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BackgroundDietary sugar, especially in liquid form, increases risk of dental caries, adiposity, and type 2 diabetes. The United Kingdom Soft Drinks Industry Levy (SDIL) was announced in March 2016 and implemented in April 2018 and charges manufacturers and importers at £0.24 per litre for drinks with over 8 g sugar per 100 mL (high levy category), £0.18 per litre for drinks with 5 to 8 g sugar per 100 mL (low levy category), and no charge for drinks with less than 5 g sugar per 100 mL (no levy category). Fruit juices and milk-based drinks are exempt. We measured the impact of the SDIL on price, product size, number of soft drinks on the marketplace, and the proportion of drinks over the lower levy threshold of 5 g sugar per 100 mL.Methods and findingsWe analysed data on a total of 209,637 observations of soft drinks over 85 time points between September 2015 and February 2019, collected from the websites of the leading supermarkets in the UK. The data set was structured as a repeat cross-sectional study. We used controlled interrupted time series to assess the impact of the SDIL on changes in level and slope for the 4 outcome variables. Equivalent models were run for potentially levy-eligible drink categories ('intervention' drinks) and levy-exempt fruit juices and milk-based drinks ('control' drinks). Observed results were compared with counterfactual scenarios based on extrapolation of pre-SDIL trends. We found that in February 2019, the proportion of intervention drinks over the lower levy sugar threshold had fallen by 33.8 percentage points (95% CI: 33.3-34.4, p < 0.001). The price of intervention drinks in the high levy category had risen by £0.075 (£0.037-0.115, p < 0.001) per litre-a 31% pass through rate-whilst prices of intervention drinks in the low levy category and no levy category had fallen and risen by smaller amounts, respectively. Whilst the product size of branded high levy and low levy drinks barely changed after implementation of the SDIL (-7 mL [-23 to 11 mL] and 16 mL [6-27ml], respectively), there were large changes to product size of own-brand drinks with an increase of 172 mL (133-214 mL) for high levy drinks and a decrease of 141 mL (111-170 mL) for low levy drinks. The number of available drinks that were in the high levy category when the SDIL was announced was reduced by 3 (-6 to 12) by the implementation of the SDIL. Equivalent models for control drinks provided little evidence of impact of the SDIL. These results are not sales weighted, so do not give an account of how sugar consumption from drinks may have changed over the time period.ConclusionsThe results suggest that the SDIL incentivised many manufacturers to reduce sugar in soft drinks. Some of the cost of the levy to manufacturers and importers was passed on to consumers as higher prices but not always on targeted drinks. These changes could reduce population exposure to liquid sugars and associated health risks.

ObjectivesTraditional methods for creating food composition tables struggle to cope with the large number of products and the rapid pace of change in the food and drink marketplace. This paper introduces foodDB, a big data approach to the analysis of this marketplace, and presents analyses illustrating its research potential.DesignfoodDB has been used to collect data weekly on all foods and drinks available on six major UK supermarket websites since November 2017. As of June 2018, foodDB has 3 193 171 observations of 128 283 distinct food and drink products measured at multiple timepoints.MethodsWeekly extraction of nutrition and availability data of products was extracted from the webpages of the supermarket websites. This process was automated with a codebase written in Python.ResultsAnalyses using a single weekly timepoint of 97 368 total products in March 2018 identified 2699 ready meals and pizzas, and showed that lower price ready meals had significantly lower levels of fat, saturates, sugar and salt (p

