Applications of Machine Learning in Chemical and Biological Oceanography.

Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorize complex systems based on a large amount of data. ML is applied in various areas including natural science, engineering, space exploration, and even gaming development. This review focuses on the use of machine learning in the field of chemical and biological oceanography.

Meta-analysis cum machine learning approaches address the structure and biogeochemical potential of marine copepod associated bacteriobiomes.

Copepods are the dominant members of the zooplankton community and the most abundant form of life. It is imperative to obtain insights into the copepod-associated bacteriobiomes (CAB) in order to identify specific bacterial taxa associated within a copepod, and to understand how they vary between different copepods. Analysing the potential genes within the CAB may reveal their intrinsic role in biogeochemical cycles.

Bioconversion of chitin and concomitant production of chitinase and N-acetylglucosamine by novel Achromobacter xylosoxidans isolated from shrimp waste disposal area.

Marine pollution is a significant issue in recent decades, with the increase in industries and their waste harming the environment and ecosystems. Notably, the rise in shellfish industries contributes to tons of shellfish waste composed of up to 58% chitin. Chitin, the second most ample polymer next to cellulose, is insoluble and resistant to degradation.

Metagenomic 16S rDNA amplicon data of microbial diversity and its predicted metabolic functions in the Southern Ocean (Antarctic).

Antarctica holds about 70% of all the freshwater on the planet in the form of ice. The seawater, it chills, affect the currents and temperature everywhere. Global warming risks the melting of the icecaps as it has already increased the ocean temperature by 1 °C to the West Antarctic peninsula since 1955.

Metagenomic data of vertical distribution and abundance of bacterial diversity in the hypersaline sediments of Mad Boon-mangrove ecosystem, Bay of Bengal.

Bacterial diversity studies in hypersaline soil often yield novel organisms and contribute to our understanding of this extreme environment. Soil from Mad Boon is previously uncharacterized, with dense mangrove forest in one side and hypersaline soil in another side of backwater located in Southeast coast of Tamil Nadu, India. We surveyed to characterize the structure and diversity of the bacterial community.

Pyrosequencing-Based Seasonal Observation of Prokaryotic Diversity in Pneumatophore-Associated Soil of Avicennia marina.

Pneumatophores are aerial roots developing from the main roots of mangrove plants away from the gravity. The below ground pneumatophore-associated soil prokaryotic community of Avicennia marina was studied by amplicon pyrosequencing (39,378 reads) during monsoon and summer seasons. Apart from the most dominant phylum Proteobacteria in both seasons, the second most were Acidobacteria (summer) and Cyanobacteria/Chloroplast (monsoon).