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Insights from field chemical immobilization of northern elephant seals over two decades
Abstract Chemical immobilization is essential for ensuring the safety and welfare of wildlife and researchers during animal handling. We analyzed observation data from 1,464 immobilization procedures on 625 northern elephant seals ( Mirounga angustirostris ) conducted between 2004 and 2025 to describe our practices and insights. Inductions used tiletamine-zolazepam (Telazol®), with supplemental doses of ketamine, diazepam, and/or Telazol for augmentation (required to achieve satisfactory induction) and maintenance (required to maintain chemical immobilization for the procedure duration). Procedures spanned a wide range of body masses (57–668 kg) across the annual cycle (i.e., seasons) and life stages, including juveniles of both sexes and adult females up to 18 years old. For adult females, 68% received Telazol induction doses within ± 0.1 mg/kg of the 1.0 mg/kg target, indicating accurate mass estimation and a broad safety margin when paired with vigilant monitoring and respiratory support as needed. Lower induction doses were associated with a higher likelihood of augmentation drug administration, whereas higher doses were more often associated with faster-than-typical inductions and the administration of a respiratory stimulant. In procedures without augmentation or respiratory stimulant administration, smaller juveniles received higher mass-specific induction doses than adults across the observed body size range, regardless of sex. These findings show that age- and season-based mass estimates can refine dosing precision within a safe range. These findings describe recorded field decisions and procedural events (e.g., timing and sequence of drug administration) rather than direct physiological measurements of anesthetic depth or safety. Our long-term dataset provides empirically derived guidance to improve immobilization efficacy, safety, and consistency for elephant seals and related species.
Measuring cardiac stroke volume through in-ear audio sensing
Abstract Stroke volume, the volume of blood ejected by the left ventricle during a contraction, is a key metric of cardiovascular health. Currently, stroke volume is measured in clinic with specialised equipment. While purpose-made wearables exist to measure stroke volume, no solution relies solely on commodity devices. We present a deep learning system for stroke volume estimation from in-ear audio of earbuds. We combine generative self-supervised/transfer learning, a transformer-based autoencoder, to predict average stroke volume in unseen subjects. With data from 23 healthy participants, we compare our estimations to clinically validated device estimations. We achieve a mean absolute error of 5.24 ml, a Pearson correlation of r=0.94 between average predicted stroke volume and average true stroke volume, and a Percentage Error in the limits of agreement of 11.05% (within clinical range for stroke volume measurement devices). These findings open the doors to longitudinal, scalable and affordable cardiovascular measurement out of clinic.
Interpretable diabetes risk prediction: a comparative study of tree-based algorithms using SHAP and LIME
Discovering heuristics in a complex SAT solver with large language models
Abstract The Satisfiability problem (SAT) is fundamental in computational complexity theory and has a wide range of industrial applications. Optimizing modern SAT solvers in real-world settings is quite challenging due to their intricate architectures. While automatic configuration frameworks have been developed, they rely on manually constrained search spaces. Here we develop AutoModSAT, a framework that uses large language models (LLMs) to automatically optimize SAT solvers. AutoModSAT combines an LLM-compatible modular solver design, unsupervised prompt optimization to diversify generated functions, and an efficient search procedure based on presearch strategy and a (1 + λ ) evolutionary algorithm. Extensive experiments across a wide range of datasets demonstrate that AutoModSAT achieves 40% performance improvement over the baseline solver and 30% improvement over the state-of-the-art solvers. Moreover, AutoModSAT also attains a notable speedup compared to the parameter-tuned alternatives of the state-of-the-art solvers over most of the test datasets. These results demonstrate the potential of LLM-guided heuristic discovery for optimizing complex SAT solvers.
The sustainability evaluation using SDG indicators under a geometric integrated threshold score framework
Ultrafast reset-free integrated polarization tracking enabled by thin-film lithium tantalate
Audio-visual timing underlies sex differences in the perception of social displays
A magic methyl effect in the active site of a methyltransferase ribozyme
Abstract The chemical richness of RNAs is greatly enhanced by post-transcriptional modifications with RNA methylation as the most prominent type. RNA modifications modulate the stability, folding and interaction pattern of RNA molecules. Furthermore, emerging data suggests RNA modifications also directly regulate the activity of catalytic RNA molecules, i.e., ribozymes. Here, we employ classical and hybrid quantum-classical (QM/MM) molecular dynamics (MD) simulations to investigate the reaction mechanism of an artificial methyltransferase ribozyme MTR1. Importantly, we pinpoint how 2’- O -methylations of active site nucleotides synergistically enhance ribozyme activity by reducing the conformational flexibility of the ribose rings and rigidifying the active site. Finally, the herein reported crystal structure of the modified MTR1, solved at 2.6 Å resolution, validates the results of our simulations. Taken together, our work supports the purported central role of modified RNA for early RNA catalysis and may guide rational design of more efficient ribozymes.
