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Normal view

Received — 18 September 2026 ⏭

Advancing cancer detection and treatment using longitudinal routine clinical data

✇Cell
By: Fei Liu · Kai Wang · Hui Xu · Cheng Tang · Xian Shen · Meihao Wang · Lei Yang · Li Yang · Li Liu · Changxi Hu · Gen Li · Wei Wu · Zixing Zou · Bingzhou Li · Sian Liu · Jin Kang · Jungho Kong · Ting Li · Io Nam Wong · Xiaoying Huang · Gang Chen · Wenyang Lu · Ian Ziyar · Charlotte L. Zhang · Yiwen Sun · Weihong Lin · Caiwen Ou · Manson Fok · Taiwa Hou · Winston Wang · Kanmin Xue · Yun Yin · Hao Zhu · Jonathan Gootenberg · Omar O. Abudayyeh · Michael Karin · Alexandre Loupy · John E.J. Rasko · Trey Ideker · Huiyan Luo · Eric Oermann · Kang Zhang · International Consortium of Digital Twins in Healthcare and Medicine
27 July 2026 at 08:00
Liu et al. develop Oncoformer, a multimodal transformer that reads routine laboratory tests and chest X-rays already collected in everyday care. Across more than 3.6 million individuals, it detects cancer, infers tumor stage, and stratifies treatment response and recurrence risk, pointing toward risk-adapted cancer care built on data already in hand.
Received — 15 September 2026 ⏭

MP-Bench: Evaluating Voice Agents as a Multiparty Conversation Participant

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yi-Jen Shih · Shih-Yun Shan Kuan · Guan-Ting Lin · Kai-Wei Chang · Siddhant Arora · Shu-wen Yang · Abdelrahman Mohamed · Shinji Watanabe · Hung-yi Lee · David Harwath
14 September 2026 at 12:00
arXiv:2609.13076v1 Announce Type: cross Abstract: Conversational voice agents have advanced significantly, offering increasingly natural human-machine interactions through both cascaded and end-to-end architectures. However, while recent benchmarks extensively evaluate dyadic interactions and passive audio comprehension, they largely overlook a prevalent real-world scenario: multi-party conversations. Evaluating agents in these settings is fundamentally more challenging than in dyadic interactions due to the exponentially greater conversational complexity. For voice agents to integrate seamlessly into human group dynamics, they must not only generate contextually appropriate responses but also demonstrate a nuanced understanding of open turn-taking. To address this gap, we introduce Multiparty Bench (MP-Bench), the first benchmark specifically designed to objectively evaluate conversational speech systems as active participants within multi-party contexts. MP-Bench assesses agent behavior along two primary dimensions: turn-taking awareness and response appropriateness. Additionally, we incorporate comprehension-based question-answering tasks as a complementary evaluation. By benchmarking 12 voice agents, we find that real-time voice agents stay at or below 22% on multiparty comprehension and remain near chance on multiparty turn-taking, exposing an open challenge for real-time voice agents under multiparty scenario.
Received — 10 September 2026 ⏭

Revisiting the Shape Convention of Transformer Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Feng-Ting Liao · Guan-Ting Yi · Tzu-Quan Lin · Meng-Hsi Chen · Da-shan Shiu
10 September 2026 at 12:00
arXiv:2602.06471v2 Announce Type: replace-cross Abstract: The architectural shape of dense Transformers has remained remarkably stable: narrow-wide-narrow feed-forward networks (FFNs) consume most non-embedding parameters. Motivated by theoretical and empirical evidences that residual wide-narrow-wide (hourglass) MLPs remain expressive despite bottlenecks, we revisit whether this architectural convention is necessary for dense language models. We study Hourglass Transformers, which replace the conventional FFN with residual stacks of hourglass sub-MLPs and use hourglass attention to decouple residual-stream width from attention width. This exposes a practical depth-width trade-off: compressing the FFN intermediate dimension allows wider hidden states and fewer layers at matched parameter budgets. Across model scales from 113M to 8B parameters, Hourglass Transformers achieve language-modeling and downstream performance comparable to conventional Transformers, while improving training compute efficiency by $8.7\%$ at matched average downstream accuracy across the 906M, 3B, and 8B scales. After long-context extension, the 8B Hourglass model also outperforms its matched conventional baseline across 4k-64k context lengths. At 64k context, the reduced attention layer count lowers both computation and KV-cache requirements, yielding up to $1.93\times$ faster token decoding and $50\%$ lower KV-cache memory at the 1B scale. These results identify hourglass structures as a practical architecture-efficiency alternative for compute- and latency-conscious Transformer design.

