Let W be a simply laced Weyl group of finite type and rank n. If W has type $E_7$, $E_8$ or $D_n$ for n even, then the root system of W has subsystems of type $nA_1$. This gives rise to an irreducible Macdonald representation of W spanned by n-roots, which are products of n orthogonal roots in the symmetric algebra of the reflection representation. We prove that in these cases, the set of all maximal sets of orthogonal positive roots has the structure of a quasiparabolic set in the sense of Rains–Vazirani. The quasiparabolic structure can be described in terms of certain quadruples of orthogonal positive roots which we call crossings, nestings and alignments. This leads to nonnesting and noncrossing bases for the Macdonald representation, as well as some highly structured partially ordered sets. We use the $8$-roots in type $E_8$ to give a concise description of a graph that is known to be non-isomorphic but quantum isomorphic to the orthogonality graph of the $E_8$ root system.

Flavonoids, found in plant foods, are becoming increasingly recognised for their health benefits(1). A valid, reliable and short dietary assessment tool is necessary to assess flavonoid intake, as current methods are burdensome for researchers and participants. This study aimed to evaluate the validity and reproducibility of a flavonoid food frequency questionnaire (Flav-Q), which was derived from the Kent & Charlton Flavonoid Food Frequency Questionnaire (FFQ)(2). The Flav-Q contains 23 items and was validated against repeated 24-hour dietary recalls in an Australian adult population (18y+). The Flav-Q was administered at four time-points over 12 months period (n = 80). At each time-point, two 24-hour dietary recall surveys were completed using Intake-24(3). Usual flavonoid intake was assessed by cross-referencing food lists with the Phenol-Explorer database and averaged using the multiple source method (MSM) for participants who had at least 4 recalls. The criterion validity of the Flav-Q at baseline was compared against the usual intake using the Wilcoxon signed-rank test, Spearman’s correlation coefficient, Bland-Altman plots, and Cohen’s kappa (κ)(4). The reproducibility of the baseline Flav-Q (Flav_Q1) was compared with time points 2, 3, and 4. Mean total flavonoid intake was higher for Flav-Q1 compared to usual intake (443.2 mg/day vs 234.4 mg/day, p < 0.001) and overestimated subclass intake except for flavanones. Moderate to strong correlations were found between Flav-Q1 and usual intake for total flavonoids (r = 0.66, p < 0.001; κ = 0.45, p < 0.001) and subclasses flavan-3-ols (r = 0.72, p < 0.001; κ = 0.53; p < 0.001)), flavonols (r = 0.55, p < 0.001; κ = 0.40, p < 0.001), flavanones (r = 0.49, p < 0.001; κ = 0.30, p = 0.007), and a weaker non-significant correlation for anthocyanin (r = 0.38, p < 0.001; κ = 0.15, p = 0.18) and flavones (r = 0.34, p < 0.001; κ = 20, p = 0.07). Bland-Altman plots showed a large bias and wide limits of agreement (61.64%) for total flavonoid intake. Flav-Q demonstrated high reproducibility across all timepoints (Flav-Q1 vs Flav-Q2 r = 0.82, p < 0.001; κ = 0.70, p < 0.001), Flav-Q1 vs Flav-Q3 (r = 0.68, p < 0.001; κ = 0.47, p < 0.001), Flav-Q1 vs Flav-Q4 (r = 0.63, p < 0.001; κ = 0.47, p < 0.001). Mean percentage differences between repeated timepoints for total flavonoid ranged from 19% to 31%, with Bland-Altman plots showing good levels of agreement. Overall, the Flav-Q tool was reproducible and demonstrated some agreement for assessing the intake of total flavonoid and its subclasses. However, further validation to determine reasons for over-estimation is necessary.

Objectives/Goals: Undergraduate Medical Education (UME) may apply Just-in-Time training (JITT) to provide medical students with learning experiences closely aligned with real-time clinical needs. The purpose of this scoping review is to offer an overview of the implementation of JITT training in UME. Methods/Study Population: Following the five-stage framework by Arksey and O’Malley to methodically collect and analyze studies on JITT in UME, five electronic databases were searched, and a supplemental search for grey literature was conducted. Studies exploring the integration of JITT principles into UME clinical training and their time to follow-up after training were included. Bloom’s Taxonomy was used to assess educational goals of JITT interventions. Results/Anticipated Results: The review yielded 21 studies across 4 countries. The majority were cohort studies (13) and randomized control trials (5). Assessment definitions and use of JITT varied widely. Most studies focused on short-term outcomes, defined by being measured immediately after JITT session (15) or at the end of JITT-based rotation or clerkship (3). Three studies evaluated outcomes at a period longer than 2 weeks after completion of session or clerkship. Attitudes (9), followed by skills (8) were the most common educational goals of intervention. The efficacy and utility of JITT in improving educational goal acquisition was demonstrated in 90% (17/19) of the studies with reported outcomes. Discussion/Significance of Impact: The introduction of JITT in UME has been shown to meet the immediate needs of healthcare environments; however, evidence is limited in the evaluation of longer-term outcomes. Further research to determine the impact of JITT on long-term learning retention and education goal acquisition in UME is merited.

