Centrality dependence of identified particle elliptic flow in relativistic heavy ion collisions at sqrt(s)= 7.7--62.4 GeV

Elliptic flow (v_2) values for identified particles at midrapidity in Au + Au collisions measured by the STAR experiment in the Beam Energy Scan at the Relativistic Heavy Ion Collider at sqrt{s_{NN}}= 7.7--62.4 GeV are presented for three centrality classes. The centrality dependence and the data at sqrt{s_{NN}}= 14.5 GeV are new. Except at the lowest beam energies we observe a similar relative v_2 baryon-meson splitting for all centrality classes which is in agreement within 15% with the number-of-constituent quark scaling. The larger v_2 for most particles relative to antiparticles, already observed for minimum bias collisions, shows a clear centrality dependence, with the largest difference for the most central collisions. Also, the results are compared with A Multiphase Transport Model and fit with a Blast Wave model.Comment: 14 pages, 12 figures, Phys. Rev. C, to be published. Data tables available at https://drupal.star.bnl.gov/STAR/publications/centrality-dependence-identified-particle-elliptic-flow-relativistic-heavy-ion-collisi

A discrete firefly algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system with recycling policy

A real-world newspaper distribution problem with recycling policy is tackled in this work. In order to meet all the complex restrictions contained in such a problem, it has been modeled as a rich vehicle routing problem, which can be more specifically considered as an asymmetric and clustered vehicle routing problem with simultaneous pickup and deliveries, variable costs and forbidden paths (AC-VRP-SPDVCFP). This is the first study of such a problem in the literature. For this reason, a benchmark composed by 15 instances has been also proposed. In the design of this benchmark, real geographical positions have been used, located in the province of Bizkaia, Spain. For the proper treatment of this AC-VRP-SPDVCFP, a discrete firefly algorithm (DFA) has been developed. This application is the first application of the firefly algorithm to any rich vehicle routing problem. To prove that the proposed DFA is a promising technique, its performance has been compared with two other well-known techniques: an evolutionary algorithm and an evolutionary simulated annealing. Our results have shown that the DFA has outperformed these two classic meta-heuristics

Genome-wide signatures of environmental adaptation in European aspen (Populus tremula) under current and future climate conditions

Future climate change has been predicted to disrupt local adaptation in many perennial plants, such as forest trees, but the magnitude and location of these effects are thus far poorly understood. Here, we assess local adaptation to current climate in European aspen (Populus tremula) by using environmental association analyses to identify genetic variants associated with two representative climate variables describing current day variation in temperature and precipitation. We also analysed patterns of genetic differentiation between southern and northern populations and observe that regions of high genetic differentiation are enriched for SNPs that are significantly associated with climate. Using variants associated with climate, we examined patterns of isolation by distance and environment and used spatial modelling to predict the geographic distribution of genomic variation in response to two scenarios of future climate change. We show that climate conditions at a northern reference site will correspond to climate conditions experienced by current day populations located 4-8 latitude degrees further south. By assessing the relationship between phenotypic traits and vegetative fitness, we also demonstrate that southern populations harbour genetic variation that likely would be adaptive further north under both climate change scenarios. Current day populations at the lagging edge of the distribution in Sweden can therefore serve as sources for introducing adaptive alleles onto northern populations, but the likelihood of this largely depends on naturally occurring levels of gene flow

Serum IL-7 as diagnostic biomarker for Rheumatoid Arthritis: validation with EULAR-2010 classification criteria

OBJECTIVES: Despite the well-established value of currently used classification criteria for the early diagnosis of rheumatoid arthritis (RA) there is a constant demand for novel biomarkers notably in autoantibody-negative patients. Interleukin 7 (IL-7) has been reported as a candidate diagnostic biomarker based on ACR-1987 criteria. However, clinical practice has moved to using the EULAR 2010 classification criteria. Therefore, to advance the use of IL-7 alongside the RA biomarker pipeline, we repeated the original study in a new cohort. METHODS: 255 patients were recruited. IL-7 was quantified by ELISA. Univariate and regression analyses were used to model RA diagnosis. RESULTS: 123 patients were diagnosed with RA (EULAR 2010) while 132 were classified as non-RA. In univariate analysis, RA was associated with autoantibodies and SE-positivity, higher joint counts, DAS28 (all p<0.001) and CRP (p=0.024). IL-7 was lower in RA (p=0.05). Logistic regression analysis in 227 patients with complete data set confirmed IL-7 was the second best predictive marker (p=0.035) following SJC (p=0.007) with good model fit (AUROC=0.889). A second model investigated 147 ACPA-negative patients: lower IL7 was the second best predictive marker (p=0.075) behind SJC (p=0.013). CONCLUSIONS: This study validates our previous results from a UK cohort using EULAR 2010 criteria although the predictive power associated with IL-7 is lower than in the study using ACR 1987 criteria (both French/UK cohorts). IL-7 remains a potential biomarker for ACPA-negative RA although further validation with larger numbers of ACPA-negative patients is still needed notably to translate these results into clinical applicability