A clinically derived lipid-endothelial signature links serum multi-omics to immune exclusion and clinical stratification in hepatocellular carcinoma

✇Omics in Hepatocellular
By: Luxi Yang · Qiaoying Jin · Lu Chen · Yating Liu · Yumin Li
2 September 2026 at 18:00

Ther Adv Med Oncol. 2026 Aug 31;18:17588359261481797. doi: 10.1177/17588359261481797. eCollection 2026.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) is driven by extensive metabolic reprogramming, vascular remodeling, and immune microenvironmental dysfunction. Although numerous stratification signatures have been proposed, few are grounded in clinically derived serum multi-omics and biologically linked to endothelial remodeling, endothelial regulation, and immune exclusion.

OBJECTIVES: This study aimed to identify a serum-derived lipid-endothelial program associated with immune exclusion and clinical stratification in HCC.

DESIGN: A translational multi-omics study integrating clinically collected serum samples, public transcriptomic cohorts, single-cell RNA sequencing, and experimental validation.

METHODS: Proteomic and metabolomic sequencing was performed on serum samples from patients with HCC and normal controls. Dysregulated pathways were integrated with transcriptomic data from TCGA-LIHC and ICGC-LIRI-JP cohorts to identify genes jointly associated with lipid metabolism and leukocyte transendothelial migration. A risk score was calculated using the expression of PON1, TXNRD1, CLDN4, CLDN6, CYP2C9, and CTSA. Higher expression of TXNRD1, CLDN4, CLDN6, and CTSA contributed to a higher risk score, whereas PON1 and CYP2C9 contributed protective coefficients. Immune contexture, tumor mutation burden, and exploratory therapeutic sensitivity patterns were further evaluated using transcriptome-based drug sensitivity prediction, followed by single-cell RNA sequencing and experimental expression validation.

RESULTS: Serum multi-omics analysis revealed prominent dysregulation of lipid metabolic pathways and leukocyte transendothelial migration-related processes in HCC. Integrative analysis identified a six-gene lipid-endothelial signature (PON1, TXNRD1, CLDN4, CLDN6, CYP2C9, and CTSA) that stratified patients into high- and low-risk groups. In the TCGA-LIHC cohort, high-risk patients had significantly poorer overall survival than low-risk patients (log-rank P < 0.0001), and this survival-stratifying association was externally supported in the ICGC-LIRI-JP cohort (log-rank P = 0.016). The high-risk phenotype was associated with immune-excluded features, distinct somatic mutation patterns, and altered predicted sensitivity to several selected anticancer agents. Single-cell analysis and experimental assays further supported the association between the six-gene program, malignant epithelial states, endothelial-related remodeling, and immune microenvironmental heterogeneity.

CONCLUSION: This study defines a clinically derived lipid-endothelial program associated with immune exclusion, adverse prognosis, and potential differences in therapeutic vulnerability in HCC. The proposed signature provides a biologically informed framework for prognostic assessment and may support future evaluation of targeted interventions in HCC.

