The global food system puts enormous pressure on the environment. Managing these pressures requires understanding not only where they occur (i.e., where food is produced), but also who drives them (i.e., where food is consumed). However, the size and complexity of global supply chains make it difficult to trace food production to consumption. Here, we provide the most comprehensive dataset of bilateral trade flows of environmental pressures stemming from food production from producing to consuming nations. The dataset provides environmental pressures for greenhouse gas emissions, water use, nitrogen and phosphorus pollution, and the area of land/water occupancy of food production for crops and animals from land, freshwater, and ocean systems. To produce these data, we improved upon reported food trade and production data to identify producing and consuming nations for each food item, allowing us to match food flows with appropriate environmental pressure data. These data provide a resource for research on sustainable global food consumption and the drivers of environmental impact.

Multicenter clinical trials are essential for evaluating interventions but often face significant challenges in study design, site coordination, participant recruitment, and regulatory compliance. To address these issues, the National Institutes of Health’s National Center for Advancing Translational Sciences established the Trial Innovation Network (TIN). The TIN offers a scientific consultation process, providing access to clinical trial and disease experts who provide input and recommendations throughout the trial’s duration, at no cost to investigators. This approach aims to improve trial design, accelerate implementation, foster interdisciplinary teamwork, and spur innovations that enhance multicenter trial quality and efficiency. The TIN leverages resources of the Clinical and Translational Science Awards (CTSA) program, complementing local capabilities at the investigator’s institution. The Initial Consultation process focuses on the study’s scientific premise, design, site development, recruitment and retention strategies, funding feasibility, and other support areas. As of 6/1/2024, the TIN has provided 431 Initial Consultations to increase efficiency and accelerate trial implementation by delivering customized support and tailored recommendations. Across a range of clinical trials, the TIN has developed standardized, streamlined, and adaptable processes. We describe these processes, provide operational metrics, and include a set of lessons learned for consideration by other trial support and innovation networks.

Vitamin A deficiency (VAD) poses significant health risks and is prevalent in children and adolescents in India. This study aimed to determine the effect of seasonal variation and availability of vitamin A-rich (VA-rich) foods on serum retinol in adolescents. Data on serum retinol levels from adolescents (n 2297, mean age 14 years) from the Comprehensive National Nutrition Survey (2016–2018) in India were analysed, with VAD defined as serum retinol < 0·7 µmol/L. Five states were selected based on a comparable under-five mortality rate and the seasonal spread of the data collection period. Dietary data from adolescents and children ≤ 4 years old were used to assess VA-rich food consumption. A linear mixed model framework was employed to analyse the relationship between serum retinol, month of the year and VA-rich food consumption, with a priori ranking to control for multiple hypothesis testing. Consumption of VA-rich foods, particularly fruits and vegetables/roots and tubers, showed seasonal patterns, with higher consumption during summer and monsoon months. Significant associations were found between serum retinol concentrations and age, month of sampling, consumption of VA-rich foods and fish. VAD prevalence was lowest in August, coinciding with higher consumption of VA-rich fruits and foods. Findings highlight the importance of considering seasonality in assessing VAD prevalence and careful interpretation of survey findings. Intentional design, analysis and reporting of surveys to capture seasonal variation is crucial for accurate assessment and interpretation of VAD prevalence, including during monitoring and evaluation of programmes, and to ensure that public health strategies are appropriately informed.

The First Large Absorption Survey in H i (FLASH) is a large-area radio survey for neutral hydrogen in and around galaxies in the intermediate redshift range $0.4\lt z\lt1.0$, using the 21-cm H i absorption line as a probe of cold neutral gas. The survey uses the ASKAP radio telescope and will cover 24,000 deg$^2$ of sky over the next five years. FLASH breaks new ground in two ways – it is the first large H i absorption survey to be carried out without any optical preselection of targets, and we use an automated Bayesian line-finding tool to search through large datasets and assign a statistical significance to potential line detections. Two Pilot Surveys, covering around 3000 deg$^2$ of sky, were carried out in 2019-22 to test and verify the strategy for the full FLASH survey. The processed data products from these Pilot Surveys (spectral-line cubes, continuum images, and catalogues) are public and available online. In this paper, we describe the FLASH spectral-line and continuum data products and discuss the quality of the H i spectra and the completeness of our automated line search. Finally, we present a set of 30 new H i absorption lines that were robustly detected in the Pilot Surveys, almost doubling the number of known H i absorption systems at $0.4\lt z\lt1$. The detected lines span a wide range in H i optical depth, including three lines with a peak optical depth $\tau\gt1$, and appear to be a mixture of intervening and associated systems. Interestingly, around two-thirds of the lines found in this untargeted sample are detected against sources with a peaked-spectrum radio continuum, which are only a minor (5–20%) fraction of the overall radio-source population. The detection rate for H i absorption lines in the Pilot Surveys (0.3 to 0.5 lines per 40 deg$^2$ ASKAP field) is a factor of two below the expected value. One possible reason for this is the presence of a range of spectral-line artefacts in the Pilot Survey data that have now been mitigated and are not expected to recur in the full FLASH survey. A future paper in this series will discuss the host galaxies of the H i absorption systems identified here.

