A Geographically Weighted Regression Analysis of General Election Turnout in the Republic of Ireland

Turnout in the 2002 general election confirm e d a downwar d trend in Irish voter pa rticipation levels, that first becam e evident in the early 1980s and was to culm inate in th e lowest turnout fo r an Irish general election since the foundation of the st ate (Figure 1). Given this context, voter turnout is a topic that requires further analysis within the Irish con t ex t, and two ap proaches to address i ng this top i c ex ists, with each approach having its own distin ctive adv a ntages and dis a dvantages. The surveying approach draws on individual-level data, and has the ad vantage of being able to draw on a wide range of variables, including social-psychological variables, an d m a ke direct inferences about the behaviour, and sources of the behaviour, of individuals. However, questionnaire surveys generally te nd to overestim ate turnout levels, with poten tial bias in su rvey re spon se rates resu lting in the under-rep res e ntation of non-voters in survey sam p les. The second type of data – aggregate data – is subject to problem s arising from the lim ited range of variables that can be accessed in seeking explanations of low turnout (u sually lim ited to data that can be drawn from census analyses) and the cross-lev e l infere nce problem that arises in any attem p t to m a ke inferences about individual-leve l behaviour from aggregate-leve l data (Achen and Shively, 1995). However, the big advantage of aggregate data lies in th e accuracy of the estim a tion of the dependent variable (turnout) and in the potential it offers for spatial and censu s-based analysis of variations in the turnout variable. Lower levels of aggregation allow fo r a larger num ber of cases to be analysed and for more detailed pictures of spatial varia tions in turnout leve ls to be gleaned

JUMP-LANDING MECHANICS IN PATELLAR TENDINOPATHY IN ELITE JUNIOR BASKETBALL ATHLETES

The purpose of this study was to identify key modifiable jump-landing variables associated with patellar tendinopathy (PT). Thirty-six junior elite basketball players (18 men, 18 women) were recruited (8 PT, 11 controls, 17 excluded from statistical analysis). Three-dimensional (3D) landing technique during a stop-jump task and patellar tendon ultrasounds were recorded. A series of mixed-design factorial analyses of variance were used to determine any significant between-group differences. Athletes with PT utilised a lower ground reaction force (GRF) loading rate (LR) via increasing their time duration from initial foot-ground contact (IC) to peak vertical GRF (Fv). This strategy of a lower LR did not lead to those athletes with PT decreasing their peak GRF nor patellar tendon forces (FPT) in comparison to the controls

MetaboClust : Using interactive time-series cluster analysis to relate metabolomic data with perturbed pathways

Motivation Modern analytical techniques such as LC-MS, GC-MS and NMR are increasingly being used to study the underlying dynamics of biological systems by tracking changes in metabolite levels over time. Such techniques are capable of providing information on large numbers of metabolites simultaneously, a feature that is exploited in non-targeted studies. However, since the dynamics of specific metabolites are unlikely to be known a priori this presents an initial subjective challenge as to where the focus of the investigation should be. Whilst a number of feed-forward software tools are available for manipulation of metabolomic data, no tool centralizes on clustering and focus is typically directed by a workflow that is chosen in advance. Results We present an interactive approach to time-course analyses and a complementary implementation in a software package, MetaboClust. This is presented through the analysis of two LC-MS time-course case studies on plants (Medicago truncatula and Alopecurus myosuroides). We demonstrate a dynamic, user-centric workflow to clustering with intrinsic visual feedback at all stages of analysis. The software is used to apply data correction, generate the time-profiles, perform exploratory statistical analysis and assign tentative metabolite identifications. Clustering is used to group metabolites in an unbiased manner, allowing pathway analysis to score metabolic pathways, based on their overlap with clusters showing interesting trends

Determining the proximity effect-induced magnetic moment in graphene by polarized neutron reflectivity and x-ray magnetic circular dichroism

We report the magnitude of the induced magnetic moment in CVD-grown epitaxial and rotated-domain graphene in proximity with a ferromagnetic Ni film, using polarized neutron reflectivity (PNR) and X-ray magnetic circular dichroism (XMCD). The XMCD spectra at the C K-edge confirm the presence of a magnetic signal in the graphene layer, and the sum rules give a magnetic moment of up to ∼0.47 μB/C atom induced in the graphene layer. For a more precise estimation, we conducted PNR measurements. The PNR results indicate an induced magnetic moment of ∼0.41 μB/C atom at 10 K for epitaxial and rotated-domain graphene. Additional PNR measurements on graphene grown on a nonmagnetic Ni9Mo1 substrate, where no magnetic moment in graphene is measured, suggest that the origin of the induced magnetic moment is due to the opening of the graphene’s Dirac cone as a result of the strong C pz-Ni 3d hybridization

