KIFC1 engages RUNX2/TGF-β signaling to promote lung cancer bone metastasis via disrupting bone homeostasis

Oncogene - Issue - nature.com science feeds

By: Jinyan Feng · Qingqian Zhao · Jinwu Wang · Zeyang Li · Houzhi Yang · Yuxiang Shen · Xiaoyu Yang · Yiqin Li · Yao Xu · Xiuxin Han · Chao Zhang · Guowen Wang

25 September 2026 at 08:00

Oncogene, Published online: 25 September 2026; doi:10.1038/s41388-026-03998-0

KIFC1 engages RUNX2/TGF-β signaling to promote lung cancer bone metastasis via disrupting bone homeostasis

Advanced and underlying therapeutic strategies in transformed small cell lung cancer

Omics In Lung

By: Mingfeng Wei · Zheng Li · Kaiyuan Zhu · Shixiang Guo · Lefei Hu · Fuzhi Yang · Xiaoyu Chen · Xuelin Zhang · Shuai Jiang · Xunxia Zhu · Xiaoyong Shen

11 September 2026 at 18:00

Front Med (Lausanne). 2026 Aug 27;13:1865050. doi: 10.3389/fmed.2026.1865050. eCollection 2026.

ABSTRACT

Transformed small-cell lung cancer (T-SCLC) is a clinically important form of histologic transformation and a mechanism of acquired resistance in non-small-cell lung cancer (NSCLC). It is associated with poor prognosis, with a median overall survival of only about 9-13 months. This review summarizes recent advances in the mechanisms, diagnosis, monitoring, and treatment of T-SCLC. Repeat biopsy remains the gold standard for confirming histologic transformation, whereas molecular profiling and liquid biopsy may facilitate early detection and longitudinal disease monitoring. Platinum-etoposide remains the most commonly used clinical standard after transformation, but its benefit is typically transient and durable disease control remains uncommon. Continuation of EGFR tyrosine kinase inhibitors combined with chemotherapy may prolong progression-free survival in selected patients but has not consistently improved overall survival. Anti-angiogenic therapy, particularly anlotinib, and chemo-immunotherapy have shown encouraging activity in selected patients, while emerging strategies targeting DLL3, MYC, SOX2, and epigenetic regulators may broaden the therapeutic landscape. Prospective studies integrating repeat tissue sampling, comprehensive genomic profiling, biomarker-guided patient stratification, pharmacogenomics, functional drug-sensitivity testing where feasible, and integrated multi-omics approaches are needed to advance molecularly guided and individualized treatment for T-SCLC.

PMID:42724635 | PMC:PMC13560167 | DOI:10.3389/fmed.2026.1865050

Advanced and underlying therapeutic strategies in transformed small cell lung cancer

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

By: Mingfeng Wei · Zheng Li · Kaiyuan Zhu · Shixiang Guo · Lefei Hu · Fuzhi Yang · Xiaoyu Chen · Xuelin Zhang · Shuai Jiang · Xunxia Zhu · Xiaoyong Shen

11 September 2026 at 18:00

Front Med (Lausanne). 2026 Aug 27;13:1865050. doi: 10.3389/fmed.2026.1865050. eCollection 2026.

ABSTRACT

Transformed small-cell lung cancer (T-SCLC) is a clinically important form of histologic transformation and a mechanism of acquired resistance in non-small-cell lung cancer (NSCLC). It is associated with poor prognosis, with a median overall survival of only about 9-13 months. This review summarizes recent advances in the mechanisms, diagnosis, monitoring, and treatment of T-SCLC. Repeat biopsy remains the gold standard for confirming histologic transformation, whereas molecular profiling and liquid biopsy may facilitate early detection and longitudinal disease monitoring. Platinum-etoposide remains the most commonly used clinical standard after transformation, but its benefit is typically transient and durable disease control remains uncommon. Continuation of EGFR tyrosine kinase inhibitors combined with chemotherapy may prolong progression-free survival in selected patients but has not consistently improved overall survival. Anti-angiogenic therapy, particularly anlotinib, and chemo-immunotherapy have shown encouraging activity in selected patients, while emerging strategies targeting DLL3, MYC, SOX2, and epigenetic regulators may broaden the therapeutic landscape. Prospective studies integrating repeat tissue sampling, comprehensive genomic profiling, biomarker-guided patient stratification, pharmacogenomics, functional drug-sensitivity testing where feasible, and integrated multi-omics approaches are needed to advance molecularly guided and individualized treatment for T-SCLC.

PMID:42724635 | PMC:PMC13560167 | DOI:10.3389/fmed.2026.1865050

Evaluating AI in leukocyte classification: performance of the AI system against 15 morphology experts

npj Digital Medicine

By: Wei Xu · Haoqin Jiang · Jingxian Zhang · Kun Chen · Yang Fei · Ping Guo · Yongjian He · Yi Ye · Linlin Qu · Mingkang Yang · Lizhi Yan · Di Wang · Huan Qi · Shihong Zhang · Chi Zhang · Jianbiao Wang · Ming Guan

11 April 2026 at 08:00

npj Digital Medicine, Published online: 11 April 2026; doi:10.1038/s41746-026-02601-w

Evaluating AI in leukocyte classification: performance of the AI system against 15 morphology experts

Can LLMs Learn to Reason Robustly under Noisy Supervision?