Aerosol-cloud interactions contribute significant uncertainty to modern climate model predictions. Analysis of complex observed aerosol-cloud parameter relationships is a crucial piece of reducing this uncertainty. Here, we apply two machine learning methods to explore variability in in-situ observations from the NASA ACTIVATE mission. These observations consist of flights over the Western North Atlantic Ocean, providing a large repository of data including aerosol, meteorological, and microphysical conditions in and out of clouds. We investigate this dataset using principal component analysis (PCA), a linear dimensionality reduction technique, and an autoencoder, a deep learning non-linear dimensionality reduction technique. We find that we can reduce the dimensionality of the parameter space by more than a factor of 2 and verify that the deep learning method outperforms a PCA baseline by two orders of magnitude. Analysis in the low dimensional space of both these techniques reveals two consistent physically interpretable regimes—a low pollution regime and an in-cloud regime. Through this work, we show that unsupervised machine learning techniques can learn useful information from in-situ atmospheric observations and provide interpretable results of low-dimensional variability.

Although financial stressors are implicated as risk factors for suicidal behavior, these associations might be confounded by other factors. Furthermore, a move toward high-risk subgroup definition is necessary. The authors used Swedish national registry data to examine the associations between receipt of social welfare, unemployment benefits, or early retirement (N = 627,745−2,260,753) with suicidal behavior in Cox proportional hazards models. They applied co-relative models to improve causal inference, and examined interactions with aggregate genetic risk for suicidality. All three exposures were associated with elevated suicidal behavior risk. Initial hazard ratios for suicide attempt ranged from 1.37−3.86, were similar for suicide death, and declined after controlling for psychopathology and time elapsed after exposure. Age at registration differentially impacted risk of suicidal behavior. Aggregate genetic liability for suicidality was associated with risk, but its effect was not moderated by financial stress. Financial stressors are associated with suicidal behavior risk even after controlling for psychopathology. Associations are attributable in part to familial confounding, though a potentially causal pathway was observed in most cases. Suicidality risk varied as a function of sex and age at exposure; these findings could be used to identify subgroups at high risk who warrant targeted prevention.

Inflammation and infections such as malaria affect concentrations of many micronutrient biomarkers and hence estimates of nutritional status. We aimed to assess the relationship between malaria infection and micronutrient biomarker concentrations in pre-school children (PSC), school-age children (SAC) and women of reproductive age (WRA) in Malawi and examine the potential role of malarial immunity on the relationship between malaria and micronutrient biomarkers. Data from the 2015/2016 Malawi micronutrient survey were used. The associations between current or recent malaria infection, detected by rapid diagnostic test and concentration of serum ferritin, soluble transferrin receptor (sTfR), zinc, serum folate, red blood cell folate and vitamin B12 were estimated using multivariable linear regression. Factors related to malarial immunity including age, altitude and presence of hemoglobinopathies were examined as effect modifiers. Serum ferritin, sTfR and zinc were adjusted for inflammation using the BRINDA method. Malaria infection was associated with 68 % (95 % CI 51, 86), 28 % (18, 40) and 34 % (13, 45) greater inflammation-adjusted ferritin in PSC, SAC and WRA, respectively (P < 0·001 for each). In PSC, the positive association was stronger in younger children, high altitude and children who were not carriers of the sickle cell trait. In PSC and SAC, sTfR was elevated (+ 25 % (16, 29) and + 15 % (9, 22) respectively, P < 0·001). Serum folate and erythrocyte folate were elevated in WRA with malaria (+ 18 % (3, 35) and + 11 % (1, 23), P = 0·01 and P = 0·003 respectively). Malaria affects the interpretation of micronutrient biomarker concentrations, and examining factors related to malarial immunity may be informative.

