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Simple Summary: Response to treatment and prognosis after 223Ra treatment varies among patients. Using a combination of clinical, biochemical, and radiomic data from bone scintigraphy and 18F-Fluorocholine PET/CT obtained from our prospective ChoPET-Rad study, we identified predictive and prognostic variables. The goal was to create a nomogram able to predict therapeutic failure and overall survival, aiding in the selection of more suitable candidates for this treatment. Purpose: We aimed to develop a nomogram able to predict treatment failure, skeletal events, and overall survival (OS) in patients with castration-resistant prostate cancer with bone metastases (CRPC-BM) treated with Radium-223 dichloride (223Ra). Patients and Methods: Patients from the Castilla-La Mancha Spanish region were prospectively included in the ChoPET-Rad multicenter study from January 2015 to December 2022. Patients underwent baseline, interim, and end-of-treatment bone scintigraphy (BS) and 18F-Fluorocholine PET/CT (FCH PET/CT) scans, obtaining multiple imaging radiomics as well as clinical and biochemical variables during follow-up and studying their association with the previously defined end-points. Survival analysis was performed using the Kaplan–Meier method and Cox regression. Multivariate logistic and Cox regression models were calculated, and these models were depicted by means of nomograms. Results: Median progression-free survival (PFS) and OS were 4 and 14 months (mo), respectively. The variables that showed independent and significant association with therapeutic failure were baseline alkaline phosphatase (AP) levels (p = 0.022) and the characteristics of BM on the CT portion of PET/CT (p = 0.017). In the case of OS, the significant variables were therapeutic failure (p = 0.038), the number of lines received after 223Ra (p < 0.001), average SUVmax (p = 0.002), bone marrow infiltration in FCH PET/CT (p = 0.006), and interim FCH PET/CT response (p = 0.048). Final nomograms included these variables, showing good discrimination among the 100 patients included in our study. In the study of skeletal events, only OS showed a significant association in the multivariate analysis, resulting in an inconsistent nomogram design. Conclusions: FCH PET/CT appears to be a good tool for evaluating patients eligible for treatment with 223Ra, as well as for their follow-up. Thus, findings derived from it, such as the morphological characteristics of BM in the CT, bone marrow infiltration, or the response to 223Ra in the interim study, have proven to be solid and useful variables in the creation of nomograms for predicting therapeutic failure and OS. [ABSTRACT FROM AUTHOR]

Introduction: Alzheimer's disease (AD) is a progressive neurodegenerative disorder. Current core cerebrospinal fluid (CSF) AD biomarkers, widely employed for diagnosis, require a lumbar puncture to be performed, making them impractical as screening tools. Considering the role of sleep disturbances in AD, recent research suggests quantitative sleep electroencephalography features as potential non-invasive biomarkers of AD pathology. However, quantitative analysis of comprehensive polysomnography (PSG) signals remains relatively understudied. PSG is a non-invasive test enabling qualitative and quantitative analysis of a wide range of parameters, offering additional insights alongside other biomarkers. Machine Learning (ML) gained interest for its ability to discern intricate patterns within complex datasets, offering promise in AD neuropathology detection. Therefore, this study aims to evaluate the effectiveness of a multimodal ML approach in predicting core AD CSF biomarkers. Methods: Mild-moderate AD patients were prospectively recruited for PSG, followed by testing of CSF and blood samples for biomarkers. PSG signals underwent preprocessing to extract non-linear, time domain and frequency domain statistics quantitative features. Multiple ML algorithms were trained using four subsets of input features: clinical variables (CLINVAR), conventional PSG parameters (SLEEPVAR), quantitative PSG signal features (PSGVAR) and a combination of all subsets (ALL). Cross-validation techniques were employed to evaluate model performance and ensure generalizability. Regression models were developed to determine the most effective variable combinations for explaining variance in the biomarkers. Results: On 49 subjects, Gradient Boosting Regressors achieved the best results in estimating biomarkers levels, using different loss functions for each biomarker: least absolute deviation (LAD) for the Aß42, least squares (LS) for p-tau and Huber for t-tau. The ALL subset demonstrated the lowest training errors for all three biomarkers, albeit with varying test performance. Specifically, the SLEEPVAR subset yielded the best test performance in predicting Aß42, while the ALL subset most accurately predicted p-tau and t-tau due to the lowest test errors. Conclusions: Multimodal ML can help predict the outcome of CSF biomarkers in early AD by utilizing non-invasive and economically feasible variables. The integration of computational models into medical practice offers a promising tool for the screening of patients at risk of AD, potentially guiding clinical decisions. [ABSTRACT FROM AUTHOR]

Introduction: The use of emerging tobacco and nicotine products affects tobacco use behaviors among college students. Thus, we aimed to examine transitions in tobacco use patterns and identify their predictors among smokers in a cohort of nursing students in Catalonia (Spain). Methods: We conducted a prospective longitudinal study of Catalan nursing students between 2015–2016 and 2018–2019. We examined transitions in tobacco use patterns between baseline and follow-up among smokers from: 1) daily to nondaily smoking, 2) non-daily to daily smoking, 3) cigarette-only use to polytobacco use, 4) poly-tobacco use to cigarette-only use, 5) between products, 6) reducing consumption by ≥5 cigarettes per day (CPD); and 7) quitting smoking. We applied a Generalized Linear Model with a log link (Poisson regression) and robust variance to identify predictors of reducing cigarette consumption by ≥5 CPD and quitting smoking, obtaining both crude and adjusted (APR) prevalence ratios and their 95% confidence intervals (CIs). Results: Among daily smokers at baseline, 12.1% transitioned to non-daily smoking at follow-up, while 36.2% of non-daily smokers shifted to daily smoking. Among cigarette-only users, 14.2% transitioned to poly-tobacco use, while 48.4% of polytobacco users switched to exclusive cigarette use. Among all smokers (daily and non-daily smokers), 60.8% reduced their cigarette consumption by ≥5 CPD and 28.3% quit smoking. Being a non-daily smoker (APR=0.33; 95% CI 0.19–0.55) and having lower nicotine dependence (APR=0.78; 95% CI 0.64–0.96) were inversely associated with reducing cigarette consumption, while being a non-daily smoker (APR=1.19; 95% CI: 1.08–1.31) was directly associated with quitting smoking. Conclusions: Nursing students who smoked experienced diverse transitions in tobacco use patterns over time. Evidence-based tobacco use preventive and cessation interventions are needed to tackle tobacco use among future nurses. [ABSTRACT FROM AUTHOR]

Simple Summary: A 90-day mortality predictive model for curative gastric cancer resection based on the Spanish EURECCA Esophagogastric Cancer database was externally validated using the GASTRODATA registry. The externally validated model showed a modestly worse performance compared to the original model, nevertheless maintaining its discriminating ability in clinical practice. Background: Radical gastrectomy remains the main treatment for gastric cancer, despite its high mortality. A clinical predictive model of 90-day mortality (90DM) risk after gastric cancer surgery based on the Spanish EURECCA registry database was developed using a matching learning algorithm. We performed an external validation of this model based on data from an international multicenter cohort of patients. Methods: A cohort of patients from the European GASTRODATA database was selected. Demographic, clinical, and treatment variables in the original and validation cohorts were compared. The performance of the model was evaluated using the area under the curve (AUC) for a random forest model. Results: The validation cohort included 2546 patients from 24 European hospitals. The advanced clinical T- and N-category, neoadjuvant therapy, open procedures, total gastrectomy rates, and mean volume of the centers were significantly higher in the validation cohort. The 90DM rate was also higher in the validation cohort (5.6%) vs. the original cohort (3.7%). The AUC in the validation model was 0.716. Conclusion: The externally validated model for predicting the 90DM risk in gastric cancer patients undergoing gastrectomy with curative intent continues to be as useful as the original model in clinical practice. [ABSTRACT FROM AUTHOR]

