Influence of the Atlantic inflow on trace metal enrichments in sediments and particulate matter of the NW Alboran Sea (SW Mediterranean).

Trace metal contents and fluxes in downward particulate matter and dated sediment cores of the NW Alboran Sea are analysed in this study with the aim of assessing the role of the Atlantic inflow on their transport. Increases in Zn, Cu and Pb were detected in downward particulate matter collected by sediment traps after river flooding events and after the Aznalcollar mining spill. Their arrival coincided within the recently estimated time range for river particles discharged into the Gulf of Cádiz to reach the Alboran Sea, indicating that their transfer is enhanced during events of increased river inputs of contaminated particulate matter. This also suggests that the effects of potential tailing dam failures in the Gulf of Cádiz watersheds could reach the Alboran Sea. These trace metals also increased in the sediment cores from the continental rise since the second half of the 19th century, suggesting that contaminated particles have been continuously transferred towards the Mediterranean Sea since that time, when mining concessions and production increased in the SW Iberian Pyrite Belt.

Wind turbine database for intelligent operation and maintenance strategies.

With the aim of helping researchers to develop intelligent operation and maintenance strategies, in this manuscript, an extensive 3-years Supervisory Control and Data Acquisition database of five Fuhrländer FL2500 2.5 MW wind turbines is presented. The database contains 312 analogous variables recorded at 5-minute intervals, from 78 different sensors. The reported values for each sensor are minimum, maximum, mean, and standard deviation. The database also contains the alarm events, indicating the system and subsystem and a small description. Finally, a set of functions to download specific subsets of the whole database is freely available in Matlab, R, and Python. To demonstrate the usefulness of this database, an illustrative example is given. In this example, different gearbox variables are selected to estimate a target variable to detect whether or not the estimate differs from the actual value provided for the sensor. By using this normality modelling approach, it is possible to detect rotor malfunction when the estimate differs from the actual measured value.

Special Issue "Machine Learning Methods for Biomedical Data Analysis".

Machine learning is an effective method for developing automatic algorithms for analysing sophisticated biomedical data [...].

Vulnerability of six cold-water corals to sediment resuspension from bottom trawling fishing.

Bottom trawling can significantly affect benthic communities, directly through immediate removal of sessile organisms and indirectly through sediment resuspension. Submarine canyons, often surrounded by fishing grounds, are important habitats for cold-water corals (CWC). Vulnerability of CWCs to increased suspended sediment concentration (SSC) is key to understanding the severity of bottom trawling effects on those communities. Here we show survival, growth, and physiological response of six CWCs from a Mediterranean submarine canyon (Dendrophyllia cornigera, Desmophyllum dianthus, Desmophyllum pertusum, Madrepora oculata, Leiopathes glaberrima and Muriceides lepida), exposed to a long-term, aquarium-based sedimentary disturbance experiment. Compared to cup coral and octocoral, which did not exhibit symptoms of distress, our data indicate that colonial scleractinian corals and black coral, which experienced substantial polyp mortality in enhanced SSC treatments, are more vulnerable. Indirect impact of bottom trawling could thus contribute to structural simplification of CWC communities posing an additional stressor alongside with global climate change.

A Machine Learning Approach for Predicting Non-Suicidal Self-Injury in Young Adults.

Artificial intelligence techniques were explored to assess the ability to anticipate self-harming behaviour in the mental health context using a database collected by an app previously designed to record the emotional states and activities of a group of subjects exhibiting self-harm. Specifically, the Leave-One-Subject-Out technique was used to train classification trees with a maximum of five splits. The results show an accuracy of 84.78%, a sensitivity of 64.64% and a specificity of 85.53%. In addition, positive and negative predictive values were also obtained, with results of 14.48% and 98.47%, respectively. These results are in line with those reported in previous work using a multilevel mixed-effect regression analysis. The combination of apps and AI techniques is a powerful way to improve the tools to accompany and support the care and treatment of patients with this type of behaviour. These studies also guide the improvement of apps on the user side, simplifying and collecting more meaningful data, and on the therapist side, progressing in pathology treatments. Traditional therapy involves observing and reconstructing what had happened before episodes once they have occurred. This new generation of tools will make it possible to monitor the pathology more closely and to act preventively.

EMD-based data augmentation method applied to handwriting data for the diagnosis of Essential Tremor using LSTM networks.

