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Received — 15 December 2025 ⏭ Favourites

Understanding Prompt Management in GitHub Repositories: A Call for Best Practices

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hao Li · Hicham Masri · Filipe R. Cogo · Abdul Ali Bangash · Bram Adams · Ahmed E. Hassan
15 December 2025 at 13:00
arXiv:2509.12421v2 Announce Type: replace-cross Abstract: The rapid adoption of foundation models (e.g., large language models) has given rise to promptware, i.e., software built using natural language prompts. Effective management of prompts, such as organization and quality assurance, is essential yet challenging. In this study, we perform an empirical analysis of 24,800 open-source prompts from 92 GitHub repositories to investigate prompt management practices and quality attributes. Our findings reveal critical challenges such as considerable inconsistencies in prompt formatting, substantial internal and external prompt duplication, and frequent readability and spelling issues. Based on these findings, we provide actionable recommendations for developers to enhance the usability and maintainability of open-source prompts within the rapidly evolving promptware ecosystem.

MetaVoxel: Joint Diffusion Modeling of Imaging and Clinical Metadata

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yihao Liu · Chenyu Gao · Lianrui Zuo · Michael E. Kim · Brian D. Boyd · Lisa L. Barnes · Walter A. Kukull · Lori L. Beason-Held · Susan M. Resnick · Timothy J. Hohman · Warren D. Taylor · Bennett A. Landman
15 December 2025 at 13:00
arXiv:2512.10041v2 Announce Type: replace-cross Abstract: Modern deep learning methods have achieved impressive results across tasks from disease classification, estimating continuous biomarkers, to generating realistic medical images. Most of these approaches are trained to model conditional distributions defined by a specific predictive direction with a specific set of input variables. We introduce MetaVoxel, a generative joint diffusion modeling framework that models the joint distribution over imaging data and clinical metadata by learning a single diffusion process spanning all variables. By capturing the joint distribution, MetaVoxel unifies tasks that traditionally require separate conditional models and supports flexible zero-shot inference using arbitrary subsets of inputs without task-specific retraining. Using more than 10,000 T1-weighted MRI scans paired with clinical metadata from nine datasets, we show that a single MetaVoxel model can perform image generation, age estimation, and sex prediction, achieving performance comparable to established task-specific baselines. Additional experiments highlight its capabilities for flexible inference. Together, these findings demonstrate that joint multimodal diffusion offers a promising direction for unifying medical AI models and enabling broader clinical applicability.
Received — 12 December 2025 ⏭ Favourites

Phythesis: Physics-Guided Evolutionary Scene Synthesis for Energy-Efficient Data Center Design via LLMs

✇cs.AI, q-bio.NC updates on arXiv.org
By: Minghao LI · Ruihang Wang · Rui Tan · Yonggang Wen
12 December 2025 at 13:00
arXiv:2512.10611v1 Announce Type: new Abstract: Data center (DC) infrastructure serves as the backbone to support the escalating demand for computing capacity. Traditional design methodologies that blend human expertise with specialized simulation tools scale poorly with the increasing system complexity. Recent studies adopt generative artificial intelligence to design plausible human-centric indoor layouts. However, they do not consider the underlying physics, making them unsuitable for the DC design that sets quantifiable operational objectives and strict physical constraints. To bridge the gap, we propose Phythesis, a novel framework that synergizes large language models (LLMs) and physics-guided evolutionary optimization to automate simulation-ready (SimReady) scene synthesis for energy-efficient DC design. Phythesis employs an iterative bi-level optimization architecture, where (i) the LLM-driven optimization level generates physically plausible three-dimensional layouts and self-criticizes them to refine the scene topology, and (ii) the physics-informed optimization level identifies the optimal asset parameters and selects the best asset combination. Experiments on three generation scales show that Phythesis achieves 57.3% generation success rate increase and 11.5% power usage effectiveness (PUE) improvement, compared with the vanilla LLM-based solution.
Received — 3 December 2025 ⏭ Favourites

