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Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), considered a zoonotic agent of wildlife origin, can infect various animal species, including wildlife in free-range and captive environments. Detecting susceptible species and potential reservoirs is crucial for preventing the transmission, spread, genetic evolution, and further emergence of viral variants that are major threats to global health. This study aimed to detect exposure or acute infection by SARS-CoV-2 in 420 animals from 40 different wildlife species, including terrestrial and aquatic mammals, from different regions of Spain during the 2020–2023 coronavirus disease 19 (COVID-19) pandemic. In total, 8/137 animals were positive for SARS-CoV-2 antibodies against the receptor binding domain and/or viral nucleoprotein according to independent ELISAs. However, only one ELISA-positive sample of a captive bottlenose dolphin (Tursiops truncatus) tested positive for SARS-CoV-2 neutralizing antibodies with a low titre (SNT50 38.15) according to a virus neutralization test. Cetaceans are expected to have a high risk of infection with SARS-CoV-2 according to early predictive studies due to the similarity of their angiotensin converting enzyme 2 cell receptor to that of humans. Moreover, of 283 animals analysed for SARS-CoV-2 RNA using RT-qPCR, none tested positive. Our results reinforce the importance of considering cetaceans at risk for SARS-CoV-2 infection and support taking preventive biosecurity measures when interacting with them, especially in the presence of individuals with suspected or confirmed COVID-19. Although most animals in this study tested negative for acute infection or viral exposure, ongoing surveillance of wildlife species and potentially susceptible animals is important to prevent future spillover events and detect potential novel reservoirs.

The objective of this qualitative investigation is to identify the assistive technology recognized by students with disabilities and to determine the assistive technology (software apps and devices) they require both at university and at home. A total of forty-two students, comprising 20 males and 22 females, were recruited from four different countries (Germany, Greece, Italy, and Spain) for participation in this study. The sample encompassed 10 students with visual impairments, 11 with hearing impairments, 11 with mobility impairments, and 10 with specific learning disabilities. Semi-structured interviews were conducted with the students either online or in person. Content analysis was employed to scrutinize the data obtained from these interviews. The outcomes of this analysis shed light on the assistive technology acknowledged, utilized, or desired by students with disabilities in both academic and domestic settings. The findings from this study carry practical implications for fostering inclusive and accessible education within higher education institutions, benefiting accessibility units/offices staff as well as teaching personnel.

Abstract Phylogenies are viewed as potentially powerful resources to predict missing values in trait datasets, but they are often misused. Critically, many of the imputed values that completely or partially rely on phylogenetic information are trusted without convincingly demonstrating that the data meet the requirements for the predictions to be at least minimally valuable. I discuss that phylogenetic signal, which is the mainstay of phylogenetic imputation, is often interpreted as ‘strong’ because the outcome of randomization tests has prevailed over the actual strength of the signal in determining whether it is strong or not. This circumstance has led many researchers to infer conclusions based on ‘strong’ signals that are actually way more labile than a phylogenetic random walk (i.e. Brownian motion). Although trait evolutionary trajectories that nearly fit Brownian motion are typically considered as strongly conserved, the Brownian process is subject to high levels of stochasticity that may render spurious predictions under some circumstances. To my knowledge, very few studies (if any) that rely on phylogenetically imputed information have rigorously evaluated the expected accuracy of individual predictions, despite among‐lineage variability in prediction accuracy can be dramatic even for strongly conserved traits. Here, I advocate for a Monte‐Carlo approach that is based on trait simulations to assess the prediction accuracy that is expected for each missing value in the traits of interest, which can be continuous or discrete. The framework is presented in a detailed step‐by‐step R tutorial that was conceived for non‐specialized researchers to identify highly likely spurious predictions without the need for advanced technical and statistical skills. Although phylogenetic imputation has important limitations, I suggest that leveraging advances in our understanding of such hindrances and using the technique with caution and restraint will allow trait‐based research to progress further while sampling efforts continue replacing imputed data.

Abstract The Homeric plant moly is a mysterious herb mentioned in Book 10 of the Odyssey. In the early 1980s, a pharmacological thesis to identify the plant was put forward for the first time, regarding the snowdrop (Galanthus nivalis L.) as candidate species. The proposal was inspired by the snowdrop’s acetylcholinesterase (AChE)-inhibiting properties and its alleged morphological reminiscence to other plants called moly by ancient Greek herbalists. Here, we draw from a compilation of literature from various disciplines, together with an understanding of the Homeric epic as a repository of information based on oral traditions, to (i) show that the assimilation of Homer’s moly to Galanthus nivalis is, at the very least, questionable and (ii) frame and support a new synthesis of the pharmacological thesis. We suggest that the uncertainty that revolves around the identity of Homer’s moly can be tied to an unnamed phylogenetic clade of closely related Mediterranean native species with AChE-inhibiting properties. Further, we speculate that Homer’s moly might represent an early record of an ethnobotanical complex, a sort of cultural taxon resulting from the cognitive crossbreeding of closely related taxonomic species that could have been interchangeably used due to their rough resemblance and common AChE-inhibiting properties. Such cultural taxon would have referred to the phytonym moly by the centuries-old oral traditions that ultimately crystallized in the poem. We also venture that sea daffodils (Pancratium spp.) could have greatly contributed to shaping the botanical archetype in the myth as we know it today.

This paper presents an advanced workflow for processing radar imagery stacks using Persistent Scatterer and Distributed Scatterer Interferometry (PSDS) to enhance spatial coherence and improve displacement detection accuracy. The workflow leverages Level 2 Coregistered Single Look Complex (L2-CSLC) images generated by the open-source COMPASS (Coregistered Multi-temporal Sar SLC) framework in combination with the Combined eigenvalue maximum likelihood Phase Linking (CPL) approach implemented in MiaplPy. Starting the analysis directly from Level 2 products offers a significant advantage to end-users, as they simplify processing by being pre-geocoded and ready for immediate analysis. Additionally, the open-source nature of the workflow and the use of L2-CSLC products simplify the processing pipeline, making it easier to distribute directly to users for practical applications in monitoring infrastructure stability in dynamic environments. The ISCE3-MiaplPy workflow is compared against ISCE2-MiaplPy and the European Ground Motion Service (EGMS) to assess its performance in detecting infrastructure deformations in dynamic environments, such as the Algeciras port. The results indicate that ISCE3-MiaplPy delivers denser measurements, albeit with increased noise, compared to its counterparts. This higher resolution enables a more detailed understanding of infrastructure stability and surface dynamics, which is critical for environments with ongoing human activity or natural forces.

