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Abstract Partially autonomous vehicles can help minimize human errors. However, being free from some driving subtasks can result in a low vigilance state, which can affect the driver’s attention towards the road. The present study first tested whether drivers of partially autonomous vehicles would benefit from the addition of auditory versions of the messages presented in variable message signs (VMS), particularly, when they find themselves in a monotonous driving situation. A second aim was to test whether the addition of auditory messages would also produce an indirect effect on the driver’s vigilance, improving performance on other driving subtasks not related to the message processing. Forty-three volunteers participated in a driving simulator study. They completed two tasks: (a) a VMS task, where they had to regain manual control of the car if the VMS message was critical, and (b) a car-following task, where they had to pay attention to the preceding car to respond to occasional brake events. Behavioral and EEG data were registered. Overall, results indicated that the addition of audio messages helped drivers process VMS information more effectively and maintain a higher level of vigilance throughout the driving time. These findings would provide useful information for the development of partially automated vehicles, as their design must guarantee that the driver remains attentive enough to assume control when necessary.

One of the most dangerous situations a warship may face is a missile attack launched from other ships, aircrafts, submarines or land. In addition, given the current scenario, it is not ruled out that a terrorist group may acquire missiles and use them against ships operating close to the coast, which increases their vulnerabilitydue to the limited reaction time. One of the means the ship has for its defense are decoys, designed to deceive the enemy missile. However, for their use to be effective it is necessary to obtain, in a quick way, a valid launching solution. The purpose of this article is to design a methodology to solve the problem of decoy launching and to provide the ship immediately with the necessary data to make the firing decision. To solve the problem machine learning models (neural networks and support vector machines) and a set of training data obtained in simulations will be used. The performance measures obtained with the implementation of multilayer perceptron models allow the replacement of the current procedures based on tables and launching rules with machine learning algorithms that are more flexible and adaptable to a larger number of scenarios.

Generalized additive models provide a flexible and easily-interpretable method for uncovering a nonlinear relationship between response and covariates. In many situations, the effect of a continuous covariate on the response varies across groups defined by the levels of a categorical variable. When confronted with a considerable number of groups defined by the levels of the categorical variable and a factor‐by‐curve interaction is detected in the model, it then becomes important to compare these regression curves. When the null hypothesis of equality of curves is rejected, leading to the clear conclusion that at least one curve is different, we may assume that individuals can be grouped into a number of classes whose members all share the same regression function. We propose a method that allows determining such groups with an automatic selection of their number by means of bootstrapping. The validity and behavior of the proposed method were evaluated through simulation studies. The applicability of the proposed method is illustrated using real data from an experimental study in neurology.

DesignProspective, double-blind clinical trial comparing tetanus-diphtheria vaccine administration routes, intramuscular (IM) vs. subcutaneous (SC) injection, in patients with oral anticoagulants. ISRCTN69942081.Study populationPatients treated with oral anticoagulants, 15 health centers, Vigo (Spain). Sample size, 117 in each group.Outcome variablesSafety analysis: systemic reactions and, at the vaccine administration site, erythematic, swelling, hematoma, granuloma, pain.Effectiveness analysis: differences in tetanus toxoid antibody titers.Independent variables: route, sex, age, baseline serology, number of doses administered.AnalysisFollowing the CONSORT guidelines, we performed an intention-to-treat analysis. We conducted a descriptive study of the variables included in both groups (117 in each group) and a bivariate analysis. Fewer than 5% of missing values. Imputation in baseline and final serology with the median was performed. Lost values were assumed to be values missing at random. We conducted a descriptive study of the variables and compared routes. For safety, multivariate logistic regression was applied, with each safety criterion as outcome and the independent variables. Odds ratios (ORs) were calculated. For effectiveness, a generalized additive mixed model, with the difference between final and initial antibody titers as outcome. Due to the bimodal distribution of the outcome, the normal mixture fitting with gamlssMX was used. All statistical analyses were performed with the gamlss.mx and texreg packages of the R free software environment.ResultsA previously published protocol was used across the 6-year study period. The breakdown by sex and route showed: 102 women and 132 men; and 117 IM and 117 SC, with one dose administered in over 80% of participants. There were no differences between groups in any independent variable. The second and third doses administered were not analyzed, due to the low number of cases. In terms of safety, there were no severe general reactions. Locally, significant adjusted differences were observed: in pain, by sex (male, OR: 0.39) and route (SC, OR: 0.55); in erythema, by sex (male, OR: 0.34) and route (SC, OR: 5.21); and in swelling, by sex (male, OR: 0.37) and route (SC, OR: 2.75). In terms of effectiveness, the model selected was the one adjusted for baseline serology.