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

By: Shenzhi Yang · Guangcheng Zhu · Bowen Song · Sharon Li · Haobo Wang · Xing Zheng · Yingfan Ma · Zhongqi Chen · Weiqiang Wang · Gang Chen

7 April 2026 at 12:00

arXiv:2604.03993v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) effectively trains reasoning models that rely on abundant perfect labels, but its vulnerability to unavoidable noisy labels due to expert scarcity remains critically underexplored. In this work, we take the first step toward a systematic analysis of noisy label mechanisms in RLVR. In contrast to supervised classification, most RLVR algorithms incorporate a rollout-based condition: a label's influence on training is contingent on whether the current policy can generate rollouts that realize it, a property that naturally extends to noisy labels. Based on this observation, we distinguish two types of noise: inactive noisy labels, which reduce data efficiency, and active noisy labels, which are reinforced and risk skewing the model toward incorrect distributions. From experiments on training with noisy samples, we identify an Early Correctness Coherence phenomenon: although noisy samples begin to lag behind in later stages, accuracy on both clean and noisy samples increases similarly in early training. Motivated by this dynamic, we propose Online Label Refinement (OLR), which progressively corrects potentially noisy labels with majority-voted answers when two conditions hold: a positive slope in the majority answer's rollout pass rate and stable historical consistency across updates, enabling gradual self-correction as the policy improves. We evaluate OLR on six in-distribution mathematical reasoning benchmarks (AIME24/25, AMC, MATH-500, Minerva, and Olympiad) and three out-of-distribution tasks (ARC-c, GPQA-diamond, and MMLU-pro). Across noise ratios from 0.1 to 0.9, OLR consistently improves robustness under both inactive and active noisy-label settings, achieving average gains of 3.6% to 3.9% on in-distribution benchmarks and 3.3% to 4.6% on out-of-distribution evaluations.

UniQueR: Unified Query-based Feedforward 3D Reconstruction

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

By: Chensheng Peng · Quentin Herau · Jiezhi Yang · Yichen Xie · Yihan Hu · Wenzhao Zheng · Matthew Strong · Masayoshi Tomizuka · Wei Zhan

25 March 2026 at 12:00

arXiv:2603.22851v1 Announce Type: cross Abstract: We present UniQueR, a unified query-based feedforward framework for efficient and accurate 3D reconstruction from unposed images. Existing feedforward models such as DUSt3R, VGGT, and AnySplat typically predict per-pixel point maps or pixel-aligned Gaussians, which remain fundamentally 2.5D and limited to visible surfaces. In contrast, UniQueR formulates reconstruction as a sparse 3D query inference problem. Our model learns a compact set of 3D anchor points that act as explicit geometric queries, enabling the network to infer scene structure, including geometry in occluded regions--in a single forward pass. Each query encodes spatial and appearance priors directly in global 3D space (instead of per-frame camera space) and spawns a set of 3D Gaussians for differentiable rendering. By leveraging unified query interactions across multi-view features and a decoupled cross-attention design, UniQueR achieves strong geometric expressiveness while substantially reducing memory and computational cost. Experiments on Mip-NeRF 360 and VR-NeRF demonstrate that UniQueR surpasses state-of-the-art feedforward methods in both rendering quality and geometric accuracy, using an order of magnitude fewer primitives than dense alternatives.

CBF-RL: Safety Filtering Reinforcement Learning in Training with Control Barrier Functions

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

By: Lizhi Yang · Blake Werner · Massimiliano de Sa · Aaron D. Ames

16 March 2026 at 12:00

arXiv:2510.14959v4 Announce Type: replace-cross Abstract: Reinforcement learning (RL), while powerful and expressive, can often prioritize performance at the expense of safety. Yet safety violations can lead to catastrophic outcomes in real-world deployments. Control Barrier Functions (CBFs) offer a principled method to enforce dynamic safety -- traditionally deployed online via safety filters. While the result is safe behavior, the fact that the RL policy does not have knowledge of the CBF can lead to conservative behaviors. This paper proposes CBF-RL, a framework for generating safe behaviors with RL by enforcing CBFs in training. CBF-RL has two key attributes: (1) minimally modifying a nominal RL policy to encode safety constraints via a CBF term, (2) and safety filtering of the policy rollouts in training. Theoretically, we prove that continuous-time safety filters can be deployed via closed-form expressions on discrete-time roll-outs. Practically, we demonstrate that CBF-RL internalizes the safety constraints in the learned policy -- both enforcing safer actions and biasing towards safer rewards -- enabling safe deployment without the need for an online safety filter. We validate our framework through ablation studies on navigation tasks and on the Unitree G1 humanoid robot, where CBF-RL enables safer exploration, faster convergence, and robust performance under uncertainty, enabling the humanoid robot to avoid obstacles and climb stairs safely in real-world settings without a runtime safety filter.