Abstract Background Metabolic syndrome (MetS) is a group of metabolic abnormalities characterised by hypertension, central obesity, dyslipidaemia and dysregulation of blood glucose, associated with the risk of diabetes, cardiovascular disease and overall mortality. The presence of elevated liver enzymes may precede the development of MetS, with alterations of the liver being observed that are directly related to metabolic problems. The study aims to provide the best evidence on the association between liver enzymes (ALT, AST, GGT) and MetS by determining the effect size of these biomarkers. Methods A systematic review and meta-analysis of studies indexed in PubMed and Scopus databases were performed. Study quality was assessed using the STROBE tool. The Grade Pro tool was used to evaluate the evidence, and the quantitative synthesis was performed using RevMan (Cochrane Collaboration). Results Seventeen articles comparing liver enzyme concentrations between 76,686 with MetS (MetS+) and 201,855 without MetS (MetS-) subjects were included. The concentration of ALT, AST and GGT in the MetS + subjects was significantly higher than in the control group 7.13 IU/L (CI95% 5.73–8.54; p

IntroductionUnderstanding how socioeconomic markers interact could inform future policies aimed at increasing adherence to a healthy diet.MethodsThis cross-sectional study included 437,860 participants from the UK Biobank. Dietary intake was self-reported. Were used as measures socioeconomic education level, income and Townsend deprivation index. A healthy diet score was defined using current dietary recommendations for nine food items and one point was assigned for meeting the recommendation for each. Good adherence to a healthy diet was defined as the top 75th percentile, while poor adherence was defined as the lowest 25th percentile. Poisson regression was used to investigate adherence to dietary recommendations.ResultsThere were significant trends whereby diet scores tended to be less healthy as deprivation markers increased. The diet score trends were greater for education compared to area deprivation and income. Compared to participants with the highest level of education, those with the lowest education were found to be 48% less likely to adhere to a healthy diet (95% Confidence Interval [CI]: 0.60–0.64). Additionally, participants with the lowest income level were 33% less likely to maintain a healthy diet (95% CI: 0.73–0.81), and those in the most deprived areas were 13% less likely (95% CI: 0.84–0.91).Discussion/conclussionAmong the three measured proxies of socioeconomic status – education, income, and area deprivation – low education emerged as the strongest factor associated with lower adherence to a healthy diet.

This paper describes a Long Short-Term Memory (LSTM) neural network model used to simulate the dynamics of the OC3 reference design of a Floating Offshore Wind Turbine (FOWT) spar unit. It crafts an advanced neural network with an encoder–decoder architecture capable of predicting the spar’s motion and fairlead tensions time series. These predictions are based on wind and wave excitations across various operational and extreme conditions. The LSTM network, trained on an extensive dataset from over 300 fully coupled simulation scenarios using OpenFAST, ensures a robust framework that captures the complex dynamics of a floating platform under diverse environmental scenarios. This framework’s effectiveness is further verified by thoroughly evaluating the model’s performance, leveraging comparative statistics and accuracy assessments to highlight its reliability. This methodology contributes to substantial reductions in computational time. While this research provides insights that facilitate the design process of offshore wind turbines, its primary aim is to introduce a new predictive approach, marking a step forward in the quest for more efficient and dependable renewable energy solutions.

Machine learning (ML) methods often require large volumes of labeled data to achieve meaningful performance. The expertise necessary for labeling data in medical applications like pathology presents a significant challenge in developing clinical-grade tools. Crowdsourcing approaches address this challenge by collecting labels from multiple annotators with varying degrees of expertise. In recent years, multiple methods have been adapted to learn from noisy crowdsourced labels. Among them, Gaussian Processes (GPs) have achieved excellent performance due to their ability to model uncertainty. Deep Gaussian Processes (DGPs) address the limitations of GPs using multiple layers to enable the learning of more complex representations. In this work, we develop Deep Gaussian Processes for Crowdsourcing (DGPCR) to model the crowdsourcing problem with DGPs for the first time. DGPCR models the (unknown) underlying true labels, and the behavior of each annotator is modeled with a confusion matrix among classes. We use end-to-end variational inference to estimate both DGPCR parameters and annotator biases. Using annotations from 25 pathologists and medical trainees, we show that DGPCR is competitive or superior to Scalable Gaussian Processes for Crowdsourcing (SVGPCR) and other state-of-the-art deep-learning crowdsourcing methods for breast cancer classification. Also, we observe that DGPCR with noisy labels obtains better results ( $\text{F}1=81.91$ %) than GPs ( $\text{F}1=81.57$ %) and deep learning methods ( $\text{F}1=80.88$ %) with true labels curated by experts. Finally, we show an improved estimation of annotators’ behavior.

Abstract Background The development of new aetiological premises, such as the microbiota-gut-brain axis theory, evidences the influence of dietary and nutritional patterns on mental health, affecting the patient's quality of life in terms of physical and cardiovascular health. The aim was to determine the impact of a nutritional programme focused on increasing the intake of prebiotic and probiotic food on cardio-metabolic status in individuals with schizophrenia spectrum disorders in the contextual setting of the SARS-CoV-2 era. Methods A randomised clinical trial (two-arm, double-blind, balanced-block, six-month intervention) was conducted in a group of 50 individuals diagnosed with schizophrenia spectrum disorder during the SARS-CoV-2 confinement period. The control group received conventional dietary counselling on an individual basis. In the intervention group, an individual nutritional education programme with a high content of prebiotics and probiotics (dairy and fermented foods, green leafy vegetables, high-fibre fruit, whole grains, etc.) was established. Data on cardiovascular status were collected at baseline, three and six months. In addition, anthropometric parameters were analysed monthly. Results Forty-four subjects completed follow-up and were analysed. Statistical differences (p

BackgroundMetabolic syndrome (MetS) is a constellation of risk factors increasingly present in the world’s population. People with this syndrome are at an increased risk of cardiovascular disease and type 2 diabetes mellitus. Moreover, evidence has shown that it affects different organs. MetS and its risk factors are independently associated with impaired lung function, which can be quantified through spirometric variables. ObjectiveThis study aims to determine whether a high number of MetS criteria is associated with increased lung function decline. MethodsWe conducted a descriptive cross-sectional study with a random sample of 1980 workers. Workers with acute respiratory pathology (eg, influenza), chronic respiratory pathology (eg, chronic bronchitis), or exposure to substances harmful to the lungs (eg, organic and inorganic dust) were not included. MetS was established based on harmonized criteria, and lung function was assessed according to spirometric variables. On the basis of these, classification into restrictive lung disease (RLD), obstructive lung disease, and mixed lung disease (MLD) was performed. In addition, the association between MetS and lung function was established based on analysis of covariance, linear trend analysis, and multiple linear regression. ResultsMetS was associated with worse lung function according to all the spirometric parameters analyzed (percentage of predicted forced expiratory volume in 1 second: mean 83, SD 13.8 vs mean 89.2, SD 12.8; P

Toxoplasma gondii is a generalist zoonotic parasite that involves a wide range of warm-blooded animals as intermediate hosts and felines as definitive hosts. Recent studies have proved significant positive associations between human population density and T. gondii seroprevalence in wildlife. However, there is limited data regarding T. gondii wildlife in urban areas, where the highest human density occurs. The present study aimed to analyse the T. gondii exposure in urban hedgehogs from the Metropolitan Area of Barcelona, NE Spain. One hundred eighteen hedgehogs were analysed for the presence of antibodies (modified agglutination test; n = 55) and parasite DNA (qPCR; heart = 34; brain = 60). Antibodies were detected in 69.09% of hedgehogs. T. gondii DNA was not detected in any of the analysed samples. The present study reports a high T. gondii seroprevalence in urban hedgehogs in areas surrounding Barcelona, the most densely human-populated area of NE Spain, reinforcing the association between human population density and environmental T. gondii oocysts. The lack of detection by molecular techniques warrants more studies. In the last few decades, the distribution and abundance of European hedgehogs have declined, including their urban populations. Further research is needed to investigate the impact of T. gondii on hedgehog populations.

