The environment in which people spend their lives influences their health behaviours and health outcomes. Traditional studies often use fixed descriptions and ad hoc analyses to characterise environmental exposures at static locations, relating a single environmental factor to one health aspect, even though both the environment and the exposures of people who move through it are dynamic. We introduce a digital twinning approach that semantically integrates ontological concepts and data across environmental features into machine-readable knowledge, enabling scalable and context-aware assessment of individual exposures. This work is built on The World Avatar project, which aims to create an all-encompassing digital twin based on a dynamic knowledge graph. The proposed approach deploys a computational agent to calculate exposures based on semantic representations of environmental features in time and space. The time-specific part of the exposure calculation considers both the historical context (i.e. whether features of the environment had been built) and current context (i.e., whether something was open or accessible) of the exposure. The scalability of the approach is illustrated through the construction of interoperable digital twins and the analysis of smartphone data describing movements in both the UK and Singapore. The resulting exposures capture historic year-specific changes in greenspace and time-specific accessibility based on opening hours of the food retail environment. This approach could facilitate large-scale digital studies, yielding critical insights that help create healthy cities.

BACKGROUND:Mandatory calorie labelling on menus of large out-of-home food outlets was implemented in England on 6 April 2022. Barriers and facilitators that were unforeseen before implementation may modify policy impacts. As part of a process evaluation, we aimed to examine the implementation of calorie labeling in England, focusing on business experiences and local authority enforcement to identify barriers and facilitators to achieving policy goals. METHODS:Using purposive sampling, we recruited 11 employees of large food businesses (implementers) and 9 employees of LA environmental health or trading standards departments (enforcers). Post-implementation semi-structured interviews were conducted by video conference. Interviews were audio recorded, transcribed verbatim and analysed using the Framework Method. RESULTS:Both groups of participants described a decentralised approach to delivery and enforcement, and resource constraints meant LAs were unable to assist with all business inquiries. Enforcement activity was limited because complaints about labelling from the public were rare, and enforcers prioritized acute food safety issues. Pre-implementation discussions created the presumption among enforcers that most businesses were compliant. Implementers claimed that businesses wanted to comply to safeguard their reputation and maintain customer trust. While participants supported calorie labelling, potential barriers to policy impact included a presumed lack of customer interest. Financial pressure during implementation strained business resources, and businesses suggested that customers may prioritise financial over health concerns in their decision-making. CONCLUSIONS:These findings underscore the need for central guidance, verification of adherence, and sufficient enforcement resources. To optimize policy success, future developments should consider economic contexts, customer expectations, and policy refinement, while recognizing common industry arguments against policy implementation.

In April 2022, mandatory kilocalorie (kcal) labelling in the out-of-home food sector was introduced as a policy to reduce obesity in England. Here we examined whether the implementation of this policy was associated with a consumer behaviour change. Large out-of-home food sector outlets subject to kcal labelling legislation were visited pre- and post-implementation, and customer exit surveys were conducted with 6,578 customers from 330 outlets. Kcals purchased and consumed, knowledge of purchased kcals and reported noticing and use of kcal labelling were examined. The results suggested that the introduction of the mandatory kcal labelling policy in England was not associated with a significant decrease in self-reported kcals purchased (B = 11.31, P = 0.564, 95% confidence interval (CI) -27.15 to 49.77) or consumed (B = 18.51, P = 0.279, 95% CI -15.01 to 38 52.03). Post-implementation, participants underestimated the energy content of their purchased meal less (B = 61.21, P = 0.002, 95% CI 21.57 to 100.86) and were more likely to report noticing (odds ratio 2.25, P < 0.001, 95% CI 1.84 to 2.73) and using (odds ratio 2.15, P < 0.001, 95% CI 1.62 to 2.85) kcal labelling, which may have wider public health implications.

