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Ultrastable supported oxygen evolution electrocatalyst formed by ripening-induced embedding

science 2025年第6735期387卷 791-796页

作者： Shi, Wenjuan Shen, Tonghao Xing, Chengkun Sun, Kai Yan, Qisheng Niu, Wenzhe Yang, Xiao Li, Jingjing Wei, Chenyang Wang, Ruijie Fu, Shuqing Yang, Yong Xue, Liangyao Chen, Junfeng Cui, Shiwen Hu, Xiaoyue Xie, Ke Xu, Xin Duan, Sai Xu, Yifei Zhang, Bo State Key Laboratory of Molecular Engineering of Polymers Department of Macromolecular Science Fudan University Shanghai China Collaborative Innovation Center of Chemistry for Energy Materials Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials MOE Key Laboratory of Computational Physical Sciences Department of Chemistry Fudan University Shanghai China Department of Chemistry and Department of Electrical and Computer Engineering Northwestern University Evanston IL United States Hefei National Laboratory Hefei China

The future deployment of terawatt-scale proton exchange membrane water electrolyzer (PEMWE) technology necessitates development of an efficient oxygen evolution catalyst with low cost and long lifetime. Currently, the stability of the most active iridium (Ir) catalysts is impaired by dissolution, redeposition, detachment, and agglomeration of Ir species. Here we present a ripening-induced embedding strategy that securely embeds the Ir catalyst in a cerium oxide support. Cryogenic electron tomography and all-atom kinetic Monte Carlo simulations reveal that synchronizing the growth rate of the support with the nucleation rate of Ir, regulated by sonication, is pivotal for successful synthesis. A PEMWE using this catalyst achieves a cell voltage of 1.72 volts at a current density of 3 amperes per square centimeter with an Ir loading of just 0.3 milligrams per square centimeter and a voltage degradation rate of 1.33 microvolts per hour, as demonstrated by a 6000-hour accelerated aging test. Copyright © 2025 the authors.

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GESUBNET: GENE INTERACTION INFERENCE FOR DISEASE SUBTYPE NETWORK GENERATION

arXiv

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arXiv 2024年

作者： Yang, Ziwei Chen, Zheng Liu, Xin Kotoge, Rikuto Chen, Peng Matsubara, Yasuko Sakurai, Yasushi Sun, Jimeng Bioinformatics Center Kyoto University Japan SANKEN Osaka University Japan Japan RIKEN Center for Computational Science Japan Department of Computer Science University of Illinois Urbana-Champaign United States

Retrieving gene functional networks from knowledge databases presents a challenge due to the mismatch between disease networks and subtype-specific variations. Current solutions, including statistical and deep learning methods, often fail to effectively integrate gene interaction knowledge from databases or explicitly learn subtype-specific interactions. To address this mismatch, we propose GeSubNet, which learns a unified representation capable of predicting gene interactions while distinguishing between different disease subtypes. Graphs generated by such representations can be considered subtype-specific networks. GeSubNet is a multi-step representation learning framework with three modules: First, a deep generative model learns distinct disease subtypes from patient gene expression profiles. Second, a graph neural network captures representations of prior gene networks from knowledge databases, ensuring accurate physical gene interactions. Finally, we integrate these two representations using an inference loss that leverages graph generation capabilities, conditioned on the patient separation loss, to refine subtype-specific information in the learned representation. GeSubNet consistently outperforms traditional methods, with average improvements of 30.6%, 21.0%, 20.1%, and 56.6% across four graph evaluation metrics, averaged over four cancer datasets. Particularly, we conduct a biological simulation experiment to assess how the behavior of selected genes from over 11,000 candidates affects subtypes or patient distributions. The results show that the generated network has the potential to identify subtype-specific genes with an 83% likelihood of impacting patient distribution shifts. © 2024, CC BY.

