An effective procedure has been developed to consolidate and hydrophobize decayed monumental stones by a simple sol-gel process. The sol contains silica oligomer, silica nanoparticles and a surfactant, preventing gel cracking. The effectiveness of the process on biocalcareous stone samples from an 18th century cathedral has been evaluated, and it was found that the gel creates effective linking bridges between mineral grains of the stone. Silica nanoparticles produced a significant increase in the mechanical resistance and cohesion of the stone. The application of an additional fluorinated oligomer onto the consolidated stone gave rise to a surface with lasting hydrophobicity, preventing water absorption.
Spatially-referenced geostatistical responses that are collected in environmental sciences research are often subject to detection limits, where the measures are not fully quantifiable. This leads to censoring (left, right, interval, etc), and various ad hoc statistical methods (such as choosing arbitrary detection limits, or data augmentation) are routinely employed during subsequent statistical analysis for inference and prediction. However, inference may be imprecise and sensitive to the assumptions and approximations involved in those arbitrary choices. To circumvent this, we propose an maximum likelihood estimation framework of the fixed effects and variance components and related prediction via a novel application of the Stochastic Approximation of the Expectation Maximization (SAEM) algorithm, allowing for easy and elegant estimation of model parameters under censoring. Both simulation studies and application to a real dataset on arsenic concentration collected by the Michigan Department of Environmental Quality demonstrate the advantages of our method over the available naïve techniques in terms of finite sample properties of the estimates, prediction, and robustness. The proposed methods can be implemented using the R package CensSpatial.
El propósito de este trabajo es explorar, a partir de los datos de la encuesta GEM 2012, los factores que pueden afectar la probabilidad de que un colombiano que ha retornado al país, sea emprendedor por oportunidad. La exploración se hizo a partir de la estimación de modelos de respuesta binaria. Los resultados muestran que las variables que mejor explican que un migrante retornado emprenda por oportunidad son: las expectativas positivas en el país para la creación de empresas en un horizonte de seis meses, los ahorros, y los contactos realizados durante su permanencia en el exterior. Este trabajo es uno de los primeros en abordar los factores que afectan la probabilidad de que los migrantes retornados sean emprendedores por oportunidad, sin embargo, no deja de ser de carácter exploratorio, debido a las limitaciones en las fuentes de información disponibles. Se requieren más investigaciones que profundicen en el tema.
ImportanceLatin America has implemented the world’s largest and most consolidated conditional cash transfer (CCT) programs during the last 2 decades. As a consequence of the COVID-19 pandemic, poverty rates have markedly increased, and a large number of newly low-income individuals, especially children, have been left unprotected.ObjectiveTo evaluate the association of CCT programs with child health in Latin American countries during the last 2 decades and forecast child mortality trends up to 2030 according to CCT alternative implementation options.Design, Setting, and ParticipantsThis cohort study used a multicountry, longitudinal, ecological design with multivariable negative binomial regression models, which were adjusted for all relevant demographic, socioeconomic, and health care variables, integrating the retrospective impact evaluations from January 1, 2000, to December 31, 2019, with dynamic microsimulation models to forecast potential child mortality scenarios up to 2030. The study cohort included 4882 municipalities from Brazil, Ecuador, and Mexico with adequate quality of civil registration and vital statistics according to a validated multidimensional criterion. Data analysis was performed from September 2022 to February 2023.ExposureConditional cash transfer coverage of the target (lowest-income) population categorized into 4 levels: low (0%-29.9%), intermediate (30.0%-69.9%), high (70.0%-99.9%), and consolidated (≥100%).Main Outcomes and MeasuresThe main outcomes were mortality rates for those younger than 5 years and hospitalization rates (per 1000 live births), overall and by poverty-related causes (diarrheal, malnutrition, tuberculosis, malaria, lower respiratory tract infections, and HIV/AIDS), and the mortality rates for those younger than 5 years by age groups, namely, neonatal (0-28 days), postneonatal (28 days to 1 year), infant (<1 year), and toddler (1-4 years).ResultsThe retrospective analysis included 4882 municipalities. During the study period of January 1, 2000, to December 31, 2019, mortality in Brazil, Ecuador, and Mexico decreased by 7.8% in children and 6.5% in infants, and an increase in coverage of CCT programs of 76.8% was observed in these Latin American countries. Conditional cash transfer programs were associated with significant reductions of mortality rates in those younger than 5 years (rate ratio [RR], 0.76; 95% CI, 0.75-0.76), having prevented 738 919 (95% CI, 695 641-782 104) child deaths during this period. The association of highest coverage of CCT programs was stronger with poverty-related diseases, such as malnutrition (RR, 0.33; 95% CI, 0.31-0.35), diarrhea (RR, 0.41; 95% CI, 0.40-0.43), lower respiratory tract infections (RR, 0.66, 95% CI, 0.65-0.68), malaria (RR, 0.76; 95% CI, 0.63-0.93), tuberculosis (RR, 0.62; 95% CI, 0.48-0.79), and HIV/AIDS (RR, 0.32; 95% CI, 0.28-0.37). Several sensitivity and triangulation analyses confirmed the robustness of the results. Considering a scenario of moderate economic crisis, a mitigation strategy that will increase the coverage of CCTs to protect those newly in poverty could reduce the mortality rate for those younger than 5 years by up to 17% (RR, 0.83; 95% CI, 0.80-0.85) and prevent 153 601 (95% CI, 127 441-180 600) child deaths by 2030 in Brazil, Ecuador, and Mexico.Conclusions and RelevanceThe results of this cohort study suggest that the expansion of CCT programs could strongly reduce childhood hospitalization and mortality in Latin America and should be considered an effective strategy to mitigate the health impact of the current global economic crisis in low- and middle-income countries.
Gene expression data have been very useful during the past two decades for the detection of differentially expressed genes when two (or more) biological conditions are compared. Studies seeking for differentially expressed genes are based on testing gene by gene for a mean differential expression between two conditions. Nevertheless, the global shift in gene expression when taking into account all genes present on a microarray experiment, has not yet been investigated and could provide different information on genes that could be affected by the condition under research. Such a global approach would help identifying a gene expression threshold, characteristic of a certain condition and therefore could be used for diagnosis together with the list of differentially expressed genes detected by classical methods. Moreover, characterizing genes below or above such a threshold could give new insights into the molecular mechanisms implicated functionally in each condition. Here, we present a simple methodology, based on heuristics, gene filtering, variable transformation and descriptive statistics in order to identify such global gene expression shifts and the characteristic threshold so the same can be applied by any professional that works with expression gene data and not only by statisticians. Our procedure is illustrated on a real gene expression data set comparing pathogen inoculated tomatoes with non-inoculated tomatoes. This methodology can be used for the identification of the threshold values when we have continuous variable data sets from two populations with overlapped distributional forms (histograms) in most of their percentiles.
The choice of a prior distribution is a key aspect of the Bayesian method. However, in many cases, such as the family of power links, this is not trivial. In this article, we introduce a penalized complexity prior (PC prior) of the skewness parameter for this family, which is useful for dealing with imbalanced data. We derive a general expression for this density and show its usefulness for some particular cases such as the power logit and the power probit links. A simulation study and a real data application are used to assess the efficiency of the introduced densities in comparison with the Gaussian and uniform priors. Results show improvement in point and credible interval estimation for the considered models when using the PC prior in comparison to other well‐known standard priors.
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