Objectives/Goals: We will conduct a 12-week pilot randomized controlled trial (RCT) to test the feasibility, acceptability, and preliminary efficacy of a staged-intensity whole foods intervention on hemoglobin A1c (HbA1c) change in adults, diet quality change (via the 2020 healthy eating index [HEI-2020]) in adults and offspring, and diet adherence and social determinants of health (SDOH) considerations via focus groups. Methods/Study Population: In this two-arm, parallel RCT, 30 adults with prediabetes (25–59 years) and their offspring (6–18 years) will be randomized to receive the 1) 12-week whole foods intervention which includes a 2-week feeding period (all foods/recipies provided), a 6-week customizable feeding period (3 dinners/recipies weekly), and a 4-week maintenance period (no food/recipies). The control group will receive standard of care (i.e., single RD-led diet counseling session). Primary outcomes include feasibility (≥80% retention and completion of study outcome measures) and acceptability (≥75% adult self-reported diet satisfaction). Intervention effects include 1) HbA1c change at 12-weeks in adults and 2) adult/offspring HEI-2020 scores assessed via diet records. Focus groups will assess influences of SDOH on diet adherence. Results/Anticipated Results: We have received Institutional Review Board approval, and recruitment is planned for January 2025. We will enroll 30 families from the greater Nashville, TN area. An intent-to-treat analysis will be conducted to test the preliminary effects of the whole foods diet intervention on the 12-week change in HbA1c (adults only) and 2020-HEI diet quality scores during the intervention period (adults and offspring). Focus groups will be conducted to understand how individual and family needs/preferences and SDOH may be perceived barriers or facilitators of diet adherence. Data generated from this study will be used to guide a fully powered RCT of our whole foods intervention to assess long-term effects on additional diabetes and metabolic outcomes and assessment of SDOH influences to support long-term adherence. Discussion/Significance of Impact: A healthy diet pattern is an effective nonpharmacological solution to prevent T2D, but only if it can be maintained. A family-centered whole foods diet pattern that uses “food as medicine” and considers how individual and family needs/preferences, and SDOHs could be an effective and sustainable multigenerational solution to prevent T2D in families.

In the last few decades, the study of ordinal data in which the variable of interest is not exactly observed but only known to be in a specific ordinal category has become important. To emphasize that the problem is not specific to a specific discipline we will use the neutral term coarsened observation. For single-equation models estimation of the latent linear model by Maximum Likelihood (ML) is routine. But, for higher-dimensional multivariate models it is computationally cumbersome as estimation requires the evaluation of multivariate normal distribution functions on a large scale. Our proposed alternative estimation method, based on the Generalized Method of Moments (GMM), circumvents this multivariate integration problem. It can be implemented by repeated application of standard techniques and provides a simpler and faster approach than the usual ML approach. It is applicable to multiple-equation models with $K$-dimensional error correlation matrices and ${J}_k$ response categories for the kth equation. It also yields a simple method to estimate polyserial and polychoric correlations. Comparison of our method with the outcomes of the Stata ML procedure cmp yields estimates that are not statistically different, while estimation by our method requires only a fraction of the computing time.

Analysts often seek to compare representations in high-dimensional space, e.g., embedding vectors of the same word across groups. We show that the distance measures calculated in such cases can exhibit considerable statistical bias, that stems from uncertainty in the estimation of the elements of those vectors. This problem applies to Euclidean distance, cosine similarity, and other similar measures. After illustrating the severity of this problem for text-as-data applications, we provide and validate a bias correction for the squared Euclidean distance. This same correction also substantially reduces bias in ordinary Euclidean distance and cosine similarity estimates, but corrections for these measures are not quite unbiased and are (non-intuitively) bimodal when distances are close to zero. The estimators require obtaining the variance of the latent positions. We (will) implement the estimator in free software, and we offer recommendations for related work.