The increasing capacity of today's technology represents great advances in diagnosing diseases using standard procedures supported by computer science. Deep learning techniques are able to extract the characteristics of temporal signals to study their patterns and diagnose diseases such as essential tremor. However, these techniques require a large amount of data to train the neural network and achieve good results, and the more data the network has, the more accurate the final model implemented. In this work we propose the use of a data augmentation technique to improve the accuracy of a Long short-term memory system in the diagnosis of essential tremor. For this purpose, the multivariate Empirical Mode Decomposition method will be used to decompose the original temporal signals collected from control subjects and patients with essential tremor. The time series obtained from the decomposition, covering different frequency ranges, will be randomly shuffled and combined to generate new artificial samples for each group. Then, both the generated artificial samples and part of the real samples will be used to train the LSTM network, and the remaining original samples will be used to test the model. The experimental results demonstrate the capability of the proposed method, which is compared to a set of 10 different data augmentation methods, and in all cases outperforms all other methods. In the best case, the proposed method increases the accuracy of the classifier from 83.20% to almost 93% when artificial samples are generated, which is a promising result when only small databases are available.

Effects of bottom trawling on trace metal contamination of sediments along the submarine canyons of the Gulf of Palermo (southwestern Mediterranean).

Submarine canyons are preferential pathways for transport of particulate matter and contaminants from the shelf to the deep sea. The Gulf of Palermo continental margin has a very narrow shelf (about 2-3 km wide on average) and is incised by several submarine canyons that favour shelf-slope sediment transfer. A sediment core collected on the outer shelf and six sediment cores taken at different depths along the Oreto, Eleuterio and Anerella submarine canyons were analysed to study the transfer and historical record of trace metal contamination in the Gulf of Palermo continental margin. Trace metals, major elements, organic carbon and sediment grain size were analysed in these cores, which were dated with 210Pb to assess their historical compositional evolution since the late 19th century. Hg, Pb, Cu, Zn and Cd content increased until the 1970s and 1980s, associated with the increase in urbanization and industrial activities in the Palermo area, and Hg was the contaminant that reached the highest enrichments. However, the increasing trend of these metals contamination was reversed in the 1970s and 1980s, coinciding with drastic changes in the terrigenous content and grain size of sediments in the canyon axes. These changes occurred when bottom trawling fleets expanded to deeper fishing grounds equipped with powerful trawlers around the Gulf of Palermo canyon heads and flanks and along the Oreto canyon axis. Bottom trawlers have resuspended large amounts of sediment, which have been transferred into the canyons since the 1970s and 1980s and have thus increased sediment accumulation rates. This resuspended sediment has been mixing with the sediment transferred and accumulated along the canyons, diluting and reducing its trace metal contamination levels since the expansion of the bottom trawling fleets.

Evidence of large increases in sedimentation rates due to fish trawling in submarine canyons of the Gulf of Palermo (SW Mediterranean).

Bottom trawling in submarine canyons can affect their natural sedimentation rates, but studies addressing this issue are still scarce. In the Gulf of Palermo (SW Mediterranean), bottom trawling occurs on the slope around Oreto, Arenella and Eleuterio canyons. Analyses of excess 210Pb concentrations and grain size fractions in sediment cores from their canyon axes revealed that sedimentation rates and silt contents increased in all canyons in the 1980s, due to the expansion of more powerful trawlers (>500 HP) to deeper fishing grounds. In Eleuterio and Arenella canyons, sedimentation rates increased by an order of magnitude (0.1-1.4 cm·yr-1), whereas they increased less (0.1-0.7 cm·yr-1) in Oreto Canyon, since the enhanced trawling-derived sediment fluxes into this canyon are affected by sediment resuspension from trawling along its axis. Considering the global expansion of bottom trawling, we anticipate similar alterations in other trawled canyons, with ecological consequences that should be addressed by management strategies.

A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition.

An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain-computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collected EEG signals are extremely susceptible to the contamination of electromyography (EMG) artifacts, affecting their original characteristics. Therefore, EEG denoising is an essential preprocessing step in any BCI system. Previous studies have confirmed that the combination of ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA) can effectively suppress EMG artifacts. However, the time-consuming iterative process of EEMD may limit the application of the EEMD-CCA method in real-time monitoring of BCI. Compared with the existing EEMD, the recently proposed signal serialization based EEMD (sEEMD) is a good choice to provide effective signal analysis and fast mode decomposition. In this study, an EMG denoising method based on sEEMD and CCA is discussed. All of the analyses are carried out on semi-simulated data. The results show that, in terms of frequency and amplitude, the intrinsic mode functions (IMFs) decomposed by sEEMD are consistent with the IMFs obtained by EEMD. There is no significant difference in the ability to separate EMG artifacts from EEG signals between the sEEMD-CCA method and the EEMD-CCA method (p > 0.05). Even in the case of heavy contamination (signal-to-noise ratio is less than 2 dB), the relative root mean squared error is about 0.3, and the average correlation coefficient remains above 0.9. The running speed of the sEEMD-CCA method to remove EMG artifacts is significantly improved in comparison with that of EEMD-CCA method (p < 0.05). The running time of the sEEMD-CCA method for three lengths of semi-simulated data is shortened by more than 50%. This indicates that sEEMD-CCA is a promising tool for EMG artifact removal in real-time BCI systems.