Simple Summary: We conducted a study at the Melanoma Unit of the Hospital Clinic of Barcelona to investigate brain metastases in patients with cutaneous melanoma. We collected data from patients diagnosed between January 1998 and September 2023. Patients with melanoma in situ or those with prior lung or breast cancer were excluded. Our aim was to identify factors associated with the development and survival outcomes of brain metastases. We analyzed patient demographics, tumor characteristics, and survival data. The diagnosis of brain metastases was confirmed using imaging techniques, and biopsies were performed when feasible. Our study followed strict guidelines for reporting observational studies. We found that younger age and larger primary tumor thickness increased the risk of developing brain metastases. Additionally, the presence of ulceration and microscopic satellitosis in the primary tumor were associated with a higher risk. Melanomas located on the trunk had a higher risk compared to those on the extremities. Patients with brain metastases had a median survival of around six months. Neurological symptoms and leptomeningeal involvement were associated with poorer survival outcomes. Higher number of brain lesions and elevated levels of lactate dehydrogenase (LDH) also predicted worse survival. Our findings highlight the importance of early detection and monitoring of melanoma patients, especially those at higher risk of brain metastases. Understanding these factors can aid in personalized treatment approaches and improving patient outcomes. Background: Melanoma is the cancer with the highest risk of dissemination to the central nervous system (CNS), one of the leading causes of mortality from this cancer. Objective: To identify patients at higher risk of developing CNS metastases and to evaluate associated prognostic factors. Methods: A cohort study (1998–2023) assessed patients who developed CNS melanoma metastases. Multivariate logistic regression was used to identify predictive factors at melanoma diagnosis for CNS metastasis. Cox regression analysis evaluated the CNS-independent metastasis-related variables impacting survival. Results: Out of 4718 patients, 380 (8.05%) developed CNS metastases. Multivariate logistic regression showed that a higher Breslow index, mitotic rate ≥ 1 mm2, ulceration, and microscopic satellitosis were significant risk factors for CNS metastasis development. Higher patient age and the location of the primary tumor in the upper or lower extremities were protective factors. In survival analysis, post-CNS metastasis, symptomatic disease, prior non-CNS metastases, CNS debut with multiple metastases, elevated LDH levels, and leptomeningeal involvement correlated with poorer survival. Conclusion: Predictive factors in the primary tumor independently associated with brain metastases include microscopic satellitosis, ulceration, higher Breslow index, and trunk location. Prognostic factors for lower survival in CNS disease include symptomatic disease, multiple CNS metastases, and previous metastases from different sites. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic posed a major challenge for construction companies, which were confronted with the need to prevent the enormous negative socio-psychological impact of the pandemic on their employees. The aim of this study was to evaluate the level of psychological distress among construction workers in an advanced phase of the COVID-19 pandemic in Andalusia, southern Spain. For this, a cross-sectional descriptive study was conducted using online questionnaires with data on sociodemographic variables and employment situation, COVID-19 pandemic-related data, and Goldberg's General Health Questionnaire (GHQ-12). A total of 860 questionnaires from all provinces of Andalusia, Spain, were collected between March and May 2022. Descriptive statistical analyses and non-parametric Mann–Whitney U and Chi-squared tests were performed, followed by logistic regression analysis. The incidence of psychological distress was higher among women, individuals under 43 years of age, those with a family income below EUR 1200, participants whose working conditions had been affected by the pandemic, those who had not received adequate means or specific training to protect themselves from infection, those who had experienced symptoms, those who had suffered side effects after vaccination, and those who had been hospitalised. The logistic regression analysis predicted the occurrence of psychological distress in this study by the effect of the pandemic on mental/emotional well-being, the working conditions affected during the pandemic, health-related variables, and the age of the worker. The correctly classified percentage was 75.1%. Assessing psychological distress in construction sectors may allow for the identification of vulnerable groups or even help to reduce the number of errors in daily practice and potential risks of occupational injury or illness. [ABSTRACT FROM AUTHOR]

Pressure ulcers carry a significant risk in clinical practice. This paper proposes a practical and interpretable approach to estimate the risk levels of pressure ulcers using decision tree models. In order to address the common problem of imbalanced learning in nursing classification datasets, various oversampling configurations are analyzed to improve the data quality prior to modeling. The decision trees built are based on three easily identifiable and clinically relevant pressure ulcer risk indicators: mobility, activity, and skin moisture. Additionally, this research introduces a novel tabular visualization method to enhance the usability of the decision trees in clinical practice. Thus, the primary aim of this approach is to provide nursing professionals with valuable insights for assessing the potential risk levels of pressure ulcers, which could support their decision-making and allow, for example, the application of suitable preventive measures tailored to each patient's requirements. The interpretability of the models proposed and their performance, evaluated through stratified cross-validation, make them a helpful tool for nursing care in estimating the pressure ulcer risk level. [ABSTRACT FROM AUTHOR]

The aim of this study was to develop and validate a predictive model for the establishment of skin-to-skin contact immediately after birth. A descriptive cross-sectional study was conducted during the last trimester of 2022 and the first trimester of 2023 with women who had given birth in Spain. A questionnaire containing sociodemographic, psychosocial, and health variables referring to the mother and the newborn, as well as the Bond and Attachment questionnaire (VAMF, for its name in Spanish) for the analysis of the mother–child bond and attachment, were administered. A multivariate analysis was performed, and areas under the ROC curve (AUC) with their 95% confidence intervals (CI) and the predictive characteristics of these models were estimated. In total, 1077 women participated. The prevalence of early skin-to-skin contact after delivery was 50.2% (468) in the derivation cohort and 49.8% (464) in the validation cohort. Multivariate analysis showed that prematurity, type of delivery, and birth experience were statistically significant, so they were included in the model (p ≤ 0.05). The predictive ability (AUC ROC) was good in both the derivation cohort, yielding 0.92 (95% CI: 0.89–0.95), and in the validation cohort, yielding 0.89 (95% CI: 0.85–0.93). This study developed a predictive model identifying factors facilitating early skin-to-skin contact between a mother and her newborn immediately after birth. [ABSTRACT FROM AUTHOR]

Simple Summary: Non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver condition globally. The increasing incidence of NAFLD suggests that in the upcoming years, NAFLD-related hepatocellular carcinoma (HCC) is poised to become the leading cause of this type of tumor. The aim of this study is to evaluate the survival rates of these patients and identify the primary risk factors contributing to a less favorable prognosis. To accomplish this, we have employed machine learning techniques. This introduces a novel approach for identifying these factors that can be targeted to enhance the life expectancy of these patients, offering a more personalized and effective management strategy. This enhanced management approach not only aids in the optimization of patient care but also facilitates the delivery of the most effective available treatments. Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide, with an incidence that is exponentially increasing. Hepatocellular carcinoma (HCC) is the most frequent primary tumor. There is an increasing relationship between these entities due to the potential risk of developing NAFLD-related HCC and the prevalence of NAFLD. There is limited evidence regarding prognostic factors at the diagnosis of HCC. This study compares the prognosis of HCC in patients with NAFLD against other etiologies. It also evaluates the prognostic factors at the diagnosis of these patients. For this purpose, a multicenter retrospective study was conducted involving a total of 191 patients. Out of the total, 29 presented NAFLD-related HCC. The extreme gradient boosting (XGB) method was employed to develop the reference predictive model. Patients with NAFLD-related HCC showed a worse prognosis compared to other potential etiologies of HCC. Among the variables with the worst prognosis, alcohol consumption in NAFLD patients had the greatest weight within the developed predictive model. In comparison with other studied methods, XGB obtained the highest values for the analyzed metrics. In conclusion, patients with NAFLD-related HCC and alcohol consumption, obesity, cirrhosis, and clinically significant portal hypertension (CSPH) exhibited a worse prognosis than other patients. XGB developed a highly efficient predictive model for the assessment of these patients. [ABSTRACT FROM AUTHOR]

Mathematical models for the prediction of communicable diseases preventable by vaccination helps to evaluate the impact of implemented or future vaccination programmes. So far, there has been little culture of modelling in this area, the results of which will serve to make adjustments in the planning and monitoring of these programs. The COVID-19 pandemic has provided a boost to the development of mathematical vaccination models, due to the collaboration between modelers and Public Health professionals contributing to the adjustment of the COVID-19 Vaccination Strategy in Spain. It is necessary to consolidate and continue promoting this research capacity, bring mathematical models closer to professionals and quickly incorporate the use of these tools into decision-making. [ABSTRACT FROM AUTHOR]

