Monitoring Pig Structural Soundness and Body Weight in Pork Production Systems Using Computer Vision Approaches.

As the global demand for products from food-producing animals increases with greater household economic capacity, there is an increased emphasis on the development of precision technologies for monitoring the health, product production, and wellbeing of these animals. The present review focuses on pork production. Using these systems is advantageous for enhancing pork production efficiency when trained personnel utilize these technologies to full capacity and have objective, automated, and uninterrupted streams of data collection.

Methane and nitrous oxide fluxes from reference, restored, and disturbed estuarine wetlands in Pacific Northwest, USA.

There is substantial interest in restoring tidal wetlands because of their high rates of long-term soil carbon sequestration and other valued ecosystem services. However, these wetlands are sometimes net sources of greenhouse gases (GHG) that may offset their climate cooling potential. GHG fluxes vary widely within and across tidal wetlands, so it is essential to better understand how key environmental drivers, and importantly, land management, affect GHG dynamics.

Comparing HemoCue® and Quantitative Buffy Coat® and Coulter Counter-measured haemoglobin concentrations in African children with acute uncomplicated malaria: a Bland-Altman analysis.

Background: Anaemia is a deleterious consequence of malaria, and its accurate diagnosis is crucial for effective management. However, laboratory methods for measuring haemoglobin (Hb) concentration, like the Coulter Counter and the Quantitative Buffy Coat® (QBC®), are costly and not widely accessible in resource-limited settings. The point-of-care HemoCue® test is a cheaper alternative and suitable in rural areas.

Advances in methods for characterizing dietary patterns: A scoping review.

There is a growing focus on understanding the complexity of dietary patterns and how they relate to health and other factors. Approaches that have not traditionally been applied to characterize dietary patterns, such as latent class analysis and machine learning algorithms, may offer opportunities to characterize dietary patterns in greater depth than previously considered. However, there has not been a formal examination of how this wide range of approaches has been applied to characterize dietary patterns.