Content-Based Histopathological Image Retrieval (CBHIR) is a search technique based on the visual content and histopathological features of whole-slide images (WSIs). CBHIR tools assist pathologists to obtain a faster and more accurate cancer diagnosis. Stain variation between hospitals hampers the performance of CBHIR tools. This paper explores the effects of color normalization (CN) in a recently proposed CBHIR approach to tackle this issue. In this paper, three different CN techniques were used on the CAMELYON17 (CAM17) data set, which is a breast cancer data set. CAM17 consists of images taken using different staining protocols and scanners in five hospitals. Our experiments reveal that a proper CN technique, which can transfer the color version into the most similar median values, has a positive impact on the retrieval performance of the proposed CBHIR framework. According to the obtained results, using CN as a pre-processing step can improve the accuracy of the proposed CBHIR framework to 97% (a 14% increase), compared to working with the original images.

Abstract This systematic review and meta-analysis aim to provide the best evidence on the association between metabolic syndrome (MetS) and uric acid (UA) by determining the size of the effect of this biomarker on MetS. The review protocol is registered with PROSPERO (CRD42021231124). The search covered the PubMed and Scopus databases. Methodological quality was assessed with the STROBE tool, overall risk of bias with RevMan (Cochrane Collaboration) and quality of evidence with Grade Pro. Initially, 1582 articles were identified. Then, after excluding duplicates and reviewing titles and abstracts, 1529 articles were excluded from applying the eligibility criteria. We included 43 papers (56 groups) comparing UA concentrations between subjects 91,845 with MetS and 259,931 controls. Subjects with MetS had a higher mean UA of 0.57 mg/dl (95% CI 0.54–0.61) (p

Summary: Bacterial cell-wall hydrolases must be tightly regulated during bacterial cell division to prevent aberrant cell lysis and to allow final separation of viable daughter cells. In a multidisciplinary work, we disclose the molecular dialogue between the cell-wall hydrolase LytB, wall teichoic acids, and the eukaryotic-like protein kinase StkP in Streptococcus pneumoniae. After characterizing the peptidoglycan recognition mode by the catalytic domain of LytB, we further demonstrate that LytB possesses a modular organization allowing the specific binding to wall teichoic acids and to the protein kinase StkP. Structural and cellular studies notably reveal that the temporal and spatial localization of LytB is governed by the interaction between specific modules of LytB and the final PASTA domain of StkP. Our data collectively provide a comprehensive understanding of how LytB performs final separation of daughter cells and highlights the regulatory role of eukaryotic-like kinases on lytic machineries in the last step of cell division in streptococci.

Abstract Effective approaches towards sustainability need to be informed by a diverse array of stakeholder perspectives. However, capturing these perspectives in a way that can be integrated with other forms of knowledge can represent a challenge. Here we present the first application of the conceptual framework of the Intergovernmental Science‐Policy Platform on Biodiversity and Ecosystem Services (IPBES) to a participatory assessment of local perspectives on nature, people and sustainability on Mount Kilimanjaro, Tanzania. This assessment was organized in the form of a participatory workshop with five different groups of stakeholders. Following this framework, we assembled information on the state of and trends in species diversity, Nature's Contributions to People (NCP), and on the main drivers of changes in species and habitats. Additionally, we gathered perspectives on the needs and opportunities for the sustainable management and conservation of natural resources from the individual to the international level. The various stakeholders agreed that both the condition and extent of the various habitats and NCP are declining. In line with available knowledge, the key direct drivers of change mentioned by the workshop participants were land use and climate change, whereas human population growth was singled out as the most important indirect driver. The most frequently suggested measures to address the observed decline in species diversity and its drivers were related to land and water management and to education and awareness raising. Yet, the stakeholder groups differed in the measures they suggested. The willingness of a diversity of knowledge holders to systematically engage in a structured discussion around all the elements of the IPBES framework provides support for its applicability in participatory workshops aimed at capturing nuanced and context‐based perspectives on social–ecological systems from informed stakeholders. The application of the IPBES framework enabled the comparability needed for developing narratives of stakeholder visions that can help identify new pathways towards sustainability and guide planning while retaining the context‐based nuances that remain unresolved with non‐participatory methods. Read the free Plain Language Summary for this article on the Journal blog.

Canine distemper virus (CDV) is recognised worldwide as an important pathogen in both domestic and wild carnivores. Few data are available on its impact and spread on the wildlife/wildlife–domestic animal–environment interface. This study, aimed at developing a conservation-oriented control strategy, analysed 89 sick or deceased animals from 2019 to 2023 at the Wildlife Rehabilitation Centre in Torreferrussa. RT-PCR and sequencing of the partial H gene were used to detect and analyse CDV in tissues. The total positive percentage was 20.22% (18/89), comprising 13 red foxes (44.8%), 4 European badgers (28.6%), and 1 American mink (4.5%), while 24 Eurasian otters tested negative. Phylogenetic analysis indicated that all of the CDV strains belong to the European lineage. Geographically distant individuals and different species shared the same viral strain, suggesting a strong capacity of CDV for interspecies and long-distance transmission. This calls for further research, particularly focusing on potential impacts of CDV on endangered carnivores.

The objective of the present study was to determine the effects that the reproductive season has on the motility, kinematics, morphology, and sperm morphometry of Brahman bulls evaluated with a commercial CASA system. The experiment was carried out at the Costa Rica Institute of Technology from March to August 2021. A total of eight Brahman bulls were used. A total of 28 ejaculates were collected in the pre-mating period (PMP), during it (DMP), and after it (AMP) using an electroejaculator. The sperm concentration was measured with the Accuread photometer. The motility was measured using a Spermtrack® counting chamber. The analyses were performed with the CASA-Mot ISAS®v1 system. The morphology was analyzed using a microscope with a negative phase contrast objective. Morphometry was evaluated with the CASA-Morph. The sperm concentration did not present differences between the PMP and AMP; however, it was significantly higher than DMP (p > 0.05). Regarding the progressiveness variables, linearity on forward progression (LIN), straightness (STR), and wobble (WOB) were higher (p < 0.05) DMP. A kinematic principal component analysis grouped all the variables into three factors and an effect on the reproductive period was found (p < 0.05) in the parameters of the head and middle part of the sperm, such as width and perimeter, which were greater in the PMP. The length of the sperm head in the PMP and DMP did not show differences; however, both were larger (p < 0.05) than AMP. The insertion distance of the middle piece of the sperm was significantly greater than DMP. Finally, the PMP contained cells with a larger insertion angle (p < 0.05) than AMP. These findings are important to understand the implications of reproductive status on sperm quality and to consider them in andrological evaluations.