Tightly focused proton beams generated from helical coil targets have been shown to be highly collimated across small distances, and display characteristic spectral bunching. We show, for the first time, proton spectra from such targets at high resolution via a Thomson parabola spectrometer. The proton spectral peaks reach energies above 50 MeV, with cutoffs approaching 70 MeV and particle numbers greater than 10${}^{10}$. The spectral bunch width has also been measured as low as approximately 8.5 MeV (17% energy spread). The proton beam pointing and divergence measured at metre-scale distances are found to be stable with the average pointing stability below 10 mrad, and average half-angle beam divergences of approximately 6 mrad. Evidence of the influence of the final turn of the coil on beam pointing over long distances is also presented, corroborated by particle tracing simulations, indicating the scope for further improvement and control of the beam pointing with modifying target parameters.

A highly popular method for examining the stability of a data clustering is to split the data into two parts, cluster the observations in Part A, assign the objects in Part B to their nearest centroid in Part A, and then independently cluster the Part B objects. One then examines how close the two partitions are (say, by the Rand measure). Another proposal is to split the data into k parts, and see how their centroids cluster. By means of synthetic data analyses, we demonstrate that these approaches fail to identify the appropriate number of clusters, particularly as sample size becomes large and the variables exhibit higher correlations.

Two revisions of interactive MDS data selection procedures are presented. One revision improves the estimates of the MDS parameters by adding an analysis of the volume of the spatial coordinates of stimuli. Frames of stimuli augmented by an analysis of volume should more nearly surround the swarm of stimulus points. The second revision, based on randomly ordering the list of stimuli, permits more efficient data designs to be selected by reducing the number of judgments collected but never analyzed.

This proof-of-concept study evaluated an optimization strategy for the Community Case Detection Tool (CCDT) aimed at improving community-level mental health detection and help-seeking among children aged 6–18 years. The optimization strategy, CCDT+, combined data-driven supervision with motivational interviewing techniques and behavioural nudges for community gatekeepers using the CCDT. This mixed-methods study was conducted from January to May 2023 in Palorinya refugee settlement in Uganda. We evaluated (1) the added value of the CCDT+ in improving the accuracy of detection and mental health service utilization compared to standard CCDT, and (2) implementation outcomes of the CCDT+. Of the 1026 children detected, 801 (78%) sought help, with 656 needing mental health care (PPV = 0.82; 95% CI: 0.79, 0.84). The CCDT+ significantly increased detection accuracy, with 2.34 times higher odds compared to standard CCDT (95% CI: 1.41, 3.83). Additionally, areas using the CCDT+ had a 2.05-fold increase in mental health service utilization (95% CI: 1.09, 3.83). The CCDT+ shows promise as an embedded quality-optimization process for the detection of mental health problems among children and enhance help-seeking, potentially leading to more efficient use of mental health care resources.

Globally, a third of total anthropogenic greenhouse gas emissions (GHGE) are produced by the food system(1). Estimating the carbon footprint of current diets is therefore important to consumers, businesses, and policymakers. With most home-consumed food in the United Kingdom (UK) purchased from supermarkets(2), supermarket purchasing records represent a novel data source that can provide insights into dietary patterns(3). These data are particularly useful in an environmental sustainability context as they provide information on the amounts of foods and beverages purchased, not just the amounts consumed (as in traditional dietary assessments). We estimated GHGE of foods and beverages purchased in Yorkshire and the Humber region of the UK using supermarket transaction data from primary-shopper loyalty cards over 12 months in 2022.

We mapped a UK retailer’s food and beverage products to GHGE (kg CO2-eq/kg) using data on the environmental footprint of food commodities(4), and grouped the products according to the Living Costs and Food Survey (LCFS) categories. The sustainability mapping process was guided by product sales (i.e., prioritising the most sold products and categories) and involved three stages utilising mapping approaches with different complexity, resulting in 98.6% of >28,000 store products being mapped. We estimated total GHGE of each product by multiplying the final mapped GHGE by the product weight (as sold). We then used these product-level GHGE estimations (kg CO2-eq/item) in conjunction with the sales data (number of items sold) to estimate the contribution of each product, and subsequently each LCFS category, to total GHGE from all purchases.