BackgroundIn recent years, different tools have been developed to facilitate analysis of social determinants of health (SDH) and apply this to health policy. The possibility of generating predictive models of health outcomes which combine a wide range of socioeconomic indicators with health problems is an approach that is receiving increasing attention. Our objectives are twofold: (1) to predict population health outcomes measured as hospital morbidity, taking primary care (PC) morbidity adjusted for SDH as predictors; and (2) to analyze the geographic variability of the impact of SDH-adjusted PC morbidity on hospital morbidity, by combining data sourced from electronic health records and selected operations of the National Statistics Institute (Instituto Nacional de Estadística/INE).MethodsThe following will be conducted: a qualitative study to select socio-health indicators using RAND methodology in accordance with SDH frameworks, based on indicators published by the INE in selected operations; and a quantitative study combining two large databases drawn from different Spain’s Autonomous Regions (ARs) to enable hospital morbidity to be ascertained, i.e., PC electronic health records and the minimum basic data set (MBDS) for hospital discharges. These will be linked to socioeconomic indicators, previously selected by geographic unit. The outcome variable will be hospital morbidity, and the independent variables will be age, sex, PC morbidity, geographic unit, and socioeconomic indicators.AnalysisTo achieve the first objective, predictive models will be used, with a test-and-training technique, fitting multiple logistic regression models. In the analysis of geographic variability, penalized mixed models will be used, with geographic units considered as random effects and independent predictors as fixed effects.DiscussionThis study seeks to show the relationship between SDH and population health, and the geographic differences determined by such determinants. The main limitations are posed by the collection of data for healthcare as opposed to research purposes, and the time lag between collection and publication of data, sampling errors and missing data in registries and surveys. The main strength lies in the project’s multidisciplinary nature (family medicine, pediatrics, public health, nursing, psychology, engineering, geography).

Identifying the mutational processes that shape the nucleotide composition of the mitochondrial genome (mtDNA) is fundamental to better understand how these genomes evolve. Several methods have been proposed to analyze DNA sequence nucleotide composition and skewness, but most of them lack any measurement of statistical support or were not developed taking into account the specificities of mitochondrial genomes. A new methodology is presented, which is specifically developed for mtDNA to detect compositional changes or asymmetries (AT and CG skews) based on nonparametric regression models and their derivatives. The proposed method also includes the construction of confidence intervals, which are built using bootstrap techniques. This paper introduces an R package, known as seq2R, that implements the proposed methodology. Moreover, an illustration of the use of seq2R is provided using real data, specifically two publicly available complete mtDNAs: the human (Homo sapiens) sequence and a nematode (Radopholus similis) mitogenome sequence.

Resumen: Objetivo: Validar el cuestionario STOP-Bang para la apnea moderada frente al método de referencia (polisomnografía de tipo I) en atención primaria. Método: Estudio de utilidad diagnóstica en atención primaria con una muestra estimada de 85 casos y 85 controles sanos. Con muestreo por conveniencia, 203 pacientes fueron reclutados por sus médicos en seis centros de salud. Se excluyeron 25 y se analizaron 57 mujeres y 121 hombres, de los cuales 74 tenían un índice de hipopnea-apnea (IHA) ≥15. Se evaluaron el STOP-Bang y el IHA observado en la polisomnografía en cada paciente. El tamaño de la muestra, el análisis de la curva ROC y los puntos de corte óptimos se identificaron con los paquetes easyROC, pROC y OptimalCutpoints del software libre R. Resultados: El área bajo la curva en la apnea moderada (IHA ≥15) del STOP-Bang fue 0,737 (0,667-0,808), con puntos de corte óptimos diferentes por sexo (4 en mujeres y 6 en hombres). En la validación cruzada con k = 10, el área bajo la curva para el STOP-Bang fue 0,678. Conclusiones: El STOP-Bang tiene una utilidad diagnóstica moderada para un IHA ≥15, pero superior a la de otras escalas, en una población comunitaria. Su desempeño es más adecuado en las mujeres. Abstract: Objective: We aimed to compare the diagnostic utility of the STOP-Bang questionnaire for moderate apnoea against the gold standard (type I polysomnography) in a primary care setting. Method: Study of diagnostic utility in primary care. Estimated sample: 85 cases and 85 healthy controls. In convenience sampling, 203 patients were recruited by their physicians at six health centres. Twenty-five were excluded, and 57 women and 121 men, of whom 74 had apnoea-hypopnoea index (AHI) ≥15, were analyzed. STOP-Bang was validated by comparing scores in the same patient with the apnoea-hypopnoea index observed in polysomnography, as a gold standard. Sample size, ROC curve analysis and optimal cut-off points were identified with the easyROC, pROC, and OptimalCutpoints packages. Results: The area under the curve in moderate apnoea (AHI ≥15) of the STOP-Bang was 0.777 (0.667-0.808), with optimal cut-off points different by sex (4 in women and 6 in men). In the cross-validation with k = 10, the area under the curve for the STOP-Bang was 0.678. Conclusions: The STOP-Bang presents a diagnostic moderate utility for AHI≥15, but superior to other scales, in a community population. Its performance is more appropriate in women. Palabras clave: Apnea del sueño, STOP-Bang, Utilidad diagnóstica, Sexo, Atención primaria, Keywords: Sleep apnoea, STOP-Bang, Diagnostic utility, Gender, Primary care

