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Received today β€” 7 October 2026 ⏭

Combined Transcriptomic and Histological Profiling Uncover Hepatic Regulatory Hierarchy of Triploid <em>Oncorhynchus mykiss</em> Under Interactive Salinity, Temperature and Body Weight Regimes

βœ‡Omics in Hepatocellular
By: Yanming Sui Β· Yan Ji Β· Haopeng Hu Β· Yuanhao Ren Β· Bo Qin Β· Na Ying Β· Tingting Lin Β· Siping Li Β· Hanfeng Zheng Β· Lei Li
26 September 2026 at 18:00

Biology (Basel). 2026 Sep 17;15(18):1646. doi: 10.3390/biology15181646.

ABSTRACT

Salinity, temperature and body weight dominate seawater acclimation in rainbow trout (Oncorhynchus mykiss), yet few studies simultaneously explore their main effects and potential correlative interactive patterns in hepatic responses. A 60-day L9 (33) orthogonal trial was performed on triploid rainbow trout with three gradients of body weight, temperature and salinity. Hepatic transcriptomics revealed that salinity drove global transcriptional remodeling and high salinity induced far fewer DEGs than medium salinity. WGCNA screened a salinity-positive blue module (r = 0.408, p = 0.0346), while alternative splicing confirmed extensive salinity-dependent post-transcriptional regulation. Semi-quantitative histology showed that 20 Β°C was associated with more pronounced salinity-caused hepatocellular vacuolation and karyopyknosis in the orthogonal test. Survival statistics indicated that salinity was the only factor with significant main effects (p < 0.05), and the 500 g-10 Β°C-10 ppt group obtained the highest survival. This multi-omics and histological dataset reveals a suggestive regulatory hierarchy-like pattern: salinity acts as the primary driver, temperature serves as a synergistic amplifier, and body weight plays a minor modulatory role. These findings provide a theoretical basis for developing size-specific salinity acclimation protocols in commercial triploid rainbow trout farming.

PMID:42792591 | PMC:PMC13604319 | DOI:10.3390/biology15181646

Received β€” 27 May 2026 ⏭

Authority Inversion in LLM-Mediated Ubiquitous Systems: When Models Trust Users Over Sensors

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Long Zhang Β· Zi-bo Qin Β· Wei-neng Chen
26 May 2026 at 12:00
arXiv:2605.23938v1 Announce Type: new Abstract: Large language models (LLMs) increasingly fuse heterogeneous inputs in ubiquitous systems. Yet, how LLMs implicitly allocate authority when sensor measurements and user claims conflict remains unexamined, raising critical reliability concerns for deployments where physical sensing must retain priority. Unlike explicit traditional fusion, LLMs bury authority allocation within learned representations. We discover this allocation is severely format-dependent: numerical sensor data fails to integrate into answer-relevant model directions, allowing natural-language claims to dominate the final decision, a phenomenon we term \textbf{Authority Inversion}.To diagnose and mitigate this, we develop a geometric framework of context integration, introduce two computable audit metrics, specifically the Context Integration Ratio (CIR) and Authority Alignment Index (AAI), and propose Geometric Authority Calibration (GAC), an inference-time layer-level intervention to suppress misplaced user authority. Evaluating four models (4B to 35B parameters, three architectures) across four datasets totaling 576 conflict instances reveals extreme inversion: on numerical tasks, models exhibit near-zero sensor trust (AAI = -0.805, Cohen's d = -2.14), unaffected by model capacity. Validating our geometric framework, theory-guided causal injection flips 80.2\% of incorrect decisions (vs.
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