Caspase-8 is a novel modulator of Homologous Recombination Repair in response to ionizing radiations in glioblastoma

Caspase-8 is a cysteine protease historically regarded as anti-neoplastic protein, thanks to its role in apoptosis. However, Caspase-8 expression is retained or even enhanced in several tumors, including glioblastoma (GBM), where it plays pro-tumor functions. We previously reported that it is a negative prognostic factor and contributes to resistance against DNA damaging agents, such as ionizing radiations (IR) and Temozolomide, commonly used in standard GBM treatment. We therefore investigated whether Caspase-8 may sustain DNA repair pathways proficiency in GBM. Here we uncover a novel role of Caspase-8 as promoter of the Homologous Recombination Repair (HRR). Importantly, IR promote Caspase-8 transient nuclear translocation and its recruitment to the chromatin. Moreover, Caspase-8 sustains the expression and the recruitment to the chromatin upon IR of RAD51 and CtIP, two key players of the HRR. Consistently, we identify a synthetically lethal interaction between Caspase-8 and PARP inhibition, that may enhance GBM sensitivity to IR. Remarkably, by using Caspase-8−/− murine embryo fibroblasts and a Drosophila melanogaster Caspase-8 mutant, we demonstrate that Caspase-8 plays an evolutionary conserved role in DNA repair

A brief overview of pedestrian accident modelling

Thanks to the advancement of models and methodologies to handle large amounts of data in conjunction with the increased attention of national and international governments toward safety issues, this paper aims to provide a brief overview of the main models that are used to study pedestrian crashes. The reason why this type of analysis is conducted starts from three main research questions: What are the main datasets needed to study pedestrian accidents, what are the main models utilized, and what are the main gaps that emerge in the pedestrian safety field? This proposed state-of-the-art overview starts from the analysis of statistical approaches in the context of risk factor analysis to the most recent machine learning methods to evaluate pedestrian crash severity by emphasizing the purposes for which the models are used, why they are used, and the data needed to achieve the task. The results of the analysis show how the models could be classified and the main research gaps in this field that could be useful for researchers as starting points in their studies

Affective Inflationism And Atmospheric Competence

The contribution presents an inflationary theory of atmospheres conceived as quasi-things. It proposes a distinction between prototypical, derivative-relational, and spurious-idiosyncratic atmospheres, analyzes the different levels of authority and power that atmospheric feelings can exert, and examines both the case in which an atmosphere is perceived without the perceiver being affectively involved, and the case in which the initial atmospheric impression changes over time. Finally, it outlines a theory of atmospheric competence (concerning both those who generate atmospheres and those who perceive them), at the center of which lies the possibility that an initial pathic immersion may be followed by a reflective re-emergence

A pathway for firm and dispatchable solar/wind supply through generation and markets splitting

The current mainstream strategy to allow a high share of variable renewable energy feed-in is mainly aimed at enabling new flexibility resources, but at high penetration, these resources are unlikely to be sufficient. On the contrary, the firmness and dispatchability of solar/wind could reduce/eliminate any demand for additional flexibility. In this work, we showed that solar/wind facilities can produce both variable/intermittent and baseload/dispatchable 24/365 energy by installing battery energy storage, grid forming inverters and suitable power plant controller. Then, we propose a new market design more suitable for this generation splitting approach. Using Italy as a case study, we have shown through energy simulations and cost optimization/analysis that the proposed market reform, combined with a firm energy feed-in tariff (always below 100 €/MWh), would make it profitable to reduce variable energy feed-in from large PV/wind power plants and related induced flexibility requirements by 20 %–30 %–40 %, in 2024–2030–2050. This flexibility reduction increases to 50 %–60 %–70 % dealing with the joint generation of an optimal mix of PV/wind farms. In addition, in 2050, for PV and the optimal mix of solar/wind systems, incentives below 100 €/MWh will push producers to generate only dispatchable energy. We also showed that our approach could solve or mitigate the significant misalignments between the current market structure and the techno-economic characteristics of renewables: wholesale market price volatility and cannibalization, growth of balancing prices and system-charges required to increase grid hosting capacity and adequacy

On the expressivity of the ExSpliNet KAN model

ExSpliNet is a neural network model that combines ideas of Kolmogorov networks, ensembles of probabilistic trees, and multivariate B-spline representations. In this paper, we study the expressivity of the ExSpliNet model and present two constructive approximation results that mitigate the curse of dimensionality. More precisely, we prove new error bounds for the ExSpliNet approximation of a subset of multivariate continuous functions and also of multivariate generalized bandlimited functions. The main ingredients of the proofs are a constructive version of the Kolmogorov superposition theorem, Maurey's theorem, and spline approximation results. The curse of dimensionality is lessened in the first case, while it is completely overcome in the second case. Since the considered ExSpliNet model can be regarded as a particular version of the recently introduced neural network architecture called Kolmogorov-Arnold network (KAN), our results also provide insights into the analysis of the expressivity of KANs