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Received — 10 September 2026 ⏭

Rational and computation-assisted engineering of a compact and efficient CRISPR–Cas12f genome editor

✇Molecular Therapy
By: Jingtong Liu · Shengzhou Wang · Bei Wang · Tonglu Yu · Xuan Liu · Miaomiao Li · Min-Chi Ku · Rui Wang · Renjie Chai · Wei V. Zheng · Yongming Wang
31 August 2026 at 08:00
Structure-guided design combined with protein language model-guided engineering and sgRNA optimization enables the development of a compact and highly efficient CRISPR–Cas12f genome editor. This integrated strategy substantially improves genome-editing activity while preserving high specificity, expanding the therapeutic potential of compact CRISPR systems.

An operational perturbation proteomics-based virtual cell model

✇Nature - Issue - nature.com science feeds
By: Rui Sun · Liujia Qian · Yongge Li · Tong Liu · Honghan Cheng · Xuedong Zhang · Xueya Zhou · Yuecheng Zhan · Guangmei Zhang · Zhengchao Luo · Kunpeng Ma · Chunlong Wu · Dongchen Ji · Zhangzhi Xue · Hongxue Meng · Yuhang Xiang · Dingwei Lei · Qianhe Zhou · Wenbin Hu · Yuhan Deng · Lingling Tan · Qi Xiao · Zhiwei Liu · Lei Zeng · Liqin Qian · Xuan Zheng · Qiong Hu · Nianzi Luo · Weinan E · Peijie Zhou · Han Wen · Yi Zhu · Tiannan Guo
9 September 2026 at 08:00

Nature, Published online: 09 September 2026; doi:10.1038/s41586-026-11001-9

Temporal protein-abundance measurements from systematically perturbed breast cancer cell lines were generated to develop ProteinTalks, a virtual cell model that functions as an operational tool for diverse drug discovery tasks.
Received — 27 May 2026 ⏭

Equip Pre-ranking with Target Attention by Residual Quantization

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yutong Li · Yu Zhu · Yichen Qiao · Ziyu Guan · Lv Shao · Tong Liu · Bo Zheng
26 May 2026 at 12:00
arXiv:2509.16931v3 Announce Type: replace-cross Abstract: The pre-ranking stage in industrial recommendation systems faces a fundamental conflict between efficiency and effectiveness. While powerful models like Target Attention (TA) excel at capturing complex feature interactions in the ranking stage, their high computational cost makes them infeasible for pre-ranking, which often relies on simplistic vector-product models. This disparity creates a significant performance bottleneck for the entire system. To bridge this gap, we propose TARQ, a novel pre-ranking framework. Inspired by generative models, TARQ's key innovation is to equip pre-ranking with an architecture approximate to TA by Residual Quantization. This allows us to bring the modeling power of TA into the latency-critical pre-ranking stage for the first time, establishing a new state-of-the-art trade-off between accuracy and efficiency. Extensive offline experiments and large-scale online A/B tests at Taobao demonstrate TARQ's significant improvements in ranking performance. Consequently, our model has been fully deployed in production, serving tens of millions of daily active users and yielding substantial business improvements. The code and data are available at https://github.com/zyody/tarq_sigir2026.

Multi-omics analysis of glutamine and fish collagen peptides in alleviating post-antibiotic Streptococcus pneumoniae injury in feline lung cells

✇Omics In Lung
By: Huasong Bai · Tong Liu · Hengyan Wang · Hongchen Jin · Yunliang Li · Zhanzhong Wang
16 April 2026 at 18:00

Exp Ther Med. 2026 Mar 30;31(6):148. doi: 10.3892/etm.2026.13143. eCollection 2026 Jun.