Development and multicenter validation of an AI-driven detection system for DLBCL cells in bone marrow smears
An enzyme-specific protein language model for catalytic property prediction
Hybrid African vulture and Aquila optimizer based efficient clustering approach for enhancing network longevity in wireless sensor networks
Distinct Roles of Deep and Superficial Cortical Layers in Tone Prediction, Comparison, and Adaptation in Human Auditory Cortices
Development of an efficient neutrosophic estimator for finite population mean using Monte Carlo simulation and multi-domain data
Dielectric-tuned charge and exciton dynamics in perylene bisimide supramolecular aggregates
Regularization effects on machine learning models for relative humidity prediction under semi-arid climate conditions
LncRNA MKRN3-AS1 promotes metastasis and autophagy of hepatocellular carcinoma by sponging multiple microRNAs
Abstract Hepatocellular carcinoma is one of the most common malignant tumors with poor clinical outcomes. Increasing numbers of long noncoding RNAs have been shown to be involved in the carcinogenesis and progression of HCC. However, the expressions, clinical significances, and roles of most lncRNAs in HCC are still unknown. In clinical samples, we utilized qPCR assays to measure the expression level of lncRNA MKRN3-AS1 in hepatocellular carcinoma (HCC) tissues compared with normal/adjacent tissues and analyzed its correlation with patient prognosis. Gain-of-function and loss-of-function experiments were performed in HCC cells to examine the effects of lncRNA MKRN3-AS1 on cell migration, invasion, and autophagy. For mechanistic investigation, bioinformatics analysis and molecular biology experiments were employed to verify the role of MKRN3-AS1 as a competitive endogenous RNA. Additionally, a mouse metastatic tumor model was established via tail vein injection to observe the impact of MKRN3-AS1 on lung metastasis of hepatocellular carcinoma. We found that lncRNA MKRN3-AS1 was dramatically upregulated in HCC tissues and associated with poor prognosis. Functionally, lncRNA MKRN3-AS1 promoted migration, invasion and autophagy of HCC cells. Mechanistically, MKRN3-AS1 acted as a “molecular sponge” for multiple miRNAs. By targeting miR-601, MKRN3-AS1 upregulate the expression of ZEB1, which directly promoted the invasion and migration of HCC cells. In addition, MKRN3-AS1 promoted autophagy via the miR-4487/ULK1 and miR-6881-3p/ATG5-ATG7 axes, which may enhance the stemness of HCC cells and thereby indirectly facilitate tumor invasion and migration. In mouse model, over-expression of MKRN3-AS1 facilitated lung metastasis of HCC. Collectively, The oncogenic function of MKRN3-AS1 in hepatocellular carcinoma may be attributed to its regulation of the miR-601/ZEB1, miR-4487/ULK1, and miR-6881-3p/ATG5-ATG7 signaling axes. Our findings suggest that MKRN3-AS1 may be a potential prognostic biomarker and therapeutic target for HCC.
Beyond motor ımpairment somatosensory function and body awareness as contributors to activity, participation and quality of life in children with cerebral palsy
Tracking CSF inflammatory signatures in an ovine model of leptin resistance
Abstract Leptin resistance is a key metabolic feature of diet-induced obesity in sheep, with implications for body condition management and reproductive efficiency. However, changes in central inflammatory mediators during its development remain poorly characterized in longitudinal settings. The present study examined dynamic changes in cerebrospinal fluid (CSF) cytokine, chemokine, and microRNA (miRNA) profiles in an ovine model of leptin resistance. Sixteen ovariectomized Polish Longwool ewes were assigned to control (CTRL, n = 5), food-restricted (LEAN, n = 5), and high-energy diet (OBESE, n = 6) groups over a 14-week period. CSF was collected weekly from the third brain ventricle and analysed for 13 cytokines and chemokines by Luminex multiplex immunoassay; five inflammation-related miRNAs were quantified by digital PCR. Obese animals exhibited elevated CSF IL-8, TNF-α, MIP-1α, and IP-10, alongside upregulation of miR-155-5p, miR-21-5p, miR-125b-5p, and miR-223-3p. TNF-α correlated positively with miR-155-5p ( r = 0.68, p = 0.015), while IP-10 was inversely associated with miR-146a-5p ( r = − 0.61, p = 0.036). Food-restricted animals displayed distinct CSF inflammatory profiles, with IL-1α and IL-6 exceeding obese levels at several time points, indicating that nutritional stress, independent of hyperleptinemia, can drive central inflammatory changes. These findings demonstrate coordinated CSF cytokine and miRNA signatures associated with diet-induced obesity and caloric restriction, supporting longitudinal CSF profiling as an approach for monitoring central neuroimmune dynamics in large-animal models.