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Obesity is a multifactorial disease present worldwide and correlated with hormonal alterations that may cause a decrease in reproductive outcomes and seminal quality. However, the specific mechanisms involved are unknown. This led us to examine the relationship between paternal body mass index (BMI) and clinical reproductive outcomes by evaluating the cumulative live birth rates (CLBRs) per number of embryo transfers (ETs), embryos replaced (EmbRs), and oocytes used (OUs) in consecutive treatments until achieving the first newborn. A retrospective study was performed, and Kaplan–Meier survival curves were created to observe CLBRs with regard to the paternal BMI, adjusted by relevant confounders through Cox regression models. The participants were couples undergoing intracytoplasmic sperm injection (ICSI) and ET in Spanish IVIRMA clinics using autologous gametes. The cohort was subdivided based on paternal BMI: normal (18.5–24.99 kg/m2) (N), overweight (25–29.99 kg/m2) (OV), or obese (≥30 kg/m2) (OB) patients. A total of 4750 ICSI cycles were included, encompassing 49,485 mature oocytes, 23,963 blastocysts, and 3048 ETs. When calculating CLBRs based on the number of ETs carried out until live birth was achieved, no statistically significant differences were found (p = 0.72). After adjusting for maternal age and BMI, female infertility diagnosis, the use of preimplantation genetic testing, and the number of ETs, Cox regression showed that there were no statistically significant differences between the BMI groups (HR: 0.94 [95% CI: 0.7–1.2]; p = 0.59). When calculating CLBRs considering EmbRs, there was a similarity between the BMI groups (p = 0.57). However, there were no statistically significant differences in the adjusted Cox regression (HR: 0.93 [95% CI: 0.7–1.2]; p = 0.51). Finally, when calculating CLBRs considering OUs, the results were comparable among BMI subgroups (p = 0.75), and there were no statistically significant differences with adjusted Cox regression (HR: 0.95 [95% CI: 0.8–1.2]; p = 0.66). In conclusion, paternal BMI was not associated with clinical reproductive outcomes when considering the ETs, EmbRs, or OUs needed to reach the first liveborn (LB).

The recycling and recovery of value-added secondary raw materials such as spent Zn/C batteries is crucial to reduce the environmental impact of wastes and to achieve cost-effective and sustainable processing technologies. The aim of this work is to fabricate reduced graphene oxide (rGO)-based sorbents with a desulfurization capability using recycled graphite from spent Zn/C batteries as raw material. Recycled graphite was obtained from a black mass recovered from the dismantling of spent batteries by a hydrometallurgical process. Graphene oxide (GO) obtained by the Tour’s method was comparable to that obtained from pure graphite. rGO-based sorbents were prepared by doping obtained GO with NiO and ZnO precursors by a hydrothermal route with a final annealing step. Recycled graphite along with the obtained GO, intermediate (rGO-NiO-ZnO) and final composites (rGO-NiO-ZnO-400) were characterized by Wavelength Dispersive X-ray Fluorescence (WDXRF) and X-ray diffraction (XRD) that corroborated the removal of metal impurities from the starting material as well as the presence of NiO- and ZnO-doped reduced graphene oxide. The performance of the prepared composites was evaluated by sulfidation tests under different conditions. The results revealed that the proposed rGO-NiO-ZnO composite present a desulfurization capability similar to that of commercial sorbents which constitutes a competitive alternative to syngas cleaning.

(1) Background: The main characteristic of fibromyalgia (FM) is generalized musculoskeletal pain. This may be accompanied by muscle and joint stiffness, sleep and mood disorders, anxiety and depression, cognitive dysfunction, and chronic fatigue. It is endemic in developed countries, with a higher prevalence among women than men, and its etiology is still unknown. Diagnosis is made based on chronic generalized pain and through the presence of tender points. The objective of this study was to analyze the efficacy of diathermy on pain in patients with fibromyalgia. (2) Methods: A single, blind, randomized experimental study was developed with a sample of 31 participants. Measurements were taken and recorded at three different intervals using the following measurement tools: the pressure pain threshold (PPT) at the tender points (TP) of the right and left trochanteric prominence with an algometer, the pain measurement scale, the Fibromyalgia Impact Questionnaire, the sleep quality index (PSQI, Pittsburgh), the Multidimensional Fatigue Inventory (MFI-S), and the scale for anxiety and depression (Hospital Anxiety and Depression Scale). Sociodemographic data were collected through Google Forms (age, height, weight, Body Mass Index). The intervention took place twice weekly across four weeks of sessions. (3) Results: Statistically significant results were obtained in the right and left trochanter PPT, as well as for anxiety and fatigue in the experimental group. The results obtained show that this treatment has managed to improve the quality of sleep, the impact of disease, chronic fatigue, and anxiety in patients with FM. (4) Conclusions: Diathermy is a tool that can help reduce pain. It can also improve the baseline levels of chronic fatigue, anxiety, the impact of the disease, and sleep quality in patients with fibromyalgia.

Background: Currently, educational attainment has risen significantly among young people, causing changes in the labor market where skills have become more important. Thus, tertiary education has become an “essential vehicle” to develop high-level skills that would boost students’ professional, social, and personal lives. Methods: The aim of the survey is to study the effects of an intervention program based on two methodologies, service-learning and peer mentoring, to enhance the learn-to-learn and social skills of undergraduate students. The sample was composed of 69 undergraduate students of four different degrees. The methodological design adopted was quasi-experimental pretest–posttest. The intervention consisted of 955 mentoring sessions (878 one-to-one and 77 in groups) among the undergraduate students and students in compulsory education. The undergraduate students participated as mentors. Before, they had three sessions of training. Weekly mentoring sessions were spread out during out-of-school time for 90 min each. Results: The results show a few statistically significant differences in favor of the posttest phase in strategies for the learning and social skills of the participants. Conclusions: Although the program did not obtain the expected results, these outcomes agree with the other studies that investigate intervention programs that use service-learning and peer mentoring methodologies.

This study aimed to identify potential BCL-2 small molecule inhibitors using deep neural networks (DNN) and random forest (RF), algorithms as well as molecular docking and molecular dynamics (MD) simulations to screen a library of small molecules. The RF model classified 61% (2355/3867) of molecules as ‘Active’. Further analysis through molecular docking with Vina identified CHEMBL3940231, CHEMBL3938023, and CHEMBL3947358 as top-scored small molecules with docking scores of −11, −10.9, and 10.8 kcal/mol, respectively. MD simulations validated these compounds’ stability and binding affinity to the BCL2 protein.

