Integrative multi-environmental genomic prediction in apple.

Genomic prediction for multiple environments can aid the selection of genotypes suited to specific soil and climate conditions. Methodological advances allow effective integration of phenotypic, genomic (additive, nonadditive), and large-scale environmental (enviromic) data into multi-environmental genomic prediction models. These models can also account for genotype-by-environment interaction, utilize alternative relationship matrices (kernels), or substitute statistical approaches with deep learning.

A Comprehensive Review of the Genetics of Dyslipidemias and Risk of Atherosclerotic Cardiovascular Disease.

Dyslipidemias are often diagnosed based on an individual's lipid panel that may or may not include Lp(a) or apoB. But these values alone omit key information that can underestimate risk and misdiagnose disease, which leads to imprecise medical therapies that reduce efficacy with unnecessary adverse events. For example, knowing whether an individual's dyslipidemia is monogenic can granularly inform risk and create opportunities for precision therapeutics.

Characterization of a mock up nuclear waste package using energy resolved MeV neutron analysis.

Reliable radiographic methods for characterizing nuclear waste packages non-destructively (without the need to open containers) have the potential to significantly contribute to safe handling and future disposal options, particularly for legacy waste of unknown content. Due to required shielding of waste containers and the need to characterize materials consisting of light elements, X-ray methods are not suitable. Here, energy-resolved MeV neutron radiography is demonstrated as a first-of-its-kind application for non-destructive and remote examination of mock up nuclear waste packages from a safe position using time-of-flight techniques enabled by a novel event-mode imaging detector system.

Comparison of the Efficacy of Type 2 Diabetes Group Training Courses With and Without the Integration of mHealth Support in a Controlled Trial Setting: Results of a Comparative Pilot Study.

Objective: This study assessed the effects on type 2 diabetes self-management education provided in group courses with or without a supporting smartphone application (the DM2CUA app).

Research Design And Methods: This open-label, cluster-randomized, controlled, multicenter pilot study involved three Austrian diabetes educational group courses. People with type 2 diabetes in the control group received a regular educational group course, whereas those in the intervention group received the same course plus the use of the DM2CUA app.