Molnupiravir Form I crystallizes in space group C2 (#5) with a = 6.48110(17), b = 8.71848(19), c = 27.0607(19) Å, β = 91.920(4)°, V = 1528.22(12) Å3, and Z = 4 at 295 K. The crystal structure consists of supramolecular double layers of molecules parallel to the ab-plane. The layer centers consist of hydrogen-bonded rings forming a 2D network and the outer surfaces of isopropyl groups, with van der Waals interactions between the layers. Each O atom acts as an acceptor in at least one hydrogen bond. A strong O–H⋯O hydrogen bond forms between the hydroxyl group of the oxolane ring and the carbonyl group of the oxopyrimidine ring. The other oxolane hydroxyl group forms bifurcated intra- and intermolecular hydrogen bonds. The hydroxylamino group forms an intramolecular O–H⋯N hydrogen bond with an N atom of the oxopyrimidine ring. The amino group forms an intermolecular N–H⋯N hydrogen bond to the same N atom of the ring. The powder pattern has been submitted to ICDD for inclusion in the Powder Diffraction File™ (PDF®).

We analyzed the oxygen isotope composition of biogenic apatite phosphate (δ18Op) in fossil tooth enameloid to investigate the paleoecology of Late Cretaceous sharks in the Gulf Coastal Plain of Alabama, USA. We analyzed six different shark taxa from both the Mooreville Chalk and the Blufftown Formation. We compared shark δ18Op with the δ18Op of a co-occurring poikilothermic bony fish Enchodus petrosus as a reference for ambient conditions. Enchodus petrosus tooth enamel δ18Op values are similar between formations (21.3‰ and 21.4‰ Vienna Standard Mean Ocean Water [VSMOW], respectively), suggesting minimal differences in water δ18O between formations. Most shark taxa in this study are characterized by δ18Op values that overlap with E. petrosus values, indicating they likely lived in similar habitats and were also poikilothermic. Ptychodus mortoni and Cretoxyrhina mantelli exhibit significantly lower δ18Op values than co-occurring E. petrosus (P. mortoni δ18Op is 19.1‰ VSMOW in the Mooreville Chalk; C mantelli δ18Op is 20.2‰ VSMOW in the Mooreville Chalk and 18.1‰ VSMOW in the Blufftown Formation). Excursions into brackish or freshwater habitats and thermal water-depth gradients are unlikely explanations for the lower P. mortoni and C. mantelli δ18Op values. The low P. mortoni δ18Op value is best explained by higher body temperature relative to surrounding temperatures due to active heating (e.g., mesothermy) or passive heating due to its large body size (e.g., gigantothermy). The low C. mantelli δ18Op values are best explained by a combination of mesothermy (e.g., active heating) and migration (e.g., from the Western Interior Seaway, low-latitude warmer waters, or the paleo–Gulf Stream), supporting the hypothesis that mesothermy evolved in lamniform shark taxa during the Late Cretaceous. If the anomalous P. mortoni δ18Op values are also driven by active thermoregulation, this suggests that mesothermy evolved independently in multiple families of Late Cretaceous sharks.

In the current paper, we review existing tools for solving variable selection problems in psychology. Modern regularization methods such as lasso regression have recently been introduced in the field and are incorporated into popular methodologies, such as network analysis. However, several recognized limitations of lasso regularization may limit its suitability for psychological research. In this paper, we compare the properties of lasso approaches used for variable selection to Bayesian variable selection approaches. In particular we highlight advantages of stochastic search variable selection (SSVS), that make it well suited for variable selection applications in psychology. We demonstrate these advantages and contrast SSVS with lasso type penalization in an application to predict depression symptoms in a large sample and an accompanying simulation study. We investigate the effects of sample size, effect size, and patterns of correlation among predictors on rates of correct and false inclusion and bias in the estimates. SSVS as investigated here is reasonably computationally efficient and powerful to detect moderate effects in small sample sizes (or small effects in moderate sample sizes), while protecting against false inclusion and without over-penalizing true effects. We recommend SSVS as a flexible framework that is well-suited for the field, discuss limitations, and suggest directions for future development.

Conjoint choice experiments are used widely in marketing to study consumer preferences amongst alternative products. We develop a class of choice models, belonging to the class of Poisson race models, that describe a ‘random utility’ which lends itself to a process-based description of choice. The models incorporate a dependence structure which captures the relationship between the attributes of the choice alternatives and which appropriately moderates the randomness inherent in the race. The new models are applied to conjoint choice data and are shown to have performance markedly superior to that of independent Poisson race models and of the multinomial logit model.

The purpose of the current study was to understand the prevalence and patterns of cannabinoid use among LTC residents across Canada. We gathered data on cannabinoid prescriptions among LTC residents for one year before and after recreational cannabis legalization. Multi-level modelling was used to examine the effects of demographic and diagnostic characteristics on rates of cannabinoid prescription over time. All prescriptions were for nabilone. There was a significant increase in the proportion of residents prescribed nabilone following the legalization of recreational cannabis in Canada. Residents with relatively more severe pain (based on the Minimum Data Set pain scale), a diagnosis of depression, or a diagnosis of an anxiety disorder were more likely to have received a nabilone prescription. Our results provide valuable information regarding the increasing use of synthetic cannabinoids in LTC. The implications for clinical practice and policy decision-makers are discussed.

Substance use and substance use disorders run in families. While it has long been recognized that the etiology of substance use behaviors and disorders involves a combination of genetic and environmental factors, two key questions remain largely unanswered: (1) the intergenerational transmission through which these genetic predispositions are passed from parents to children, and (2) the molecular mechanisms linking genetic variants to substance use behaviors and disorders. This article aims to provide a comprehensive conceptual framework and methodological approach for investigating the intergenerational transmission of substance use behaviors and disorders, by integrating genetic nurture analysis, gene expression imputation, and weighted gene co-expression network analysis. We also additionally describe two longitudinal cohorts — the Brisbane Longitudinal Twin Study in Australia and the Lifelines Cohort Study in the Netherlands. By applying the methodological framework to these two unique datasets, our future research will explore the complex interplay between genetic factors, gene expression, and environmental influences on substance use behaviors and disorders across different life stages and populations.

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.