Background Meat consumption could increase the risk of type 2 diabetes. However, evidence is largely based on studies of European and North American populations, with heterogeneous analysis strategies and a greater focus on red meat than on poultry. We aimed to investigate the associations of unprocessed red meat, processed meat, and poultry consumption with type 2 diabetes using data from worldwide cohorts and harmonised analytical approaches. Methods This individual-participant federated meta-analysis involved data from 31 cohorts participating in the InterConnect project. Cohorts were from the region of the Americas (n=12) and the Eastern Mediterranean (n=2), European (n=9), South-East Asia (n=1), and Western Pacific (n=7) regions. Access to individual-participant data was provided by each cohort; participants were eligible for inclusion if they were aged 18 years or older and had available data on dietary consumption and incident type 2 diabetes and were excluded if they had a diagnosis of any type of diabetes at baseline or missing data. Cohort-specific hazard ratios (HRs) and 95% CIs were estimated for each meat type, adjusted for potential confounders (including BMI), and pooled using a random-effects meta-analysis, with meta-regression to investigate potential sources of heterogeneity. Findings Among 1 966 444 adults eligible for participation, 107 271 incident cases of type 2 diabetes were identified during a median follow-up of 10 (IQR 7–15) years. Median meat consumption across cohorts was 0–110 g/day for unprocessed red meat, 0–49 g/day for processed meat, and 0–72 g/day for poultry. Greater consumption of each of the three types of meat was associated with increased incidence of type 2 diabetes, with HRs of 1·10 (95% CI 1·06–1·15) per 100 g/day of unprocessed red meat (I2=61%), 1·15 (1·11–1·20) per 50 g/day of processed meat (I2=59%), and 1·08 (1·02–1·14) per 100 g/day of poultry (I2=68%). Positive associations between meat consumption and type 2 diabetes were observed in North America and in the European and Western Pacific regions; the CIs were wide in other regions. We found no evidence that the heterogeneity was explained by age, sex, or BMI. The findings for poultry consumption were weaker under alternative modelling assumptions. Replacing processed meat with unprocessed red meat or poultry was associated with a lower incidence of type 2 diabetes. Interpretation The consumption of meat, particularly processed meat and unprocessed red meat, is a risk factor for developing type 2 diabetes across populations. These findings highlight the importance of reducing meat consumption for public health and should inform dietary guidelines. Funding The EU, the Medical Research Council, and the National Institute of Health Research Cambridge Biomedical Research Centre.

Background Meat consumption could increase the risk of type 2 diabetes. However, evidence is largely based on studies of European and North American populations, with heterogeneous analysis strategies and a greater focus on red meat than on poultry. We aimed to investigate the associations of unprocessed red meat, processed meat, and poultry consumption with type 2 diabetes using data from worldwide cohorts and harmonised analytical approaches. Methods This individual-participant federated meta-analysis involved data from 31 cohorts participating in the InterConnect project. Cohorts were from the region of the Americas (n=12) and the Eastern Mediterranean (n=2), European (n=9), South-East Asia (n=1), and Western Pacific (n=7) regions. Access to individual-participant data was provided by each cohort; participants were eligible for inclusion if they were aged 18 years or older and had available data on dietary consumption and incident type 2 diabetes and were excluded if they had a diagnosis of any type of diabetes at baseline or missing data. Cohort-specific hazard ratios (HRs) and 95% CIs were estimated for each meat type, adjusted for potential confounders (including BMI), and pooled using a random-effects meta-analysis, with meta-regression to investigate potential sources of heterogeneity. Findings Among 1 966 444 adults eligible for participation, 107 271 incident cases of type 2 diabetes were identified during a median follow-up of 10 (IQR 7-15) years. Median meat consumption across cohorts was 0-110 g/day for unprocessed red meat, 0-49 g/day for processed meat, and 0-72 g/day for poultry. Greater consumption of each of the three types of meat was associated with increased incidence of type 2 diabetes, with HRs of 110 (95% CI 106-115) per 100 g/day of unprocessed red meat (I2=61%), I 2 =61%), 115 (111-120) per 50 g/day of processed meat (I2=59%), I 2 =59%), and 108 (102-114) per 100 g/day of poultry (I2=68%). I 2 =68%). Positive associations between meat consumption and type 2 diabetes were observed in North America and in the European and Western Pacific regions; the CIs were wide in other regions. We found no evidence that the heterogeneity was explained by age, sex, or BMI. The findings for poultry consumption were weaker under alternative modelling assumptions. Replacing processed meat with unprocessed red meat or poultry was associated with a lower incidence of type 2 diabetes. Interpretation The consumption of meat, particularly processed meat and unprocessed red meat, is a risk factor for developing type 2 diabetes across populations. These findings highlight the importance of reducing meat consumption for public health and should inform dietary guidelines.

