Representative bureaucracy: does female police leadership affect gender-based violence arrests?

Representative bureaucracy theory postulates that passive representation leads to active representation of minority groups. This article investigates the passive representation of female police officers at leadership levels and the active representation of women vis-a-vis gender-based violence arrest rates in the UK. Much of the extant research on representative bureaucracy is located at street level, with evidence showing that discretionary power of minority bureaucrats can lead to active representation. This article is focused on leadership levels of a public bureaucracy. The empirical research is based upon a panel dataset of female police officers as an independent variable and gender-based violence arrest rates as a dependent variable. The analysis reveals that there is little evidence of active representation of women by female police leadership

Inflammatory and oxidative stress markers associated with decreased cervical length in pregnancy

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134275/1/aji12545_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134275/2/aji12545.pd

Statistical strategies for constructing health risk models with multiple pollutants and their interactions: possible choices and comparisons

Abstract Background As public awareness of consequences of environmental exposures has grown, estimating the adverse health effects due to simultaneous exposure to multiple pollutants is an important topic to explore. The challenges of evaluating the health impacts of environmental factors in a multipollutant model include, but are not limited to: identification of the most critical components of the pollutant mixture, examination of potential interaction effects, and attribution of health effects to individual pollutants in the presence of multicollinearity. Methods In this paper, we reviewed five methods available in the statistical literature that are potentially helpful for constructing multipollutant models. We conducted a simulation study and presented two data examples to assess the performance of these methods on feature selection, effect estimation and interaction identification using both cross-sectional and time-series designs. We also proposed and evaluated a two-step strategy employing an initial screening by a tree-based method followed by further dimension reduction/variable selection by the aforementioned five approaches at the second step. Results Among the five methods, least absolute shrinkage and selection operator regression performs well in general for identifying important exposures, but will yield biased estimates and slightly larger model dimension given many correlated candidate exposures and modest sample size. Bayesian model averaging, and supervised principal component analysis are also useful in variable selection when there is a moderately strong exposure-response association. Substantial improvements on reducing model dimension and identifying important variables have been observed for all the five statistical methods using the two-step modeling strategy when the number of candidate variables is large. Conclusions There is no uniform dominance of one method across all simulation scenarios and all criteria. The performances differ according to the nature of the response variable, the sample size, the number of pollutants involved, and the strength of exposure-response association/interaction. However, the two-step modeling strategy proposed here is potentially applicable under a multipollutant framework with many covariates by taking advantage of both the screening feature of an initial tree-based method and dimension reduction/variable selection property of the subsequent method. The choice of the method should also depend on the goal of the study: risk prediction, effect estimation or screening for important predictors and their interactions.http://deepblue.lib.umich.edu/bitstream/2027.42/112386/1/12940_2013_Article_691.pd

Associations between repeated ultrasound measures of fetal growth and biomarkers of maternal oxidative stress and inflammation in pregnancy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146468/1/aji13017_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146468/2/aji13017.pd

The Fitness Landscape of HIV-1 Gag: Advanced Modeling Approaches and Validation of Model Predictions by In Vitro Testing