Uni-X: Mitigating Modality Conflict with a Two-End-Separated Architecture for Unified Multimodal Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jitai Hao · Hao Liu · Xinyan Xiao · Qiang Huang · Jun Yu
2 December 2025 at 13:00
arXiv:2509.24365v2 Announce Type: replace-cross Abstract: Unified Multimodal Models (UMMs) built on shared autoregressive (AR) transformers are attractive for their architectural simplicity. However, we identify a critical limitation: when trained on multimodal inputs, modality-shared transformers suffer from severe gradient conflicts between vision and text, particularly in shallow and deep layers. We trace this issue to the fundamentally different low-level statistical properties of images and text, while noting that conflicts diminish in middle layers where representations become more abstract and semantically aligned. To overcome this challenge, we propose Uni-X, a two-end-separated, middle-shared architecture. Uni-X dedicates its initial and final layers to modality-specific processing, while maintaining shared parameters in the middle layers for high-level semantic fusion. This X-shaped design not only eliminates gradient conflicts at both ends but also further alleviates residual conflicts in the shared layers. Extensive experiments validate the effectiveness of Uni-X. Under identical training conditions, Uni-X achieves superior training efficiency compared to strong baselines. When scaled to 3B parameters with larger training data, Uni-X matches or surpasses 7B AR-based UMMs, achieving a GenEval score of 82 for image generation alongside strong performance in text and vision understanding tasks. These results establish Uni-X as a parameter-efficient and scalable foundation for future unified multimodal modeling. Our code is available at https://github.com/CURRENTF/Uni-X
Received — 28 November 2025 ⏭ Favourites

Single-Cell Multi-Omics in Type 2 Diabetes Mellitus: Revealing Cellular Heterogeneity and Mechanistic Insights

✇(Multiomics OR Omics) AND (Pancreatic)
By: Yijie Wei · Feitong Hong · Sijia Xie · Xinwei Luo · Xiaolong Li · Fuying Dao · Kejun Deng · Hao Lin · Hao Lyu
27 November 2025 at 19:00

Int J Mol Sci. 2025 Nov 13;26(22):11005. doi: 10.3390/ijms262211005.

ABSTRACT

Type 2 diabetes mellitus (T2DM) is a prevalent and complex metabolic disorder characterized by insulin resistance, progressive β-cell dysfunction, and severe systemic complications. Advances in single-cell multi-omics-transcriptomics, chromatin accessibility profiling, and integrative analyses-have offered unprecedented insights into the cellular heterogeneity and regulatory networks of pancreatic islets. We highlight recent discoveries in islet cell heterogeneity and β-cell pathophysiology, with a particular focus on dysfunction and dedifferentiation. We further underscore the computational frameworks that enable these discoveries, spanning data preprocessing, multi-omics integration, and machine learning-driven analyses, which collectively enable the dissection of disease-relevant cell subpopulations and the reconstruction of developmental and regulatory trajectories. We also examine how impaired signaling within islets and chronic adipose inflammation contribute to T2DM pathogenesis. Finally, we discuss key challenges in clinical translation-including limited population diversity in single-cell atlases and the interpretability of computational models-and propose future directions toward precision diagnostics and therapeutic innovation in T2DM.

PMID:41303487 | PMC:PMC12652634 | DOI:10.3390/ijms262211005

Received — 18 November 2025 ⏭ Favourites

SciAgent: A Unified Multi-Agent System for Generalistic Scientific Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xuchen Li · Ruitao Wu · Xuanbo Liu · Xukai Wang · Jinbo Hu · Zhixin Bai · Bohan Zeng · Hao Liang · Leheng Chen · Mingrui Chen · Haitian Zhong · Xuanlin Yang · Xu-Yao Zhang · Liu Liu · Jia Li · Kaiqi Huang · Jiahao Xu · Haitao Mi · Wentao Zhang · Bin Dong
18 November 2025 at 13:00
arXiv:2511.08151v2 Announce Type: replace Abstract: Recent advances in large language models have enabled AI systems to achieve expert-level performance on domain-specific scientific tasks, yet these systems remain narrow and handcrafted. We introduce SciAgent, a unified multi-agent system designed for generalistic scientific reasoning-the ability to adapt reasoning strategies across disciplines and difficulty levels. SciAgent organizes problem solving as a hierarchical process: a Coordinator Agent interprets each problem's domain and complexity, dynamically orchestrating specialized Worker Systems, each composed of interacting reasoning Sub-agents for symbolic deduction, conceptual modeling, numerical computation, and verification. These agents collaboratively assemble and refine reasoning pipelines tailored to each task. Across mathematics and physics Olympiads (IMO, IMC, IPhO, CPhO), SciAgent consistently attains or surpasses human gold-medalist performance, demonstrating both domain generality and reasoning adaptability. Additionally, SciAgent has been tested on the International Chemistry Olympiad (IChO) and selected problems from the Humanity's Last Exam (HLE) benchmark, further confirming the system's ability to generalize across diverse scientific domains. This work establishes SciAgent as a concrete step toward generalistic scientific intelligence-AI systems capable of coherent, cross-disciplinary reasoning at expert levels.
Received — 14 November 2025 ⏭ Favourites