Whole Slide Images (WSI) are widely used in histopathology for research and the diagnosis of different types of cancer. The preparation and digitization of histological tissues leads to the introduction of artifacts and variations that need to be addressed before the tissues are analyzed. WSI preprocessing can significantly improve the performance of computational pathology systems and is often used to facilitate human or machine analysis. Color preprocessing techniques are frequently mentioned in the literature, while other areas are usually ignored. In this paper, we present a detailed study of the state-of-the-art in three different areas of WSI preprocessing: Artifacts detection, color variation, and the emerging field of pathology-specific data augmentation. We include a summary of evaluation techniques along with a discussion of possible limitations and future research directions for new methods.

The annotation of large datasets is often the bottleneck in the successful application of artificial intelligence in computational pathology. For this reason recently Multiple Instance Learning (MIL) and Semi Supervised Learning (SSL) approaches are gaining popularity because they require fewer annotations. In this work we couple SSL and MIL to train a deep learning classifier that combines the advantages of both methods and overcomes their limitations. Our method is able to learn from the global WSI diagnosis and a combination of labeled and unlabeled patches. Furthermore, we propose and evaluate an efficient labeling paradigm that guarantees a strong classification performance when combined with our learning framework. We compare our method to SSL and MIL baselines, the state-of-the-art and completely supervised training. With only a small percentage of patch labels our proposed model achieves a competitive performance on SICAPv2 (Cohen’s kappa of 0.801 with 450 patch labels), PANDA (Cohen’s kappa of 0.794 with 22,023 patch labels) and Camelyon16 (ROC AUC of 0.913 with 433 patch labels). Our code is publicly available at https://github.com/arneschmidt/ssl_and_mil_cancer_classification.

Abstract Although there is growing interest in safeguarding the Tree of Life to preserve the human benefits that are directly provided by biodiversity, their evolutionary distribution remains unknown, which has hampered our understanding of the potential of phylodiversity indicators to evince them. Here, I drew on a global review of plant benefits and comprehensive phylogenetic information to breakdown their evolutionary distribution and thereby show why the commonly used Phylogenetic Diversity and Evolutionary Distinctiveness indicators can unequivocally help to preserve these natural services. Beneficial species clumped within phylogenetically overdispersed genera and closely related species often contributed very few and redundant benefits, suggesting that multiple plant lineages are required to maintain a wide variety of services. Yet, a reduced number of species stood out as multi-beneficial and evolutionarily distinct plants relative to both the entire phylogeny and the subset of beneficial species, and they collectively contributed a higher-than-expected number of records for most types of benefits. In addition to providing a clear mechanistic understanding for the recently proved success of Phylogenetic Diversity in capturing plant benefits, these findings stress the decisive role that conservation programmes aimed at protecting evolutionarily distinct taxa will play in safeguarding the beneficial potential of biodiversity for the future.

An operando DRIFT-MS system (Diffuse Reflectance Infrared Fourier Transform Spectroscopy coupled with Mass Spectrometry) was designed and set up to study the oxidative steam reforming of ethanol reaction (OSRE). This reaction involves the mixture of water, ethanol and oxygen to produce mainly hydrogen, which is a rather attractive energy carrier. Spectroscopic monitoring of the process is a key tool to contribute to the understanding of: i) the dynamics on the catalyst surface, ii) the reaction mechanism and iii) the effect of the solid's properties on the catalytic process. In this sense, this document sets forth the experimental design that allows to carry out the study under operando DRIFT-MS conditions through time for the OSRE reaction. Selection criteria for parameters, materials, configuration, and experimental conditions are included, particularly optimizing the parameters of particle size and the dilution factor with KBr as well as the temperature and flow conditions for carrying out the reaction. • Clear signals of the adsorbed species in IR that do not present interference by water in the reaction atmosphere. • Simple assembly and online product detection by MS that allow to follow the change in the products of the OSRE reaction according to the temperature. • Controlled entry of gases and quantification by loop injection.

Approximately one-third of overweight individuals, and half of those with obesity, do not have cardiometabolic disorders. For this reason, a phenotype called metabolically healthy obese (MHO) has emerged to describe this population group. The early detection of this situation could save costs associated with the development of comorbidities or pharmacological interventions. Therefore, the aim is to know the prevalence of MHO in the working population and propose variables for its detection. Cross-sectional descriptive study of 635 workers of the Cordoba City Council was carried out based on the results of the 2016 health surveillance. The outcome variables were the MHO, established based on the criteria of the IDF, NCEP—ATP III, and Aguilar—Salinas. In addition, the degree of agreement between the different MHO criteria was studied using Cohen's kappa (k), and the predictive capacity of the anthropometric variables was assessed with Receiver Operator Curves. The prevalence of MHO ranged from 6.6 to 9%. The highest agreement was reached between the IDF and NCEP-ATP III definitions (k = 0.811; 95% CI 0.724–0.898; p < 0.001). The waist-to-height ratio (WHtR) showed the highest discriminant capacity for MHO, with its best cut-off point at 0.55 for all criteria used. Sensitivity ranged from 84 to 93%. The prevalence of MHO in the working population differed according to the criteria used for diagnosis. The anthropometric variable with the highest discriminant capacity for MHO was WHtR, presenting the same cut-off point in the three criteria analyzed. Therefore, WHtR is the variable that best detects the presence of MHO.

Abstract The volume of labeled data is often the primary determinant of success in developing machine learning algorithms. This has increased interest in methods for leveraging crowds to scale data labeling efforts, and methods to learn from noisy crowd-sourced labels. The need to scale labeling is acute but particularly challenging in medical applications like pathology, due to the expertise required to generate quality labels and the limited availability of qualified experts. In this paper we investigate the application of Scalable Variational Gaussian Processes for Crowdsourcing (SVGPCR) in digital pathology. We compare SVGPCR with other crowdsourcing methods using a large multi-rater dataset where pathologists, pathology residents, and medical students annotated tissue regions breast cancer. Our study shows that SVGPCR is competitive with equivalent methods trained using gold-standard pathologist generated labels, and that SVGPCR meets or exceeds the performance of other crowdsourcing methods based on deep learning. We also show how SVGPCR can effectively learn the class-conditional reliabilities of individual annotators and demonstrate that Gaussian-process classifiers have comparable performance to similar deep learning methods. These results suggest that SVGPCR can meaningfully engage non-experts in pathology labeling tasks, and that the class-conditional reliabilities estimated by SVGPCR may assist in matching annotators to tasks where they perform well.