Ventilator-associated pneumonia (VAP) is common in Pediatric Intensive Care Units. Although early detection is crucial, current diagnostic methods are not definitive. This study aimed to identify lung ultrasound (LUS) findings and procalcitonin (PCT) values in pediatric patients with VAP to create a new early diagnosis score combined with the Clinical Pulmonary Infection Score (CPIS), the CPIS-PLUS score. Prospective longitudinal and interventional study. Pediatric patients with suspected VAP were included and classified into VAP or non-VAP groups, based on Centers of Disease Control (CDC) criteria for the final diagnosis. A chest-X-ray (CXR), LUS, and blood test were performed within the first 12 h of admission. CPIS score was calculated. A total of 108 patients with VAP suspicion were included, and VAP was finally diagnosed in 51 (47%) patients. CPIS-PLUS showed high accuracy in VAP diagnosis with a sensitivity (Sn) of 80% (95% CI 65–89%) and specificity (Sp) of 73% (95% CI 54–86%). The area under the curve (AUC) resulted in 0.86 for CPIS-PLUS vs. 0.61 for CPIS. In conclusion, this pilot study showed that CPIS-PLUS could be a potential and reliable tool for VAP early diagnosis in pediatric patients. Internal and external validations are needed to confirm the potential value of this score to facilitate VAP diagnosis in pediatric patients.

In the rapidly evolving domain of conversational agents, the integration of Large Language Models (LLMs) into Chatbot Development Platforms (CDPs) is a significant innovation. This study compares the efficacy of employing generic and fine-tuned GPT-3.5-turbo models for designing dialog flows, focusing on the intent and entity recognition crucial for dynamic conversational interactions. Two distinct approaches are introduced: a generic GPT-based system (G-GPT) leveraging the pre-trained model with complex prompts for intent and entity detection, and a fine-tuned GPT-based system (FT-GPT) employing customized models for enhanced specificity and efficiency. The evaluation encompassed the systems’ ability to accurately classify intents and recognize named entities, contrasting their adaptability, operational efficiency, and customization capabilities. The results revealed that, while the G-GPT system offers ease of deployment and versatility across various contexts, the FT-GPT system demonstrates superior precision, efficiency, and customization, although it requires initial training and dataset preparation. This research highlights the versatility of LLMs in enriching conversational features for talking assistants, from social robots to interactive chatbots. By tailoring these advanced models, the fluidity and responsiveness of conversational agents can be enhanced, making them more adaptable and effective in a variety of settings, from customer service to interactive learning environments.

Sensors based on solidly mounted resonators (SMRs) exhibit a good set of properties, such as high sensitivity, fast response, low resolution limit and low production cost, which makes them an appealing technology for sensing applications. However, they can suffer from cross-sensitivity issues, as their response can be altered by undesirable ambient factors, such as temperature and humidity variations. In this work we propose a method to discriminate humidity variations from the general frequency response using an SMR specifically manufactured to operate in a dual-mode (displaying two close resonances). The two modes behave similarly towards humidity changes (−1.94 kHZ/(%RH)) for resonance one and −1.62 kHZ/(%RH) for resonance two), whereas their performance under temperature changes is significantly different, displaying 2.64 kHZ/°C for resonance one and 34.21 kHZ/°C for resonance two. This allows for the decoupling process to be carried out in a straightforward manner. Frequency response is tracked under different humidity conditions, in the −20 °C to room temperature region, proving that this behavior is reproducible in any given environment.

This work presents the modeling and energy management of a microgrid through models developed based on physical equations for its optimal control. The microgrid’s energy management system was built with one of the most popular control algorithms in microgrid energy management systems: model predictive control. This control strategy aims to satisfy the load demand of an office located in the CIESOL bioclimatic building, which was placed in the University of Almería, using a quadratic cost function. The simulation scenarios took into account real simulation parameters provided by the microgrid of the building. For case studies of one and five days, the optimization was aimed at minimizing the input energy flows of the microgrid and the difference between the energy generated and demanded by the load, subject to a series of physical constraints for both outputs and inputs. The results of this work show how, with the correct tuning of the control strategy, the energy demand of the building is covered through the optimal management of the available energy sources, reducing the energy consumption of the public grid, regarding a wrong tuning of the controller, by 1 kWh per day for the first scenario and 7 kWh for the last.

Recent advances in Deep Learning and aerial Light Detection And Ranging (LiDAR) have offered the possibility of refining the classification and segmentation of 3D point clouds to contribute to the monitoring of complex environments. In this context, the present study focuses on developing an ordinal classification model in forest areas where LiDAR point clouds can be classified into four distinct ordinal classes: ground, low vegetation, medium vegetation, and high vegetation. To do so, an effective soft labeling technique based on a novel proposed generalized exponential function (CE-GE) is applied to the PointNet network architecture. Statistical analyses based on Kolmogorov–Smirnov and Student’s t-test reveal that the CE-GE method achieves the best results for all the evaluation metrics compared to other methodologies. Regarding the confusion matrices of the best alternative conceived and the standard categorical cross-entropy method, the smoothed ordinal classification obtains a more consistent classification compared to the nominal approach. Thus, the proposed methodology significantly improves the point-by-point classification of PointNet, reducing the errors in distinguishing between the middle classes (low vegetation and medium vegetation).

We aimed to investigate clinical and ultrasound signs of shoulder overuse injuries in professional bullfighters; side-to-side differences (dominant vs. non-dominant); and to determine potential differences according to bullfighters’ categories. An observational cross-sectional study was conducted. Thirty professional and active bullfighters were assessed. A bilateral ultrasound assessment of the subacromial bursa, long biceps head tendon (LHBT), and rotator cuff was performed to determine the presence of bursitis, subluxation, partial or total tendon rupture, tenosynovitis, or calcification. Supraspinatus tendon thickness was measured. Finally, a battery of clinical orthopedic tests (Yergason, Jobe, infraspinatus, Gerber, and bursa tests) were also performed. Most identified ultrasound findings were located in the dominant side, being the presence of bursitis (n = 9; 30%), LHBT tenosynovitis (n = 8; 26.7%), and subscapularis tendon calcification (n = 5; 16.7%) the most prevalent. No side-to-side or between-categories differences were found for supraspinatus tendon thickness (all, p > 0.05). The most frequent positive signs were the infraspinatus test (40.0%), Gerber lift-off test (33.3%), and bursitis, Jobe, and Yergason tests (all, 26.7%). Ultrasound signs were commonly found at LHBT, subacromial bursa, and rotator cuff in professional bullfighters without difference between categories and sides. No side-to-side or between-categories differences were found. Positive clinical test signs suggestive of bursitis, LHBT, and rotator cuff tendinopathy were frequently observed.

