The Dynamics of Political Participation: An Analysis of the Dynamic Interaction between Individuals and their Political Micro-Environment

While political choices are rarely isolated or simultaneous, the vast majority of empirical models in political science assume they are. This dissertation examines the dynamic interactions over time between individuals and their micro-environment, in which a single factor both influences, and is influenced by, the act of voting. These dynamic interactions occur in a surprisingly broad swathe of the current literature on American voting behavior, as implicit but unexamined elements of four major research traditions. When these interactions are present, they establish feedback cycles that pose both theoretical and statistical challenges if not analyzed appropriately. Researchers ignoring these cycles tend to underestimate long term influences on voting behavior, make unrealistic assumptions about changes in voting behavior over time, and produce biased results under certain conditions. I propose a methodology that can successfully identify and model these interactions: employing simulation models to represent dynamic interactions in an intuitive format, and using optimization techniques to conduct parameter estimation and hypothesis testing against empirical data. To guide the development of these simulation models, I outline a theoretical framework of the major pathways by which dynamic interactions can influence voting behavior. I then present two applications of this methodology, to study the dynamic impacts on voting of political mobilization, and of social conformity over time. In both cases, the models receive strong statistical support, in benchmark tests against existing econometric models and against empirical data on voting behavior. Both mobilization and social conformity have unstudied indirect impacts that can lead to an additional 1.7% to 4% increase in voter turnout beyond existing models. Targeted use of peer pressure can lead to even more significant increases in turnout - up to a 30% increase among otherwise indecisive voters. In the long term, targeted mobilization can create cadres of repeatedly-mobilized activists, which raises questions about whether political campaigns effectively use their mobilization funds to build their parties in the long term. These two simulation models also provide a foundation for a host of new research questions, ranging from the impact of high-intensity get-out-the-vote drives on future mobilization efforts, to the effects of an aging population on turnout behavior over time

Gene–gene interaction of AhRwith and within the Wntcascade affects susceptibility to lung cancer

Background: Aberrant Wnt signalling, regulating cell development and stemness, influences the development of many cancer types. The Aryl hydrocarbon receptor (AhR) mediates tumorigenesis of environmental pollutants. Complex interaction patterns of genes assigned to AhR/Wnt-signalling were recently associated with lung cancer susceptibility. Aim: To assess the association and predictive ability of AhR/Wnt-genes with lung cancer in cases and controls of European descent. Methods: Odds ratios (OR) were estimated for genomic variants assigned to the Wnt agonist and the antagonistic genes DKK2, DKK3, DKK4, FRZB, SFRP4 and Axin2. Logistic regression models with variable selection were trained, validated and tested to predict lung cancer, at which other previously identified SNPs that have been robustly associated with lung cancer risk could also enter the model. Furthermore, decision trees were created to investigate variant × variant interaction. All analyses were performed for overall lung cancer and for subgroups. Results: No genome-wide significant association of AhR/Wnt-genes with overall lung cancer was observed, but within the subgroups of ever smokers (e.g., maker rs2722278 SFRP4; OR = 1.20; 95% CI 1.13–1.27; p = 5.6 × 10–10) and never smokers (e.g., maker rs1133683 Axin2; OR = 1.27; 95% CI 1.19–1.35; p = 1.0 × 10–12). Although predictability is poor, AhR/Wnt-variants are unexpectedly overrepresented in optimized prediction scores for overall lung cancer and for small cell lung cancer. Remarkably, the score for never-smokers contained solely two AhR/Wnt-variants. The optimal decision tree for never smokers consists of 7 AhR/Wnt-variants and only two lung cancer variants. Conclusions: The role of variants belonging to Wnt/AhR-pathways in lung cancer susceptibility may be underrated in main-effects association analysis. Complex interaction patterns in individuals of European descent have moderate predictive capacity for lung cancer or subgroups thereof, especially in never smokers

Gene–gene interaction of AhRwith and within the Wntcascade affects susceptibility to lung cancer

Background Aberrant Wnt signalling, regulating cell development and stemness, influences the development of many cancer types. The Aryl hydrocarbon receptor (AhR) mediates tumorigenesis of environmental pollutants. Complex interaction patterns of genes assigned to AhR/Wnt-signalling were recently associated with lung cancer susceptibility. Aim To assess the association and predictive ability of AhR/Wnt-genes with lung cancer in cases and controls of European descent. Methods Odds ratios (OR) were estimated for genomic variants assigned to the Wnt agonist and the antagonistic genes DKK2, DKK3, DKK4, FRZB, SFRP4 and Axin2. Logistic regression models with variable selection were trained, validated and tested to predict lung cancer, at which other previously identified SNPs that have been robustly associated with lung cancer risk could also enter the model. Furthermore, decision trees were created to investigate variant x variant interaction. All analyses were performed for overall lung cancer and for subgroups. Results No genome-wide significant association of AhR/Wnt-genes with overall lung cancer was observed, but within the subgroups of ever smokers (e.g., maker rs2722278 SFRP4; OR = 1.20; 95% CI 1.13-1.27; p = 5.6 x 10(-10)) and never smokers (e.g., maker rs1133683 Axin2; OR = 1.27; 95% CI 1.19-1.35; p = 1.0 x 10(-12)). Although predictability is poor, AhR/Wnt-variants are unexpectedly overrepresented in optimized prediction scores for overall lung cancer and for small cell lung cancer. Remarkably, the score for never-smokers contained solely two AhR/Wnt-variants. The optimal decision tree for never smokers consists of 7 AhR/Wnt-variants and only two lung cancer variants. Conclusions The role of variants belonging to Wnt/AhR-pathways in lung cancer susceptibility may be underrated in main-effects association analysis. Complex interaction patterns in individuals of European descent have moderate predictive capacity for lung cancer or subgroups thereof, especially in never smokers