ABSTRACT

Streptococcus pneumoniae (SP) infection often leads to persistent lung injury even after antibiotic treatment. Despite this phenomenon, the mechanisms underlying host cell recovery remain poorly understood. Upon breaching the epithelial barrier, SP primarily targets the pulmonary interstitial cells, which constitute the major mesenchymal component of the lung. These cells serve as essential effectors of tissue repair, extracellular matrix remodeling and epithelial restoration. Therefore, a feline pulmonary interstitial cell (FCA-L2) model of SP infection was established to investigate the protective effects of glutamine (GLU) and fish collagen peptides (FCP) through integrated transcriptomic and metabolomic analyses. Cells were infected with SP (0.05 McFarland units for 4 h) and then treated with doxycycline (7.5 µg/ml for 18 h) followed by GLU (40 mM) or FCP (500 µg/ml). Notably, SP infection increased lactate dehydrogenase (LDH) release by 3.5-fold, induced secretion of IL-1β, TNF-α and IL-8, disrupted tight-junction proteins (claudin, ZO-1 and occludin) and caused oxidative imbalance and apoptosis despite antibiotic (doxycycline) treatment. However, treatment with GLU or FCP significantly reduced LDH release by ~40%, restored junctional proteins, suppressed inflammatory cytokines and enhanced antioxidant enzyme activities. Multi-omics analysis revealed that GLU promoted amino acid biosynthesis and energy metabolism and suppressed aminoacyl-tRNA synthetases and cell-cycle regulators, thereby enhancing metabolic adaptability. By contrast, FCP activated amino and nucleotide sugar metabolism, increased polyunsaturated fatty-acid synthesis and supported glycocalyx repair and membrane reconstruction. GLU and FCP provided complementary metabolic and structural protection, which mitigated post-infectious stress and promoted cellular recovery. The findings of the present study underscore the potential of bioactive food-derived compounds as adjunctive therapies that may accelerate lung tissue repair and enhance the efficacy of conventional antibiotics.

PMID:41988354 | PMC:PMC13077270 | DOI:10.3892/etm.2026.13143

Received — 17 March 2026 ⏭

OpenSage: Self-programming Agent Generation Engine

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hongwei Li · Zhun Wang · Qinrun Dai · Yuzhou Nie · Jinjun Peng · Ruitong Liu · Jingyang Zhang · Kaijie Zhu · Jingxuan He · Lun Wang · Yangruibo Ding · Yueqi Chen · Wenbo Guo · Dawn Song
16 March 2026 at 12:00
arXiv:2602.16891v2 Announce Type: replace Abstract: Agent development kits (ADKs) provide effective platforms and tooling for constructing agents, and their designs are critical to the constructed agents' performance, especially the functionality for agent topology, tools, and memory. However, current ADKs either lack sufficient functional support or rely on humans to manually design these components, limiting agents' generalizability and overall performance. We propose OpenSage, the first ADK that enables LLMs to automatically create agents with self-generated topology and toolsets while providing comprehensive and structured memory support. OpenSage offers effective functionality for agents to create and manage their own sub-agents and toolkits. It also features a hierarchical, graph-based memory system for efficient management and a specialized toolkit tailored to software engineering tasks. Extensive experiments across three state-of-the-art benchmarks with various backbone models demonstrate the advantages of OpenSage over existing ADKs. We also conduct rigorous ablation studies to demonstrate the effectiveness of our design for each component. We believe OpenSage can pave the way for the next generation of agent development, shifting the focus from human-centered to AI-centered paradigms.
Received — 17 February 2026 ⏭

LLM-Confidence Reranker: A Training-Free Approach for Enhancing Retrieval-Augmented Generation Systems

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhipeng Song · Xiangyu Kong · Xinrui Bao · Yizhi Zhou · Jiulong Jiao · Sitong Liu · Yuhang Zhou · Heng Qi
17 February 2026 at 13:00
arXiv:2602.13571v1 Announce Type: cross Abstract: Large language models (LLMs) have revolutionized natural language processing, yet hallucinations in knowledge-intensive tasks remain a critical challenge. Retrieval-augmented generation (RAG) addresses this by integrating external knowledge, but its efficacy depends on accurate document retrieval and ranking. Although existing rerankers demonstrate effectiveness, they frequently necessitate specialized training, impose substantial computational expenses, and fail to fully exploit the semantic capabilities of LLMs, particularly their inherent confidence signals. We propose the LLM-Confidence Reranker (LCR), a training-free, plug-and-play algorithm that enhances reranking in RAG systems by leveraging black-box LLM confidence derived from Maximum Semantic Cluster Proportion (MSCP). LCR employs a two-stage process: confidence assessment via multinomial sampling and clustering, followed by binning and multi-level sorting based on query and document confidence thresholds. This approach prioritizes relevant documents while preserving original rankings for high-confidence queries, ensuring robustness. Evaluated on BEIR and TREC benchmarks with BM25 and Contriever retrievers, LCR--using only 7--9B-parameter pre-trained LLMs--consistently improves NDCG@5 by up to 20.6% across pre-trained LLM and fine-tuned Transformer rerankers, without degradation. Ablation studies validate the hypothesis that LLM confidence positively correlates with document relevance, elucidating LCR's mechanism. LCR offers computational efficiency, parallelism for scalability, and broad compatibility, mitigating hallucinations in applications like medical diagnosis.
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