UK Research and Innovation

Food waste in the global food supply chain is reviewed in relation to the prospects for feeding a population of nine billion by 2050. Different definitions of food waste with respect to the complexities of food supply chains (FSCs)are discussed. An international literature review found a dearth of data on food waste and estimates varied widely; those for post-harvest losses of grain in developing countries might be overestimated. As much of the post-harvest loss data for developing countries was collected over 30 years ago, current global losses cannot be quantified. A significant gap exists in the understanding of the food waste implications of the rapid development of 'BRIC' economies. The limited data suggest that losses are much higher at the immediate post-harvest stages in developing countries and higher for perishable foods across industrialized and developing economies alike. For affluent economies, post-consumer food waste accounts for the greatest overall losses. To supplement the fragmentary picture and to gain a forward view, interviews were conducted with international FSC experts. The analyses highlighted the scale of the problem, the scope for improved system efficiencies and the challenges of affecting behavioural change to reduce post-consumer waste in affluent populations.

BACKGROUND: Underweight and obesity are associated with adverse health outcomes throughout the life course. We estimated the individual and combined prevalence of underweight or thinness and obesity, and their changes, from 1990 to 2022 for adults and school-aged children and adolescents in 200 countries and territories. METHODS: ). For school-aged children and adolescents, we report thinness (BMI <2 SD below the median of the WHO growth reference) and obesity (BMI >2 SD above the median). FINDINGS: From 1990 to 2022, the combined prevalence of underweight and obesity in adults decreased in 11 countries (6%) for women and 17 (9%) for men with a posterior probability of at least 0·80 that the observed changes were true decreases. The combined prevalence increased in 162 countries (81%) for women and 140 countries (70%) for men with a posterior probability of at least 0·80. In 2022, the combined prevalence of underweight and obesity was highest in island nations in the Caribbean and Polynesia and Micronesia, and countries in the Middle East and north Africa. Obesity prevalence was higher than underweight with posterior probability of at least 0·80 in 177 countries (89%) for women and 145 (73%) for men in 2022, whereas the converse was true in 16 countries (8%) for women, and 39 (20%) for men. From 1990 to 2022, the combined prevalence of thinness and obesity decreased among girls in five countries (3%) and among boys in 15 countries (8%) with a posterior probability of at least 0·80, and increased among girls in 140 countries (70%) and boys in 137 countries (69%) with a posterior probability of at least 0·80. The countries with highest combined prevalence of thinness and obesity in school-aged children and adolescents in 2022 were in Polynesia and Micronesia and the Caribbean for both sexes, and Chile and Qatar for boys. Combined prevalence was also high in some countries in south Asia, such as India and Pakistan, where thinness remained prevalent despite having declined. In 2022, obesity in school-aged children and adolescents was more prevalent than thinness with a posterior probability of at least 0·80 among girls in 133 countries (67%) and boys in 125 countries (63%), whereas the converse was true in 35 countries (18%) and 42 countries (21%), respectively. In almost all countries for both adults and school-aged children and adolescents, the increases in double burden were driven by increases in obesity, and decreases in double burden by declining underweight or thinness. INTERPRETATION: The combined burden of underweight and obesity has increased in most countries, driven by an increase in obesity, while underweight and thinness remain prevalent in south Asia and parts of Africa. A healthy nutrition transition that enhances access to nutritious foods is needed to address the remaining burden of underweight while curbing and reversing the increase in obesity. FUNDING: UK Medical Research Council, UK Research and Innovation (Research England), UK Research and Innovation (Innovate UK), and European Union.

In remote sensing, principal components analysis is usually performed using unstandardized variables. However, the use of standardized variables yields significantly different results. In this paper principal components of two LANDSAT MSS subscenes were separately calculated using both methods. The result indicate substantial improvement in signal-to-noise ratio and image enhancement by using standardized variables in the principal components analysis.

The sensitivity of tropical forest carbon to climate is a key uncertainty in predicting global climate change. Although short-term drying and warming are known to affect forests, it is unknown if such effects translate into long-term responses. Here, we analyze 590 permanent plots measured across the tropics to derive the equilibrium climate controls on forest carbon. Maximum temperature is the most important predictor of aboveground biomass (-9.1 megagrams of carbon per hectare per degree Celsius), primarily by reducing woody productivity, and has a greater impact per °C in the hottest forests (>32.2°C). Our results nevertheless reveal greater thermal resilience than observations of short-term variation imply. To realize the long-term climate adaptation potential of tropical forests requires both protecting them and stabilizing Earth's climate.

