SuperConga: An open-source framework for mesoscopic superconductivity

We present SuperConga, an open-source framework for simulating equilibrium properties of unconventional and ballistic singlet superconductors, confined to two-dimensional (2D) mesoscopic grains in a perpendicular external magnetic field, at arbitrary low temperatures. It aims at being both fast and easy to use, enabling research without access to a computer cluster, and visualization in real-time with OpenGL. The core is written in C++ and CUDA, exploiting the embarrassingly parallel nature of the quasiclassical theory of superconductivity by utilizing the parallel computational power of modern graphics processing units. The framework self-consistently computes both the superconducting order-parameter and the induced vector potential and finds the current density, free energy, induced flux density, local density of states (LDOS), and the magnetic moment. A user-friendly Python frontend is provided, enabling simulation parameters to be defined via intuitive configuration files, or via the command-line interface, without requiring a deep understanding of implementation details. For example, complicated geometries can be created with relative ease. The framework ships with simple tools for analyzing and visualizing the results, including an interactive plotter for spectroscopy. An overview of the theory is presented, as well as examples showcasing the framework\u27s capabilities and ease of use. The framework is free to download from https://gitlab.com/superconga/superconga, which also links to the extensive user manual, containing even more examples, tutorials, and guides. To demonstrate and benchmark SuperConga, we study the magnetostatics, thermodynamics, and spectroscopy of various phenomena. In particular, we study flux quantization in solenoids, vortex physics, surface Andreev bound-states, and a "phase crystal."We compare our numeric results with analytics and present experimental observables, e.g., the magnetic moment and LDOS, measurable with, for example, scanning probes, STM, and magnetometry

Classification complexity in myoelectric pattern recognition

Background: Limb prosthetics, exoskeletons, and neurorehabilitation devices can be intuitively controlled using myoelectric pattern recognition (MPR) to decode the subject\u27s intended movement. In conventional MPR, descriptive electromyography (EMG) features representing the intended movement are fed into a classification algorithm. The separability of the different movements in the feature space significantly affects the classification complexity. Classification complexity estimating algorithms (CCEAs) were studied in this work in order to improve feature selection, predict MPR performance, and inform on faulty data acquisition. Methods: CCEAs such as nearest neighbor separability (NNS), purity, repeatability index (RI), and separability index (SI) were evaluated based on their correlation with classification accuracy, as well as on their suitability to produce highly performing EMG feature sets. SI was evaluated using Mahalanobis distance, Bhattacharyya distance, Hellinger distance, Kullback-Leibler divergence, and a modified version of Mahalanobis distance. Three commonly used classifiers in MPR were used to compute classification accuracy (linear discriminant analysis (LDA), multi-layer perceptron (MLP), and support vector machine (SVM)). The algorithms and analytic graphical user interfaces produced in this work are freely available in BioPatRec. Results: NNS and SI were found to be highly correlated with classification accuracy (correlations up to 0.98 for both algorithms) and capable of yielding highly descriptive feature sets. Additionally, the experiments revealed how the level of correlation between the inputs of the classifiers influences classification accuracy, and emphasizes the classifiers\u27 sensitivity to such redundancy. Conclusions: This study deepens the understanding of the classification complexity in prediction of motor volition based on myoelectric information. It also provides researchers with tools to analyze myoelectric recordings in order to improve classification performance

Cigarette smoking and gastric cancer in the Stomach Cancer Pooling (StoP) Project

Tobacco smoking is a known cause of gastric cancer, but several aspects of the association remain imprecisely quantified. We examined the relation between cigarette smoking and the risk of gastric cancer using a uniquely large dataset of 23 epidemiological studies within the \ue2\u80\u98Stomach cancer Pooling (StoP) Project\ue2\u80\u99, including 10 290 cases and 26 145 controls. We estimated summary odds ratios (ORs) and the corresponding 95% confidence intervals (CIs) by pooling study-specific ORs using random-effects models. Compared with never smokers, the ORs were 1.20 (95% CI: 1.09\ue2\u80\u931.32) for ever, 1.12 (95% CI: 0.99\ue2\u80\u931.27) for former, and 1.25 (95% CI: 1.11\ue2\u80\u931.40) for current cigarette smokers. Among current smokers, the risk increased with number of cigarettes per day to reach an OR of 1.32 (95% CI: 1.10\ue2\u80\u931.58) for smokers of more than 20 cigarettes per day. The risk increased with duration of smoking, to reach an OR of 1.33 (95% CI: 1.14\ue2\u80\u931.54) for more than 40 years of smoking and decreased with increasing time since stopping cigarette smoking (P for trend<0.01) and became similar to that of never smokers 10 years after stopping. Risks were somewhat higher for cardia than noncardia gastric cancer. Risks were similar when considering only studies with information on Helicobacter pylori infection and comparing all cases to H. pylori+ controls only. This study provides the most precise estimate of the detrimental effect of cigarette smoking on the risk of gastric cancer on the basis of individual data, including the relationship with dose and duration, and the decrease in risk following stopping smoking

Alcohol consumption and gastric cancer risk\u2014A pooled analysis within the StoP project consortium