Viral immune evasion by sequence variation is a major hindrance to HIV-1 vaccine design. To address this challenge, our group has developed a computational model, rooted in physics, that aims to predict the fitness landscape of HIV-1 proteins in order to design vaccine immunogens that lead to impaired viral fitness, thus blocking viable escape routes. Here, we advance the computational models to address previous limitations, and directly test model predictions against in vitro fitness measurements of HIV-1 strains containing multiple Gag mutations. We incorporated regularization into the model fitting procedure to address finite sampling. Further, we developed a model that accounts for the specific identity of mutant amino acids (Potts model), generalizing our previous approach (Ising model) that is unable to distinguish between different mutant amino acids. Gag mutation combinations (17 pairs, 1 triple and 25 single mutations within these) predicted to be either harmful to HIV-1 viability or fitness-neutral were introduced into HIV-1 NL4-3 by site-directed mutagenesis and replication capacities of these mutants were assayed in vitro. The predicted and measured fitness of the corresponding mutants for the original Ising model (r = −0.74, p = 3.6×10−6) are strongly correlated, and this was further strengthened in the regularized Ising model (r = −0.83, p = 3.7×10−12). Performance of the Potts model (r = −0.73, p = 9.7×10−9) was similar to that of the Ising model, indicating that the binary approximation is sufficient for capturing fitness effects of common mutants at sites of low amino acid diversity. However, we show that the Potts model is expected to improve predictive power for more variable proteins. Overall, our results support the ability of the computational models to robustly predict the relative fitness of mutant viral strains, and indicate the potential value of this approach for understanding viral immune evasion, and harnessing this knowledge for immunogen design

Associations between mixtures of urinary phthalate metabolites with gestational age at delivery: a time to event analysis using summative phthalate risk scores

Abstract Background Preterm birth is a significant public health concern and exposure to phthalates has been shown to be associated with an increased odds of preterm birth. Even modest reductions in gestational age at delivery could entail morbid consequences for the neonate and analyzing data with this additional information may be useful. In the present analysis, we consider gestational age at delivery as our outcome of interest and examine associations with multiple phthalates. Methods Women were recruited early in pregnancy as part of a prospective, longitudinal birth cohort at the Brigham and Women’s Hospital in Boston, Massachusetts. Urine samples were collected at up to four time points during gestation for urinary phthalate metabolite measurement, and birth outcomes were recorded at delivery. From this population, we selected all 130 cases of preterm birth (< 37 weeks of gestation) as well as 352 random controls. We conducted analysis with both geometric average of the exposure concentrations across the first three visits as well as using repeated measures of the exposure. Two different time to event models were used to examine associations between nine urinary phthalate metabolite concentrations and time to delivery. Two different approaches to constructing a summative phthalate risk score were also considered. Results The single-pollutant analysis using a Cox proportional hazards model showed the strongest association with a hazard ratio (HR) of 1.21 (95% confidence interval (CI): 1.09, 1.33) per interquartile range (IQR) change in average log-transformed mono-2-ethyl-5-carboxypentyl phthalate (MECPP) concentration. Using the accelerated failure time model, we observed a 1.19% (95% CI: 0.26, 2.11%) decrease in gestational age in association with an IQR change in average log-transformed MECPP. We next examined associations with an environmental risk score (ERS). The fourth quartile of ERS was significantly associated with a HR of 1.44 (95% CI: 1.19, 1.75) and a reduction of 2.55% (95% CI: 0.76, 4.30%) in time to delivery (in days) compared to the first quartile. Conclusions On average, pregnant women with higher urinary metabolite concentrations of individual phthalates have shorter time to delivery. The strength of the observed associations are amplified with the risk scores when compared to individual pollutants.https://deepblue.lib.umich.edu/bitstream/2027.42/144517/1/12940_2018_Article_400.pd

The importance of Icelandic ice sheet growth and retreat on mantle CO2 flux

Climate cycles may significantly affect the eruptive behavior of terrestrial volcanoes due to pressure changes caused by glacial loading, which raises the possibility that climate change may modulate CO2 degassing via volcanism. In Iceland, magmatism is likely to have been influenced by glacial activity. To explore if deglaciation therefore impacted CO2 flux we coupled a model of glacial loading over the last ∼120 ka to melt generation and transport. We find that a nuanced relationship exists between magmatism and glacial activity. Enhanced CO2 degassing happened prior to the main phase of late‐Pleistocene deglaciation, and it is sensitive to the duration of the growth of the ice sheet entering into the LGM, as well as the rate of ice loss. Ice sheet growth depresses melting in the upper mantle, creating a delayed pulse of CO2 out‐gassing as the magmatic system recovers from the effects of loading