Traffic safety is an important social problem. Many accidents are due to non-compliance with traffic regulations. Serious or repeated offenses are sanctioned with penalty points or court conviction, and sanctions can lead to disqualification from driving. This paper explores the relevance of alcohol consumption and personality factors as predictors of driving disqualification. The aim of the study is to determine whether the behaviors of persistent offenders and their propensity for law-breaking are related to their characteristics and patterns of drinking. A sample of 358 drivers participated in the study: 126 non-offender habitual drivers and 232 persistent traffic offenders disqualified from driving for serious or repeated traffic offenses, 127 of them after conviction, 105 without conviction (by accumulation of penalties). Participants were given a battery of tests measuring a set of explanatory personality and alcohol consumption factors. We used a cross-sectional study design and performed statistical analysis of variance and regression searching for differences among the groups. The results reveal group effects, with significant differences in a number of factors between traffic offenders and non-offenders, and between both categories of offenders in a number of variables, including traffic violations that lead to demerit points and/or loss of a driver’s license and crash involvement. Certain variables, including problem drinking, high levels of activity or excitement, penchant for thrill or sensation seeking, and propensity to hostility while driving, can accurately predict group membership. Alcohol disorders are the best predictors of disqualification from driving for serious or repeat traffic offenses, both penalized and convicted.

The increasing concern about bacterial resistance has made the rational prescription of antibiotics even more urgent. The non-pharmacological measures established to reduce the impact of the SARS-CoV-2 pandemic have modified the epidemiology of pediatric infections and, consequently, the use of antibiotics. Interrupted time series (ITS) analyses are quasi-experimental studies that allow for the estimation of causal effects with observational data in “natural experiments”, such as changes in health policies or pandemics. The effect of the SARS-CoV-2 pandemic on the incidence of infectious diseases and the use of antibiotics between 2018 and 2020 in the Health Area of Vigo (Galicia, Spain) was quantified and analyzed. This paper outlines a real-world data study with administrative records from primary care services provided for the pediatric population. The records were related to episodes classified as infectious by the International Classification of Primary Care (ICPC-2) and oral medication in the therapeutic subgroup J01, corresponding to antibiotics for systemic use, according to the World Health Organization’s Anatomical Therapeutic Chemical (ATC) classification system. The records were classified according to incident episodes, age, dose per inhabitant, and year. Segmented regression models were applied using an algorithm that automatically identifies the number and position of the change points. During the SARS-CoV-2 pandemic, the number of infectious diseases being transmitted between individuals, through the air and through the fecal–oral route, significantly decreased, and a slight decrease in infections transmitted via other mechanisms (urinary tract infections) was also found. In parallel, during the months of the pandemic, there has been a marked and significant reduction in antibacterial agent utilization, mainly of penicillins, cephalosporins, and macrolides.

We present the R npregfast package via some applications involved with the study of living organisms. The package implements nonparametric estimation procedures in regression models with or without factor-by-curve interactions. The main feature of the package is its ability to perform inference regarding these models. Namely, the implementation of different procedures to test features of the estimated regression curves: on the one hand, the comparisons between curves which may vary across groups defined by levels of a categorical variable or factor; on the other hand, the comparisons of some critical points of the curve (e.g., maxima, minima or inflection points), studying for this purpose the derivatives of the curve.

Supervised classification of 3D point clouds using machine learning algorithms and handcrafted local features as covariates frequently depends on the size of the neighborhood (scale) around each point used to determine those features. It is therefore crucial to estimate the scale or scales providing the best classification results. In this work, we propose three methods to estimate said scales, all of them based on calculating the maximum values of the distance correlation (DC) functions between the features and the label assigned to each point. The performance of the methods was tested using simulated data, and the method presenting the best results was applied to a benchmark data set for point cloud classification. This method consists of detecting the local maximums of DC functions previously smoothed to avoid choosing scales that are very close to each other. Five different classifiers were used: linear discriminant analysis, support vector machines, random forest, multinomial logistic regression and multilayer perceptron neural network. The results obtained were compared with those from other strategies available in the literature, being favorable to our approach.

