Diagnosis of the health status of mooring systems for floating offshore wind turbines using autoencoders

Floating offshore wind turbines (FOWTs) show promise in terms of energy production, availability, and sustainability, but remain unprofitable due to high maintenance costs. This work proposes a deep learning algorithm to detect mooring line degradation and failure by monitoring the dynamic response of the publicly available DeepCWind OC4 semi-submersible platform. This study implements an autoencoder capable of predicting multiple forms of damage occurring at once, with various levels of severity. Given the scarcity of real data, simulations performed in OpenFAST, recreating both healthy and damaged mooring systems, are used to train and validate the algorithm. The novelty of the proposed approach consists of using a set of key statistical metrics describing the platform’s displacements and rotations as input layer for the autoencoder. The statistics of the responses are calculated at 33-minute-long sea states under a broad spectrum of metocean and wind conditions. An autoencoder is trained using these parameters to discover that the proposed algorithm is capable of detecting mild anomalies caused by biofouling and anchor displacements, with correlation coefficients up to 98.51\% and 99.16\%, respectively. These results are encouraging for the continuous health monitoring of FOWT mooring systems using easily measurable quantities to plan preventive maintenance actions adequately

Predictive Maintenance of Floating Offshore Wind Turbine Mooring Lines using Deep Neural Networks

The recent massive deployment of onshore wind farms has caused controversy to arise mainly around the issues of land occupation, noise and visual pollution and impact on wildlife. Fixed offshore turbines, albeit beneficial in those aspects, become economically unfeasible when installed far away from coastlines. The possibility of installing floating offshore wind turbines is currently hindered by their excessive operation and maintenance costs. We have developed a comprehensive model to help companies plan their operations in advance by detecting failure in mooring lines in almost real time using supervised deep learning techniques. Given the lack of real data, we have coupled numerical methods and OpenFAST simulations to build a dataset containing the displacements and rotations of a turbine's floating platform across all directions. These time series and their corresponding frequency spectra are used to obtain a set of key statistical parameters, including means and standard deviations, peak frequencies, and several relevant momenta. We have designed and trained a Deep Neural Network to understand and distinguish amongst a series of common failure modes for mooring lines considering a range of metocean and structural conditions. We have obtained promising results when monitoring severe changes in the line's mass and damping using short time spans, achieving a 95.7% validation accuracy when detecting severe biofouling failure.N Gorostidi has received funding from the Spanish Ministry of Science and Innovation project DEEPINVERSE, with reference PID2019-108111RB-I00 (FEDER/AEI). V Nava has received funding from the project IA4TES - Inteligencia Artificial para la Transición Energética Sostenible funded by Ministry of Economic Affairs and Digital Transformation (MIA.2021.M04.0008); the “BCAM Severo Ochoa” accreditation of excellence (SEV-2017-0718); and the Basque Government through the BERC 2022-2025 program, the Elkartek project EXPERTIA (KK-2021/00048)

Temperature enhances the functional diversity of dissolved organic matter utilization by coastal marine bacteria

Although bulk bacterial metabolism in response to temperature has been determined for different oceanic regions, the impact of temperature on the functional diversity of dissolved organic matter (DOM) utilization has been largely unexplored. Here, we hypothesized that besides modifying the rates of carbon utilization, temperature can also alter the diversity of substrates utilized. The patterns of utilization of 31 model DOM compounds (as represented in Biolog EcoPlates™) by bacterioplankton were assessed using inocula from surface waters of the southern Bay of Biscay continental shelf over 1 year. Bacteria utilized more polymers and carbohydrates in late spring and summer than in winter, likely reflecting changes in substrate availability linked to the release and accumulation of DOM in phytoplankton post-bloom conditions. Seawater temperature correlated positively with the number of substrates utilized (i.e. functional richness) and this relationship was maintained in monthly experimental incubations spanning 3°C below and above in situ values. The enhancement of functional richness with experimental warming displayed a unimodal response to ambient temperature, peaking at 16°C. This temperature acted as a threshold separating nutrient-sufficient from nutrient-deficient conditions at the study site, suggesting that trophic conditions will be critical in the response of microbial DOM utilization to future warming

SEOM Clinical Guidelines For Endometrial Cancer (2017)

Endometrial cancer (EC) is the most common gynecological cancer in developed countries. Most patients are diagnosed at an early stage with a low risk of relapse. However, there is a group of patients with a high risk of relapse and poor prognosis. Despite the recent publication of randomized trials, the adjuvant treatment of high-risk EC is still to be defined and there are many open questions about the best approach and the right timing. Unfortunately, the survival of metastatic or recurrent EC is short, due to the poor results of chemotherapy and the lack of a second line of treatment. Advances in the knowledge of the molecular abnormalities in EC have permitted the development of promising targeted therapies

Seasonal dynamics of natural Ostreococcus viral infection at the single cell level using VirusFISH

