New Institutionalism Through a Gender Lens:Towards a Feminist Institutionalism?

New institutionalism (NI) may no longer qualify as being ‘new’, but since re-emphasizing institutions as a central explanatory variable in political analysis over two decades ago, it continues to provide scholars with a useful perspective through which to analyse political dynamics and outcomes that shape everyday life. The renewed focus on institutions has rebalanced the structure/agency scales back toward the former without losing important insights about the role and impact of political actors. NI has allowed for greater understanding about the co-constitutive nature of politics: the various ways in which actors bring about or resist change in institutions; and the way institutions shape the nature of actors’ behaviour through the construction of rules, norms and policies. <br/

Trace-gas metabolic versatility of the facultative methanotroph Methylocella silvestris

The climate-active gas methane is generated both by biological processes and by thermogenic decomposition of fossil organic material, which forms methane and short-chain alkanes, principally ethane, propane and butane1, 2. In addition to natural sources, environments are exposed to anthropogenic inputs of all these gases from oil and gas extraction and distribution. The gases provide carbon and/or energy for a diverse range of microorganisms that can metabolize them in both anoxic3 and oxic zones. Aerobic methanotrophs, which can assimilate methane, have been considered to be entirely distinct from utilizers of short-chain alkanes, and studies of environments exposed to mixtures of methane and multi-carbon alkanes have assumed that disparate groups of microorganisms are responsible for the metabolism of these gases. Here we describe the mechanism by which a single bacterial strain, Methylocella silvestris, can use methane or propane as a carbon and energy source, documenting a methanotroph that can utilize a short-chain alkane as an alternative to methane. Furthermore, during growth on a mixture of these gases, efficient consumption of both gases occurred at the same time. Two soluble di-iron centre monooxygenase (SDIMO) gene clusters were identified and were found to be differentially expressed during bacterial growth on these gases, although both were required for efficient propane utilization. This report of a methanotroph expressing an additional SDIMO that seems to be uniquely involved in short-chain alkane metabolism suggests that such metabolic flexibility may be important in many environments where methane and short-chain alkanes co-occur

Effects of gestational age at birth on cognitive performance : a function of cognitive workload demands

Objective: Cognitive deficits have been inconsistently described for late or moderately preterm children but are consistently found in very preterm children. This study investigates the association between cognitive workload demands of tasks and cognitive performance in relation to gestational age at birth. Methods: Data were collected as part of a prospective geographically defined whole-population study of neonatal at-risk children in Southern Bavaria. At 8;5 years, n = 1326 children (gestation range: 23–41 weeks) were assessed with the K-ABC and a Mathematics Test. Results: Cognitive scores of preterm children decreased as cognitive workload demands of tasks increased. The relationship between gestation and task workload was curvilinear and more pronounced the higher the cognitive workload: GA2 (quadratic term) on low cognitive workload: R2 = .02, p<0.001; moderate cognitive workload: R2 = .09, p<0.001; and high cognitive workload tasks: R2 = .14, p<0.001. Specifically, disproportionally lower scores were found for very (<32 weeks gestation) and moderately (32–33 weeks gestation) preterm children the higher the cognitive workload of the tasks. Early biological factors such as gestation and neonatal complications explained more of the variance in high (12.5%) compared with moderate (8.1%) and low cognitive workload tasks (1.7%). Conclusions: The cognitive workload model may help to explain variations of findings on the relationship of gestational age with cognitive performance in the literature. The findings have implications for routine cognitive follow-up, educational intervention, and basic research into neuro-plasticity and brain reorganization after preterm birth

