Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations.

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022.

[Predictions of three mathematical models related with the COVID-19 Vaccination Strategy in Spain. June 2021.].

The Ministry of Health has coordinated three studies that have estimated the impact of the COVID-19 Vaccination Strategy in Spain. The models aim to help how to establish priority population groups for vaccination, in an initial context of dose limitation. With the same epidemiological and vaccine information, the results of this three different mathematical models point in the same direction: combined with physical distancing, staggered vaccination, starting with the high risk groups, would prevent 60% of infections, 42% of hospitalizations and 60% of mortality in the population.

Comparing quantile regression methods for probabilistic forecasting of NO pollution levels.

High concentration episodes for NO2 are increasingly dealt with by authorities through traffic restrictions which are activated when air quality deteriorates beyond certain thresholds. Foreseeing the probability that pollutant concentrations reach those thresholds becomes thus a necessity. Probabilistic forecasting, as oposed to point-forecasting, is a family of techniques that allow for the prediction of the expected distribution function instead of a single future value.

Handwritten Geometrical Patterns in the Evaluation of Motor Symptoms in Psychotic Disorders.

The analysis of handwriting has been used in several contexts. For example, handwriting has shown to be of value in the study of motor symptoms in neurological and mental disorders. In the present work, the geometric analysis of handwriting patterns is proposed as a tool to evaluate motor symptoms in psychotic disorders.

Precise automatic classification of 46 different pollen types with convolutional neural networks.

In palynology, the visual classification of pollen grains from different species is a hard task which is usually tackled by human operators using microscopes. Many industries, including medical and pharmaceutical, rely on the accuracy of this manual classification process, which is reported to be around 67%. In this paper, we propose a new method to automatically classify pollen grains using deep learning techniques that improve the correct classification rates in images not previously seen by the models.

Introspective and Neurophysiological Measures of Mind Wandering in Schizophrenia.

Patients with schizophrenia have often been considered to be "in their own world". However, this casual observation has not been proven by scientific evidence so far. This can be explained because scientific research has usually addressed cognition related to the processing of external stimuli, but only recently have efforts been made to explain thoughts, images and feelings not directly related to the external environment.

Direct assessment of health impacts on hospital admission from traffic intensity in Madrid.

In this paper we establish the attributable risk on respiratory and cardiovascular disorders related to traffic intensity in Madrid. In contrast to previous related studies, the proposed approach directly associates road traffic counts to patient emergency admission rates instead of using primary air pollutants. By applying Shapley values over gradient boosting machines, a first selection step is performed among all traffic observation points based on their influence on patient emergency admissions at Gregorio Marañon hospital.

Handwriting movements for assessment of motor symptoms in schizophrenia spectrum disorders and bipolar disorder.

The main aim of the present study was to explore the value of several measures of handwriting in the study of motor abnormalities in patients with bipolar or psychotic disorders. 54 adult participants with a schizophrenia spectrum disorder or bipolar disorder and 44 matched healthy controls, participated in the study. Participants were asked to copy a handwriting pattern consisting of four loops, with an inking pen on a digitizing tablet.

EEG Multiscale Complexity in Schizophrenia During Picture Naming.

Unlabelled: Patients with schizophrenia show cognitive deficits that are evident both behaviourally and with EEG recordings. Recent studies have suggested that non-linear analyses of EEG might more adequately reflect the complex, irregular, non-stationary behavior of neural processes than more traditional ERP measures. Non-linear analyses have been mainly applied to EEGs from patients at rest, whereas differences in complexity might be more evident during task performance.

Improving classification of pollen grain images of the POLEN23E dataset through three different applications of deep learning convolutional neural networks.

In palynology, the visual classification of pollen grains from different species is a hard task which is usually tackled by human operators using microscopes. Its complete automatization would save a high quantity of resources and provide valuable improvements especially for allergy-related information systems, but also for other application fields as paleoclimate reconstruction, quality control of honey based products, collection of evidences in criminal investigations or fabric dating and tracking. This paper presents three state-of-the-art deep learning classification methods applied to the recently published POLEN23E image dataset.

Spatial Analysis of Handwritten Texts as a Marker of Cognitive Control.