Asking the Right Questions: Benchmarking Large Language Models in the Development of Clinical Consultation Templates

✇cs.AI, q-bio.NC updates on arXiv.org
By: Liam G. McCoy · Fateme Nateghi Haredasht · Kanav Chopra · David Wu · David JH Wu · Abass Conteh · Sarita Khemani · Saloni Kumar Maharaj · Vishnu Ravi · Arth Pahwa · Yingjie Weng · Leah Rosengaus · Lena Giang · Kelvin Zhenghao Li · Olivia Jee · Daniel Shirvani · Ethan Goh · Jonathan H. Chen
13 November 2025 at 13:00
arXiv:2508.01159v2 Announce Type: replace-cross Abstract: This study evaluates the capacity of large language models (LLMs) to generate structured clinical consultation templates for electronic consultation. Using 145 expert-crafted templates developed and routinely used by Stanford's eConsult team, we assess frontier models -- including o3, GPT-4o, Kimi K2, Claude 4 Sonnet, Llama 3 70B, and Gemini 2.5 Pro -- for their ability to produce clinically coherent, concise, and prioritized clinical question schemas. Through a multi-agent pipeline combining prompt optimization, semantic autograding, and prioritization analysis, we show that while models like o3 achieve high comprehensiveness (up to 92.2\%), they consistently generate excessively long templates and fail to correctly prioritize the most clinically important questions under length constraints. Performance varies across specialties, with significant degradation in narrative-driven fields such as psychiatry and pain medicine. Our findings demonstrate that LLMs can enhance structured clinical information exchange between physicians, while highlighting the need for more robust evaluation methods that capture a model's ability to prioritize clinically salient information within the time constraints of real-world physician communication.
Received — 12 November 2025 ⏭ Favourites

SciAgent: A Unified Multi-Agent System for Generalistic Scientific Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xuchen Li · Ruitao Wu · Xuanbo Liu · Xukai Wang · Jinbo Hu · Zhixin Bai · Bohan Zeng · Hao Liang · Leheng Chen · Mingrui Chen · Haitian Zhong · Xuanlin Yang · Xu-Yao Zhang · Liu Liu · Jia Li · Kaiqi Huang · Jiahao Xu · Haitao Mi · Wentao Zhang · Bin Dong
12 November 2025 at 13:00
arXiv:2511.08151v1 Announce Type: new Abstract: Recent advances in large language models have enabled AI systems to achieve expert-level performance on domain-specific scientific tasks, yet these systems remain narrow and handcrafted. We introduce SciAgent, a unified multi-agent system designed for generalistic scientific reasoning-the ability to adapt reasoning strategies across disciplines and difficulty levels. SciAgent organizes problem solving as a hierarchical process: a Coordinator Agent interprets each problem's domain and complexity, dynamically orchestrating specialized Worker Systems, each composed of interacting reasoning Sub-agents for symbolic deduction, conceptual modeling, numerical computation, and verification. These agents collaboratively assemble and refine reasoning pipelines tailored to each task. Across mathematics and physics Olympiads (IMO, IMC, IPhO, CPhO), SciAgent consistently attains or surpasses human gold-medalist performance, demonstrating both domain generality and reasoning adaptability. Additionally, SciAgent has been tested on the International Chemistry Olympiad (IChO) and selected problems from the Humanity's Last Exam (HLE) benchmark, further confirming the system's ability to generalize across diverse scientific domains. This work establishes SciAgent as a concrete step toward generalistic scientific intelligence-AI systems capable of coherent, cross-disciplinary reasoning at expert levels.
Received — 5 November 2025 ⏭ Favourites

