State of the art of the cow-calf systems in beef and dairy cattle (Bos taurus) operations in EU, USA, and Brazil from 1998 to 2023.

Cow-calf systems represent a significant research area in animal husbandry, with differences depending on the final product (meat or milk). This study aimed to apply text mining and topic analysis on literature describing cow-calf systems in European, American, and Brazilian beef and dairy sectors between 1998 and 2023. Additionally, cow-calf contact (CCC) literature data was manually extracted. Our findings revealed the presence of 11 research areas among literature on cow-calf systems, with different priorities identified in the beef and dairy sectors. Beef industry mainly focused on animal proficiency and nutrition, while dairy on animal welfare and CCC, which showed a growing trend as emerging research topic, mostly in the EU. Current debates around calf welfare and EU's planned animal welfare legislation revision appeared to be driving the increasing interest in this topic. Studies in the beef sector were mainly localized in Brazil, showing that research in different contexts and species is important for CCC implementation. Manual data extraction showed considerable variation in the retained CCC documents regarding sample size, type of contact, methods and CCC duration. Learning about the varied CCC approaches used in beef and dairy farms in different locations, concentrating on their strengths and weaknesses, will help to develop novel solutions to global challenges. Adopting validated and robust indicators would help scientists and policymakers to monitor the system's quality. To improve CCC feasibility, match consumer demands, and move towards One Welfare and One Health, future research should focus on a variety of situations to overcome the current shortcomings.

Unsupervised learning and natural language processing highlight research trends in a superbug.

Introduction: Antibiotic-resistant Acinetobacter baumannii is a very important nosocomial pathogen worldwide. Thousands of studies have been conducted about this pathogen. However, there has not been any attempt to use all this information to highlight the research trends concerning this pathogen. Methods: Here we use unsupervised learning and natural language processing (NLP), two areas of Artificial Intelligence, to analyse the most extensive database of articles created (5,500+ articles, from 851 different journals, published over 3 decades). Results: K-means clustering found 113 theme clusters and these were defined with representative terms automatically obtained with topic modelling, summarising different research areas. The biggest clusters, all with over 100 articles, are biased toward multidrug resistance, carbapenem resistance, clinical treatment, and nosocomial infections. However, we also found that some research areas, such as ecology and non-human infections, have received very little attention. This approach allowed us to study research themes over time unveiling those of recent interest, such as the use of Cefiderocol (a recently approved antibiotic) against A. baumannii. Discussion: In a broader context, our results show that unsupervised learning, NLP and topic modelling can be used to describe and analyse the research themes for important infectious diseases. This strategy should be very useful to analyse other ESKAPE pathogens or any other pathogens relevant to Public Health.