Evaluating recent methods to overcome spatial confounding

The concept of spatial confounding is closely connected to spatial regression, although no general definition has been established. A generally accepted idea of spatial confounding in spatial regression models is the change in fixed effects estimates that may occur when spatially correlated random effects collinear with the covariate are included in the model. Different methods have been proposed to alleviate spatial confounding in spatial linear regression models, but it is not clear if they provide correct fixed effects estimates. In this article, we consider some of those proposals to alleviate spatial confounding such as restricted regression, the spatial+ model, and transformed Gaussian Markov random fields. The objective is to determine which one provides the best estimates of the fixed effects. Dowry death data in Uttar Pradesh in 2001, stomach cancer incidence data in Slovenia in the period 1995-2001 and lip cancer incidence data in Scotland between the years 1975-1980 are analyzed. Several simulation studies are conducted to evaluate the performance of the methods in different scenarios of spatial confounding. Results reflect that the spatial+ method seems to provide fixed effects estimates closest to the true value

A one-step spatial+ approach to mitigate spatial confounding in multivariate spatial areal models

Ecological spatial areal models encounter the well-known and challenging problem of spatial confounding. This issue makes it arduous to distinguish between the impacts of observed covariates and spatial random effects. Despite previous research and various proposed methods to tackle this problem, finding a definitive solution remains elusive. In this paper, we propose a one-step version of the spatial+ approach that involves dividing the covariate into two components. One component captures large-scale spatial dependence, while the other accounts for short-scale dependence. This approach eliminates the need to separately fit spatial models for the covariates. We apply this method to analyze two forms of crimes against women, namely rapes and dowry deaths, in Uttar Pradesh, India, exploring their relationship with socio-demographic covariates. To evaluate the performance of the new approach, we conduct extensive simulation studies under different spatial confounding scenarios. The results demonstrate that the proposed method provides reliable estimates of fixed effects and posterior correlations between different responses

High-dimensional order-free multivariate spatial disease mapping

Despite the amount of research on disease mapping in recent years, the use of multivariate models for areal spatial data remains limited due to difficulties in implementation and computational burden. These problems are exacerbated when the number of small areas is very large. In this paper, we introduce an order-free multivariate scalable Bayesian modelling approach to smooth mortality (or incidence) risks of several diseases simultaneously. The proposal partitions the spatial domain into smaller subregions, fits multivariate models in each subdivision and obtains the posterior distribution of the relative risks across the entire spatial domain. The approach also provides posterior correlations among the spatial patterns of the diseases in each partition that are combined through a consensus Monte Carlo algorithm to obtain correlations for the whole study region. We implement the proposal using integrated nested Laplace approximations (INLA) in the R package bigDM and use it to jointly analyse colorectal, lung, and stomach cancer mortality data in Spanish municipalities. The new proposal permits the analysis of big data sets and provides better results than fitting a single multivariate model

Estimating unemployment in very small areas

In the last few years, European countries have shown a deep interest in applying small area techniques to produce reliable estimates at county level. However, the specificity of every European country and the heterogeneity of the available auxiliary information, make the use of a common methodology a very difficult task. In this study, the performance of several design-based, model-assisted, and model-based estimators using different auxiliary information for estimating unemployment at small area level is analyzed. The results are illustrated with data from Navarre, an autonomous region located at the north of Spain and divided into seven small areas. After discussing pros and cons of the different alternatives, a composite estimator is chosen, because of its good trade-off between bias and variance. Several methods for estimating the prediction error of the proposed estimator are also provided

Induction of radiata pine somatic embryogenesis at high temperatures provokes a long-term decrease in dna methylation/hydroxymethylation and differential expression of stress-related genes

Based on the hypothesis that embryo development is a crucial stage for the formation of stable epigenetic marks that could modulate the behaviour of the resulting plants, in this study, radiata pine somatic embryogenesis was induced at high temperatures (23¿ C, eight weeks, control; 40¿ C, 4 h; 60¿ C, 5 min) and the global methylation and hydroxymethylation levels of emerging embryonal masses and somatic plants were analysed using LC-ESI-MS/ MS-MRM. In this context, the expression pattern of six genes previously described as stress-mediators was studied throughout the embryogenic process until plant level to assess whether the observed epigenetic changes could have provoked a sustained alteration of the transcriptome. Results indicated that the highest temperatures led to hypomethylation of both embryonal masses and somatic plants. Moreover, we detected for the first time in a pine species the presence of 5-hydroxymethylcytosine, and revealed its tissue specificity and potential involvement in heat-stress responses. Additionally, a heat shock protein-coding gene showed a down-regulation tendency along the process, with a special emphasis given to embryonal masses at first subculture and ex vitro somatic plants. Likewise, the transcripts of several proteins related with translation, oxidative stress response, and drought resilience were differentially expressed

Calbindin D28k Expression and the Absence of Apoptosis in the Cerebellum of Solatium bonariense L-lntoxicated Bovines

Solanum bonariense intoxication is characterized by cerebellar neuronal vacuolation, degeneration, and necrosis. Cerebellar Purkinje cells seem especially susceptible, but more research is needed to determine the pathogenesis of neuronal necrosis and the mechanism of Purkinje cell susceptibility. Calbindin D28k (CbD28k) is highly expressed in Purkinje cells and has been used as a marker for normal and degenerative Purkinje cells. The goal of this study was to describe S bonariense-induced disease by ascertaining Purkinje cell-specific degenerative changes using CbD28k expression and to correlate this with apoptosis in Purkinje cells, as determined using TUNEL (transferase-mediated dUTP-biotin nick end-labeling) and ultrastructural changes. In all cases, an increase in both dose and duration of S bonariense intoxication resulted in a decrease in the number of Purkinje cells. CbD28k immunohistochemistry was an excellent marker for Purkinje cells because immunoreactivity did not change in normal or degenerative tissues. This finding suggests that excessive calcium excitatory stimulation does not induce rapid neuronal degeneration and death. As found in previous studies, TUNEL tests and electron microscopy suggest that Purkinje cell degeneration and death are not occurring via an apoptotic process. These findings suggest that S bonariense poisoning induces progressive Purkinje cell death that is not mediated by excitotoxicity or apoptotic activation.Facultad de Ciencias Veterinaria