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Sexual dimorphism in the complete Drosophila male central nervous system connectome

The Drosophila whole male central nervous system connectome enables end-to-end analysis of sensorimotor circuits. Comparison with existing female datasets shows that brain-wide wiring differences between the sexes are concentrated in higher centers.

A framework for building a synthetic cell from the SynCell Asia Initiative

Nature Biotechnology, Published online: 26 May 2026; doi:10.1038/s41587-026-03153-w

Building a living cell from scratch requires overcoming a bottleneck that has remained unresolved despite decades of progress: orchestrating the spatiotemporal integration of core functional modules. To tackle this barrier, the SynCell Asia Initiative outlines a strategy for developing core functional modules followed by their systems-level integration through the establishment of a centralized, artificial intelligence (AI)-driven biofoundry.

Integrating clinical and multiomics evidence based on disease module theory: deciphering the comorbidity network of psoriasis vulgaris via the Ising model for mechanistic insights

Front Immunol. 2026 Apr 14;17:1744789. doi: 10.3389/fimmu.2026.1744789. eCollection 2026.

ABSTRACT

Psoriasis vulgaris (PV), a chronic immune-mediated inflammatory dermatosis, is associated with a significant burden of systemic comorbidities. Traditional comorbidity research methods struggle to reveal its complex interconnectedness. Based on large-scale retrospective cohort data, we constructed a PV comorbidity network using the Ising model from statistical physics. Weighted network centrality analysis was used to identify core and hub nodes and elucidate shared molecular mechanisms at the multiomics level (nontargeted proteomics and lipid peroxidation metabolomics). Finally, the impact of IL-17A inhibition (IL-17Ai) on PV and atherosclerosis (assessed by carotid Doppler color ultrasound) was evaluated using a prospective intervention study. The Ising model identified atherosclerosis- coronary heart disease (CHD) as the core comorbidity (degree centrality >10), with pulmonary nodules, hypertension, and fatty liver serving as key hub nodes (betweenness centrality >60). Multiomics analysis revealed a core molecular mechanism in PV, involving immune inflammation, oxidative stress, lipid metabolism disorder, and coagulation abnormalities, where the oxidative stress molecule GPX3 acts as a critical hub. Following IL-17Ai intervention, both skin lesions and early atherosclerosis markers significantly improved, accompanied by downregulation of the proinflammatory peripheral blood factor S100A9 and upregulation of anti-inflammatory lipid peroxidation metabolites (e.g., 17(R)-RVD1). This study systematically revealed the modular hierarchical structure of PV comorbidities at the network topology and molecular mechanism levels, confirming the central role of the IL-17 signaling pathway in driving the comorbidity network. This conclusion was further clinically validated by IL-17Ai intervention outcomes. This research provides theoretical and clinical evidence for early identification, prioritized management, and "one drug, multiple targets" therapeutic strategies for treating PV comorbidities.

PMID:42058202 | PMC:PMC13121148 | DOI:10.3389/fimmu.2026.1744789

ImplicitRM: Unbiased Reward Modeling from Implicit Preference Data for LLM alignment

arXiv:2603.23184v1 Announce Type: cross Abstract: Reward modeling represents a long-standing challenge in reinforcement learning from human feedback (RLHF) for aligning language models. Current reward modeling is heavily contingent upon experimental feedback data with high collection costs. In this work, we study \textit{implicit reward modeling} -- learning reward models from implicit human feedback (e.g., clicks and copies) -- as a cost-effective alternative. We identify two fundamental challenges in implicit reward modeling: (1) Implicit preference data lacks definitive negative samples, which makes standard positive-negative classification methods inapplicable; (2) Implicit preference data suffers from user preference bias, where different responses have different propensities to elicit user feedback actions, which exacerbates the difficulty of distinguishing definitive negative samples. To address these challenges, we propose ImplicitRM, which aims to learn unbiased reward models from implicit preference data. ImplicitRM stratifies training samples into four latent groups via a stratification model. Building on this, it derives a learning objective through likelihood maximization, which we prove is theoretically unbiased, effectively resolving both challenges. Experiments demonstrate that ImplicitRM learns accurate reward models across implicit preference datasets. Code is available on our project website.