Background/ObjectivesThe microbiota plays a vital role in the two-way communication between the gastrointestinal tract and numerous neuropsychiatric disorders, such as schizophrenia. Besides, the microbiota modulation through the use of psychobiotics (prebiotics and probiotics with nutraceutical action) is related to the improvement of the physical and psychopathological health. The objective to this study was to test the efficacy of prebiotic/probiotic dietary modulation in patients diagnosed with schizophrenia, attending to the nutritional and cardio-metabolic impact.MethodsTwo-arms, double-blind, randomized in balanced blocks clinical trial of 6 months of intervention, will be developed in a group of 50 individuals. The control group will receive conventional dietary advice individually from specialized mental health nurses. In the intervention group, an individual dietetic-nutritional education program with high prebiotic and probiotic content (dairy and fermented foods, green leafy vegetables, high-fiber fruit, whole grains, etc.) will be developed by these nurses. Data will be collected on the psychopathological state, and blood test (at the beginning, at 3 and 6 months). The estimation of intestinal microbiota and the usual nutritional pattern will also be assessed at the beginning and 6 months, using a stool test. To evaluate the degree of adherence, the intervention group will fill a specific weekly record of the main dishes/food consumed. Anthropometric parameters will also be analyzed monthly.DiscussionThe study is anticipated to establish feasibility an adequate dietary modulation with a high simbiotic content, leads to a significant improvement in the nutritional status and cardio-metabolic. Furthermore, it is presumed to reach a degree of evidence that allows establishing nutritional management as an effective therapeutic intervention in the psychopathological treatment of patients with schizophrenia spectrum disorders.Clinical Trial Registration[www.ClinicalTrials.gov], identifier [NCT04366401].

Historical climate adaptation can give insight into the potential for adaptation to contemporary changing climates. Here Bennett et al. investigate thermal tolerance evolution across much of the tree of life and find different effects of ancestral climate on the subsequent evolution of ectotherms vs. endotherms.

The penicillin-binding proteins are the enzyme catalysts of the critical transpeptidation crosslinking polymerization reaction of bacterial peptidoglycan synthesis and the molecular targets of the penicillin antibiotics. Here, we report a combined crystallographic, small-angle X-ray scattering (SAXS) in-solution structure, computational and biophysical analysis of PBP1 of Staphylococcus aureus (saPBP1), providing mechanistic clues about its function and regulation during cell division. The structure reveals the pedestal domain, the transpeptidase domain, and most of the linker connecting to the “penicillin-binding protein and serine/threonine kinase associated” (PASTA) domains, but not its two PASTA domains, despite their presence in the construct. To address this absence, the structure of the PASTA domains was determined at 1.5 Å resolution. Extensive molecular-dynamics simulations interpret the PASTA domains of saPBP1 as conformationally mobile and separated from the transpeptidase domain. This conclusion was confirmed by SAXS experiments on the full-length protein in solution. A series of crystallographic complexes with β-lactam antibiotics (as inhibitors) and penta-Gly (as a substrate mimetic) allowed the molecular characterization of both inhibition by antibiotics and binding for the donor and acceptor peptidoglycan strands. Mass-spectrometry experiments with synthetic peptidoglycan fragments revealed binding by PASTA domains in coordination with the remaining domains. The observed mobility of the PASTA domain in saPBP1 could play a crucial role for in vivo interaction with its glycosyltransferase partner in the membrane or with other components of the divisome machinery, as well as for coordination of transpeptidation and polymerization processes in the bacterial divisome.

The pediatric population has various criteria for measuring metabolic syndrome (MetS). The diversity of consensus for diagnosis has led to different non-comparable reported prevalence. Given the increase in its prevalence in pediatric ages, it is necessary to develop efficient methods to encourage early detection. Consequently, early screening for the risk of MetS could favor timely action in preventing associated comorbidities in adulthood. This study aimed to establish the diagnostic capacity of models that use non-invasive (anthropometric) and invasive (serum biomarkers) variables for the early detection of MetS in Chilean children. A cross-sectional study was carried out on 220 children aged 6 to 11. Multivariate logistic regressions and discriminant analyses were applied to determine the diagnostic capacity of invasive and non-invasive variables. Based on these results, four diagnostic models were created and compared: (i) anthropometric, (ii) hormonal (insulin, leptin, and adiponectin), (iii) Lipid A (high-density cholesterol lipoprotein [HDL-c] and triglycerides [TG]) and (iv) Lipid B (TG/HDL-c). The prevalence of MetS was 26.8%. Lipid biomarkers (HDL-c and TG) and their ratio (TG/HDL-c) presented higher diagnostic capacity, above 80%, followed by body mass index (BMI, 0.71–0.88) and waist-to-height ratio (WHtR, 0.70–0.87). The lipid model A was the most accurate (sensitivity [S] = 62.7%, specificity [E] = 96.9%, validity index 87.7%), followed by the anthropometric model (S = 69.5%, E = 88.8% and validity index = 83.6%). In conclusion, detecting MetS was possible through invasive and non-invasive methods tested in overweight and obese children. The proposed models based on anthropometric variables, or serum biomarkers of the lipid model A, presented acceptable validity indices. Moreover, they were higher than those that measured adipokines, leptin, and adiponectin. The anthropometric model was the most cost-effective and easy to apply in different environments.

Toxoplasma gondii infection in healthy animals is often asymptomatic. However, some species with little history of contact with the parasite, such as marsupials and New World primates, present high mortality rates after infection. Despite its potential conservation concern, T. gondii infection in insectivorous bats has received little attention, and its impact on bat populations’ health is unknown. To assess the putative role of insectivorous bats in the cycle of T. gondii, samples of three species of bats (Pipistrellus pipistrellus, P. pygmaeus and P. kuhlii) collected between 2019 and 2021 in NE Spain were tested for the presence of the parasite using a qPCR. All tissues resulted negative (0.0% prevalence with 95% CI: [0.0–2.6]) for the presence of T. gondii. Unlike previous studies on insectivorous bats from Europe, Asia and America, the present study suggests that Pipistrellus spp. bats do not play a significant role in the epidemiology of T. gondii in NE Spain. Further studies are encouraged to elucidate both the epidemiology of T. gondii and its potential impact on the health of microchiropteran species in Europe.

V2X (Vehicle to everything) communications can be currently supported by standards based on IEEE 802.11p (e.g. DSRC or ITS-G5) or LTE-V2X (also known as Cellular V2X or C-V2X) technologies. There has been an intense debate in the community on which technology achieves best performance. However, existing studies do not take into account the variability present in the generation and size of V2X messages. This variability can significantly impact the operation and performance of the Medium Access Control (MAC). This study progresses the state of the art by conducting an in-depth evaluation of both technologies under different message traffic patterns. In particular, we consider aperiodic and periodic messages of constant or variable size based on the standardized ETSI Cooperative Awareness Messages (CAMs). This study considers different scenarios and possible configurations of IEEE 802.11p and LTE-V2X. We demonstrate that IEEE 802.11p can better cope with variations in the size and time interval between messages. We also demonstrate (and characterize) that the LTE-V2X sensing-based semi-persistent scheduling faces certain inefficiencies when transmitting aperiodic messages of variable size. These inefficiencies result in that IEEE 802.11p generally outperforms LTE-V2X when transmitting aperiodic messages of variable size except when the channel load is very low.

Type III CRISPR-Cas RNases from the Csm and Csx families are activated by cyclic oligoadenylates (cOA). Here the authors present the cOA bound Sulfolobus islandicus Csx1 structure, which forms a hexamer and reveal an allosteric mechanism for the activation of Csx1 RNase.

