Gastrointestinal nematode infections in German sheep

The objective of the present study was to determine the prevalence and variation of natural gastrointestinal nematode (GIN) infections in lambs according to birth type, gender and breed based on individual faecal egg counts (FEC) from various regions in Germany. A total of 3,924 lambs (3 to 15 months old) with different genetic backgrounds (Merinoland, German Blackhead Mutton, Rhoen, Texel and Merino long-wool) were individually sampled during the grazing period between 2006 and 2008. Furthermore, pooled faecal samples from each of the farms were cultured in order to differentiate the third-stage larvae of the nematode spp. Sixty-three percent of the lambs were infected with GIN. The infections were mostly low to moderate and involved several nematode species. The Trichostrongylus spp. was the predominant species based on the percentage of larvae in faecal cultures. Only 11.4% of the lambs were free of Eimeria oocysts. Tapeworm eggs were encountered in 13.2% of all samples. The prevalence of GIN infections varied significantly (P < 0.001) among farms. A significantly higher FEC (P < 0.05) was observed in multiple-born lambs when compared with singletons. Moreover, male lambs were more susceptible to infection than females (P < 0.001). No significant differences (P > 0.05) were observed between breeds regarding FEC. Inter-individual variations were higher than inter-breed differences, which may indicate the possibility of selection within these breeds for parasites resistance as described in earlier studies

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Biodiversity of secondary metabolites compounds isolated from phylum actinobacteria and its therapeutic applications

The current review aims to summarise the biodiversity and biosynthesis of novel secondary metabolites compounds, of the phylum Actinobacteria and the diverse range of secondary metabolites produced that vary depending on its ecological environments they inhabit. Actinobacteria creates a wide range of bioactive substances that can be of great value to public health and the pharmaceutical industry. The literature analysis process for this review was conducted using the VOSviewer software tool to visualise the bibliometric networks of the most relevant databases from the Scopus database in the period between 2010 and 22 March 2021. Screening and exploring the available literature relating to the extreme environments and ecosystems that Actinobacteria inhabit aims to identify new strains of this major microorganism class, producing unique novel bioactive compounds. The knowledge gained from these studies is intended to encourage scientists in the natural product discovery field to identify and characterise novel strains containing various bioac-tive gene clusters with potential clinical applications. It is evident that Actinobacteria adapted to survive in extreme environments represent an important source of a wide range of bioactive com-pounds. Actinobacteria have a large number of secondary metabolite biosynthetic gene clusters. They can synthesise thousands of subordinate metabolites with different biological actions such as anti-bacterial, anti-parasitic, anti-fungal, anti-virus, anti-cancer and growth-promoting compounds. These are highly significant economically due to their potential applications in the food, nutrition and health industries and thus support our communities’ well-being