Vitamin D is an essential nutrient to be consumed in the habitual dietary intake, whose deficiency is associated with various disturbances. This study represents a validation of vitamin D status estimation using a semi-quantitative FFQ, together with data from additional physical activity and lifestyle questionnaires. This information was combined to forecast the serum vitamin D status. Different statistical methods were applied to estimate the vitamin D status using predictors based on diet and lifestyle. Serum vitamin D was predicted using linear regression (with leave-one-out cross-validation) and random forest models. Intraclass correlation coefficients, Lin's agreement coefficients, Bland–Altman plots and other methods were used to assess the accuracy of the predicted v. observed serum values. Data were collected in Spain. A total of 220 healthy volunteers aged between 18 and 78 years were included in this study. They completed validated questionnaires and agreed to provide blood samples to measure serum 25-hydroxyvitamin D (25(OH)D) levels. The common final predictors in both models were age, sex, sunlight exposure, vitamin D dietary intake (as assessed by the FFQ), BMI, time spent walking, physical activity and skin reaction after sun exposure. The intraclass correlation coefficient for the prediction was 0·60 (95 % CI: 0·52, 0·67; P < 0·001) using the random forest model. The magnitude of the correlation was moderate, which means that our estimation could be useful in future epidemiological studies to establish a link between the predicted 25(OH)D values and the occurrence of several clinical outcomes in larger cohorts. [ABSTRACT FROM AUTHOR]

Background: Stroke is a highly prevalent disease that can provoke severe disability. We evaluate a predictive model based on the Minimum Basic Data Set (MBDS) compiled by the Spain Health Ministry, obtained for the period 2008–2012 for patients with ischaemic stroke in Spain, to establish the model's validity and to optimise its calibration. The MBDS is the main clinical-administrative database for hospitalisations recorded in Spain, and to our knowledge, no predictive models for stroke mortality have previously been developed using this resource. The main study aim is to perform an external validation and recalibration of the coefficients of this predictive model with respect to a chronologically later cohort. Material and Methods: External validation (testing the model on a different cohort to assess its performance) and recalibration (validation with optimisation of model coefficients) were performed using the MBDS for patients admitted for ischaemic stroke in the period 2016–2018. A cohort study was designed, in which a recalibrated model was obtained by applying the variables of the original model without their coefficients. The variables from the original model were then applied to the subsequent cohort, together with the coefficients from the initial model. The areas under the curve (AUC) of the recalibration and the external validation procedure were compared. Results: The recalibrated model produced an AUC of 0.743 and was composed of the following variables: age (odds ratio, OR:1.073), female sex (OR:1.143), ischaemic heart disease (OR:1.192), hypertension (OR:0.719), atrial fibrillation (OR:1.414), hyperlipidaemia (OR:0.652), heart failure (OR:2.133) and posterior circulation stroke (OR: 0.755). External validation produced an AUC of 0.726. Conclusions: The recalibrated clinical model thus obtained presented moderate-high discriminant ability and was generalisable to predict death for patients with ischaemic stroke. Rigorous external validation slightly decreased the AUC but confirmed the validity of the baseline model for the chronologically later cohort. [ABSTRACT FROM AUTHOR]

Determining the factors that pre-adapt plant species to successfully establish and spread outside of their native ranges constitutes a powerful approach with great potential for management. While this source-area approach accounts for the bias associated with species' regions of origin, it has been only implemented in pools of species known to be established elsewhere. We argue that, in regions with well-known introduction histories, such as the Mediterranean Biome, the consideration of co-dominant non-introduced species as a control group allows a better understanding of the invasion process. For this purpose, we used occurrence data from GBIF and trait data from previous studies to find predictors of establishment and invasion. We compare the frequency, climatic niche and functional traits of 149 co-dominant plant species in their native region in southern Spain, considering whether they have colonised other Mediterranean-climate regions or not and their level of invasion. We found that large native ranges and diverse climatic niches were the best predictors of species establishment abroad. Moreover, coloniser species had longer bloom periods, higher growth rates and greater resource acquisition, whereas coloniser species becoming invasive had also greater reproductive height and nitrogen use efficiency. This framework has the potential to improve prediction models and management practices to prevent the harmful impacts from species in invaded communities. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has put a huge challenge on the Indian health infrastructure. With a larger number of people getting affected during the second wave, hospitals were overburdened, running out of supplies and oxygen. Hence, predicting new COVID-19 cases, new deaths, and total active cases multiple days in advance can aid better utilization of scarce medical resources and prudent pandemic-related decision-making. The proposed method uses gated recurrent unit networks as the main predicting model. A study is conducted by building four models pre-trained on COVID-19 data from four different countries (United States of America, Brazil, Spain, and Bangladesh) and fine-tuned on India's data. Since the four countries chosen have experienced different types of infection curves, the pre-training provides a transfer learning to the models incorporating diverse situations into account. Each of the four models then gives 7-day ahead predictions using the recursive learning method for the Indian test data. The final prediction comes from an ensemble of the predictions of the different models. This method with two countries, Spain and Bangladesh, is seen to achieve the best performance amongst all the combinations as well as compared to other traditional regression models. [ABSTRACT FROM AUTHOR]

During the outbreak of a disease caused by a pathogen with unknown characteristics, the uncertainty of its progression parameters can be reduced by devising methods that, based on rational assumptions, exploit available information to provide actionable insights. In this study, performed a few (~6) weeks into the outbreak of COVID-19 (caused by SARS-CoV-2), one of the most important disease parameters, the average time-to-recovery, was calculated using data publicly available on the internet (daily reported cases of confirmed infections, deaths, and recoveries), and fed into an algorithm that matches confirmed cases with deaths and recoveries. Unmatched cases were adjusted based on the matched cases calculation. The mean time-to-recovery, calculated from all globally reported cases, was found to be 18.01 days (SD 3.31 days) for the matched cases and 18.29 days (SD 2.73 days) taking into consideration the adjusted unmatched cases as well. The proposed method used limited data and provided experimental results in the same region as clinical studies published several months later. This indicates that the proposed method, combined with expert knowledge and informed calculated assumptions, could provide a meaningful calculated average time-to-recovery figure, which can be used as an evidence-based estimation to support containment and mitigation policy decisions, even at the very early stages of an outbreak. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak was a major event that greatly impacted the economy and the health systems around the world. Understanding the behavior of the virus and being able to perform long-term and short-term future predictions of the daily new cases is a working field for machine learning methods and mathematical models. This paper compares Verhulst's, Gompertz´s, and SIR models from the point of view of their efficiency to describe the behavior of COVID-19 in Spain. These mathematical models are used to predict the future of the pandemic by first solving the corresponding inverse problems to identify the model parameters in each wave separately, using as observed data the daily cases in the past. The posterior distributions of the model parameters are then inferred via the Metropolis–Hastings algorithm, comparing the robustness of each prediction model and making different representations to visualize the results obtained concerning the posterior distribution of the model parameters and their predictions. The knowledge acquired is used to perform predictions about the evolution of both the daily number of infected cases and the total number of cases during each wave. As a main conclusion, predictive models are incomplete without a corresponding uncertainty analysis of the corresponding inverse problem. The invariance of the output (posterior prediction) with respect to the forward predictive model that is used shows that the methodology shown in this paper can be used to adopt decisions in real practice (public health). [ABSTRACT FROM AUTHOR]