SUMMARY:Extensive human health data from cohort studies, national registries, and biobanks can reveal lifecourse risk factors impacting health. Combining these sources offers increased statistical power, rare outcome detection, replication of findings, and extended study periods. Traditionally, this required data transfer to a central location or separate partner analyses with pooled summary statistics, posing ethical, legal, and time constraints. Federated analysis-which involves remote data analysis without sharing individual-level data-is a promising alternative. One promising solution is DataSHIELD (https://datashield.org/), an open-source R based implementation. To enable federated analysis, data owners need a user-friendly way to install the federated infrastructure and manage users and data. Here, we present MOLGENIS Armadillo: a lightweight server for federated analysis solutions such as DataSHIELD. AVAILABILITY AND IMPLEMENTATION:Armadillo is implemented as a collection of three packages freely available under the open source licence LGPLv3: two R packages downloadable from the Comprehensive R Archive Network (CRAN) ("MolgenisArmadillo" and "DSMolgenisArmdillo") and one Java application ("ArmadilloService") as jar and docker images via Github (https://github.com/molgenis/molgenis-service-armadillo).

Background and objectives On 6 April 2022, the UK government implemented mandatory kilocalorie (kcal) labelling regulations for food and drink products sold in the out-of-home food sector (OHFS) in England. Previous assessments of kcal labelling practices in the UK OHFS found a low prevalence of voluntary implementation and poor compliance with labelling recommendations. This study aimed to examine changes in labelling practices preimplementation versus post implementation of mandatory labelling regulations in 2022.Methods In August–December 2021 (preimplementation) and August–November 2022 (post implementation), large OHFS businesses (250 or more employees) subject to labelling regulations were visited. At two time points, a researcher visited the same 117 food outlets (belonging to 90 unique businesses) across four local authorities in England. Outlets were rated for compliance with government regulations for whether kcal labelling was provided at any or all point of choice, provided for all eligible food and drink items, provided per portion for sharing items, if labelling was clear and legible and if kcal reference information was displayed.Results There was a significant increase (21% preimplementation vs 80% post implementation, OR=40.98 (95% CI 8.08 to 207.74), p<0.001) in the proportion of outlets providing any kcal labelling at point-of-choice post implementation. Only 15% of outlets met all labelling compliance criteria post implementation, with a minority of outlets not presenting labelling in a clear (33%) or legible (29%) way.Conclusion The number of large businesses in the OHFS providing kcal labelling increased following the implementation of mandatory labelling regulations. However, around one-fifth of eligible outlets sampled were not providing kcal labelling 4–8 months after the regulations came into force, and the majority of businesses only partially complied with government guidance. More effective enforcement may be required to further improve kcal labelling practices in the OHFS in England.Preregistration Study protocol and analysis strategy preregistered on Open Science Framework (https://osf.io/pfnm6/).

BackgroundNeighbourhood exposure to takeaways can contribute negatively to diet and diet-related health outcomes. Urban planners within local authorities (LAs) in England can modify takeaway exposure through denying planning permission to new outlets in management zones around schools. LAs sometimes refer to these as takeaway "exclusion zones". Understanding the long-term impacts of this intervention on the takeaway retail environment and health, an important policy question, requires methods to forecast future takeaway growth and subsequent population-level exposure to takeaways. In this paper we describe a novel two-stage method to achieve this.MethodsWe used historic data on locations of takeaways and a time-series auto-regressive integrated moving average (ARIMA) model, to forecast numbers of outlets within management zones to 2031, based on historical trends, in six LAs with different urban/rural characteristics across England. Forecast performance was evaluated based on root mean squared error (RMSE) and mean absolute scaled error (MASE) scores in time-series cross-validation. Using travel-to-work data from the 2011 UK census, we then translated these forecasts of the number of takeaways within management zones into population-level exposures across home, work and commuting domains.ResultsOur ARIMA models outperformed exponential smoothing equivalents according to RMSE and MASE. The model was able to forecast growth in the count of takeaways up to 2031 across all six LAs, with variable growth rates by RUC (min-max: 39.4-79.3%). Manchester (classified as a non-London urban with major conurbation LA) exhibited the highest forecast growth rate (79.3%, 95% CI 61.6, 96.9) and estimated population-level takeaway exposure within management zones, increasing by 65.5 outlets per capita to 148.2 (95% CI 133.6, 162.7) outlets. Overall, urban (vs. rural) LAs were forecast stronger growth and higher population exposures.ConclusionsOur two-stage forecasting approach provides a novel way to estimate long-term future takeaway growth and population-level takeaway exposure. While Manchester exhibited the strongest growth, all six LAs were forecast marked growth that might be considered a risk to public health. Our methods can be used to model future growth in other types of retail outlets and in other areas.

