Helminth parasites of the wood mouse, Apodemus sylvaticus (n = 440), were surveyed in five localities, comprising woodland and grassland sites, in Southern England. Seven species of helminths were identified, among which Heligmosomoides polygyrus and Syphacia stroma were dominant (prevalence = 79.1% and 54.1%, respectively). Less common species were the trematode Corrigia vitta (14.8%), cestodes Catenotaenia pusilla (8.4%), Hydatigera taeniaeformis (4.1%) and Microsomacanthus crenata (3.4%) and the nematode Aonchotheca murissylvatici (0.2%). Differences in prevalences between localities were found for H. polygyrus, H. taeniaeformis and M. crenata and in abundances of H. polygyrus, S. stroma and C. vitta. Age-dependent increases in both parameters were identified among species and for helminth species richness. The only species to show significant host sex bias was S. stroma with prevalence values being higher in male mice. A number of different methods for exploiting raw data, and data corrected for significant confounding factors, were used to determine whether there were significant associations (prevalence) between species or quantitative interactions (abundance). The strongest evidence for a positive association was shown in concurrent infections with the trematode C. vitta and the cestode C. pusilla (significant in the whole dataset and evident in each locality, both sexes and both age classes). The abundance of C. pusilla was also higher in mice with C. vitta and vice versa. Overall, however, there was little support for associations or quantitative interactions between species, especially after data had been corrected for significant extrinsic/intrinsic factors, and we conclude that the helminths of wood mice in these communities are largely non-interactive and hence, perhaps better referred to as assemblages.

Recent research has shown that interaction effects may often be nonlinear (Hainmueller, Mummolo, and Xu [2019, Political Analysis 27, 163–192]). As standard interaction effect specifications assume a linear interaction effect, that is, the moderator conditions the effect at a constant rate, this can lead to bias. However, allowing nonlinear interaction effects, without accounting for other nonlinearities and nonlinear interaction effects, can also lead to biased estimates. Specifically, researchers can infer nonlinear interaction effects, even though the true interaction effect is linear, when variables used for covariate adjustment that are correlated with the moderator have a nonlinear effect upon the outcome of interest. We illustrate this bias with simulations and show how diagnostic tools recommended in the literature are unable to uncover the issue. We show how using the adaptive Lasso to identify relevant nonlinearities among variables used for covariate adjustment can avoid this issue. Moreover, the use of regularized estimators, which allow for a fuller set of nonlinearities, both independent and interactive, is more generally shown to avoid this bias and more general forms of omitted interaction bias.

Answer set programs used in real-world applications often require that the program is usable with different input data. This, however, can often lead to contradictory statements and consequently to an inconsistent program. Causes for potential contradictions in a program are conflicting rules. In this paper, we show how to ensure that a program $\mathcal{P}$ remains non-contradictory given any allowed set of such input data. For that, we introduce the notion of conflict-resolving ${\lambda}$-extensions. A conflict-resolving ${\lambda}$-extension for a conflicting rule r is a set ${\lambda}$ of (default) literals such that extending the body of r by ${\lambda}$ resolves all conflicts of r at once. We investigate the properties that suitable ${\lambda}$-extensions should possess and building on that, we develop a strategy to compute all such conflict-resolving ${\lambda}$-extensions for each conflicting rule in $\mathcal{P}$. We show that by implementing a conflict resolution process that successively resolves conflicts using ${\lambda}$-extensions eventually yields a program that remains non-contradictory given any allowed set of input data.

Diet and physical activity (PA) have been studied extensively in epidemiology as single or combined lifestyle factors; however, their interaction has not been studied thoroughly. Studying potential synergisms between lifestyle components with a comprehensive interaction analysis, including additive measures of interaction, provides key insights into the nature of their joint effect and helps target interventions more effectively. First, a comprehensive review was conducted to assess the potential research gap regarding reported interaction analyses conducted in studies assessing the Mediterranean diet (MedDiet) in combination with PA on all-cause mortality. Thereafter, we prospectively assessed the joint association of the MedDiet with PA on all-cause mortality in the Seguimiento Universidad de Navarra (SUN) cohort, followed by both multiplicative and additive interaction analyses. The conjoint effect of low adherence to the MedDiet and low PA observed an increased risk greater than the individual risk factors, suggesting a potential additive interaction or synergism between both exposures, with relative risk due to interaction (RERI) and (95 % confidence interval (95 % CI)) = 0·46 (–0·83 to 1·75) and attributable proportion (95 % CI) due to interaction of 36 % (–0·62, 1·34). No multiplicative interaction was detected. Studying interactions between lifestyle factors, such as the MedDiet and PA, is particularly relevant given the current research gaps in studying the complexities of combined aspects of lifestyle in comparison with isolated behaviours. Our findings underline the important public health message of adhering to both the MedDiet and PA for the prevention of premature mortality.

Analyzing variation in treatment effects across subsets of the population is an important way for social scientists to evaluate theoretical arguments. A common strategy in assessing such treatment effect heterogeneity is to include a multiplicative interaction term between the treatment and a hypothesized effect modifier in a regression model. Unfortunately, this approach can result in biased inferences due to unmodeled interactions between the effect modifier and other covariates, and including these interactions can lead to unstable estimates due to overfitting. In this paper, we explore the usefulness of machine learning algorithms for stabilizing these estimates and show how many off-the-shelf adaptive methods lead to two forms of bias: direct and indirect regularization bias. To overcome these issues, we use a post-double selection approach that utilizes several lasso estimators to select the interactions to include in the final model. We extend this approach to estimate uncertainty for both interaction and marginal effects. Simulation evidence shows that this approach has better performance than competing methods, even when the number of covariates is large. We show in two empirical examples that the choice of method leads to dramatically different conclusions about effect heterogeneity.

This paper presents an aeromechanics investigation of tiltrotor aircraft through the conversion regime of flight. The effects of the rotors-on-wing, rotors-on-empennage and wing-on-empennage interactions were investigated singularly and collectively to assess their impacts on trim behaviour, performance and conversion boundaries. The rotors-on-wing download was found to be dominant in the prediction of hover and low-speed flight performance and had a degrading effect overall. The fuselage pitch attitude and stick position were found to be significantly affected by the empennage interaction cases throughout the conversion domain. The large flap/flaperon settings used to alleviate the rotor download contributed considerably to the low-speed trim behaviour. The conversion boundaries were found to be insensitive to all the interaction cases, though the min-speed boundary was reduced marginally due to the wing-on-empennage interaction. The results showed that the combined interactions were important factors to accurately predict the trim behaviour and aircraft performance throughout the conversion corridor.