690 research outputs found

    Large UK retailers' initiatives to reduce consumers' emissions: a systematic assessment

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    In the interest of climate change mitigation, policy makers, businesses and non-governmental organisations have devised initiatives designed to reduce in-use emissions whilst, at the same time, the number of energy-consuming products in homes, and household energy consumption, is increasing. Retailers are important because they are at the interface between manufacturers of products and consumers and they supply the vast majority of consumer goods in developed countries like the UK, including energy using products. Large retailers have a consistent history of corporate responsibility reporting and have included plans and actions to influence consumer emissions within them. This paper adapts two frameworks to use them for systematically assessing large retailers’ initiatives aimed at reducing consumers’ carbon emissions. The Framework for Strategic Sustainable Development (FSSD) is adapted and used to analyse the strategic scope and coherence of these initiatives in relation to the businesses’ sustainability strategies. The ISM ‘Individual Social Material’ framework is adapted and used to analyse how consumer behaviour change mechanisms are framed by retailers. These frameworks are used to analyse eighteen initiatives designed to reduce consumer emissions from eight of the largest UK retail businesses, identified from publicly available data. The results of the eighteen initiatives analysed show that the vast majority were not well planned nor were they strategically coherent. Secondly, most of these specific initiatives relied solely on providing information to consumers and thus deployed a rather narrow range of consumer behaviour change mechanisms. The research concludes that leaders of retail businesses and policy makers could use the FSSD to ensure processes, and measurements are comprehensive and integrated, in order to increase the materiality and impact of their initiatives to reduce consumer emissions in use. Furthermore, retailers could benefit from exploring different models of behaviour change from the ISM framework in order to access a wider set of tools for transformative system change

    Lin28 Enhances Tissue Repair by Reprogramming Cellular Metabolism

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    SummaryRegeneration capacity declines with age, but why juvenile organisms show enhanced tissue repair remains unexplained. Lin28a, a highly conserved RNA-binding protein expressed during embryogenesis, plays roles in development, pluripotency, and metabolism. To determine whether Lin28a might influence tissue repair in adults, we engineered the reactivation of Lin28a expression in several models of tissue injury. Lin28a reactivation improved hair regrowth by promoting anagen in hair follicles and accelerated regrowth of cartilage, bone, and mesenchyme after ear and digit injuries. Lin28a inhibits let-7 microRNA biogenesis; however, let-7 repression was necessary but insufficient to enhance repair. Lin28a bound to and enhanced the translation of mRNAs for several metabolic enzymes, thereby increasing glycolysis and oxidative phosphorylation (OxPhos). Lin28a-mediated enhancement of tissue repair was negated by OxPhos inhibition, whereas a pharmacologically induced increase in OxPhos enhanced repair. Thus, Lin28a enhances tissue repair in some adult tissues by reprogramming cellular bioenergetics.PaperCli

    Preferential regulation of stably expressed genes in the human genome suggests a widespread expression buffering role of microRNAs

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    In this study, we comprehensively explored the stably expressed genes (SE genes) and fluctuant genes (FL genes) in the human genome by a meta-analysis of large scale microarray data. We found that these genes have distinct function distributions. miRNA targets are shown to be significantly enriched in SE genes by using propensity analysis of miRNA regulation, supporting the hypothesis that miRNAs can buffer whole genome expression fluctuation. The expression-buffering effect of miRNA is independent of the target site number within the 3'-untranslated region. In addition, we found that gene expression fluctuation is positively correlated with the number of transcription factor binding sites in the promoter region, which suggests that coordination between transcription factors and miRNAs leads to balanced responses to external perturbations

    PCA-based lung motion model

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    Organ motion induced by respiration may cause clinically significant targeting errors and greatly degrade the effectiveness of conformal radiotherapy. It is therefore crucial to be able to model respiratory motion accurately. A recently proposed lung motion model based on principal component analysis (PCA) has been shown to be promising on a few patients. However, there is still a need to understand the underlying reason why it works. In this paper, we present a much deeper and detailed analysis of the PCA-based lung motion model. We provide the theoretical justification of the effectiveness of PCA in modeling lung motion. We also prove that under certain conditions, the PCA motion model is equivalent to 5D motion model, which is based on physiology and anatomy of the lung. The modeling power of PCA model was tested on clinical data and the average 3D error was found to be below 1 mm.Comment: 4 pages, 1 figure. submitted to International Conference on the use of Computers in Radiation Therapy 201

    Semiautomated Device for Batch Extraction of Metabolites from Tissue Samples

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    ABSTRACT: Metabolomics has become a mainstream analytical strategy for investigating metabolism. The quality of data derived from these studies is proportional to the consistency of the sample preparation. Although considerable research has been devoted to finding optimal extraction protocols, most of the established methods require extensive sample handling. Manual sample preparation can be highly effective in the hands of skilled technicians, but an automated tool for purifying metabolites from complex biological tissues would be of obvious utility to the field. Here, we introduce the semiautomated metabolite batch extraction device (SAMBED), a new tool designed to simplify metabolomics sample preparation. We discuss SAMBED’s design and show that SAMBED-based extractions are of comparable quality to extracts produced through traditional methods (13 % mean coefficient of variation from SAMBED versus 16 % from manual extractions). Moreover, we show that aqueous SAMBED-based methods ca

