Artificial intelligence to assist specialists in the detection of haematological diseases.

Artificial intelligence, particularly the growth of neural network research and development, has become an invaluable tool for data analysis, offering unrivalled solutions for image generation, natural language processing, and personalised suggestions. In the meantime, biomedicine has been presented as one of the pressing challenges of the 21st century. The inversion of the age pyramid, the increase in longevity, and the negative environment due to pollution and bad habits of the population have led to a necessity of research in the methodologies that can help to mitigate and fight against these changes.

Nutritional Modulation of Associations between Prenatal Exposure to Persistent Organic Pollutants and Childhood Obesity: A Prospective Cohort Study.

Background: Prenatal exposure to persistent organic pollutants (POPs) may contribute to the development of childhood obesity and metabolic disorders. However, little is known about whether the maternal nutritional status during pregnancy can modulate these associations.

Objectives: The main objective was to characterize the joint associations and interactions between prenatal levels of POPs and nutrients on childhood obesity.

Molecular phylogenetics, speciation, and long distance dispersal in tardigrade evolution: A case study of the genus Milnesium.

Microorganisms (sensu lato, i.e., including micrometazoans) are thought to have cosmopolitan geographic distributions due to their theoretically unlimited dispersal capabilities, a consequence of their tiny size, population dynamics, and resistant forms.

MuPeG-The Multiple Person Gait Framework.

Gait recognition is being employed as an effective approach to identify people without requiring subject collaboration. Nowadays, developed techniques for this task are obtaining high performance on current datasets (usually more than 90 % of accuracy). However, those datasets are simple as they only contain one subject in the scene at the same time.

A cross-dataset deep learning-based classifier for people fall detection and identification.

Background And Objective: Fall detection is an important problem for vulnerable sectors of the population such as elderly people, who frequently live alone. Note that a fall can be very dangerous for them if they cannot ask for help. Hence, in those situations, an automatic system that detected and informed to emergency services about the fall and subject identity could help to save lives.