NMR Metabolomics Defining Genetic Variation in Pea Seed Metabolites

Nuclear magnetic resonance (NMR) spectroscopy profiling was used to provide an unbiased assessment of changes to the metabolite composition of seeds and to define genetic variation for a range of pea seed metabolites. Mature seeds from recombinant inbred lines, derived from three mapping populations for which there is substantial genetic marker linkage information, were grown in two environments/years and analyzed by non-targeted NMR. Adaptive binning of the NMR metabolite data, followed by analysis of quantitative variation among lines for individual bins, identified the main genomic regions determining this metabolic variability and the variability for selected compounds was investigated. Analysis by t-tests identified a set of bins with highly significant associations to genetic map regions, based on probability (p) values that were appreciably lower than those determined for randomized data. The correlation between bins showing high mean absolute deviation and those showing low p-values for marker association provided an indication of the extent to which the genetics of bin variation might be explained by one or a few loci. Variation in compounds related to aromatic amino acids, branched-chain amino acids, sucrose-derived metabolites, secondary metabolites and some unidentified compounds was associated with one or more genetic loci. The combined analysis shows that there are multiple loci throughout the genome that together impact on the abundance of many compounds through a network of interactions, where individual loci may affect more than one compound and vice versa. This work therefore provides a framework for the genetic analysis of the seed metabolome, and the use of genetic marker data in the breeding and selection of seeds for specific seed quality traits and compounds that have high commercial value

Síntese e avaliação catalítica de catalisadores microporoso, mesoporosos e micro-mesoporosos

As propriedades e potencialidades dos materiais porosos estão em constantes estudos e usos nas mais variadas áreas da ciência. Esses materiais são atribuídos em classes de acordo com o ordenamento dos seus blocos estruturantes. Suas propriedades estão intrinsecamente relacionadas pela sua capacidade de catalisar as reações químicas. Neste trabalho, catalisadores do tipo HAlZSM-12, HAlMCM-41, HAlMCM-48, AlSBA-15 (Si/Al= 25, 50, 75) e HAlZSM-12/HAlMCM-41, HAlZSM-12/HAlMCM-48, HAlZSM-12/AlSBA-15 foram sintetizados pelo método hidrotérmico, submetidas a processos de calcinação e troca iônica e caracterizados por difratometria de raios-X. No presente trabalho também avaliou-se o potencial catalítico dos catalisadores na pirólise catalítica do ácido oléico em escala de bancada usando a termogravimetria. _________________________________________________________________________________________ ABSTRACT: The properties and potential of porous materials are in constant studies and uses in various areas of science. These materials are attributed to classes according to their structural ordering of blocks. Their properties are intrinsically related by their ability to catalyze chemical reactions. In this study, catalysts of type HAlZSM-12, HAlMCM-41, HAlMCM-48, AlSBA-15 (Si/Al 25, 50, 75) and composites HAlZSM-12/HAlMCM-41, HAlZSM-12/HAlMCM-48, HAlZSM-12/AlSBA-15 were synthesized by hydrothermal method, subjected to calcination and ion exchange processes and characterized by X-ray diffraction. In this study also were evaluated the catalytic potential of catalysts in the catalytic pyrolysis of oleic acid in micro-scale tests using thermogravimetric (TG)

pygwb: Python-based library for gravitational-wave background searches

The collection of gravitational waves (GWs) that are either too weak or too numerous to be individually resolved is commonly referred to as the gravitational-wave background (GWB). A confident detection and model-driven characterization of such a signal will provide invaluable information about the evolution of the Universe and the population of GW sources within it. We present a new, user-friendly Python--based package for gravitational-wave data analysis to search for an isotropic GWB in ground--based interferometer data. We employ cross-correlation spectra of GW detector pairs to construct an optimal estimator of the Gaussian and isotropic GWB, and Bayesian parameter estimation to constrain GWB models. The modularity and clarity of the code allow for both a shallow learning curve and flexibility in adjusting the analysis to one's own needs. We describe the individual modules which make up {\tt pygwb}, following the traditional steps of stochastic analyses carried out within the LIGO, Virgo, and KAGRA Collaboration. We then describe the built-in pipeline which combines the different modules and validate it with both mock data and real GW data from the O3 Advanced LIGO and Virgo observing run. We successfully recover all mock data injections and reproduce published results.Comment: 32 pages, 14 figure