    Circulating microRNAs Reveal Time Course of Organ Injury in a Porcine Model of Acetaminophen-Induced Acute Liver Failure

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    Acute liver failure is a rare but catastrophic condition which can progress rapidly to multi-organ failure. Studies investigating the onset of individual organ injury such as the liver, kidneys and brain during the evolution of acute liver failure, are lacking. MicroRNAs are short, non-coding strands of RNA that are released into the circulation following tissue injury. In this study, we have characterised the release of both global microRNA and specific microRNA species into the plasma using a porcine model of acetaminophen-induced acute liver failure. Pigs were induced to acute liver failure with oral acetaminophen over 19h±2h and death occurred 13h±3h thereafter. Global microRNA concentrations increased 4h prior to acute liver failure in plasma (P<0.0001) but not in isolated exosomes, and were associated with increasing plasma levels of the damage-associated molecular pattern molecule, genomic DNA (P<0.0001). MiR122 increased around the time of onset of acute liver failure (P<0.0001) and was associated with increasing international normalised ratio (P<0.0001). MiR192 increased 8h after acute liver failure (P<0.0001) and was associated with increasing creatinine (P<0.0001). The increase in miR124-1 occurred concurrent with the pre-terminal increase in intracranial pressure (P<0.0001) and was associated with decreasing cerebral perfusion pressure (P<0.002)

    Allele-specific miRNA-binding analysis identifies candidate target genes for breast cancer risk

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    Most breast cancer (BC) risk-associated single-nucleotide polymorphisms (raSNPs) identified in genome-wide association studies (GWAS) are believed to cis-regulate the expression of genes. We hypothesise that cis-regulatory variants contributing to disease risk may be affecting microRNA (miRNA) genes and/or miRNA binding. To test this, we adapted two miRNA-binding prediction algorithms-TargetScan and miRanda-to perform allele-specific queries, and integrated differential allelic expression (DAE) and expression quantitative trait loci (eQTL) data, to query 150 genome-wide significant ( P≀5×10-8 ) raSNPs, plus proxies. We found that no raSNP mapped to a miRNA gene, suggesting that altered miRNA targeting is an unlikely mechanism involved in BC risk. Also, 11.5% (6 out of 52) raSNPs located in 3'-untranslated regions of putative miRNA target genes were predicted to alter miRNA::mRNA (messenger RNA) pair binding stability in five candidate target genes. Of these, we propose RNF115, at locus 1q21.1, as a strong novel target gene associated with BC risk, and reinforce the role of miRNA-mediated cis-regulation at locus 19p13.11. We believe that integrating allele-specific querying in miRNA-binding prediction, and data supporting cis-regulation of expression, improves the identification of candidate target genes in BC risk, as well as in other common cancers and complex diseases.Funding Agency Portuguese Foundation for Science and Technology CRESC ALGARVE 2020 European Union (EU) 303745 Maratona da Saude Award DL 57/2016/CP1361/CT0042 SFRH/BPD/99502/2014 CBMR-UID/BIM/04773/2013 POCI-01-0145-FEDER-022184info:eu-repo/semantics/publishedVersio

    Predicting functional responses in agro-ecosystems from animal movement data to improve management of invasive pests

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    Functional responses describe how changing resource availability affects con- sumer resource use, thus providing a mechanistic approach to prediction of the invasibility and potential damage of invasive alien species (IAS). However, functional responses can be context dependent, varying with resource characteristics and availability, consumer attributes, and environmental variables. Identifying context dependencies can allow invasion and damage risk to be predicted across different ecoregions. Understanding how ecological factors shape the functional response in agro-ecosystems can improve predictions of hotspots of highest impact and inform strategies to mitigate damage across locations with varying crop types and avail- ability. We linked heterogeneous movement data across different agro-ecosystems to predict ecologically driven variability in the functional responses. We applied our approach to wild pigs (Sus scrofa), one of the most successful and detrimental IAS worldwide where agricultural resource depredation is an important driver of spread and establishment. We used continental- scale movement data within agro-ecosystems to quantify the functional response of agricul- tural resources relative to availability of crops and natural forage. We hypothesized that wild pigs would selectively use crops more often when natural forage resources were low. We also examined how individual attributes such as sex, crop type, and resource stimulus such as dis- tance to crops altered the magnitude of the functional response. There was a strong agricul- tural functional response where crop use was an accelerating function of crop availability at low density (Type III) and was highly context dependent. As hypothesized, there was a reduced response of crop use with increasing crop availability when non-agricultural resources were more available, emphasizing that crop damage levels are likely to be highly heterogeneous depending on surrounding natural resources and temporal availability of crops. We found sig- nificant effects of crop type and sex, with males spending 20% more time and visiting crops 58% more often than females, and both sexes showing different functional responses depend- ing on crop type. Our application demonstrates how commonly collected animal movement data can be used to understand context dependencies in resource use to improve our under- standing of pest foraging behavior, with implications for prioritizing spatiotemporal hotspots of potential economic loss in agro-ecosystems
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