Background: For the rising number of people living with dementia, cost-effective community-based interventions to support psychosocial care are needed. The FindMyApps program helps people with dementia and their caregivers learn to use tablet computers and find user-friendly apps that facilitate self-management and engagement in meaningful activities. This definitive trial builds on previous feasibility pilot trials of FindMyApps and further evaluates cost-effectiveness.

Method: This is a protocol for a non-blinded randomized controlled trial (RCT) with two arms (intervention and usual care). 150 dyads (person with dementia and their carer) will be recruited. Participants must be resident in the community, with a diagnosis of Mild Cognitive Impairment or mild dementia (Mini Mental-State Examination 17-26, or Global Deterioration Scale 3-4. Dyads will be randomly assigned in equal proportions to receive either the FindMyApps intervention (experimental arm) or usual care (control arm). Primary outcomes measured at 3 months will be: patient self-management and social participation; caregiver sense of competence. Data will be collected through questionnaires filled in by the researcher (patient outcomes) or participants themselves (carer outcomes). In addition to a main effect analysis, a cost-effectiveness analysis will take place. In line with Medical Research Council (MRC) guidance for the evaluation of complex interventions, a process analysis will be undertaken, to identify factors that may influence trial outcomes. Semi-structured interviews and remotely collected data regarding use of the FindMyApps app will support the process analysis.

Result: Results of this study are expected in 2022. The study will be adequately powered to detect at least a moderate effect size of the intervention with respect to the primary outcomes.

Conclusion: This study will investigate the effectiveness and cost-effectiveness of the FindMyApps intervention. The results of the study will provide strong evidence to support or oppose scaling up implementation of the intervention. This is also an example of how the MRC framework for the evaluation of complex interventions can be implemented in practice. In a field which is often criticized for a lack of high quality evidence, randomized controlled trials should be applied more frequently designed for the robust and transparent evaluation of digital tools and technologies.

Modern nanomaterials contain complexity that spans all three dimensions—from multigate semiconductors to clean energy nanocatalysts to complex block copolymers. For nanoscale characterization, it has been a long-standing goal to observe and quantify the three-dimensional (3D) structure—not just surfaces, but the entire internal volume and the chemical arrangement. Electron tomography estimates the complete 3D structure of nanomaterials from a series of two-dimensional projections taken across many viewing angles. Since its first introduction in 1968, electron tomography has progressed substantially in resolution, dose, and chemical sensitivity. In particular, scanning transmission electron microscope tomography has greatly enhanced the study of 3D nanomaterials by providing quantifiable internal morphology and spectroscopic detection of elements. Combined with recent innovations in computational reconstruction algorithms and 3D visualization tools, scientists can interactively dissect volumetric representations and extract meaningful statistics of specimens. This article highlights the maturing field of electron tomography and the widening scientific applications that utilize 3D structural, chemical, and functional imaging at the nanometer and subnanometer length scales.

Several dietary factors have been extensively investigated for associations with risk of breast cancer, but to date unequivocal evidence only exists for alcohol consumption. We sought to systematically evaluate the association between 92 dietary factors and breast cancer risk using a nutrient-wide association study approach. Using data from 272,098 women participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) study, we assessed dietary intake of 92 foods and nutrients estimated by dietary questionnaires. Cox regression with age as the time scale and adjustment for potential confounders, was used to quantify the association between each food or nutrient and risk of breast cancer. A false discovery rate (FDR) of 0.05 was used to select the set of foods and nutrients to evaluate in the independent replication cohort, the Netherlands Cohort Study (NLCS). During a median follow-up time of 15 years, 10,979 incident invasive breast cancers were identified in the women from the EPIC study. Six foods and nutrients were associated with risk of breast cancer when controlling the FDR at 0.05. Higher intake of alcohol overall was associated with a higher risk of breast cancer (hazard ratio (HR) for a 1 SD increment in intake = 1.05, 95% confidence interval (CI) 1.03–1.07), as was beer/cider intake and wine intake (HRs per 1 SD increment = 1.05, 95% CI 1.03–1.06 and 1.04, 95% CI 1.02–1.06, respectively), whereas higher intakes of fibre, apple/pear, and carbohydrates were associated with a lower risk of breast cancer (HRs per 1 SD increment = 0.96, 95% CI 0.94–0.98; 0.96, 95% CI 0.94–0.99; and 0.96, 95% CI 0.95–0.98, respectively). When evaluated in the NLCS (2368 invasive breast cancer cases), estimates for each of these foods and nutrients were similar in magnitude and direction, with the exception of beer/cider intake, which was not associated with risk of breast cancer in the NLCS. Our findings confirm the well-established increased risk of breast cancer associated with alcohol consumption, and suggest that higher intake of dietary fibre, and possibly fruit and carbohydrates, might be associated with reduced breast cancer risk.

Electron microscopy is uniquely suited for atomic-resolution imaging of heterogeneous and complex materials, where composition, physical, and electronic structure need to be analyzed simultaneously. Historically, the technique has demonstrated optimal performance at room temperature, since practical aspects such as vibration, drift, and contamination limit exploration at extreme temperature regimes. Conversely, quantum materials that exhibit exotic physical properties directly tied to the quantum mechanical nature of electrons are best studied (and often only exist) at extremely low temperatures. As a result, emergent phenomena, such as superconductivity, are typically studied using scanning probe-based techniques that can provide exquisite structural and electronic characterization, but are necessarily limited to surfaces. In this article, we focus not on the various methods that have been used to examine quantum materials at extremely low temperatures, but on what could be accomplished in the field of quantum materials if the power of electron microscopy to provide structural analysis at the atomic scale was extended to extremely low temperatures.