Factors associated with the prevalence of helminths in Mangalarga Marchador horses in southern of Minas Gerais, Brazil

ABSTRACT: Horses are highly susceptible to parasitism. Helminth infections cause great harm to the animals and to their breeders. This study aimed at evaluating socioeconomic, cultural and management factors associated with the prevalence of gastrointestinal helminths of horses. A total of 40 farmas the Mangalarga Marchador horse breed were visited in southern Minas Gerais, Brazil, where interviews were conducted. Horse feces were collected on the farms and coproparasitological laboratory tests were conducted to quantify the infection and to identify parasites. Data were tabulated in Epidata and analyzed using the SPSS 20.0 software. A great similarity between breeds was observed, specifically in their profiles, as well as in their animal management techniques and in their parasite control habits. The cyathostome was the most prevalent helminth, followed by Oxyuris and large strongyles. The farms which prioritize only equine production are less likely to have animals with massive helminth infection

Answer ALS, a large-scale resource for sporadic and familial ALS combining clinical and multi-omics data from induced pluripotent cell lines.

Answer ALS is a biological and clinical resource of patient-derived, induced pluripotent stem (iPS) cell lines, multi-omic data derived from iPS neurons and longitudinal clinical and smartphone data from over 1,000 patients with ALS. This resource provides population-level biological and clinical data that may be employed to identify clinical-molecular-biochemical subtypes of amyotrophic lateral sclerosis (ALS). A unique smartphone-based system was employed to collect deep clinical data, including fine motor activity, speech, breathing and linguistics/cognition. The iPS spinal neurons were blood derived from each patient and these cells underwent multi-omic analytics including whole-genome sequencing, RNA transcriptomics, ATAC-sequencing and proteomics. The intent of these data is for the generation of integrated clinical and biological signatures using bioinformatics, statistics and computational biology to establish patterns that may lead to a better understanding of the underlying mechanisms of disease, including subgroup identification. A web portal for open-source sharing of all data was developed for widespread community-based data analytics