Reduced incidence of foot-related hospitalisation and amputation amongst persons with diabetes in Queensland, Australia

Objective To determine trends in the incidence of foot-related hospitalisation and amputation amongst persons with diabetes in Queensland (Australia) between 2005 and 2010 that coincided with changes in state-wide ambulatory diabetic foot-related complication management. Methods All data from cases admitted for the principal reason of diabetes foot-related hospitalisation or amputation in Queensland from 2005–2010 were obtained from the Queensland Hospital Admitted Patient Data Collection dataset. Incidence rates for foot-related hospitalisation (admissions, bed days used) and amputation (total, minor, major) cases amongst persons with diabetes were calculated per 1,000 person-years with diabetes (diabetes population) and per 100,000 person-years (general population). Age-sex standardised incidence and age-sex adjusted Poisson regression models were also calculated for the general population. Results There were 4,443 amputations, 24,917 hospital admissions and 260,085 bed days used for diabetes foot-related complications in Queensland. Incidence per 1,000 person-years with diabetes decreased from 2005 to 2010: 43.0% for hospital admissions (36.6 to 20.9), 40.1% bed days (391 to 234), 40.0% total amputations (6.47 to 3.88), 45.0% major amputations (2.18 to 1.20), 37.5% minor amputations (4.29 to 2.68) (p < 0.01 respectively). Age-sex standardised incidence per 100,000 person-years in the general population also decreased from 2005 to 2010: 23.3% hospital admissions (105.1 to 80.6), 19.5% bed days (1,122 to 903), 19.3% total amputations (18.57 to 14.99), 26.4% major amputations (6.26 to 4.61), 15.7% minor amputations (12.32 to 10.38) (p < 0.01 respectively). The age-sex adjusted incidence rates per calendar year decreased in the general population (rate ratio (95% CI)); hospital admissions 0.949 (0.942–0.956), bed days 0.964 (0.962–0.966), total amputations 0.962 (0.946–0.979), major amputations 0.945 (0.917–0.974), minor amputations 0.970 (0.950–0.991) (p < 0.05 respectively). Conclusions There were significant reductions in the incidence of foot-related hospitalisation and amputation amongst persons with diabetes in the population of Queensland over a recent six-year period.

Avian diversification patterns across the K-Pg boundary: Influence of calibrations, datasets, and model misspecification

Birds represent the most diverse extant tetrapod clade, with ca. 10,000 extant species, and the timing of the crown avian radiation remains hotly debated. The fossil record supports a primarily Cenozoic radiation of crown birds, whereas molecular divergence dating analyses generally imply that this radiation was well underway during the Cretaceous. Furthermore, substantial differences have been noted between published divergence estimates. These have been variously attributed to clock model, calibration regime, and gene type. One underappreciated phenomenon is that disparity between fossil ages and molecular dates tends to be proportionally greater for shallower nodes in the avian Tree of Life. Here, we explore potential drivers of disparity in avian divergence dates through a set of analyses applying various calibration strategies and coding methods to a mitochondrial genome dataset and an 18-gene nuclear dataset, both sampled across 72 taxa. Our analyses support the occurrence of two deep divergences (i.e., the Palaeognathae/Neognathae split and the Galloanserae/Neoaves split) well within the Cretaceous, followed by a rapid radiation of Neoaves near the K-Pg boundary. However, 95% highest posterior density intervals for most basal divergences in Neoaves cross the boundary, and we emphasize that, barring unreasonably strict prior distributions, distinguishing between a rapid Early Paleocene radiation and a Late Cretaceous radiation may be beyond the resolving power of currently favored divergence dating methods. In contrast to recent observations for placental mammals, constraining all divergences within Neoaves to occur in the Cenozoic does not result in unreasonably high inferred substitution rates. Comparisons of nuclear DNA (nDNA) versus mitochondrial DNA (mtDNA) datasets and NT- versus RY-coded mitochondrial data reveal patterns of disparity that are consistent with substitution model misspecifications that result in tree compression/tree extension artifacts, which may explain some discordance between previous divergence estimates based on different sequence types. Comparisons of fully calibrated and nominally calibrated trees support a correlation between body mass and apparent dating error. Overall, our results are consistent with (but do not require) a Paleogene radiation for most major clades of crown birds.

Statistical tools for analyzing water quality data

Water quality data are often collected at different sites over time to improve water quality management. Water quality data usually exhibit the following characteristics: non-normal distribution, presence of outliers, missing values, values below detection limits (censored), and serial dependence. It is essential to apply appropriate statistical methodology when analyzing water quality data to draw valid conclusions and hence provide useful advice in water management. In this chapter, we will provide and demonstrate various statistical tools for analyzing such water quality data, and will also introduce how to use a statistical software R to analyze water quality data by various statistical methods. A dataset collected from the Susquehanna River Basin will be used to demonstrate various statistical methods provided in this chapter. The dataset can be downloaded from website http://www.srbc.net/programs/CBP/nutrientprogram.htm.

Deeper and wider fully convolutional network coupled with conditional random fields for scene labeling

Deep convolutional neural networks (DCNNs) have been employed in many computer vision tasks with great success due to their robustness in feature learning. One of the advantages of DCNNs is their representation robustness to object locations, which is useful for object recognition tasks. However, this also discards spatial information, which is useful when dealing with topological information of the image (e.g. scene labeling, face recognition). In this paper, we propose a deeper and wider network architecture to tackle the scene labeling task. The depth is achieved by incorporating predictions from multiple early layers of the DCNN. The width is achieved by combining multiple outputs of the network. We then further refine the parsing task by adopting graphical models (GMs) as a post-processing step to incorporate spatial and contextual information into the network. The new strategy for a deeper, wider convolutional network coupled with graphical models has shown promising results on the PASCAL-Context dataset.