MemSearcher: Training LLMs to Reason, Search and Manage Memory via End-to-End Reinforcement Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qianhao Yuan · Jie Lou · Zichao Li · Jiawei Chen · Yaojie Lu · Hongyu Lin · Le Sun · Debing Zhang · Xianpei Han
5 November 2025 at 13:00
arXiv:2511.02805v1 Announce Type: cross Abstract: Typical search agents concatenate the entire interaction history into the LLM context, preserving information integrity but producing long, noisy contexts, resulting in high computation and memory costs. In contrast, using only the current turn avoids this overhead but discards essential information. This trade-off limits the scalability of search agents. To address this challenge, we propose MemSearcher, an agent workflow that iteratively maintains a compact memory and combines the current turn with it. At each turn, MemSearcher fuses the user's question with the memory to generate reasoning traces, perform search actions, and update memory to retain only information essential for solving the task. This design stabilizes context length across multi-turn interactions, improving efficiency without sacrificing accuracy. To optimize this workflow, we introduce multi-context GRPO, an end-to-end RL framework that jointly optimize reasoning, search strategies, and memory management of MemSearcher Agents. Specifically, multi-context GRPO samples groups of trajectories under different contexts and propagates trajectory-level advantages across all conversations within them. Trained on the same dataset as Search-R1, MemSearcher achieves significant improvements over strong baselines on seven public benchmarks: +11% on Qwen2.5-3B-Instruct and +12% on Qwen2.5-7B-Instruct relative average gains. Notably, the 3B-based MemSearcher even outperforms 7B-based baselines, demonstrating that striking a balance between information integrity and efficiency yields both higher accuracy and lower computational overhead. The code and models will be publicly available at https://github.com/icip-cas/MemSearcher
Received — 28 October 2025 ⏭ Favourites

Beyond Accuracy: Rethinking Hallucination and Regulatory Response in Generative AI

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zihao Li · Weiwei Yi · Jiahong Chen
27 October 2025 at 12:00
arXiv:2509.13345v2 Announce Type: replace-cross Abstract: Hallucination in generative AI is often treated as a technical failure to produce factually correct output. Yet this framing underrepresents the broader significance of hallucinated content in language models, which may appear fluent, persuasive, and contextually appropriate while conveying distortions that escape conventional accuracy checks. This paper critically examines how regulatory and evaluation frameworks have inherited a narrow view of hallucination, one that prioritises surface verifiability over deeper questions of meaning, influence, and impact. We propose a layered approach to understanding hallucination risks, encompassing epistemic instability, user misdirection, and social-scale effects. Drawing on interdisciplinary sources and examining instruments such as the EU AI Act and the GDPR, we show that current governance models struggle to address hallucination when it manifests as ambiguity, bias reinforcement, or normative convergence. Rather than improving factual precision alone, we argue for regulatory responses that account for languages generative nature, the asymmetries between system and user, and the shifting boundaries between information, persuasion, and harm.
Received — 23 October 2025 ⏭ Favourites

Single-cell multi-omics analysis reveals cancer regulatory elements of transcriptional programs and clinical implications

✇Omics In Lung
By: Xiaowei Tang · Qiaoling Zhang · Zichu Shen · Jian Xiao · Minghao Li · Xiangyan Meng · Chenyi Wang · Guangze Zhang · Anhang Liu · Yuxin Yin
21 October 2025 at 18:00

Cell Death Dis. 2025 Oct 21;16(1):746. doi: 10.1038/s41419-025-08060-7.

ABSTRACT

The regulatory mechanisms governing transcriptional programs in the cancer genome remain elusive, particularly those concerning cell-type specificity. We carefully curated single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) and single-cell RNA sequencing (scRNA-seq) data from eight distinct carcinoma tissues, including breast, skin, colon, endometrium, lung, ovary, liver, and kidney. Using single-cell multi-omics analysis, we identified extensive open chromatin regions and constructed peak-gene link networks, which can reveal distinct cancer gene regulation and genetic risks. We further explored conserved epigenetic regulation across cell types within cancer and elucidated their functional implications. Moreover, we identified cell-type-associated transcription factors (TFs) that regulate key cellular functions, such as the TEAD family of TFs, which widely control cancer-related signaling pathways in tumor cells. In colon cancer, we further identified tumor-specific TFs that are more highly activated in tumor cells than in normal epithelial cells, including CEBPG, LEF1, SOX4, TCF7, and TEAD4, which are pivotal in driving malignant transcriptional programs and represent potential therapeutic targets, as corroborated by single-cell sequencing data from multiple sources and in vitro experiments. Our findings provide a comprehensive understanding of the regulatory dynamics underlying carcinomas and offer valuable insights into potential therapeutic interventions.