Autoimmune and autoinflammatory mechanisms in uveitis

The eye, as currently viewed, is neither immunologically ignorant nor sequestered from the systemic environment. The eye utilises distinct immunoregulatory mechanisms to preserve tissue and cellular function in the face of immune-mediated insult; clinically, inflammation following such an insult is termed uveitis. The intra-ocular inflammation in uveitis may be clinically obvious as a result of infection (e.g. toxoplasma, herpes), but in the main infection, if any, remains covert. We now recognise that healthy tissues including the retina have regulatory mechanisms imparted by control of myeloid cells through receptors (e.g. CD200R) and soluble inhibitory factors (e.g. alpha-MSH), regulation of the blood retinal barrier, and active immune surveillance. Once homoeostasis has been disrupted and inflammation ensues, the mechanisms to regulate inflammation, including T cell apoptosis, generation of Treg cells, and myeloid cell suppression in situ, are less successful. Why inflammation becomes persistent remains unknown, but extrapolating from animal models, possibilities include differential trafficking of T cells from the retina, residency of CD8(+) T cells, and alterations of myeloid cell phenotype and function. Translating lessons learned from animal models to humans has been helped by system biology approaches and informatics, which suggest that diseased animals and people share similar changes in T cell phenotypes and monocyte function to date. Together the data infer a possible cryptic infectious drive in uveitis that unlocks and drives persistent autoimmune responses, or promotes further innate immune responses. Thus there may be many mechanisms in common with those observed in autoinflammatory disorders

Hydrodynamics of the VanA-type VanS histidine kinase: an extended solution conformation and first evidence for interactions with vancomycin

VanA-type resistance to glycopeptide antibiotics in clinical enterococci is regulated by the VanSARA two-component signal transduction system. The nature of the molecular ligand that is recognised by the VanSA sensory component has not hitherto been identified. Here we employ purified, intact and active VanSA membrane protein (henceforth referred to as VanS) in analytical ultracentrifugation experiments to study VanS oligomeric state and conformation in the absence and presence of vancomycin. A combination of sedimentation velocity and sedimentation equilibrium in the analytical ultracentrifuge (SEDFIT, SEDFIT-MSTAR and MULTISIG analysis) showed that VanS in the absence of the ligand is almost entirely monomeric (molar mass M = 45.7 kDa) in dilute aqueous solution with a trace amount of high molar mass material (M ~ 200 kDa). The sedimentation coefficient s suggests the monomer adopts an extended conformation in aqueous solution with an equivalent aspect ratio of ~ (12+2). In the presence of vancomycin over a 33% increase in the sedimentation coefficient is observed with the appearance of additional higher s components, demonstrating an interaction, an observation consistent with our circular dichroism measurements. The two possible causes of this increase in s – either a ligand induced dimerization and/or compaction of the monomer are considered

Detection of regulator genes and eQTLs in gene networks

Genetic differences between individuals associated to quantitative phenotypic traits, including disease states, are usually found in non-coding genomic regions. These genetic variants are often also associated to differences in expression levels of nearby genes (they are "expression quantitative trait loci" or eQTLs for short) and presumably play a gene regulatory role, affecting the status of molecular networks of interacting genes, proteins and metabolites. Computational systems biology approaches to reconstruct causal gene networks from large-scale omics data have therefore become essential to understand the structure of networks controlled by eQTLs together with other regulatory genes, and to generate detailed hypotheses about the molecular mechanisms that lead from genotype to phenotype. Here we review the main analytical methods and softwares to identify eQTLs and their associated genes, to reconstruct co-expression networks and modules, to reconstruct causal Bayesian gene and module networks, and to validate predicted networks in silico.Comment: minor revision with typos corrected; review article; 24 pages, 2 figure

The genetic architecture of low-temperature adaptation in the wine yeast Saccharomyces cerevisiae

[Background] Low-temperature growth and fermentation of wine yeast can enhance wine aroma and make them highly desirable traits for the industry. Elucidating response to cold in Saccharomyces cerevisiae is, therefore, of paramount importance to select or genetically improve new wine strains. As most enological traits of industrial importance in yeasts, adaptation to low temperature is a polygenic trait regulated by many interacting loci.[Results] In order to unravel the genetic determinants of low-temperature fermentation, we mapped quantitative trait loci (QTLs) by bulk segregant analyses in the F13 offspring of two Saccharomyces cerevisiae industrial strains with divergent performance at low temperature. We detected four genomic regions involved in the adaptation at low temperature, three of them located in the subtelomeric regions (chromosomes XIII, XV and XVI) and one in the chromosome XIV. The QTL analysis revealed that subtelomeric regions play a key role in defining individual variation, which emphasizes the importance of these regions’ adaptive nature.[Conclusions] The reciprocal hemizygosity analysis (RHA), run to validate the genes involved in low-temperature fermentation, showed that genetic variation in mitochondrial proteins, maintenance of correct asymmetry and distribution of phospholipid in the plasma membrane are key determinants of low-temperature adaptation.This work has been financially supported from the Spanish Government through MINECO and FEDER funds (AGL2013-47300-C3-3-R and PCIN-2015-143 grants) and from Generalitat Valenciana through PROMETEOII/2014/042 grant, awarded to JMG. This study has been carried out in the context of the European Project ERA-IB “YeastTempTation” EGR thanks the Spanish government for an FPI grant BES-2011-044498 and MM also thanks the Generalitat Valenciana for a VALi+d ACIF/2015/194 grant. We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI).Peer reviewe