关键词： Generative adversarial networks

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scDrug: From single-cell RNA-seq to drug response prediction

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computational and Structural Biotechnology Journal 2023年 21卷 150-157页

作者： Hsieh, Chiao-Yu Wen, Jian-Hung Lin, Shih-Ming Tseng, Tzu-Yang Huang, Jia-Hsin Huang, Hsuan-Cheng Juan, Hsueh-Fen Taiwan AI Labs Taipei10351 Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei11221 Taiwan Department of Life Science National Taiwan University Taipei10617 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei10617 Taiwan Center for Computational and Systems Biology National Taiwan University Taipei10617 Taiwan

Single-cell RNA sequencing (scRNA-seq) technology allows massively parallel characterization of thousands of cells at the transcriptome level. scRNA-seq is emerging as an important tool to investigate the cellular components and their interactions in the tumor microenvironment. scRNA-seq is also used to reveal the association between tumor microenvironmental patterns and clinical outcomes and to dissect cell-specific effects of drug treatment in complex tissues. Recent advances in scRNA-seq have driven the discovery of biomarkers in diseases and therapeutic targets. Although methods for prediction of drug response using gene expression of scRNA-seq data have been proposed, an integrated tool from scRNA-seq analysis to drug discovery is required. We present scDrug as a bioinformatics workflow that includes a one-step pipeline to generate cell clustering for scRNA-seq data and two methods to predict drug treatments. The scDrug pipeline consists of three main modules: scRNA-seq analysis for identification of tumor cell subpopulations, functional annotation of cellular subclusters, and prediction of drug responses. scDrug enables the exploration of scRNA-seq data readily and facilitates the drug repurposing process. scDrug is freely available on GitHub at https://***/ailabstw/scDrug. © 2022 The Authors

关键词： RNA

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Modularity-based selection of the number of slices in temporal network clustering

arXiv

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arXiv 2023年

作者： Seiron, Patrik Lindegren, Axel Magnani, Matteo Rohner, Christian Murata, Tsuyoshi Holme, Petter Department of Information Technology Uppsala University Sweden Department of Computer Science Tokyo Institute of Technology Japan Department of Computer Science Aalto University Finland Center for Computational Social Science Kobe University Japan

A popular way to cluster a temporal network is to transform it into a sequence of networks, also called slices, where each slice corresponds to a time interval and contains the vertices and edges existing in that interval. A reason to perform this transformation is that after a network has been sliced, existing algorithms designed to find clusters in multilayer networks can be used. However, to use this approach, we need to know how many slices to generate. This chapter discusses how to select the number of slices when generalized modularity is used to identify the clusters. Copyright © 2023, The Authors. All rights reserved.

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Systems-based approaches to study immunometabolism

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Cellular & Molecular Immunology 2022年第3期19卷 409-420页

作者： Vinee Purohit Allon Wagner Nir Yosef Vijay K.Kuchroo Evergrande Center for Immunologic Diseases and Ann Romney Center for Neurologic Diseases Brigham and Women’s HospitalHarvard Medical SchoolBostonMA02115USA Broad Institute of MIT and Harvard CambridgeMA02141USA Department of Electrical Engineering and Computer Science University of CaliforniaBerkeleyCA94720USA Center for Computational Biology University of CaliforniaBerkeleyCA94720USA

Technical advances at the interface of biology and computation,such as single-cell RNA-sequencing(scRNA-seq),reveal new layers of complexity in cellular *** emerging area of investigation using the systems biology approach is the study of the metabolism of immune *** diverse spectra of immune cell phenotypes,sparsity of immune cell numbers in vivo,limitations in the number of metabolites identified,dynamic nature of cellular metabolism and metabolic fluxes,tissue specificity,and high dependence on the local milieu make investigations in immunometabolism challenging,especially at the single-cell *** this review,we define the systemic nature of immunometabolism,summarize cell-and system-based approaches,and introduce mathematical modeling approaches for systems interrogation of metabolic changes in immune *** close the review by discussing the applications and shortcomings of metabolic modeling *** systems-oriented studies of metabolism expected to become a mainstay of immunological research,an understanding of current approaches toward systems immunometabolism will help investigators make the best use of current resources and push the boundaries of the discipline.

关键词： Immunometabolism Metabolic techniques GSMM Metabolic modeling Systems biology

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Gene Set Priorization Guided by Regulatory Networks with p-values through Kernel Mixed Model 26th

Gene Set Priorization Guided by Regulatory Networks with p...

