Records for 52,362 lactations over a 10-yr period from 260 dairy farms in North America that used a common commercial software for record keeping were evaluated for potential risk factors for twinning. Records were evaluated for the associations of reproductive disease, parity, production, drug therapy, and the occurrence of subsequent twins. The rate of twinning on these farms steadily increased over the observation period from 1.4% in 1983 to 2.4% in 1993. The rate of twinning also increased as parity of the cow increased, from 1.0% for cows in their first lactation to > 4.1% for cows in their fifth or higher lactation. No association between twinning and season of year was detected. A multivariate logistical regression analysis found that the rate of twinning increased with increases in milk production, incidence of cystic ovarian disease, and the use of common pharmaceuticals, including GnRH, PGF2 alpha, and antibiotics. Results of the regression model also indicated that the single most important reason for the recent increase in the rate of twinning was a concurrent increase in milk production.
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http://dx.doi.org/10.3168/jds.S0022-0302(98)75659-0 | DOI Listing |
Ocul Surf
January 2025
Department of Twins Research and Genetic Epidemiology, King's College London, London, United Kingdom. Electronic address:
Purpose: To test the association between serum inflammatory markers and dry eye disease (DED) using a hypothesis-free proteomic approach in a population-based cohort.
Methods: A total of 2602 unselected community-based participants (mean age 61.5 (range 21-92 years), 94.
Eur J Pediatr
January 2025
Institute of Clinical Medicine and Department of Pediatrics, University of Eastern Finland, Kuopio, Finland.
Unlabelled: Twin pregnancies are associated with higher risks of adverse maternal and neonatal outcomes compared to singleton pregnancies. This retrospective nationwide cohort study analyzed trends in twin pregnancy outcomes in Finland from 2008 to 2023 using data from the Finnish Medical Birth Register. Outcomes assessed included perinatal mortality, stillbirths, neonatal mortality, neonatal intensive care unit (NICU) admissions, and hospitalization rates at one week of age.
View Article and Find Full Text PDFJ Appl Crystallogr
January 2024
NIST Center for Neutron Research, National Institute of Standards and Technology, Gaithersburg, Maryland, USA.
Neutron reflectometry (NR) is a powerful technique for interrogating the structure of thin films at interfaces. Because NR measurements are slow and instrument availability is limited, measurement efficiency is paramount. One approach to improving measurement efficiency is active learning (AL), in which the next measurement configurations are selected on the basis of information gained from the partial data collected so far.
View Article and Find Full Text PDFBehav Sci (Basel)
December 2024
Smart Design Lab, School of Design, Northumbria University, Newcastle upon Tyne NE1 8ST, UK.
Nostalgic scenes can trigger nostalgia to a considerable extent and can be effectively used as a nostalgic trigger that contributes to the psychological comfort of the elderly and immigrant populations, but a design system has not been adequately studied. Therefore, the design principles and digital twin (DT) design system of nostalgic scenes is proposed in this study. It focuses on the construction of a nostalgic scene DT model based on the system of system (SoS) theory.
View Article and Find Full Text PDFShock
January 2025
Department of Industrial and Systems Engineering, University of Florida, P.O. Box 116595, Gainesville, FL, 32611, USA.
Understanding clinical trajectories of sepsis patients is crucial for prognostication, resource planning, and to inform digital twin models of critical illness. This study aims to identify common clinical trajectories based on dynamic assessment of cardiorespiratory support using a validated electronic health record data that covers retrospective cohort of 19,177 patients with sepsis admitted to ICUs of Mayo Clinic Hospitals over eight-year period. Patient trajectories were modeled from ICU admission up to 14 days using an unsupervised machine learning two-stage clustering method based on cardiorespiratory support in ICU and hospital discharge status.
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