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Data on toxic effects are at large missing the prevailing understanding of the risks of industrial chemicals. Thyroid hormone (TH) system disruption includes interferences of the life cycle of the thyroid hormones and may occur in various organs. In the current study, high-throughput screening data available for 14 putative molecular initiating events of adverse outcome pathways, related to disruption of the TH system, were used to develop 19 in silico models for identification of potential thyroid hormone system-disrupting chemicals. The conformal prediction framework with the underlying Random Forest was used as a wrapper for the models allowing for setting the desired confidence level and controlling the error rate of predictions. The trained models were then applied to two different databases: (i) an in-house database comprising xenobiotics identified in human blood and ii) currently used chemicals registered in the Swedish Product Register, which have been predicted to have a high exposure index to consumers. The application of these models showed that among currently used chemicals, fewer were overall predicted as active compared to chemicals identified in human blood. Chemicals of specific concern for TH disruption were identified from both databases based on their predicted activity.

Motivated by the development of optimal dispatching strategies for prehospital resources, we model the spatial distribution of ambulance call events in the Swedish municipality Skellefteå during 2014–2018 in order to identify important spatial covariates and discern hotspot regions. Our large-scale multivariate data point pattern of call events consists of spatial locations and marks containing the associated priority levels and sex labels. The covariates used are related to road network coverage, population density, and socio-economic status. For each marginal point pattern, we model the associated intensity function by means of a log-linear function of the covariates and their interaction terms, in combination with lasso-like elastic-net regularized composite/Poisson process likelihood estimation. This enables variable selection and collinearity adjustment as well as reduction of variance inflation from overfitting and bias from underfitting. To incorporate mobility adjustment, reflecting people’s movement patterns, we also include a nonparametric (kernel) intensity estimate as an additional covariate. The kernel intensity estimation performed here exploits a new heuristic bandwidth selection algorithm. We discover that hotspot regions occur along dense parts of the road network. A mean absolute error evaluation of the fitted model indicates that it is suitable for designing prehospital resource dispatching strategies. Supplementary materials accompanying this paper appear online.

Exposing sires to various environmental manipulations has demonstrated that paternal effects can be non-trivial also in species where male investment in offspring is almost exclusively limited to sperm. Whether paternal effects also have a genetic component (i.e. paternal indirect genetic effects (PIGEs)) in such species is however largely unknown, primarily because of methodological difficulties separating indirect from direct effects of genes. PIGEs may nevertheless be important since they have the capacity to contribute to evolutionary change. Here we use

Statistical methods are commonly used to monitor quality in manufacturing processes. We consider a set of problems where the probability that a unit is defect, i.e., the defect probability, is influenced by a large number of sub-processes, and where the overall aim is to monitor the defect probability and link abnormalities in quality to observed production variables. We developed a simulation framework for studying the performances of statistical methods used to solve the considered problem. Fourteen prediction procedures were obtained by combining six prediction methods (linear regression, logistic regression, LASSO, penalized logistic regression, support vector machines, gradient boosting decision trees) and two pre-processing procedures. These prediction procedures were evaluated on four types of simulated datasets with different relationships between the explanatory variables and the defect probabilities. Additionally, two established methods for variable selection were compared to a novel method called mixed moments selection (MMS). MMS was more robust than the other methods, performed well on all dataset types, and can easily be combined with any type of prediction method. Additionally, it was shown that it can be advantageous to complement the original explanatory variables with their squared values prior to analyzing the data. Overall, a procedure combining MMS, including additional quadratic terms and using PLR had the best performance. The proposed framework can be applied to evaluate any type of prediction procedure for the general problem we consider. This would increase the understanding of different procedures and facilitate the selection of procedures for a specific problem.