Introduction: the management of musculoskeletal pain through the application of dry needling (DN) is effective. The application of this technique can carry very infrequent major risks on muscles, such as on the iliocostalis lumborum due to its proximity to the kidney and the peritoneum. It is important to establish a DN protocol based on the different anthropometric variables of the subjects. Main objective: the main objective of this study was to investigate the correlation between different anthropometric variables and the skin-kidney and skin-peritoneum distances to establish the size of the needle that could perform DN in the iliocostalis lumborum muscle without risk. Design: a cross-sectional observational study was conducted. Methodology: a total of 68 healthy subjects were evaluated. Demographic and anthropometric data, such as age, gender, weight, height, body mass index (BMI), chest (xiphoid process and axilla) and abdomen circumferences, and skinfold thickness were collected. The measurements of skin-upper and lower edge of the iliocostalis lumborum muscle and the skin-peritoneum and/or kidney in the regions of L2 and L4, and on both sides, were assessed using ultrasound imaging. Results: a multiple linear regression analysis was performed, confirming that, in L2 without compression, gender significantly predicted the distance, with the distance being greater in women than in men. The measurement without compression increased with age up to 50 years, and it also increased with higher measurements for the chest-triceps, iliac crest, and thigh skinfold thickness, and decreased with higher measurement for the abdominal circumference. It was verified that the measurement with compression in L2 decreased as the neutral axillary circumference and the skinfold thickness in the abdomen-iliac crest increased, while the distance increased with larger measurements obtained in the neutral abdominal circumference and in the skinfold thickness of the chest-triceps. It was also verified that the measurement with compression in L4 increased up to a body mass index of 25 and then decreased even if the index increased further, and it decreased as the skinfold thickness in the abdomen-iliac crest decreased and increased as the measurements of the neutral abdominal circumference and the skinfold thickness in the chest-triceps increased. In L4 without compression, the gender variable significantly predicted changes in the measurement, with women tending to have a smaller distance compared to men. Conclusions: the measurements of the neutral abdominal circumference, chest-triceps, and abdomen-iliac crest skinfold thickness could help clinicians predict the skin-kidney and skin-peritoneum distances for dry needling of the iliocostalis lumborum with the methodology described. [ABSTRACT FROM AUTHOR]

Background: Endoscopic submucosal dissection has become the primary option of treatment for early gastric cancer. However, lymph node metastasis may lead to poor prognosis. We analyzed factors related to lymph node metastasis in EGC patients, and we developed a construction prediction model with machine learning using data from a retrospective series. Methods: Two independent cohorts' series were evaluated including 305 patients with EGC from China as cohort I and 35 patients from Spain as cohort II. Five classifiers obtained from machine learning were selected to establish a robust prediction model for lymph node metastasis in EGC. Results: The clinical variables such as invasion depth, histologic type, ulceration, tumor location, tumor size, Lauren classification, and age were selected to establish the five prediction models: linear support vector classifier (Linear SVC), logistic regression model, extreme gradient boosting model (XGBoost), light gradient boosting machine model (LightGBM), and Gaussian process classification model. Interestingly, all prediction models of cohort I showed accuracy between 70 and 81%. Furthermore, the prediction models of the cohort II exhibited accuracy between 48 and 82%. The areas under curve (AUC) of the five models between cohort I and cohort II were between 0.736 and 0.830. Conclusions: Our results support that the machine learning method could be used to predict lymph node metastasis in early gastric cancer and perhaps provide another evaluation method to choose the suited treatment for patients. [ABSTRACT FROM AUTHOR]

Background: Oral cavity cancer (OCC) and oropharyngeal cancer (OPC) are two common malignancies whose mortality is worryingly increasing worldwide. However, few studies have estimated the mortality trends for these cancers in the coming years. This study analysed the mortality rates for OCC and OPC observed between 1980 and 2019 to generate a predictive model for the next 25 years in Spain. Methods: Mid-year population data and death certificates for the period 1980–2019 were obtained from the Spanish National Institute of Statistics. The Nordpred program (Norwegian Cancer Registry, Oslo, Norway) was used to calculate adjusted mortality rates as well as estimated mortality projections with an age-period-cohort model for the period 2020–2044. Results: The specific mortality rate per 100,000 inhabitants for OCC decreased from 2.36 (1980–1984) to 2.17 (2015–2019) and is expected to decline to 1.68 (2040–2044), particularly in males. For OPC, mortality rates rose from 0.67 (1980–1984) to 1.23 (2015–2019) and are projected to drop to 0.71 (2040–2044). In the group of females > 65 years predictions showed rising mortality rates for both OCC and OPC. The predictive model projects more deaths in females than in males for OCC in the period 2040–2044, while deaths for OPC will decrease in males and gradually increase in females. Conclusions: Although OCC mortality rates have been found to decrease in males in the last observed decades, there is still room to improve them in females > 65 years in the future by promoting campaigns against smoking and alcohol consumption. OPC mortality will become a growing health problem. Vaccination campaigns for the prevention of human papillomavirus-associated cancers may have a long-term impact on the mortality of these cancers, which should be evaluated in upcoming studies. Clinical relevance: Our findings highlighted the importance of closely monitoring OCC and OPC mortality rates in the coming years by age group and sex, and the need to continue preventive measures against the main known risk factors, such as tobacco, alcohol, and human papillomavirus infection. [ABSTRACT FROM AUTHOR]

Background. This study aimed to compare the efficacy and safety of Femto-LASIK, PRK, and Artiflex/Artisan phakic lens implantation in the surgical correction of myopia at different moments of postoperative follow-up; to propose a linear predictive model of visual acuity without correction at five years of refractive procedures; and to evaluate its validity. Methods. A retrospective observational analysis was performed. Patients were clinically reviewed after three months, one year, two years, and five years. Univariate and bivariate analyses and a multivariate linear regression model were performed. Results. Six hundred seventy-nine eyes were analyzed: 18.9% Artiflex, 2.8% Artisan, 42.3% Femto-LASIK, and 36.1% PRK. There were significant differences in effectiveness and safety after five years when comparing Artiflex/Artisan versus PRK and Femto-LASIK (p < 0.01). The linear regression model explained 30.32% of the patients' visual acuity variability after five years. Conclusions. PRK surgery, Femto-Lasik, and Artiflex/Artisan type phakic lens implantation are effective, safe, and predictable techniques with stable refractive results. Phakic lenses magnify myopic patients who improve their UCVA and BCVA. Concerning phakic lens implantation, corneal endothelial cells remain stable. The predictive model calculated that surgery with a phakic lens increased the UCVA result at five years, and surgery with PRK slightly decreased the long-term results. [ABSTRACT FROM AUTHOR]

The present article aimed to test a predictive model based on children's perception of autonomy support exercised by their physical education teachers in establishing a state of high motivational quality, which in turn leads to greater intention and physical activity. Participants were 502 elementary school students aged 9 to 11 years (52.59% males (9.47 ± 0.53 years old) and 47.41% females (9.54 ± 0.53 years old)), who completed a self-reported questionnaire of perceived autonomy support from physical education teachers, self-determination, intention to be physically active, and physical activity. Results showed that perceived autonomy support was positively related to autonomous motivation, which in turn was associated with intention and physical activity. In addition, the model did not present variations with respect to gender and confirmed the indirect effects of autonomous motivation on intention and practice of physical activity. Finally, we conclude on the importance of an adequate perception of autonomy support in students by their teachers for the establishment of an appropriate motivational state that could promote greater intention and physical activity. Thus, the motivational state acquired in physical education classes can be translated into intention and practice of physical activity in different contexts of students' daily lives. [ABSTRACT FROM AUTHOR]

The exploration of patterns of health beliefs about COVID-19 among nursing students may be beneficial to identify behaviors, attitudes and knowledge about contagion risk. We sought to analyze the variables of risk perception, perceived risk factors, coping style, sense of coherence and knowledge of preventive measures as possible predictors of having suffered from COVID-19. Participants were nursing students from 13 universities in Spain. Sociodemographic and health variables were collected. To test the independent variables, the Perception Risk Coping Knowledge (PRCK-COVID-19) scale was created and validated because there was no specific survey for young people adapted to the pandemic situation of COVID-19. It was validated with adequate psychometric properties. A total of 1562 students (87.5% female, mean age 21.5 ± 5.7 years) responded. The high perception of the risk of contagion, the high level of knowledge and a coping style focused on the situation were notable. Significant differences by gender were found in the coping styles, problem-focused, avoidance and knowledge scales, with women scoring higher in all categories. The multiple regression analysis was significant (F = 3.68; p < 0.001). The predictor variables were the coping styles subscale search for support and the intrinsic and extrinsic perceived risk factors. Our model predicts that nursing students with a social support-based coping style are at a higher risk of becoming infected with COVID-19, based on their own health belief model. [ABSTRACT FROM AUTHOR]