Brand Suicide? Memory and Liking of Negative Brand Names

Negative brand names are surprisingly common in the marketplace (e.g., Poison perfume; Hell pizza, and Monster energy drink), yet their effects on consumer behavior are currently unknown. Three studies investigated the effects of negative brand name valence on brand name memory and liking of a branded product. Study 1 demonstrates that relative to nonnegative brand names, negative brand names and their associated logos are better recognised. Studies 2 and 3 demonstrate that negative valence of a brand name tends to have a detrimental influence on product evaluation with evaluations worsening as negative valence increases. However, evaluation is also dependent on brand name arousal, with high arousal brand names resulting in more positive evaluations, such that moderately negative brand names are equally as attractive as some non-negative brand names. Study 3 shows evidence for affective habituation, whereby the effects of negative valence reduce with repeated exposures to some classes of negative brand name

A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies

Gene expression measurements are influenced by a wide range of factors, such as the state of the cell, experimental conditions and variants in the sequence of regulatory regions. To understand the effect of a variable of interest, such as the genotype of a locus, it is important to account for variation that is due to confounding causes. Here, we present VBQTL, a probabilistic approach for mapping expression quantitative trait loci (eQTLs) that jointly models contributions from genotype as well as known and hidden confounding factors. VBQTL is implemented within an efficient and flexible inference framework, making it fast and tractable on large-scale problems. We compare the performance of VBQTL with alternative methods for dealing with confounding variability on eQTL mapping datasets from simulations, yeast, mouse, and human. Employing Bayesian complexity control and joint modelling is shown to result in more precise estimates of the contribution of different confounding factors resulting in additional associations to measured transcript levels compared to alternative approaches. We present a threefold larger collection of cis eQTLs than previously found in a whole-genome eQTL scan of an outbred human population. Altogether, 27% of the tested probes show a significant genetic association in cis, and we validate that the additional eQTLs are likely to be real by replicating them in different sets of individuals. Our method is the next step in the analysis of high-dimensional phenotype data, and its application has revealed insights into genetic regulation of gene expression by demonstrating more abundant cis-acting eQTLs in human than previously shown. Our software is freely available online at http://www.sanger.ac.uk/resources/software/peer/

Genome-wide fine-scale recombination rate variation in Drosophila melanogaster

Estimating fine-scale recombination maps of Drosophila from population genomic data is a challenging problem, in particular because of the high background recombination rate. In this paper, a new computational method is developed to address this challenge. Through an extensive simulation study, it is demonstrated that the method allows more accurate inference, and exhibits greater robustness to the effects of natural selection and noise, compared to a well-used previous method developed for studying fine-scale recombination rate variation in the human genome. As an application, a genome-wide analysis of genetic variation data is performed for two Drosophila melanogaster populations, one from North America (Raleigh, USA) and the other from Africa (Gikongoro, Rwanda). It is shown that fine-scale recombination rate variation is widespread throughout the D. melanogaster genome, across all chromosomes and in both populations. At the fine-scale, a conservative, systematic search for evidence of recombination hotspots suggests the existence of a handful of putative hotspots each with at least a tenfold increase in intensity over the background rate. A wavelet analysis is carried out to compare the estimated recombination maps in the two populations and to quantify the extent to which recombination rates are conserved. In general, similarity is observed at very broad scales, but substantial differences are seen at fine scales. The average recombination rate of the X chromosome appears to be higher than that of the autosomes in both populations, and this pattern is much more pronounced in the African population than the North American population. The correlation between various genomic features—including recombination rates, diversity, divergence, GC content, gene content, and sequence quality—is examined using the wavelet analysis, and it is shown that the most notable difference between D. melanogaster and humans is in the correlation between recombination and diversity