Using the community-based breeding program (CBBP) model as a collaborative platform to develop the African Goat Improvement Network-Image collection protocol (AGIN-ICP) with mobile technology for data collection and management of livestock phenotypes.

The African Goat Improvement Network Image Collection Protocol (AGIN-ICP) is an accessible, easy to use, low-cost procedure to collect phenotypic data via digital images. The AGIN-ICP collects images to extract several phenotype measures including health status indicators (anemia status, age, and weight), body measurements, shapes, and coat color and pattern, from digital images taken with standard digital cameras or mobile devices. This strategy is to quickly survey, record, assess, analyze, and store these data for use in a wide variety of production and sampling conditions.

Alignment of major-groove hydrogen bond arrays uncovers shared information between different DNA sequences that bind the same protein.

Protein-DNA binding is of a great interest due to its importance in many biological processes. Previous studies have presented many factors responsible for the recognition and specificity, but understanding the minimal informational requirements for proteins that bind to multiple DNA-sites is still an understudied area of bioinformatics. Here we focus on the hydrogen bonds displayed by the target DNA in the major groove that take part in protein-binding.

PreciseEdge raster RGB image segmentation algorithm reduces user input for livestock digital body measurements highly correlated to real-world measurements.

Computer vision is a tool that could provide livestock producers with digital body measures and records that are important for animal health and production, namely body height and length, and chest girth. However, to build these tools, the scarcity of labeled training data sets with uniform images (pose, lighting) that also represent real-world livestock can be a challenge. Collecting images in a standard way, with manual image labeling is the gold standard to create such training data, but the time and cost can be prohibitive.

Application of machine learning in understanding atherosclerosis: Emerging insights.

Biological processes are incredibly complex-integrating molecular signaling networks involved in multicellular communication and function, thus maintaining homeostasis. Dysfunction of these processes can result in the disruption of homeostasis, leading to the development of several disease processes including atherosclerosis. We have significantly advanced our understanding of bioprocesses in atherosclerosis, and in doing so, we are beginning to appreciate the complexities, intricacies, and heterogeneity atherosclerosi.

Expression of a truncated ATHB17 protein in maize increases ear weight at silking.

ATHB17 (AT2G01430) is an Arabidopsis gene encoding a member of the α-subclass of the homeodomain leucine zipper class II (HD-Zip II) family of transcription factors. The ATHB17 monomer contains four domains common to all class II HD-Zip proteins: a putative repression domain adjacent to a homeodomain, leucine zipper, and carboxy terminal domain. However, it also possesses a unique N-terminus not present in other members of the family.