In this paper, the aerial biomass of citrus plantations in Spain was evaluated using destructive methods. Before cutting down the trees, their geometric variables were measured: trunk diameter at 10 cm from the ground (Dt), trunk perimeter at 10 cm from the ground (Pm), mean crown diameter (Dc), canopy height (Hc), and maximum crown height (Hmax). After geometric characterization of the tree, it was felled. This was performed with a chainsaw about 10 cm above the ground. After cutting down, trees with and without leaves were weighed, and biomass variables such as moisture, calorific value, elemental composition, and proximate analysis were measured. The predictive models obtained showed an r2 of 0.78. According to our analysis, in plantations in Spain, where the average plantation pattern is 4 × 4 m, the amount of carbon stored in a plot is 15 t of C per hectare. If leaves and wood are counted, the energy density in citrus plots can be estimated at 900 MJ/tree. However, if only wood is included in the calculation, the accumulated energy per tree is 750.3 MJ/tree, which represents 5.6 × 105 MJ/ha. [ABSTRACT FROM AUTHOR]

In this paper, we perform an analysis about the effect of the perception of corruption in the activity of the credit insurance or CDS market in Spain during the period between 2008 and 2018. To measure the perception of corruption, we use Google Trends tool for the keyword “corrupcion”. On the other hand, to proxy the activity of the CDS market, we use the evolution of the 5-year CDSs. Subsequently, an empirical analysis is carried out through regression methodology and out-of-sample analysis to verify the predictive capacity of the model. The results show that perceived corruption impacts the evolution of CDSs, and its influence is greater in the years in which the country's economy does not face a crisis. [ABSTRACT FROM AUTHOR]

Simple Summary: Colic syndrome poses a potentially devastating situation in equine patients. Therefore, the prediction of survival has been an important research area over the years, especially in surgical cases. However, prognoses and survival rates are highly diverse in different populations. Many clinically relevant and numerical variables have been used in several large-scale studies. We aimed to determine the survival percentage and other predictive variables in a specific population with concrete characteristics in Spain, in an attempt to predict prognosis and survival using only easily accessible variables. The extrinsic and intrinsic characteristics of an equine population may influence the onset of gastrointestinal lesions and affect the survival rate of patients. The equine population in Spain has been the focus of a small number of studies, none of which have involved more than one surgical center. In this retrospective cohort study, we aimed to analyze the survival rate, identify the variables that influenced death, and generate multivariate models using clinical variables. Data were collected from the clinical records of two surgical referral centers in the same region, and a total of 566 horses met the inclusion criteria. The statistical analysis was divided into three parts: The first and second included logistic analysis, in order to identify the variables most closely associated with survival. The third part assessed all previous variables in terms of survival and hospitalization time, using a COX survival analysis. The main risk factors associated with intra-operative mortality were related to seasonality (winter and summer), patient age (older than 9 years), distance from the hospital, the presence of a strangulating lesion, and the bowel segment affected (small intestine). Furthermore, the main factors associated with mortality during hospitalization were the characteristics of the lesions (strangulating) and the differences between surgical centers. The models generated in this study have good predictive value and use only reliable and easily obtainable variables. The most reliable characteristics are those related to the type of colic and the location of the lesion. [ABSTRACT FROM AUTHOR]

Introduction: Lockdowns due to the COVID-19 pandemic radically changed nursing education. Along with social isolation, the transition to distance education affected the well-being of students in several countries, particularly Portugal and Spain. Objectives: To identify which variables are predictors of psychological well-being for Portuguese and Spanish nursing students during mandatory lockdowns. Methods: A multicenter, cross-sectional, descriptive, correlational study involving a sample of 1075 students (944 women, mean age 22.46 + /−4.95 years). Data were collected from an online questionnaire which applied the following scales: Perceived Stress Scale (α =.820); Brief COPE-14 Subscales (α =.430 < 0.930); Well-being Manifestations Measure Scale (α =.940); Herth Hope Index (α =.850). A multiple regression model was created to predict the psychological well-being of nursing students. Results: The following predictor variables were identified in the model of the psychological well-being of students during the COVID-19 pandemic: perceived stress (β =.405; p ≤.001); hope (β =.404; p ≤.001); and the mechanisms of active coping (β =.405; p ≤.001), planning (β =.097; p ≤.001), and positive reinterpretation (β =.053; p =.12). These five variables predicted 62.0% of the nursing students' psychological well-being (R 2 =.620; F = 350.82; p ≤.001). Conclusion: Promoting students' mental health is essential, especially in periods of great adversity, such as a pandemic. Our results lead the way for the design and validation of an intervention program that addresses the five variables identified as predictors of students' psychological well-being. [ABSTRACT FROM AUTHOR]

The diagnosis of alcohol use disorder (AUD) remains a difficult challenge, and some patients may not be adequately diagnosed. This study aims to identify an optimum combination of laboratory markers to detect alcohol consumption, using data science. An analytical observational study was conducted with 337 subjects (253 men and 83 women, with a mean age of 44 years (10.61 Standard Deviation (SD)). The first group included 204 participants being treated in the Addictive Behaviors Unit (ABU) from Albacete (Spain). They met the diagnostic criteria for AUD specified in the Diagnostic and Statistical Manual of mental disorders fifth edition (DSM-5). The second group included 133 blood donors (people with no risk of AUD), recruited by cross-section. All participants were also divided in two groups according to the WHO classification for risk of alcohol consumption in Spain, that is, males drinking more than 28 standard drink units (SDUs) or women drinking more than 17 SDUs. Medical history and laboratory markers were selected from our hospital's database. A correlation between alterations in laboratory markers and the amount of alcohol consumed was established. We then created three predicted models (with logistic regression, classification tree, and Bayesian network) to detect risk of alcohol consumption by using laboratory markers as predictive features. For the execution of the selection of variables and the creation and validation of predictive models, two tools were used: the scikit-learn library for Python, and the Weka application. The logistic regression model provided a maximum AUD prediction accuracy of 85.07%. Secondly, the classification tree provided a lower accuracy of 79.4%, but easier interpretation. Finally, the Naive Bayes network had an accuracy of 87.46%. The combination of several common biochemical markers and the use of data science can enhance detection of AUD, helping to prevent future medical complications derived from AUD. [ABSTRACT FROM AUTHOR]

Simple Summary: Magnetic-resonance-imaging-based predictive models (MRI-PMs) improve the MRI prediction of clinically significant prostate cancer (csPCa) in prostate biopsies. Risk calculators (RC) provide easy individual assessment of csPCa likelihood. MRI-PMs have been analysed in overall populations of men suspected to have PCa, but they have never been analysed according to the prostate imaging-report and data system (PI-RADS) categories. Therefore, the true clinical usefulness of MRI-PMs regarding the specific PI-RADS categories is unknown. A new and externally validated MRI-PM for csPCa was developed in the metropolitan area of Barcelona, and a web-RC designed with the new option of selecting the csPCa probability threshold. The development cohort comprised 1486 men scheduled to undergo a 3-tesla multiparametric MRI (mpMRI) and guided and/or systematic biopsies in one academic institution of Barcelona. The external validation cohort comprised 946 men in whom the same diagnostic approach was carried out as in the development cohort, in two other academic institutions of the same metropolitan area. CsPCa was detected in 36.9% of men in the development cohort and 40.8% in the external validation cohort (p = 0.054). The area under the curve of mpMRI increased from 0.842 to 0.897 in the developed MRI-PM (p < 0.001), and from 0.743 to 0.858 in the external validation cohort (p < 0.001). A selected 15% threshold avoided 40.1% of prostate biopsies and missed 5.4% of the 36.9% csPCa detected in the development cohort. In men with PI-RADS <3, 4.3% would be biopsied and 32.3% of all existing 4.2% of csPCa would be detected. In men with PI-RADS 3, 62% of prostate biopsies would be avoided and 28% of all existing 12.4% of csPCa would be undetected. In men with PI-RADS 4, 4% of prostate biopsies would be avoided and 0.6% of all existing 43.1% of csPCa would be undetected. In men with PI-RADS 5, 0.6% of prostate biopsies would be avoided and none of the existing 42.0% of csPCa would be undetected. The Barcelona MRI-PM presented good performance on the overall population; however, its clinical usefulness varied regarding the PI-RADS category. The selection of csPCa probability thresholds in the designed RC may facilitate external validation and outperformance of MRI-PMs in specific PI-RADS categories. [ABSTRACT FROM AUTHOR]