When incorporating sales, the LCFS categories with the highest contributions to total GHGE included ‘beef’ (19.6%), ‘milk’ (9.8%), ‘cheese and curd’ (8.6%), ‘ready meals’ (6.9%), and ‘poultry’ (5.5%). The LCFS categories among the lowest contributors to total GHGE included ‘confectionery products’ (0.2%), ‘pasta products’ (0.4%) and ‘soft drinks’ (0.5%). Although some LCFS categories had higher GHGE per kg for their products, they were sold in smaller quantities, and therefore, their contributions to total GHGE were lower in total. For example, ‘lamb’ was in the top five LCFS categories with the highest GHGE per kg (39.7 kg CO2-eq/kg) but contributed to 1.4.% of total estimated GHGE when incorporating sales information, which was less than ‘bread’ (2.2%) and ‘yoghurt’ (1.7%).

Our results highlight that although some foods might be very GHGE-intensive on a per weight basis, they have a lower overall GHGE impact if they are not frequently purchased in the population. These supermarket sales data are an important resource to understanding and subsequently tackling the environmental impact of the food system. Further research, including other environmental sustainability metrics (e.g., water and land use), is needed to provide a more comprehensive picture of the environmental footprint of foods and beverages purchased by UK consumers.

Current dietary patterns are suboptimal for both human and planetary health(1,2). With growing consumer and business concerns around food sustainability, estimating the environmental footprint of foods and diets is pertinent. In many countries, supermarkets are the primary provider of foods and beverages; therefore, supermarket purchasing records represent a novel source of population dietary data that offers advantages over traditional methods(3). We developed a method for mapping greenhouse gas emissions (GHGE) to food and beverage products from a high-street retailer’s portfolio, to enable the estimation of the environmental footprint of population diets when linked with sales information.

We used data from the food and beverage portfolio of a high-street retailer in the United Kingdom (UK), including product name/description, categorisation, ingredients, and weight. We mapped these products to GHGE (kg CO2-eq/kg) using a global database on the average environmental footprint of food commodities(4). This mapping process involved three stages utilising different mapping approaches, guided by product sales data, which we extracted from the retailer’s loyaltycard transactions for Yorkshire and the Humber (UK) region during 2022. Stage 1 involved categorising the products into Living Costs and Food Survey food categories and mapping each category to GHGE, where possible (food-category approach). Stage 2 involved splitting selected food categories (based on complexity, necessity of a better mapping, and sales) and creating a sub-category-specific mapping based on an indicator product, which was selected as most popular using sales data (food-sub-category approach). The indicator-product mapping represented a weighted average GHGE value calculated using information on product ingredients and their estimated proportions (ingredient approach). Stage 3 utilised word-searches in product descriptions to distinguish further between product types within selected prioritised subcategories. We used the estimated product-level GHGE (mapped GHGE × product weight) and sales data to estimate food-category contributions to total GHGE and assess how these estimations change by mapping stage.

Of >28,000 products, 77.7%, 98.0% and 98.6% were mapped to GHGE at the end of stages 1, 2 and 3, respectively. Of the final product mappings, 40% were at a food-category level and 60% at least at a sub-category level. We calculated 153 product-specific GHGE using ingredients information for prioritised indicator products. When using mappings from stage 3 vs 1, the contributions of ‘savoury snacks’ and ‘chocolate’ to total GHGE were approximately four and two times higher respectively, due largely to improved mapping that accounted for product sub-category and ingredients.

Mapping environmental sustainability metrics to a retail product dataset is feasible when using a staged approach, guided and prioritised by sales data. However, mapping approach and the estimations’ variability should be considered. This method could be used for estimating the environmental footprint from food purchasing data, helping to inform responses towards promoting healthier and more sustainable diets.

Evidence on the effectiveness and implementation of mental health and psychosocial support (MHPSS) interventions for men in humanitarian settings is limited. Moreover, engagement and retention of men in such interventions has been challenging. Adaptations may therefore be required to improve the appropriateness and acceptability of these interventions for men. This study conducted formative research and examined the feasibility of combining an MHPSS intervention, Self-Help Plus, with a brief intervention to reduce harmful alcohol use among refugee men in Uganda. We conducted a cluster randomized feasibility trial comparing the combined alcohol intervention and Self-Help Plus, Self-Help Plus alone and enhanced usual care. Participants were 168 South Sudanese refugee men in Rhino Settlement who reported moderate or high levels of psychological distress. Session attendance was adequate: all sessions had at least 69% of participants present. Participant outcome measures, including symptoms of psychological distress, functional impairment, self-defined problems, depressive symptoms, post-traumatic stress symptoms, overall substance use risk, substance specific risk (alcohol, cannabis, stimulants and sedatives) and well-being, were sensitive to change. A combined approach to addressing mental health and alcohol use appears feasible among men in refugee settings, but further research is needed to examine the effectiveness of combined interventions among men.