A comprehensive characterization of regulatory elements in the chicken genome across tissues will have substantial impacts on both fundamental and applied research. Here, we systematically identified and characterized regulatory elements in the chicken genome by integrating 377 genome-wide sequencing datasets from 23 adult tissues. In total, we annotated 1.57 million regulatory elements, representing 15 distinct chromatin states, and predicted about 1.2 million enhancer-gene pairs and 7662 super-enhancers. This functional annotation of the chicken genome should have wide utility on identifying regulatory elements accounting for gene regulation underlying domestication, selection, and complex trait regulation, which we explored. In short, this comprehensive atlas of regulatory elements provides the scientific community with a valuable resource for chicken genetics and genomics.
An experiment was conducted to investigate the effects of different dietary threonine (Thr) levels and immune stress on Pekin ducklings' growth performance, carcass traits, serum immune parameters, and intestinal mucin 2 (MUC2) and nuclear factor kB (NF-κB) gene expressions. A total of 320 Pekin ducklings was randomly assigned to a 5 × 2 factorial arrangement of treatments. Each treatment group consisted of 4 replicate pens with 8 ducks per pen. Ducklings were fed 5 graded levels of Thr: 0.49, 0.56, 0.60, 0.65, and 0.76% from hatch to 21 d of age. At 11 d of age, ducks in the stressed groups were challenged with bovine serum albumin (BSA), and ducks in the unstressed groups were injected with normal saline water. The results showed that increasing Thr supplementation from 0.49 to 0.56% in the diet can improve BWG; feed consumption; weight and relative weight of breast and leg; weight of liver, bursa of Fabricius, spleen, and thymus; serum natural immune globulin A (IgA) concentration; and MUC2 gene expression in the ileum of 21-day-old Pekin ducks, significantly (P < 0.05). Immune stress with BSA had a significant effect on 21-day-old Pekin ducklings' BWG, feed consumption, and weight and relative weight of breast and thymus (P < 0.05), but no interaction between BSA and dietary Thr content was noticed in our experiment in 21-day-old Pekin ducks (P < 0.05). Dietary Thr requirements of the unstressed groups and stressed groups based on broken-line model analyses for ducks' BWG were 0.705 and 0.603%, respectively, and for ducks' feed consumption were 0.724 and 0.705%, respectively.
Animal dimensions are essential indicators for monitoring their growth rate, diet efficiency, and health status. A computer vision system is a recently emerging precision livestock farming technology that overcomes the previously unresolved challenges pertaining to labor and cost. Depth sensor cameras can be used to estimate the depth or height of an animal, in addition to two-dimensional information. Collecting top-view depth images is common in evaluating body mass or conformational traits in livestock species. However, in the depth image data acquisition process, manual interventions are involved in controlling a camera from a laptop or where detailed steps for automated data collection are not documented. Furthermore, open-source image data acquisition implementations are rarely available. The objective of this study was to 1) investigate the utility of automated top-view dairy cow depth data collection methods using picture- and video-based methods, 2) evaluate the performance of an infrared cut lens, 3) and make the source code available. Both methods can automatically perform animal detection, trigger recording, capture depth data, and terminate recording for individual animals. The picture-based method takes only a predetermined number of images whereas the video-based method uses a sequence of frames as a video. For the picture-based method, we evaluated 3- and 10-picture approaches. The depth sensor camera was mounted 2.75 m above-the-ground over a walk-through scale between the milking parlor and the free-stall barn. A total of 150 Holstein and 100 Jersey cows were evaluated. A pixel location where the depth was monitored was set up as a point of interest. More than 89% of cows were successfully captured using both picture- and video-based methods. The success rates of the picture- and video-based methods further improved to 92% and 98%, respectively, when combined with an infrared cut lens. Although both the picture-based method with 10 pictures and the video-based method yielded accurate results for collecting depth data son cows, the former was more efficient in terms of data storage. The current study demonstrates automated depth data collection frameworks and a Python implementation available to the community, which can help facilitate the deployment of computer vision systems for dairy cows.
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