cs.AI, q-bio.NC updates on arXiv.org
Improving Ensemble Forecasts of Abnormally Deflecting Tropical Cyclones with Fused Atmosphere-Ocean-Terrain Data Qixiang Li · Shuwei Huo · Chong Wang · Xiaofeng Li · Yuan Zhou
arXiv:2603.29200v1 Announce Type: cross Abstract: Deep learning-based tropical cyclone (TC) forecasting methods have demonstrated significant potential and application advantages, as they feature much lower computational cost and faster operation speed than numerical weather prediction models. However, existing deep learning methods still have key limitations: they can only process a single type of sequential trajectory data or homogeneous meteorological variables, and fail to achieve accurate
1 April 2026 at 12:00

Improving Ensemble Forecasts of Abnormally Deflecting Tropical Cyclones with Fused Atmosphere-Ocean-Terrain Data

cs.AI, q-bio.NC updates on arXiv.org

By: Qixiang Li · Shuwei Huo · Chong Wang · Xiaofeng Li · Yuan Zhou

1 April 2026 at 12:00

arXiv:2603.29200v1 Announce Type: cross Abstract: Deep learning-based tropical cyclone (TC) forecasting methods have demonstrated significant potential and application advantages, as they feature much lower computational cost and faster operation speed than numerical weather prediction models. However, existing deep learning methods still have key limitations: they can only process a single type of sequential trajectory data or homogeneous meteorological variables, and fail to achieve accurate forecasting of abnormal deflected TCs. To address these challenges, we present two groundbreaking contributions. First, we have constructed a multimodal and multi-source dataset named AOT-TCs for TC forecasting in the Northwest Pacific basin. As the first dataset of its kind, it innovatively integrates heterogeneous variables from the atmosphere, ocean, and land, thus obtaining a comprehensive and information-rich meteorological dataset. Second, based on the AOT-TCs dataset, we propose a forecasting model that can handle both normal and abnormally deflected TCs. This is the first TC forecasting model to adopt an explicit atmosphere-ocean-terrain coupling architecture, enabling it to effectively capture complex interactions across physical domains. Extensive experiments on all TC cases in the Northwest Pacific from 2017 to 2024 show that our model achieves state-of-the-art performance in TC forecasting: it not only significantly improves the forecasting accuracy of normal TCs but also breaks through the technical bottleneck in forecasting abnormally deflected TCs.

cs.AI, q-bio.NC updates on arXiv.org
Scalable AI-assisted Workflow Management for Detector Design Optimization Using Distributed Computing Derek Anderson · Amit Bashyal · Markus Diefenthaler · Cristiano Fanelli · Wen Guan · Tanja Horn · Alex Jentsch Meifeng Lin · Tadashi Maeno · Kei Nagai · Hemalata Nayak · Connor Pecar · Karthik Suresh · Fang-Ying Tsai · Anselm Vossen · Tianle Wang · Torre Wenaus
arXiv:2603.30014v1 Announce Type: cross Abstract: The Production and Distributed Analysis (PanDA) system, originally developed for the ATLAS experiment at the CERN Large Hadron Collider (LHC), has evolved into a robust platform for orchestrating large-scale workflows across distributed computing resources. Coupled with its intelligent Distributed Dispatch and Scheduling (iDDS) component, PanDA supports AI/ML-driven workflows through a scalable and flexible workflow engine. We present an AI-as
1 April 2026 at 12:00

Scalable AI-assisted Workflow Management for Detector Design Optimization Using Distributed Computing

cs.AI, q-bio.NC updates on arXiv.org

By: Derek Anderson · Amit Bashyal · Markus Diefenthaler · Cristiano Fanelli · Wen Guan · Tanja Horn · Alex Jentsch Meifeng Lin · Tadashi Maeno · Kei Nagai · Hemalata Nayak · Connor Pecar · Karthik Suresh · Fang-Ying Tsai · Anselm Vossen · Tianle Wang · Torre Wenaus

1 April 2026 at 12:00

arXiv:2603.30014v1 Announce Type: cross Abstract: The Production and Distributed Analysis (PanDA) system, originally developed for the ATLAS experiment at the CERN Large Hadron Collider (LHC), has evolved into a robust platform for orchestrating large-scale workflows across distributed computing resources. Coupled with its intelligent Distributed Dispatch and Scheduling (iDDS) component, PanDA supports AI/ML-driven workflows through a scalable and flexible workflow engine. We present an AI-assisted framework for detector design optimization that integrates multi-objective Bayesian optimization with the PanDA--iDDS workflow engine to coordinate iterative simulations across heterogeneous resources. The framework addresses the challenge of exploring high-dimensional parameter spaces inherent in modern detector design. We demonstrate the framework using benchmark problems and realistic studies of the ePIC and dRICH detectors for the Electron-Ion Collider (EIC). Results show improved automation, scalability, and efficiency in multi-objective optimization. This work establishes a flexible and extensible paradigm for AI-driven detector design and other computationally intensive scientific applications.