OBJECTIVE:Survival models are used extensively in biomedical sciences, where they allow the investigation of the effect of exposures on health outcomes. It is desirable to use diverse data sets in survival analyses, because this offers increased statistical power and generalisability of results. However, there are often challenges with bringing data together in one location or following an analysis plan and sharing results. DataSHIELD is an analysis platform that helps users to overcome these ethical, governance and process difficulties. It allows users to analyse data remotely, using functions that are built to restrict access to the detailed data items (federated analysis). Previous works have provided survival modelling functionality in DataSHIELD (dsSurvival package), but there is a requirement to provide functions that offer privacy enhancing survival curves that retain useful information.RESULTS:We introduce an enhanced version of the dsSurvival package which offers privacy enhancing survival curves for DataSHIELD. Different methods for enhancing privacy were evaluated for their effectiveness in enhancing privacy while maintaining utility. We demonstrated how our selected method could enhance privacy in different scenarios using real survival data. The details of how DataSHIELD can be used to generate survival curves can be found in the associated tutorial.

Food environment research predominantly focuses on the spatial distribution of out-of-home food outlets. However, the healthiness of food choices available within these outlets has been understudied, largely due to resource constraints. In this study, we propose an innovative, low-resource approach to characterise the healthiness of out-of-home food outlets at scale. Menu healthiness scores were calculated for food outlets on JustEat, and a deep learning model was trained to predict these scores for all physical out-of-home outlets in Great Britain, based on outlet names. Our findings highlight the "double burden" of the unhealthy food environment in deprived areas where there tend to be more out-of-home food outlets, and these outlets tend to be less healthy. This methodological advancement provides a nuanced understanding of out-of-home food environments, with potential for automation and broad geographic application.

Many advancements of mobile cameras aim to reach the visual quality of professional DSLR cameras. Great progress was shown over the last years in optimizing the sharp regions of an image and in creating virtual portrait effects with artificially blurred backgrounds. Bokeh is the aesthetic quality of the blur in out-of-focus areas of an image. This is a popular technique among professional photographers, and for this reason, a new goal in computational photography is to optimize the Bokeh effect itself.This paper introduces EBokehNet, a efficient state-of-the-art solution for Bokeh effect transformation and rendering. Our method can render Bokeh from an all-in-focus image, or transform the Bokeh of one lens to the effect of another lens without harming the sharp foreground regions in the image. Moreover we can control the shape and strength of the effect by feeding the lens properties i.e. type (Sony or Canon) and aperture, into the neural network as an additional input. Our method is a winning solution at the NTIRE 2023 Lens-to-Lens Bokeh Effect Transformation Challenge, and state-of-the-art at the EBB benchmark.

Background In April 2022, mandatory calorie labelling in the out-of-home food sector (OHFS) was introduced as a policy to reduce obesity in England. The policy requires food outlets belonging to large (>250 employees) businesses in England selling food for immediate consumption to provide caloriel labelling on all unpackaged food and non-alcoholic drink items. To date, there has been no evaluation of this national public health policy. We aimed to determine likely effectiveness by examining change in customer energy intake pre vs. post implementation of mandatory calorie labelling in the OHFS sector in England. Methods We conducted intercept surveys with 6548 participants (pre-assessment, 2021 N=3308, post-assessment, 2022 N=3240) as they left OHFS outlets. Outlets were representatively sampled. The number of calories participants purchased and consumed during OHFS visits were measured through the use of till receipts and self-reports by participants. Demographic information was collected to examine any differential effects by participant age, gender, ethnicity and socioeconomic status. Multiple regression models were conducted to examine pre vs. post policy implementation difference in energy intake, adjusting for local area, outlet and participant characteristics pre vs. policy waves. Effects were also examined according to local area deprivation using the index of multiple deprivation (IMD, 1–5). Results The mean number of calories purchased (1012 kcals, SD = 632) and consumed (915 kcals, SD = 578) at baseline was high. Multiple-regression models indicated that there was no effect of policy on energy purchased (-7 kcals [95% CIs -49 to 35 kcals], p=0.746), or consumed (- 0.5 kcals [95% CIs -37 to 36 kcals], p=0.977) pre vs post implementation. Across both waves of data collection participants from more deprived areas (IMD = 1) tended to purchase and consume more energy than participants from the least deprived areas (IMD = 5), but there was no evidence across analyses that effects of policy differed by IMD or any participant level characteristics. Discussion The introduction of mandatory calorie labelling policy in England was not associated with a decrease in energy purchased and consumed in large OHFS outlets. The implementation of mandatory calorie labelling alone may be unlikely to lead to significant impacts on obesity in England, although further evaluation is warranted.