BackgroundTraditional methods of dietary assessment have their limitations and commercial sources of food sales and purchase data are increasingly suggested as an additional source to measuring diet at the population level. However, the potential uses of food sales data are less well understood. The aim of this review is to establish how sales data on food and soft drink products from third-party companies have been used in public health nutrition research.MethodsA search of five electronic databases was conducted in February-March 2018 for studies published in peer-reviewed journals that had used food sales or purchase data from a commercial company to analyse trends and patterns in food purchases or in the nutritional composition of foods. Study quality was evaluated using the National Institutes of Health (NIH) Quality Assessment Tool for Cohort and Cross-Sectional Studies.ResultsOf 2919 papers identified in the search, 68 were included. The selected studies used sales or purchase data from four companies: Euromonitor, GfK, Kantar and Nielsen. Sales and purchase data have been used to evaluate interventions, including the impact of the saturated fat tax in Denmark, the soft drink and junk food taxes in Mexico and supplemental nutrition programmes in the USA. They have also been used to identify trends in the nutrient composition of foods over time and patterns in food purchasing, including socio-demographic variations in purchasing.ConclusionFood sales and purchase data are a valuable tool for public health nutrition researchers and their use has increased markedly in the last four years, despite the cost of access, the lack of transparency on data-collection methods and restrictions on publication. The availability of product and brand-level sales data means they are particularly useful for assessing how changes by individual food companies can impact on diet and public health.

India, being a developing country, faces big challenges in ensuring water, sanitation, and hygiene (WASH) for all. This case study presents the performance evaluation of a large wastewater management and sanitation-related infrastructure in a metropolitan city in North India. "Dravyavati River Project" is the major sanitation program of the water-stressed Jaipur city based on the concept of river rejuvenation of the long-lost Dravyavati River which flows across the city. The project envisages integrated urban water management such that it aims at the collection and treatment of wastewater (sewage network and treatment plants), safe disposal, ensuring continuous unpolluted flow, geological and ecological integrity to strengthen public health, to reduce the impact of water stress on the total water cycle by promoting groundwater recharge, and improvement in biodiversity. The technical assessment is based on the primary and secondary data collection of field samples and laboratory analysis of influent and effluent samples collected from the five sewage treatment plants (STPs). The results suggest that the project has largely delivered the envisaged environment, public well-being, and ecological and socioeconomic benefits, but there are substantial gaps in the conceived outputs and actual performance. The challenge lies in bridging these gaps and overcoming operational inefficiencies to ensure the sustainability of the Dravyavati River rejuvenation., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

People have a unique ability to represent other people's internal thoughts and feelings-their mental states. Mental state knowledge has a rich conceptual structure, organized along key dimensions, such as valence. People use this conceptual structure to guide social interactions. How do people acquire their understanding of this structure? Here we investigate an underexplored contributor to this process: observation of mental state dynamics. Mental states-including both emotions and cognitive states-are not static. Rather, the transitions from one state to another are systematic and predictable. Drawing on prior cognitive science, we hypothesize that these transition dynamics may shape the conceptual structure that people learn to apply to mental states. Across nine behavioral experiments ( N = 1,439), we tested whether the transition probabilities between mental states causally shape people's conceptual judgments of those states. In each study, we found that observing frequent transitions between mental states caused people to judge them to be conceptually similar. Computational modeling indicated that people translated mental state dynamics into concepts by embedding the states as points within a geometric space. The closer two states are within this space, the greater the likelihood of transitions between them. In three neural network experiments, we trained artificial neural networks to predict real human mental state dynamics. The networks spontaneously learned the same conceptual dimensions that people use to understand mental states. Together these results indicate that mental state dynamics-and the goal of predicting them-shape the structure of mental state concepts. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

