Considerable technological advances have been made in the automated detection of estrus in dairy cattle, but few studies have evaluated their relative performance on the same animals or assessed cow-related factors that affect their performance. Our objective was to assess the performance and reliability of three devices commercially available in France for cow estrus detection. The devices were a pedometer (PM; Afitag) and two activity meters (AM1; Heatime-RuminAct, and AM2; HeatPhone). Two algorithms were tested for AM2. We fitted 63 lactating Holstein cows with the three detectors from calving to 90 days after calving. The onset and pattern of cyclicity were monitored from 7 to 90 days postpartum measuring progesterone concentration in milk twice weekly. A total of 211 ovulations were identified. Cyclicity was classified as normal in 60% of cows (38/63). Calculated over the operating period of all the devices (179 periods of estrus), the sensitivities and positive predictive values were, respectively, 71% and 71% for PM, 62% and 84% for AM1, 61% and 67% for the first algorithm of AM2, and 62% and 87% for the second algorithm of AM2. Both activity meters had a lower sensitivity but a higher positive predictive value than the PM (P < 0.05). For all devices, the performance in estrus detection was much poorer at the first postpartum ovulation than at subsequent ovulations (P < 0.05). Lactation rank and milk production affected some devices (P < 0.05). These devices could be used to reinforce visual observations, especially after 50 days postpartum, the minimum recommended delay to insemination. However, their full benefit remains to be verified in different farming systems and taking into account the specific objectives of the dairy farmer.
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http://dx.doi.org/10.1016/j.theriogenology.2014.06.010 | DOI Listing |
Disabil Rehabil
January 2025
Sydney School of Health Sciences, Faculty of Medicine & Health, The University of Sydney, Sydney, Australia.
Purpose: To investigate potential mechanisms of a digital rehabilitation intervention associated with improved mobility among adults undertaking rehabilitation.
Materials And Methods: Causal mediation analysis of the AMOUNT trial (ACTRN12614000936628). Participants were randomised to digitally-enabled rehabilitation (virtual reality video games, activity monitors, and handheld computer devices prescribed by a physiotherapist) and usual care or usual care alone.
Polymers (Basel)
January 2025
Center for Micro-Electro Mechanical Systems (CMEMS), Campus Azurém, University of Minho, 4800-058 Guimarães, Portugal.
Indwelling medical devices, such as urinary catheters, often experience bacterial colonization, forming biofilms that resist antibiotics and the host's immune defenses through quorum sensing (QS), a chemical communication system. This study explores the development of antimicrobial coatings by immobilizing acylase, a quorum-quenching enzyme, on sandblasted polydimethylsiloxane (PDMS) surfaces. PDMS, commonly used in medical devices, was sandblasted to increase its surface roughness, enhancing acylase attachment.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Instituto de Ciencias Aplicadas y Tecnología (ICAT), Universidad Nacional Autónoma de México, Ciudad de México C.P. 04510, Mexico.
Mobility is essential for individuals with physical disabilities, and wheelchairs significantly enhance their quality of life. Recent advancements focus on developing sophisticated control systems for effective and efficient interaction. This study evaluates the usability and performance of three wheelchair control modes manual, automatic, and voice controlled using a virtual reality (VR) simulation tool.
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January 2025
Centre of Mechanical Technology and Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal.
To automate the quality control of painted surfaces of heating devices, an automatic defect detection and classification system was developed by combining deflectometry and bright light-based illumination on the image acquisition, deep learning models for the classification of non-defective (OK) and defective (NOK) surfaces that fused dual-modal information at the decision level, and an online network for information dispatching and visualization. Three decision-making algorithms were tested for implementation: a new model built and trained from scratch and transfer learning of pre-trained networks (ResNet-50 and Inception V3). The results revealed that the two illumination modes employed widened the type of defects that could be identified with this system, while maintaining its lower computational complexity by performing multi-modal fusion at the decision level.
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January 2025
College of Sport and Health Science, Ritsumeikan University, Kusatsu 525-8577, Shiga, Japan.
This study aimed to assess the intraday reliability of markerless gait analysis using an RGB-D camera versus a traditional three-dimensional motion analysis (3DMA) system with and without a simulated walking assistant. Gait assessments were conducted on 20 healthy adults walking on a treadmill with a focus on spatiotemporal parameters gathered using the RGB-D camera and 3DMA system. The intraday reliability of the RGB-D camera was evaluated using intraclass correlation coefficients (ICC 1, 1), while its consistency with the 3DMA system was determined using ICC (2, 1).
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