BACKGROUND: An unknown proportion of people who had an apparently mild COVID-19 infection continue to suffer with persistent symptoms, including chest pain, shortness of breath, muscle and joint pains, headaches, cognitive impairment ('brain fog'), and fatigue. Post-acute COVID-19 ('long-COVID') seems to be a multisystem disease, sometimes occurring after a mild acute illness; people struggling with these persistent symptoms refer to themselves as 'long haulers'. AIM: To explore experiences of people with persisting symptoms following COVID-19 infection, and their views on primary care support received. DESIGN & SETTING: Qualitative methodology, with semi-structured interviews to explore perspectives of people with persisting symptoms following suspected or confirmed COVID-19 infection. Participants were recruited via social media between July-August 2020. METHOD: Interviews were conducted by telephone or video call, digitally recorded, and transcribed with consent. Thematic analysis was conducted applying constant comparison techniques. People with experience of persisting symptoms contributed to study design and data analysis. RESULTS: of enduring and managing symptoms and accessing care; living with uncertainty, helplessness and fear, particularly over whether recovery is possible; the importance of finding the 'right' GP (understanding, empathy, and support needed); and recovery and rehabilitation: what would help? CONCLUSION: This study will raise awareness among primary care professionals, and commissioners, of long-COVID and the range of symptoms people are experiencing. Patients require their GP to believe their symptoms and to demonstrate empathy and understanding. Ongoing support by primary care professionals during recovery and rehabilitation is crucial.

The application of state-of-the-art deep-learning approaches to the protein modeling problem has expanded the "high-accuracy" category in CASP14 to encompass all targets. Building on the metrics used for high-accuracy assessment in previous CASPs, we evaluated the performance of all groups that submitted models for at least 10 targets across all difficulty classes, and judged the usefulness of those produced by AlphaFold2 (AF2) as molecular replacement search models with AMPLE. Driven by the qualitative diversity of the targets submitted to CASP, we also introduce DipDiff as a new measure for the improvement in backbone geometry provided by a model versus available templates. Although a large leap in high-accuracy is seen due to AF2, the second-best method in CASP14 out-performed the best in CASP13, illustrating the role of community-based benchmarking in the development and evolution of the protein structure prediction field.

BACKGROUND: Azithromycin, an antibiotic with potential antiviral and anti-inflammatory properties, has been used to treat COVID-19, but evidence from community randomised trials is lacking. We aimed to assess the effectiveness of azithromycin to treat suspected COVID-19 among people in the community who had an increased risk of complications. METHODS: In this UK-based, primary care, open-label, multi-arm, adaptive platform randomised trial of interventions against COVID-19 in people at increased risk of an adverse clinical course (PRINCIPLE), we randomly assigned people aged 65 years and older, or 50 years and older with at least one comorbidity, who had been unwell for 14 days or less with suspected COVID-19, to usual care plus azithromycin 500 mg daily for three days, usual care plus other interventions, or usual care alone. The trial had two coprimary endpoints measured within 28 days from randomisation: time to first self-reported recovery, analysed using a Bayesian piecewise exponential, and hospital admission or death related to COVID-19, analysed using a Bayesian logistic regression model. Eligible participants with outcome data were included in the primary analysis, and those who received the allocated treatment were included in the safety analysis. The trial is registered with ISRCTN, ISRCTN86534580. FINDINGS: The first participant was recruited to PRINCIPLE on April 2, 2020. The azithromycin group enrolled participants between May 22 and Nov 30, 2020, by which time 2265 participants had been randomly assigned, 540 to azithromycin plus usual care, 875 to usual care alone, and 850 to other interventions. 2120 (94%) of 2265 participants provided follow-up data and were included in the Bayesian primary analysis, 500 participants in the azithromycin plus usual care group, 823 in the usual care alone group, and 797 in other intervention groups. 402 (80%) of 500 participants in the azithromycin plus usual care group and 631 (77%) of 823 participants in the usual care alone group reported feeling recovered within 28 days. We found little evidence of a meaningful benefit in the azithromycin plus usual care group in time to first reported recovery versus usual care alone (hazard ratio 1·08, 95% Bayesian credibility interval [BCI] 0·95 to 1·23), equating to an estimated benefit in median time to first recovery of 0·94 days (95% BCI -0·56 to 2·43). The probability that there was a clinically meaningful benefit of at least 1·5 days in time to recovery was 0·23. 16 (3%) of 500 participants in the azithromycin plus usual care group and 28 (3%) of 823 participants in the usual care alone group were hospitalised (absolute benefit in percentage 0·3%, 95% BCI -1·7 to 2·2). There were no deaths in either study group. Safety outcomes were similar in both groups. Two (1%) of 455 participants in the azothromycin plus usual care group and four (1%) of 668 participants in the usual care alone group reported admission to hospital during the trial, not related to COVID-19. INTERPRETATION: Our findings do not justify the routine use of azithromycin for reducing time to recovery or risk of hospitalisation for people with suspected COVID-19 in the community. These findings have important antibiotic stewardship implications during this pandemic, as inappropriate use of antibiotics leads to increased antimicrobial resistance, and there is evidence that azithromycin use increased during the pandemic in the UK. FUNDING: UK Research and Innovation and UK Department of Health and Social Care.