Protected areas (PAs) have been created with the purpose of preserving biodiversity, acting as refuges from anthropogenic pressures. Traditionally, PAs have been designed and managed to represent mainly taxonomic diversity, ignoring other diversity facets such as its functional and phylogenetic components. Yet, functional and phylogenetic diversity are, respectively, connected with species’ roles on ecosystems and evolutionary history held within communities. Here, we focused on the amphibian, reptile, resident breeding bird, and non-flying mammal faunas of the national and natural parks of the Iberian Peninsula, to evaluate whether these PAs are adequately representing regional functional, phylogenetic, and taxonomic diversity of each group. Specifically, we computed functional and phylogenetic diversity within each PA, and then compared those values to the ones obtained from a random assembly of species from the regional pool, that was defined as the region encompassing the PA and a neighboring area of 50 km beyond its boundary. We also calculated the proportion of species in each regional pool that were present within the PAs. In general, the functional and phylogenetic diversity of amphibians, reptiles and non-flying mammals found within PAs did not differ significantly from random expectations generated from the species pertaining to the regional pool, although a few PAs showed a higher diversity. In contrast, resident breeding birds presented lower functional and phylogenetic diversity than expected by chance in many of the PAs, which could relate to climatic variables and the habitat specificity of some species. The proportion of species from the regional pools that are present in the PAs was high for amphibians, reptiles and mammals, and slightly lower for birds. These results suggest that the Iberian natural and national parks are effectively capturing the functional, phylogenetic and taxonomic diversity of most tetrapod assemblages present at the regional level. Future studies should identify priority areas to expand the representation of these biodiversity components, and assess potential effects of climate and land-use changes on current patterns.

In January 2020, the storm Gloria hit the Western Mediterranean Sea causing severe coastal damages, destruction of infrastructures, flooding and several casualties. This extreme event was characterized by strong Eastern winds, record-breaking waves heights and periods, and a storm surge that locally beat the record along Valencia’s coastline. This paper analyses the dynamic evolution of sea level during this storm. The study employs both the in situ data and the operational forecasts of the PORTUS early warning system. Tide gauge data are analyzed on the different temporal scales that contribute to total sea level: long-term and seasonal, tides and storm surges, and higher frequency oscillations. It was found that, due to the unusual long wave periods, infragravity waves were generated and dominate the high frequency energy band, contributing significantly to extreme sea level records. This is a relevant finding, since this kind of oscillations are usually associated with larger basins, where swell can develop and propagate. The impact of sea level rise is also analyzed and considered relevant. A multi-model ensemble storm surge forecasting system is employed to study the event. The system was able to correctly forecast the surge, and the measured data were always inside the confidence bands of the system. The differences of the results obtained by the available operational forecasting system integrated into the ensemble, including those from Copernicus Marine Service, are described. All the models provided useful forecasts during the event, but differences with measured data are described and connected with the known limitations in physics (for example, barotropic vs. baroclinic) and set-up of the models (model domain, lack of tides and different inverse barometer implementations at the open boundaries amongst others).

The good performance of the port activities in terminals is mainly conditioned by the dynamic response of the moored ship system at a berth. An adequate definition of the highly multivariate processes involved in the response of a moored ship at a berth is crucial for an appropriate characterization of port operability. The availability of an efficient forecast system of the movements of moored ships is essential for the planning, performance, and safety of the development of port operations. In this paper, an inference model to predict moored ship motions, based on a semi-supervised Machine Learning methodology, is presented. A comparison with different supervised and unsupervised Machine Learning techniques, as well as with existing Deep Learning-based models for predicting moored ship motions, has been performed. The highest performance of the semi-supervised Machine Learning-based model has been obtained. Additionally, the influence of infragravity wave parameters introduced as predictor variables in the model has been analyzed and compared with the typical ocean waves, wind, and sea level as predictor variables. The prediction model has been developed and validated with an available dataset of measured data from field campaigns in the Outer Port of Punta Langosteira (A Coruña, Spain).

Humanity faces the challenge of conserving the attributes of biodiversity that may be essential to secure human wellbeing. Among all the organisms that are beneficial to humans, plants stand out as the most important providers of natural resources. Therefore, identifying plant uses is critical to preserve the beneficial potential of biodiversity and to promote basic and applied research on the relationship between plants and humans. However, much of this information is often uncritical, contradictory, of dubious value or simply not readily accessible to the great majority of scientists and policy makers. Here, we compiled a genus-level dataset of plant-use records for all accepted vascular plant taxa (13489 genera) using the information gathered in the 4th Edition of Mabberley's plant-book, the most comprehensive global review of plant classification and their uses published to date. From 1974 to 2017 all the information was systematically gathered, evaluated, and synthesized by David Mabberley, who reviewed over 1000 botanical sources including modern Floras, monographs, periodicals, handbooks, and authoritative websites. Plant uses were arranged across 28 standard categories of use following the Economic Botany Data Collection Standard guidelines, which resulted in a binary classification of 9478 plant-use records pertaining human and animal nutrition, materials, fuels, medicine, poisons, social and environmental uses. Of all the taxa included in the dataset, 33% were assigned to at least one category of use, the most common being "ornamental" (26%), "medicine" (16%), "human food" (13%) and "timber" (8%). In addition to a readily available binary matrix for quantitative analyses, we provide a control text matrix that links the former to the description of the uses in Mabberley's plant-book. We hope this dataset will serve to establish synergies between scientists and policy makers interested in plant-human interactions and to move towards the complete compilation and classification of the nature's contributions to people upon which the wellbeing of future generations may depend.

Climate change is a major driver of biodiversity decline with pervasive effects in biodiversity hotspots, where many endemic and threatened species thrive. However, the biological drivers of extinction susceptibility remain largely elusive, which hampers the implementation of effective conservation policies. Here, we advocate for the use of phylogenies as a complementary tool to inform policy makers. If we assume that the traits that determine extinction susceptibility are somewhat evolutionarily conserved, identifying the clades that accumulate a disproportionate amount of threatened species may help to mitigate potential increases in extinction risk among currently unthreatened species in these clades, even if the underlying biological drivers are unknown. We focused on the complete endemic angiosperm flora of a Mediterranean hotpot (Iberian Peninsula) to examine phylogenetic patterns in extinction risk expressed as IUCN categories (Least Concern “LC”, Near Threatened “NT”, Vulnerable “VU”, Endangered “EN” and Critically Endangered “CR”) using alpha and beta diversity metrics, comparative methods and a “hot node” approach. Phylogenetic diversity was significantly low for EN species and marginally significant for NT and CR, while LC and VU categories showed random pattern. Phylogenetic beta diversity (PBD) between IUCN categories was intermediate (0.40 – 0.61) and predominantly due to the “true” turnover component of PBD. Phylogenetic turnover was significantly low between NT – VU and VU – EN, suggesting that closely related species tend to show different threat status. In contrast, the comparisons involving the CR category sit toward the higher tail of the distribution, indicating a somewhat higher degree of clade specificity for CR species. In line with these patterns, phylogenetic signal in extinction risk was rather low (lambda = 0.23). Several of the “hot” clades that accumulated a significantly high number of species with the same threat status were specific to certain IUCN categories, yet few of them were observed across the categories. Most notably, the Caryophyllales stood out as the main threat-accumulating lineage, particularly within the Plumbaginaceae. All in all, our results indicate that few phylogenetic clades concentrate a great fraction of the extinction-risk gradient in the endemic flora of the western Mediterranean, and monitoring programs should pay particular attention to these extinction-prone lineages.

