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DNA double-strand breaks (DSBs) elicit an elaborate response to signal damage and trigger repair via two major pathways: nonhomologous end-joining (NHEJ), which functions throughout the interphase, and homologous recombination (HR), restricted to S/G2 phases. The DNA damage response relies, on post-translational modifications of nuclear factors to coordinate the mending of breaks. Ubiquitylation of histones and chromatin-associated factors regulates DSB repair and numerous E3 ubiquitin ligases are involved in this process. Despite significant progress, our understanding of ubiquitimmediated DNA damage response regulation remains incomplete. Here, we have performed a localization screen to identify RING/U-box E3 ligases involved in genome maintenance. Our approach uncovered 7 novel E3 ligases that are recruited to microirradiation stripes, suggesting potential roles in DNA damage signaling and repair. Among these factors, the DELTEX family E3 ligase DTX2 is rapidly mobilized to lesions in a poly ADP-ribosylation-dependent manner. DTX2 is recruited and retained at DSBs via its WWE and DELTEX conserved C-terminal domains. In cells, both domains are required for optimal binding to mono and poly ADP-ribosylated proteins withWWEs playing a prominent role in this process. Supporting its involvement in DSB repair, DTX2 depletion decreases HR efficiency and moderately enhances NHEJ. Furthermore, DTX2 depletion impeded BRCA1 foci formation and increased 53BP1 accumulation at DSBs, suggesting a fine-tuning role for this E3 ligase in repair pathway choice. Finally, DTX2 depletion sensitized cancer cells to X-rays and PARP inhibition and these susceptibilities could be rescued by DTX2 reexpression. Altogether, our work identifies DTX2 as a novel ADP-ribosylationdependent regulator of HR-mediated DSB repair. [ABSTRACT FROM AUTHOR]

In machine learning, some models can make uncertain and imprecise predictions, they are called evidential models. These models may also be able to handle imperfect labeling and take into account labels that are richer than the commonly used hard labels, containing uncertainty and imprecision. This paper proposes an Evidential Decision Tree, and an Evidential Random Forest. These two models use a distance and a degree of inclusion to allow the model to group observations whose response elements are included in each other into a single node. Experimental results showed better performance for the presented methods compared to other evidential models and to recent Cautious Random Forests when the data is noisy. The models also offer a better robustness to the overfitting effect when using datasets that are effectively uncertainly and imprecisely labeled by the contributors. The proposed models are also able to predict rich labels, an information that can be used in other approaches, such as active learning. • Source uncertainty and imprecision can be modeled in richer labels. • Richer labels improve classification performance. • Evidential decision tree and evidential random forest are robust to overfitting. [ABSTRACT FROM AUTHOR]

Crowdsourcing is the outsourcing of tasks to a crowd of contributors on a dedicated platform. The crowd on these platforms is very diversified and includes various profiles of contributors which generates data of uneven quality. However, majority voting, which is the aggregating method commonly used in platforms, gives equal weight to each contribution. To overcome this problem, we propose a method, MONITOR, which estimates the contributor's profile and aggregates the collected data by taking into account their possible imperfections thanks to the theory of belief functions. To do so, MONITOR starts by estimating the profile of the contributor through his qualification for the task and his behavior. Crowdsourcing campaigns have been carried out to collect the necessary data to test MONITOR on real data in order to compare it to existing approaches. The results of the experiments show that thanks to the use of the MONITOR method, we obtain a better rate of correct answer after aggregation of the contributions compared to the majority voting. Our contributions in this article are for the first time the proposal of a model that takes into account both the qualification of the contributor and his behavior in the estimation of his profile. For the second one, the weakening and the aggregation of the answers according to the estimated profiles. • Proposition of a model for estimate the profile of users of crowdsourcing platforms. • Profile estimation based on contributor qualification and behavior. • Aggregation of human answers, including imperfection, using the Dempster theory. • Aggregation including the estimated profile of contributors. [ABSTRACT FROM AUTHOR]