Unsupervised Anomaly detection (AD) requires building a notion of normalcy, distinguishing in-distribution (ID) and out-of-distribution (OOD) data, using only available ID samples. Recently, large gains were made on this task for the domain of natural images using self-supervised contrastive feature learning as a first step followed by kNN or traditional one-class classifiers for feature scoring. Learned representations that are non-uniformly distributed on the unit hypersphere have been shown to be beneficial for this task. We go a step further and investigate how the geometrical compactness of the ID feature distribution makes isolating and detecting outliers easier, especially in the realistic situation when ID training data is polluted (i.e. ID data contains some OOD data that is used for learning the feature extractor parameters). We propose novel architectural modifications to the self-supervised feature learning step, that enable such compact distributions for ID data to be learned. We show that the proposed modifications can be effectively applied to most existing self-supervised objectives, with large gains in performance. Furthermore, this improved OOD performance is obtained without resorting to tricks such as using strongly augmented ID images (e.g. by 90 degree rotations) as proxies for the unseen OOD data, as these impose overly prescriptive assumptions about ID data and its invariances. We perform extensive studies on benchmark datasets for one-class OOD detection and show state-of-the-art performance in the presence of pollution in the ID data, and comparable performance otherwise. We also propose and extensively evaluate a novel feature scoring technique based on the angular Mahalanobis distance, and propose a simple and novel technique for feature ensembling during evaluation that enables a big boost in performance at nearly zero run-time cost compared to the standard use of model ensembling or test time augmentations. Source code is available Here

Optimizing research on the developmental origins of health and disease (DOHaD) involves implementing initiatives maximizing the use of the available cohort study data; achieving sufficient statistical power to support subgroup analysis; and using participant data presenting adequate follow-up and exposure heterogeneity. It also involves being able to undertake comparison, cross-validation, or replication across data sets. To answer these requirements, cohort study data need to be findable, accessible, interoperable, and reusable (FAIR), and more particularly, it often needs to be harmonized. Harmonization is required to achieve or improve comparability of the putatively equivalent measures collected by different studies on different individuals. Although the characteristics of the research initiatives generating and using harmonized data vary extensively, all are confronted by similar issues. Having to collate, understand, process, host, and co-analyze data from individual cohort studies is particularly challenging. The scientific success and timely management of projects can be facilitated by an ensemble of factors. The current document provides an overview of the ‘life course’ of research projects requiring harmonization of existing data and highlights key elements to be considered from the inception to the end of the project.

Weakly-supervised vision-language (V-L) pre-training (W-VLP) aims at learning cross-modal alignment with little or no paired data, such as aligned images and captions. Recent W-VLP methods, which pair visual features with object tags, help achieve performances comparable with some VLP models trained with aligned pairs in various V-L downstream tasks. This, however, is not the case in cross-modal retrieval (XMR). We argue that the learning of such a W-VLP model is curbed and biased by the object tags of limited semantics. We address the lack of paired V-L data for model supervision with a novel Visual Vocabulary based Feature Hallucinator (WFH), which is trained via weak supervision as a W-VLP model, not requiring images paired with captions. WFH generates visual hallucinations from texts, which are then paired with the originally unpaired texts, allowing more diverse interactions across modalities. Empirically, WFH consistently boosts the prior W-VLP works, e.g. U-VisualBERT (U-VB), over a variety of V-L tasks, i.e. XMR, Visual Question Answering, etc. Notably, benchmarked with recall@{1,5,10}, it consistently improves U-VB on image-to-text and text-to-image retrieval on two popular datasets Flickr30K and MSCOCO. Meanwhile, it gains by at least 14.5% in cross-dataset generalization tests on these XMR tasks. Moreover, in other V-L downstream tasks considered, our WFH models are on par with models trained with paired V-L data, revealing the utility of unpaired data. These results demonstrate greater generalization of the proposed W-VLP model with WFH.

