In recent years, the consumption of metformin has increased not only due to the higher prevalence of type 2 diabetes, but also due to their usage for other indications such as cancer and polycystic ...ovary syndrome. Consequently, metformin is currently among the highest drug by weight released into the aquatic environments. Since the toxic effects of this drug on aquatic species has been scarcely explored, the aim of this work was to investigate the influence of metformin on the development and redox balance of zebrafish (Danio rerio) embryos. For this purpose, zebrafish embryos (4 hpf) were exposed to 1, 10, 20, 30, 40, 50, 75 and 100 μg/L metformin until 96 hpf. Metformin significantly accelerated the hatching process in all exposure groups. Moreover, this drug induced several morphological alterations on the embryos, affecting their integrity and consequently leading to their death. The most frequent malformations found on the embryos included malformation of tail, scoliosis, pericardial edema and yolk deformation. Regarding oxidative balance, metformin significantly induced the activity of antioxidant enzymes and the levels of oxidative damage biomarkers. However, our IBR analisis demonstrated that oxidative damage biomarkers got more influence over the embryos. Together these results demonstrated that metformin may affect the embryonic development of zebrafish and that oxidative stress may be involved in the generation of this embryotoxic process.
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•Environmental relevant concentrations of metformin may disrupt the embryogenesis of fish.•An anticipated hatching process was observed in all treatment groups.•Oxidative damage on embryos increased in a concentration dependent manner.•Inhibition of mitochondrial complex I may induce abnormalities on zebrafish embryos.•Metformin has a biphasic dose-response, with more severe effects at lower concentrations.
The previous Fuzzy Rule-Based Classification Systems (FRBCSs) for Big Data problems consist in concurrently learning multiple Chi et al. FRBCSs whose rule bases are then aggregated. The problem of ...this approach is that different models are obtained when varying the configuration of the cluster, becoming less accurate as more computing nodes are added. Our aim with this work is to design a new FRBCS for Big Data classification problems (CHI-BD) which is able to provide exactly the same model as the one that would be obtained by the original Chi et al. algorithm if it could be executed with this quantity of data. In order to do so, we take advantage of the suitability of the Chi et al. algorithm for the MapReduce paradigm, solving the problems of the previous approach, which lead us to obtain the same model (i.e., classification accuracy) regardless of the number of computing nodes considered.
Polish border guards can't stop a few dozen fast-wheeled APCs and IFVs. If such a force would cross through any road at the eastern border of Poland, it would pass almost intact. Afterward, it would ...be an easy target, but only before it could hide in a dense urban area. Is eastern Poland prepared for such a surprise attack of under a hundred vehicles that could arrive in many city centres within an hour after the border crossing? If a Russian force arrives at even a medium-sized city centre, they could occupy and hold such a position for weeks and use local civilians as human shields. They could force political negotiations holding also precious city infrastructure hostage. Lack of fast and decisive decision-making which is highly likely (because any bombardment will mean a loss of life of civilians) will only make any attempt to regain the city more difficult.
Metformin is one the most prescribed drug to treat type 2 diabetes. In wastewater treatment plants, this drug is bacterially transformed to guanylurea, which occurs at higher concentrations in the ...aquatic environments than its parent compound. Since there is a huge knowledge gap about the toxicity of this metabolite on aquatic organisms, we aimed to investigate the impact of guanylurea on the embryonic development and oxidative stress biomarkers of zebrafish (Danio rerio). For this effect, zebrafish embryos (4 h post fertilization) were exposed to 25, 50, 100, 200, 250, 25,000, 50,000, 75,000 μg/L guanylurea until 96 h post fertilization. Guanylurea led to a significant delay in the hatching process in all exposure groups. Furthermore, this transformation product affected the embryonic development of fish, inducing severe body alterations and consequently leading to their death. The most pronounced malformations were malformation of tail, scoliosis, pericardial edema, yolk deformation and craniofacial malformation. Concerning oxidative stress response, we demonstrated that guanylurea induced the antioxidant activity of superoxide dismutase, catalase, and glutathione peroxidase in zebrafish embryos. In addition, the levels of lipid peroxidation, protein carbonyl and hydroperoxide content were also increased in the embryos exposed to this transformation product. However, the integrated biomarker response (IBR) analysis carried out in this study demonstrated that oxidative damage biomarkers got more influence over the embryos than antioxidant enzymes. Thus, we can conclude that guanylurea induces oxidative stress in zebrafish embryos, and that this transformation product impair the normal development of this freshwater organism.
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•Guanylurea delayed the hatching process in all treatment groups.•Developmental abnormalities induced by guanylurea led to the death of zebrafish embryos.•Embryos exposed to guanylurea showed an increase in activity of antioxidant enzymes.•Oxidative damage biomarkers levels increased as guanylurea concentration increased.•Environmentally relevant concentrations of guanylurea pose a threat to aquatic species.
