Factors influencing early electric vehicle adoption in Ireland

The 5th French Association of Environmental and Resource Economists Annual Conference (FAERE 2018), Aix-en-Provence, France, 30-31 August 2018The objective of this work is to analyse the key determinants of electric vehicle uptake amongst early adopters. Transport accounts for about a quarter of Europe’s total greenhouse gas emissions and has not achieved similar reductions in emissions as other sectors. However, there is an opportunity to achieve lower emissions through the widespread use of electric vehicles. Due to the rising awareness of the link between emissions and global warming, the European Union has set serious targets for renewable energy and greenhouse gas emissions that member states must achieve by 2020 and 2030. Although considerable progress has been made in reaching targets, efforts in the transport sector have been lagging in many countries, with a significant boost required in electric vehicle roll-out if transport-specific targets are to be met. One reason for this lack of progress is possibly an incomplete understanding of the motivations behind consumer uptake, which in turn, hampers policy design to encourage adoption. Here, for the first time, the case study of Ireland is used to analyse socio-demographic and neighbourhood characteristics such as charging infrastructure, dealers and other EV adopters, to identify the key determinants of electric vehicle adoption in the early phase of technology diffusion. From our exploratory data analysis, social class which represents whether the population consists of skilled, semi-skilled or unskilled workers, appears to be the principal factor affecting EV uptake in Ireland. This variable may proxy for income effects, implying that the average wealth of a neighbourhood matters for EV ownership. There also appears to be clustering in EV adopters, possibly due to unobserved peer effects. The OLS model performs poorly for our dataset. Our future work will help determine the significant predictors of adoption based on a spatial econometric approach that explicitly models relationships between agents in the model such that the restrictive assumptions of OLS models can be relaxed to allow for interdependence between individual actors

Mechanism of enhancement of ferroelectricity of croconic acid with temperature

A microscopic study of the thermal behaviour of atomic motions in the organic ferroelectric croconic acid is presented in the temperature range 5–300 K.</p

A Novel Biclustering Approach to Association Rule Mining for Predicting HIV-1–Human Protein Interactions

Identification of potential viral-host protein interactions is a vital and useful approach towards development of new drugs targeting those interactions. In recent days, computational tools are being utilized for predicting viral-host interactions. Recently a database containing records of experimentally validated interactions between a set of HIV-1 proteins and a set of human proteins has been published. The problem of predicting new interactions based on this database is usually posed as a classification problem. However, posing the problem as a classification one suffers from the lack of biologically validated negative interactions. Therefore it will be beneficial to use the existing database for predicting new viral-host interactions without the need of negative samples. Motivated by this, in this article, the HIV-1–human protein interaction database has been analyzed using association rule mining. The main objective is to identify a set of association rules both among the HIV-1 proteins and among the human proteins, and use these rules for predicting new interactions. In this regard, a novel association rule mining technique based on biclustering has been proposed for discovering frequent closed itemsets followed by the association rules from the adjacency matrix of the HIV-1–human interaction network. Novel HIV-1–human interactions have been predicted based on the discovered association rules and tested for biological significance. For validation of the predicted new interactions, gene ontology-based and pathway-based studies have been performed. These studies show that the human proteins which are predicted to interact with a particular viral protein share many common biological activities. Moreover, literature survey has been used for validation purpose to identify some predicted interactions that are already validated experimentally but not present in the database. Comparison with other prediction methods is also discussed

Genome-wide association study reveals novel genetic loci:a new polygenic risk score for mitral valve prolapse