Introduction. The mortality risk in children admitted to Pediatric Intensive Care Units (PICU) is usually estimated by means of validated scales, which only include objective data among their items. Human perceptions may also add relevant information to prognosticate the risk of death, and the tool to use this subjective data is fuzzy logic. The objective of our study was to develop a mathematical model to predict mortality risk based on the subjective perception of PICU staff and to evaluate its accuracy compared to validated scales. Methods. A prospective observational study in two PICUs (one in Spain and another in Latvia) was performed. Children were consecutively included regardless of the cause of admission along a two-year period. A fuzzy set program was developed for the PICU staff to record the subjective assessment of the patients' mortality risk expressed through a short range and a long range, both between 0% and 100%. Pediatric Index of Mortality 2 (PIM2) and Therapeutic Intervention Scoring System 28 (TISS28) were also prospectively calculated for each patient. Subjective and objective predictions were compared using the logistic regression analysis. To assess the prognostication ability of the models a stratified B -random K -fold cross-validation was performed. Results. Five hundred ninety-nine patients were included, 308 in Spain (293 survivors, 15 nonsurvivors) and 291 in Latvia (282 survivors, 9 nonsurvivors). The best logistic classification model for subjective information was the one based on MID (midpoint of the short range), whereas objective information was the one based on PIM2. Mortality estimation performance was 86.3% for PIM2, 92.6% for MID, and the combination of MID and PIM2 reached 96.4%. Conclusions. Subjective assessment was as useful as validated scales to estimate the risk of mortality. A hybrid model including fuzzy information and probabilistic scales (PIM2) seems to increase the accuracy of prognosticating mortality in PICU. [ABSTRACT FROM AUTHOR]

We analysed data on the weekly mortality of seniors aged 65 and older in the member states of the EU27. We aimed to model normal mortality in the time before covid-19 and use this model to analyse the deviations of true values from model predictions in the time after the emergence of covid-19. We used linear regression with autocorrelated errors to model normal mortality, which shows seasonality with increased mortality during the winter months. In the time before covid-19 deviations above three sigma occurred regularly, but deviations above five sigma were rare. In contrast, we detected deviations above five sigma in all but three member states after the emergence of covid-19. The member state with the most extreme deviation was Spain (32 sigma), the member state with the longest continuous deviation above five sigma was Czechia (25 weeks), and the member state with the highest excess mortality was Slovakia (12.8 deaths per 1000 seniors). We attempted to explain differences in extreme deviations with multivariate linear regression analysis. Extreme deviations were lower in member states with higher health care expenditure in euros per inhabitant. [ABSTRACT FROM AUTHOR]

Background: Several studies have proposed personalized strategies based on women's individual breast cancer risk to improve the effectiveness of breast cancer screening. We designed and internally validated an individualized risk prediction model for women eligible for mammography screening. Methods: Retrospective cohort study of 121,969 women aged 50 to 69 years, screened at the long-standing population-based screening program in Spain between 1995 and 2015 and followed up until 2017. We used partly conditional Cox proportional hazards regression to estimate the adjusted hazard ratios (aHR) and individual risks for age, family history of breast cancer, previous benign breast disease, and previous mammographic features. We internally validated our model with the expected-to-observed ratio and the area under the receiver operating characteristic curve. Results: During a mean follow-up of 7.5 years, 2,058 women were diagnosed with breast cancer. All three risk factors were strongly associated with breast cancer risk, with the highest risk being found among women with family history of breast cancer (aHR: 1.67), a proliferative benign breast disease (aHR: 3.02) and previous calcifications (aHR: 2.52). The model was well calibrated overall (expected-to-observed ratio ranging from 0.99 at 2 years to 1.02 at 20 years) but slightly overestimated the risk in women with proliferative benign breast disease. The area under the receiver operating characteristic curve ranged from 58.7% to 64.7%, depending of the time horizon selected. Conclusions: We developed a risk prediction model to estimate the short- and long-term risk of breast cancer in women eligible for mammography screening using information routinely reported at screening participation. The model could help to guiding individualized screening strategies aimed at improving the risk-benefit balance of mammography screening programs. [ABSTRACT FROM AUTHOR]

COVID-19 is a pandemic which has spread to more than 200 countries. Its high transmission rate makes it difficult to control. To date, no specific treatment has been found as a cure for the disease. Therefore, prediction of COVID-19 cases provides a useful insight to mitigate the disease. This study aims to model and predict COVID-19 cases. Eight countries: Italy, New Zealand, the USA, Brazil, India, Pakistan, Spain, and South Africa which are in different phases of COVID-19 distribution as well as in different socioeconomic and geographical characteristics were selected as test cases. The Alpha-Sutte Indicator approach was utilized as the modelling strategy. The capability of the approach in modelling COVID-19 cases over the ARIMA method was tested in the study. Data consist of accumulated COVID-19 cases present in the selected countries from the first day of the presence of cases to September 26, 2020. Ten percent of the data were used to validate the modelling approach. The analysis disclosed that the Alpha-Sutte modelling approach is appropriate in modelling cumulative COVID-19 cases over ARIMA by reporting 0.11%, 0.33%, 0.08%, 0.72%, 0.12%, 0.03%, 1.28%, and 0.08% of the mean absolute percentage error (MAPE) for the USA, Brazil, Italy, India, New Zealand, Pakistan, Spain, and South Africa, respectively. Differences between forecasted and real cases of COVID-19 in the validation set were tested using the paired t -test. The differences were not statistically significant, revealing the effectiveness of the modelling approach. Thus, predictions were generated using the Alpha-Sutte approach for each country. Therefore, the Alpha-Sutte method can be recommended for short-term forecasting of cumulative COVID-19 incidences. The authorities in the health care sector and other administrators may use the predictions to control and manage the COVID-19 cases. [ABSTRACT FROM AUTHOR]

In this paper, the fractional-order generalization of the susceptible-infected-recovered (SIR) epidemic model for predicting the spread of the COVID-19 disease is presented. The time-domain model implementation is based on the fixed-step method using the nabla fractional-order difference defined by Grünwald-Letnikov formula. We study the influence of fractional order values on the dynamic properties of the proposed fractional-order SIR model. In modeling the COVID-19 transmission, the model's parameters are estimated while using the genetic algorithm. The model prediction results for the spread of COVID-19 in Italy and Spain confirm the usefulness of the introduced methodology. [ABSTRACT FROM AUTHOR]

INTRODUCTION On March 11, 2020, WHO declared COVID-19 a pandemic and called on governments to impose drastic measures to fight it. It is vitally important for government health authorities and leaders to have reliable estimates of infected cases and deaths in order to apply the necessary measures with the resources at their disposal. OBJECTIVE Test the validity of the logistic regression and Gompertz curve to forecast peaks of confirmed cases and deaths in Cuba, as well as total number of cases. METHODS An inferential, predictive study was conducted using logistic and Gompertz growth curves, adjusted with the least squares method and informatics tools for analysis and prediction of growth in COVID-19 cases and deaths. Italy and Spain--countries that have passed the initial peak of infection rates--were studied, and it was inferred from the results of these countries that their models were applicable to Cuba. This hypothesis was tested by applying goodness-of-fit and significance tests on its parameters. RESULTS Both models showed good fit, low mean square errors, and all parameters were highly significant. CONCLUSIONS The validity of models was confirmed based on logistic regression and the Gompertz curve to forecast the dates of peak infections and deaths, as well as total number of cases in Cuba. [ABSTRACT FROM AUTHOR]

Purpose - The purpose of this study is to construct the first short-term financial distress prediction model for the Spanish banking sector. Design/methodology/approach - The concept of financial distress covers a range of different types of financial problems, in addition to bankruptcy, which is not common in the sector. The methodology used to predict financial problems was artificial neural networks using traditional financial variables according to the capital, assets, management, earnings, liquidity and sensibility system, as well as a series of macroeconomic variables, the impact of which has been proven in a number of studies. Findings - The results obtained show that artificial neural networks are a highly suitable method for studying financial distress in Spanish credit institutions and for predicting all cases in which an entity has short-term financial problems. Originality/value - This is the first work that tries to build a model of artificial neural networks to predict the financial distress in the Spanish banking system, grouping under the concept of financial distress, apart from bankruptcy, other financial problems that affect the viability of these entities. [ABSTRACT FROM AUTHOR]