Background Inconsistent associations between neighbourhood out-of-home food environment and dietary outcomes have been reported. One reason may be that previous studies have primarily focused on the location and type of out-of-home food outlets, rather than the healthiness of options served. We investigated associations between different measures of the neighbourhood out-of-home food environment, incorporating menu healthiness, with frequency of out-of-home meal purchases and diet quality. Methods We used Ordnance Survey Points of Interest data, a dataset containing all food outlets in Great Britain (GB), to define various measures of participants' out-of-home food outlet exposure. We linked this with cross-sectional survey data in adults living in GB from the International Food Policy Study in 2021 (n=3,523). Exposures included availability, proximity, and relative composition of out-of-home food outlets in a number of different neighbourhood buffers around the home (i.e., 500–1600 m), and incorporated healthiness scores (0–12, with 0 the least healthy) based on menu attributes such as variety of vegetables sold. Outcomes were the number of meals purchased out-of-home in the past 7 days and the Healthy Diet Indicator (HDI) derived from a 24hr dietary recall. In multiverse analyses, where multiple analytical choices can be tested, we used generalised linear regression models with survey weights to investigate the impact of different exposure on outcomes, controlling for multiple testing. Results GB adults had access to an average of 98 (95CI% 91, 104) out-of-home food outlets within 1600 m of their home, with an average healthiness score of 6.7 (95%CI 6.6, 6.7). The number of out-of-home food outlets available, regardless of their healthiness, was positively associated with the number of meals purchased out-of-home across all neighbourhood definitions; e.g. for every additional 100 out-of-home outlets within 1600 m of home, there was an increase of 0.10 (95%CI 0.02, 0.17) out-of-home meals purchased per week. Proximity, relative composition, and menu healthiness of neighbourhood out-of-home outlets were not associated with out-of-home meal purchase frequency. None of the exposure measures were associated with HDI. Conclusion The only aspect of the neighbourhood out-of-home food environment associated with the number of meals purchased out-of-home was the number of out-of-home food outlets. Menu healthiness of out-of-home food outlets was not associated with how often adults living in GB purchased out-of-home meals or diet quality. Interventions focusing on mitigating the proliferation of out-of-home food outlets may be more effective in promoting healthy dietary behaviour than those that focus on food served. Our findings are limited by reliance on self-reported dietary data.

Background Mandatory calorie labelling on menus of large out-of-home food outlets was implemented in England in April 2022. Early quantitative findings indicate implementation was not associated with a change in calories purchased or consumed. We conducted post-implementation interviews to understand the experience and process of implementation and enforcement of the calorie labelling regulations and provide additional context for interpretation of quantitative findings. Methods Using purposive sampling, we recruited 11 employees of large food businesses (regulation implementers) and 9 of local authority (LA) environmental health or trading standards departments (regulation enforcers). Semi-structured interviews directed by topic guides were conducted by video conference and lasted up to 60 minutes. Interviews were audio recorded, transcribed verbatim and analysed using the Framework Method. Results Interviews revealed interdependent economic, regulatory, business, and consumer contexts that together may explain the success, or lack thereof, of the policy. Implementers reported that calorie labelling was implemented at a time of global financial pressure, making extra resource demands on businesses during an already difficult time. Both groups of participants described a decentralised approach to delivery and enforcement, whereby delivery was primarily carried out by businesses partnering with LAs. Resource constraints meant LAs were not equally able to assist with business inquiries. Participants reported little enforcement activity and few complaints about calorie labels. Calorie labelling was perceived by enforcers as low priority ('somewhere near the bottom') compared to acute food threats such as allergens; LAs were not provided with additional support; and pre-implementation discussions with businesses created 'presumed compliance.' Businesses complied because they did not want to 'lose customers' trust.' Participants were generally supportive of the idea of calorie labelling but described reasons why they thought it would have little impact on consumer behaviour. These included a presumed lack of consumer interest, an unhealthy food environment that labelling would not change, ingrained eating habits, lack of additional messaging about how consumers should use calorie labels and a financial context that may encourage consumers to focus on 'full bellies' and 'value-for-money' over health. Conclusion Despite financial challenges, large businesses implemented calorie labelling in partnership with LAs. Resource constraints meant LAs engaged in little enforcement activity, relying instead on pre-implementation partnerships with businesses. LAs require additional resources to carry out effective enforcement activity. Several reasons for the limited impact on consumer purchasing and consumption were raised. We did not conduct interviews with central government policymakers or consumers who would both likely provide useful additional insights.

