Integrating computer vision algorithms and RFID system for identification and tracking of group-housed animals: an example with pigs.

Precision livestock farming aims to individually and automatically monitor animal activity to ensure their health, well-being, and productivity. Computer vision has emerged as a promising tool for this purpose. However, accurately tracking individuals using imaging remains challenging, especially in group housing where animals may have similar appearances.

Impact of multi-output and stacking methods on feed efficiency prediction from genotype using machine learning algorithms.

Feeding represents the largest economic cost in meat production; therefore, selection to improve traits related to feed efficiency is a goal in most livestock breeding programs. Residual feed intake (RFI), that is, the difference between the actual and the expected feed intake based on animal's requirements, has been used as the selection criteria to improve feed efficiency since it was proposed by Kotch in 1963. In growing pigs, it is computed as the residual of the multiple regression model of daily feed intake (DFI), on average daily gain (ADG), backfat thickness (BFT), and metabolic body weight (MW).

Disentangling the causal relationship between rabbit growth and cecal microbiota through structural equation models.

Background: The effect of the cecal microbiome on growth of rabbits that were fed under different regimes has been studied previously. However, the term "effect" carries a causal meaning that can be confounded because of potential genetic associations between the microbiome and production traits. Structural equation models (SEM) can help disentangle such a complex interplay by decomposing the effect on a production trait into direct host genetics effects and indirect host genetic effects that are exerted through microbiota effects.

Analysis of the causal structure of traits involved in sow lactation feed efficiency.

Background: Feed efficiency during lactation involves a set of phenotypic traits that form a complex system, with some traits exerting causal effects on the others. Information regarding such interrelationships can be used to predict the effect of external interventions on the system, and ultimately to optimize management practices and multi-trait selection strategies. Structural equation models can be used to infer the magnitude of the different causes of such interrelationships.

Feline immunodeficiency virus and feline leukemia virus infection in free-ranging guignas (Leopardus guigna) and sympatric domestic cats in human perturbed landscapes on Chiloé Island, Chile.

Feline immunodeficiency virus (FIV) and feline leukemia virus (FeLV) are two of the most common viruses affecting domestic cats (Felis catus). During the last two decades, reports show that both viruses also infect or affect other species of the family Felidae. Human landscape perturbation is one of the main causes of emerging diseases in wild animals, facilitating contact and transmission of pathogens between domestic and wild animals.

Molecular characterization, distribution, and dynamics of hepatitis C virus genotypes in blood donors in Colombia.

Hepatitis C virus (HCV) is a frequent cause of acute and chronic hepatitis and a leading cause for cirrhosis of the liver and hepatocellular carcinoma. HCV is classified in six major genotypes and more than 70 subtypes. In Colombian blood banks, serum samples were tested for anti-HCV antibodies using a third-generation ELISA.

Localized hand-foot syndrome after intra-arterial hepatic chemotherapy with floxuridine: a clinical case.

Hand-foot syndrome is a toxic effect of some chemotherapy agents such as 5-fluorouracil (5-FU), capecitabine and liposomal doxorubicin. The symptoms and signs are localized erythema and paresthesia. Floxuridine (FUDR) is an analogue of 5-FU, used for arterial hepatic infusion in patients affected by liver metastases from colorectal cancer.