An investigation into the reasons for the rejection of congestion charging by the citizens of Edinburgh

In February 2005, residents of Edinburgh, a medium-sized city in the United Kingdom, were given the opportunity to vote in a referendum on the introduction of a road user charging scheme, which had been in development for almost a decade. The public voted against the scheme by a ratio of 3:1 and it was consequently abandoned. This paper describes the evolution of the scheme, and presents results of research to determine the principle factors responsible for the public's overwhelming opposition to the scheme. The research used a postal, self-completion questionnaire that was distributed to 1300 randomlyselected households in central and southern Edinburgh three months after the referendum. The questionnaire responses were analysed to assess the influence of several factors on the way respondents voted in the referendum. Car use was shown to be the principle determinant of voting behaviour, with car owners strongly opposing the scheme while non-car owners only weakly supported it. The public’s limited understanding of the scheme increased the strength of the opposing vote. Further, the public were largely unconvinced that the scheme would have achieved its dual objectives of reduced congestion and improved public transport. The findings suggest that more attention should have been paid to designing a simpler, more easily communicated, scheme and convincing residents, particularly public transport users, of its benefits. Some other aspects of the scheme that militated against its successful introduction are also briefly identified

Terminology and Provision for Students with Learning Difficulties: An Examination of Australian State Government Education Department Websites

Students with learning difficulties form the largest group of students with additional needs in Australian mainstream classrooms. However the terminology surrounding these students differs broadly across the country. A consistent and shared understanding of the term learning difficulties is vital, as this impacts the identification and equitable provision of support for students experiencing difficulties with learning. The website of each Australian state/territory government education department was examined to determine to how students with learning difficulties are formally identified and supported. It was found that considerable differences, and even conflicting information, exist both within and across education systems. Implications and the significance of this situation are discussed

Colour unwound - disentangling colours for azimuthal asymmetries in Drell-Yan scattering

It has been suggested that a colour-entanglement effect exists in the Drell-Yan cross section for the 'double T-odd' contributions at low transverse momentum $Q_T$, rendering the colour structure different from that predicted by the usual factorisation formula [1]. These T-odd contributions can come from the Boer-Mulders or Sivers transverse momentum dependent distribution functions. The different colour structure should be visible already at the lowest possible order that gives a contribution to the double Boer-Mulders (dBM) or double Sivers (dS) effect, that is at the level of two gluon exchanges. To discriminate between the different predictions, we compute the leading-power contribution to the low-$Q_T$ dBM cross section at the two-gluon exchange order in the context of a spectator model. The computation is performed using a method of regions analysis with Collins subtraction terms implemented. The results conform with the predictions of the factorisation formula. In the cancellation of the colour entanglement, diagrams containing the three-gluon vertex are essential. Furthermore, the Glauber region turns out to play an important role - in fact, it is possible to assign the full contribution to the dBM cross section at the given order to the region in which the two gluons have Glauber scaling. A similar disentanglement of colour is found for the dS effect.Comment: 36 pages, 11 figures; v2: typos corrected/ reference added, v3: minor corrections/ small explanations added/ references added, v4: very minor correction/ small explanations added/ references added (this version has been accepted for publication in SciPost

Reply to Janssen et al. Comment on “Sobczyk, M.K.; Gaunt, T.R. The Effect of Circulating Zinc, Selenium, Copper and Vitamin K1 on COVID-19 Outcomes: A Mendelian Randomization Study. Nutrients 2022, 14, 233

In their correspondence arising from our recent manuscript [...

The Effect of Circulating Zinc, Selenium, Copper and Vitamin K1 on COVID-19 Outcomes:A Mendelian Randomization Study

Background & Aims: Previous results from observational, interventional studies and in vitro experiments suggest that certain micronutrients possess anti-viral and immunomodulatory activities. In particular, it has been hypothesized that zinc, selenium, copper and vitamin K(1) have strong potential for prophylaxis and treatment of COVID-19. We aimed to test whether genetically predicted Zn, Se, Cu or vitamin K(1) levels have a causal effect on COVID-19 related outcomes, including risk of infection, hospitalization and critical illness. Methods: We employed a two-sample Mendelian Randomization (MR) analysis. Our genetic variants derived from European-ancestry GWAS reflected circulating levels of Zn, Cu, Se in red blood cells as well as Se and vitamin K(1) in serum/plasma. For the COVID-19 outcome GWAS, we used infection, hospitalization or critical illness. Our inverse-variance weighted (IVW) MR analysis was complemented by sensitivity analyses including a more liberal selection of variants at a genome-wide sub-significant threshold, MR-Egger and weighted median/mode tests. Results: Circulating micronutrient levels show limited evidence of association with COVID-19 infection, with the odds ratio [OR] ranging from 0.97 (95% CI: 0.87–1.08, p-value = 0.55) for zinc to 1.07 (95% CI: 1.00–1.14, p-value = 0.06)—i.e., no beneficial effect for copper was observed per 1 SD increase in exposure. Similarly minimal evidence was obtained for the hospitalization and critical illness outcomes with OR from 0.98 (95% CI: 0.87–1.09, p-value = 0.66) for vitamin K(1) to 1.07 (95% CI: 0.88–1.29, p-value = 0.49) for copper, and from 0.93 (95% CI: 0.72–1.19, p-value = 0.55) for vitamin K(1) to 1.21 (95% CI: 0.79–1.86, p-value = 0.39) for zinc, respectively. Conclusions: This study does not provide evidence that supplementation with zinc, selenium, copper or vitamin K(1) can prevent SARS-CoV-2 infection, critical illness or hospitalization for COVID-19

