Stackmeincke5515

The dingo population on world heritage-listed K'gari-Fraser Island (K'gari) is amongst the most well-known in Australia. However, an absence of population genetic data limits capacity for informed conservation management. We used 9 microsatellite loci to compare the levels of genetic diversity and genetic structure of 175 K'gari dingo tissue samples with 264 samples from adjacent mainland regions. Our results demonstrated that the K'gari population has significantly lower genetic diversity than mainland dingoes (AR, HE, PAR; p  less then  0.05) with a fourfold reduction in effective population size (Ne = 25.7 vs 103.8). There is also strong evidence of genetic differentiation between the island and mainland populations. These results are in accordance with genetic theory for small, isolated, island populations, and most likely the result of low initial diversity and founder effects such as bottlenecks leading to decreased diversity and drift. As the first study to incorporate a large sample set of K'gari dingoes, this provides invaluable baseline data for future research, which should incorporate genetic and demographic monitoring to ensure long-term persistence. Given that human-associated activities will continue to result in dingo mortality, it is critical that genetic factors are considered in conservation management decisions to avoid deleterious consequences for this iconic dingo population.Structural covariance conceptualizes how morphologic properties of brain regions are related to one another (across individuals). It can provide unique information to cortical structure (e.g., thickness) about the development of functionally meaningful networks. The current study investigated how structural covariance networks develop during the transition from childhood to adolescence, a period characterized by marked structural re-organization. Participants (N = 192; scans = 366) completed MRI assessments between 8.5 and 14.5 years of age. A sliding window approach was used to create "age-bins", and structural covariance networks (based on cortical thickness) were created for each bin. Next, generalized additive models were used to characterize trajectories of age-related changes in network properties. Results revealed nonlinear trajectories with "peaks" in mean correlation and global density that are suggestive of a period of convergence in anatomical properties across the cortex during early adolescence, prior to regional specialization. "Hub" regions in sensorimotor cortices were present by late childhood, but the extent and strength of association cortices as "hubs" increased into mid-adolescence. Moreover, these regional changes were found to be related to rates of thinning across the cortex. In the context of neurocognitive networks, the frontoparietal, default mode, and attention systems exhibited age-related increases in within-network and between-network covariance. These regional and modular developmental patterns are consistent with continued refinement of socioemotional and other complex executive functions that are supported by higher-order cognitive networks during early adolescence.Peru has the highest burden of multidrug-resistant tuberculosis in the Americas region. Since 1999, the annual number of extensively drug-resistant tuberculosis (XDR-TB) Peruvian cases has been increasing, becoming a public health challenge. The objective of this study was to perform genomic characterization of Mycobacterium tuberculosis strains obtained from Peruvian patients with XDR-TB diagnosed from 2011 to 2015 in Peru. Whole genome sequencing (WGS) was performed on 68 XDR-TB strains from different regions of Peru. 58 (85.3%) strains came from the most populated districts of Lima and Callao. Concerning the lineages, 62 (91.2%) strains belonged to the Euro-American Lineage, while the remaining 6 (8.8%) strains belonged to the East-Asian Lineage. Most strains (90%) had high-confidence resistance mutations according to pre-established WHO-confident grading system. Discordant results between microbiological and molecular methodologies were caused by mutations outside the hotspot regions analysed by commercial molecular assays (rpoB I491F and inhA S94A). Cluster analysis using a cut-off ≤ 10 SNPs revealed that only 23 (34%) strains evidenced recent transmission links. This study highlights the relevance and utility of WGS as a high-resolution approach to predict drug resistance, analyse transmission of strains between groups, and determine evolutionary patterns of circulating XDR-TB strains in the country.In this work we propose to use Deep Learning to automatically calculate the coordinates of the vertebral corners in sagittal x-rays images of the thoracolumbar spine and, from those landmarks, to calculate relevant radiological parameters such as L1-L5 and L1-S1 lordosis and sacral slope. For this purpose, we used 10,193 images annotated with the landmarks coordinates as the ground truth. N-acetylcysteine inhibitor We realized a model that consists of 2 steps. In step 1, we trained 2 Convolutional Neural Networks to identify each vertebra in the image and calculate the landmarks coordinates respectively. In step 2, we refined the localization using cropped images of a single vertebra as input to another convolutional neural network and we used geometrical transformations to map the corners to the original image. For the localization tasks, we used a differentiable spatial to numerical transform (DSNT) as the top layer. We evaluated the model both qualitatively and quantitatively on a set of 195 test images. The median localization errors relative to the vertebrae dimensions were 1.98% and 1.68% for x and y coordinates respectively. All the predicted angles were highly correlated with the ground truth, despite non-negligible absolute median errors of 1.84°, 2.43° and 1.98° for L1-L5, L1-S1 and SS respectively. Our model is able to calculate with good accuracy the coordinates of the vertebral corners and has a large potential for improving the reliability and repeatability of measurements in clinical tasks.We present an experimental demonstration of the feasibility of the first 20 + Mb/s Gaussian modulated coherent state continuous variable quantum key distribution system with a locally generated local oscillator at the receiver (LLO-CVQKD). To increase the signal repetition rate, and hence the potential secure key rate, we equip our system with high-performance, wideband devices and design the components to support high repetition rate operation. We have successfully trialed the signal repetition rate as high as 500 MHz. To reduce the system complexity and correct for any phase shift during transmission, reference pulses are interleaved with quantum signals at Alice. Customized monitoring software has been developed, allowing all parameters to be controlled in real-time without any physical setup modification. We introduce a system-level noise model analysis at high bandwidth and propose a new 'combined-optimization' technique to optimize system parameters simultaneously to high precision. We use the measured excess noise, to predict that the system is capable of realizing a record 26.