This study aimed to investigate whether changes in progastrin-releasing peptide (ProGRP) levels correlate with treatment response and can be used to optimize clinical management of patients with small-cell lung cancer. Patients with small-cell lung cancer (any stage) receiving chemotherapy were eligible. ProGRP was measured in serum/plasma at baseline and after each chemotherapy cycle using the Elecsys ® ProGRP assay (Roche Diagnostics). Treatment response was assessed by computed tomography scan. The primary objective was to examine whether changes in ProGRP levels correlated with computed tomography scan results after two cycles of chemotherapy. The prognostic value of ProGRP among patients receiving first-line chemotherapy was also assessed. Overall, 261 patients from six centers were eligible. Among patients with elevated baseline ProGRP (>100 pg/mL), a ProGRP decline after Cycle 2 was associated with nonprogression (area under the curve: 84%; 95% confidence interval: 72.8–95.1; n = 141). ProGRP changes from baseline to end of Cycle 1 were predictive of response, as determined by computed tomography scan 3 weeks later (area under the curve: 87%; 95% confidence interval: 74.1−99.2; n = 137). This was enhanced by repeat measurements, with a 92% area under the curve (95% confidence interval: 85.3−97.8) among patients with ProGRP data after both Cycles 1 and 2 (n = 123); if a patient experienced a ≥25% decline in ProGRP after Cycle 1, and ProGRP remained stable or decreased after Cycle 2, the probability of finding progression on the interim computed tomography scan at the end of Cycle 2 was almost zero (sensitivity: 100%, specificity: 71%). Both ProGRP levels at baseline and at the end of first-line chemotherapy were prognostic; the latter provided a moderately improved hazard ratio of 2.43 (95% confidence interval: 1.33–4.46; n = 110) versus 1.87 (95% confidence interval: 1.04–3.37; n = 216). In summary, for patients with small-cell lung cancer and elevated baseline ProGRP levels, ProGRP may be a simple, reliable, and repeatable tool for monitoring response to chemotherapy and provide valuable prognostic information.

ObjectiveTo examine the effectiveness of self-weighing for weight loss in men for 6 months.MethodsIn the present study, 54 men, mean age of 40.1 ± 11.1 years, with overweight or obesity, were recruited and randomly assigned into two groups: control group (CG), without weight self-monitoring and intervention group (IG), with weight self-monitoring. Both groups received the same nutritional and educational advice and the establishment of a weight target to reach in the weight loss program. Subjects of IG also had individualized motivating content to improve self-management for 24 weeks. Anthropometric indices were measured at baseline and weekly for 24 weeks.Results When the group assigned after randomization was introduced in the analysis, its influence was significant in weight loss (F1.52 = 19.465, ± 2 = 0.272, p < 0.001) and in the decrease in body fat percentage (F1.52 = 8,306, ± 2 = 0.132, p < 0.01).ConclusionStudy results indicate that self-weighing can help patients to lose additional weight. Our findings have implications in the emerging area of the behavioral approach of patients undergoing weight-loss treatment, as well as clinical care processes.Clinical Trial Registrationwww.ClinicalTrials.gov, identifier NCT04032249.

Nearly 40% of the world’s population lives within 100 kilometres of the coast with the risk that this implies in terms of exposure to the effects of climate change. Ocean energy, according to the IPCC (Intergovernmental Panel on Climate Change) in 2019, has been identified as one of the measures for mitigating these effects. In addition, ocean energy can play an essential role in achieving some of the SDGs (Sustainable Development Goals) set at the Paris Climate Summit in 2015, namely SDG 7 (clean and affordable energy) and SDG 13 (climate action) and could have a substantial impact on others such as SDG 1 (poverty eradication), SDG 2 (end hunger), SDG 5 (gender equality), SDG 6 (universal energy access), SDG 8 (promote sustainable economic growth), SDG 9 (build resilient infrastructure), SDG 14 (sustainable conservation of oceans and seas) and SDG 17 (promote sustainable development cooperation). There are several projects under development around the world aimed at extracting energy from waves. However, to date, no technology has been found that, in general terms, is superior to others. There are several conditioning factors that prevent this type of energy from reaching the level of maturity of other marine renewable energies. These are mainly economic, technological, environmental, and regulatory, to mention the most important. This article aims to analyse the approaches that other researchers have adopted to evaluate wave energy projects and, through a prospective method of expert consultation such as the Delphi methodology, will present the most generally accepted criteria for successful wave energy projects. Subsequently, the validity of these results will be analysed for the case of the use of the energy produced for self-consumption in ports.

West Nile virus lineage 2 (WNV-L2) emerged in Europe in 2004; since then, it has spread across the continent, causing outbreaks in humans and animals. During 2017 and 2020, WNV-L2 was detected and isolated from four northern goshawks in two provinces of Catalonia (north-eastern Spain). In order to characterise the first Spanish WNV-L2 isolates and elucidate the potential overwintering of the virus in this Mediterranean region, complete genome sequencing, phylogenetic analyses, and a study of phenotypic characterisation were performed. Our results showed that these Spanish isolates belonged to the central-southern WNV-L2 clade. In more detail, they were related to the Lombardy cluster that emerged in Italy in 2013 and has been able to spread westwards, causing outbreaks in France (2018) and Spain (2017 and 2020). Phenotypic characterisation performed in vitro showed that these isolates presented characteristics corresponding to strains of moderate to high virulence. All these findings evidence that these WNV-L2 strains have been able to circulate and overwinter in the region, and are pathogenic, at least in northern goshawks, which seem to be very susceptible to WNV infection and may be good indicators of WNV-L2 circulation. Due to the increasing number of human and animal cases in Europe in the last years, this zoonotic flavivirus should be kept under extensive surveillance, following a One-Health approach.

The surveillance for West Nile virus (WNV) in Catalonia (northeastern Spain) has consistently detected flaviviruses not identified as WNV. With the aim of characterizing the flaviviruses circulating in Catalonia, serum samples from birds and horses collected between 2010 and 2019 and positive by panflavivirus competition ELISA (cELISA) were analyzed by microneutralization test (MNT) against different flaviviruses. A third of the samples tested were inconclusive by MNT, highlighting the limitations of current diagnostic techniques. Our results evidenced the widespread circulation of flaviviruses, in particular WNV, but also Usutu virus (USUV), and suggest that chicken and horses could serve as sentinels for both viruses. In several regions, WNV and USUV overlapped, but no significant geographical aggregation was observed. Bagaza virus (BAGV) was not detected in birds, while positivity to tick-borne encephalitis virus (TBEV) was sporadically detected in horses although no endemic foci were observed. So far, no human infections by WNV, USUV, or TBEV have been reported in Catalonia. However, these zoonotic flaviviruses need to be kept under surveillance, ideally within a One Health framework.