Estimating the real burden of disease under a pandemic situation: The SARS-CoV2 case.

The present paper introduces a new model used to study and analyse the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) epidemic-reported-data from Spain. This is a Hidden Markov Model whose hidden layer is a regeneration process with Poisson immigration, Po-INAR(1), together with a mechanism that allows the estimation of the under-reporting in non-stationary count time series. A novelty of the model is that the expectation of the unobserved process's innovations is a time-dependent function defined in such a way that information about the spread of an epidemic, as modelled through a Susceptible-Infectious-Removed dynamical system, is incorporated into the model. In addition, the parameter controlling the intensity of the under-reporting is also made to vary with time to adjust to possible seasonality or trend in the data. Maximum likelihood methods are used to estimate the parameters of the model.

MADloy: robust detection of mosaic loss of chromosome Y from genotype-array-intensity data.

BACKGROUND: Accurate protocols and methods to robustly detect the mosaic loss of chromosome Y (mLOY) are needed given its reported role in cancer, several age-related disorders and overall male mortality. Intensity SNP-array data have been used to infer mLOY status and to determine its prominent role in male disease. However, discrepancies of reported findings can be due to the uncertainty and variability of the methods used for mLOY detection and to the differences in the tissue-matrix used. RESULTS: We created a publicly available software tool called MADloy (Mosaic Alteration Detection for LOY) that incorporates existing methods and includes a new robust approach, allowing efficient calling in large studies and comparisons between methods. MADloy optimizes mLOY calling by correctly modeling the underlying reference population with no-mLOY status and incorporating B-deviation information. We observed improvements in the calling accuracy to previous methods, using experimentally validated samples, and an increment in the statistical power to detect associations with disease and mortality, using simulation studies and real dataset analyses. To understand discrepancies in mLOY detection across different tissues, we applied MADloy to detect the increment of mLOY cellularity in blood on 18 individuals after 3 years and to confirm that its detection in saliva was sub-optimal (41%). We additionally applied MADloy to detect the down-regulation genes in the chromosome Y in kidney and bladder tumors with mLOY, and to perform pathway analyses for the detection of mLOY in blood. CONCLUSIONS: MADloy is a new software tool implemented in R for the easy and robust calling of mLOY status across different tissues aimed to facilitate its study in large epidemiological studies.

Potential Therapeutic Effects of the Neural Stem Cell-Targeting Antibody Nilo1 in Patient-Derived Glioblastoma Stem Cells.

Glioblastoma (GBM) is the most devastating and least treatable brain tumor with median survival <15 months and extremely high recurrence rates. Promising results of immune checkpoint blockade obtained from pre-clinical studies in mice did not translate to clinic, and new strategies are urgently needed, particularly those targeting GBM stem cells (GSCs) that are held responsible for drug resistance and tumor recurrence. Patient-derived GSC cultures are critical for finding effective brain tumor therapies. Here, we investigated the ability of the recently described monoclonal antibody Nilo1 to specifically recognize GSCs isolated from GBM surgical samples. We employed five patient-derived GSC cultures with different stemness marker expression and differentiation potential, able to recapitulate original tumors when xenotransplanted in vivo. To answer whether Nilo1 has any functional effects in patient-derived GSCs lines, we treated the cells with Nilo1 in vitro and analyzed cell proliferation, cell cycle, apoptosis, sphere formation, as well as the expression of stem vs. differentiation markers. All tested GSCs stained positively for Nilo1, and the ability of Nilo1 to recognize GSCs strongly relied on their stem-like phenotype. Our results showed that a subset of patient-derived GSCs were sensitive to Nilo1 treatment. In three GSC lines Nilo1 triggered differentiation accompanied by the induction of p21. Most strikingly, in one GSC line Nilo1 completely abrogated self-renewal and led to Bax-associated apoptosis. Our data suggest that Nilo1 targets a molecule functionally relevant for stemness maintenance and pinpoint Nilo1 as a novel antibody-based therapeutical strategy to be used either alone or in combination with cytotoxic drugs for GSC targeting. Further pre-clinical studies are needed to validate the effectiveness of GSC-specific Nilo1 targeting in vivo.

Trace metal variability controlled by hydrodynamic processes in a polluted inner shelf environment (Besòs prodelta, NW Mediterranean).

