1,763 research outputs found

    A new mass-loss rate prescription for red supergiants

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    Evolutionary models have shown the substantial effect that strong mass-loss rates (⁠M˙s) can have on the fate of massive stars. Red supergiant (RSG) mass-loss is poorly understood theoretically, and so stellar models rely on purely empirical M˙–luminosity relations to calculate evolution. Empirical prescriptions usually scale with luminosity and effective temperature, but M˙ should also depend on the current mass and hence the surface gravity of the star, yielding more than one possible M˙ for the same position on the Hertzsprung–Russell diagram. One can solve this degeneracy by measuring M˙ for RSGs that reside in clusters, where age and initial mass (Minit) are known. In this paper we derive M˙ values and luminosities for RSGs in two clusters, NGC 2004 and RSGC1. Using newly derived Minit measurements, we combine the results with those of clusters with a range of ages and derive an Minit-dependent M˙ prescription. When comparing this new prescription to the treatment of mass-loss currently implemented in evolutionary models, we find models drastically overpredict the total mass-loss, by up to a factor of 20. Importantly, the most massive RSGs experience the largest downward revision in their mass-loss rates, drastically changing the impact of wind mass-loss on their evolution. Our results suggest that for most initial masses of RSG progenitors, quiescent mass-loss during the RSG phase is not effective at removing a significant fraction of the H-envelope prior to core-collapse, and we discuss the implications of this for stellar evolution and observations of SNe and SN progenitors

    Energy minimization problem in two-level dissipative quantum control: meridian case

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    International audienceWe analyze the energy-minimizing problem for a two-level dissipative quantum system described by the Kossakowsky-Lindblad equation. According to the Pontryagin Maximum Principle (PMP), minimizers can be selected among normal and abnormal extremals whose dynamics are classified according to the values of the dissipation parameters. Our aim is to improve our previous analysis concerning 2D solutions in the case where the Hamiltonian dynamics are integrable

    The Age of Westerland 1 Revisited

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    The cluster Westerlund 1 (Wd1) is host to a large variety of post-main-sequence (MS) massive stars. The simultaneous presence of these stars can only be explained by stellar models if the cluster has a finely tuned age of 4–5 Myr, with several published studies independently claiming ages within this range. At this age, stellar models predict that the cool supergiants (CSGs) should have luminosities of log(L L☉)» 5.5, close to the empirical luminosity limit. Here, we test that prediction using archival data and new photometry from Stratospheric Observatory for Infrared Astronomy to estimate bolometric luminosities for the CSGs. We find that these stars are on average 0.4 dex too faint to be 5 Myr old, regardless of which stellar evolutionary model is used, and instead are indicative of a much older age of 10.4-+1.21.3 Myr. We argue that neither systematic uncertainties in the extinction law nor stellar variability can explain this discrepancy. In reviewing various independent age estimates of Wd1 in the literature, we first show that those based on stellar diversity are unreliable. Second, we reanalyze Wd1’s pre-MS stars employing the Damineli extinction law, finding an age of 7.2-+2.31.1 Myr; older than that of previous studies, but which is vulnerable to systematic errors that could push the age close to 10 Myr. However, there remains significant tension between the CSG age and that inferred from the eclipsing binary W13. We conclude that stellar evolutionary models cannot explain Wd1 under the single-age paradigm. Instead, we propose that the stars in the Wd1 region formed over a period of several megayears

    Re-embedding agency at the workplace scale: workers and labour control in Glasgow call centres

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    Following recent calls for the development of a more embedded sense of labour agency, this paper focuses on the scale of the workplace which is largely absent from recent labour geography debates. Drawing on studies in the labour process tradition, the paper presents empirical research on call centre work in Glasgow, utilising this to revisit the concept of local Labour Control Regimes (LCR). We argue that rather than being simply imposed by capital and the state ‘from above’, workplace control should be seen as the product of a dialectical process of interaction and negotiation between management and labour. Labour’s indeterminacy can influence capital in case specific ways as firms adapt to labour agency and selectively tolerate and collude with certain practices and behaviours. Workers’ learned behaviour and identities are shown to affect not only recruitment patterns in unexpected ways, but also modes of accepted conduct in call centres. Accordingly, the case is made for the influence of subtle – yet pervasive – worker agency expressed at the micro-scale of the labour process itself. This, it is argued, exerts a degree of ‘bottom-up’ pressure on key fractions of capital within the local LCR

    Uptake of oxLDL and IL-10 production by macrophages requires PAFR and CD36 recruitment into the same lipid rafts