npj Digital Medicine
A randomized trial of a digitally delivered, home-based neuromodulation and mindfulness intervention for pain management in older adults with knee osteoarthritis Juyoung Park · Chiyoung Lee · Lifeng Lin · Mindy J. Fain · Ilknur Telkes · Stephen Dahmer · Lindsey Park · Jason Hoang · Hongyu Miao · Hyochol Ahn
npj Digital Medicine, Published online: 31 March 2026; doi:10.1038/s41746-026-02577-7A randomized trial of a digitally delivered, home-based neuromodulation and mindfulness intervention for pain management in older adults with knee osteoarthritis
31 March 2026 at 08:00

A randomized trial of a digitally delivered, home-based neuromodulation and mindfulness intervention for pain management in older adults with knee osteoarthritis

npj Digital Medicine

By: Juyoung Park · Chiyoung Lee · Lifeng Lin · Mindy J. Fain · Ilknur Telkes · Stephen Dahmer · Lindsey Park · Jason Hoang · Hongyu Miao · Hyochol Ahn

31 March 2026 at 08:00

npj Digital Medicine, Published online: 31 March 2026; doi:10.1038/s41746-026-02577-7

A randomized trial of a digitally delivered, home-based neuromodulation and mindfulness intervention for pain management in older adults with knee osteoarthritis

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
AI-guided multi-omics analysis identifies NPC1-modulated susceptibility to SARS-CoV-2 infection under PM(2.5) exposure Guoqing Feng · Zheng Dong · Limei Ke · Weilai Zhou · Yu Tian · Xingtian Li · Wenxin Xiang · Yanjun Li · Qi Huang · Linfeng Liu · Bo Yin · Shouyi Yan · Jianxiu Liu · Xindong Ma · Huaiyong Chen · Miao He · Ke Hao · Sijin Liu · Qian Di
Nat Commun. 2026 Mar 30. doi: 10.1038/s41467-026-71196-3. Online ahead of print.ABSTRACTExposure to airborne fine particulate matter (PM2.5) has been linked to increased risk of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, yet the underlying mechanisms remain unclear. Here, by leveraging a fine-tuned foundation model of single-cell transcriptomics, we uncover shared transcriptional signatures between PM2.5 exposure and SARS-CoV-2 infection. We further validate this
30 March 2026 at 18:00

AI-guided multi-omics analysis identifies NPC1-modulated susceptibility to SARS-CoV-2 infection under PM(2.5) exposure

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: Guoqing Feng · Zheng Dong · Limei Ke · Weilai Zhou · Yu Tian · Xingtian Li · Wenxin Xiang · Yanjun Li · Qi Huang · Linfeng Liu · Bo Yin · Shouyi Yan · Jianxiu Liu · Xindong Ma · Huaiyong Chen · Miao He · Ke Hao · Sijin Liu · Qian Di

30 March 2026 at 18:00

Nat Commun. 2026 Mar 30. doi: 10.1038/s41467-026-71196-3. Online ahead of print.

ABSTRACT

Exposure to airborne fine particulate matter (PM_2.5) has been linked to increased risk of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, yet the underlying mechanisms remain unclear. Here, by leveraging a fine-tuned foundation model of single-cell transcriptomics, we uncover shared transcriptional signatures between PM_2.5 exposure and SARS-CoV-2 infection. We further validate this association using population-level epidemiological analyses and perform genome-wide association studies (GWAS) to identify genetic variants that modulate infection risk under PM_2.5 exposure. In addition, we identify NPC1 as a key modulator involved in SARS-CoV-2 infection efficiency under virus-laden PM_2.5 exposure through integrative functional genomic analyses and in vitro experiments. Our findings suggest that PM_2.5 facilitates viral entry through an NPC1-modulated endo-lysosomal pathway, providing a mechanistic explanation for observed pollution-related susceptibility. By integrating artificial intelligence (AI)-guided transcriptomics, epidemiology, GWAS, functional genomics, and in vitro verification, our study elucidates how environmental and genetic factors jointly influence SARS-CoV-2 susceptibility. This work highlights how AI-assisted multi-omics integration systematically decodes the health impacts of environmental exposures from molecular to population levels and informs air quality policy and infectious disease preparedness.

PMID:41912520 | DOI:10.1038/s41467-026-71196-3

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