Is local office a springboard for women to Dáil Éireann?

Previous research has found the single transferable vote electoral system is relatively friendly to women candidates. Despite this, female representation in the Irish Parliament remains substantially lower than in most other democracies. Drawing on pipeline theory and localism, we assess the impact of local office-holding on the success of male and female major party candidates in the 2007 and 2011 Irish general elections. We find previous experience in local office is a key springboard to higher office for men and women, and when women serve in local government the likelihood of election increases significantly

Dirac plasmons in bipartite lattices of metallic nanoparticles

We study theoretically "graphene-like" plasmonic metamaterials constituted by two-dimensional arrays of metallic nanoparticles, including perfect honeycomb structures with and without inversion symmetry, as well as generic bipartite lattices. The dipolar interactions between localised surface plasmons in different nanoparticles gives rise to collective plasmons that extend over the whole lattice. We study the band structure of collective plasmons and unveil its tunability with the orientation of the dipole moments associated with the localised surface plasmons. Depending on the dipole orientation, we identify a phase diagram of gapless or gapped phases in the collective plasmon dispersion. We show that the gapless phases in the phase diagram are characterised by collective plasmons behaving as massless chiral Dirac particles, in analogy with electrons in graphene. When the inversion symmetry of the honeycomb structure is broken, collective plasmons are described as gapped chiral Dirac modes with an energy-dependent Berry phase. We further relax the geometric symmetry of the honeycomb structure by analysing generic bipartite hexagonal lattices. In this case we study the evolution of the phase diagram and unveil the emergence of a sequence of topological phase transitions when one hexagonal sublattice is progressively shifted with respect to the other.Comment: 20 pages, 10 figures, 3 videos; published version (2D Materials, focus on artificial graphene

An observational cohort feasibility study to identify microvesicle and miRNA biomarkers of acute kidney injury following paediatric cardiac surgery

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Objectives: Micro-RNA, small noncoding RNA fragments involved in gene regulation, and microvesicles, membrane-bound particles less than 1 μm known to regulate cellular processes including responses to injury, may serve as disease-specific biomarkers of acute kidney injury. We evaluated the feasibility of measuring these signals as well as other known acute kidney injury biomarkers in a mixed pediatric cardiac surgery population. Design: Single center prospective cohort feasibility study. Setting: PICU. Patients: Twenty-four children (≤ 17 yr) undergoing cardiac surgery with cardiopulmonary bypass without preexisting inflammatory state, acute kidney injury, or extracorporeal life support. Interventions: None. Measurements and Main Results: Acute kidney injury was defined according to modified Kidney Diseases Improving Global Outcomes criteria. Blood and urine samples were collected preoperatively and at 6–12 and 24 hours. Microvesicles derivation was assessed using flow cytometry and NanoSight analysis. Micro-RNAs were isolated from plasma and analyzed by microarray and quantitative real-time polymerase chain reaction. Data completeness for the primary outcomes was 100%. Patients with acute kidney injury (n = 14/24) were younger, underwent longer cardiopulmonary bypass, and required greater inotrope support. Acute kidney injury subjects had different fractional content of platelets and endothelial-derived microvesicles before surgery. Platelets and endothelial microvesicles levels were higher in acute kidney injury patients. A number of micro-RNA species were differentially expressed in acute kidney injury patients. Pathway analysis of candidate target genes in the kidney suggested that the most often affected pathways were phosphatase and tensin homolog and signal transducer and activator of transcription 3 signaling. Conclusions: Microvesicles and micro-RNAs expression patterns in pediatric cardiac surgery patients can be measured in children and potentially serve as tools for stratification of patients at risk of acute kidney injury

Dirac-like Plasmons in Honeycomb Lattices of Metallic Nanoparticles

Copyright © 2013 American Physical SocietyWe consider a two-dimensional honeycomb lattice of metallic nanoparticles, each supporting a localized surface plasmon, and study the quantum properties of the collective plasmons resulting from the near-field dipolar interaction between the nanoparticles. We analytically investigate the dispersion, the effective Hamiltonian, and the eigenstates of the collective plasmons for an arbitrary orientation of the individual dipole moments. When the polarization points close to the normal to the plane, the spectrum presents Dirac cones, similar to those present in the electronic band structure of graphene. We derive the effective Dirac Hamiltonian for the collective plasmons and show that the corresponding spinor eigenstates represent Dirac-like massless bosonic excitations that present similar effects to electrons in graphene, such as a nontrivial Berry phase and the absence of backscattering off smooth inhomogeneities. We further discuss how one can manipulate the Dirac points in the Brillouin zone and open a gap in the collective plasmon dispersion by modifying the polarization of the localized surface plasmons, paving the way for a fully tunable plasmonic analogue of graphene

Birth weight as a risk factor for neonatal mortality: Breed-specific approach to identify at-risk puppies