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    Macrophage interaction with oxidized low-density lipoprotein (oxLDL) leads to its differentiation into foam cells and cytokine production, contributing to atherosclerosis development. In a previous study, we showed that CD36 and the receptor for platelet-activating factor (PAFR) are required for oxLDL to activate gene transcription for cytokines and CD36. Here, we investigated the localization and physical interaction of CD36 and PAFR in macrophages stimulated with oxLDL. We found that blocking CD36 or PAFR decreases oxLDL uptake and IL-10 production. OxLDL induces IL-10 mRNA expression only in HEK293T expressing both receptors (PAFR and CD36). OxLDL does not induce IL-12 production. The lipid rafts disruption by treatment with βCD reduces the oxLDL uptake and IL-10 production. OxLDL induces co-immunoprecipitation of PAFR and CD36 with the constitutive raft protein flotillin-1, and colocalization with the lipid raft-marker GM1-ganglioside. Finally, we found colocalization of PAFR and CD36 in macrophages from human atherosclerotic plaques. Our results show that oxLDL induces the recruitment of PAFR and CD36 into the same lipid rafts, which is important for oxLDL uptake and IL-10 production. This study provided new insights into how oxLDL interact with macrophages and contributing to atherosclerosis development

    Star Clusters

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    This review concentrates almost entirely on globular star clusters. It emphasises the increasing realisation that few of the traditional problems of star cluster astronomy can be studied in isolation: the influence of the Galaxy affects dynamical evolution deep in the core, and the spectrum of stellar masses; in turn the evolution of the core determines the highest stellar densities, and the rate of encounters. In this way external tidal effects indirectly influence the formation and evolution of blue stragglers, binary pulsars, X-ray sources, etc. More controversially, the stellar density appears to influence the relative distribution of normal stars. In the opposite sense, the evolution of individual stars governs much of the early dynamics of a globular cluster, and the existence of large numbers of primordial binary stars has changed important details of our picture of the dynamical evolution. New computational tools which will become available in the next few years will help dynamical theorists to address these questions.Comment: 10 pages, 3 figures, Te

    Socioeconomic differentials in the immediate mortality effects of the national Irish smoking ban

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    This article has been made available through the Brunel Open Access Publishing Fund.Background: Consistent evidence has demonstrated that smoking ban policies save lives, but impacts on health inequalities are uncertain as few studies have assessed post-ban effects by socioeconomic status (SES) and findings have been inconsistent. The aim of this study was to assess the effects of the national Irish smoking ban on ischemic heart disease (IHD), stroke, and chronic obstructive pulmonary disease (COPD) mortality by discrete and composite SES indicators to determine impacts on inequalities. Methods: Census data were used to assign frequencies of structural and material SES indicators to 34 local authorities across Ireland with a 2000–2010 study period. Discrete indicators were jointly analysed through principal component analysis to generate a composite index, with sensitivity analyses conducted by varying the included indicators. Poisson regression with interrupted time-series analysis was conducted to examine monthly age and gender-standardised mortality rates in the Irish population, ages ≥35 years, stratified by tertiles of SES indicators. All models were adjusted for time trend, season, influenza, and smoking prevalence. Results: Post-ban mortality reductions by structural SES indicators were concentrated in the most deprived tertile for all causes of death, while reductions by material SES indicators were more equitable across SES tertiles. The composite indices mirrored the results of the discrete indicators, demonstrating that post-ban mortality decreases were either greater or similar in the most deprived when compared to the least deprived for all causes of death. Conclusions: Overall findings indicated that the national Irish smoking ban reduced inequalities in smoking-related mortality. Due to the higher rates of smoking-related mortality in the most deprived group, even equitable reductions across SES tertiles resulted in decreases in inequalities. The choice of SES indicator was influential in the measurement of effects, underscoring that a differentiated analytical approach aided in understanding the complexities in which structural and material factors influence mortality

    Mendelian randomization for studying the effects of perturbing drug targets [version 1; peer review: awaiting peer review]

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    Drugs whose targets have genetic evidence to support efficacy and safety are more likely to be approved after clinical development. In this paper, we provide an overview of how natural sequence variation in the genes that encode drug targets can be used in Mendelian randomization analyses to offer insight into mechanism-based efficacy and adverse effects. Large databases of summary level genetic association data are increasingly available and can be leveraged to identify and validate variants that serve as proxies for drug target perturbation. As with all empirical research, Mendelian randomization has limitations including genetic confounding, its consideration of lifelong effects, and issues related to heterogeneity across different tissues and populations. When appropriately applied, Mendelian randomization provides a useful empirical framework for using population level data to improve the success rates of the drug development pipeline

    Mendelian randomization for studying the effects of perturbing drug targets [version 2; peer review: 3 approved, 1 approved with reservations]

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    Drugs whose targets have genetic evidence to support efficacy and safety are more likely to be approved after clinical development. In this paper, we provide an overview of how natural sequence variation in the genes that encode drug targets can be used in Mendelian randomization analyses to offer insight into mechanism-based efficacy and adverse effects. Large databases of summary level genetic association data are increasingly available and can be leveraged to identify and validate variants that serve as proxies for drug target perturbation. As with all empirical research, Mendelian randomization has limitations including genetic confounding, its consideration of lifelong effects, and issues related to heterogeneity across different tissues and populations. When appropriately applied, Mendelian randomization provides a useful empirical framework for using population level data to improve the success rates of the drug development pipeline
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