Magnetic Field Amplification in Galaxy Clusters and its Simulation

We review the present theoretical and numerical understanding of magnetic field amplification in cosmic large-scale structure, on length scales of galaxy clusters and beyond. Structure formation drives compression and turbulence, which amplify tiny magnetic seed fields to the microGauss values that are observed in the intracluster medium. This process is intimately connected to the properties of turbulence and the microphysics of the intra-cluster medium. Additional roles are played by merger induced shocks that sweep through the intra-cluster medium and motions induced by sloshing cool cores. The accurate simulation of magnetic field amplification in clusters still poses a serious challenge for simulations of cosmological structure formation. We review the current literature on cosmological simulations that include magnetic fields and outline theoretical as well as numerical challenges.Comment: 60 pages, 19 Figure

Researching the gender division of unpaid domestic work: practices, relationships, negotiations, and meanings

The paper focuses on the potential of quantitative research methods for sociologists who research the gender division of unpaid domestic work. To begin, it reflects on the emergence of the sociological interest in unpaid domestic work and identifies an early core concern with making invisible work visible. It is argued that quantitative research methods provide us with the most valuable opportunities for ‘recognising’ unpaid domestic work since they facilitate larger scale representative projects. However the data in most of the large scale surveys are scant, and fail to reflect developments in the conceptualisation of unpaid domestic work. Four areas of concern to contemporary sociology are identified: domestic work practices, relationships, negotiations and meanings. Given the complex questions that these four sub- topics raise, the paper proposes a range of sub-areas as a focus for ongoing sociological research into unpaid domestic work. It is concluded that despite the methodological challenges presented, detailed indicators of the multiple dimensions of unpaid domestic work need to be agreed so that valid information can be collected as routinely in large scale surveys as are those on paid work

Genome-wide interaction study of smoking behavior and non-small cell lung cancer risk in Caucasian population.

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer. Both environmental and genetic risk factors contribute to lung carcinogenesis. We conducted a genome-wide interaction analysis between SNPs and smoking status (never vs ever smokers) in a European-descent population. We adopted a two-step analysis strategy in the discovery stage: we first conducted a case-only interaction analysis to assess the relationship between SNPs and smoking behavior using 13,336 NSCLC cases. Candidate SNPs with p-value less than 0.001 were further analyzed using a standard case-control interaction analysis including 13970 controls. The significant SNPs with p-value less than 3.5x10-5 (correcting for multiple tests) from the case-control analysis in the discovery stage were further validated using an independent replication dataset comprising 5377 controls and 3054 NSCLC cases. We further stratified the analysis by histological subtypes. Two novel SNPs, rs6441286 and rs17723637, were identified for overall lung cancer risk. The interaction odds ratio and meta-analysis p-value for these two SNPs were 1.24 with 6.96x10-7 and 1.37 with 3.49x10-7, respectively. Additionally, interaction of smoking with rs4751674 was identified in squamous cell lung carcinoma with an odds ratio of 0.58 and p-value of 8.12x10-7. This study is by far the largest genome-wide SNP-smoking interaction analysis reported for lung cancer. The three identified novel SNPs provide potential candidate biomarkers for lung cancer risk screening and intervention. The results from our study reinforce that gene-smoking interactions play important roles in the etiology of lung cancer and account for part of the missing heritability of this disease

Evolution of the UML Interactions Metamodel

UML Interactions represent one of the three different behavior kinds of the UML. In general, they specify the exchange of messages among parts of a system. Although UML Interactions can reside on different level of abstractions, they seem to be sufficiently elaborated for a higher-level of abstraction where they are used for sketching the communication among parts. Its metamodel reveals some fuzziness and imprecision where definitions should be accurate and concise, though. In this paper, we propose improvements to the UML Interactions’ metamodel for Message arguments and Loop CombinedFragments that make them more versatile. We will justify the needs for the improvements by precisely showing the shortcomings of the related parts of the metamodel. We demonstrate the expressiveness of the improvements by applying them to examples that current Interactions definition handles awkwardlyacceptedVersio

Four decades of opposing natural and human-induced artificial selection acting on Windermere pike (Esox lucius)

The ability of natural selection to drive local adaptation has been appreciated ever since Darwin. Whether human impacts can impede the adaptive process has received less attention. We tested this hypothesis by quantifying natural selection and harvest selection acting on a freshwater fish (pike) over four decades. Across the time series, directional natural selection tended to favour large individuals whereas the fishery targeted large individuals. Moreover, non-linear natural selection tended to favour intermediate sized fish whereas the fishery targeted intermediate sized fish because the smallest and largest individuals were often not captured. Thus, our results unequivocally demonstrate that natural selection and fishery selection often acted in opposite directions within this natural system. Moreover, the two selective factors combined to produce reduced fitness overall and stronger stabilizing selection relative to natural selection acting alone. The long-term ramifications of such human-induced modifications to adaptive landscapes are currently unknown and certainly warrant further investigation

A Fully General Operational Semantics for UML 2.0 Sequence Diagrams with Potential and Mandatory Choice

Abstract. UML sequence diagrams is a specification language that has proved itself to be of great value in system development. When put to applications such as simulation, testing and other kinds of automated analysis there is a need for formal semantics. Such methods of auto-mated analysis are by nature operational, and this motivates formalizing an operational semantics. In this paper we present an operational seman-tics for UML 2.0 sequence diagrams that we believe gives a solid starting point for developing methods for automated analysis. The operational semantics has been proved to be sound and complete with respect to a denotational semantics for the same language. It handles negative be-havior as well as potential and mandatory choice. We are not aware of any other operational semantics for sequence diagrams of this strength.