Abstract: In numerous species, low birth weight is a risk factor for neonatal mortality. In the canine species, definition of a low birth weight is complex due to the huge interbreed variability in size. To identify puppies at higher risk of neonatal death, data from 6,694 puppies were analysed. The data were collected from 75 French breeding kennels, examining 27 breeds and totaling 1,202 litters of puppies. Generalised linear mixed models allowed to identify birth weight, birth weight heterogeneity within the litter, and size of the breeding kennel as significant risk factors for neonatal mortality. Receiver Operating Characteristics (ROC) and classification and regression tree (CART) analyses were combined to define breed specific thresholds for birth weight allowing the identification of puppies at higher risk of neonatal mortality. Due to differences in birth weights between breeds, including when belonging to the same breed size, analyses were conducted at the breed level. First, ROC analysis thresholds were successfully established for 12 breeds (area under the ROC ≥ 0.70; sensitivity ≥ 75%; specificity: 45–68%) and they ranged from 162 g in the Maltese to 480 g in the Bernese Mountain dog. Secondly, CART analysis thresholds from 22 breeds ranged from 105 g in the Maltese and 436 g in the Boxer. Puppies were grouped into three categories according to birth weight: low, moderate and high risk of neonatal mortality (higher than the ROC threshold, between ROC and CART thresholds, and lower than the CART threshold respectively). In the current study, 44% of the puppies were classified as at moderate risk and 5.3% for a high risk of neonatal mortality. Thresholds defined by CART analysis (and not ROC analysis) were used to define low birth weight puppies and were sometimes quite different between breeds with similar birth weight distributions suggesting a variable relationship between birth weight reduction and neonatal death. These results allow the identification of puppies at an increased risk of neonatal death, thus requiring specific nursing to improve their chances of survival. With these high risk puppies identified, both animal welfare and kennel productivity is predicted to improve

MGMT methylation analysis of glioblastoma on the Infinium methylation BeadChip identifies two distinct CpG regions associated with gene silencing and outcome, yielding a prediction model for comparisons across datasets, tumor grades, and CIMP-status

The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) gene is an important predictive biomarker for benefit from alkylating agent therapy in glioblastoma. Recent studies in anaplastic glioma suggest a prognostic value for MGMT methylation. Investigation of pathogenetic and epigenetic features of this intriguingly distinct behavior requires accurate MGMT classification to assess high throughput molecular databases. Promoter methylation-mediated gene silencing is strongly dependent on the location of the methylated CpGs, complicating classification. Using the HumanMethylation450 (HM-450K) BeadChip interrogating 176 CpGs annotated for the MGMT gene, with 14 located in the promoter, two distinct regions in the CpG island of the promoter were identified with high importance for gene silencing and outcome prediction. A logistic regression model (MGMT-STP27) comprising probes cg1243587 and cg12981137 provided good classification properties and prognostic value (kappa=0.85; log-rank p<0.001) using a training-set of 63 glioblastomas from homogenously treated patients, for whom MGMT methylation was previously shown to be predictive for outcome based on classification by methylation-specific PCR. MGMT-STP27 was successfully validated in an independent cohort of chemo-radiotherapy-treated glioblastoma patients (n=50; kappa=0.88; outcome, log-rank p<0.001). Lower prevalence of MGMT methylation among CpG island methylator phenotype (CIMP) positive tumors was found in glioblastomas from The Cancer Genome Atlas than in low grade and anaplastic glioma cohorts, while in CIMP-negative gliomas MGMT was classified as methylated in approximately 50% regardless of tumor grade. The proposed MGMT-STP27 prediction model allows mining of datasets derived on the HM-450K or HM-27K BeadChip to explore effects of distinct epigenetic context of MGMT methylation suspected to modulate treatment resistance in different tumor type

BioRock:new experiments and hardware to investigate microbe–mineral interactions in space

In this paper, we describe the development of an International Space Station experiment, BioRock. The purpose of this experiment is to investigate biofilm formation and microbe–mineral interactions in space. The latter research has application in areas as diverse as regolith amelioration and extraterrestrial mining. We describe the design of a prototype biomining reactor for use in space experimentation and investigations on in situ Resource Use and we describe the results of pre-flight tests

Human intestinal tissue tropism of intimin epsilon O103 Escherichia coli

Human intestinal in vitro organ culture was used to assess the tissue tropism of human isolates of Escherichia coli O103:H2 and O103:H- that express intimin F. Both strains showed tropism for follicle associated epithelium and limited adhesion to other regions of the small and large intestine. This is similar to the tissue tropism shown by intimin gamma enterohaemorrhagic (EHEC) O157:H7, but distinct from that of intimin a enteropathogenic (EPEC) O127:H6. (C) 2002 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserve

Machine learning-based clinical decision support for infection risk prediction

BackgroundHealthcare-associated infection (HAI) remains a significant risk for hospitalized patients and a challenging burden for the healthcare system. This study presents a clinical decision support tool that can be used in clinical workflows to proactively engage secondary assessments of pre-symptomatic and at-risk infection patients, thereby enabling earlier diagnosis and treatment.MethodsThis study applies machine learning, specifically ensemble-based boosted decision trees, on large retrospective hospital datasets to develop an infection risk score that predicts infection before obvious symptoms present. We extracted a stratified machine learning dataset of 36,782 healthcare-associated infection patients. The model leveraged vital signs, laboratory measurements and demographics to predict HAI before clinical suspicion, defined as the order of a microbiology test or administration of antibiotics.ResultsOur best performing infection risk model achieves a cross-validated AUC of 0.88 at 1 h before clinical suspicion and maintains an AUC &gt;0.85 for 48 h before suspicion by aggregating information across demographics and a set of 163 vital signs and laboratory measurements. A second model trained on a reduced feature space comprising demographics and the 36 most frequently measured vital signs and laboratory measurements can still achieve an AUC of 0.86 at 1 h before clinical suspicion. These results compare favorably against using temperature alone and clinical rules such as the quick sequential organ failure assessment (qSOFA) score. Along with the performance results, we also provide an analysis of model interpretability via feature importance rankings.ConclusionThe predictive model aggregates information from multiple physiological parameters such as vital signs and laboratory measurements to provide a continuous risk score of infection that can be deployed in hospitals to provide advance warning of patient deterioration