Differential requirement for RecFOR pathway components in Thermus thermophilus.

Recombinational repair is an important mechanism that allows DNA replication to overcome damaged templates, so the DNA is duplicated timely and correctly. The RecFOR pathway is one of the common ways to load RecA, while the RuvABC complex operates in the resolution of DNA intermediates. We have generated deletions of recO, recR and ruvB genes in Thermus thermophilus, while a recF null mutant could not be obtained. The recO deletion was in all cases accompanied by spontaneous loss of function mutations in addA or addB genes, which encode a helicase-exonuclease also key for recombination. The mutants were moderately affected in viability and chromosome segregation. When we generated these mutations in a Δppol/addAB strain, we observed that the transformation efficiency was maintained at the typical level of Δppol/addAB, which is 100-fold higher than that of the wild type. Most mutants showed increased filamentation phenotypes, especially ruvB, which also had DNA repair defects. These results suggest that in T. thermophilus (i) the components of the RecFOR pathway have differential roles, (ii) there is an epistatic relationship of the AddAB complex over the RecFOR pathway and (iii) that neither of the two pathways or their combination is strictly required for viability although they are necessary for normal DNA repair and chromosome segregation.

Mapping Motor Cortex Stimulation to Muscle Responses: A Deep Neural Network Modeling Approach.

A deep neural network (DNN) that can reliably model muscle responses from corresponding brain stimulation has the potential to increase knowledge of coordinated motor control for numerous basic science and applied use cases. Such cases include the understanding of abnormal movement patterns due to neurological injury from stroke, and stimulation based interventions for neurological recovery such as paired associative stimulation. In this work, potential DNN models are explored and the one with the minimum squared errors is recommended for the optimal performance of the M2M-Net, a network that maps transcranial magnetic stimulation of the motor cortex to corresponding muscle responses, using: a finite element simulation, an empirical neural response profile, a convolutional autoencoder, a separate deep network mapper, and recordings of multi-muscle activation. We discuss the rationale behind the different modeling approaches and architectures, and contrast their results. Additionally, to obtain a comparative insight of the trade-o between complexity and performance analysis, we explore different techniques, including the extension of two classical information criteria for M2M-Net. Finally, we find that the model analogous to mapping the motor cortex stimulation to a combination of direct and synergistic connection to the muscles performs the best, when the neural response profile is used at the input.