The detection of building footprints and road networks has many useful applications including the monitoring of urban development, real-time navigation, etc. Taking into account that a great deal of ...human attention is required by these remote sensing tasks, a lot of effort has been made to automate them. However, the vast majority of the approaches rely on very high-resolution satellite imagery (<2.5 m) whose costs are not yet affordable for maintaining up-to-date maps. Working with the limited spatial resolution provided by high-resolution satellite imagery such as Sentinel-1 and Sentinel-2 (10 m) makes it hard to detect buildings and roads, since these labels may coexist within the same pixel. This paper focuses on this problem and presents a novel methodology capable of detecting building and roads with sub-pixel width by increasing the resolution of the output masks. This methodology consists of fusing Sentinel-1 and Sentinel-2 data (at 10 m) together with OpenStreetMap to train deep learning models for building and road detection at 2.5 m. This becomes possible thanks to the usage of OpenStreetMap vector data, which can be rasterized to any desired resolution. Accordingly, a few simple yet effective modifications of the U-Net architecture are proposed to not only semantically segment the input image, but also to learn how to enhance the resolution of the output masks. As a result, generated mappings quadruplicate the input spatial resolution, closing the gap between satellite and aerial imagery for building and road detection. To properly evaluate the generalization capabilities of the proposed methodology, a data-set composed of 44 cities across the Spanish territory have been considered and divided into training and testing cities. Both quantitative and qualitative results show that high-resolution satellite imagery can be used for sub-pixel width building and road detection following the proper methodology.
System downtime and unplanned outages massively affect plant productivity; therefore, the reliability, availability, maintainability, and safety (RAMS) disciplines, together with fault diagnosis and ...condition monitoring (CM), are mandatory in energy applications. This article focuses on the optimization of a maintenance plan for the yaw system used in an onshore wind turbine (WT). A complete reliability-centered maintenance (RCM) procedure is applied to the system to identify which maintenance action is the optimal solution in terms of cost, safety, and availability. The scope of the research is to propose a new customized decision-making diagram inside the RCM assessment to reduce the subjectivity of the procedure proposed in the standard and save the cost by optimizing maintenance decisions, making the projects more cost-efficient and cost-effective. This article concludes by proposing a new diagnostic method based on a data-driven CM system to efficiently monitor the health and detect damages in the WT by means of measurements of critical parameters of the tested system. This article highlights how a reliability analysis, during the early phase of the design, is a very helpful and powerful means to guide the maintenance decision and the data-driven CM.
The Covid-19 pandemic that began in the city of Wuhan in China has caused a huge number of deaths worldwide. Countries have introduced spatial restrictions on movement and social distancing in ...response to the rapid rate of SARS-Cov-2 transmission among its populations. Research originality lies in the taken global perspective revealing indication of significant relationships between changes in mobility and the number of Covid-19 cases. The study uncovers a time offset between the two applied databases, Google Mobility and John Hopkins University, influencing correlations between mobility and pandemic development. Analyses reveals a link between the introduction of lockdown and the number of new Covid-19 cases. Types of mobility with the most significant impact on the development of the pandemic are “retail and recreation areas", "transit stations", "workplaces" "groceries and pharmacies”. The difference in the correlation between the lockdown introduced and the number of SARS-COV-2 cases is 81%, when using a 14-day weighted average compared to the 7-day average. Moreover, the study reveals a strong geographical diversity in human mobility and its impact on the number of new Covid-19 cases.
The concept of passive revolution is one of the most important in Gramscian
theory. It is an abstraction that explains historical processes of popular mobilisation that
end in the predominance of ...stability over change. This paper analyses the use of this
concept over three different historical periods and reflects on its usefulness in analysing
current political phenomena. Through an empirical exercise of analysis of 31 in-depth
interviews with leaders of associations that participated in the Chilean social revolt, the
conclusion is that the so-called social outburst of 18 October is heading towards a closure
in the form of a passive revolution. This, through a gradual institutionalisation that shows a
resolution “from above” and without popular protagonism. Also, social organisations’
critical participation confronting the fear of “winning nothing” after months of continuous
mobilisation and attrition
El concepto de revolución pasiva es uno de los más importantes en la teoría
gramscina. Se trata de una abstracción que explica procesos históricos de movilización
popular que terminan en el predominio de la estabilidad frente al cambio. Este trabajo
analiza el uso de este concepto a lo largo de tres periodos históricos diferentes y reflexiona
sobre su utilidad a la hora de analizar fenómenos políticos actuales. A través de un ejercicio
empírico de análisis de 31 entrevistas en profundidad a dirigentes de asociaciones que
participaron en la revuelta social chilena, se llega a la conclusión de que el llamado estallido
social del 18 de octubre se encamina a un cierre en forma de revolución pasiva. Esto, a
través de una institucionalización paulatina que evidencia una resolución “por arriba” y sin
protagonismo de las clases subalternas. De participación crítica de las organizaciones
sociales ante el miedo a “no ganar nada” tras meses de movilización continuada y desgaste.
Semantic segmentation of remote sensing images has many practical applications such as urban planning or disaster assessment. Deep learning-based approaches have shown their usefulness in ...automatically segmenting large remote sensing images, helping to automatize these tasks. However, deep learning models require large amounts of labeled data to generalize well to unseen scenarios. The generation of global-scale remote sensing datasets with high intraclass variability presents a major challenge. For this reason, data augmentation techniques have been widely applied to artificially increase the size of the datasets. Among them, photometric data augmentation techniques such as random brightness, contrast, saturation, and hue have been traditionally applied aiming at improving the generalization against color spectrum variations, but they can have a negative effect on the model due to their synthetic nature. To solve this issue, sensors with high revisit times such as Sentinel-1 and Sentinel-2 can be exploited to realistically augment the dataset. Accordingly, this paper sets out a novel realistic multi-temporal color data augmentation technique. The proposed methodology has been evaluated in the building and road semantic segmentation tasks, considering a dataset composed of 38 Spanish cities. As a result, the experimental study shows the usefulness of the proposed multi-temporal data augmentation technique, which can be further improved with traditional photometric transformations.
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