Production of a new chitinase from Nocardiopsis halophila TN-X8 utilizing bio-waste from the blue swimming crab: enzyme characterization and immobilization.

In accordance with the framework of the Circular Blue Bioeconomy in the Mediterranean region, the objective of this study was to evaluate the biotransformation of blue swimming crab (Portunus segnis) residues obtained from the port of Sfax by an extracellular chitinase produced by Nocardiopsis halophila strain TN-X8 isolated from Chott El Jerid (Tozeur, Tunisia). From the analysis of multiple extremophilic Actinomycetota, it was determined that strain TN-X8 exclusively utilized 60 g/L of raw blue swimming crab as its carbon and energy source, achieving a chitinase activity of approximately 950 U/mL following a 6-day incubation period at 40 °C. Pure chitinase, designated as ChiA-Nh30, was obtained after heat treatment, followed by ammonium sulfate fractionation and Sephacryl® S-200 column chromatography.

A multimodal dynamical variational autoencoder for audiovisual speech representation learning.

High-dimensional data such as natural images or speech signals exhibit some form of regularity, preventing their dimensions from varying independently. This suggests that there exists a lower dimensional latent representation from which the high-dimensional observed data were generated. Uncovering the hidden explanatory features of complex data is the goal of representation learning, and deep latent variable generative models have emerged as promising unsupervised approaches.

Does Anisakis spp. infestation affect the proximate composition, fatty acids, and minerals contents of its host Merluccius merlucccius?

Fish play an important role in human nutrition. They are not only a great source of protein and healthy fats, but also a unique source of essential nutrients such as omega-3 fatty acids. Moreover, most fish are parasitized, and some of these parasites are able to influence the reallocation of resources in their favor and thus reduce the nutritional quality of the fish.

Predicting congenital syphilis cases: A performance evaluation of different machine learning models.

Background: Communicable diseases represent a huge economic burden for healthcare systems and for society. Sexually transmitted infections (STIs) are a concerning issue, especially in developing and underdeveloped countries, in which environmental factors and other determinants of health play a role in contributing to its fast spread. In light of this situation, machine learning techniques have been explored to assess the incidence of syphilis and contribute to the epidemiological surveillance in this scenario.