Untangling Neolithic and Bronze Age mitochondrial lineages in South Asia

Two key moments shaped the extant South Asian gene pool within the last 10 thousand years (ka): the Neolithic period, with the advent of agriculture and the rise of the Harappan/Indus Valley Civilisation; and Late Bronze Age events that witnessed the abrupt fall of the Harappan Civilisation and the arrival of Indo-European speakers. This study focuses on the phylogeographic patterns of mitochondrial haplogroups H2 and H13 in the Indian Subcontinent and incorporates evidence from recently released ancient genomes from Central and South Asia. It found signals of Neolithic arrivals from Iran and later movements in the Bronze Age from Central Asia that derived ultimately from the Steppe. This study shows how a detailed mtDNA phylogeographic approach, combining both modern and ancient variation, can provide evidence of population movements, even in a scenario of strong male bias such as in the case of the Bronze Age Steppe dispersals

Crowdsourcing, Citizen Science or Volunteered Geographic Information? The Current State of Crowdsourced Geographic Information

Citizens are increasingly becoming an important source of geographic information, sometimes entering domains that had until recently been the exclusive realm of authoritative agencies. This activity has a very diverse character as it can, amongst other things, be active or passive, involve spatial or aspatial data and the data provided can be variable in terms of key attributes such as format, description and quality. Unsurprisingly, therefore, there are a variety of terms used to describe data arising from citizens. In this article, the expressions used to describe citizen sensing of geographic information are reviewed and their use over time explored, prior to categorizing them and highlighting key issues in the current state of the subject. The latter involved a review of ~100 Internet sites with particular focus on their thematic topic, the nature of the data and issues such as incentives for contributors. This review suggests that most sites involve active rather than passive contribution, with citizens typically motivated by the desire to aid a worthy cause, often receiving little training. As such, this article provides a snapshot of the role of citizens in crowdsourcing geographic information and a guide to the current status of this rapidly emerging and evolving subject

Farm Area Segmentation in Satellite Images Using DeepLabv3+ Neural Networks

Farm detection using low resolution satellite images is an important part of digital agriculture applications such as crop yield monitoring. However, it has not received enough attention compared to high-resolution images. Although high resolution images are more efficient for detection of land cover components, the analysis of low-resolution images are yet important due to the low-resolution repositories of the past satellite images used for timeseries analysis, free availability and economic concerns. In this paper, semantic segmentation of farm areas is addressed using low resolution satellite images. The segmentation is performed in two stages; First, local patches or Regions of Interest (ROI) that include farm areas are detected. Next, deep semantic segmentation strategies are employed to detect the farm pixels. For patch classification, two previously developed local patch classification strategies are employed; a two-step semi-supervised methodology using hand-crafted features and Support Vector Machine (SVM) modelling and transfer learning using the pretrained Convolutional Neural Networks (CNNs). For the latter, the high-level features learnt from the massive filter banks of deep Visual Geometry Group Network (VGG-16) are utilized. After classifying the image patches that contain farm areas, the DeepLabv3+ model is used for semantic segmentation of farm pixels. Four different pretrained networks, resnet18, resnet50, resnet101 and mobilenetv2, are used to transfer their learnt features for the new farm segmentation problem. The first step results show the superiority of the transfer learning compared to hand-crafted features for classification of patches. The second step results show that the model trained based on resnet50 achieved the highest semantic segmentation accuracy.acceptedVersionPeer reviewe

rasterdiv ‐ an Information Theory tailored R package for measuring ecosystem heterogeneity from space: to the origin and back

Ecosystem heterogeneity has been widely recognized as a key ecological indicator of several ecological functions, diversity patterns and change, metapopulation dynamics, population connectivity or gene flow. In this paper, we present a new R package—rasterdiv—to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns. The rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open-source algorithms

A pragmatic cluster randomized trial evaluating the impact of a community pharmacy intervention on statin adherence: rationale and design of the Community Pharmacy Assisting in Total Cardiovascular Health (CPATCH) study

<p>Abstract</p> <p>Background</p> <p>Traditional randomized controlled trials are considered the gold standard for evaluating the efficacy of a treatment. However, in adherence research, limitations to this study design exist, especially when evaluating real-world applicability of an intervention. Although adherence interventions by community pharmacists have been tested, problems with internal and external validity have limited the usefulness of these studies, and further well-designed and well-conducted research is needed. We aimed to determine the real-world effectiveness of a community pharmacy adherence intervention using a robust study design. This novel design integrates cluster randomization and an outcome evaluation of medication adherence using a population-based administrative data source in the province of Saskatchewan, Canada.</p> <p>Methods/Design</p> <p>Community pharmacies from across the province of Saskatchewan, Canada were randomized to deliver an adherence intervention to their patients or usual care. Intervention pharmacies were trained to employ a practical adherence strategy targeted at new users of statin medications. While randomization and implementation of the intervention occurred at the community pharmacy level, the outcome analysis will occur at the level of the individual subjects. The primary outcome is the mean statin adherence among all eligible new users of statin medications. Secondary outcomes include the proportion of new statin users who exhibit adherence ≥80%, and persistence with statin use.</p> <p>Discussion</p> <p>This novel study design was developed to combine the rigor of a randomized trial with a pragmatic approach to implementing and capturing the results in a real-world fashion. We believe this approach can serve as an example for future study designs evaluating practice-based adherence interventions.</p> <p>Trial Registration</p> <p>ClinicalTrials.gov no. NCT00971412.</p

Feasibility of low-dose CT with model-based iterative image reconstruction in follow-up of patients with testicular cancer.

Purpose: We examine the performance of pure model-based iterative reconstruction with reduced-dose CT in follow-up of patients with early-stage testicular cancer. Methods: Sixteen patients (mean age 35.6 ± 7.4 years) with stage I or II testicular cancer underwent conventional dose (CD) and low-dose (LD) CT acquisition during CT surveillance. LD data was reconstructed with model-based iterative reconstruction (LD–MBIR). Datasets were objectively and subjectively analysed at 8 anatomical levels. Two blinded clinical reads were compared to gold-standard assessment for diagnostic accuracy. Results: Mean radiation dose reduction of 67.1% was recorded. Mean dose measurements for LD–MBIR were: thorax – 66 ± 11 mGy cm (DLP), 1.0 ± 0.2 mSv (ED), 2.0 ± 0.4 mGy (SSDE); abdominopelvic – 128 ± 38 mGy cm (DLP), 1.9 ± 0.6 mSv (ED), 3.0 ± 0.6 mGy (SSDE). Objective noise and signal-to-noise ratio values were comparable between the CD and LD–MBIR images. LD–MBIR images were superior (p < 0.001) with regard to subjective noise, streak artefact, 2-plane contrast resolution, 2-plane spatial resolution and diagnostic acceptability. All patients were correctly categorised as positive, indeterminate or negative for metastatic disease by 2 readers on LD–MBIR and CD datasets. Conclusions: MBIR facilitated a 67% reduction in radiation dose whilst producing images that were comparable or superior to conventional dose studies without loss of diagnostic utility

A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly Algorithm (FFA) in Wireless Networks

This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF–FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model’s performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF–FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF–FFA model can be applied as an efficient technique for the accurate prediction of vertical handover