Achieving Business Practicability of Model-Driven Cross-Platform Apps

Due to the incompatibility of mobile device platforms such as Android and iOS, apps have to be developed separately for each target platform. Cross-platform development approaches based on Web technology have significantly improved over the last years. However, since they do not lead to native apps, these frameworks are not feasible for all kinds of business apps. Moreover, the way apps are developed is cumbersome. Advanced cross-platform approaches such as MD2, which is based on model-driven development (MDSD) techniques, are a much more powerful yet less mature choice. We discuss business implications of MDSD for apps and introduce MD2 as our proposed solution to fulfill typical requirements. Moreover, we highlight a business-oriented enhancement that further increases MD2's business practicability. We generalize our findings and sketch the path towards more versatile MDSD of apps

Achieving Business Practicability of Model-Driven Cross-Platform Apps

-Due to the incompatibility of mobile device platforms such as Android and iOS, apps have to be developed separately for each target platform. Cross-platform development approaches based on Web technology have significantly improved over the last years. However, since they do not lead to native apps, these frameworks are not feasible for all kinds of business apps. Moreover, the way apps are developed is cumbersome. Advanced cross-platform approaches such as MD2, which is based on model-driven development (MDSD) techniques, are a much more powerful yet less mature choice. We discuss business implications of MDSD for apps and introduce MD2 as our proposed solution to fulfill typical requirements. Moreover, we highlight a business-oriented enhancement that further increases MD2's business practicability. We generalize our findings and sketch the path towards more versatile MDSD of app

Associations of Health App Use and Perceived Effectiveness in People With Cardiovascular Diseases and Diabetes: Population-Based Survey

Background: Mobile health apps can help to change health-related behaviors and manage chronic conditions in patients with cardiovascular diseases (CVDs) and diabetes mellitus, but a certain level of health literacy and electronic health (eHealth) literacy may be needed. Objective: The aim of this study was to identify factors associated with mobile health app use in individuals with CVD or diabetes and detect relations with the perceived effectiveness of health apps among app users. Methods: The study used population-based Web-based survey (N=1500) among Germans, aged 35 years and older, with CVD, diabetes, or both. A total of 3 subgroups were examined: (1) Individuals with CVD (n=1325), (2) Individuals with diabetes (n=681), and (3) Individuals with CVD and diabetes (n=524). Sociodemographics, health behaviors, CVD, diabetes, health and eHealth literacy, characteristics of health app use, and characteristics of apps themselves were assessed by questionnaires. Linear and logistic regression models were applied. Results: Overall, patterns of factors associated with health app use were comparable in individuals with CVD or diabetes or both. Across subgroups, about every fourth patient reported using apps for health-related purposes, with physical activity and weight loss being the most prominent target behaviors. Health app users were younger, more likely to be female (except in those with CVD and diabetes combined), better educated, and reported more physical activity. App users had higher eHealth literacy than nonusers. Those users who perceived the app to have a greater effectiveness on their health behaviors tended to be more health and eHealth literate and rated the app to use more behavior change techniques (BCTs). Conclusions: There are health- and literacy-related disparities in the access to health app use among patients with CVD, diabetes, or both, which are relevant to specific health care professionals such as endocrinologists, dieticians, cardiologists, or general practitioners. Apps containing more BCTs had a higher perceived effect on people’s health, and app developers should take the complexity of needs into account. Furthermore, eHealth literacy appears to be a requirement to use health apps successfully, which should be considered in health education strategies to improve health in patients with CVD and diabetes

Deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks

Brain extraction in magnetic resonance imaging (MRI) data is an important segmentation step in many neuroimaging preprocessing pipelines. Image segmentation is one of the research fields in which deep learning had the biggest impact in recent years enabling high precision segmentation with minimal compute. Consequently, traditional brain extraction methods are now being replaced by deep learning-based methods. Here, we used a unique dataset comprising 568 T1-weighted (T1w) MR images from 191 different studies in combination with cutting edge deep learning methods to build a fast, high-precision brain extraction tool called deepbet. deepbet uses LinkNet, a modern UNet architecture, in a two stage prediction process. This increases its segmentation performance, setting a novel state-of-the-art performance during cross-validation with a median Dice score (DSC) of 99.0% on unseen datasets, outperforming current state of the art models (DSC = 97.8% and DSC = 97.9%). While current methods are more sensitive to outliers, resulting in Dice scores as low as 76.5%, deepbet manages to achieve a Dice score of > 96.9% for all samples. Finally, our model accelerates brain extraction by a factor of ~10 compared to current methods, enabling the processing of one image in ~2 seconds on low level hardware

Transethnic Genome-Wide Association Study Provides Insights in the Genetic Architecture and Heritability of Long QT Syndrome

BACKGROUND: Long QT syndrome (LQTS) is a rare genetic disorder and a major preventable cause of sudden cardiac death in the young. A causal rare genetic variant with large effect size is identified in up to 80% of probands (genotype positive) and cascade family screening shows incomplete penetrance of genetic variants. Furthermore, a proportion of cases meeting diagnostic criteria for LQTS remain genetically elusive despite genetic testing of established genes (genotype negative). These observations raise the possibility that common genetic variants with small effect size contribute to the clinical picture of LQTS. This study aimed to characterize and quantify the contribution of common genetic variation to LQTS disease susceptibility. METHODS: We conducted genome-wide association studies followed by transethnic meta-analysis in 1656 unrelated patients with LQTS of European or Japanese ancestry and 9890 controls to identify susceptibility single nucleotide polymorphisms. We estimated the common variant heritability of LQTS and tested the genetic correlation between LQTS susceptibility and other cardiac traits. Furthermore, we tested the aggregate effect of the 68 single nucleotide polymorphisms previously associated with the QT-interval in the general population using a polygenic risk score. RESULTS: Genome-wide association analysis identified 3 loci associated with LQTS at genome-wide statistical significance (P&lt;5×10-8) near NOS1AP, KCNQ1, and KLF12, and 1 missense variant in KCNE1(p.Asp85Asn) at the suggestive threshold (P&lt;10-6). Heritability analyses showed that ≈15% of variance in overall LQTS susceptibility was attributable to common genetic variation (h2SNP 0.148; standard error 0.019). LQTS susceptibility showed a strong genome-wide genetic correlation with the QT-interval in the general population (rg=0.40; P=3.2×10-3). The polygenic risk score comprising common variants previously associated with the QT-interval in the general population was greater in LQTS cases compared with controls (P&lt;10-13), and it is notable that, among patients with LQTS, this polygenic risk score was greater in patients who were genotype negative compared with those who were genotype positive (P&lt;0.005). CONCLUSIONS: This work establishes an important role for common genetic variation in susceptibility to LQTS. We demonstrate overlap between genetic control of the QT-interval in the general population and genetic factors contributing to LQTS susceptibility. Using polygenic risk score analyses aggregating common genetic variants that modulate the QT-interval in the general population, we provide evidence for a polygenic architecture in genotype negative LQTS.</p