Inactivation of GSK3β by Ser phosphorylation prevents thymocyte necroptosis and impacts Tcr repertoire diversity.

The assembly of Tcrb and Tcra genes require double negative (DN) thymocytes to undergo multiple rounds of programmed DNA double-strand breaks (DSBs), followed by their efficient repair. However, mechanisms governing cell cycle checkpoints and specific survival pathways during the repair process remain unclear. Here, we report high-resolution scRNA-seq analyses of individually sorted mouse DN3 and DN4 thymocytes, which reveals a G2M cell cycle checkpoint, in addition to the known G1 checkpoint, during Tcrb and Tcra recombination.

Reverse-engineered Electro-Fenton for the selective synthesis of oxalic or oxamic acid through the degradation of acetaminophen: A novel green electrocatalytic refinery approach.

The Electro-Fenton process (EF) has been conventionally applied to efficiently degrade refractory and/or toxic pollutants. However, in this work, EF was used as a reverse engineering tool to selectively synthesize highly value-added products (oxalic or oxamic acid) through the degradation of the model pollutant acetaminophen, a widely used analgesic and antipyretic drug. It was found that the production of either oxalic or oxamic acid is dictated by the applied current density.

A benchmark for Rey-Osterrieth complex figure test automatic scoring.

The Rey-Osterrieth complex figure (ROCF) test is a neuropsychological task that can be useful for early detection of cognitive decline in the elderly population. Several computer vision systems have been proposed to automate this complex analysis task, but the lack of public benchmarks does not allow a fair comparison of these systems. To advance in that direction, we present a benchmarking framework for the automatic scoring of the ROCF test that provides: the ROCFD528 dataset, which is the first open dataset of ROCF line drawings; and experimental results obtained by several modern deep learning models, which can be used as a baseline for comparing new proposals.

Early detection of mild cognitive impairment through neuropsychological tests in population screenings: a decision support system integrating ontologies and machine learning.

Machine learning (ML) methodologies for detecting Mild Cognitive Impairment (MCI) are progressively gaining prevalence to manage the vast volume of processed information. Nevertheless, the black-box nature of ML algorithms and the heterogeneity within the data may result in varied interpretations across distinct studies. To avoid this, in this proposal, we present the design of a decision support system that integrates a machine learning model represented using the Semantic Web Rule Language (SWRL) in an ontology with specialized knowledge in neuropsychological tests, the NIO ontology.