In numerous systems of animal production, there is increasing interest in the use of three-dimensional (3D)-imaging technology on farms for its ability to easily and safely measure traits of interest in living animals. With this information, it is possible to evaluate multiple morphological indicators of interest, either directly or indirectly, and follow them through time. Several tools for this purpose were developed, but one of their main weaknesses was their sensitivity to light and animal movement, which limited their potential for large-scale application on farms. To address this, a new device, called Deffilait3D and based on depth camera technology, was developed. In tests on 31 Holstein dairy cows and 13 Holstein heifers, the values generated for most measured indicators were highly repeatable and reproducible, with coefficients of variation lower than 4%. A comparison of measurements obtained from both Deffilait3D and the previous validated system, called Morpho3D, revealed a high degree of similarity for most selected traits, e.g., less than 0.2% variation for animal volume and 1.2% for chest depth, with the highest degree of difference (8%) noted for animal surface area. Previously published equations used to estimate body weight with the Morpho3D device were equally valid using Deffilait3D. This new device was able to record 3D images regardless of the movement of animals and it is affected only by direct daylight. The ongoing step is now to develop methods for automated analysis and extraction from images, which should enable the rapid development of new tools and potentially lead to the large-scale adoption of this type of device on commercial farms.
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http://dx.doi.org/10.1093/tas/txae018 | DOI Listing |
Angew Chem Int Ed Engl
January 2025
Tsinghua University, Chemistry, HeTian Building Dept. of Chemistry, Tsinghua University, Beijing, P. R. China, 100084, Beijing, CHINA.
Expanded heterohelicene composing of alternating linearly and angularly fused multi-resonance (MR) skeleton has garnered wide interest for their promising narrowband emission. Herein, a pair of sym- and asym-expanded heterohelicene isomers are firstly developed by merging boron/oxygen (B/O)-embedded MR triangulene and indolo[3,2,1-jk]carbazole units via one-shot synthesis. Owing to the fully resonating extended helical skeleton, the target heterohelicenes exhibit significantly narrowed spectra bandwidth while emission red-shifting, thus affording deep-blue narrowband emission with peak at around 460 nm, full-width-at-half-maximum (FWHM) of merely 18 nm and near-unity photoluminescence quantum yields.
View Article and Find Full Text PDFNat Commun
January 2025
Institute of Theoretical Computer Science, Graz University of Technology, Graz, Austria.
Recent experimental studies in the awake brain have identified a rule for synaptic plasticity that is instrumental for the instantaneous creation of memory traces in area CA1 of the mammalian brain: Behavioral Time scale Synaptic Plasticity. This one-shot learning rule differs in five essential aspects from previously considered plasticity mechanisms. We introduce a transparent model for the core function of this learning rule and establish a theory that enables a principled understanding of the system of memory traces that it creates.
View Article and Find Full Text PDFMacromolecules
December 2024
Dainton Building, Department of Chemistry, University of Sheffield, Brook Hill, Sheffield, South Yorkshire S3 7HF, U.K.
We report the reversible addition-fragmentation chain transfer (RAFT) dispersion polymerization of 2-hydroxyethyl methacrylate (HEMA) in -dodecane using a poly(lauryl methacrylate) (PLMA) precursor at 90 °C. This formulation is an example of polymerization-induced self-assembly (PISA), which leads to the formation of a colloidal dispersion of spherical PLMA-PHEMA nanoparticles at 10-20% w/w solids. PISA syntheses involving polar monomers in non-polar media have been previously reported but this particular system offers some unexpected and interesting challenges in terms of both synthesis and characterization.
View Article and Find Full Text PDFFront Artif Intell
December 2024
Robert Bosch Center for Data Science and Artificial Intelligence, Indian Institute of Technology Madras, Chennai, India.
We study a contextual bandit setting where the agent has access to causal side information, in addition to the ability to perform multiple targeted experiments corresponding to potentially different context-action pairs-simultaneously in one-shot within a budget. This new formalism provides a natural model for several real-world scenarios where parallel targeted experiments can be conducted and where some domain knowledge of causal relationships is available. We propose a new algorithm that utilizes a novel entropy-like measure that we introduce.
View Article and Find Full Text PDFMed Phys
December 2024
Smart Medical Imaging, Learning and Engineering (SMILE) Lab, Medical UltraSound Image Computing (MUSIC) Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China.
Background: Most existing deep learning-based registration methods are trained on single-type images to address same-domain tasks, resulting in performance degradation when applied to new scenarios. Retraining a model for new scenarios requires extra time and data. Therefore, efficient and accurate solutions for cross-domain deformable registration are in demand.
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