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26th International Conference on Research in computational Molecular Biology, RECOMB 2022

作者： Wang, Haohan Lopez, Oscar L. Wu, Wei Xing, Eric P. Language Technologies Institute School of Computer Science Carnegie Mellon University Pittsburgh United States Alzheimer’s Disease Research Center University of Pittsburgh Medical Center Pittsburgh United States Computational Biology Department School of Computer Science Carnegie Mellon University Pittsburgh United States Machine Learning Department School of Computer Science Carnegie Mellon University Pittsburgh United States

ISBN: (纸本)9783031047480

The transcriptome association study has helped prioritize many causal genes for detailed study and thus further helped the development of many therapeutic strategies for multiple diseases. How- ever, prioritizing the causal gene only does not seem always to be able to offer sufficient guidance to the downstream analysis. Thus, in this paper, we propose to perform the association studies from another perspective: we aim to prioritize genes with a tradeoff between the pursuit of the causality evidence and the interest of the genes in the pathway. We introduce a new method for transcriptome association study by incorporating the information of gene regulatory networks. In addition to directly building the regularization into variable selection methods, we also expect the method to report p-values of the associated genes so that these p-values have been empirically proved trustworthy by geneticists. Thus, we introduce a high-dimension variable selection method with the following two merits: it has a flexible modeling power that allows the domain experts to consider the structure of covariates so that prior knowledge, such as the gene regulatory network, can be integrated;it also calculates the p-value, with a practical manner widely accepted by geneticists, so that the identified covariates can be directly assessed with statistical guarantees. With simulations, we demonstrate the empirical strength of our method against other high-dimension variable selection methods. We further apply our method to Alzheimer’s disease, and our method identifies interesting sets of genes. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genes

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Radiologist-in-the-Loop Self-Training for Generalizable CT Metal Artifact Reduction

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IEEE Transactions on Medical Imaging 2025年第6期44卷 2504-2514页

作者： Ma, Chenglong Li, Zilong Li, Yuanlin Han, Jing Zhang, Junping Zhang, Yi Liu, Jiannan Shan, Hongming Institute of Science and Technology for Brain-Inspired Intelligence MOE Frontiers Center for Brain Science Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Shanghai200433 China Fudan University Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Shanghai200433 China Shanghai Jiao Tong University School of Medicine Department of Oral Maxillofacial Head and Neck Oncology Shanghai Ninth People’s Hospital Shanghai200025 China Shanghai Jiao Tong University College of Stomatology Shanghai200240 China National Center for Stomatology Shanghai200011 China Shanghai Research Institute of Stomatology National Clinical Research Center for Oral Diseases Shanghai Key Laboratory of Stomatology Shanghai200011 China Sichuan University School of Cyber Science and Engineering Sichuan Chengdu610065 China

Metal artifacts in computed tomography (CT) images can significantly degrade image quality and impede accurate diagnosis. Supervised metal artifact reduction (MAR) methods, trained using simulated datasets, often struggle to perform well on real clinical CT images due to a substantial domain gap. Although state-of-the-art semi-supervised methods use pseudo ground-truths generated by a prior network to mitigate this issue, their reliance on a fixed prior limits both the quality and quantity of these pseudo ground-truths, introducing confirmation bias and reducing clinical applicability. To address these limitations, we propose a novel radiologist-in-the-loop self-training framework for MAR, termed RISE-MAR, which can integrate radiologists’ feedback into the semi-supervised learning process, progressively improving the quality and quantity of pseudo ground-truths for enhanced generalization on real clinical CT images. For quality assurance, we introduce a clinical quality assessor model that emulates radiologist evaluations, effectively selecting high-quality pseudo ground-truths for semi-supervised training. For quantity assurance, our self-training framework iteratively generates additional high-quality pseudo ground-truths, expanding the clinical dataset and further improving model generalization. Extensive experimental results on multiple clinical datasets demonstrate the superior generalization performance of our RISE-MAR over state-of-the-art methods, advancing the development of MAR models for practical application. © 1982-2012 IEEE.

关键词： computerized tomography

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Treeffuser: probabilistic predictions via conditional diffusions with gradient-boosted trees 24

Treeffuser: probabilistic predictions via conditional diffus...