Spinal muscular atrophy (SMA) is a devastating genetic neurodegenerative disease caused by the insufficient production of Survival Motor Neuron (SMN) protein. It presents different phenotypes with frequent contractures and dislocations, scoliosis, and pain. This study aims to report the prevalence and description of pain and how it affects daily life by analyzing a new ad hoc questionnaire. An observational study of patients under 18 years of age with SMA was conducted at two referral centers in Spain. Data were analyzed using a descriptive analysis and a Bayesian ordinal regression model to assess the association with clinical and demographic variables. Fifty-one individuals were included in this study, 27% of whom reported pain with a median duration of 5.2 years and a mean Visual Analogic Scale (VAS) score of 5. Notably, 77% were receiving disease-modifying treatment, with more than 50% receiving analgesic treatment. The Bayesian model showed that functional status, lower limb contractures, and number of visits have a high probability (>90%) of influencing pain. Thus, the prevalence of pain in the SMA population under 18 years is substantial, and its presence could be associated with lower limb contractures, better functional status, and higher RULM (Revised Upper Limb Module) scores.

Background: In recent years, there has been an evident delay in childbearing and concerns have been raised about whether this increase in age affects reproductive outcomes. This study aimed to evaluate the effect of paternal age on obstetrical and perinatal outcomes in couples undergoing in vitro fertilization or intracytoplasmic sperm injection using autologous sperm and oocytes. Methods: This retrospective study evaluated obstetrical and perinatal outcomes from 14,125 couples that were arbitrarily divided into three groups according to paternal age at conception: ≤30 (n = 1164), 31–40 (n = 11,668) and >40 (n = 1293). Statistics consisted of a descriptive analysis followed by univariate and multivariate models, using the youngest age group as a reference. Results: The study showed significantly longer pregnancies for the fathers aged 31–40 compared to ≤30 years. However, there were no significant differences for the type of delivery, gestational diabetes, anaemia, hypertension, delivery threat, premature rupture of membranes, preterm birth, very preterm birth, and the neonate’s sex, weight, low birth weight, very low birth weight, length, cranial perimeter, Apgar score and neonatal intensive care unit admission. Conclusion: Despite our promising results for older fathers, as paternal age was not associated with clinically relevant obstetrical and perinatal outcomes, future well-designed studies are necessary as it has been associated with other important disorders.

(1) Fibromyalgia (FM) is a chronic musculoskeletal condition with multiple symptoms primarily affecting women. An imbalance in cytokine levels has been observed, suggesting a chronic low-grade inflammation. The main aim of the meta-analysis was to examine the effect of multimodal rehabilitation on cytokine levels and other predominant variables in patients with FM. Furthermore, to examine which non-pharmacological tools have been used to investigate the effects that these can have on cytokines in FM patients. (2) Methods: Searches were conducted in PubMed, Scopus, Web of Science, Cochrane, and ScienceDirect databases. This systematic review and metanalysis followed the PRISMA statement protocol. The methodological quality of the studies was assessed using the PEDro scale, the risk of bias followed the Cochrane Manual 5.0.1, and the GRADE system was used for rating the certainty of evidence. (3) Results: Of 318 studies found, eight were finally selected, with a sample size of 320 women with a mean age of 57 ± 20. The proinflammatory cytokines IL-1β, IL-6, IL-8 and TNF-α were the most studied. Resistance exercise, aquatic exercise, dynamic contractions, cycling, treadmill, and infrared therapy were the main non-pharmacological tools used. (4) Conclusions: The systematic review with meta-analysis found evidence of elevated cytokine levels in patients with FM, suggesting low chronic inflammation and a possible contribution to central sensitization and chronic pain. However, the effects of physiotherapeutic interventions on cytokine levels are variable, highlighting the importance of considering different factors and the need for further research.

The widespread use of mobile phones in daily life makes them a fundamental tool for the study of human behavior. In particular, they can be used as a source of additional information to help to diagnose diseases. This work is based on contrasted dual-tasking tests where cognitive performance is studied by performing tasks of high cognitive load while walking. In this case, we study significant differences in mobile device use among groups of people of different ages and examine whether they are more characteristic when the interaction takes place on the move. A study is conducted by monitoring the interaction with the mobile device for one consecutive week and analyzing the correlations between these interactions and the participants’ ages. Additionally, a user profiling model is designed to help to use this ground truth in future works focused on the early diagnosis of cognitive deficits. The results obtained contribute to preliminarily characterizing how age-related normotypical cognitive decline affects interactions with mobile devices. In addition, the pilot study generates a dataset with monitored events and interactions of 45 users that includes more than 4.5 million records.

We explored the influence of habitat structure on bird density and species richness in the poorly known bird communities in the steppes of Eastern Morocco, along a 200 km long N–S gradient of increasing aridity. The birds were surveyed, and habitat structure was measured in 44 transects regularly distributed along the gradient and during the winter and spring seasons in two consecutive years. After applying a principal component analysis (PCA), five axes were identified, including one related to the latitude–altitude–soil-type gradient and another describing the development of herbaceous vegetation. Generalized linear models were used to explore the relations between bird density and species richness with PCA axes in each season, considering both the entire community and groups of granivorous, insectivorous, and mixed-diet species. More than 90% of the birds were year-round residents, with larks dominating the community in both seasons. We conclude that a distinct multifactorial response can be identified for each functional group of species. In the winter, the community is mainly affected by the structure of the habitat, while aridity (and its assumed relation to primary production) is less influential. In the spring, habitat structure continues to have the greatest explanatory power, but location along the aridity gradient becomes more relevant. These findings reveal the interaction of the negative effects of climatic and anthropogenic changes in the habitat available to these bird communities, with a greater impact expected on birds with diets that include seeds, as well as a general shift of optimal breeding conditions toward more northerly latitudes.

Retirement migration within Europe has increased enormously since the 1990s. It now involves millions of elderly Europeans moving from Central/Northern Europe (UK, Germany, Scandinavia) to Mediterranean countries (Malta, Portugal, Spain) in search of a better quality of life. Most previous research departs from an ethnographic perspective to look at the personal experiences and motivations of retirement migrants. In this paper, we adopt a macro-level perspective to address the use that retirement migrants make of the European framework of social rights. We aim to understand (a) to what extent do retirement migrants living in Spain ask for help from the local Social Services when they enter into dependency? (b) Do retirement migrants engage in strategies to maximize their welfare rights? To answer these questions, we carried out qualitative phone interviews with the coordinators of Social Services that cover 80 (out of 119) of the Spanish municipalities with larger numbers of retirement migrants (more than 30% of elderly residents are foreigners).

Intensive soil tillage and the resulting erosion constitutes one of the main problems in olive growing. Palliative practices such as implementing vegetable covers are encouraged. Recently, the method of adding inert green roofs to the soil, either alone or in combination with adventitious vegetation, has stood out. Assessing this agronomic measure is our main objective. This research was conducted in 2022 in the Jaén province (southern Spain), wherein olive groves with different managements were selected: (i) conventional, free from vegetation, (ii) ecologically managed olive grove containing a cover of adventitious vegetation (VC), and (iii) ecologically managed olive grove in which the remains of pruning are added to the adventitious cover (VC-MIX). Vegetation inventories and beneficial insect monitoring were performed using a combined device with chromatic and olfactory trapping. The olive moth (Prays oleae) and lacewing predators were selected as the indicator species. Both the beneficial insect diversity and relative plant abundance were higher in the VC-MIX, where the highest rates of predation by lacewings were found (88%). In turn, these parameters in the VC olive surpassed those of the conventional olive grove. The factors involved in the notable differences between the three management types are discussed.

