Modeling improvements in digestible amino acids by a consensus bacterial 6-phytase variant in grower pigs.

Data from 8 datasets generated from 5 independent experiments that determined the effects of a consensus bacterial 6-phytase variant (PhyG) on apparent ileal digestibility (AID) of amino acids (AA) in growing pigs (~21 to 45 kg body weight) were combined and modeled to test the hypothesis that the phytase results in significant improvements in ileal AA digestibility. The aim was to generate accurate and robust dose-related predictions of the digestible AA contributions of the phytase. The 5 experiments were conducted in Spain, Australia, USA, and Brazil and incorporated variation in diet composition (ingredient composition, phytate-phosphorus (PP) content, limestone solubility), diet form, animal breed and sex.

FSID: a novel approach to human activity recognition using few-shot weight imprinting.

Accurate recognition of human activities from gait sensory data plays a vital role in healthcare and wellness monitoring. However, conventional deep learning models for Human Activity Recognition (HAR) often require large labeled datasets and extensive training, which limits their effectiveness in real-world scenarios with scarce or imbalanced data. These models also struggle to generalize to rare or unseen activities, making them less suitable for dynamic and personalized healthcare settings.

Application of energy and nutrient reductions to a mixed-cereal diet without added inorganic phosphate, supplemented with a novel phytase alone or with a xylanase-β-glucanase combination, achieved a production benefit in pigs from wean to finish.

This experiment tested the hypothesis that supplementation of a nutrient- and energy-reduced mixed-cereal diet with phytase, xylanase and β-glucanase, over an entire wean-to-finish growth cycle, would result in growth performance outcomes that were not different from those achieved by pigs fed an unsupplemented, nutritionally-adequate diet. A total of 192 weaned pigs [DanBred × Pi, initial body weight (BW) 7.2 ± 0.

Semi-supervised contour-driven broad learning system for autonomous segmentation of concealed prohibited baggage items.

With the exponential rise in global air traffic, ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation security. Although X-ray baggage monitoring is now standard, manual screening has several limitations, including the propensity for errors, and raises concerns about passenger privacy. To address these drawbacks, researchers have leveraged recent advances in deep learning to design threat-segmentation frameworks.

Remodelling of Rea1 linker domain drives the removal of assembly factors from pre-ribosomal particles.

The ribosome maturation factor Rea1 (or Midasin) catalyses the removal of assembly factors from large ribosomal subunit precursors and promotes their export from the nucleus to the cytosol. Rea1 consists of nearly 5000 amino-acid residues and belongs to the AAA+ protein family. It consists of a ring of six AAA+ domains from which the ≈1700 amino-acid residue linker emerges that is subdivided into stem, middle and top domains.