Effect of the down-regulation of the high Grain Protein Content (GPC) genes on the wheat transcriptome during monocarpic senescence

<p>Abstract</p> <p>Background</p> <p>Increasing the nutrient concentration of wheat grains is important to ameliorate nutritional deficiencies in many parts of the world. Proteins and nutrients in the wheat grain are largely derived from the remobilization of degraded leaf molecules during monocarpic senescence. The down-regulation of the NAC transcription factor <it>Grain Protein Content </it>(<it>GPC</it>) in transgenic wheat plants delays senescence (>3 weeks) and reduces the concentration of protein, Zn and Fe in the grain (>30%), linking senescence and nutrient remobilization.</p> <p>Based on the early and rapid up-regulation of <it>GPC </it>in wheat flag leaves after anthesis, we hypothesized that this transcription factor is an early regulator of monocarpic senescence. To test this hypothesis, we used high-throughput mRNA-seq technologies to characterize the effect of the <it>GPC </it>down-regulation on the wheat flag-leaf transcriptome 12 days after anthesis. At this early stage of senescence <it>GPC </it>transcript levels are significantly lower in transgenic GPC-RNAi plants than in the wild type, but there are still no visible phenotypic differences between genotypes.</p> <p>Results</p> <p>We generated 1.4 million 454 reads from early senescing flag leaves (average ~350 nt) and assembled 1.2 million into 30,497 contigs that were used as a reference to map 145 million Illumina reads from three wild type and four GPC-RNAi plants. Following normalization and statistical testing, we identified a set of 691 genes differentially regulated by <it>GPC </it>(431 ≥ 2-fold change). Transcript level ratios between transgenic and wild type plants showed a high correlation (<it>R </it>= 0.83) between qRT-PCR and Illumina results, providing independent validation of the mRNA-seq approach. A set of differentially expressed genes were analyzed across an early senescence time-course.</p> <p>Conclusions</p> <p>Monocarpic senescence is an active process characterized by large-scale changes in gene expression which begins considerably before the appearance of visual symptoms of senescence. The mRNA-seq approach used here was able to detect small differences in transcript levels during the early stages of senescence. This resulted in an extensive list of <it>GPC</it>-regulated genes, which includes transporters, hormone regulated genes, and transcription factors. These <it>GPC</it>-regulated genes, particularly those up-regulated during senescence, provide valuable entry points to dissect the early stages of monocarpic senescence and nutrient remobilization in wheat.</p

Roadmap for Optical Tweezers 2023

Optical tweezers are tools made of light that enable contactless pushing, trapping, and manipulation of objects ranging from atoms to space light sails. Since the pioneering work by Arthur Ashkin in the 1970s, optical tweezers have evolved into sophisticated instruments and have been employed in a broad range of applications in life sciences, physics, and engineering. These include accurate force and torque measurement at the femtonewton level, microrheology of complex fluids, single micro- and nanoparticle spectroscopy, single-cell analysis, and statistical-physics experiments. This roadmap provides insights into current investigations involving optical forces and optical tweezers from their theoretical foundations to designs and setups. It also offers perspectives for applications to a wide range of research fields, from biophysics to space exploration

On the mechanisms governing gas penetration into a tokamak plasma during a massive gas injection

A new 1D radial fluid code, IMAGINE, is used to simulate the penetration of gas into a tokamak plasma during a massive gas injection (MGI). The main result is that the gas is in general strongly braked as it reaches the plasma, due to mechanisms related to charge exchange and (to a smaller extent) recombination. As a result, only a fraction of the gas penetrates into the plasma. Also, a shock wave is created in the gas which propagates away from the plasma, braking and compressing the incoming gas. Simulation results are quantitatively consistent, at least in terms of orders of magnitude, with experimental data for a D 2 MGI into a JET Ohmic plasma. Simulations of MGI into the background plasma surrounding a runaway electron beam show that if the background electron density is too high, the gas may not penetrate, suggesting a possible explanation for the recent results of Reux et al in JET (2015 Nucl. Fusion 55 093013)