Evaluation of risk attitude as a predictor of substance related risk taking

Decision making is a complex process influenced by a number of factors. Mathematical models of risk have been developed to deconstruct the decision making process into the components that are most influential. Risk attitude, or a person's preferred level of risk, has been identified as an important factor for decisions involving risk or risk taking. Risk attitude is related to a variety of risk taking behaviors, particularly those involving financial risk (e.g., investing in stocks). However, risk attitude has not been studied in the context of substance related risk taking behaviors. The goal of this study was to determine the utility of risk attitude as a predictor of substance related risk taking. Furthermore, this study was designed to test the associations between risk attitude and conceptually similar personality traits, such as impulsivity. The results of this study suggest that individuals who were risk seeking were more likely to experience negative consequences due to alcohol despite similar levels of alcohol consumption. Those who were risk seeking also engaged in risky sexual behaviors with new partners more frequently. Finally, those who were risk seeking scored higher on measures of impulsivity relative to those who were risk averse. These results provide initial evidence that risk attitude is related to substance related risk taking. Future research should examine the contextual variables that influence risk attitude (e.g., alcohol intoxication)

Nanoscale periodicity in stripe-forming systems at high temperature: Au/W(110)

We observe using low-energy electron microscopy the self-assembly of monolayer-thick stripes of Au on W(110) near the transition temperature between stripes and the non-patterned (homogeneous) phase. We demonstrate that the amplitude of this Au stripe phase decreases with increasing temperature and vanishes at the order-disorder transition (ODT). The wavelength varies much more slowly with temperature and coverage than theories of stress-domain patterns with sharp phase boundaries would predict, and maintains a finite value of about 100 nm at the ODT. We argue that such nanometer-scale stripes should often appear near the ODT.Comment: 5 page

How Polarized Have We Become? A Multimodal Classification of Trump Followers and Clinton Followers

Polarization in American politics has been extensively documented and analyzed for decades, and the phenomenon became all the more apparent during the 2016 presidential election, where Trump and Clinton depicted two radically different pictures of America. Inspired by this gaping polarization and the extensive utilization of Twitter during the 2016 presidential campaign, in this paper we take the first step in measuring polarization in social media and we attempt to predict individuals' Twitter following behavior through analyzing ones' everyday tweets, profile images and posted pictures. As such, we treat polarization as a classification problem and study to what extent Trump followers and Clinton followers on Twitter can be distinguished, which in turn serves as a metric of polarization in general. We apply LSTM to processing tweet features and we extract visual features using the VGG neural network. Integrating these two sets of features boosts the overall performance. We are able to achieve an accuracy of 69%, suggesting that the high degree of polarization recorded in the literature has started to manifest itself in social media as well.Comment: 16 pages, SocInfo 2017, 9th International Conference on Social Informatic

Inferring Population Preferences via Mixtures of Spatial Voting Models

Understanding political phenomena requires measuring the political preferences of society. We introduce a model based on mixtures of spatial voting models that infers the underlying distribution of political preferences of voters with only voting records of the population and political positions of candidates in an election. Beyond offering a cost-effective alternative to surveys, this method projects the political preferences of voters and candidates into a shared latent preference space. This projection allows us to directly compare the preferences of the two groups, which is desirable for political science but difficult with traditional survey methods. After validating the aggregated-level inferences of this model against results of related work and on simple prediction tasks, we apply the model to better understand the phenomenon of political polarization in the Texas, New York, and Ohio electorates. Taken at face value, inferences drawn from our model indicate that the electorates in these states may be less bimodal than the distribution of candidates, but that the electorates are comparatively more extreme in their variance. We conclude with a discussion of limitations of our method and potential future directions for research.Comment: To be published in the 8th International Conference on Social Informatics (SocInfo) 201

The HPS electromagnetic calorimeter

The Heavy Photon Search experiment (HPS) is searching for a new gauge boson, the so-called “heavy photon.” Through its kinetic mixing with the Standard Model photon, this particle could decay into an electron-positron pair. It would then be detectable as a narrow peak in the invariant mass spectrum of such pairs, or, depending on its lifetime, by a decay downstream of the production target. The HPS experiment is installed in Hall-B of Jefferson Lab. This article presents the design and performance of one of the two detectors of the experiment, the electromagnetic calorimeter, during the runs performed in 2015–2016. The calorimeter's main purpose is to provide a fast trigger and reduce the copious background from electromagnetic processes through matching with a tracking detector. The detector is a homogeneous calorimeter, made of 442 lead-tungstate (PbWO4) scintillating crystals, each read out by an avalanche photodiode coupled to a custom trans-impedance amplifier

A MAGIC population-based genome-wide association study reveals functional association of GhRBB1_A07 gene with superior fiber quality in cotton

Title: Quantile-quantile (Q-Q) Plot of six fiber traits generated from GWAS analysis following mixed linear model (MLM) using GAPIT software. A) Fiber elongation (ELO), B) Micronaire (MIC), C) Short fiber content (SFC), D) Fiber strength (STR), E) Upper half mean fiber length (UHM), and F) Uniformity index (UI). Description of data: Q-Q plots of six fiber traits generated from GWAS analysis following MLM are included in this figure. The X and Y axis have the expected and observed negative logarithm 10 of p value, respectively generated during GWAS analysis. (DOCX 207 kb

Phosphorylation of the androgen receptor is associated with reduced survival in hormonerefractory prostate cancer patients

Cell line studies demonstrate that the PI3K/Akt pathway is upregulated in hormone-refractory prostate cancer (HRPC) and can result in phosphorylation of the androgen receptor (AR). The current study therefore aims to establish if this has relevance to the development of clinical HRPC. Immunohistochemistry was employed to investigate the expression and phosphorylation status of Akt and AR in matched hormone-sensitive and -refractory prostate cancer tumours from 68 patients. In the hormone-refractory tissue, only phosphorylated AR (pAR) was associated with shorter time to death from relapse (<i>P</i>=0.003). However, when an increase in expression in the transition from hormone-sensitive to -refractory prostate cancer was investigated, an increase in expression of PI3K was associated with decreased time to biochemical relapse (<i>P</i>=0.014), and an increase in expression of pAkt<sup>473</sup> and pAR<sup>210</sup> were associated with decreased disease-specific survival (<i>P</i>=0.0019 and 0.0015, respectively). Protein expression of pAkt<sup>473</sup> and pAR<sup>210</sup> also strongly correlated (<i>P</i><0.001, c.c.=0.711) in the hormone-refractory prostate tumours. These results provide evidence using clinical specimens, that upregulation of the PI3K/Akt pathway is associated with phosphorylation of the AR during development of HRPC, suggesting that this pathway could be a potential therapeutic target