Abstract Background Pleural effusions present a diagnostic challenge. Approximately 20% are associated with cancer and some 50% require invasive procedures to perform diagnosis. Determination of tumour markers may help to identify patients with malignant effusions. Two strategies are used to obtain high specificity in the differential diagnosis of malignant pleural effusions: a) high cut-off, and b) fluid/serum (F/S) ratio and low cut-off. The aim of this study is to compare these two strategies and to establish whether the identification of possible false positives using benign biomarkers – ADA, CRP and % of polymorphonuclear cells – improves diagnostic accuracy. Methods We studied 402 pleural effusions, 122 of them malignant. Benign biomarkers were determined in pleural fluid, and CEA, CA72-4, CA19-9 and CA15-3 in pleural fluid and serum. Results Establishing a cut-off value for each TM for a specificity of 100%, a joint sensitivity of 66.5% was obtained. With the F/S strategy and low cut-off points, sensitivity was 77% and specificity 98.2%, Subclassifying cases with negative benign biomarkers, both strategies achieved a specificity of 100%; sensitivity was 69.9% for single determination and 80.6% for F/S ratio. Conclusions The best interpretation of TM in the differential diagnosis of malignant pleural effusions is obtained using the F/S ratio in the group with negative benign biomarkers.

Background: Metabolic Syndrome (MetS) has a high prevalence in children, and its presence increases in those with a high BMI. This fact confirms the need for early detection to avoid the development of other comorbidities. Non-invasive variables are presented as a cost-effective and easy to apply alternative in any clinical setting. Aim: To propose a non-invasive method for the early diagnosis of MetS in overweight and obese Chilean children. Methods: We conducted a cross-sectional study on 221 children aged 6 to 11 years. We carried out multivariate logistic regressions, receiver operating characteristic curves, and discriminant analysis to determine the predictive capacity of non-invasive variables. The proposed new method for early detection of MetS is based on clinical decision trees. Results: The prevalence of MetS was 26.7%. The area under the curve for the BMI and waist circumference was 0.827 and 0.808, respectively. Two decision trees were calculated: the first included blood pressure (≥104.5/69 mmHg), BMI (≥23.5 Kg/m2) and WHtR (≥0.55); the second used BMI (≥23.5 Kg/m2) and WHtR (≥0.55), with validity index of 74.7% and 80.5%, respectively. Conclusions: Early detection of MetS is possible through non-invasive methods in overweight and obese children. Two models (Clinical decision trees) based on anthropometric (non-invasive) variables with acceptable validity indexes have been presented. Clinical decision trees can be applied in different clinical and non-clinical settings, adapting to the tools available, being an economical and easy to measurement option. These methods reduce the use of blood tests to those patients who require confirmation.

Pansharpening is a technique that fuses a low spatial resolution multispectral image and a high spatial resolution panchromatic one to obtain a multispectral image with the spatial resolution of the latter while preserving the spectral information of the multispectral image. In this paper we propose a variational Bayesian methodology for pansharpening. The proposed methodology uses the sensor characteristics to model the observation process and Super-Gaussian sparse image priors on the expected characteristics of the pansharpened image. The pansharpened image, as well as all model and variational parameters, are estimated within the proposed methodology. Using real and synthetic data, the quality of the pansharpened images is assessed both visually and quantitatively and compared with other pansharpening methods. Theoretical and experimental results demonstrate the effectiveness, efficiency, and flexibility of the proposed formulation.

Performance prediction systems allow knowing the learning status of students during a term and produce estimations on future status, what is invaluable information for teachers. The majority of current systems statically classify students once in time and show results in simple visual modes. This paper presents an innovative system with progressive, time-dependent and probabilistic performance predictions. The system produces by-weekly probabilistic classifications of students in three groups: high, medium or low performance. The system is empirically tested and data is gathered, analysed and presented. Predictions are shown as point graphs over time, along with calculated learning trends. Summary blocks are with latest predictions and trends are also provided for teacher efficiency. Moreover, some methods for selecting best moments for teacher intervention are derived from predictions. Evidence gathered shows potential to give teachers insights on students' learning trends, early diagnose learning status and selecting best moment for intervention.

The aim of this Special Issue is to compile a set of research works that highlight the use of gamification and other advanced technologies as powerful tools for motivation during learning. We have been fortunate to obtain a representative sample of the current research activity in this field.

The revenue of a ship company, a terminal, a port and even the whole logistic chain largely depends on the performance of (un)loading operations inside port areas. However, they are conditioned by met-ocean agents that exert stresses on fixed structures, berthed ships and mooring systems that can affect the comfort, productivity and safety of the operations. Under extreme conditions, operations may be interrupted, producing periods of inactivity, economic losses and even jeopardizing the terminal’s reliability. Therefore, it is crucial to develop strategies for characterizing and predicting operability in docks and anchorages. The present paper aims to assess the main approaches up to date to address the operability on berthed ships and to explore present and future strategies. To accomplish this, main factors affecting the operability are reviewed and different methodologies for the characterization and calculation of operability are detailed, together with some results regarding the perception of vulnerability from the Spanish port community. Rather than aiming for generic mono-parametric thresholds, the results highlight the need for addressing this challenge in a specific way for each dock and anchorage, broadening field monitoring and considering the varied peculiarities and the specific perception and experience of main port’s actors.

Damage in rubble mound breakwaters has been addressed for more than 80 years and, as reported in “Part I: Historical review of damage models”, a considerable number of hydraulic instability models have been proposed up to date. Most of them were developed from small-scale physical tests, based on damage descriptors not always parameterized nor measured in the same way, which indeed complicates the comparison and reproducibility from different experimental data sources. The latter is increased by the lack of a standardized methodological approach inside an experimental and measuring process that involves many sources of uncertainty. Currently, the latest innovations applicable to damage measurement, together with the growing demand of reliable decision-making tools for conservation/maintenance strategies and structural/operational risk management, venture an upcoming proliferation of prototype monitoring, as well as new approaches aimed to characterize the stochastic nature of damage evolution. In this context, this paper is meant to review the concept of damage in rubble mound breakwaters, the different proposals for its parameterization, the past and present measuring techniques, and main challenges in the near future.

The term “damage” in rubble mound breakwaters is usually related to the foremost failure mode of this kind of coastal structures: their hydraulic instability. The characterization of the breakwater response against wave action was and will be the goal of hundreds of studies. Because of the large amount of information, the present review on damage in rubble mound breakwaters is divided in two papers, which are closely linked but conceptually different; whereas Part II is focused on the various approaches for defining and measuring damage, Part I summarizes the diverse strategies for modelling damage development and progression. The present paper compiles 146 references on this topic, chronologically discussed over almost a century of history: from 1933 to 2020. It includes 23 formulations of hydraulic stability models and 11 formulations of damage progression models, together with main advances and shortcomings up to date. The future of rubble mound design is linked to risk-based tools and advanced management strategies, for which deeper comprehension about the spatial and temporal evolution of damage during the useful life of each particular structure is required. For this aim, damage progression probabilistic models, full-scale monitoring and standardization will presumably be some of the key challenges in the upcoming years.