HIPred:an integrative approach to predicting haploinsufficient genes

Abstract Motivation A major cause of autosomal dominant disease is haploinsufficiency, whereby a single copy of a gene is not sufficient to maintain the normal function of the gene. A large proportion of existing methods for predicting haploinsufficiency incorporate biological networks, e.g. protein-protein interaction networks that have recently been shown to introduce study bias. As a result, these methods tend to perform best on well-studied genes, but underperform on less studied genes. The advent of large genome sequencing consortia, such as the 1000 genomes project, NHLBI Exome Sequencing Project and the Exome Aggregation Consortium creates an urgent need for unbiased haploinsufficiency prediction methods. Results Here, we describe a machine learning approach, called HIPred, that integrates genomic and evolutionary information from ENSEMBL, with functional annotations from the Encyclopaedia of DNA Elements consortium and the NIH Roadmap Epigenomics Project to predict haploinsufficiency, without the study bias described earlier. We benchmark HIPred using several datasets and show that our unbiased method performs as well as, and in most cases, outperforms existing biased algorithms. Availability and Implementation HIPred scores for all gene identifiers are available at: https://github.com/HAShihab/HIPred. Supplementary information Supplementary data are available at Bioinformatics online. </jats:sec

A Pathway-centric Approach to Rare Variant Association Analysis

Current endeavours in rare variant analysis are typically underpowered when investigating association signals from individual genes. We undertook an approach to rare variant analysis which utilises biological pathway information to analyse functionally relevant genes together. Conventional filtering approaches for rare variant analysis are based on variant consequence and are therefore confined to coding regions of the genome. Therefore, we undertook a novel approach to this process by obtaining functional annotations from the Combined Annotation Dependent Depletion (CADD) tool, which allowed potentially deleterious variants from intronic regions of genes to be incorporated into analyses. This work was undertaken using whole-genome sequencing data from the UK10K project. Rare variants from the KEGG pathway for arginine and proline metabolism were collectively associated with systolic blood pressure (P=3.32x10(−5)) based on analyses using the optimal sequence kernel association test. Variants along this pathway also showed evidence of replication using imputed data from the Avon Longitudinal Study of Parents and Children cohort (P=0.02). Subsequent analyses found that the strength of evidence diminished when analysing genes in this pathway individually, suggesting that they would have been overlooked in a conventional gene-based analysis. Future studies that adopt similar approaches to investigate polygenic effects should yield value in better understanding the genetic architecture of complex disease

Incorporating non-coding annotations into rare variant analysis

BackgroundThe success of collapsing methods which investigate the combined effect of rare variants on complex traits has so far been limited. The manner in which variants within a gene are selected prior to analysis has a crucial impact on this success, which has resulted in analyses conventionally filtering variants according to their consequence. This study investigates whether an alternative approach to filtering, using annotations from recently developed bioinformatics tools, can aid these types of analyses in comparison to conventional approaches.Methods & resultsWe conducted a candidate gene analysis using the UK10K sequence and lipids data, filtering according to functional annotations using the resource CADD (Combined Annotation-Dependent Depletion) and contrasting results with 'nonsynonymous' and 'loss of function' consequence analyses. Using CADD allowed the inclusion of potentially deleterious intronic variants, which was not possible when filtering by consequence. Overall, different filtering approaches provided similar evidence of association, although filtering according to CADD identified evidence of association between ANGPTL4 and High Density Lipoproteins (P = 0.02, N = 3,210) which was not observed in the other analyses. We also undertook genome-wide analyses to determine how filtering in this manner compared to conventional approaches for gene regions. Results suggested that filtering by annotations according to CADD, as well as other tools known as FATHMM-MKL and DANN, identified association signals not detected when filtering by variant consequence and vice versa.ConclusionIncorporating variant annotations from non-coding bioinformatics tools should prove to be a valuable asset for rare variant analyses in the future. Filtering by variant consequence is only possible in coding regions of the genome, whereas utilising non-coding bioinformatics annotations provides an opportunity to discover unknown causal variants in non-coding regions as well. This should allow studies to uncover a greater number of causal variants for complex traits and help elucidate their functional role in disease