Glioblastoma (GB) is the most prevalent primary brain cancer and the most aggressive form of glioma because of its poor prognosis and high recurrence. To confirm the importance of epigenetics in glioma, we explored The Cancer Gene Atlas (TCGA) database and we found that several histone/DNA modifications and chromatin remodeling factors were affected at transcriptional and genetic levels in GB compared to lower-grade gliomas. We associated these alterations in our own cohort of study with a significant reduction in the bulk levels of acetylated lysines 9 and 14 of histone H3 in high-grade compared to low-grade tumors. Within GB, we performed an RNA-seq analysis between samples exhibiting the lowest and highest levels of acetylated H3 in the cohort; these results are in general concordance with the transcriptional changes obtained after histone deacetylase (HDAC) inhibition of GB-derived cultures that affected relevant genes in glioma biology and treatment (e.g., A2ML1, CD83, SLC17A7, TNFSF18). Overall, we identified a transcriptional signature linked to histone acetylation that was potentially associated with good prognosis, i.e., high overall survival and low rate of somatic mutations in epigenetically related genes in GB. Our study identifies lysine acetylation as a key defective histone modification in adult high-grade glioma, and offers novel insights regarding the use of HDAC inhibitors in therapy.

Monitoring temporal dynamics in genetic diversity is of great importance for conservation, especially for threatened species that are suffering a rapid population decline and increased fragmentation. Here, we investigate temporal variation in genetic diversity, structure, and gene flow in the Dupont’s lark (Chersophilus duponti) across most of its range. This species shows increasing levels of population fragmentation, substantial population declines, and severe range contraction, so temporal losses of genetic diversity, increasing differentiation, and decreasing gene flow are expected when comparing present day data with previous situations. To address this, we resampled sites (nine regions in two countries) after 12–15 years (five-to-seven generations) and assessed changes in genetic parameters using 11 microsatellite markers. We found no substantial loss in genetic diversity over time at the species level, but we detected considerable variation among regions in the amount of allelic diversity and heterozygosity lost over time. Temporal variation in allele frequencies (common, rare, and private alleles), and changes in genetic differentiation and gene flow over time suggest a major role of connectivity for the stability of the overall metapopulation. Our results agree with the hypothesis that connectivity rescues genetic diversity via immigration and gene flow. However, evidence of recent genetic bottleneck and the substantial changes detected in some regions are clear signs of genetic erosion and may be signalling a rapid decline of the populations. Urgent actions must be carried out to stop and reverse human impacts on this threatened lark and its habitat.

Cognitive deficits are very difficult to diagnose during the initial stages; tests typically consist of a patient performing punctual dual-task activities, which are subjectively analyzed to determine the cognitive decline impact on gait. This work supports novel and objective diagnosis methods by stating a baseline on how neurotypical aging affects dual tasks while using a smartphone on the move. With this aim, we propose a twofold research question: Which mobile device tasks performed on the move (dual tasking) have characteristic changes in gait parameters, and which are especially characteristic at older ages? An experiment was conducted with 30 healthy participants where they performed 15 activities (1 single task, 2 traditional dual-tasks and 12 mobile-based dual-tasks) while walking about 50 m. Participants wore a wireless motion tracker (15 sensors) that made the concise analysis of gait possible. The results obtained characterized the gait parameters affected by mobile-based dual-tasking and the impact of normal cognitive decline due to aging. The statistical analysis shows that using smartphone-based dual-tasking produces more significant results than traditional dual-tasking. In the study, 3 out of 10 gait parameters were very significantly affected (p < 0.001) when using the traditional dual tasks, while 5 out of 10 parameters were very significantly affected (p < 0.001) in mobile-based dual-tasking. Moreover, the most characteristic tasks and gait parameters were identified through the obtained results. Future work will focus on applying this knowledge to improve the early diagnosis of MCI.

Liver transplantation outcomes have improved in recent years. However, with the emergence of expanded donor criteria, tools to better assist donor–recipient matching have become necessary. Most of the currently proposed scores based on conventional biostatistics are not good classifiers of a problem that is considered “unbalanced.” In recent years, the implementation of artificial intelligence in medicine has experienced exponential growth. Deep learning, a branch of artificial intelligence, may be the answer to this classification problem. The ability to handle a large number of variables with speed, objectivity, and multi-objective analysis is one of its advantages. Artificial neural networks and random forests have been the most widely used deep classifiers in this field. This review aims to give a brief overview of D–R matching and its evolution in recent years and how artificial intelligence may be able to provide a solution.

Anthracycline-induced cardiotoxicity is the most severe collateral effect of chemotherapy originated by an excess of oxidative stress in cardiomyocytes that leads to cardiac dysfunction. We assessed clinical data from patients with breast cancer receiving anthracyclines and searched for discriminating microRNAs between patients that developed cardiotoxicity (cases) and those that did not (controls), using RNA sequencing and regression analysis. Serum levels of 25 microRNAs were differentially expressed in cases versus controls within the first year after anthracycline treatment, as assessed by three different regression models (elastic net, Robinson and Smyth exact negative binomial test and random forest). MiR-4732-3p was the only microRNA identified in all regression models and was downregulated in patients that experienced cardiotoxicity. MiR-4732-3p was also present in neonatal rat cardiomyocytes and cardiac fibroblasts and was modulated by anthracycline treatment. A miR-4732-3p mimic was cardioprotective in cardiac and fibroblast cultures, following doxorubicin challenge, in terms of cell viability and ROS levels. Notably, administration of the miR-4732-3p mimic in doxorubicin-treated rats preserved cardiac function, normalized weight loss, induced angiogenesis, and decreased apoptosis, interstitial fibrosis and cardiac myofibroblasts. At the molecular level, miR-4732-3p regulated genes of TGFβ and Hippo signaling pathways. Overall, the results indicate that miR-4732-3p is a novel biomarker of cardiotoxicity that has therapeutic potential against anthracycline-induced heart damage.

The widespread trend of pursuing higher efficiencies in radial turbochargers led to the prompting of this work. A 3D-printed model of the static parts of a radial variable geometry turbine, the vaned nozzle, and the volute, was developed. This model was up-scaled from the actual reference turbine to place sensors and characterize the flow around the nozzle vanes, including the tip gap. In this study, a computational model of the scaled-up turbine was carried out to verify the results in two ways. For this model, firstly compared with an already validated CFD turbine model of the real device (which includes a rotor), its operating range was extended to different nozzle positions, and we checked the issues with rotor–stator interactions as well as the influence of elements such as the screws of the turbine stator. After showing results for different nozzle openings, another purpose of the study was to check the effect of varying the clearance over the tip of the stator vanes on the tip leakage flow since the 3D-printed model has variable gap height configurations.