AIMS: Mitral valve prolapse (MVP) is a common valvular heart disease with a prevalence of >2% in the general adult population. Despite this high incidence, there is a limited understanding of the molecular mechanism of this disease, and no medical therapy is available for this disease. We aimed to elucidate the genetic basis of MVP in order to better understand this complex disorder. METHODS AND RESULTS: We performed a meta-analysis of six genome-wide association studies that included 4884 cases and 434 649 controls. We identified 14 loci associated with MVP in our primary analysis and 2 additional loci associated with a subset of the samples that additionally underwent mitral valve surgery. Integration of epigenetic, transcriptional, and proteomic data identified candidate MVP genes including LMCD1, SPTBN1, LTBP2, TGFB2, NMB, and ALPK3. We created a polygenic risk score (PRS) for MVP and showed an improved MVP risk prediction beyond age, sex, and clinical risk factors. CONCLUSION: We identified 14 genetic loci that are associated with MVP. Multiple analyses identified candidate genes including two transforming growth factor-beta signalling molecules and spectrin beta. We present the first PRS for MVP that could eventually aid risk stratification of patients for MVP screening in a clinical setting. These findings advance our understanding of this common valvular heart disease and may reveal novel therapeutic targets for intervention. KEY QUESTION: Expand our understanding of the genetic basis for mitral valve prolapse (MVP). Uncover relevant pathways and target genes for MVP pathophysiology. Leverage genetic data for MVP risk prediction. KEY FINDING: Sixteen genetic loci were significantly associated with MVP, including 13 novel loci. Interesting target genes at these loci included LTBP2, TGFB2, ALKP3, BAG3, RBM20, and SPTBN1. A risk score including clinical factors and a polygenic risk score, performed best at predicting MVP, with an area under the receiver operating characteristics curve of 0.677. TAKE-HOME MESSAGE: Mitral valve prolapse has a polygenic basis: many genetic variants cumulatively influence pre-disposition for disease. Disease risk may be modulated via changes to transforming growth factor-beta signalling, the cytoskeleton, as well as cardiomyopathy pathways. Polygenic risk scores could enhance the MVP risk prediction

Morbidity and mortality from road injuries: results from the Global Burden of Disease Study 2017

BackgroundThe global burden of road injuries is known to follow complex geographical, temporal and demographic patterns. While health loss from road injuries is a major topic of global importance, there has been no recent comprehensive assessment that includes estimates for every age group, sex and country over recent years.MethodsWe used results from the Global Burden of Disease (GBD) 2017 study to report incidence, prevalence, years lived with disability, deaths, years of life lost and disability-adjusted life years for all locations in the GBD 2017 hierarchy from 1990 to 2017 for road injuries. Second, we measured mortality-to-incidence ratios by location. Third, we assessed the distribution of the natures of injury (eg, traumatic brain injury) that result from each road injury.ResultsGlobally, 1 243 068 (95% uncertainty interval 1 191 889 to 1 276 940) people died from road injuries in 2017 out of 54 192 330 (47 381 583 to 61 645 891) new cases of road injuries. Age-standardised incidence rates of road injuries increased between 1990 and 2017, while mortality rates decreased. Regionally, age-standardised mortality rates decreased in all but two regions, South Asia and Southern Latin America, where rates did not change significantly. Nine of 21 GBD regions experienced significant increases in age-standardised incidence rates, while 10 experienced significant decreases and two experienced no significant change.ConclusionsWhile road injury mortality has improved in recent decades, there are worsening rates of incidence and significant geographical heterogeneity. These findings indicate that more research is needed to better understand how road injuries can be prevented

Multi-ethnic genome-wide association study for atrial fibrillation

Atrial fibrillation (AF) affects more than 33 million individuals worldwide and has a complex heritability. We conducted the largest meta-analysis of genome-wide association studies (GWAS) for AF to date, consisting of more than half a million individuals, including 65,446 with AF. In total, we identified 97 loci significantly associated with AF, including 67 that were novel in a combined-ancestry analysis, and 3 that were novel in a European-specific analysis. We sought to identify AF-associated genes at the GWAS loci by performing RNA-sequencing and expression quantitative trait locus analyses in 101 left atrial samples, the most relevant tissue for AF. We also performed transcriptome-wide analyses that identified 57 AF-associated genes, 42 of which overlap with GWAS loci. The identified loci implicate genes enriched within cardiac developmental, electrophysiological, contractile and structural pathways. These results extend our understanding of the biological pathways underlying AF and may facilitate the development of therapeutics for AF