PMID:42682961 | PMC:PMC13530516 | DOI:10.1177/17588359261481797

Received — 27 May 2026 ⏭

APT-Agent: Automated Penetration Testing using Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: William Guanting Li (University of Queensland) · Alsharif Abuadbba (CSIRO Data61) · Kristen Moore (CSIRO Data61) · Dan Dongseong Kim (University of Queensland)
26 May 2026 at 12:00
arXiv:2605.24949v1 Announce Type: cross Abstract: Penetration testing is essential to securing modern web infrastructures, yet traditional manual methods struggle to keep pace with their scale and complexity. Large Language Models (LLMs) offer new opportunities for automating these tasks, but existing approaches face two persistent challenges: hallucination of technical entities and insufficient long-term contextual memory. To address these issues, we present APT-Agent, a fully automated LLM-driven penetration testing framework that systematically orchestrates reconnaissance, exploitation, and exfiltration. APT-Agent introduces a hybrid rectification module to recover hallucinated commands and a command-specific memory architecture to preserve operational context across multi-step attack sequences. We evaluate our APT-Agent on Metasploitable 2 against seven vulnerable services spanning web, database, and network protocols. APT-Agent achieves an 84.29% end-to-end exploitation success rate, compared to 48.57% (Script Kiddie) and 18.57% (PentestGPT) under matched conditions. By reducing cognitive burden and minimizing reliance on human intervention, APT-Agent represents a step toward scalable, reliable, and cognitively efficient automation for penetration testing.
  • ✇cs.AI, q-bio.NC updates on arXiv.org
  • Contractual Skills: A GovernSpec Design Framework for Enterprise AI Agents Ting Liu
    arXiv:2605.22634v2 Announce Type: replace-cross Abstract: Skills have become a practical packaging mechanism for agent instructions, workflows, scripts, and reference materials. In enterprise settings, however, a skill often needs to express more than task guidance: goals, input boundaries, permissions, human approval points, evidence requirements, output contracts, quality criteria, verification steps, and handoff rules. This paper proposes contractual skills, a GovernSpec-inspired design fram
     
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Contractual Skills: A GovernSpec Design Framework for Enterprise AI Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ting Liu
26 May 2026 at 12:00
arXiv:2605.22634v2 Announce Type: replace-cross Abstract: Skills have become a practical packaging mechanism for agent instructions, workflows, scripts, and reference materials. In enterprise settings, however, a skill often needs to express more than task guidance: goals, input boundaries, permissions, human approval points, evidence requirements, output contracts, quality criteria, verification steps, and handoff rules. This paper proposes contractual skills, a GovernSpec-inspired design framework for organizing SKILL.md files as readable task contracts while preserving lightweight skill discovery and progressive loading. The framework clarifies the boundary between contractual skills, GovernSpec YAML contracts, Model Context Protocol (MCP) surfaces, tool adapters, runtime guardrails, tracing, and evaluation systems. We evaluate the framework with three offline empirical studies. The first text-generation experiment covers three enterprise skills, fifteen synthetic tasks, four instruction conditions, and eight generation models, producing 960 outputs and 1680 cross-judge score records. The second study is a public-skill A/B expansion: eight public skills are compared with contractual rewrites across forty-eight synthetic tasks, six generation models, two repeats, 1152 outputs, and two complete judge files. In this setting, contractual skills raise mean quality from 4.692 to 4.914 and reduce critical-error rate from 0.083 to 0.013. The third study is an offline tool-calling challenge with eight models and 192 simulated tool-call records. The results suggest that contractual skills are best understood as a governance layer that makes task intent, boundaries, and acceptance criteria explicit, not as a standalone safety mechanism.

Pan-neurodegeneration proteomics reveals disease subtypes and molecular signatures

✇Cell
By: Him K. Shrestha · Huan Sun · Jay M. Yarbro · DongGeun Lee · Danting Liu · Erming Wang · Meghan McReynolds · Nan Zhang · Boer Xie · Shu Yang · Kaiwen Yu · Suresh Poudel · Yuxin Li · Zuo-Fei Yuan · Dehui Kong · Minghui Wang · Zhen Wang · Mingming Niu · Hong Wang · Masihuz Zaman · Ju Wang · David R. Vanderwall · Yu Sun · Zhiping Wu · Ping-Chung Chen · Bing Bai · Anthony A. High · Júlia Faura · Chunyu Liu · David A. Bennett · Erik C.B. Johnson · Nicholas T. Seyfried · Allan I. Levey · Vahram Haroutunian · Geidy E. Serrano · Thomas G. Beach · Michael DeTure · Takahisa Kanekiyo · Ronald C. Petersen · Guojun Bu · Pamela J. McLean · Dennis W. Dickson · Rosa Rademakers · Gang Yu · Xusheng Wang · Bin Zhang · Junmin Peng
23 March 2026 at 08:00
A pan-neurodegeneration atlas built from multilayer, deep proteomics of 2,279 brain samples across 6 major diseases integrates whole proteome, detergent-insoluble proteome, and posttranslational modifications to enable intra- and inter-disease comparisons to reveal disease-specific subtypes and dysregulated pathways, while identifying shared changes such as GPNMB upregulation and NPTX2 downregulation.
Received — 8 April 2026 ⏭