Patients with high cholesterol and glucose levels are at high risk for cardiovascular disease. The Sterol Regulatory Element Binding Protein (SREBP) system regulates genes involved in lipid, cholesterol and glucose pathways. Autosomal Dominant Hypercholesterolemias (ADHs) are a group of diseases with increased cholesterol levels. They affect 1 out of every 500 individuals. About 20–30% of patients do not present any mutation in the known genes (LDLR, APOB and PCSK9). ADHs constitute a good model to identify the genes involved in the alteration of lipid levels or possible therapeutic targets. In this paper, we studied whether a mutation in the SREBP system could be responsible for ADH and other metabolic alterations present in these patients. Forty-one ADH patients without mutations in the main responsible genes were screened by direct sequencing of SREBP system genes. A luciferase reporter assay of the found mutation and an oral glucose tolerance test in carriers and non-carriers were performed. We found a novel mutation in the SREBF2 gene that increases transcription levels and cosegregates with hypercholesterolemia, and we found increased glucose levels in one family. SREBP2 is known to be involved in cholesterol synthesis, plasma levels and glucose metabolism in humans. The found mutation may involve the SREBF2 gene in hypercholesterolemia combined with hyperglycemia.

Because of the changes in society, the educational scope must implement teaching–learning methodologies that help students to develop the competences that will be necessary in their academic–professional journey. This study presents a teaching innovation experience that is based on the flipped classroom methodology, which was carried out with 136 students (academic year: 2019–2020) in the subject of “Theory and History of Physical Education, Physical Activity and Sport” of the degree of Physical Activity and Sport Sciences of the University of Seville. The methodology of the study combines qualitative and quantitative approaches (mixed methods) through a pre-experimental design. The results show that there were significant differences in the acquisition of knowledge after the application of the methodology, which had a significant impact on the students’ competence levels. Moreover, the students presented high levels of satisfaction in different areas. This allows for the conclusion that it is important for this methodology to continue in later courses, given its contribution to the competences that are related to the formal aspects and that are linked to research and organisation. Recommendations for practice are presented at the end of this article.

The aim of this study is to determine the perceptions of university students toward teaching–learning processes during the COVID-19 pandemic. This research gathers inquiries made worldwide on the perceptions of students in higher education during a state of alarm. The proposed objectives were (a) to analyse the perception of students toward teaching–learning processes in university; (b) to determine the assessment given by students about the changes that took place in university teaching as a result of COVID-19; and (c) to explore resources (hardware and software), professional collaboration, digital pedagogy and student empowerment (motivation) regarding digital education and recent changes in university teaching due to the pandemic. This study used a non-experimental, descriptive design based on opinion polls or surveys. The results show a positive correlation between digital pedagogy, student motivation and digital environments. As a conclusion, we encourage the scientific community to continue delving into the motivation, collaboration and reflective exchange of experiences, self-learning and promotion of initiatives that foster the development of competencies in future teachers. It is also important to continue the research on integrated designs in training processes in university, tutoring and continuous evaluation, as they are key for digital transformation in universities.

Abdominal aortic aneurysm (AAA), is a complex disorder characterized by vascular vessel wall remodeling. LIGHT (TNFSF14) is a proinflammatory cytokine associated with vascular disease. In the present study, the impact of genetic inactivation of Light was investigated in dissecting AAA induced by angiotensin II (AngII) in the Apolipoprotein E-deficient (Apoe−/−) mice. Studies in aortic human (ah) vascular smooth muscle cells (VSMC) to study potential translation to human pathology were also performed. AngII-treated Apoe−/−Light−/− mice displayed increased abdominal aorta maximum diameter and AAA severity compared with Apoe−/− mice. Notably, reduced smooth muscle α-actin+ area and Acta2 and Col1a1 gene expression were observed in AAA from Apoe−/−Light−/− mice, suggesting a loss of VSMC contractile phenotype compared with controls. Decreased Opn and augmented Sox9 expression, which are associated with detrimental and non-contractile osteochondrogenic VSMC phenotypes, were also seen in AngII-treated Apoe−/−Light−/− mouse AAA. Consistent with a role of LIGHT preserving VSMC contractile characteristics, LIGHT-treatment of ahVSMCs diminished the expression of SOX9 and of the pluripotency marker CKIT. These effects were partly mediated through lymphotoxin β receptor (LTβR) as the silencing of its gene ablated LIGHT effects on ahVSMCs. These studies suggest a protective role of LIGHT through mechanisms that prevent VSMC trans-differentiation in an LTβR-dependent manner.

Lipids of different unsaturation degree were added to dairy ewe diet to test the hypothesis that unsaturated oils would modulate milk fatty acid (FA) profile without impairing or even improving feed efficiency. To this aim, we examined milk FA profile and efficiency metrics (feed conversion ratio (FCR), energy conversion ratio (ECR), residual feed intake (RFI), and residual energy intake (REI)) in 40 lactating ewes fed a diet with no lipid supplementation (Control) or supplemented with 3 fats rich in saturated, monounsaturated and polyunsaturated FA (i.e., purified palmitic acid (PA), olive oil (OO), and soybean oil (SBO)). Compared with PA, addition of OO decreased milk medium-chain saturated FA and improved the concentration of potentially health-promoting FA, such as cis-9 18:1, trans-11 18:1, cis-9 trans-11 CLA, and 4:0, with no impact on feed efficiency metrics. Nevertheless, FA analysis and decreases in FCR and ECR suggested that SBO supplementation would be a better nutritional strategy to further improve milk FA profile and feed efficiency in dairy ewes. The paradox of differences observed depending on the metric used to estimate feed efficiency (i.e., the lack of variation in RFI and REI vs. changes in FCR and ECR) does not allow solid conclusions to be drawn in this regard.

The development of professional practices during the university stage is a fundamental factor for quality skills development. For many students, it is the first real experience in a professional context, so continuous monitoring by teachers is necessary. This article presents an innovative proposal to develop the follow-up of the professional practices of the degree in Primary Education, and two Masters’ in Psychopedagogy and Special Educational Needs using a learning management system (LMS) (Blackboard). The experience was developed by a team of teachers from the departments of Didactics and Educational Organization and Research Methods and Educational Diagnosis of the University of Seville (Spain). The aspects to be studied are the development of communication, reflection, and collaborative learning processes during the internship period. After an explicit agreement, 24 students (10 from the course “Professional Practices I” in undergraduate students; and the others from the Master’s) committed to periodically using the blog designed ad hoc for this experience. A content analysis of the speeches posted on the blog was carried out, examining the changes, the advantages, and the disadvantages that this model entailed. It allowed observing similarities and differences between both groups of students. As the main conclusion, there were some differences between the two groups of students, regarding the number, type, and contents of interventions; there were no differences in the assessment of the methodology, all the students thought that it was a very positive assessment of the experience for generating information exchange networks among colleagues and teachers. Finally, the relevance of constant monitoring of the academic tutor was highlighted.