SmartBench: Evaluating LLMs in Smart Homes with Anomalous Device States and Behavioral Contexts

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

By: Qingsong Zou · Zhi Yan · Zhiyao Xu · Kuofeng Gao · Jingyu Xiao · Yong Jiang

10 March 2026 at 12:00

arXiv:2603.06636v1 Announce Type: cross Abstract: Due to the strong context-awareness capabilities demonstrated by large language models (LLMs), recent research has begun exploring their integration into smart home assistants to help users manage and adjust their living environments. While LLMs have been shown to effectively understand user needs and provide appropriate responses, most existing studies primarily focus on interpreting and executing user behaviors or instructions. However, a critical function of smart home assistants is the ability to detect when the home environment is in an anomalous state. This involves two key requirements: the LLM must accurately determine whether an anomalous condition is present, and provide either a clear explanation or actionable suggestions. To enhance the anomaly detection capabilities of next-generation LLM-based smart home assistants, we introduce SmartBench, which is the first smart home dataset designed for LLMs, containing both normal and anomalous device states as well as normal and anomalous device state transition contexts. We evaluate 13 mainstream LLMs on this benchmark. The experimental results show that most state-of-the-art models cannot achieve good anomaly detection performance. For example, Claude-Sonnet-4.5 achieves only 66.1% detection accuracy on context-independent anomaly categories, and performs even worse on context-dependent anomalies, with an accuracy of only 57.8%. More experimental results suggest that next-generation LLM-based smart home assistants are still far from being able to effectively detect and handle anomalous conditions in the smart home environment. Our dataset is publicly available at https://github.com/horizonsinzqs/SmartBench.

Property-driven Protein Inverse Folding With Multi-Objective Preference Alignment

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

By: Xiaoyang Hou · Junqi Liu · Chence Shi · Xin Liu · Zhi Yang · Jian Tang

10 March 2026 at 12:00

arXiv:2603.06748v1 Announce Type: cross Abstract: Protein sequence design must balance designability, defined as the ability to recover a target backbone, with multiple, often competing, developability properties such as solubility, thermostability, and expression. Existing approaches address these properties through post hoc mutation, inference-time biasing, or retraining on property-specific subsets, yet they are target dependent and demand substantial domain expertise or careful hyperparameter tuning. In this paper, we introduce ProtAlign, a multi-objective preference alignment framework that fine-tunes pretrained inverse folding models to satisfy diverse developability objectives while preserving structural fidelity. ProtAlign employs a semi-online Direct Preference Optimization strategy with a flexible preference margin to mitigate conflicts among competing objectives and constructs preference pairs using in silico property predictors. Applied to the widely used ProteinMPNN backbone, the resulting model MoMPNN enhances developability without compromising designability across tasks including sequence design for CATH 4.3 crystal structures, de novo generated backbones, and real-world binder design scenarios, making it an appealing framework for practical protein sequence design.

Kunlun: Establishing Scaling Laws for Massive-Scale Recommendation Systems through Unified Architecture Design

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

By: Bojian Hou · Xiaolong Liu · Xiaoyi Liu · Jiaqi Xu · Yasmine Badr · Mengyue Hang · Sudhanshu Chanpuriya · Junqing Zhou · Yuhang Yang · Han Xu · Qiuling Suo · Laming Chen · Yuxi Hu · Jiasheng Zhang · Huaqing Xiong · Yuzhen Huang · Chao Chen · Yue Dong · Yi Yang · Shuo Chang · Xiaorui Gan · Wenlin Chen · Santanu Kolay · Darren Liu · Jade Nie · Chunzhi Yang · Ellie Wen · Jiyan Yang · Huayu Li

17 February 2026 at 13:00

arXiv:2602.10016v2 Announce Type: replace-cross Abstract: Deriving predictable scaling laws that govern the relationship between model performance and computational investment is crucial for designing and allocating resources in massive-scale recommendation systems. While such laws are established for large language models, they remain challenging for recommendation systems, especially those processing both user history and context features. We identify poor scaling efficiency as the main barrier to predictable power-law scaling, stemming from inefficient modules with low Model FLOPs Utilization (MFU) and suboptimal resource allocation. We introduce Kunlun, a scalable architecture that systematically improves model efficiency and resource allocation. Our low-level optimizations include Generalized Dot-Product Attention (GDPA), Hierarchical Seed Pooling (HSP), and Sliding Window Attention. Our high-level innovations feature Computation Skip (CompSkip) and Event-level Personalization. These advances increase MFU from 17% to 37% on NVIDIA B200 GPUs and double scaling efficiency over state-of-the-art methods. Kunlun is now deployed in major Meta Ads models, delivering significant production impact.

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