PMID:41120274 | PMC:PMC12541060 | DOI:10.1038/s41419-025-08060-7

Received — 17 September 2025 ⏭ Favourites

Behavior Change Strategies in Digital Exercise Interventions for Adolescent Idiopathic Scoliosis: Scoping Review

✇Journal of Medical Internet Research
By: Yufeng Li · Fangyuan Chang · Wen Zhang · Zihan Ren · Yawen Chen · Zhao Liu
17 September 2025 at 02:00
Background: Adolescent idiopathic scoliosis is a common spinal deformity typically treated with exercise therapy. Despite the increasing use of digital technologies in interventions, there remains a gap in understanding how to effectively integrate behavior change techniques (BCTs) and behavior theories within these digital solutions. Objective: This review aims to identify the digital characteristics of interventions and the BCTs used, and to analyze potential theoretical mechanisms with the Theoretical Domains Framework and the capability, opportunity, motivation, and behavior model. Methods: We conducted a scoping review according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A total of 5 databases, including PubMed, Web of Science, Embase, Cochrane Library, and CINAHL, were selected for screening eligible studies up to April 4, 2024. We included studies of any design type that involved patients with adolescent idiopathic scoliosis using digital interventions for exercise rehabilitation, including qualitative, quantitative, or mixed methods studies, and study protocols with detailed descriptions of digital interventions. Two researchers independently screened studies and extracted data into tables for descriptive analysis. The Mixed Methods Appraisal Tool was used to assess the quality of studies. Results: Out of the 3267 identified papers, 21 (0.64%) studies were included. The most frequently used technologies were videoconferencing (n=7) and instructional videos (n=5). The three most common BCT clusters were “Shaping Knowledge” (n=19), “Social Support” (n=16), and “Antecedents” (n=16). “Knowledge” was the most used mechanism of action (n=21), followed by “Skills” (n=16), “Environmental Context and Resources” (n=16), and “Social Influences” (n=16). The studies primarily addressed “Capability” and “Opportunity,” with less emphasis on “Motivation,” particularly “Automatic Motivation.” Conclusions: This review identified common digital technologies and their characteristics, analyzed potential mechanisms of behavior change in interventions, and provided recommendations for technology utilization. Future research should further evaluate the effectiveness of digital technologies while enhancing patient motivation and user experience. Trial Registration: PROSPERO CRD42024530851; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024530851
Received — 3 September 2025 ⏭ Favourites

Protein lipoylation in cancer: metabolic reprogramming and therapeutic potential

✇Cell Death Discovery nature.com science feeds
By: Sainan Li · Yingchao Liu · Wanye Hu · Aoli Deng · Xueying Ren · Lulu Chen · Yajuan Lu · Yunyi Wu · Hangqi Huang · Jinghao Cao · Jing Du · Jun Xia · Yanchun Li
2 September 2025 at 08:00

Cell Death Discovery, Published online: 02 September 2025; doi:10.1038/s41420-025-02718-z

Protein lipoylation in cancer: metabolic reprogramming and therapeutic potential
Received — 15 June 2025 ⏭ Favourites

An organoid co-culture model for probing systemic anti-tumor immunity in lung cancer

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Kaiyi Li · Chang Liu · Xizhao Sui · Chao Li · Ting Zhang · Tian Zhao · Dong Zhang · Hainan Wu · Yuhan Liu · Shuai Wang · Yingshun Yang · Baobao Lin · Wenyan Wang · Fan Yang · Xiaofang Chen · Peng Liu
13 June 2025 at 18:00

Cell Stem Cell. 2025 Jun 6:S1934-5909(25)00191-2. doi: 10.1016/j.stem.2025.05.011. Online ahead of print.