The aim of the present work was focused on optimising the yield and quality of the biocrude obtained by hydrothermal liquefaction (HTL) of Nannochloropsis gaditana. Temperature, reaction time and microalga concentration were the variables used to carry out an experimental factorial design with a central composite design. The responses chosen were the biocrude yield and the nitrogen and oxygen content in the biocrude phase. A second-order model was obtained to predict the responses as a function of these variables. Temperature is the most determining factor with a positive influence on biocrude yield. The maximum biocrude yield (42.3 ± 0.8 wt%) was obtained at 320 °C, 10 min of reaction and 10 wt% microalgae concentration, and the nitrogen and oxygen content significantly decreased with respect to their corresponding levels in the initial microalgal biomass. The HHV value of the biocrude was 35.7 MJ/kg. The biocrude was composed of 30% of linear and branched hydrocarbons.

The application of MACS non-apoptotic sperm selection in infertility clinics is controversial since the published literature does not agree on its effect on reproductive outcomes. Therefore, it is not part of the routine clinical practice. Classical measures of reproductive success (pregnancy or live birth rates per ovarian stimulation) introduce a bias in the evaluation of a technique’s effect, since only the best embryo is transferred. This retrospective, multicenter, observational study evaluated the impact of MACS on reproductive outcomes, measuring results in classical parameters and cumulative live birth rates (CLBR). Data from ICSI cycles using autologous oocyte in Spanish IVIRMA fertility clinics from January 2008 to February 2020 were divided into two groups according to their semen processing: standard practice (reference: 46,807 patients) versus an added MACS sperm selection (1779 patients). Only when measured as CLBR per embryo transferred and per MII oocyte used was the difference between groups statistically significant. There were no significant differences between MACS and reference groups on pregnancy and live birth rates. In conclusion, results suggest that non-apoptotic sperm selection by MACS on unselected males prior to ICSI with autologous oocytes has limited clinical impact, showing a subtle increase in CLBR per embryo transferred.

Glioblastoma (GBM) is the most frequent and aggressive primary brain tumor and is associated with a poor prognosis. Despite the use of combined treatment approaches, recurrence is almost inevitable and survival longer than 14 or 15 months after diagnosis is low. It is therefore necessary to identify new therapeutic targets to fight GBM progression and recurrence. Some publications have pointed out the role of glioma stem cells (GSCs) as the origin of GBM. These cells, with characteristics of neural stem cells (NSC) present in physiological neurogenic niches, have been proposed as being responsible for the high resistance of GBM to current treatments such as temozolomide (TMZ). The protein Kinase C (PKC) family members play an essential role in transducing signals related with cell cycle entrance, differentiation and apoptosis in NSC and participate in distinct signaling cascades that determine NSC and GSC dynamics. Thus, PKC could be a suitable druggable target to treat recurrent GBM. Clinical trials have tested the efficacy of PKCβ inhibitors, and preclinical studies have focused on other PKC isozymes. Here, we discuss the idea that other PKC isozymes may also be involved in GBM progression and that the development of a new generation of effective drugs should consider the balance between the activation of different PKC subtypes.

Meteorological data are extensively used to perform environmental learning. Soft Computing (SC) and Machine Learning (ML) techniques represent a valuable support in many research areas, but require datasets containing information related to the topic under study. Such datasets are not always available in an appropriate format and its preparation and pre-processing implies a lot of time and effort by researchers. This paper presents a novel software tool with a user-friendly GUI to create datasets by means of management and data integration of meteorological observations from two data sources: the National Data Buoy Center and the National Centers for Environmental Prediction and for Atmospheric Research Reanalysis Project. Such datasets can be created using buoys and reanalysis data through customisable procedures, in terms of temporal resolution, predictive and objective variables, and can be used by SC and ML methodologies for prediction tasks (classification or regression). The objective is providing the research community with an automated and versatile system for the casuistry that entails well-formed and quality data integration, potentially leading to better prediction models. The software tool can be used as a supporting tool for coastal and ocean engineering applications, sustainable energy production, or environmental modelling; as well as for decision-making in the design and building of coastal protection structures, marine transport, ocean energy converters, and well-planned running of offshore and coastal engineering activities. Finally, to illustrate the applicability of the proposed tool, a case study to classify waves depending on their significant height and to predict energy flux in the Gulf of Alaska is presented.

This systematic review and meta-analysis sought to gain further insight into the relationship between cortisol reactivity and chronic widespread pain in patients with fibromyalgia. The studies selected were those conducted in adults with fibromyalgia that were random controlled, non-controlled or observational. Studies were excluded if they examined diseases other than fibromyalgia or if they did not report on pain or cortisol. Twelve studies met inclusion criteria. Data were extracted into tabular format using predefined data fields by two reviewers and assessed for accuracy by a third reviewer. The methodological quality of the studies was assessed using the PEDro scale. Data Synthesis: Of 263 studies identified, 12 were selected for our review and 10 were finally included as their methodological quality was good. In the meta-analysis, we calculated effect sizes of interventions on pain indicators and cortisol levels in patients with fibromyalgia. A small overall effect of all the interventions was observed on pain tolerance and pressure pain thresholds, yet this effect lacked significance (ES = 0.150; 95%CI 0.932–1.550; p > 0.05). Conclusions: While some effects of individual nonpharmacological therapeutic interventions were observed on both cortisol levels and measures of pain, our results suggest much further work is needed to elucidate the true relationship between chronic widespread pain and cortisol levels in patients with fibromyalgia.

Affective computing is a branch of artificial intelligence that aims at processing and interpreting emotions. In this study, we implemented sensors/actuators into a stuffed toy mammoth, which allows the toy to have an affective and cognitive basis to its communication. The goal is for therapists to use this as a tool during their therapy sessions that work with patients with mood disorders. The toy detects emotion and provides a dialogue that would guide a session aimed at working with emotional regulation and perception. These technical capabilities are possible by employing IBM Watson’s services, implemented into a Raspberry Pi Zero. In this paper, we delve into its evaluation with neurotypical adolescents, a panel of experts, and other professionals. The evaluation aims were to perform a technical and application validation for use in therapy sessions. The results of the evaluations are generally positive, with an 87% accuracy for emotion recognition, and an average usability score of 77.5 for experts (n = 5), and 64.35 for professionals (n = 23). We add to that information some of the issues encountered, its effects on applicability, and future work to be done.