Abstract We describe the scientific and technical implementation of two models for a core set of experiments contributing to the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The models used are the physical atmosphere‐land‐ocean‐sea ice model HadGEM3‐GC3.1 and the Earth system model UKESM1 which adds a carbon‐nitrogen cycle and atmospheric chemistry to HadGEM3‐GC3.1. The model results are constrained by the external boundary conditions (forcing data) and initial conditions. We outline the scientific rationale and assumptions made in specifying these. Notable details of the implementation include an ozone redistribution scheme for prescribed ozone simulations (HadGEM3‐GC3.1) to avoid inconsistencies with the model's thermal tropopause, and land use change in dynamic vegetation simulations (UKESM1) whose influence will be subject to potential biases in the simulation of background natural vegetation. We discuss the implications of these decisions for interpretation of the simulation results. These simulations are expensive in terms of human and CPU resources and will underpin many further experiments; we describe some of the technical steps taken to ensure their scientific robustness and reproducibility.

Prior to the SARS-CoV-2 pandemic, antibiotic resistance was listed as the major global health care priority. Some analyses, including the O'Neill report, have predicted that deaths due to drug-resistant bacterial infections may eclipse the total number of cancer deaths by 2050. Although fungal infections remain in the shadow of public awareness, total attributable annual deaths are similar to, or exceeds, global mortalities due to malaria, tuberculosis or HIV. The impact of fungal infections has been exacerbated by the steady rise of antifungal drug resistant strains and species which reflects the widespread use of antifungals for prophylaxis and therapy, and in the case of azole resistance in Aspergillus, has been linked to the widespread agricultural use of antifungals. This review, based on a workshop hosted by the Medical Research Council and the University of Exeter, illuminates the problem of antifungal resistance and suggests how this growing threat might be mitigated.

COVID-19 is a global challenge that demands researchers, policy makers, and governments address multiple dimensions which go far beyond the implications of this pandemic for health and wellbeing. Just as the UN Sustainable Development Goals call for focus on the connections between development policy sectors, the pandemic has exposed the complex global interdependencies that underpin economies and highlighted fault lines in societal structures that perpetuate ethnic, economic, social, and gender inequalities.

OBJECTIVE: Necrotising enterocolitis (NEC) remains one of the primary causes of morbidity and mortality in neonates and alternative strategies are needed. Stem cells have become a therapeutic option for other intestinal diseases, which share some features with NEC. We tested the hypothesis that amniotic fluid stem (AFS) cells exerted a beneficial effect in a neonatal rat model of NEC. DESIGN: Rats intraperitoneally injected with AFS cells and their controls (bone marrow mesenchymal stem cells, myoblast) were analysed for survival, behaviour, bowel imaging (MRI scan), histology, bowel absorption and motility, immunofluorescence for AFS cell detection, degree of gut inflammation (myeloperoxidase and malondialdehyde), and enterocyte apoptosis and proliferation. RESULTS: AFS cells integrated in the bowel wall and improved rat survival and clinical conditions, decreased NEC incidence and macroscopic gut damage, improved intestinal function, decreased bowel inflammation, increased enterocyte proliferation and reduced apoptosis. The beneficial effect was achieved via modulation of stromal cells expressing cyclooxygenase 2 in the lamina propria, as shown by survival studies using selective and non-selective cyclooxygenase 2 inhibitors. Interestingly, AFS cells differentially expressed genes of the Wnt/β-catenin pathway, which regulate intestinal epithelial stem cell function and cell migration and growth factors known to maintain gut epithelial integrity and reduce mucosal injury. CONCLUSIONS: We demonstrated here for the first time that AFS cells injected in an established model of NEC improve survival, clinical status, gut structure and function. Understanding the mechanism of this effect may help us to develop new cellular or pharmacological therapies for infants with NEC.

