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Received β€” 10 September 2026 ⏭

Self-Evolving Scientific Agent Designs Physically Reasoned White-Box Fluid Control

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Boai Sun Β· Wenjin Guo Β· Zongmin Yu Β· Liu Yang
10 September 2026 at 12:00
arXiv:2606.08405v4 Announce Type: replace Abstract: While neural networks excel in autonomous control, their black-box nature makes control decisions difficult to interpret and diagnose in dynamic fluids. Here, we show how self-evolving scientific agents can design explicit, neural-network-free white-box controllers by iteratively interpreting simulation evidence, accumulating control knowledge and refining controller code. We demonstrate this approach on an underactuated two-joint swimmer navigating unsteady flows via joint angular accelerations. Starting from a target-blind propulsive controller, the agent gradually constructs key mechanisms, including travelling-wave propulsion, body-frame guidance, phase-selective steering, redirect bursts and adaptive relief. The resulting controllers reach targets and generalize across changes in target position, wake geometry, cylinder count, and inflow speed without revision. Moreover, 2D control priors transfer successfully to accelerate 3D adaptation. Our work demonstrates that self-evolving agents can autonomously design physically reasoned and generalizable white-box fluid control, showing a promising paradigm beyond traditional reinforcement learning and black-box neural network control.

Enteric glial serotonin signaling drives anti-tumor immunity in colorectal cancer

βœ‡Cell
By: Liu Yang Β· Michael A. Wheeler
3 September 2026 at 08:00
Peripheral serotonergic signaling has been implicated in diverse physiological processes, yet its role in coordinating glial-immune interactions remains poorly understood. In this issue of Cell, Wen and colleagues identify enteric glial cells as critical effectors of peripheral 5-HT2AR agonism, uncovering a serotonergic neuroimmune circuit that drives cytotoxic T cell-mediated immunity against colorectal cancer.
Received β€” 27 May 2026 ⏭

Integrative bioinformatics and experimental validation reveal quercetin as a potential multi-target therapeutic agent in hepatocellular carcinoma

βœ‡Omics in Hepatocellular
By: Longfei Zeng Β· Gang Zhu Β· Shaozhen Tang Β· Tao Wu Β· Liu Yang Β· Xin Zhang Β· Li Dai
18 May 2026 at 18:00

Cytotechnology. 2026 Jun;78(3):119. doi: 10.1007/s10616-026-00993-x. Epub 2026 May 14.

ABSTRACT

Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer worldwide, with increasing incidence and mortality rates. Although several targeted therapies are currently available, the therapeutic outcomes remain unsatisfactory due to the high heterogeneity and drug resistance of HCC. Therefore, novel molecular mechanisms and therapeutic strategies urgently need to be explored. In this study, we obtained the GSE39791 dataset from the GEO database and identified 1,186 differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was conducted to obtain 776 key module genes, which were intersected with 11,671 HCC-related genes from the GeneCards database, resulting in 226 candidate genes. A protein-protein interaction (PPI) network was constructed using the STRING database, and the top 20 hub genes were identified using the MNC algorithm in Cytoscape. Among these, the five most significant hub genes-RFC4, TOP2A, AURKA, HSP90AA1, and MCM4-were selected for further analysis. KEGG enrichment analysis was performed to explore their functional pathways. Potential therapeutic agents were predicted using the CMap database, and molecular docking was conducted via AutoDock Vina. To validate the computational predictions, a quercetin intervention model was established. The optimal dose was determined through CCK-8 assays in HepG2 cells, and the expression of the five hub genes was examined in normal liver cells (LO2), HepG2 cells, and HepG2 cells treated with quercetin using RT-qPCR. The five hub genes-RFC4, TOP2A, AURKA, HSP90AA1, and MCM4-were significantly overexpressed in both HCC tissues and cell lines. Enrichment analysis revealed that these genes were mainly involved in cancer-related pathways, including the cell cycle, p53 signaling pathway, and FoxO signaling pathway. Drug prediction analysis showed that quercetin exhibited a negative regulatory pattern with respect to HCC and displayed binding energies below - 5 kcal/mol with all five hub proteins. CCK-8 assays confirmed the dose-dependent inhibitory effect of quercetin on HepG2 cell viability. RT-qPCR results demonstrated that quercetin significantly downregulated the expression of the five hub genes, consistent with the bioinformatics predictions. This study integrated multi-omics analysis and experimental validation to identify five core genes closely associated with HCC and suggested that quercetin may exert anti-HCC effects partly associated with the regulation of these genes. Our findings offer new insights into the molecular mechanisms of HCC and provide a promising strategy for the development of targeted therapeutics.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10616-026-00993-x.

PMID:42145839 | PMC:PMC13176377 | DOI:10.1007/s10616-026-00993-x

Received β€” 13 April 2026 ⏭

Plasmin promotes hepatocellular carcinoma invasion and metastasis via CXCR4-mediated activation of PI3K/AKT/mTOR signaling

βœ‡Oncogene - Issue - nature.com science feeds
By: Zhiyuan Shen Β· Yuexing Yang Β· Tianyu Mao Β· Fangyan Wu Β· Fengming Li Β· Liu Yang Β· Min Yu Β· Bing Zhao
9 April 2026 at 08:00

Oncogene, Published online: 09 April 2026; doi:10.1038/s41388-026-03775-z

Plasmin promotes hepatocellular carcinoma invasion and metastasis via CXCR4-mediated activation of PI3K/AKT/mTOR signaling
Received β€” 17 March 2026 ⏭

Graph In-Context Operator Networks for Generalizable Spatiotemporal Prediction

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Chenghan Wu Β· Zongmin Yu Β· Boai Sun Β· Liu Yang
16 March 2026 at 12:00
arXiv:2603.12725v1 Announce Type: cross Abstract: In-context operator learning enables neural networks to infer solution operators from contextual examples without weight updates. While prior work has demonstrated the effectiveness of this paradigm in leveraging vast datasets, a systematic comparison against single-operator learning using identical training data has been absent. We address this gap through controlled experiments comparing in-context operator learning against classical operator learning (single-operator models trained without contextual examples), under the same training steps and dataset. To enable this investigation on real-world spatiotemporal systems, we propose GICON (Graph In-Context Operator Network), combining graph message passing for geometric generalization with example-aware positional encoding for cardinality generalization. Experiments on air quality prediction across two Chinese regions show that in-context operator learning outperforms classical operator learning on complex tasks, generalizing across spatial domains and scaling robustly from few training examples to 100 at inference.
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