A 2 X 2 factorially arranged trial was conducted to compare effects of implant (zeranol) and frame size on weight and compositional gain, and plasma hormone concentrations. Angus, Charolais X Hereford and Hereford X Angus yearling steers (34 steers averaging 270 kg body weight) were randomly assigned to treatments of small (SF) vs large frame (LF) and implant (I) vs no implant (NI). Steers were implanted at 0 and 97 d and individually fed an 81% whole shelled corn and 11.5% corn silage-based diet (dry basis) for a 175-d period. Shrunk weights and body measurements for frame size determination were taken initially and at approximately 28-d intervals; blood was collected via venipuncture at 14-d intervals for analyses of insulin (IN), triiodothyronine (T3), thyroxine (T4) and glucose concentrations. Steers were also counted in a whole body counter for measurement of 40K content and prediction of whole body protein and fat. The I steers showed an improvement (P less than .05) in daily gain regardless of frame size for the total trial. The I LF steers required 18% more dry matter to attain higher daily gain for 97 to 175 d; I steers were more efficient (P less than .05) at converting dry matter to gain during 0 to 97 d and 0 to 175 d. Daily fat deposition was increased (P less than .05) in I steers, while protein deposition was not affected by I. Plasma IN concentrations were numerically elevated (P less than .10) in I steers regardless of frame size, during the initial 97 d. Implant did not influence (P greater than .10) plasma T3, T4 and glucose concentrations regardless of frame size. Steers responded differently to zeranol implant over time regarding plasma T4 concentrations (P less than .003). Steers differing in frame size responded similarly in rate of gain, in feed conversion and in patterns of plasma insulin concentrations to zeranol implants.
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http://dx.doi.org/10.2527/jas1987.6541113x | DOI Listing |
Polymers (Basel)
December 2024
Industrial Chemistry Laboratory, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece.
Well-defined amorphous/semi-crystalline statistical copolymers of n-dodecyl isocyanate, DDIC, and allyl isocyanate, ALIC, were synthesized via coordination polymerization using the chiral half-titanocene complex CpTiCl(O-(S)-2-Bu) as an initiator. In the frame of the terminal model, the monomer reactivity ratios of the statistical copolymers were calculated using both well-known linear graphical methods and the computer program COPOINT. The molecular and structural characteristics of the copolymers were also calculated.
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Eötvös Lorand University (ELTE), Faculty of Informatics, Savaria Institute of Technology, Karolyi Gaspar tér 4, 9700 Szombathely, Hungary.
The microstructural features of polycrystals determine numerous properties, whereas the evolution of crystallographic texture is responsible for the anisotropy of particular properties. Therefore, it is of crucial importance to find proper quantitative indicators, which reflect the nature of microstructure and texture characteristics. This is partially performed by the assessment of the average grain size and texture intensity that provide basic information on the microstructural features evolved; however, often, the basic quantitative indicators are not capable of revealing the complete microstructural state especially when the system is highly heterogeneous.
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Department of Surgery, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania.
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Animals (Basel)
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College of Information Engineering, Northwest A&F University, Yangling 712100, China.
Livestock behavior serves as a crucial indicator of physiological health. Leveraging deep learning techniques to automatically recognize dairy goat behaviors, particularly abnormal ones, enables early detection of potential health and environmental issues. To address the challenges of recognizing small-target behaviors in complex environments, a multi-scale and lightweight behavior recognition model for dairy goats called GSCW-YOLO was proposed.
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Maize Research Institute, Guangxi Academy of Agricultural Sciences, Nanning, 530007 Guangxi China.
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