Bacillus Coagulans Enhance the Immune Function of the Intestinal Mucosa of Yellow Broilers

<div><p>ABSTRACT This experiment was conducted to investigate the effects of Bacillus coagulans on the growth performance and immune functions of the intestinal mucosa of yellow broilers. Three hundred and sixty one-day-old yellow chicks were randomly allocated to four treatments groups with six replicates of 15 chicks each. The broilers were randomly subjected to one of the following treatments for 28 days: control group (group1, fed a basal diet) and three treatments (group 2, 3, 4) fed the basal diet supplemented with 100, 200, or 300 mg/kg Bacillus coagulans , respectively). The results showed that for 28 days, compared with the control diet, the dietary addition of 200 mg/kg Bacillus coagulans significantly decreased the feed/gain ratio (F/G) (p<0.05), improved the thymus index, spleen index and bursa index (p<0.05), increased the villus height to crypt depth ratio (V/C) in the duodenum (p<0.05), increased the number of secretory immunoglobulin (sIgA) positive cells ( p<0.05). The dietary addition of 200 mg/kg Bacillus coagulans promoted a significant increase in Lactobacillus spp. populations and suppressed Escherichia coli replication in cecum, compared with the control (p<0.05). Moreover, the dietary addition of 200 mg/kg Bacillus coagulans also significantly enhanced the levels of interferon alpha (IFNα), toll-like receptor (TLR3), and melanoma differentiation-associated protein 5(MDA5) in the duodenum (p<0.05). In conclusion, the dietary addition of Bacillus coagulans significantly improved broiler performance, and enhanced the intestinal mucosal barrier and immune function. The optimal dosage of Bacillus coagulans for yellow broilers was determined as 2×108 cfu/kg.</p></div

Development of a device and algorithm research for akhal-teke activity level analysis

Featured Application: This research introduces a novel wearable device that uses an acceleration threshold behavior recognition method to classify horse activities into three levels: low (standing), medium (walking), and high (trotting, cantering, and galloping). The recognition algorithm is directly implemented in the hardware, which horses wear during their training sessions. This device allows for the real-time analysis of horse activity levels and the accurate calculation of the time spent in each activity state. This method provides scientific data support for horse training, facilitating the optimization of training programs. This study demonstrated that wearable devices can distinguish between different levels of horse activity, categorized into three types based on the horse’s gaits: low activity (standing), medium activity (walking), and high activity (trotting, cantering, and galloping). Current research in activity level classification predominantly relies on deep learning techniques, known for their effectiveness but also their demand for substantial data and computational resources. This study introduces a combined acceleration threshold behavior recognition method tailored for wearable hardware devices, enabling these devices to classify the activity levels of horses directly. The approach comprises three sequential phases: first, a combined acceleration interval counting method utilizing a non-linear segmentation strategy for preliminary classification; second, a statistical analysis of the variance among these segments, coupled with multi-level threshold processing; third, a method using variance-based proximity classification for recognition. The experimental results show that the initial stage achieved an accuracy of 87.55% using interval counting, the second stage reached 90.87% with variance analysis, and the third stage achieved 91.27% through variance-based proximity classification. When all three stages are combined, the classification accuracy improves to 92.74%. Extensive testing with the Xinjiang Wild Horse Group validated the feasibility of the proposed solution and demonstrated its practical applicability in real-world scenarios