Publications by authors named "J M Poyatos"

Currently, there is a growing concern about water scarcity. The rising demand for wastewater treatment systems that facilitate the reuse of wastewater has resulted in a focus on the use of microalgae in sustainable treatments. These methods not only eliminate nutrients from the wastewater but also produce biomass that can be used to obtain high-value products.

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Complexity in biology is often described using a multi-map hierarchical architecture, where the genotype, representing the encoded information, is mapped to the functional level, known as the phenotype, which is then connected to a latent phenotype we refer to as fitness. This underlying architecture governs the processes driving evolution. Furthermore, natural selection, along with other neutral forces, can, in turn, modify these maps.

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Phenotype prediction is at the center of many questions in biology. Prediction is often achieved by determining statistical associations between genetic and phenotypic variation, ignoring the exact processes that cause the phenotype. Here, we present a framework based on genome-scale metabolic reconstructions to reveal the mechanisms behind the associations.

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In recent years, Deep Learning models have shown a great performance in complex optimization problems. They generally require large training datasets, which is a limitation in most practical cases. Transfer learning allows importing the first layers of a pre-trained architecture and connecting them to fully-connected layers to adapt them to a new problem.

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Every day, large quantities of wastewater are discharged from various sources that could be reused. Wastewater contains nutrients such as nitrogen or phosphorus, which can be recovered. Microalgae-based technologies have attracted attention in this sector, as they are able to bioremediate wastewater, harnessing its nutrients and generating algal biomass useful for different downstream uses, as well as having other advantages.

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