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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€” 25 February 2026 ⏭

Can Multimodal LLMs See Science Instruction? Benchmarking Pedagogical Reasoning in K-12 Classroom Videos

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Yixuan Shen Β· Peng He Β· Honglu Liu Β· Yuyang Ji Β· Tingting Li Β· Tianlong Chen Β· Kaidi Xu Β· Feng Liu
24 February 2026 at 13:00
arXiv:2602.18466v1 Announce Type: cross Abstract: K-12 science classrooms are rich sites of inquiry where students coordinate phenomena, evidence, and explanatory models through discourse; yet, the multimodal complexity of these interactions has made automated analysis elusive. Existing benchmarks for classroom discourse focus primarily on mathematics and rely solely on transcripts, overlooking the visual artifacts and model-based reasoning emphasized by the Next Generation Science Standards (NGSS). We address this gap with SciIBI, the first video benchmark for analyzing science classroom discourse, featuring 113 NGSS-aligned clips annotated with Core Instructional Practices (CIP) and sophistication levels. By evaluating eight state-of-the-art LLMs and Multimodal LLMs, we reveal fundamental limitations: current models struggle to distinguish pedagogically similar practices, suggesting that CIP coding requires instructional reasoning beyond surface pattern matching. Furthermore, adding video input yields inconsistent gains across architectures. Crucially, our evidence-based evaluation reveals that models often succeed through surface shortcuts rather than genuine pedagogical understanding. These findings establish science classroom discourse as a challenging frontier for multimodal AI and point toward human-AI collaboration, where models retrieve evidence to accelerate expert review rather than replace it.
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