Predicting anomalous emission of pollutants into the atmosphere well in advance is crucial for industries emitting such elements, since it allows them to take corrective measures aimed to avoid such emissions and their consequences. In this work, we propose a functional location-scale model to predict in advance pollution episodes where two pollutants are involved. Functional generalized additive models (FGAMs) are used to estimate the means and variances of the model, as well as the correlation between both pollutants. The method not only forecasts the concentrations of both pollutants, it also estimates an uncertainty region where the concentrations of both pollutants should be located, given a specific level of uncertainty. The performance of the model was evaluated using real data of SO 2 and NO x emissions from a coal-fired power station, obtaining good results.

One major goal in clinical applications of multi-state models is the estimation of transition probabilities. The usual nonparametric estimator of the transition matrix for non-homogeneous Markov processes is the Aalen-Johansen estimator (Aalen and Johansen 1978). However, two problems may arise from using this estimator: first, its standard error may be large in heavy censored scenarios; second, the estimator may be inconsistent if the process is non-Markovian. The development of the R package TPmsm has been motivated by several recent contributions that account for these estimation problems. Estimation and statistical inference for transition probabilities can be performed using TPmsm. The TPmsm package provides seven different approaches to three-state illness-death modeling. In two of these approaches the transition probabilities are estimated conditionally on current or past covariate measures. Two real data examples are included for illustration of software usage.

We analyze the utility of multiscale supervised classification algorithms for object detection and extraction from laser scanning or photogrammetric point clouds. Only the geometric information (the point coordinates) was considered, thus making the method independent of the systems used to collect the data. A maximum of five features (input variables) was used, four of them related to the eigenvalues obtained from a principal component analysis (PCA). PCA was carried out at six scales, defined by the diameter of a sphere around each observation. Four multiclass supervised classification models were tested (linear discriminant analysis, logistic regression, support vector machines, and random forest) in two different scenarios, urban and forest, formed by artificial and natural objects, respectively. The results obtained were accurate (overall accuracy over 80% for the urban dataset, and over 93% for the forest dataset), in the range of the best results found in the literature, regardless of the classification method. For both datasets, the random forest algorithm provided the best solution/results when discrimination capacity, computing time, and the ability to estimate the relative importance of each variable are considered together.

Educación y Formación 2020, el nuevo marco estratégico para la cooperación europea en el ámbito de la educación y formación, pone de manifiesto la necesidad de intensificar la movilidad transnacional con fines educativos, al haberse constatado su positiva repercusión en la formación universitaria y en el acceso al mercado laboral de los jóvenes europeos. En este contexto ha de destacarse la "movilidad virtual" que, de forma complementaria a la movilidad física y apoyándose en las crecientes posibilidades que nos ofrecen las TIC, será uno de los próximos retos de la oferta educativa virtual realizada por las universidades españolas. En este trabajo centramos nuestra atención en las ventajas que supone la utilización del Portfolio Europeo de las Lenguas en su versión electrónica (e-PEL) como herramienta metodológica que posibilita el desarrollo de competencias genéricas -como el aprendizaje a lo largo de la vida, la autoevaluación o la aplicación de las TIC- facilitando así la motivación y la movilidad internacional. Así mismo, presentamos también un ejemplo de actividad en la que se integran el aprendizaje de ambas competencias (TIC y lengua extranjera) y que confirma al e-PEL como una herramienta necesaria en la formación universitaria europea.

L’elaboració dels plans d’estudi per als títols universitaris oficials nous suposa un gran repte, perquè demana dotar de sentit l’ensenyament a partir de les competències que han d’adquirir els estudiants i incidir, de manera especial, en l’avaluació d’aquestes i en la seva concreció en resultats d’aprenentatge. Així mateix, tingui’s en compte que, des de la selecció i la definició de les competències fins a la concreció en resultats d’aprenentatge, existeix tot un procés llarg i reflexiu —concreció de capacitats, indicadors de realització, activitats d’aprenentatge, criteris i instruments d’avaluació—, el desenvolupament del qual abordem en aquest treball, amb la intenció de facilitar al professorat el disseny i la posada en pràctica d’aquest model educatiu nou, mitjançant una formació combinada (b-learning) i prenent com a referència alguna de les matèries i/o de les assignatures impartides.

In longitudinal studies of disease, patients can experience several events across a followup period. Analysis of such studies can be successfully performed by multi-state models. In the multi-state framework, issues of interest include the study of the relationship between covariates and disease evolution, estimation of transition probabilities, and survival rates. This paper introduces p3state.msm, a software application for R which performs inference in an illness-death model. It describes the capabilities of the program for estimating semi-parametric regression models and for implementing nonparametric estimators for several quantities. The main feature of the package is its ability for obtaining nonMarkov estimates for the transition probabilities. Moreover, the methods can also be used in progressive three-state models. In such a model, estimators for other quantities, such as the bivariate distribution function (for sequentially ordered events), are also given. The software is illustrated using data from the Stanford Heart Transplant Study.