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

作者： Nicolas Beltran-Velez Alessandro Antonio Grande Achille Nazaret Alp Kucukelbir David Blei Department of Computer Science Columbia University New York Halvorsen Center for Computational Oncology Memorial Sloan Kettering Cancer Center New York and Irving Institute for Cancer Dynamics Columbia University New York Department of Computer Science and Irving Institute for Cancer Dynamics Columbia University New York Department of Computer Science Columbia University New York and Fero Labs New York Department of Computer Science and Department of Statistics Columbia University New York

ISBN: (纸本)9798331314385

Probabilistic prediction aims to compute predictive distributions rather than single point predictions. These distributions enable practitioners to quantify uncertainty, compute risk, and detect outliers. However, most probabilistic methods assume parametric responses, such as Gaussian or Poisson distributions. When these assumptions fail, such models lead to bad predictions and poorly calibrated uncertainty. In this paper, we propose Treeffuser, an easy-to-use method for probabilistic prediction on tabular data. The idea is to learn a conditional diffusion model where the score function is estimated using gradient-boosted trees. The conditional diffusion model makes Treeffuser flexible and non-parametric, while the gradient-boosted trees make it robust and easy to train on CPUs. Treeffuser learns well-calibrated predictive distributions and can handle a wide range of regression tasks—including those with multivariate, multimodal, and skewed responses. We study Treeffuser on synthetic and real data and show that it outperforms existing methods, providing better calibrated probabilistic predictions. We further demonstrate its versatility with an application to inventory allocation under uncertainty using sales data from Walmart. We implement Treeffuser in https://***/blei-lab/treeffuser.

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Amortized variational inference: when and why? 24

Amortized variational inference: when and why?

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Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence

作者： Charles C. Margossian David M. Blei Center for Computational Mathematics Flatiron Institute New York New York Department of Computer Science and Statistics Columbia University New York New York

In a probabilistic latent variable model, factorized (or mean-field) variational inference (F-VI) fits a separate parametric distribution for each latent variable. Amortized variational inference (A-VI) instead learns a common inference function, which maps each observation to its corresponding latent variable's approximate posterior. Typically, A-VI is used as a step in the training of variational autoencoders, however it stands to reason that A-VI could also be used as a general alternative to F-VI. In this paper we study when and why A-VI can be used for approximate Bayesian inference. We derive conditions on a latent variable model which are necessary, sufficient, and verifiable under which A-VI can attain F-VI's optimal solution, thereby closing the amortization gap. We prove these conditions are uniquely verified by simple hierarchical models, a broad class that encompasses many models in machine learning. We then show, on a broader class of models, how to expand the domain of AVI's inference function to improve its solution, and we provide examples, e.g. hidden Markov models, where the amortization gap cannot be closed.

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Large Language Models in Psychiatry:Current Applications,Limitations,and Future Scope

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Big Data Mining and Analytics 2024年第4期7卷 1148-1168页

作者： Zhe Liu Yihang Bao Shuai Zeng Ruiyi Qian Miaohan Deng An Gu Jianye Li Weidi Wang Wenxiang Cai Wenhao Li Han Wang Dong Xu Guan Ning Lin Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicineand School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai 200030China Department of Electrical Engineering and Computer Science University of MissouriColumbiaMO 65211USA School of Information Science and Technology Institute of Computational BiologyNortheast Normal UniversityChangchun 130024China

With the advancements in Artificial Intelligence(AI)technology,Large Language Models(LLMs)provide outstanding capabilities for natural language understanding and generation,enhancing various *** psychiatry,LLMs can empower healthcare by analyzing vast amounts of medical data to improve diagnostic accuracy,enhance therapeutic communication,and personalize patient care with their strength in understanding and generating human-like *** clinical AI,developing and utilizing robust and interpretable models has been a longstanding *** survey investigates the current psychiatric practice of LLMs,along with a series of corpus resources that could be used for training psychiatric *** discuss the limitations concerning LLM reproducibility,capabilities,usability,interpretability in clinical settings,and ethical ***,we propose potential future directions for research,clinical application,and education in psychiatric ***,we discuss the challenge of integrating LLMs into the evolving landscape of healthcare in real-world scenarios.

关键词： Artificial Intelligence(AI) Large Language Model(LLM) psychiatry medical application

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