People express their own emotions and perceive others' emotions via a variety of channels, including facial movements, body gestures, vocal prosody, and language. Studying these channels of affective behavior offers insight into both the experience and perception of emotion. Prior research has predominantly focused on studying individual channels of affective behavior in isolation using tightly controlled, non-naturalistic experiments. This approach limits our understanding of emotion in more naturalistic contexts where different channels of information tend to interact. Traditional methods struggle to address this limitation: manually annotating behavior is time-consuming, making it infeasible to do at large scale; manually selecting and manipulating stimuli based on hypotheses may neglect unanticipated features, potentially generating biased conclusions; and common linear modeling approaches cannot fully capture the complex, nonlinear, and interactive nature of real-life affective processes. In this methodology review, we describe how deep learning can be applied to address these challenges to advance a more naturalistic affective science. First, we describe current practices in affective research and explain why existing methods face challenges in revealing a more naturalistic understanding of emotion. Second, we introduce deep learning approaches and explain how they can be applied to tackle three main challenges: quantifying naturalistic behaviors, selecting and manipulating naturalistic stimuli, and modeling naturalistic affective processes. Finally, we describe the limitations of these deep learning methods, and how these limitations might be avoided or mitigated. By detailing the promise and the peril of deep learning, this review aims to pave the way for a more naturalistic affective science., Competing Interests: Conflict of InterestOn behalf of all authors, the corresponding author states that there is no conflict of interest., (© The Society for Affective Science 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Objectives: To assess the efficacy of ramelteon in preventing delirium, an acute neuropsychiatric condition associated with increased morbidity and mortality, in the perioperative, ICU setting., Design: Parallel-arm, randomized, double-blinded, placebo-controlled trial., Setting: Academic medical center in La Jolla, California., Patients: Patients greater than or equal to 18 years undergoing elective pulmonary thromboendarterectomy., Interventions: Ramelteon 8 mg or matching placebo starting the night prior to surgery and for a maximum of six nights while in the ICU., Measurements and Main Results: Incident delirium was measured twice daily using the Confusion Assessment Method-ICU. The safety outcome was coma-free days assessed by the Richmond Agitation-Sedation Scale. One-hundred twenty participants were enrolled and analysis completed in 117. Delirium occurred in 22 of 58 patients allocated to placebo versus 19 of 59 allocated to ramelteon (relative risk, 0.8; 95% CI, 0.5-1.4; p = 0.516). Delirium duration, as assessed by the number of delirium-free days was also similar in both groups (placebo median 2 d [interquartile range, 2-3 d] vs ramelteon 3 d [2-5 d]; p = 0.181). Coma-free days was also similar between groups (placebo median 2 d [interquartile range, 1-3 d] vs ramelteon 3 d [2-4 d]; p = 0.210). We found no difference in ICU length of stay (median 4 d [interquartile range, 3-5 d] vs 4 d [3-6 d]; p = 0.349), or in-hospital mortality (four vs three deaths; relative risk ratio, 0.7; 95% CI, 0.2-3.2; p = 0.717), all placebo versus ramelteon, respectively., Conclusions: Ramelteon 8 mg did not prevent postoperative delirium in patients admitted for elective cardiac surgery.

Pancreatic ductal adenocarcinoma (PDAC) typically has nonspecific symptoms and is often found too late to treat. Because diagnosis of PDAC involves complex, invasive, and expensive procedures, screening populations at increased risk will depend on developing rapid, sensitive, specific, and cost-effective tests. Exosomes, which are nanoscale vesicles shed into blood from tumors, have come into focus as valuable entities for noninvasive liquid biopsy diagnostics. However, rapid capture and analysis of exosomes with their protein and other biomarkers have proven difficult. Here, we present a simple method integrating capture and analysis of exosomes and other extracellular vesicles directly from whole blood, plasma, or serum onto an AC electrokinetic microarray chip. In this process, no pretreatment or dilution of sample is required, nor is it necessary to use capture antibodies or other affinity techniques. Subsequent on-chip immunofluorescence analysis permits specific identification and quantification of target biomarkers within as little as 30 min total time. In this initial validation study, the biomarkers glypican-1 and CD63 were found to reflect the presence of PDAC and thus were used to develop a bivariate model for detecting PDAC. Twenty PDAC patient samples could be distinguished from 11 healthy subjects with 99% sensitivity and 82% specificity. In a smaller group of colon cancer patient samples, elevated glypican-1 was observed for metastatic but not for nonmetastatic disease. The speed and simplicity of ACE exosome capture and on-chip biomarker detection, combined with the ability to use whole blood, will enable seamless "sample-to-answer" liquid biopsy screening and improve early stage cancer diagnostics.