Ostreococcus is a cosmopolitan marine genus of phytoplankton found in mesotrophic and oligotrophic waters, and the smallest free-living eukaryotes known to date, with a cell diameter close to 1 μm. Ostreococcus has been extensively studied as a model system to investigate viral–host dynamics in culture, yet the impact of viruses in naturally occurring populations is largely unknown. Here, we used Virus Fluorescence in situ Hybridization (VirusFISH) to visualize and quantify viral-host dynamics in natural populations of Ostreococcus during a seasonal cycle in the central Cantabrian Sea (Southern Bay of Biscay). Ostreococcus were predominantly found during summer and autumn at surface and 50 m depth, in coastal, mid-shelf and shelf waters, representing up to 21% of the picoeukaryotic communities. Viral infection was only detected in surface waters, and its impact was variable but highest from May to July and November to December, when up to half of the population was infected. Metatranscriptomic data available from the mid-shelf station unveiled that the Ostreococcus population was dominated by the species O. lucimarinus. This work represents a proof of concept that the VirusFISH technique can be used to quantify the impact of viruses on targeted populations of key microbes from complex natural communities.Preprint5,84

On building physics-based AI models for the design and SHM of mooring systems

Expert systems in industrial processes are modelled using physics-based approaches, data-driven models or hybrid approaches in which however the underlying physical models generally constitute a separate block with respect to the Artificial Intelligence (AI) technique(s). This work applies the novel concept of “imbrication”-a physics-based AI approach-to the mooring system of offshore renewable energy devices to achieve a complete integration of both perspectives. This approach can reduce the size of the training dataset and computational time while delivering algorithms with higher generalization capability and explicability. We first undertake the design of the mooring system by developing a surrogate model coupled with a Bayesian optimiser. Then, we analyse the structural health monitoring of the mooring system by designing a supervised Deep Neural Network architecture. Herein, we describe the characteristics of the imbrication process, analyse preliminary results of our investigation and provide considerations for orienting further research work and sector applicability

Novel interactions between phytoplankton and bacteria shape microbial seasonal dynamics in coastal ocean waters

Trophic interactions between marine phytoplankton and heterotrophic bacteria are at the base of the biogeochemical carbon cycling in the ocean. However, the specific interactions taking place between phytoplankton and bacterial taxa remain largely unexplored, particularly out of phytoplankton blooming events. Here, we applied network analysis to a 3.5-year time-series dataset to assess the specific associations between different phytoplankton and bacterial taxa along the seasonal scale, distinguishing between free-living and particle-attached bacteria. Using a newly developed network post-analysis technique we removed bacteria-phytoplankton correlations that were primarily driven by environmental parameters, to detect potential biotic interactions. Our results indicate that phytoplankton dynamics may be a strong driver of the inter-annual variability in bacterial community composition. We found the highest abundance of specific bacteria-phytoplankton associations in the particle-attached fraction, indicating a tighter bacteria-phytoplankton association than in the free-living fraction. In the particle-associated fraction we unveiled novel potential associations such as the one between Planctomycetes taxa and the diatom Leptocylindrus spp. Consistent correlations were also found between free-living bacterial taxa and different diatoms, including novel associations such as those between SAR11 with Naviculales diatom order, and between Actinobacteria and Cylindrotheca spp. We also confirmed previously known associations between Rhodobacteraceae and Thalassiosira spp. Our results expand our view on bacteria-phytoplankton associations, suggesting that taxa-specific interactions may largely impact the seasonal dynamics of heterotrophic bacterial communities

More, smaller bacteria in response to ocean's warming?

Heterotrophic bacteria play a major role in organic matter cycling in the ocean. Although the high abundances and relatively fast growth rates of coastal surface bacterioplankton make them suitable sentinels of global change, past analyses have largely overlooked this functional group. Here, time series analysis of a decade of monthly observations in temperate Atlantic coastal waters revealed strong seasonal patterns in the abundance, size and biomass of the ubiquitous flow-cytometric groups of low (LNA) and high nucleic acid (HNA) content bacteria. Over this relatively short period, we also found that bacterioplankton cells were significantly smaller, a trend that is consistent with the hypothesized temperature-driven decrease in body size. Although decadal cell shrinking was observed for both groups, it was only LNA cells that were strongly coherent, with ecological theories linking temperature, abundance and individual size on both the seasonal and interannual scale. We explain this finding because, relative to their HNA counterparts, marine LNA bacteria are less diverse, dominated by members of the SAR11 clade. Temperature manipulation experiments in 2012 confirmed a direct effect of warming on bacterial size. Concurrent with rising temperatures in spring, significant decadal trends of increasing standing stocks (3% per year) accompanied by decreasing mean cell size (-1% per year) suggest a major shift in community structure, with a larger contribution of LNA bacteria to total biomass. The increasing prevalence of these typically oligotrophic taxa may severely impact marine food webs and carbon fluxes by an overall decrease in the efficiency of the biological pump.Versión del editor5,064