The main goal of this study is to carry out an analysis about the bankrupt of companies in the hospitality sector in Spain from 2007 to 2017, applying prediction formulas of business failure. The development of models for insolvency prediction began in the United States with the pioneering works of Beaver and Altman in the 1960s. After years of methodological improvements and studies applied to different sectors, it has not been possible to unify amethod applicable bythe Academia. Therefore, the realization of this study has been using the formula Z of Altmanof 1968 and1983and compare them with the formula Z of Amat et al. of 2017. The two methods of Z-Score applied to the companies of the hospitality sector in Spain in bankruptcy process show us a highly percentage of matches in the exercises before the official bankruptcy process, being Altman the one that obtains a better approach to the total number of bankruptcy companies. [ABSTRACT FROM AUTHOR]

A multinational firm uses sophisticated, state-of-the-art methods to design and implement customer satisfaction improvement programs in the United States and Spain. Quasi-experimental analysis reveals a complex and surprising picture that highlights (1) implementation issues (empowerment), (2) a construct of residual satisfaction not captured by customer needs, and (3) the managerial need for combining nonequivalent controls and nonequivalent dependent variables. [ABSTRACT FROM AUTHOR]

Academic performance during the first year of university is correlated with future academic success, and is considered to be a determining factor in the reduction of dropouts. In the present study, we describe a new academic performance indicator for the first term of first‐year science degrees students at La Laguna University in Spain. We are interested in identifying the most important previous academic factors for predicting the success of first‐year students at university. Specifically, multiple linear regression models were used to identify such predictors of academic success. We report that, for all of the analyzed science degrees, the best predictor of academic success is high school grade point average. In addition, we obtained predictive models for estimating the value of the new academic performance indicator. Using these models, we can predict future academic success, which may help identify students at risk of failure at the beginning of the course. This in turn would ensure early implementation of educational interventions or strategies to increase academic achievement of such students. [ABSTRACT FROM AUTHOR]

Background: Atrial fibrillation (AF) is caused by different mechanisms but current treatment strategies do not target these mechanisms. Stratified therapy based on mechanistic drivers and biomarkers of AF have the potential to improve AF prevention and management outcomes. We will integrate mechanistic insights with known pathophysiological drivers of AF in models predicting recurrent AF and prevalent AF to test hypotheses related to AF mechanisms and response to rhythm control therapy.Methods: We will harmonise and combine baseline and outcome data from 12 studies collected by six centres from the United Kingdom, Germany, France, Spain, and the Netherlands which assess prevalent AF or recurrent AF. A Delphi process and statistical selection will be used to identify candidate clinical predictors. Prediction models will be developed in patients with AF for AF recurrence and AF-related outcomes, and in patients with or without AF at baseline for prevalent AF. Models will be used to test mechanistic hypotheses and investigate the predictive value of plasma biomarkers.Discussion: This retrospective, harmonised, individual patient data analysis will use information from 12 datasets collected in five European countries. It is envisioned that the outcome of this analysis would provide a greater understanding of the factors associated with recurrent and prevalent AF, potentially allowing development of stratified approaches to prevention and therapy management. [ABSTRACT FROM AUTHOR]

This article presents a study on the pH control of raceway photobioreactors (PBRs) using a learning-based model predictive control (LBMPC) approach. The primary objective is to enhance the control performance and improve microalgae biomass production. The research builds upon existing control solutions for PBRs found in the literature by integrating the robust and adaptive capabilities of the LBMPC. Specifically, the control algorithm is modified to include a pre-established Linear Quadratic Tracking with a Feedforward controller, facilitating the formulation of the conventional LBMPC. To validate the proposed controller, implementation is performed in an existing facility at the IFAPA center, located near the University of Almería in Southern Spain. The experiment involves two parallel PBRs operating under the same meteorological conditions with different microalgae strains and media (freshwater and wastewater). The LBMPC demonstrates satisfactory results and is compared with the conventional nominal MPC strategy through an analogous experiment. The LBMPC outperforms the conventional approach, achieving up to four times superior performance in terms of the average error index. The results highlight the importance of employing robust adaptive control strategies for highly nonlinear and multi-disturbed systems like the variant biological-chemical microalgae process in PBRs since the complexity associated with adapting the system's model can negatively impact the performance of conventional model-based controllers. • A learning-based predictive control is applied to an actual photobioreactor. • The proposed strategy uses adaptive and performance features for pH control. • The trials were carried out in two parallel reactors with different media (freshwater and wastewater). • The adaptive controller performed better than the nominal-based controller. [ABSTRACT FROM AUTHOR]

The modeling and mapping of soil organic carbon (SOC) has advanced through the rapid growth of Earth observation data (e.g., Sentinel) collection and the advent of appropriate tools such as the Google Earth Engine (GEE). However, the effects of differing optical and radar sensors on SOC prediction models remain uncertain. This research aims to investigate the effects of different optical and radar sensors (Sentinel-1/2/3 and ALOS-2) on SOC prediction models based on long-term satellite observations on the GEE platform. We also evaluate the relative impact of four synthetic aperture radar (SAR) acquisition configurations (polarization mode, band frequency, orbital direction and time window) on SOC mapping with multiband SAR data from Spain. Twelve experiments involving different satellite data configurations, combined with 4027 soil samples, were used for building SOC random forest regression models. The results show that the synthesis mode and choice of satellite images, as well as the SAR acquisition configurations, influenced the model accuracy to varying degrees. Models based on SAR data involving cross-polarization, multiple time periods and "ASCENDING" orbits outperformed those involving copolarization, a single time period and "DESCENDING" orbits. Moreover, combining information from different orbital directions and polarization modes improved the soil prediction models. Among the SOC models based on long-term satellite observations, the Sentinel-3-based models (R2 = 0.40) performed the best, while the ALOS-2-based model performed the worst. In addition, the predictive performance of MSI/Sentinel-2 (R2 = 0.35) was comparable with that of SAR/Sentinel-1 (R2 = 0.35); however, the combination (R2 = 0.39) of the two improved the model performance. All the predicted maps involving Sentinel satellites had similar spatial patterns that were higher in northwest Spain and lower in the south. Overall, this study provides insights into the effects of different optical and radar sensors and radar system parameters on soil prediction models and improves our understanding of the potential of Sentinels in developing soil carbon mapping. • Our results revealed that the composite mode, radar data utilization strategies and sensor selection influenced outputs. • The mixed information of different orbital directions and polarization modes improved the mapping accuracy. • The predictive performance of Sentinel-1 and Sentinel-2 was comparable. • The accuracy rankings based on satellite observations were as follows: Sentinel-3 > Sentinel-2 > Sentinel-1 > ALOS-2. • The synergistic utilization of the Sentinel series gave better prediction models. [ABSTRACT FROM AUTHOR]

A phenomenon that also causes impairment in the received signal strength of the RF signal in satellite links operating at frequencies above 10 GHz, especially in those systems that operate at higher frequencies with small fade margins, is the tropospheric scintillation that occurs in the lower layer of the troposphere. In order to estimate the intensity, i.e. the variance between the scintillation amplitude fluctuations, there are several models in the literature, whose accuracy depends on the locality in which the models are implemented. In this Letter, new models are developed for the prediction of tropospheric scintillation that adjusts to localities in Spain, specifically Madrid city, based on measurements of the aforementioned phenomenon reported from Spain and the bios-inspired technique Cuckoo Search (CS). The results obtained, evaluated in terms of the root mean square error, were totally satisfactory, being the most outstanding cases the improved versions of the Ortgies-T, Statistical Temperature and Humidity 2 and Statistical Temperature and Refractivity 2 models. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive statistical analysis of the cosmogenic radioisotope 7 Be measured in surface air in ten stations over Spain for a 9 years period (from January 2006 to December 2014). Besides the analysis of 7 Be seasonal and inter-annual variability, 7 Be frequency distributions and its correlations with meteorological variables observed in the 10 sampling sites were investigated. The second part of this paper focuses on a feasibility study for the application of the Artificial Neural Networks (ANNs) to predict monthly 7 Be activity concentrations using meteorological variables, PM10 concentrations and the sunspot number as input parameters. Notwithstanding the low correlations found between 7 Be and input parameter, the performance of the ANNs, as evaluated by the relevant statistical parameters, demonstrates their capability to correctly predict 7 Be monthly activities in the 10 Spanish sampling sites. [ABSTRACT FROM AUTHOR]

