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A secure and scalable IoT access control framework with dynamic attribute updates and policy hiding
Study on salt deposition distribution and environmental effect of seawater cooling tower
The chain mediating effect of self-respect and self-control on peer relationship and early adolescent phone dependence
Creating an ‘all comers’ research group in quantitative history
CALCR interaction with ANTXR1 drives gastric tumor growth and metastasis via AKT signaling pathway
Long COVID activists fought Trump team’s research cuts and won ― for now
Clinical outcomes of plate-haptic diffractive multifocal toric IOL in cataract eyes with long axial length and corneal astigmatism
Modulation instability, bifurcation analysis, and ion-acoustic wave solutions of generalized perturbed KdV equation with M-fractional derivative
Region-specific brain decellularized extracellular matrix promotes cell recovery in an in vitro model of stroke
AI race in 2025 is tighter than ever before
High entropy alloy property predictions using a transformer-based language model
Abstract This study introduces a language transformer-based machine learning model to predict key mechanical properties of high-entropy alloys (HEAs), addressing the challenges due to their complex, multi-principal element compositions and limited experimental data. By pre-training the transformer on extensive synthetic materials data and fine-tuning it with specific HEA datasets, the model effectively captures intricate elemental interactions through self-attention mechanisms. This approach mitigates data scarcity issues via transfer learning, enhancing predictive accuracy for properties like elongation (%) and ultimate tensile strength compared to traditional regression models such as random forests and Gaussian processes. The model’s interpretability is enhanced by visualizing attention weights, revealing significant elemental relationships that align with known metallurgical principles. This work demonstrates the potential of transformer models to accelerate materials discovery and optimization, enabling accurate property predictions, thereby advancing the field of materials informatics. To fully realize the model’s potential in practical applications, future studies should incorporate more advanced preprocessing methods, realistic constraints during synthetic dataset generation, and more refined tokenization techniques.
Gut bacteriome dynamics in high altitude-adapted chicken lines: a key to future poultry therapeutics
Abstract High-altitude-adapted chickens harbor a unique gut bacteriome essential for their survival under extremely cold and hypoxic environment, however, little is known about their population and functional dynamics, limiting their application in poultry production. Hence, this study employed amplicon-based metagenomics to examine the gut bacterial diversity and their functional profile in two high-altitude-adapted chicken lines, e.g. LEHBRO-1 and LEHBRO-3. The results revealed significant variations in taxonomic abundance at the phylum level, with Firmicutes, Proteobacteria, Bacteroidetes, and Actinobacteria predominating in LEHBRO-1, whereas Firmicutes, Proteobacteria, Bacteroidetes, Planctomycetes, and Actinobacteria predominated in LEHBRO-3. Genus-level diversity and Linear Discriminant Analysis Effect Size (LEfSe) biomarker analysis also substantiated the differences in the gut bacterial communities between the two chicken lines. Furthermore, functional profiling revealed enrichment of carbohydrate, nucleotide, lipid, amino acid, fatty acid, energy, and glycan metabolic pathways in the gut bacteriomes of these high-altitude chicken lines. The Statistical Analysis of Metagenomic Profiles (STAMP) for metabolic profiling identified a significant difference in purine and protein metabolism between these two chicken lines. These findings indicate the unique gut bacteriome and their functional diversity in high-altitude-adapted chickens, which would provide a foundation for future research on gut therapeutics to improve chicken health and productivity in high-altitude areas.