Mckenziemygind4614

Mycobacterium tuberculosis (Mtb) strains of Beijing lineage have caused great concern because of their rapid emergence of drug resistance and worldwide spread. DNA mutation rates that reflect evolutional adaptation to host responses and the appearance of drug resistance have not been elucidated in human-infected Beijing strains. We tracked and obtained an original Mtb isolate of Beijing lineage from the 1999 tuberculosis outbreak in Japan, as well as five other isolates that spread in humans, and two isolates from the patient caused recurrence. Three isolates were from patients who developed TB within one year after infection (rapid-progressor, RP), and the other three isolates were from those who developed TB more than one year after infection (slow-progressor, SP). We sequenced genomes of these isolates and analyzed the propensity and rate of genomic mutations. Generation time versus mutation rate curves were significantly higher for RP. The ratio of oxidative versus non-oxidation damages induced mutations was higher in SP than RP, suggesting that persistent Mtb are exposed to oxidative stress in the latent state. Our data thus demonstrates that higher mutation rates of Mtb Beijing strains during human infection is likely to account for the higher adaptability and an emergence ratio of drug resistance.The energy needed in controlling a complex network is a problem of practical importance. Recent works have focused on the reduction of control energy either via strategic placement of driver nodes, or by decreasing the cardinality of nodes to be controlled. However, optimizing control energy with respect to target nodes selection has yet been considered. In this work, we propose an iterative method based on Stiefel manifold optimization of selectable target node matrix to reduce control energy. We derive the matrix derivative gradient needed for the search algorithm in a general way, and search for target nodes which result in reduced control energy, assuming that driver nodes placement is fixed. Our findings reveal that the control energy is optimal when the path distances from driver nodes to target nodes are minimized. learn more We corroborate our algorithm with extensive simulations on elementary network topologies, random and scale-free networks, as well as various real networks. The simulation results show that the control energy found using our algorithm outperforms heuristic selection strategies for choosing target nodes by a few orders of magnitude. Our work may be applicable to opinion networks, where one is interested in identifying the optimal group of individuals that the driver nodes can influence.An amendment to this paper has been published and can be accessed via a link at the top of the paper.The maintenance of genomic stability relies on DNA damage sensor kinases that detect DNA lesions and phosphorylate an extensive network of substrates. The Mec1/ATR kinase is one of the primary sensor kinases responsible for orchestrating DNA damage responses. Despite the importance of Mec1/ATR, the current network of its identified substrates remains incomplete due, in part, to limitations in mass spectrometry-based quantitative phosphoproteomics. Phosphoproteomics suffers from lack of redundancy and statistical power for generating high confidence datasets, since information about phosphopeptide identity, site-localization, and quantitation must often be gleaned from a single peptide-spectrum match (PSM). Here we carefully analyzed the isotope label swapping strategy for phosphoproteomics, using data consistency among reciprocal labeling experiments as a central filtering rule for maximizing phosphopeptide identification and quantitation. We demonstrate that the approach allows drastic reduction of false positive quantitations and identifications even from phosphopeptides with a low number of spectral matches. Application of this approach identifies new Mec1/ATR-dependent signaling events, expanding our understanding of the DNA damage signaling network. Overall, the proposed quantitative phosphoproteomic approach should be generally applicable for investigating kinase signaling networks with high confidence and depth.Amiodarone is an anti-arrhythmic drug that was approved by the US Food and Drug Administration (FDA) in 1985. Pre-clinical studies suggest that Amiodarone induces cytotoxicity in several types of cancer cells, thus making it a potential candidate for use as an anti-cancer treatment. However, it is also known to cause a variety of severe side effects. We hypothesized that in addition to the cytotoxic effects observed in cancer cells Amiodarone also has an indirect effect on angiogensis, a key factor in the tumor microenvironment. In this study, we examined Amiodarone's effects on a murine tumor model comprised of U-87 MG glioblastoma multiforme (GBM) cells, known to form highly vascularized tumors. We performed several in vitro assays using tumor and endothelial cells, along with in vivo assays utilizing three murine models. Low dose Amiodarone markedly reduced the size of GBM xenograft tumors and displayed a strong anti-angiogenic effect, suggesting dual cancer fighting properties. Our findings lay the ground for further research of Amiodarone as a possible clinical agent that, used in safe doses, maintains its dual properties while averting the drug's harmful side effects.To purpose of this paper was to assess the feasibility of volumetric breast density estimations on MRI without segmentations accompanied with an explainability step. A total of 615 patients with breast cancer were included for volumetric breast density estimation. A 3-dimensional regression convolutional neural network (CNN) was used to estimate the volumetric breast density. Patients were split in training (N = 400), validation (N = 50), and hold-out test set (N = 165). Hyperparameters were optimized using Neural Network Intelligence and augmentations consisted of translations and rotations. The estimated densities were evaluated to the ground truth using Spearman's correlation and Bland-Altman plots. The output of the CNN was visually analyzed using SHapley Additive exPlanations (SHAP). Spearman's correlation between estimated and ground truth density was ρ = 0.81 (N = 165, P  less then  0.001) in the hold-out test set. The estimated density had a median bias of 0.70% (95% limits of agreement = - 6.8% to 5.