WNT7A correlates with immunosuppression and predicts adverse prognosis in lung adenocarcinoma: Potential implication of the NF-kappaB/CCL2 Axis

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Xiang Guo · Zhengfang Xiao · Yao Zhang · Li Wang · Hui Hu · Ting Liu · Yingjun Kong
7 April 2026 at 18:00

Cytokine. 2026 Apr 6;202:157144. doi: 10.1016/j.cyto.2026.157144. Online ahead of print.

ABSTRACT

BACKGROUND: The remodeling of the tumor immune microenvironment (TME) is a pivotal determinant of therapeutic efficacy and clinical outcome in Lung Adenocarcinoma (LUAD). While WNT signaling is a known oncogenic driver, the specific immunomodulatory role of WNT7A and its potential crosstalk with inflammatory pathways in LUAD remain to be fully elucidated. We sought to define the prognostic value of WNT7A and explore the molecular mechanisms by which it may foster an immunosuppressive TME.

METHODS: We performed a multi-omics analysis utilizing the TCGA-LUAD cohort (N = 508) and validated findings in an independent external cohort (GSE30219, N = 293). The prognostic significance of WNT7A was evaluated using Kaplan-Meier and multivariate Cox regression analyses. TME composition was dissected via ssGSEA, focusing on myeloid-derived suppressor cell (MDSC) infiltration. Mechanistic pathways were identified using Gene Set Enrichment Analysis (GSEA) and gene co-expression networks.

RESULTS: High WNT7A expression was identified as a significant predictor of poor Overall Survival (OS) in the TCGA cohort (P < 0.05) and validated in the external cohort (P < 0.05). Multivariate analysis confirmed WNT7A as an independent prognostic risk factor (HR = 1.085, P = 0.036). Immunologically, WNT7A expression was positively correlated with MDSC infiltration (R = 0.43, P < 0.001), suggesting a shift towards an immune-tolerant phenotype. Mechanistically, GSEA revealed a robust activation of inflammatory signaling in the high-WNT7A group. Specifically, the TNFA Signaling via NF-κB pathway was significantly enriched(NES = 2.52, P < 0.001). Consistent with this pathway activation, WNT7A showed a statistically significant positive correlation with CCL2 (P < 0.001), a critical chemokine for MDSC recruitment, implicating the NF-κB/CCL2 axis in this process.

CONCLUSION: WNT7A serves as a prognostic biomarker linked to immune evasion in LUAD, potentially by modulating the NF-κB/CCL2/MDSC axis. This study identifies WNT7A as a potential therapeutic target to remodel the immune microenvironment, providing a rationale for future investigations into WNT-targeted strategies to improve immunotherapy efficacy.

PMID:41946008 | DOI:10.1016/j.cyto.2026.157144

WNT7A correlates with immunosuppression and predicts adverse prognosis in lung adenocarcinoma: Potential implication of the NF-kappaB/CCL2 Axis

✇Omics In Lung
By: Xiang Guo · Zhengfang Xiao · Yao Zhang · Li Wang · Hui Hu · Ting Liu · Yingjun Kong
7 April 2026 at 18:00

Cytokine. 2026 Apr 6;202:157144. doi: 10.1016/j.cyto.2026.157144. Online ahead of print.