ABSTRACT

Deciphering interactions between tumor micro- and systemic immune macroenvironments is essential for developing more effective cancer diagnosis and therapeutic strategies. Here, we established a gel-liquid interface (GLI) co-culture model of lung cancer organoids (LCOs) and paired peripheral-blood mononuclear cells (PBMCs), featuring enhanced interactions between immune cells and tumor organoids for optimized simulation of in vivo systemic anti-tumor immunity. By constructing a cohort of lung cancer patients, we demonstrated that the responses of GLI models under αPD1 treatment reflected the immunotherapy outcomes of the corresponding patients precisely. Furthermore, we dissected the various tumor immune processes mediated by PBMC-derived T cells within GLI models through functional multi-omics analyses, along with the characterization of circulating tumor-reactive T cells (GNLY+CD44+CD9+) with effector memory-like phenotypes as a potential indicator of immunotherapy efficacy. Our findings indicate that the GLI co-culture model can be used to develop diagnostic strategies for precision immunotherapies, as well as understanding the underlying mechanisms.

PMID:40513558 | DOI:10.1016/j.stem.2025.05.011

Received — 8 February 2025 ⏭ Favourites

A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Xihao Li · Han Chen · Margaret Sunitha Selvaraj · Eric Van Buren · Hufeng Zhou · Yuxuan Wang · Ryan Sun · Zachary R McCaw · Zhi Yu · Min-Zhi Jiang · Daniel DiCorpo · Sheila M Gaynor · Rounak Dey · Donna K Arnett · Emelia J Benjamin · Joshua C Bis · John Blangero · Eric Boerwinkle · Donald W Bowden · Jennifer A Brody · Brian E Cade · April P Carson · Jenna C Carlson · Nathalie Chami · Yii-Der Ida Chen · Joanne E Curran · Paul S de Vries · Myriam Fornage · Nora Franceschini · Barry I Freedman · Charles Gu · Nancy L Heard-Costa · Jiang He · Lifang Hou · Yi-Jen Hung · Marguerite R Irvin · Robert C Kaplan · Sharon L R Kardia · Tanika N Kelly · Iain Konigsberg · Charles Kooperberg · Brian G Kral · Changwei Li · Yun Li · Honghuang Lin · Ching-Ti Liu · Ruth J F Loos · Michael C Mahaney · Lisa W Martin · Rasika A Mathias · Braxton D Mitchell · May E Montasser · Alanna C Morrison · Take Naseri · Kari E North · Nicholette D Palmer · Patricia A Peyser · Bruce M Psaty · Susan Redline · Alexander P Reiner · Stephen S Rich · Colleen M Sitlani · Jennifer A Smith · Kent D Taylor · Hemant
7 February 2025 at 19:00

Nat Comput Sci. 2025 Feb 7. doi: 10.1038/s43588-024-00764-8. Online ahead of print.

ABSTRACT

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally scalable analytical pipeline for functionally informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits in 61,838 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered and replicated new associations with lipid traits missed by single-trait analysis.

PMID:39920506 | DOI:10.1038/s43588-024-00764-8

Received — 24 December 2024 ⏭ Favourites

Synthesis of portimines reveals the basis of their anti-cancer activity

✇Nature - Issue - nature.com science feeds
By: Junchen Tang · Weichao Li · Tzu-Yuan Chiu · Francisco Martínez-Peña · Zengwei Luo · Christine T. Chong · Qijia Wei · Nathalia Gazaniga · Thomas J. West · Yi Yang See · Luke L. Lairson · Christopher G. Parker · Phil S. Baran
20 September 2023 at 08:00

Nature, Published online: 20 September 2023; doi:10.1038/s41586-023-06535-1

A scalable total synthesis of portimines enables structural reassignment of portimine B and in-depth functional evaluation of portimine A, revealing that portimine A induces translation inhibition selectively in human cancer cells and is efficacious in vivo tumour-clearance models.

Scientific discovery in the age of artificial intelligence

✇Nature - Issue - nature.com science feeds
By: Hanchen Wang · Tianfan Fu · Yuanqi Du · Wenhao Gao · Kexin Huang · Ziming Liu · Payal Chandak · Shengchao Liu · Peter Van Katwyk · Andreea Deac · Anima Anandkumar · Karianne Bergen · Carla P. Gomes · Shirley Ho · Pushmeet Kohli · Joan Lasenby · Jure Leskovec · Tie-Yan Li
2 August 2023 at 08:00

Nature, Published online: 02 August 2023; doi:10.1038/s41586-023-06221-2

The advances in artificial intelligence over the past decade are examined, with a discussion on how artificial intelligence systems can aid the scientific process and the central issues that remain despite advances.
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