An association between heavy alcohol drinking and gastric cancer risk has been recently reported, but the issue is still open to discussion and quantification. We investigated the role of alcohol drinking on gastric cancer risk in the \u201cStomach cancer Pooling (StoP) Project,\u201d a consortium of epidemiological studies. A total of 9,669 cases and 25,336 controls from 20 studies from Europe, Asia and North America were included. We estimated summary odds-ratios (ORs) and the corresponding 95% confidence intervals (CIs) by pooling study-specific ORs using random-effects meta-regression models. Compared with abstainers, drinkers of up to 4 drinks/day of alcohol had no increase in gastric cancer risk, while the ORs were 1.26 (95% CI, 1.08\u20131.48) for heavy (>4 to 6 drinks/day) and 1.48 (95% CI 1.29\u20131.70) for very heavy (>6 drinks/day) drinkers. The risk for drinkers of >4 drinks/day was higher in never smokers (OR 1.87, 95% CI 1.35\u20132.58) as compared with current smokers (OR 1.14, 95% CI 0.93\u20131.40). Somewhat stronger associations emerged with heavy drinking in cardia (OR 1.61, 95% CI 1.11\u20132.34) than in non-cardia (OR 1.28, 95% CI 1.13\u20131.45) gastric cancers, and in intestinal-type (OR 1.54, 95% CI 1.20\u20131.97) than in diffuse-type (OR 1.29, 95% CI 1.05\u20131.58) cancers. The association was similar in strata of H. pylori infected (OR = 1.52, 95% CI 1.16\u20132.00) and noninfected subjects (OR = 1.69, 95% CI 0.95\u20133.01). Our collaborative pooled-analysis provides definite, more precise quantitative evidence than previously available of an association between heavy alcohol drinking and gastric cancer risk

Education and gastric cancer risk-An individual participant data meta-analysis in the StoP project consortium

Low socioeconomic position (SEP) is a strong risk factor for incidence and premature mortality from several cancers. Our study aimed at quantifying the association between SEP and gastric cancer (GC) risk through an individual participant data meta-analysis within the \u201cStomach cancer Pooling (StoP) Project\u201d. Educational level and household income were used as proxies for the SEP. We estimated pooled odds ratios (ORs) and the corresponding 95% confidence intervals (CIs) across levels of education and household income by pooling study-specific ORs through random-effects meta-analytic models. The relative index of inequality (RII) was also computed. A total of 9,773 GC cases and 24,373 controls from 25 studies from Europe, Asia and America were included. The pooled OR for the highest compared to the lowest level of education was 0.60 (95% CI, 0.44\u20130.84), while the pooled RII was 0.45 (95% CI, 0.29\u20130.69). A strong inverse association was observed both for noncardia (OR 0.39, 95% CI, 0.22\u20130.70) and cardia GC (OR 0.47, 95% CI, 0.22\u20130.99). The relation was stronger among H. pylori negative subjects (RII 0.14, 95% CI, 0.04\u20130.48) as compared to H. pylori positive ones (RII 0.29, 95% CI, 0.10\u20130.84), in the absence of a significant interaction (p = 0.28). The highest household income category showed a pooled OR of 0.65 (95% CI, 0.48\u20130.89), while the corresponding RII was 0.40 (95% CI, 0.22\u20130.72). Our collaborative pooled-analysis showed a strong inverse relationship between SEP indicators and GC risk. Our data call for public health interventions to reduce GC risk among the more vulnerable groups of the population

Impact of smoking and smoking cessation on cardiovascular events and mortality among older adults: Meta-analysis of Individual participant data from prospective cohort studies of the CHANCES consortium

Objective: To investigate the impact of smoking and smoking cessation on cardiovascular mortality, acute coronary events, and stroke events in people aged 60 and older, and to calculate and report risk advancement periods for cardiovascular mortality in addition to traditional epidemiological relative risk measures. Design: Individual participant meta-analysis using data from 25 cohorts participating in the CHANCES consortium. Data were harmonised, analysed separately employing Cox proportional hazard regression models, and combined by meta-analysis. Results: Overall, 503?905 participants aged 60 and older were included in this study, of whom 37?952 died from Cardiovascular Diseases. Random effects meta-analysis of the association of smoking status with cardiovascular mortality yielded a summary hazard ratio of 2.07 (95% CI 1.82 to 2.36) for current smokers and 1.37 (1.25 to 1.49) for former smokers compared with never smokers. Corresponding summary estimates for risk advancement periods were 5.50 years (4.25 to 6.75) for current smokers and 2.16 years (1.38 to 2.39) for former smokers. The excess risk in smokers increased with cigarette consumption in a dose-response manner, and decreased continuously with time since smoking cessation in former smokers. Relative risk estimates for acute coronary events and for stroke events were somewhat lower than for cardiovascular mortality, but patterns were similar. Conclusions: Our study corroborates and expands evidence from previous studies in showing that smoking is a strong independent risk factor of cardiovascular events and mortality even at older age, advancing cardiovascular mortality by more than five years, and demonstrating that smoking cessation in these age groups is still beneficial in reducing the excess risk