ABSTRACT

BACKGROUND: The remodeling of the tumor immune microenvironment (TME) is a pivotal determinant of therapeutic efficacy and clinical outcome in Lung Adenocarcinoma (LUAD). While WNT signaling is a known oncogenic driver, the specific immunomodulatory role of WNT7A and its potential crosstalk with inflammatory pathways in LUAD remain to be fully elucidated. We sought to define the prognostic value of WNT7A and explore the molecular mechanisms by which it may foster an immunosuppressive TME.

METHODS: We performed a multi-omics analysis utilizing the TCGA-LUAD cohort (N = 508) and validated findings in an independent external cohort (GSE30219, N = 293). The prognostic significance of WNT7A was evaluated using Kaplan-Meier and multivariate Cox regression analyses. TME composition was dissected via ssGSEA, focusing on myeloid-derived suppressor cell (MDSC) infiltration. Mechanistic pathways were identified using Gene Set Enrichment Analysis (GSEA) and gene co-expression networks.

RESULTS: High WNT7A expression was identified as a significant predictor of poor Overall Survival (OS) in the TCGA cohort (P < 0.05) and validated in the external cohort (P < 0.05). Multivariate analysis confirmed WNT7A as an independent prognostic risk factor (HR = 1.085, P = 0.036). Immunologically, WNT7A expression was positively correlated with MDSC infiltration (R = 0.43, P < 0.001), suggesting a shift towards an immune-tolerant phenotype. Mechanistically, GSEA revealed a robust activation of inflammatory signaling in the high-WNT7A group. Specifically, the TNFA Signaling via NF-κB pathway was significantly enriched(NES = 2.52, P < 0.001). Consistent with this pathway activation, WNT7A showed a statistically significant positive correlation with CCL2 (P < 0.001), a critical chemokine for MDSC recruitment, implicating the NF-κB/CCL2 axis in this process.

CONCLUSION: WNT7A serves as a prognostic biomarker linked to immune evasion in LUAD, potentially by modulating the NF-κB/CCL2/MDSC axis. This study identifies WNT7A as a potential therapeutic target to remodel the immune microenvironment, providing a rationale for future investigations into WNT-targeted strategies to improve immunotherapy efficacy.

PMID:41946008 | DOI:10.1016/j.cyto.2026.157144

Readable Minds: Emergent Theory-of-Mind-Like Behavior in LLM Poker Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hsieh-Ting Lin · Tsung-Yu Hou
7 April 2026 at 12:00
arXiv:2604.04157v1 Announce Type: new Abstract: Theory of Mind (ToM) -- the ability to model others' mental states -- is fundamental to human social cognition. Whether large language models (LLMs) can develop ToM has been tested exclusively through static vignettes, leaving open whether ToM-like reasoning can emerge through dynamic interaction. Here we report that autonomous LLM agents playing extended sessions of Texas Hold'em poker progressively develop sophisticated opponent models, but only when equipped with persistent memory. In a 2x2 factorial design crossing memory (present/absent) with domain knowledge (present/absent), each with five replications (N = 20 experiments, ~6,000 agent-hand observations), we find that memory is both necessary and sufficient for ToM-like behavior emergence (Cliff's delta = 1.0, p = 0.008). Agents with memory reach ToM Level 3-5 (predictive to recursive modeling), while agents without memory remain at Level 0 across all replications. Strategic deception grounded in opponent models occurs exclusively in memory-equipped conditions (Fisher's exact p
Received — 6 April 2026 ⏭
Received — 4 April 2026 ⏭

Countering Catastrophic Forgetting of Large Language Models for Better Instruction Following via Weight-Space Model Merging

