking framework for evaluating cross-dataset prediction generalization in DRP models. Our framework incorporates five publicly available drug screening datasets, six standardized DRP models, and a scalable workflow for systematic evaluation. To assess model generalization, we introduce a set of evaluation metrics that quantify both absolute performance (e.g., predictive accuracy across datasets) and relative performance (e.g., performance drop compared to within-dataset results), enabling a more comprehensive assessment of model transferability. Our results reveal substantial performance drops when models are tested on unseen datasets, underscoring the importance of rigorous generalization assessments. While several models demonstrate relatively strong cross-dataset generalization, no single model consistently outperforms across all datasets. Furthermore, we identify CTRPv2 as the most effective source dataset for training, yielding higher generalization scores across target datasets. By sharing this standardized evaluation framework with the community, our study aims to establish a rigorous foundation for model comparison, and accelerate the development of robust DRP models for real-world applications. © 2025, CC BY.

关键词： Prediction models

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Time-dependent reduction of somatic variants in lichen striatus as evidence for the clearance of mutated skin cells

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Journal of the American Academy of Dermatology 2025年第6期92卷 1377-1379页

作者： Lee, Byunghyuk Lim, Dong Min Shin, Kihyuk Lee, Hyun Jung Lee, Jungsoo Kim, Hoon-Soo Kim, Byung-Soo Kim, Moon-Bum Kim, Kihun Kim, Won Kyu Kang, Junho Ko, Hyun-Chang Kim, Yun Hak Department of Dermatology College of Medicine Pusan National University Yangsan South Korea Interdisciplinary Program of Genomic Data Science Pusan National University Yangsan South Korea Department of Dermatology Pusan National University Yangsan Hospital Yangsan South Korea Research Institute for Convergence of Biomedical Science and Technology Pusan National University Yangsan Hospital Yangsan South Korea Department of Pathology School of Medicine Pusan National University Yangsan South Korea Department of Biomedical Informatics School of Medicine Pusan National University Yangsan South Korea Department of Anatomy School of Medicine Pusan National University Yangsan South Korea Natural Product Research Center Korea Institute of Science and Technology Gangneung South Korea Department of Research Keimyung University Dongsan Medical Center Daegu South Korea

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