Anti-XPA monoclonal antibody

Nucleotide excision repair (NER) removes a wide range of DNA lesions, including UV-induced photoproducts and bulky base adducts. XPA is an essential protein in eukaryotic NER, although reports about its stoichiometry and role in damage recognition are controversial. Here, by PeakForce Tapping atomic force microscopy, we show that human XPA binds and bends DNA by similar to 60 degrees as a monomer. Furthermore, we observe XPA specificity for the helix-distorting base adduct N-(2'-deoxyguanosin-8-yl)-2-acetylaminofluorene over non-damaged dsDNA. Moreover, single molecule fluorescence microscopy reveals that DNA-bound XPA exhibits multiple modes of linear diffusion between paused phases. The presence of DNA damage increases the frequency of pausing. Truncated XPA, lacking the intrinsically disordered N- and C-termini, loses specificity for DNA lesions and shows less pausing on damaged DNA. Our data are consistent with a working model in which monomeric XPA bends DNA, displays episodic phases of linear diffusion along DNA, and pauses in response to DNA damage. It is not fully understood how XPA interacts with a DNA lesion during nucleotide excision repair. Here, the authors use single molecule analysis to study XPA-DNA interactions, including the DNA bend angle, protein stoichiometry, and diffusive properties during damage search.

Today reducing power consumption is a major concern especially when it concerns small embedded devices. Power optimization is required all along the design flow but particularly in the first steps where it has the strongest impact. In this work, we propose new power models based on neural networks that predict the power consumed by digital operators implemented on Field Programmable Gate Arrays (FPGAs). These operators are interconnected and the statistical information of data patterns are propagated among them. The obtained results make an overall power estimation of a specific design possible. A comparison is performed to evaluate the accuracy of our power models against the estimations provided by the Xilinx Power Analyzer (XPA) tool. Our approach is verified at system-level where different processing systems are implemented. A mean absolute percentage error which is less than 8% is shown versus the Xilinx classic flow dedicated to power estimation.