✇cs.AI, q-bio.NC updates on arXiv.org
By: Mengxian Lyu · Cheng Peng · Ziyi Chen · Mengyuan Zhang · Jieting Li Lu · Yonghui Wu
3 April 2026 at 12:00
arXiv:2604.01538v1 Announce Type: cross Abstract: Large language models have been adopted in the medical domain for clinical documentation to reduce clinician burden. However, studies have reported that LLMs often "forget" a significant amount of instruction-following ability when fine-tuned using a task-specific medical dataset, a critical challenge in adopting general-purpose LLMs for clinical applications. This study presents a model merging framework to efficiently adapt general-purpose LLMs to the medical domain by countering this forgetting issue. By merging a clinical foundation model (GatorTronLlama) with a general instruct model (Llama-3.1-8B-Instruct) via interpolation-based merge methods, we seek to derive a domain-adapted model with strong performance on clinical tasks while retaining instruction-following ability. Comprehensive evaluation across medical benchmarks and five clinical generation tasks (e.g., radiology and discharge summarization) shows that merged models can effectively mitigate catastrophic forgetting, preserve clinical domain expertise, and retain instruction-following ability. In addition, our model merging strategies demonstrate training efficiency, achieving performance on par with fully fine-tuned baselines under severely constrained supervision (e.g., 64-shot vs. 256-shot). Consequently, weight-space merging constitutes a highly scalable solution for adapting open-source LLMs to clinical applications, facilitating broader deployment in resource-constrained healthcare environments.
Received — 2 April 2026 ⏭
Received — 26 March 2026 ⏭

Dual Antitumor Effects of Solanum incanum L. in Lung and Colonic Adenocarcinomas via TRAIL-Mediated Extrinsic Apoptotic Pathway: A Network Pharmacology and Multi-Omics Investigation

✇Omics In Lung
By: Chih-Ting Lin · Li-Ren Yeh · Jiun-Ting Wu · Chao-Ming Hung · Chang-Yin Lee
21 March 2026 at 18:00

Curr Med Chem. 2026 Mar 17. doi: 10.2174/0109298673415187251212085809. Online ahead of print.

ABSTRACT

BACKGROUND: Solanum incanum L. (S. incanum) is a Traditional Chinese Medicine (TCM) known for its heat-clearing and detoxifying properties. This study evaluates the anti-cancer potential of S. incanum in lung and colorectal adenocarcinoma. Also, we aim to confirm the connection between the lung and the large intestine, as proposed in TCM theory.

METHODS: This study employed network pharmacology analysis, MTT and morphological analysis, Western blotting, and molecular docking to investigate the anti-cancer effects of S. incanum, particularly through its activation of the TRAIL-mediated extrinsic apoptotic pathway.

RESULTS: This study identified 15 active compounds in S. incanum, with glycosylated alkaloids such as α-solanine and solasonine highlighted as key bioactive compounds. KEGG and GO analyses revealed that S. incanum influences apoptosis-related pathways, particularly the extrinsic apoptotic pathway, regulating mediators such as CASP3, CASP8, and various TNF- related receptors. The anticancer potential was assessed through cellular studies using A549 and HT29 cell lines, with MTT cell viability assays showing that α-solanine induces apoptosis, with IC50 values between 20 and 30 μM. Western blot analysis demonstrated that S. incanum activates apoptosis via the TRAIL-mediated extrinsic apoptotic pathway, promoting CASP8, CASP3, and PARP1 cleavage in a dose-dependent manner, affecting DNA synthesis. Molecular docking further identified α-solanine and solasonine as compounds that bind to critical proteins in the TRAIL-related apoptotic pathway, confirming their roles in promoting apoptosis.

DISCUSSION: The shared activation of the TRAIL-mediated apoptotic pathway in both lung and colorectal adenocarcinoma models suggests a common molecular mechanism. These findings provide experimental evidence linking traditional ethnopharmacological concepts with apoptosis-related anti-cancer activity.

CONCLUSIONS: This study emphasizes that the main compound of S. incanum, α-solanine, supports the ethnopharmacological concept of "Exterior-interior Pairing of the Lung and Large Intestine", thereby demonstrating that both lung and colorectal adenocarcinomas share a common anticancer pathway through a TRAIL-related apoptotic mechanism.