In recent years, Raman spectroscopy has undergone major advancements in its ability to probe deeply through turbid media such as biological tissues. This progress has been facilitated by the advent of a range of specialist techniques based around spatially offset Raman spectroscopy (SORS) to enable non-invasive probing of living tissue through depths of up to 5 cm. This represents an improvement in depth penetration of up to two orders of magnitude compared to what can be achieved with conventional Raman methods. In combination with the inherently high molecular specificity of Raman spectroscopy, this has therefore opened up entirely new prospects for a range of new analytical applications across multiple fields including medical diagnosis and disease monitoring. This article discusses SORS and related variants of deep Raman spectroscopy such as transmission Raman spectroscopy (TRS), micro-SORS and surface enhanced spatially offset Raman spectroscopy (SESORS), and reviews the progress made in this field during the past 5 years including advances in non-invasive cancer diagnosis, monitoring of neurotransmitters, and assessment of bone disease.

Reward the creation of analyses for policymakers that are inclusive, rigorous, transparent and accessible, urge Christl A. Donnelly and colleagues. Reward the creation of analyses for policymakers that are inclusive, rigorous, transparent and accessible.

Abstract The underwater Hunga Tonga-Hunga Ha-apai volcano erupted in the early hours of 15th January 2022, and injected volcanic gases and aerosols to over 50 km altitude. Here we synthesise satellite, ground-based, in situ and radiosonde observations of the eruption to investigate the strength of the stratospheric aerosol and water vapour perturbations in the initial weeks after the eruption and we quantify the net radiative impact across the two species using offline radiative transfer modelling. We find that the Hunga Tonga-Hunga Ha-apai eruption produced the largest global perturbation of stratospheric aerosols since the Pinatubo eruption in 1991 and the largest perturbation of stratospheric water vapour observed in the satellite era. Immediately after the eruption, water vapour radiative cooling dominated the local stratospheric heating/cooling rates, while at the top-of-the-atmosphere and surface, volcanic aerosol cooling dominated the radiative forcing. However, after two weeks, due to dispersion/dilution, water vapour heating started to dominate the top-of-the-atmosphere radiative forcing, leading to a net warming of the climate system.

Abstract Cryptocurrencies and blockchain technology are playing an increasingly important role for organizations that seek to build social and solidarity‐based finance. Blockchain technology has emerged as a potential disruptor for the financial industry. However, cryptocurrencies and blockchain technology may help develop organizations that seek to build social and solidarity‐based finance.

In the life cycle of a material, there will be either chemical or physical change due to varying environmental factors such as biological activity, light, heat, moisture, and chemical conditions. This process leads to polymer property change as pertains to functional deterioration because of the physical, biological, and chemical reactions that result in chemical transformations and bond scission and thus can be regarded as polymer degradation. Due to the present demand for sustainable polymers, bio-based polymers have been identified as a solution. There is therefore a need to compare the sustainability impacts of bio-based polymers, to maximize their use in functional use stage and still withhold the bio-degradation capability. This study focuses are poly (lactic acid) (PLA), Poly (ε-caprolactone) (PCL), polyhydroxyalkanoates (PHA), and polyamides (PA) as biopolymers of interest due to their potential in technological applications, stability, and biodegradability. For preparing bio-based value-added products, an appropriate selection of the fabrication or functional modification process is a very important factor for particular industrial or biomedical applications. The literature review indicates that in vivo is preferred to in vitro because it suits an overall study of the experiment’s effects on a living subject. This study will explore these features in detail. In particular, the review will cover processing and biodegradation pathways for each of the biopolymers. In addition, thermal degredation and photodegradation are covered, and future trends and conclusions are drawn.

