We wanted to find out how selenium (Se) affects broiler chicken performance, meat physicochemical properties, and selenium deposition in the tissues of broilers. Each of the 96 experimental pens had 30 chickens and included a total of 2,880 one-day-old broilers (Cobb 500 strain). A factorial design of four-by-three (SY + SS) and eight replicates (SY + SS) was used for the 12 experimental treatments, with selenium levels ranging from 0.15 to 0.60 ppm and organic (SY) or inorganic (SS) sources of selenium and their relationship (SY + SS). There were no differences in performance (P > 0.05) across Se levels or sources. 106 g/day of ADFI, 63 g/day of ADG, and 1.6844 kg/kg of FCR were found to be the averaging values for these three parameters: ADFI, ADG, and FCR. (P > 0.05) There were no variations in pH (5.79) or shear force between treatments (30.08 kgf). This resulted in the loss of 21.92 percent of breast flesh in the birds given 0.15 ppm Se, which was statistically significant. Adding 0.60 ppm of organic Se to the diet reduced cooking losses the most, according to the study (15.87 percent). It rose from 0.97 mg/kg (0.15 ppm) of selenium to 2.43 mg/kg (0.60 ppm) of selenium in the liver when SY was used. Se concentration in breast meat rose from 0.23 mg/kg to 1.42 mg/kg when SY intake increased. Supplementing the food with 0.15 ppm of Se from any source is thus effective in maintaining normal avian performance. As compared to the SY, the SY was more effective in depositing Se into the liver and breast muscle.
This article aims to determine the time-dependent heat coefficient together with the temperature solution for a type of semi-linear time-fractional inverse source problem by applying a method based on the finite difference scheme and Tikhonov regularization. An unconditionally stable implicit finite difference scheme is used as a direct (forward) solver. While by the MATLAB routine lsqnonlin from the optimization toolbox, the inverse problem is reformulated as nonlinear least square minimization and solved efficiently. Since the problem is generally incorrect or ill-posed that means any error inclusion in the input data will produce a large error in the output data. Therefore, the Tikhonov regularization technique is applie
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Sickle cell disease (SCD) is a hereditary ailment that can cause severe pain and suffering to people who are affected. However, with continued investment in research and treatment options, we can make progress towards improving the lives of those with SCD. Over 40% of patients experience painful vaso-occlusive crises (VOCs), so we must work towards finding solutions and providing support for those living with this condition, These episodes, a hallmark of SCD, significantly contribute to morbidity, mortality, and a diminished quality of life, while also incurring substantial healthcare costs. Chronic pain particularly affects older adolescents and adults with SCD, with over half reporting daily discomfort. Opioid-based analgesics, though sti
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The dynamic development of computer and software technology in recent years was accompanied by the expansion and widespread implementation of artificial intelligence (AI) based methods in many aspects of human life. A prominent field where rapid progress was observed are high‐throughput methods in biology that generate big amounts of data that need to be processed and analyzed. Therefore, AI methods are more and more applied in the biomedical field, among others for RNA‐protein binding sites prediction, DNA sequence function prediction, protein‐protein interaction prediction, or biomedical image classification. Stem cells are widely used in biomedical research, e.g., leukemia or other disease studies. Our proposed approach of
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