PMID:41863159 | DOI:10.2174/0109298673415187251212085809

Human-specific features of the cerebellum and ZP2-regulated synapse development

✇Cell
By: Suel-Kee Kim · Adriana Cherskov · Aastha Sindhwani · Sang-Hun Choi · Hyojin Kim · Ming-Li Li · Menglei Zhang · Xoel Mato-Blanco · Yuting Liu · Nicola Micali · David M. Young · Mark Estacion · Yueqi Zhang · José Manuel Ruiz-Jiménez · Anandita Nadkarni · Victor Luria · Suvimal Kumar Sindhu · Ipsita Chatterjee · Akemi Shibata · Dan Liang · Hyesun Cho · Saejeong Park · Ana Spajic · Rothem Kovner · Martina Glavan · Rachel J. Chen · Ryan D. Risgaard · Xinyun Li · Sirisha Pochareddy · Amir Karger · Anita Huttner · Yury M. Morozov · Etienne W. Daadi · Carlo Colantuoni · Kevin T. Gobeske · John J. Ely · Patrick R. Hof · Marcel M. Daadi · Chet C. Sherwood · Alvaro Duque · Shaojie Ma · Andre M.M. Sousa · Stephen G. Waxman · Pasko Rakic · Gabriel Santpere · Stephan J. Sanders · Nenad Sestan
11 March 2026 at 08:00
Human-specific transcriptomic and regulatory features are present in the cerebellum, with ZP2 playing a key role in synapse regulation. ZP2 expression is induced by pontine mossy fibers, leading to decreased synaptic proteins and neuronal activity, which provides insights into the evolutionary development of the human cerebellum.
Received — 14 March 2026 ⏭

NFATC2::NUTM2 Fusion Defines a Novel Primary Pulmonary Epithelial Tumor With a Distinctive Immunophenotype

✇Pulmonary nodule
By: Lanlan Feng · Qiao Liu · Xiaoyan Liu · Shumei Wang · Junting Li · Yu Han · Xiaorong Mu · Yuanyuan Wang · Junfeng Jiang · Chun Wu · Xiujuan Han · Shaojun Zhu · Fuqin Zhang · Jing Tian · Zhuo Wang · Wei Zhang · Li Gong
13 March 2026 at 18:00

Am J Surg Pathol. 2026 Jun 1;50(6):695-704. doi: 10.1097/PAS.0000000000002533. Epub 2026 Mar 13.

ABSTRACT

With the application of molecular techniques in pathologic diagnosis, several novel primary pulmonary epithelial tumors have been continuously discovered and classified under the WHO classification of thoracic tumors. Recently, a pulmonary tumor with NFATC2 :: NUTM2B fusion was first documented, but the spectrum of NFATC2::NUTM2 fusion variants and their associated pathologic features remains incompletely characterized. Coincidentally, we also found and described 6 primary pulmonary tumors harboring recurrent NFATC2::NUTM2A/E fusions through integrated genomic analysis. These patients, including 4 females and 2 males, with a median age of 53 years, presented with incidentally detected peripheral lung nodules composed of monotonous epithelioid cells arranged in cords, nests, and trabeculae within a prominent desmoplastic stroma. All tumors exhibited a consistent immunophenotype: CK5/6+/GATA3+/calponin+/EMA+/DOG1 (perinuclear dot-like staining)/p63-. High-throughput chromosome conformation capture (Hi-C) analysis showed the structural variation of NFATC2::NUTM2E in all 6 cases, whereas RNA sequencing detected the fusion transcripts in 5 cases ( NFATC2::NUTM2A , n=2; NFATC2::NUTM2E , n=3). Ultrastructural examination of 1 case suggested epithelial differentiation. All patients remained disease-free after complete resection (median follow-up: 24 mo; range: 9 to 41 mo). These findings define a novel primary pulmonary tumor entity driven by NFATC2::NUTM2 fusions, and characterized by a distinctive immunophenotype, expanding the spectrum of NUTM2 -associated neoplasms. Our study underscores the utility of multiomics approaches for characterizing rare neoplasms and provides a diagnostic framework for this entity.

