RESUMEN
Seaweeds are macroalgae, which can be of many different morphologies, sizes, colors, and chemical profiles. They include brown, red, and green seaweeds. Brown seaweeds have been more investigated and exploited in comparison to other seaweed types for their use in animal feeding studies due to their large sizes and ease of harvesting. Recent in vitro and in vivo studies suggest that plant secondary compound-containing seaweeds (e.g., halogenated compounds, phlorotannins, etc.) have the potential to mitigate enteric methane (CH4) emissions from ruminants when added to the diets of beef and dairy cattle. Red seaweeds including Asparagopsis spp. are rich in crude protein and halogenated compounds compared to brown and green seaweeds. When halogenated-containing red seaweeds are used as the active ingredient in ruminant diets, bromoform concentration can be used as an indicator of anti-methanogenic properties. Phlorotannin-containing brown seaweed has also the potential to decrease CH4 production. However, numerous studies examined the possible anti-methanogenic effects of marine seaweeds with inconsistent results. This work reviews existing data associated with seaweeds and in vitro and in vivo rumen fermentation, animal performance, and enteric CH4 emissions in ruminants. Increased understanding of the seaweed supplementation related to rumen fermentation and its effect on animal performance and CH4 emissions in ruminants may lead to novel strategies aimed at reducing greenhouse gas emissions while improving animal productivity.
RESUMEN
Understanding the role that the environment plays in influencing public health often involves collecting and studying large, complex data sets. There have been a number of private and public efforts to gather sufficient information and confront significant unknowns in the field of environmental public health, yet there is a persistent and largely unmet need for findable, accessible, interoperable, and reusable (FAIR) data. Even when data are readily available, the ability to create, analyze, and draw conclusions from these data using emerging computational tools, such as augmented and artificial inteligence (AI) and machine learning, requires technical skills not currently implemented on a programmatic level across research hubs and academic institutions. We argue that collaborative efforts in data curation and storage, scientific computing, and training are of paramount importance to empower researchers within environmental sciences and the broader public health community to apply AI approaches and fully realize their potential. Leaders in the field were asked to prioritize challenges in incorporating big data in environmental public health research: inconsistent implementation of FAIR principles in data collection and sharing, a lack of skilled data scientists and appropriate cyber-infrastructures, and limited understanding of possibilities and communication of benefits were among those identified. These issues are discussed, and actionable recommendations are provided.