Quantum field theory predicts a nonlinear response of the vacuum to strong electromagnetic fields of macroscopic extent. This fundamental tenet has remained experimentally challenging and is yet to be tested in the laboratory. A particularly distinct signature of the resulting optical activity of the quantum vacuum is vacuum birefringence. This offers an excellent opportunity for a precision test of nonlinear quantum electrodynamics in an uncharted parameter regime. Recently, the operation of the high-intensity Relativistic Laser at the X-ray Free Electron Laser provided by the Helmholtz International Beamline for Extreme Fields has been inaugurated at the High Energy Density scientific instrument of the European X-ray Free Electron Laser. We make the case that this worldwide unique combination of an X-ray free-electron laser and an ultra-intense near-infrared laser together with recent advances in high-precision X-ray polarimetry, refinements of prospective discovery scenarios and progress in their accurate theoretical modelling have set the stage for performing an actual discovery experiment of quantum vacuum nonlinearity.

The Automated Meteorology—Ice—Geophysics Observation System 3 (AMIGOS-3) is a multi-sensor on-ice ocean mooring and weather, camera and precision GPS measurement station, controlled by a Python script. The station is designed to be deployed on floating ice in the polar regions and operate unattended for up to several years. Ocean mooring sensors (SeaBird MicroCAT and Nortek Aquadopp) record conductivity, temperature and depth (reported at 10 min intervals), and current velocity (hourly intervals). A Silixa XT fiber-optic distributed temperature sensing system provides a temperature profile time-series through the ice and ocean column with a cadence of 6 d−1 to 1 week−1 depending on available station power. A subset of the station data is telemetered by Iridium modem. Two-way communication, using both single-burst data and file transfer protocols, facilitates station data collection changes and power management. Power is supplied by solar panels and a sealed lead-acid battery system. Two AMIGOS-3 systems were installed on the Thwaites Eastern Ice Shelf in January 2020, providing data well into 2022. We discuss the components of the system and present several of the data sets, summarizing observed climate, ice and ocean conditions.

Tversky's contrast model of proximity was initially formulated to account for the observed violations of the metric axioms often found in empirical proximity data. This set-theoretic approach models the similarity/dissimilarity between any two stimuli as a linear (or ratio) combination of measures of the common and distinctive features of the two stimuli. This paper proposes a new spatial multidimensional scaling (MDS) procedure called TSCALE based on Tversky's linear contrast model for the analysis of generally asymmetric three-way, two-mode proximity data. We first review the basic structure of Tversky's conceptual contrast model. A brief discussion of alternative MDS procedures to accommodate asymmetric proximity data is also provided. The technical details of the TSCALE procedure are given, as well as the program options that allow for the estimation of a number of different model specifications. The nonlinear estimation framework is discussed, as are the results of a modest Monte Carlo analysis. Two consumer psychology applications are provided: one involving perceptions of fast-food restaurants and the other regarding perceptions of various competitive brands of cola softdrinks. Finally, other applications and directions for future research are mentioned.

We use childhood exposure to disasters as a natural experiment inducing variations in adulthood outcomes. Following the fetal origin hypothesis, we hypothesize that children from households with greater famine exposure will have poorer health outcomes. Employing a unique dataset from Bangladesh, we test this hypothesis for the 1974–75 famine that was largely caused by increased differences between the price of coarse rice and agricultural wages, together with the lack of entitlement to foodgrains for daily wage earners. People from northern regions of Bangladesh were unequally affected by this famine that spanned several months in 1974 and 1975. We find that children surviving the 1974–75 famine have lower health outcomes during their adulthood. Due to the long-lasting effects of such adverse events and their apparent human capital and growth implications, it is important to enact and enforce public policies aimed at ameliorating the immediate harms of such events through helping the poor.

Inflammation and infections such as malaria affect micronutrient biomarker concentrations and hence estimates of nutritional status. It is unknown whether correction for C-reactive protein (CRP) and α1-acid glycoprotein (AGP) fully captures the modification in ferritin concentrations during a malaria infection, or whether environmental and sociodemographic factors modify this association. Cross-sectional data from eight surveys in children aged 6–59 months (Cameroon, Cote d’Ivoire, Kenya, Liberia, Malawi, Nigeria and Zambia; n 6653) from the Biomarkers Reflecting Inflammation and Nutritional Determinants of Anaemia (BRINDA) project were pooled. Ferritin was adjusted using the BRINDA adjustment method, with values < 12 μg/l indicating iron deficiency. The association between current or recent malaria infection, detected by microscopy or rapid test kit, and inflammation-adjusted ferritin was estimated using pooled multivariable linear regression. Age, sex, malaria endemicity profile (defined by the Plasmodium falciparum infection prevalence) and malaria diagnostic methods were examined as effect modifiers. Unweighted pooled malaria prevalence was 26·0 % (95 % CI 25·0, 27·1) and unweighted pooled iron deficiency was 41·9 % (95 % CI 40·7, 43·1). Current or recent malaria infection was associated with a 44 % (95 % CI 39·0, 52·0; P < 0·001) increase in inflammation-adjusted ferritin after adjusting for age and study identifier. In children, ferritin increased less with malaria infection as age and malaria endemicity increased. Adjustment for malaria increased the prevalence of iron deficiency, but the effect was small. Additional information would help elucidate the underlying mechanisms of the role of endemicity and age in the association between malaria and ferritin.