PMID:41821426 | DOI:10.1097/PAS.0000000000002533

NFATC2::NUTM2 Fusion Defines a Novel Primary Pulmonary Epithelial Tumor With a Distinctive Immunophenotype

✇Omics In Lung
By: Lanlan Feng · Qiao Liu · Xiaoyan Liu · Shumei Wang · Junting Li · Yu Han · Xiaorong Mu · Yuanyuan Wang · Junfeng Jiang · Chun Wu · Xiujuan Han · Shaojun Zhu · Fuqin Zhang · Jing Tian · Zhuo Wang · Wei Zhang · Li Gong
13 March 2026 at 18:00

Am J Surg Pathol. 2026 Mar 13. doi: 10.1097/PAS.0000000000002533. Online ahead of print.

ABSTRACT

With the application of molecular techniques in pathologic diagnosis, several novel primary pulmonary epithelial tumors have been continuously discovered and classified under the WHO classification of thoracic tumors. Recently, a pulmonary tumor with NFATC2::NUTM2B fusion was first documented, but the spectrum of NFATC2::NUTM2 fusion variants and their associated pathologic features remains incompletely characterized. Coincidentally, we also found and described 6 primary pulmonary tumors harboring recurrent NFATC2::NUTM2A/E fusions through integrated genomic analysis. These patients, including 4 females and 2 males, with a median age of 53 years, presented with incidentally detected peripheral lung nodules composed of monotonous epithelioid cells arranged in cords, nests, and trabeculae within a prominent desmoplastic stroma. All tumors exhibited a consistent immunophenotype: CK5/6+/GATA3+/calponin+/EMA+/DOG1 (perinuclear dot-like staining)/p63-. High-throughput chromosome conformation capture (Hi-C) analysis showed the structural variation of NFATC2::NUTM2E in all 6 cases, whereas RNA sequencing detected the fusion transcripts in 5 cases (NFATC2::NUTM2A, n=2; NFATC2::NUTM2E, n=3). Ultrastructural examination of 1 case suggested epithelial differentiation. All patients remained disease-free after complete resection (median follow-up: 24 mo; range: 9 to 41 mo). These findings define a novel primary pulmonary tumor entity driven by NFATC2::NUTM2 fusions, and characterized by a distinctive immunophenotype, expanding the spectrum of NUTM2-associated neoplasms. Our study underscores the utility of multiomics approaches for characterizing rare neoplasms and provides a diagnostic framework for this entity.

PMID:41821426 | DOI:10.1097/PAS.0000000000002533

Received — 5 March 2026 ⏭

T2S-Bench & Structure-of-Thought: Benchmarking and Prompting Comprehensive Text-to-Structure Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qinsi Wang · Hancheng Ye · Jinhee Kim · Jinghan Ke · Yifei Wang · Martin Kuo · Zishan Shao · Dongting Li · Yueqian Lin · Ting Jiang · Chiyue Wei · Qi Qian · Wei Wen · Helen Li · Yiran Chen
5 March 2026 at 13:00
arXiv:2603.03790v1 Announce Type: cross Abstract: Think about how human handles complex reading tasks: marking key points, inferring their relationships, and structuring information to guide understanding and responses. Likewise, can a large language model benefit from text structure to enhance text-processing performance? To explore it, in this work, we first introduce Structure of Thought (SoT), a prompting technique that explicitly guides models to construct intermediate text structures, consistently boosting performance across eight tasks and three model families. Building upon this insight, we present T2S-Bench, the first benchmark designed to evaluate and improve text-to-structure capabilities of models. T2S-Bench includes 1.8K samples across 6 scientific domains and 32 structural types, rigorously constructed to ensure accuracy, fairness, and quality. Evaluation on 45 mainstream models reveals substantial improvement potential: the average accuracy on the multi-hop reasoning task is only 52.1%, and even the most advanced model achieves 58.1% node accuracy in end-to-end extraction. Furthermore, on Qwen2.5-7B-Instruct, SoT alone yields an average +5.7% improvement across eight diverse text-processing tasks, and fine-tuning on T2S-Bench further increases this gain to +8.6%. These results highlight the value of explicit text structuring and the complementary contributions of SoT and T2S-Bench. Dataset and eval code have been released at https://t2s-bench.github.io/T2S-Bench-Page/.
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