Abstract Motivation DataSHIELD is an open-source software infrastructure enabling the analysis of data distributed across multiple databases (federated data) without leaking individuals’ information (non-disclosive). It has applications in many scientific domains, ranging from biosciences to social sciences and including high-throughput genomic studies. R is the language used to interact with (and build) DataSHIELD. This creates difficulties for researchers who do not have experience writing R code or lack the time to learn how to use the DataSHIELD functions. To help new researchers use the DataSHIELD infrastructure and to improve the user-friendliness for experienced researchers, we present ShinyDataSHIELD. Implementation ShinyDataSHIELD is a web application with an R backend that serves as a graphical user interface (GUI) to the DataSHIELD infrastructure. General features The version of the application presented here includes modules to perform: (i) exploratory analysis through descriptive summary statistics and graphical representations (scatter plots, histograms, heatmaps and boxplots); (ii) statistical modelling (generalized linear fixed and mixed-effects models, survival analysis through Cox regression); (iii) genome-wide association studies (GWAS); and (iv) omic analysis (transcriptomics, epigenomics and multi-omic integration). Availability ShinyDataSHIELD is publicly hosted online [https://datashield-demo.obiba.org/], the source code and user guide are deposited on Zenodo DOI 10.5281/zenodo.6500323, freely available to non-commercial users under ‘Commons Clause’ License Condition v1.0. Docker images are also available [https://hub.docker.com/r/brgelab/shiny-data-shield].

We present the new Bokeh Effect Transformation Dataset (BETD), and review the proposed solutions for this novel task at the NTIRE 2023 Bokeh Effect Transformation Challenge. Recent advancements of mobile photography aim to reach the visual quality of full-frame cameras. Now, a goal in computational photography is to optimize the Bokeh effect itself, which is the aesthetic quality of the blur in out-of-focus areas of an image. Photographers create this aesthetic effect by benefiting from the lens optical properties.The aim of this work is to design a neural network capable of converting the the Bokeh effect of one lens to the effect of another lens without harming the sharp foreground regions in the image. For a given input image, knowing the target lens type, we render or transform the Bokeh effect accordingly to the lens properties. We build the BETD using two full-frame Sony cameras, and diverse lens setups.To the best of our knowledge, we are the first attempt to solve this novel task, and we provide the first BETD dataset and benchmark for it. The challenge had 99 registered participants. The submitted methods gauge the state-of-the-art in Bokeh effect rendering and transformation.

In several studies, exploratory dietary patterns (DP), derived by principal component analysis, were inversely or positively associated with incident type 2 diabetes (T2D). However, findings remained study-specific, inconsistent and rarely replicated. This study aimed to investigate the associations between DPs and T2D in multiple cohorts across the world. This federated meta-analysis of individual participant data was based on 25 prospective cohort studies from 5 continents including a total of 390,664 participants with a follow-up for T2D (3.8–25.0 years). After data harmonization across cohorts we evaluated 15 previously identified T2D-related DPs for association with incident T2D estimating pooled incidence rate ratios (IRR) and confidence intervals (CI) by Piecewise Poisson regression and random-effects meta-analysis. 29,386 participants developed T2D during follow-up. Five DPs, characterized by higher intake of red meat, processed meat, French fries and refined grains, were associated with higher incidence of T2D. The strongest association was observed for a DP comprising these food groups besides others (IRRpooled per 1 SD = 1.104, 95% CI 1.059–1.151). Although heterogeneity was present (I2 = 85%), IRR exceeded 1 in 18 of the 20 meta-analyzed studies. Original DPs associated with lower T2D risk were not confirmed. Instead, a healthy DP (HDP1) was associated with higher T2D risk (IRRpooled per 1 SD = 1.057, 95% CI 1.027–1.088). Our findings from various cohorts revealed positive associations for several DPs, characterized by higher intake of red meat, processed meat, French fries and refined grains, adding to the evidence-base that links DPs to higher T2D risk. However, no inverse DP–T2D associations were confirmed.