In order to deal with an increasingly complex world, we need ever more sophisticated computational models that can help us make decisions wisely and understand the potential consequences of choices. But creating a model requires far more than just raw data and technical skills: it requires a close collaboration between model commissioners, developers, users and reviewers. Good modelling requires its users and commissioners to understand more about the whole process, including the different kinds of purpose a model can have and the different technical bases. This paper offers a guide to the process of commissioning, developing and deploying models across a wide range of domains from public policy to science and engineering. It provides two checklists to help potential modellers, commissioners and users ensure they have considered the most significant factors that will determine success. We conclude there is a need to reinforce modelling as a discipline, so that misconstruction is less likely; to increase understanding of modelling in all domains, so that the misuse of models is reduced; and to bring commissioners closer to modelling, so that the results are more useful.

Laser-matter interactions in laser additive manufacturing (LAM) occur on short time scales (10−6–10−3 s) and have traditionally proven difficult to characterise. We investigate these interactions during LAM of stainless steel SS316L and 13-93 bioactive glass powders using a custom built LAM process replicator (LAMPR) with in situ and operando synchrotron X-ray real-time radiography. This reveals a wide range of melt track solidification phenomena as well as spatter and porosity formation. We hypothesise that the SS316L powder absorbs the laser energy at its surface while the trace elements in the 13-93 bioactive glass powder absorb and remit the infra-red radiation. Our results show that a low viscosity melt, e.g. 8 mPa s for SS316L, tends to generate spatter (diameter up to 250 μm and an average spatter velocity of 0.26 m s−1) and form a melt track by molten pool wetting. In contrast, a high viscosity melt, e.g. 2 Pa s for 13-93 bioactive glass, inhibits spatter formation by damping the Marangoni convection, forming a melt track via viscous flow. The viscous flow in 13-93 bioactive glass resists pore transport; combined with the reboil effect, this promotes pore growth during LAM, resulting in a pore size up to 600 times larger than that exhibited in the SS316L sample.

Abstract. The distribution of data contributed to the Coupled Model Intercomparison Project Phase 6 (CMIP6) is via the Earth System Grid Federation (ESGF). The ESGF is a network of internationally distributed sites that together work as a federated data archive. Data records from climate modelling institutes are published to the ESGF and then shared around the world. It is anticipated that CMIP6 will produce approximately 20 PB of data to be published and distributed via the ESGF. In addition to this large volume of data a number of value-added CMIP6 services are required to interact with the ESGF; for example the citation and errata services both interact with the ESGF but are not a core part of its infrastructure. With a number of interacting services and a large volume of data anticipated for CMIP6, the CMIP Data Node Operations Team (CDNOT) was formed. The CDNOT coordinated and implemented a series of CMIP6 preparation data challenges to test all the interacting components in the ESGF CMIP6 software ecosystem. This ensured that when CMIP6 data were released they could be reliably distributed.

In most sports, it is found that the important performance indicators (PI's) vary from coach to coach. Therefore, if sets of PI's can be identified and clear operational definitions defined, there is significant scope/benefit for consultancy and research, particularly in commercially orientated sports such as soccer. The aim of this study was to use the unique opportunity of a large number of performance analysts coming together to discuss this problem and its application to soccer, and define sets of performance indicators for each position in soccer. In the early spring of 2011, staff from 9 universities, from all over Europe, brought 51 level 3 Sports Science students to Hungary for an Intensive Programme in Performance Analysis of Sport (IPPAS). The 15 staff, all experts in PA, had a total of over 200 years of experience of PA between them. The most experienced 'experts' (N=5) acted as mentors, introducing the area, defining the aims and managing the groups. The rest (N=10) and the 51 students were distributed evenly as possible across 7 groups, in which their aim was to define the key PI's for one of the positions in soccer. The positions used were:-Goalkeepers; Full Backs; Centre Backs; Holding Midfield; Attacking Midfield; Wide Midfield and Strikers. In conclusion, 7 sets of KPI'S, were defined for each of these classifications within 5 category sets: Physiological, Tactical, Technical -Defending,Technical -Attacking, and Psychological. These KPI's were different from position to position within the team, particularly for the Goal Keeper. The KPI's for the outfield players were very similar, differing only in their order of importance. This enabled a 'generic' set of skills required for outfield players in soccer.