The ionosphere provides a channel that is able to propagate electromagnetic waves for long-haul communications, allowing Non-Line-Of-Sight (NLOS) radio communications in the high-frequency band (HF). Nonetheless, its performance as a communications channel mainly depends on solar activity, as Earth’s diurnal and seasonal cycles modify the ionospheric ionization by the sun and consequently the channel performance. La Salle and the Observatori de l’Ebre have conducted oblique soundings of a 12,760 km ionospheric channel from Livingston Island (South Shetland Archipelago—Antarctica −62.7∘S, 299.6∘E) to Cambrils (Spain −41.0∘N, 1.0∘E) to evaluate this evidence and analyze the characteristics of this particular channel. The final goal of the project is to establish a stable communications link to be used as a backup for low throughput data transmission from the various geomagnetic and meteorological sensors located in the Spanish Antarctic Station. The aim of this paper is to confirm the relationship between the channel availability of narrowband and wideband communications and the resulting channel parameters with the solar and ionospheric activity for four consecutive sounding campaigns.

The purpose of this study was to evaluate the relationship between blood redox status, dose and antioxidant dietary intake of different hospital staff groups exposed to low doses of ionizing radiation (LDIR) (Interventional Radiology and Cardiology, Radiation Oncology, and Nuclear Medicine) and non-exposed. Personal dose equivalent (from last year and cumulative), plasma antioxidant markers (total antioxidant capacity, extracellular superoxide dismutase activity, and glutathione/oxidized glutathione ratio), oxidative stress markers (nitrites and nitrates, and lipid peroxidation) and dietary intake (antioxidant capacity using ORAC values) were collected and analyzed from 28 non-exposed healthcare workers and 42 healthcare workers exposed to LDIR. Hospital staff exposed to LDIR presented a redox imbalance in blood that seems to correlate with dose. Workers from the Nuclear Medicine Unit were the most affected group with the lowest value of plasma antioxidant response and the highest value of plasma thiobarbituric acid reactive substances, TBARS (indicator of lipid peroxidation) of all four groups. Cumulative personal dose equivalent positively correlated with nitrites and negatively correlated with total antioxidant capacity in blood. The diet of healthcare workers from Nuclear Medicine Unit had higher ORAC values than the diet of non-exposed. Therefore, occupational exposure to LDIR, especially for the Nuclear Medicine Unit, seems to produce an imbalanced redox status in blood that would correlate with cumulative personal dose equivalent.

Background: Differential diagnosis of Alzheimer’s disease (AD) is a complex task due to the clinical similarity among neurodegenerative diseases. Previous studies showed the role of lipid peroxidation in early AD development. However, the clinical validation of potential specific biomarkers in minimally invasive samples constitutes a great challenge in early AD diagnosis. Methods: Plasma samples from participants classified into AD (n = 138), non-AD (including MCI and other dementias not due to AD) (n = 70) and healthy (n = 50) were analysed. Lipid peroxidation compounds (isoprostanes, isofurans, neuroprostanes, neurofurans) were determined by ultra-performance liquid chromatography coupled with tandem mass spectrometry. Statistical analysis for biomarkers’ clinical validation was based on Elastic Net. Results: A two-step diagnosis model was developed from plasma lipid peroxidation products to diagnose early AD specifically, and a bootstrap validated AUC of 0.74 was obtained. Conclusion: A promising AD differential diagnosis model was developed. It was clinically validated as a screening test. However, further external validation is required before clinical application.

Concentrator photovoltaic (CPV) is used to obtain cheaper and more stable renewable energy. Methods which predict the energy production of a power system under specific circumstances are highly important to reach the goal of using this system as a part of a bigger one or of making it integrated with the grid. In this paper, the development of a model to predict the energy of a High CPV (HCPV) system using an Artificial Neural Network (ANN) is described. This system is located at the University of Rabat. The performed experiments show a quick prediction with encouraging results for a very short-term prediction horizon, considering the small amount of data available. These conclusions are based on the processes of obtaining the ANN models and detailed discussion of the results, which have been validated using real data.

Currently, one of the challenges of universities is attracting talent in students, researchers, and teachers. The transition from high school to college requires a student to take a succession of decisions that will shape their future. For this reason, knowledge of the motivations of the students, their family, and their personal environment, to choose a particular degree and/or university to pursue their higher studies, would allow universities to efficiently adjust their recruitment strategies. In this article, a study was developed based on a structural equation model of the access to the Spanish Public University System (SUPE), which can help with supply and demand problems, recruitment actions and policies, and other strategic decisions. This was done through an extensive survey of first-year students of Spanish universities. The results allowed us to obtain the parameters of the model, which showed that the fit between the model and the data obtained were excellent at a global level and acceptable as well in all knowledge areas. The objective of the structural model was to provide a general view of the behavior of the students when deciding the degree and university in which they are going to study, and can help in the decision making of university leaders and to understand some behaviors of the Spanish Public University System.

Over the past few decades, the aerodynamic improvements of turbocharger turbines contributed significantly to the overall efficiency augmentation and the advancements in downsizing of internal combustion engines. Due to the compact size of automotive turbochargers, the experimental measurement of the complex internal aerodynamics has been insufficiently studied. Hence, turbine designs mostly rely on the results of numerical simulations and the validation of zero-dimensional parameters as efficiency and reduced mass flow. To push the aerodynamic development even further, a precise validation of three-dimensional flow patterns predicted by applied computational fluid dynamics (CFD) methods is in need. This paper presents the design of an up-scaled volute-stator model, which allows optical experimental measurement techniques. In a preliminary step, numerical results indicate that the enlarged geometry will be representative of the flow patterns and characteristic non-dimensional numbers at defined flow sections of the real size turbine. Limitations due to rotor-stator interactions are highlighted. Measurement sections of interest for available measurement techniques are predefined.

Previous studies showed a relationship between lipid oxidation biomarkers from plasma samples and Alzheimer’s Disease (AD), constituting a promising diagnostic tool. In this work we analyzed whether these plasma biomarkers could reflect specific brain oxidation in AD. In this work lipid peroxidation compounds were determined in plasma and cerebrospinal fluid (CSF) samples from AD and non-AD (including other neurological pathologies) participants, by means of an analytical method based on liquid chromatography coupled with mass spectrometry. Statistical analysis evaluated correlations between biological matrices. The results did not show satisfactory correlations between plasma and CSF samples for any of the studied lipid peroxidation biomarkers (isoprostanes, neuroprostanes, prostaglandines, dihomo-isoprostanes). However, some of the analytes showed correlations with specific CSF biomarkers for AD and with neuropsychological tests (Mini-Mental State Examination (MMSE), Repeatable Battery for the Assessment of Neuropsychological Status (RBANS)). In conclusion, lipid peroxidation biomarkers in CSF samples do not reflect their levels in plasma samples, and no significant differences were observed between participant groups. However, some of the analytes could be useful as cognitive decline biomarkers.