Influenza surveillance is critical to monitoring the situation during epidemic seasons and predictive mathematic models may aid the early detection of epidemic patterns. The objective of this study was to design a real-time spatial predictive model of ILI (Influenza Like Illness) incidence rate in Catalonia using one- and two-week forecasts. The available data sources used to select explanatory variables to include in the model were the statutory reporting disease system and the sentinel surveillance system in Catalonia for influenza incidence rates, the official climate service in Catalonia for meteorological data, laboratory data and Google Flu Trend. Time series for every explanatory variable with data from the last 4 seasons (from 2010–2011 to 2013–2014) was created. A pilot test was conducted during the 2014–2015 season to select the explanatory variables to be included in the model and the type of model to be applied. During the 2015–2016 season a real-time model was applied weekly, obtaining the intensity level and predicted incidence rates with 95% confidence levels one and two weeks away for each health region. At the end of the season, the confidence interval success rate (CISR) and intensity level success rate (ILSR) were analysed. For the 2015–2016 season a CISR of 85.3% at one week and 87.1% at two weeks and an ILSR of 82.9% and 82% were observed, respectively. The model described is a useful tool although it is hard to evaluate due to uncertainty. The accuracy of prediction at one and two weeks was above 80% globally, but was lower during the peak epidemic period. In order to improve the predictive power, new explanatory variables should be included. [ABSTRACT FROM AUTHOR]

The Spanish scale to quantify or qualify bodily harm resulting from any unintentional traffic accident prior to 1 January 2016 is established by Royal Legislative Decree (RDL) 8/2004. This scale assigns points to the sequelae, which are converted into Euros using a table that is updated annually. The objective of this study is to develop a predictive model of sequelae points that will enable the estimation of compensation a short time after the accident. This will facilitate the calculation of the money reserve and rapid access to compensation for the injured party. To conduct this study, we developed a database with information from 999 individuals who had suffered car crash injuries which were evaluated according to the scale contained in RDL 8/2004 for medical experts. Predictive models based on logistic regression models were designed on this database. To choose the best model, we calculated Mallow's Cp. The use of hurdle models made it possible to predict the points received by an injured party within a relatively short period of time after the accident. Once these points are known, it is a simple matter to calculate the corresponding compensation. The prediction models developed provide an easy way to predict the compensation to be awarded to the injured party. These models use days of hospitalization, sex, age and the results of international scales based on the Abbreviated Injury Scale. These variables can be used soon after the occurrence of the crash. [ABSTRACT FROM PUBLISHER]

Background: The cat flea, Ctenocephalides felis, is the most prevalent flea species detected on dogs and cats in Europe and other world regions. The status of flea infestation today is an evident public health concern because of their cosmopolitan distribution and the flea-borne diseases transmission. This study determines the spatial distribution of the cat flea C. felis infesting dogs in Spain. Using geospatial tools, models were constructed based on entomological data collected from dogs during the period 2013-2015. Bioclimatic zones, covering broad climate and vegetation ranges, were surveyed in relation to their size. Results: The models builded were obtained by negative binomial regression of several environmental variables to show impacts on C. felis infestation prevalence: land cover, bioclimatic zone, mean summer and autumn temperature, mean summer rainfall, distance to urban settlement and normalized difference vegetation index. In the face of climate change, we also simulated the future distributions of C. felis for the global climate model (GCM) "GFDL-CM3" and for the representative concentration pathway RCP45, which predicts their spread in the country. Conclusions: Predictive models for current climate conditions indicated the widespread distribution of C. felis throughout Spain, mainly across the central northernmost zone of the mainland. Under predicted conditions of climate change, the risk of spread was slightly greater, especially in the north and central peninsula, than for the current situation. The data provided will be useful for local veterinarians to design effective strategies against flea infestation and the pathogens transmitted by these arthropods. [ABSTRACT FROM AUTHOR]

Parabolic-trough solar collector fields are large-scale systems, so the application of centralized optimization-based control methods to these systems is often not suitable for real-time control. As such, this paper formulates a novel coalitional control approach as an appropriate alternative to the centralized scheme. The key idea is to split the overall solar collector field into smaller subsystems, each of them governed by a local controller. Then, controllers are clustered into coalitions to solve a local optimization-based problem related to the corresponding subset of subsystems, so that an approximate solution of the original centralized problem can be obtained in a decentralized fashion. However, the operational constraints of the solar collector field couple the optimization problems of the multiple coalitions, thus limiting the ability to solve them in a fully decentralized manner. To overcome this issue, a novel population-dynamics-assisted resource allocation strategy is proposed as a mechanism to decouple the local optimization problems of the multiple coalitions. The proposed coalitional methodology allows to solve the multiple local subproblems in parallel, hence reducing the overall computational burden, while guaranteeing the satisfaction of the operational constraints and without significantly compromising the overall performance. The effectiveness of proposed approach is shown through numerical simulations of a 10- and 100-loop version of the ACUREX solar collector field of Plataforma Solar de Almería , Spain. • Decentralized control of large-scale parabolic-trough solar collector fields. • Reducing the computational burden of model predictive controllers for solar plants. • Splitting large-scale solar plants by clustering model predictive controllers. • Improving the scalability of model predictive control for large-scale solar plants. • Evolutionary game theoretical assistance for model predictive control in solar plants. [ABSTRACT FROM AUTHOR]

Wildfires have changed in recent decades. The catastrophic wildfires make it necessary to have accurate predictive models on a country scale to organize firefighting resources. In Mediterranean countries, the number of wildfires is quite high but they are mainly concentrated around summer months. Because of seasonality, there are territories where the number of fires is zero in some months and is overdispersed in others. Zero-inflated negative binomial mixed models are adapted to this type of data because they can describe patterns that explain both number of fires and their non-occurrence and also provide useful prediction tools. In addition to model-based predictions, a parametric bootstrap method is applied for estimating mean squared errors and constructing prediction intervals. The statistical methodology and developed software are applied to model and to predict number of wildfires in Spain between 2002 and 2015 by provinces and months. [Display omitted] • Negative binomial mixed models are suitable for predicting wildfires and its overdispersion. • Binomial logit mixed models capture the excess of zeros. • Random effects, varying with month and province, model temporal and spatial variability. • Forecasts under expected scenarios give valuable information for decision-making. [ABSTRACT FROM AUTHOR]

The rapid expansion of photovoltaics within the global energy market requires increasing use of land, a limited resource where different uses compete. In this respect, photovoltaic systems that combine electricity generation with land use for crops are becoming increasingly important. This article proposes an analytical model validated in an operating bifacial photovoltaic plant during the summer months. This analytical model considers view factors and heat transfer calculations, as well as an electrical model of the photovoltaic modules. A study was carried out on a commercial solar power plant (11.24 MWp) in Seville, southern Spain, where different vegetal species were planted in two strings, and string performance was monitored. Nine crop species with different irrigation and light requirements, offering different albedo parameters and thermal characteristics. The results show advantages from the point of view of module temperature and power gain. This paper provides regressions for the characterisation of the photovoltaic agricultural plant, with R2 higher than 85%, both for module temperature and energy exported in the string with crops. [Display omitted] [ABSTRACT FROM AUTHOR]

The occurrence of outliers in real-world phenomena is quite usual. If these anomalous data are not properly treated, unreliable models can be generated. Many approaches in the literature are focused on a posteriori detection of outliers. However, a new methodology to a priori predict the occurrence of such data is proposed here. Thus, the main goal of this work is to predict the occurrence of outliers in time series, by using, for the first time, imbalanced classification techniques. In this sense, the problem of forecasting outlying data has been transformed into a binary classification problem, in which the positive class represents the occurrence of outliers. Given that the number of outliers is much lower than the number of common values, the resultant classification problem is imbalanced. To create training and test sets, robust statistical methods have been used to detect outliers in both sets. Once the outliers have been detected, the instances of the dataset are labeled accordingly. Namely, if any of the samples composing the next instance are detected as an outlier, the label is set to one. As a study case, the methodology has been tested on electricity demand time series in the Spanish electricity market, in which most of the outliers were properly forecast. [ABSTRACT FROM AUTHOR]