Documento de la Sociedad Española de Hipertensión-Liga Española para la Lucha contra la Hipertensión Arterial (SEH-LELHA) sobre las guías ACC/AHA 2017 de hipertensión arterial

The American College of Cardiology (ACC) and the American Heart Association (AHA) have recently published their guidelines for the prevention, detection, evaluation, and management of hypertension in adults. The most controversial issue is the classification threshold at 130/80 mmHg, which will allow a large number of patients to be diagnosed as hypertensive who were previously considered normotensive. Blood pressure (BP) is considered normal (<120 mmHg systolic and <80 mmHg diastolic), elevated (120-129 and <80 mmHg), stage 1 (130-139 or 80-89 mmHg), and stage 2 (≥140 or ≥90 mmHg). Out-of-office BP measurements are recommended to confirm the diagnosis of hypertension and for titration of BP-lowering medication. In management, cardiovascular risk would be determinant since those with grade 1 hypertension and an estimated 10-year risk of atherosclerotic cardiovascular disease ≥10%, and those with cardiovascular disease, chronic kidney disease and/or diabetes will require pharmacological treatment, the rest being susceptible to non-pharmacological treatment up to the 140/90 mmHg threshold. These recommendations would allow patients with level 1 hypertension and high atherosclerotic cardiovascular disease to benefit from pharmacological therapies and all patients could also benefit from improved non-pharmacological therapies. However, this approach should be cautious because inadequate BP measurement and/or lack of systematic atherosclerotic cardiovascular disease calculation could lead to overestimation in diagnosing hypertension and to overtreatment. Guidelines are recommendations, not impositions, and the management of hypertension should be individualized, based on clinical decisions, preferences of the patients, and an adequate balance between benefits and risks

Comparison of clinical rating scales in genetic frontotemporal dementia within the GENFI cohort

BACKGROUND: Therapeutic trials are now underway in genetic forms of frontotemporal dementia (FTD) but clinical outcome measures are limited. The two most commonly used measures, the Clinical Dementia Rating (CDR)+National Alzheimer’s Disease Coordinating Center (NACC) Frontotemporal Lobar Degeneration (FTLD) and the FTD Rating Scale (FRS), have yet to be compared in detail in the genetic forms of FTD. METHODS: The CDR+NACC FTLD and FRS were assessed cross-sectionally in 725 consecutively recruited participants from the Genetic FTD Initiative: 457 mutation carriers (77 microtubule-associated protein tau (MAPT), 187 GRN, 193 C9orf72) and 268 family members without mutations (non-carrier control group). 231 mutation carriers (51 MAPT, 92 GRN, 88 C9orf72) and 145 non-carriers had available longitudinal data at a follow-up time point. RESULTS: Cross-sectionally, the mean FRS score was lower in all genetic groups compared with controls: GRN mutation carriers mean 83.4 (SD 27.0), MAPT mutation carriers 78.2 (28.8), C9orf72 mutation carriers 71.0 (34.0), controls 96.2 (7.7), p<0.001 for all comparisons, while the mean CDR+NACC FTLD Sum of Boxes was significantly higher in all genetic groups: GRN mutation carriers mean 2.6 (5.2), MAPT mutation carriers 3.2 (5.6), C9orf72 mutation carriers 4.2 (6.2), controls 0.2 (0.6), p<0.001 for all comparisons. Mean FRS score decreased and CDR+NACC FTLD Sum of Boxes increased with increasing disease severity within each individual genetic group. FRS and CDR+NACC FTLD Sum of Boxes scores were strongly negatively correlated across all mutation carriers (r_{s} =−0.77, p<0.001) and within each genetic group (r_{s} =−0.67 to −0.81, p<0.001 in each group). Nonetheless, discrepancies in disease staging were seen between the scales, and with each scale and clinician-judged symptomatic status. Longitudinally, annualised change in both FRS and CDR+NACC FTLD Sum of Boxes scores initially increased with disease severity level before decreasing in those with the most severe disease: controls −0.1 (6.0) for FRS, −0.1 (0.4) for CDR+NACC FTLD Sum of Boxes, asymptomatic mutation carriers −0.5 (8.2), 0.2 (0.9), prodromal disease −2.3 (9.9), 0.6 (2.7), mild disease −10.2 (18.6), 3.0 (4.1), moderate disease −9.6 (16.6), 4.4 (4.0), severe disease −2.7 (8.3), 1.7 (3.3). Sample sizes were calculated for a trial of prodromal mutation carriers: over 180 participants per arm would be needed to detect a moderate sized effect (30%) for both outcome measures, with sample sizes lower for the FRS. CONCLUSIONS: Both the FRS and CDR+NACC FTLD measure disease severity in genetic FTD mutation carriers throughout the timeline of their disease, although the FRS may be preferable as an outcome measure. However, neither address a number of key symptoms in the FTD spectrum, for example, motor and neuropsychiatric deficits, which future scales will need to incorporate