High Frequency (HF) communications through ionospheric reflection is a widely used technique specifically for maritime, aeronautical, and emergency services communication with remote areas due to economic and management reasons, and also as backup system. Although long distance radio links can be established beyond line-of-sight, the availability, the usable frequencies and the capacity of the channel depends on the state of the ionosphere. The main factors that affect the ionosphere are day-night, season, sunspot number, polar aurora and earth magnetic field. These effects impair the transmitted wave, which suffers attenuation, time and frequency dispersion. In order to increase the knowledge of this channel, the ionosphere has been sounded by means of narrowband and wideband waveforms by the research community all over the world in several research initiatives. This work intends to be a review of remarkable projects for vertical sounding with a world wide network and for oblique sounding for high latitude, mid latitude, and trans-equatorial latitude.

Phosphorylation and O-GlcNAcylation are widespread post-translational modifications (PTMs), often sharing protein targets. Numerous studies have reported the phosphorylation of plant viral proteins. In plants, research on O-GlcNAcylation lags behind that of other eukaryotes, and information about O-GlcNAcylated plant viral proteins is extremely scarce. The potyvirus Plum pox virus (PPV) causes sharka disease in Prunus trees and also infects a wide range of experimental hosts. Capsid protein (CP) from virions of PPV-R isolate purified from herbaceous plants can be extensively modified by O-GlcNAcylation and phosphorylation. In this study, a combination of proteomics and biochemical approaches was employed to broaden knowledge of PPV CP PTMs. CP proved to be modified regardless of whether or not it was assembled into mature particles. PTMs of CP occurred in the natural host Prunus persica, similarly to what happens in herbaceous plants. Additionally, we observed that O-GlcNAcylation and phosphorylation were general features of different PPV strains, suggesting that these modifications contribute to general strategies deployed during plant-virus interactions. Interestingly, phosphorylation at a casein kinase II motif conserved among potyviral CPs exhibited strain specificity in PPV; however, it did not display the critical role attributed to the same modification in the CP of another potyvirus, Potato virus A.

In the complex political scene surrounding the death of Francisco Franco, Spanish female architects were crossing borders to try and understand what was happening abroad. This article provides unpublished data on the various experiences of female graduates in Spain when they shared their enthusiasm, concerns and energy with colleagues from other countries at international conferences that took place before the arrival of democracy. For almost four decades, between 1939 and 1975, Spanish female architects were limited by the patriarchal system’s own barriers and by the political barriers imposed by Franco’s regime. This paper aims to organise and articulate women’s memories, proving the implicit acceptance of patriarchal ideas and models at the start of the 20th century, the timidity of the congress resolutions in the sixties and the later awakening provided by UIFA (Union Internationale des Femmes Architectes) congresses. Finally, it is worth examining the metamorphosis that occurred in free western societies in the 20th century, with respect to the role played by women as a user and as a professional, through the attentive gaze of women architects from a nondemocratic country.

La Salle and Ebro Observatory have been involved in remote sensing projects in Antarctica for the last 11 years (approximately one solar cycle). The Ebro Observatory has been monitoring and analyzing the geomagnetic and the ionospheric activity in the Antarctic Spanish station Juan Carlos I (ASJI) (62.7°S, 299.6°E) for more than eighteen and ten years, respectively. La Salle has two main goals in the project. The first one is the data transmission and reception from Antarctica to Spain to obtain a historical series of measurements of channel sounding of this 12,760-km ionospheric HF (high frequency) radio link. The second one is the establishment of a stable data low power communication system between the ASJI and Cambrils, Spain (41.0°N, 1.0°E), to transmit the data from the remote sensors located on the island. In this paper, both narrowband and wideband soundings have been carried out to figure out the channel availability performed using a frequency range from 2 to 30 MHz with 0.5 MHz step during the 24 h of the day, encompassing wider channel measurements than previously done, in terms of hours and frequency. This paper presents the results obtained for the austral summer in 2014, using a monopole antenna at the transmitter and an inverted V on the receiver side. These results led us to the final physical layer design for the long Remote Sens. 2015, 7 11713 haul link, dividing the day into two parts: daytime, with low data throughput design, and nighttime, reaching high data throughput.

A new gait phase detection system for continuous monitoring based on wireless sensorized insoles is presented. The system can be used in gait analysis mobile applications, and it is designed for real-time demarcation of gait phases. The system employs pressure sensors to assess the force exerted by each foot during walking. A fuzzy rule-based inference algorithm is implemented on a smartphone and used to detect each of the gait phases based on the sensor signals. Additionally, to provide a solution that is insensitive to perturbations caused by non-walking activities, a probabilistic classifier is employed to discriminate walking forward from other low-level activities, such as turning, walking backwards, lateral walking, etc. The combination of these two algorithms constitutes the first approach towards a continuous gait assessment system, by means of the avoidance of non-walking influences.

The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by combining object-based image analysis and advanced machine learning methods. In this investigation, we evaluated the C4.5 decision tree, logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) neural network methods, both as single classifiers and combined in a hierarchical classification, for the mapping of nine major summer crops (both woody and herbaceous) from ASTER satellite images captured in two different dates. Each method was built with different combinations of spectral and textural features obtained after the segmentation of the remote images in an object-based framework. As single classifiers, MLP and SVM obtained maximum overall accuracy of 88%, slightly higher than LR (86%) and notably higher than C4.5 (79%). The SVM+SVM classifier (best method) improved these results to 89%. In most cases, the hierarchical classifiers considerably increased the accuracy of the most poorly classified class (minimum sensitivity). The SVM+SVM method offered a significant improvement in classification accuracy for all of the studied crops compared to the conventional decision tree classifier, ranging between 4% for safflower and 29% for corn, which suggests the application of object-based image analysis and advanced machine learning methods in complex crop classification tasks.

The study of cognitive responses and processes while using applications is a critical field in human−computer interaction. This paper aims to determine the mental effort required for different typical tasks with smartphones. Mental effort is typically associated with the concept of cognitive load, and has been studied by analyzing electroencephalography (EEG) signals. Thus, this paper shows the results of analyzing the cognitive load of a set of characteristic tasks on smartphones. To determine the set of tasks to analyze, this paper proposes a taxonomy of smartphone-based actions defined after considering the related proposals in the literature and identifying the significant characteristics of the tasks to classify them. The EEG data was obtained through an experiment with real users doing tasks from the aforementioned taxonomy. The results show significant differences in the cognitive load of each task category and identify those tasks that involve a higher degree of mental effort. The results will be the starting point of the M4S project that aims to contribute to the early diagnosis of mild cognitive impairment through monitoring everyday dual-tasking in terms of interaction with smartphones.