We explore the idea that cognitive demands of the handwriting would influence the degree of automaticity of the handwriting process, which in turn would affect the geometric parameters of texts. We compared the heterogeneity of handwritten texts in tasks with different cognitive demands; the heterogeneity of texts was analyzed with lacunarity, a measure of geometrical invariance. In Experiment 1, we asked participants to perform two tasks that varied in cognitive demands: transcription and exposition about an autobiographical episode.

Probabilistic forecasting for extreme NO pollution episodes.

In this study, we investigate the convenience of quantile regression to predict extreme concentrations of NO. Contrarily to the usual point-forecasting, where a single value is forecast for each horizon, probabilistic forecasting through quantile regression allows for the prediction of the full probability distribution, which in turn allows to build models specifically fit for the tails of this distribution. Using data from the city of Madrid, including NO concentrations as well as meteorological measures, we build models that predict extreme NO concentrations, outperforming point-forecasting alternatives, and we prove that the predictions are accurate, reliable and sharp.

What are the most important variables for Poaceae airborne pollen forecasting?

In this paper, the problem of predicting future concentrations of airborne pollen is solved through a computational intelligence data-driven approach. The proposed method is able to identify the most important variables among those considered by other authors (mainly recent pollen concentrations and weather parameters), without any prior assumptions about the phenological relevance of the variables. Furthermore, an inferential procedure based on non-parametric hypothesis testing is presented to provide statistical evidence of the results, which are coherent to the literature and outperform previous proposals in terms of accuracy.

Predicting the Poaceae pollen season: six month-ahead forecasting and identification of relevant features.

In this paper, we approach the problem of predicting the concentrations of Poaceae pollen which define the main pollination season in the city of Madrid. A classification-based approach, based on a computational intelligence model (random forests), is applied to forecast the dates in which risk concentration levels are to be observed. Unlike previous works, the proposal extends the range of forecasting horizons up to 6 months ahead.

Interference control commonalities in patients with schizophrenia, bipolar disorder, and borderline personality disorder.

Background: Whereas deficits in executive functioning have been widely reported in schizophrenia and, somewhat less, in bipolar disorder, few studies have addressed this issue in people diagnosed with borderline personality disorder. Importantly, no studies to date have compared the ability to cope with interfering information in all three groups of patients. Impairment in executive control has been associated with reduced daily functioning.

Multiscale Lempel-Ziv complexity for EEG measures.

Objective: To demonstrate that the classical calculation of Lempel-Ziv complexity (LZC) has an important limitation when applied to EEGs with rapid rhythms, and to propose a multiscale approach that overcomes this limitation.

Methods: We have evaluated, both with simulated and real EEGs, whether LZC calculation neglects functional characteristics of rapid EEG rhythms. In addition, we have proposed a procedure to obtain multiple binarization sequences that yield a spectrum of LZC, and we have explored whether complexity would be better captured using this computation.

Time series modeling and forecasting using memetic algorithms for regime-switching models.

In this brief, we present a novel model fitting procedure for the neuro-coefficient smooth transition autoregressive model (NCSTAR), as presented by Medeiros and Veiga. The model is endowed with a statistically founded iterative building procedure and can be interpreted in terms of fuzzy rule-based systems. The interpretability of the generated models and a mathematically sound building procedure are two very important properties of forecasting models.

Equivalences between neural-autoregressive time series models and fuzzy systems.

Soft computing (SC) emerged as an integrating framework for a number of techniques that could complement one another quite well (artificial neural networks, fuzzy systems, evolutionary algorithms, probabilistic reasoning). Since its inception, a distinctive goal has been to dig out the deep relationships among their components. This paper considers two wide families of SC models.

SatDNA Analyzer: a computing tool for satellite-DNA evolutionary analysis.

Unlabelled: satDNA Analyzer is a program, implemented in C++, for the analysis of the patterns of variation at each nucleotide position considered independently amongst all units of a given satellite-DNA family when comparing it between a pair of species. The program classifies each site accordingly as monomorphic or polymorphic, discriminates shared from non-shared polymorphisms and classifies each non-shared polymorphism according to the model proposed by Strachan et al. in six different stages of transition during the spread of a variant repeat unit toward its fixation.