Trace metal pollution of coastal sediment is monitored in many countries to control its evolution and the effectiveness of preventive and corrective measures. However, temporal variability of trace metal pollution is not always due to changes in pollution management, as natural processes can induce a significant variability in the trace metal content of sediment and particulate matter, especially in strongly polluted coastal areas. To study this variability, time series of trace metals in particulate matter and bottom sediments were recorded along with hydrographic and hydrodynamic parameters in the most highly polluted zone of the Besòs River prodelta. Two benthic tripods equipped with current meters, turbidimeters and sediment traps were deployed at 20 and 30 m water depth from late-September to mid-June and sediment cores were taken four times at each site during the deployment period. Trace metal content in the trapped particulate matter and the surface sediment increased during storm events, which can resuspend and erode several cm of subsurface sediments with higher pollution levels from earlier industrial times. After the storms, significant accumulation of less polluted sediment began, and near-bottom currents redistributed it, decreasing trace metal contents in surface sediments and trapped particulate matter. Therefore, energy conditions previous to monitoring sampling must be considered in order to evaluate the evolution of trace metals in inner shelf polluted sediments.

Effects of flushing flows on the transport of mercury-polluted particulate matter from the Flix Reservoir to the Ebro Estuary.

Polluted sediments retained in water reservoirs are potential sources of deleterious materials downstream, especially during floods or flushing flows (FFs). Their interaction with these events is important for determining potential risks and evaluating management actions. In the Ebro River, the Flix Reservoir accumulated a deposit of more than 3 × 105 t of industrial waste with high Hg concentrations. Because suspended particulate matter (SPM) is the main driver of Hg pollution downriver, this study analyses the transport of particulate matter and Hg pollution from the Flix Reservoir to the Ebro Estuary during FFs. Time series of currents, turbidity and downward particulate matter fluxes were obtained by current meters, turbidimeters and sediment traps assembled in benthic tripods. They were deployed in the reservoir and at two locations in the estuary during two recording periods that each captured a flushing flow (FF) event. In addition, SPM samples were collected during the study period at several locations along the river course, from upstream of the Flix Reservoir down to the river mouth, to measure the suspended particulate matter and associated Hg mobilized downstream. A continuous background level of Hg pollution was observed during the deployment periods, but the Hg and particulate matter fluxes increased by between one and two orders of magnitude during FFs. Though the two events reached similar water discharges, the first FF was after the wet season and generated lower particulate matter concentrations and fluxes, but higher Hg contents than the second, which occurred after the dry season. The higher available particulate matter in the second event diluted the polluted Hg particle load more than the first event. Thus, similar FFs may result in different Hg concentration and sediment transport episodes, largely depending on the previous hydrological regime and the river sediment availability. These findings should be considered for FF management.

Automatic ham classification method based on support vector machine model increases accuracy and benefits compared to manual classification.

The thickness of the subcutaneous fat (SFT) is a very important parameter in the ham, since determines the process the ham will be submitted. This study compares two methods to predict the SFT in slaughter line: an automatic system using an SVM model (Support Vector Machine) and a manual measurement of the fat carried out by an experienced operator, in terms of accuracy and economic benefit. These two methods were compared to the golden standard obtained by measuring SFT with a ruler in a sample of 400 hams equally distributed within each SFT class. The results show that the SFT prediction made by the SVM model achieves an accuracy of 75.3%, which represents an improvement of 5.5% compared to the manual measurement. Regarding economic benefits, SVM model can increase them between 12 and 17%. It can be concluded that the classification using SVM is more accurate than the one performed manually with an increase of the economic benefit for sorting.

Genetic identification of a war-evacuated child in search of his own identity for more than seventy years.

V. M. E. was evacuated when he was a young boy in 1939. He left an aunt and cousins in Spain (G. E. family). He was adopted in Belgium by the D. family and thus his new name became V. D. He has been unable to remember his childhood before his adoption, a symptomatology compatible with amnesia for personal identity, presumably because he may have suffered a head contusion before or during his exodus. Identification tests were performed on blood samples from V. D. and V. G. E., a mitochondrial cousin of the missing boy. V. G. E. and the missing boy have a common mitochondrial ancestor, their maternal grandmother. The mitochondrial profile of both samples turned out to be highly specific, which allowed the genetic identification of V. D. as V. M. E. As a result, V. D. has reclaimed his past and reunited with his former family in Spain after more than seven decades. As far as we know, this is the first report describing the application of mitochondrial DNA in the identification of a person evacuated during the Spanish Civil War suffering from amnesia for personal identity.

