This study uses a rational choice approach to argue that an under-theorized and rarely tested cause of governmental discrimination against religious minorities is its popularity. Specifically, we argue that self-interested politicians are more likely to enact discriminatory policies when they believe said discrimination will be popular. These policies, in turn, have payoffs via increased public perceptions of governmental legitimacy. Using the Religion and State project, round 3 and World Values Survey data for members of the majority religion between 1990 and 2014 in 58 Christian-majority countries, we demonstrate that prejudice against members of other religions predicts increased governmental religious discrimination, which is, in turn, associated with higher confidence in government, legislatures, and political parties. While our results are specific to discrimination against religious minorities, this suggests that when discrimination against minorities in general is popular, politicians are likely to oblige.

Site-specific weed management (on the scale of a few meters or less) has the potential to greatly reduce pesticide use and its associated environmental and economic costs. A prerequisite for site-specific weed management is the availability of accurate maps of the weed population that can be generated quickly and cheaply. Improvements and cost reductions in unmanned aerial vehicles (UAVs) and camera technology mean these tools are now readily available for agricultural use. We used UAVs to collect aerial images captured in both RGB and multispectral formats of 12 cereal fields (wheat [Triticum aestivum L.] and barley [Hordeum vulgare L.]) across eastern England. These data were used to train machine learning models to generate prediction maps of locations of black-grass (Alopecurus myosuroides Huds.), a prolific weed in UK cereal fields. We tested machine learning and data set resampling methods to obtain the most accurate system for predicting the presence and absence of weeds in new out-of-sample fields. The accuracy of the system in predicting the absence of A. myosuroides is 69% and its presence above 5 g in weight with 77% accuracy in new out-of-sample fields. This system generates prediction maps that can be used by either agricultural machinery or autonomous robotic platforms for precision weed management. Improvements to the accuracy can be made by increasing the number of fields and samples in the data set and the length of time over which data are collected to gather data across the entire growing season.

Most assume that when governments support a religion, they do so in the hope that they will increase their legitimacy. However, a growing literature implies that support for religion may decrease a government’s legitimacy for three reasons. First, political secularism, an ideology mandating the separation of religion and state or state restrictions on religion, is increasingly popular. Second, state support for religion can undermine religious vitality. Third, support for religion entails an element of government control over religion which can undermine the perceived authenticity of a religion. We test this support–legitimacy relationship in Christian-majority countries from 1990 to 2014 using the Religion and State and World Values Survey data, comprising 54 countries and 126 country years. We find that state support for religion is associated with lower levels of individual confidence in government. We posit this has important implications for our understanding of the underpinnings of legitimacy.

The US government invests substantial sums to control the HIV/AIDS epidemic. To monitor progress toward epidemic control, PEPFAR, or the President’s Emergency Plan for AIDS Relief, oversees a data reporting system that includes standard indicators, reporting formats, information systems, and data warehouses. These data, reported quarterly, inform understanding of the global epidemic, resource allocation, and identification of trouble spots. PEPFAR has developed tools to assess the quality of data reported. These tools made important contributions but are limited in the methods used to identify anomalous data points. The most advanced consider univariate probability distributions, whereas correlations between indicators suggest a multivariate approach is better suited. For temporal analysis, the same tool compares values to the averages of preceding periods, though does not consider underlying trends and seasonal factors. To that end, we apply two methods to identify anomalous data points among routinely collected facility-level HIV/AIDS data. One approach is Recommender Systems, an unsupervised machine learning method that captures relationships between users and items. We apply the approach in a novel way by predicting reported values, comparing predicted to reported values, and identifying the greatest deviations. For a temporal perspective, we apply time series models that are flexible to include trend and seasonality. Results of these methods were validated against manual review (95% agreement on non-anomalies, 56% agreement on anomalies for recommender systems; 96% agreement on non-anomalies, 91% agreement on anomalies for time series). This tool will apply greater methodological sophistication to monitoring data quality in an accelerated and standardized manner.

Perturbations to the gut microbiome are implicated in altered neurodevelopmental trajectories that may shape life span risk for emotion dysregulation and affective disorders. However, the sensitive periods during which the microbiome may influence neurodevelopment remain understudied. We investigated relationships between gut microbiome composition across infancy and temperament at 12 months of age. In 67 infants, we examined if gut microbiome composition assessed at 1–3 weeks, 2, 6, and 12 months of age was associated with temperament at age 12 months. Stool samples were sequenced using the 16S Illumina MiSeq platform. Temperament was assessed using the Infant Behavior Questionnaire-Revised (IBQ-R). Beta diversity at age 1–3 weeks was associated with surgency/extraversion at age 12 months. Bifidobacterium and Lachnospiraceae abundance at 1–3 weeks of age was positively associated with surgency/extraversion at age 12 months. Klebsiella abundance at 1–3 weeks was negatively associated with surgency/extraversion at 12 months. Concurrent composition was associated with negative affectivity at 12 months, including a positive association with Ruminococcus-1 and a negative association with Lactobacillus. Our findings support a relationship between gut microbiome composition and infant temperament. While exploratory due to the small sample size, these results point to early and late infancy as sensitive periods during which the gut microbiome may exert effects on neurodevelopment.