SIRT3 deacetylates STEAP4 to modulate cuproptosis sensitivity via mitochondrial metabolic reprogramming in HBV-related HCC

Cell Death Differ. 2026 Mar 16. doi: 10.1038/s41418-026-01713-w. Online ahead of print.

ABSTRACT

Hepatitis B virus (HBV) infection remains a leading etiological driver of hepatocellular carcinoma (HCC). Cuproptosis is a recently defined copper-dependent form of regulated cell death that selectively eliminates mitochondria-dependent cells; whether HBV rewires this vulnerability remains unknown. Here we unveil a novel HBV X protein (HBx)-driven mechanism of cuproptosis evasion. Integrative analysis of clinical specimens, HBx-transgenic (HBx-Tg) mice, and multi-omics datasets revealed marked downregulation of STEAP4 (six-transmembrane epithelial antigen of prostate 4), a metalloreductase essential for cuproptosis sensitivity, in HBV-positive HCC. Mechanistically, HBx attenuates sirtuin 3 (SIRT3), impairing deacetylation of STEAP4 at lysine 404 and abolishing its mitochondrial targeting. Consequently, cells switch from the tricarboxylic acid (TCA) cycle respiration to glycolysis, reducing sensitivity to the copper ionophore elesclomol (ES). Restoring STEAP4 expression or pharmacological activation of SIRT3 with honokiol (HKL) re-instated mitochondrial STEAP4 localization and re-sensitized HBV-related HCC cells to cuproptosis; combination with ES produced synergistic tumor suppression in vitro and in orthotopic models. Collectively, our findings establish the SIRT3-STEAP4 axis as a novel regulator of cuproptosis resistance in HBV-related HCC. HBx-mediated repression of SIRT3 disrupts STEAP4 deacetylation and mitochondrial targeting, fostering metabolic reprogramming and evasion of copper-induced cell death. The results provide a pre-clinical rationale for copper-directed combination strategies in HBV-associated HCC.

PMID:41840161 | DOI:10.1038/s41418-026-01713-w

Improving Diffusion Planners by Self-Supervised Action Gating with Energies

arXiv:2603.02650v1 Announce Type: cross Abstract: Diffusion planners are a strong approach for offline reinforcement learning, but they can fail when value-guided selection favours trajectories that score well yet are locally inconsistent with the environment dynamics, resulting in brittle execution. We propose Self-supervised Action Gating with Energies (SAGE), an inference-time re-ranking method that penalises dynamically inconsistent plans using a latent consistency signal. SAGE trains a Joint-Embedding Predictive Architecture (JEPA) encoder on offline state sequences and an action-conditioned latent predictor for short horizon transitions. At test time, SAGE assigns each sampled candidate an energy given by its latent prediction error and combines this feasibility score with value estimates to select actions. SAGE can integrate into existing diffusion planning pipelines that can sample trajectories and select actions via value scoring; it requires no environment rollouts and no policy re-training. Across locomotion, navigation, and manipulation benchmarks, SAGE improves the performance and robustness of diffusion planners.

CellINR: Implicitly Overcoming Photo-induced Artifacts in 4D Live Fluorescence Microscopy

arXiv:2508.19300v2 Announce Type: replace-cross Abstract: 4D live fluorescence microscopy is often compromised by prolonged high intensity illumination which induces photobleaching and phototoxic effects that generate photo-induced artifacts and severely impair image continuity and detail recovery. To address this challenge, we propose the CellINR framework, a case-specific optimization approach based on implicit neural representation. The method employs blind convolution and structure amplification strategies to map 3D spatial coordinates into the high frequency domain, enabling precise modeling and high-accuracy reconstruction of cellular structures while effectively distinguishing true signals from artifacts. Experimental results demonstrate that CellINR significantly outperforms existing techniques in artifact removal and restoration of structural continuity, and for the first time, a paired 4D live cell imaging dataset is provided for evaluating reconstruction performance, thereby offering a solid foundation for subsequent quantitative analyses and biological research. The code and dataset will be public.
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