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Glyphosate (Gly) is the active ingredient of several widely used herbicide formulations. Studies on Gly and glyphosate-based herbicide (GBH) exposure in different experimental models have suggested that the nervous system represented a key target for its toxicity, especially the prefrontal cortex (PFC). However, it is still unknown whether exposure to GBH affects higher brain functions dependent on PFC circuitry. The present work aimed to examine the effects of subtoxic doses of GBH on social cognition and cognitive flexibility as two functions belonging to higher brain function in mice. To do so, adult male mice were exposed daily to GBH by gavage at doses of 250 or 500 mg/kg for a sub-chronic period lasting 6 weeks. Then, mice were subjected to behavioral testing using the three-chamber and the Barnes maze paradigms. Our results indicate that GBH did not affect sociability. However, we found that GBH affects social cognition expressed by a lower discrimination index in the three-chamber test. Moreover, spatial memories evaluated during the probe trial, and cognitive flexibility evaluated during the reversal probe, were affected in mice exposed to GBH. Based on these results, exposure to subtoxic doses of GBH led to neurobehavioral alterations affecting the integrity of social cognition and cognitive flexibility functions. Finally, these data urge a thorough investigation of the cellular and molecular mechanisms underlying these alterations., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

pathological pain and Attention-deficit/hyperactivity disorder (ADHD) are two complex multifactorial syndromes. The comorbidity of ADHD and altered pain perception is well documented in children, adolescents, and adults. According to pathophysiological investigations, the dopaminergic system's dysfunction provides a common basis for ADHD and comorbid pain. Growing evidence suggests that oxidative stress may be crucial in both pathologies. Recent studies revealed that a small peptide encompassing the redox-active site of selenoprotein T (PSELT), protects dopaminergic neurons and fibers as well as lesioned nerves in animal models. The current study aims to examine the effects of PSELT treatment on ADHD-like symptoms and pain sensitivity, as well as the role of catecholaminergic systems in these effects. Our results demonstrated that intranasal administration of PSELT reduced the hyperactivity in the open field, decreased the impulsivity displayed by 6-OHDA-lesioned male mice in the 5-choice serial reaction time task test and improved attentional performance. In addition, PSELT treatment significantly increased the nociception threshold in both normal and inflammatory conditions. Furthermore, anti-hyperalgesic activity was antagonized with sulpiride pre-treatment, but not by phentolamine, or propranolol pre-treatments. The present study suggests that PSELT reduces the severity of ADHD symptoms in mice and possesses potent antinociceptive effects which could be related to the involvement of D2/D3 dopaminergic receptors., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

In this paper, we present an approach that combines data-driven and physical modelling for predicting the runoff occurrence and volume at catchment scale. With that aim, we first estimated the runoff volume from recorded storms aided by the Green-Ampt infiltration model. Then, we used machine learning algorithms, namely LightGBM (LGBM) and Deep Neural Network (DNN), to predict the outputs of the physical model fed on a set of atmospheric variables (relative humidity, temperature, atmospheric pressure, and wind velocity) collected before or immediately after the beginning of the storm. Results for a small urban catchment in Madrid show DNN performed better in predicting the runoff occurrence and volume. Moreover, enriching the input primary atmospheric variables with auxiliary variables (e.g., storm intensity data recorded during the first hour, or rain volume and intensity estimates obtained from auxiliary regression methods) largely increased the model performance. We show in this manuscript data-driven algorithms shaped by physical criteria can be successfully generated by allowing the data-driven algorithm learn from the output of physical models. It represents a novel approach for physics-informed data-driven algorithms shifting from common practices in hydrological modelling through machine learning., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Sergio Zubelzu reports financial support was provided by Spain Ministry of Science and Innovation., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)