FANCA is a component of the Fanconi anemia (FA) core complex that activates DNA interstrand crosslink repair by monoubiquitination of FANCD2. Here, we report that purified FANCA protein catalyzes bidirectional single-strand annealing (SA) and strand exchange (SE) at a level comparable to RAD52, while a disease-causing FANCA mutant, F1263Δ, is defective in both activities. FANCG, which directly interacts with FANCA, dramatically stimulates its SA and SE activities. Alternatively, FANCB, which does not directly interact with FANCA, does not stimulate this activity. Importantly, five other patient-derived FANCA mutants also exhibit deficient SA and SE, suggesting that the biochemical activities of FANCA are relevant to the etiology of FA. A cell-based DNA double-strand break (DSB) repair assay demonstrates that FANCA plays a direct role in the single-strand annealing sub-pathway (SSA) of DSB repair by catalyzing SA, and this role is independent of the canonical FA pathway and RAD52.

The edge platform has evolved to become a part of a distributed computing environment. While typical edges do not have enough processing power to train machine learning models in real time, it is common to generate models in the cloud for use on the edge. The pattern of heterogeneous Internet of Things (IoT) data is dependent on individual circumstances. It is not easy to guarantee prediction performance when a monolithic model is used without considering the spatial characteristics of the space generating those data. In this paper, we propose a collaborative framework using a new method to select the best model for the edge from candidate models of cloud based on sample data correlation. This method lets the edge use the most suitable model without any training tasks on the edge side, and it also minimizes privacy issues. We apply the proposed method to predict future fine particulate matter concentration in an individual space. The results suggest that our method can provide better performance than the previous method.

No abstract

No abstract

No abstract