End members and species defined with permissible ranges of composition are presented for the true micas, the brittle micas and the interlayer-cation-deficient micas. The determination of the crystallochemical formula for different available chemical data is outlined, and a system of modifiers and suffixes is given to allow the expression of unusual chemical substitutions or polytypic stacking arrangements. Tables of mica synonyms, varieties, ill-defined materials and a list of names formerly or erroneously used for micas are presented. The Mica Subcommittee was appointed by the Commission on New Minerals and Mineral Names (“Commission”) of the International Mineralogical Association (IMA). The definitions and recommendations presented were approved by the Commission.

Knowledge graphs have become a common approach for knowledge representation. Yet, the application of graph methodology is elusive due to the sheer number and complexity of knowledge sources. In addition, semantic incompatibilities hinder efforts to harmonize and integrate across these diverse sources. As part of The Biomedical Translator Consortium, we have developed a knowledge graph–based question-answering system designed to augment human reasoning and accelerate translational scientific discovery: the Translator system. We have applied the Translator system to answer biomedical questions in the context of a broad array of diseases and syndromes, including Fanconi anemia, primary ciliary dyskinesia, multiple sclerosis, and others. A variety of collaborative approaches have been used to research and develop the Translator system. One recent approach involved the establishment of a monthly “Question-of-the-Month (QotM) Challenge” series. Herein, we describe the structure of the QotM Challenge; the six challenges that have been conducted to date on drug-induced liver injury, cannabidiol toxicity, coronavirus infection, diabetes, psoriatic arthritis, and ATP1A3-related phenotypes; the scientific insights that have been gleaned during the challenges; and the technical issues that were identified over the course of the challenges and that can now be addressed to foster further development of the prototype Translator system. We close with a discussion on Large Language Models such as ChatGPT and highlight differences between those models and the Translator system.

Diets and dietary constituents that we consume have a considerable impact on disease risk. Intriguingly these effects may be modulated to some extent by sex. Lack of female representation in nutritional studies as well as a lack of stratification by sex has and continues to limit our understanding of these sex × diet interactions. Here we provide an overview of the current and available literature describing how exposure to certain dietary patterns (Western-style diet, Mediterranean diet, vegetarian/vegan, ketogenic diet) and dietary constituents (dietary fibre, PUFA and plant bioactive) influences disease risk in a sex-specific manner. Interestingly, these sex differences appear to be highly disease-specific. The identification of such sex differences in response to diet stresses the importance of sex stratification in nutritional research.

Systems Engineering (SE) is becoming increasingly relevant in industrial application since more stakeholders are involved in engineering activities. To implement SE, companies have to adapt existing engineering processes and methods. This adaption requires knowledge about new methods as well as their integration into the engineering activities. In order to ensure goal-oriented identification of methods for different SE activities in this contribution an action field profile and the Systems Engineering Method Matrix are proposed. The development of both tools is driven by the assumption that most SE activities and methods can be described based on the artefacts the deliver. In order to get feedback about the proposed tools, semi-structured interviews with two industry partners were conducted, focussing on the tool's usability. These interviews underline the basic usability of the tools and their support to identify SE activities to be supported by (new) methods. Moreover, requirements for further development and adaption are derived from the interviews.

The stable core, an inner model of the form $\langle L[S],\in , S\rangle $ for a simply definable predicate S, was introduced by the first author in [8], where he showed that V is a class forcing extension of its stable core. We study the structural properties of the stable core and its interactions with large cardinals. We show that the $\operatorname {GCH} $ can fail at all regular cardinals in the stable core, that the stable core can have a discrete proper class of measurable cardinals, but that measurable cardinals need not be downward absolute to the stable core. Moreover, we show that, if large cardinals exist in V, then the stable core has inner models with a proper class of measurable limits of measurables, with a proper class of measurable limits of measurable limits of measurables, and so forth. We show this by providing a characterization of natural inner models $L[C_1, \dots , C_n]$ for specially nested class clubs $C_1, \dots , C_n$, like those arising in the stable core, generalizing recent results of Welch [29].

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.

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.