Multiple immunofluorescence assay identifies upregulation of Active ß-catenin in prostate cancer.

OBJECTIVES: To apply a systems pathology-based approach to the quantification of nuclear Active ß-catenin and human leukocyte antigen class I, and assess the biomarker involvement in a cohort of prostate tumor patients. RESULTS: The systems pathology approach applied allows a precise quantification of the marker expression in the different cell compartments as well as the determination of the areas that coexpress two markers. Our data shows that the accumulation of ß-catenin in the nuclear compartment is significantly decreased in the adjacent normal areas when compared to tumor of the same patients (p < 0.001). In conclusion, the application of this novel multiple immunofluorescence assay demonstrates that the upregulation of Active ß-catenin is a relatively common feature of prostate tumor development, and further supports the activation of the Wnt/ß-catenin pathway in prostate cancer progression.

On-line Ham Grading using pattern recognition models based on available data in commercial pig slaughterhouses.

The thickness of the subcutaneous fat in hams is one of the most important factors for the dry-curing process and largely determines its final quality. This parameter is usually measured in slaughterhouses by a manual metrical measure to classify hams. The aim of the present study was to propose an automatic classification method based on data obtained from a carcass automatic classification equipment (AutoFom) and intrinsic data of the pigs (sex, breed, and weight) to simulate the manual classification system. The evaluated classification algorithms were decision tree, support vector machines (SVM), k-nearest neighbour and discriminant analysis. A total of 4000 hams selected by breed and sex were classified as thin (0-10 mm), standard (11-15 mm), semi-fat (16-20 mm) and fat (>20 mm). The most reliable model, with a percentage of success of 73%, was SVM with Gaussian kernel, including all data available. These results suggest that the proposed classification method can be a useful online tool in slaughterhouses to classify hams.

Time-series analysis for porcine reproductive and respiratory syndrome in the United States.

Industry-driven voluntary disease control programs for swine diseases emerged in North America in the early 2000's, and, since then, those programs have been used for monitoring diseases of economic importance to swine producers. One example of such initiatives is Dr. Morrison's Swine Health Monitoring Project, a nation-wide monitoring program for swine diseases including the porcine reproductive and respiratory syndrome (PRRS). PRRS has been extensively reported as a seasonal disease in the U.S., with predictable peaks that start in fall and are extended through the winter season. However, formal time series analysis stratified by geographic region has never been conducted for this important disease across the U.S. The main objective of this study was to use approximately seven years of PRRS incidence data in breeding swine herds to conduct time-series analysis in order to describe the temporal patterns of PRRS outbreaks at the farm level for five major swine-producing states across the U.S. including the states of Minnesota, Iowa, North Carolina, Nebraska and Illinois. Data was aggregated retrospectively at the week level for the number of herds containing animals actively shedding PRRS virus. Basic descriptive statistics were conducted followed by autoregressive integrated moving average (ARIMA) modelling, conducted separately for each of the above-mentioned states. Results showed that there was a difference in the nature of PRRS seasonality among states. Of note, when comparing states, the typical seasonal pattern previously described for PRRS could only be detected for farms located in the states of Minnesota, North Carolina and Nebraska. For the other two states, seasonal peaks every six months were detected within a year. In conclusion, we showed that epidemic patterns are not homogeneous across the U.S, with major peaks of disease occurring through the year. These findings highlight the importance of coordinating alternative control strategies in different regions considering the prevailing epidemiological patterns.

Discrete Cosine Transform for the Analysis of Essential Tremor.

Essential tremor (ET) is the most common movement disorder. In fact, its prevalence is about 20 times higher than that of Parkinson's disease. In addition, studies have shown that a high percentage of cases, between 50 and 70%, are estimated to be of genetic origin. The gold standard test for diagnosis, monitoring and to differentiate between both pathologies is based on the drawing of the Archimedes' spiral. Our major challenge is to develop the simplest system able to correctly classify Archimedes' spirals, therefore we will exclusively use the information of the x and y coordinates. This is the minimum information provided by any digitizing device. We explore the use of features from drawings related to the Discrete Cosine Transform as part of a wider cross-study for the diagnosis of essential tremor held at Biodonostia. We compare the performance of these features against other classic and already analyzed ones. We outperform previous results using a very simple system and a reduced set of features. Because the system is simple, it will be possible to implement it in a portable device (microcontroller), which will receive the x and y coordinates and will issue the classification result. This can be done in real time, and therefore without needing any extra job from the medical team. In future works these new drawing-biomarkers will be integrated with the ones obtained in the previous Biodonostia study. Undoubtedly, the use of this technology and user-friendly tools based on indirect measures could provide remarkable social and economic benefits.