Modern American populism: Analyzing the economics behind the Silent Majority, the Tea Party and Trumpism

This article researches populism, more specifically, Modern American Populism (MAP), constructed of white, rural, and economically oppressed reactionarianism, which was borne out of the political upheaval of the 1960’s Civil Rights movement. The research looks to explain the causes of populism and what leads voters to support populist movements and politicians. The research focuses on economic anxiety as the main cause but also examines an alternative theory of racial resentment. In an effort to answer the question, what causes populist movements and motivations, I apply a research approach that utilizes qualitative and quantitative methods. There is an examination of literature that defines populism, its causes and a detailed discussion of the case studies, including the 1972 election of Richard Nixon; the Tea Party election of 2010; and the 2016 election of Donald Trump. In addition, statistical data analysis was run using American National Election Studies (ANES) surveys associated with each specific case study. These case studies were chosen because they most represent forms of populist movements in modern American history. While ample qualitative evidence suggested support for the hypothesis that economic anxiety is a necessary condition for populist voting patterns that elected Nixon, the Tea Party and Trump, the statistical data only supported the hypothesis in two cases, 2010 and 2016, with 1972 coming back inconclusive. The data also suggested that both economic anxiety and racial resentment played a role in 2010 and 2016, while having no significant effect in 1972 in either case. This suggests that further research needs to be conducted into additional populist case studies, as well as an examination into the role economic anxiety and economic crises play on racial resentment and racially motivated voting behavior

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding “big data” that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding ‘big data’ that is conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them

Trichodysplasia spinulosa is characterized by active polyomavirus infection

Background: Recently a new polyomavirus was identified in a patient with trichodysplasia spinulosa (TS), a rare follicular skin disease of immunocompromised patients characterized by facial spines and overgrowth of inner root sheath cells. Seroepidemiological studies indicate that TSPyV is ubiquitous and latently infects 70% of the healthy individuals. Objective: To corroborate the relationship between active TSPyV infection and TS disease by analyzing the presence, load, and precise localization of TSPyV infection in TS patients and in controls. Study design: TS lesional and non-lesional skin samples were retrieved from TS patients through a PubMed search. Samples were analyzed for the presence and load of TSPyV DNA with quantitative PCR, and for expression and localization of viral protein with immunofluorescence. Findings obtained in TS patients (n= 11) were compared to those obtained in healthy controls (n= 249). Results: TSPyV DNA detection was significantly associated with disease (P<0.001), with 100% positivity of the lesional and 2% of the control samples. Quantification revealed high TSPyV DNA loads in the lesional samples (∼10 6copies/cell), and low viral loads in the occasionally TSPyV-positive non-lesional and control samples (<10 2copies/cell). TSPyV VP1 protein expression was detected only in lesional TS samples, restricted to the nuclei of inner root sheath cells over-expressing trichohyalin. Conclusions: The high prevalence and load of TSPyV DNA only in TS lesions, and the abundant expression of TSPyV protein in the affected hair follicle cells demonstrate a tight relation between TSPyV infection and TS disease, and indicate involvement of active TSPyV infection in TS pathogenesis