Searching edges in the overlap of two plane graphs

Consider a pair of plane straight-line graphs, whose edges are colored red and blue, respectively, and let n be the total complexity of both graphs. We present a O(n log n)-time O(n)-space technique to preprocess such pair of graphs, that enables efficient searches among the red-blue intersections along edges of one of the graphs. Our technique has a number of applications to geometric problems. This includes: (1) a solution to the batched red-blue search problem [Dehne et al. 2006] in O(n log n) queries to the oracle; (2) an algorithm to compute the maximum vertical distance between a pair of 3D polyhedral terrains one of which is convex in O(n log n) time, where n is the total complexity of both terrains; (3) an algorithm to construct the Hausdorff Voronoi diagram of a family of point clusters in the plane in O((n+m) log^3 n) time and O(n+m) space, where n is the total number of points in all clusters and m is the number of crossings between all clusters; (4) an algorithm to construct the farthest-color Voronoi diagram of the corners of n axis-aligned rectangles in O(n log^2 n) time; (5) an algorithm to solve the stabbing circle problem for n parallel line segments in the plane in optimal O(n log n) time. All these results are new or improve on the best known algorithms.Comment: 22 pages, 6 figure

Seasonal predictability of the 2010 Russian heat wave

The atmospheric blocking over eastern Europe and western Russia that prevailed during July and August of 2010 led to the development of a devastating Russian heat wave. Therefore the question of whether the event was predictable or not is highly important. The principal aim of this study is to examine the predictability of this high-impact atmospheric event on a seasonal timescale. To this end, a set of dynamical seasonal simulations have been carried out using an atmospheric global circulation model (AGCM). The impact of various model initializations on the predictability of this large-scale event and its sensitivity to the initial conditions has been also investigated. The ensemble seasonal simulations are based on a modified version of the lagged-average forecast method using different lead-time initializations of the model. The results indicated that only a few individual members reproduced the main features of the blocking system 3 months ahead. Most members missed the phase space and the propagation of the system, setting limitations in the predictability of the event

Prediction of atrial fibrillation and stroke using machine learning models in UK Biobank

Objective: Atrial fibrillation (AF) is the most common cardiac arrythmia, and it is associated with increased risk for ischemic stroke, which is underestimated, as AF can be asymptomatic. The aim of this study was to develop optimal ML models for prediction of AF in the population, and secondly for ischemic stroke in AF patients. Methods: To develop ML models for prediction of 1) AF in the general population and 2) ischemic stroke in patients with AF we constructed XGBoost, LightGBM, Random Forest, Deep Neural Network, Support Vector Machine and Lasso penalised logistic regression models using UK-Biobank's extensive real-world clinical data, questionnaires, as well as biochemical and genetic data, and their predictive performances were compared. Ranking and contribution of the different features was assessed by SHapley Additive exPlanations (SHAP) analysis. The clinical tool CHA2DS2-VASc for prediction of ischemic stroke among AF patients, was used for comparison to the best performing ML model. Findings: The best performing model for AF prediction was LightGBM, with an area-under-the-roc-curve (AUROC) of 0.729 (95% confidence intervals (CI): 0.719, 0.738). The best performing model for ischemic stroke prediction in AF patients was XGBoost with AUROC of 0.631 (95% CI: 0.604, 0.657). The improved AUROC in the XGBoost model compared to CHA2DS2-VASc was statistically significant based on DeLong's test (p-value = 2.20E-06). In addition, the SHAP analysis showed that several peripheral blood biomarkers (e.g. creatinine, glycated haemoglobin, monocytes) were associated with ischemic stroke, which are not considered by CHA2DS2-VASc. Implications: The best performing ML models presented have the potential for clinical use, but further validation in independent studies is required. Our results endorse the incorporation of some routinely measured blood biomarkers for ischemic stroke prediction in AF patients

Cytogenetic behavior of cryoprotectant DMSO

IVF (in vitro fertilization) is now used worldwide to overcome female or male infertility. Cryopreservation of human embryos provides the clearest opportunity to improve the clinical results obtained with IVF. Cryoprotective agents (CPA) are used to minimize freezing injuries. DMSO has been the most widely used CPA, however, high concentrations of CPAs in the vitrification solution have been shown to be detrimental to the cell. In order to determine the effect of DMSO solutions (5%, 10% and 20%) on genetic stability and/or subsequent DNA repair, we have investigated its ability to induce Sister Chromatid Exchanges (SCEs) and Proliferation Rate Index (PRI) in normal human lymphocyte cultures of peripheral blood, due to the fact that the study cannot be conducted on embryos and to the limited number of spare available embryos, the corresponding accessible experimental material was T lymphocyte. The blood samples were taken from three different healthy donors (conducting experimental procedure in triplicate). After the effect of DMSO solutions on blood according to the instructions of kit K-SIBV-500, lymphocytes are harvested and cultured with suitable technique to assess SCEs and PRI. The results show that all three DMSO concentrations cause a statistically dose depended significant increase of SCE frequency of the lymphocytes (p<0.001) and raise the need for more research regarding the safe and effective use of cryoprotectant

Illumination diagnosis for retrieval of reflections from ambient-noise seismic data in the Siilinjärvi mining site, Finland

Reflection seismic methods are becoming popular in mineral exploration, because they allow high-resolution delineation of the exploration targets, even at great depths. Seismic interferometry can be used to retrieve reflections from passive seismic data, removing the need for active seismic sources and, therefore, reducing the cost and environmental impact of exploration. The retrieval of reflections can be challenging, since passive seismic records are typically dominated by surface waves. Therefore, illumination diagnosis, a method which allows the isolation of the portions of the passive data where body-wave signals are stronger, can be a valuable step that improves the quality of the reflections retrieved from seismic interferometry and reduces the overall computational cost of the processing stage. Here, we validate the performance of the method to effectively isolate the portions of the passive data dominated by body waves and apply it on an ambient-noise seismic dataset acquired in the Siilinjärvi mining site in Finland

COVID-19 susceptibility variants associate with blood clots, thrombophlebitis and circulatory diseases.

Epidemiological studies suggest that individuals with comorbid conditions including diabetes, chronic lung, inflammatory and vascular disease, are at higher risk of adverse COVID-19 outcomes. Genome-wide association studies have identified several loci associated with increased susceptibility and severity for COVID-19. However, it is not clear whether these associations are genetically determined or not. We used a Phenome-Wide Association (PheWAS) approach to investigate the role of genetically determined COVID-19 susceptibility on disease related outcomes. PheWAS analyses were performed in order to identify traits and diseases related to COVID-19 susceptibility and severity, evaluated through a predictive COVID-19 risk score. We utilised phenotypic data in up to 400,000 individuals from the UK Biobank, including Hospital Episode Statistics and General Practice data. We identified a spectrum of associations between both genetically determined COVID-19 susceptibility and severity with a number of traits. COVID-19 risk was associated with increased risk for phlebitis and thrombophlebitis (OR = 1.11, p = 5.36e-08). We also identified significant signals between COVID-19 susceptibility with blood clots in the leg (OR = 1.1, p = 1.66e-16) and with increased risk for blood clots in the lung (OR = 1.12, p = 1.45 e-10). Our study identifies significant association of genetically determined COVID-19 with increased blood clot events in leg and lungs. The reported associations between both COVID-19 susceptibility and severity and other diseases adds to the identification and stratification of individuals at increased risk, adverse outcomes and long-term effects