Over 2023, many universities and policy organisations in the higher education (HE) sector are working to create guiding principles and guidelines for the use of generative artificial intelligence (AI) in HE Teaching and Learning (T&L). Despite these guidelines, students remain unsure if and how they should use AI. This article discusses the AI information sessions held over the Autumn 2023 term in the Department of Classics at the University of Reading, which aimed to provide students with the knowledge and tools to make informed judgements about using AI in their studies. These sessions discussed the benefits and drawbacks of generative AI, highlighting training data, content policy, environmental impact, and examples of potential uses. Staff and student participants were surveyed before and after these information sessions to gather their opinions surrounding AI use. Although at least 60% of participants had previously used generative AI, 80% of participants were apprehensive of or against using generative AI tools for learning purposes following the AI information sessions. By providing staff and students with the ethical considerations surrounding generative AI, they can make an informed judgement about using AI in their work without misplaced faith or excessive fear.

Background: After a transient ischemic attack (TIA) or minor stroke, the long-term risk of subsequent stroke is uncertain. Methods: Electronic databases were searched for observational studies reporting subsequent stroke during a minimum follow-up of 1 year in patients with TIA or minor stroke. Unpublished data on number of stroke events and exact person-time at risk contributed by all patients during discrete time intervals of follow-up were requested from the authors of included studies. This information was used to calculate the incidence of stroke in individual studies, and results across studies were pooled using random-effects meta-analysis. Results: Fifteen independent cohorts involving 129794 patients were included in the analysis. The pooled incidence rate of subsequent stroke per 100 person-years was 6.4 events in the first year and 2.0 events in the second through tenth years, with cumulative incidences of 14% at 5 years and 21% at 10 years. Based on 10 studies with information available on fatal stroke, the pooled case fatality rate of subsequent stroke was 9.5% (95% CI, 5.9 – 13.8). Conclusions: One in five patients is expected to experience a subsequent stroke within 10 years after a TIA or minor stroke, with every tenth patient expected to die from their subsequent stroke.

A healthy diet is at the forefront of measures to prevent type 2 diabetes. Certain vegetable and fish oils, such as pine nut oil (PNO), have been demonstrated to ameliorate the adverse metabolic effects of a high-fat diet. The present study investigates the involvement of the free fatty acid receptors 1 (FFAR1) and 4 (FFAR4) in the chronic activity of hydrolysed PNO (hPNO) on high-fat diet-induced obesity and insulin resistance. Male C57BL/6J wild-type, FFAR1 knockout (-/-) and FFAR4-/- mice were placed on 60 % high-fat diet for 3 months. Mice were then dosed hPNO for 24 d, during which time body composition, energy intake and expenditure, glucose tolerance and fasting plasma insulin, leptin and adiponectin were measured. hPNO improved glucose tolerance and decreased plasma insulin in the wild-type and FFAR1-/- mice, but not the FFAR4-/- mice. hPNO also decreased high-fat diet-induced body weight gain and fat mass, whilst increasing energy expenditure and plasma adiponectin. None of these effects on energy balance were statistically significant in FFAR4-/- mice, but it was not shown that they were significantly less than in wild-type mice. In conclusion, chronic hPNO supplementation reduces the metabolically detrimental effects of high-fat diet on obesity and insulin resistance in a manner that is dependent on the presence of FFAR4.

The paper explores the integration of emotional design elements in the development of medical devices to enhance user acceptance and adherence. It emphasizes the importance of a user-centered approach, acknowledging both functional and emotional needs. The study compares two cases within healthcare design, highlighting the impact of emotional design on users' perception of medical devices. Despite the different stages of development in the two cases, both employed a higher level of refflective design, aiming to create a lasting impact on users' identity using the products.