Search results for "CAS"

showing 10 items of 26112 documents

Analysis of salivary detection of P16INK4A and RASSF1A promoter gene methylation and its association with oral squamous cell carcinoma in a Colombian…

2019

Background Epigenetic factors play a fundamental role in the etiopathogenesis of oral squamous cell carcinoma (OSCC). This study evaluated if salivary detection of P16INK4A/RASSF1A gene promoter methylation might be linked to the clinical/histological features of OSCC in a Colombian population. Material and Methods Methylation-specific polymerase chain reaction (MSP-PCR) was used to detect the methylation frequency of P16INK4A/RASSF1A genes in DNA obtained from whole saliva collected of 40 healthy controls (HC) and 43 OSCC patients. Determination of the clinical performance of MSP-PCR assay was based on standard algorithms derived from two-way contingency table analysis. The association of …

010407 polymersSalivaPopulationBiology01 natural scienceslaw.inventionlaw0502 economics and businessEpigeneticseducationneoplasmsGeneral DentistryGenePolymerase chain reactioneducation.field_of_studyOral Medicine and PathologyResearch05 social sciencesPromoterMethylation:CIENCIAS MÉDICAS [UNESCO]0104 chemical sciencesstomatognathic diseasesUNESCO::CIENCIAS MÉDICASDNA methylationCancer research050211 marketingJournal of Clinical and Experimental Dentistry
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2019

Abstract. This study uses the synergy of multi-resolution soil moisture (SM) satellite estimates from the Soil Moisture Ocean Salinity (SMOS) mission, a dense network of ground-based SM measurements, and a soil–vegetation–atmosphere transfer (SVAT) model, SURFEX (externalized surface), module ISBA (interactions between soil, biosphere and atmosphere), to examine the benefits of the SMOS level 4 (SMOS-L4) version 3.0, or “all weather” high-resolution soil moisture disaggregated product (SMOS-L43.0; ∼1 km). The added value compared to SMOS level 3 (SMOS-L3; ∼25 km) and SMOS level 2 (SMOS-L2; ∼15 km) is investigated. In situ SM observations over the Valencia anchor station (VAS; SMOS calibrati…

010504 meteorology & atmospheric sciences0208 environmental biotechnologyFlood forecastingInitializationBiosphere02 engineering and technologyVegetation01 natural sciences020801 environmental engineeringClimatologySpatial ecologyEnvironmental scienceSatelliteSpatial variabilityWater content0105 earth and related environmental sciencesHydrology and Earth System Sciences
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SMOS Level-2 Soil Moisture Product Evaluation in Rain-Fed Croplands of the Pampean Region of Argentina

2016

A field campaign was carried out to evaluate the Soil Moisture (SM) MIR-SMUDP2 product (v5.51) generated from the data of the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) aboard the Soil Moisture and Ocean Salinity (SMOS) mission. The study area was the Pampean Region of Argentina, which was selected because it is a vast area of flatlands containing quite homogeneous rain-fed croplands, which are considered SMOS nominal land uses and hardly affected by radio-frequency interference contamination. Transects of ground handheld SM measurements were performed using ThetaProbe ML2x probes within four Icosahedral Snyder Equal Area Earth (ISEA) grid nodes, where permanent SM statio…

010504 meteorology & atmospheric sciences0211 other engineering and technologiesSoil science02 engineering and technologyAtmospheric sciences01 natural sciencesStandard deviationCiencias de la Tierra y relacionadas con el Medio AmbienteSOIL MOISTURE (SM)Electrical and Electronic EngineeringPRODUCT EVALUATIONWater contentField campaign021101 geological & geomatics engineering0105 earth and related environmental sciencesPhysicsRadiometerSOIL MOISTURE AND OCEAN SALINITY (SMOS)GROUND MEASUREMENTSNegative biasHomogeneousProduct (mathematics)Random errorGeneral Earth and Planetary SciencesMeteorología y Ciencias AtmosféricasCIENCIAS NATURALES Y EXACTASIEEE Transactions on Geoscience and Remote Sensing
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Potential impacts of a future Nordic bioeconomy on surface water quality

2020

AbstractNordic water bodies face multiple stressors due to human activities, generating diffuse loading and climate change. The ‘green shift’ towards a bio-based economy poses new demands and increased pressure on the environment. Bioeconomy-related pressures consist primarily of more intensive land management to maximise production of biomass. These activities can add considerable nutrient and sediment loads to receiving waters, posing a threat to ecosystem services and good ecological status of surface waters. The potential threats of climate change and the ‘green shift’ highlight the need for improved understanding of catchment-scale water and element fluxes. Here, we assess possible bio…

010504 meteorology & atmospheric sciencesClimate ChangeVDP::Landbruks- og Fiskerifag: 900::Landbruksfag: 910Geography Planning and DevelopmentLand managementClimate changemaankäyttö010501 environmental sciences01 natural sciencesEnvironmental Effects of a Green Bio-EconomyEcosystem servicesVDP::Matematikk og Naturvitenskap: 400::Zoologiske og botaniske fag: 480::Økologi: 488Environmental ChemistryProduction (economics)Humans14. Life underwaterVDP::Landbruks- og Fiskerifag: 900::Fiskerifag: 920Ecosystem0105 earth and related environmental sciences2. Zero hungerBiomass (ecology)EcologyLand usebusiness.industryEnvironmental resource managementSurface waterGeneral Medicine15. Life on landModels TheoreticalvedenlaatuBioeconomy6. Clean waterWater qualitypintavesi13. Climate actionLand useEnvironmental scienceWater qualitybusinessbiotalousSurface waterForecasting
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Statistical retrieval of atmospheric profiles with deep convolutional neural networks

2019

Abstract Infrared atmospheric sounders, such as IASI, provide an unprecedented source of information for atmosphere monitoring and weather forecasting. Sensors provide rich spectral information that allows retrieval of temperature and moisture profiles. From a statistical point of view, the challenge is immense: on the one hand, “underdetermination” is common place as regression needs to work on high dimensional input and output spaces; on the other hand, redundancy is present in all dimensions (spatial, spectral and temporal). On top of this, several noise sources are encountered in the data. In this paper, we present for the first time the use of convolutional neural networks for the retr…

010504 meteorology & atmospheric sciencesComputer science0211 other engineering and technologiesWeather forecasting02 engineering and technologycomputer.software_genreAtmospheric measurements01 natural sciencesConvolutional neural networkLinear regressionRedundancy (engineering)Information retrievalInfrared measurementsComputers in Earth SciencesEngineering (miscellaneous)021101 geological & geomatics engineering0105 earth and related environmental sciencesArtificial neural networkbusiness.industryDeep learningDimensionality reductionPattern recognitionAtomic and Molecular Physics and OpticsComputer Science Applications13. Climate actionNoise (video)Artificial intelligencebusinesscomputerNeural networksISPRS Journal of Photogrammetry and Remote Sensing
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Applications of a new set of methane line parameters to the modeling of Titan's spectrum in the 1.58 μm window

2012

International audience; In this paper we apply a recently released set of methane line parameters (Wang et al., 2011) to the modeling of Titan spectra in the 1.58 mu m window at both low and high spectral resolution. We first compare the methane absorption based on this new set of methane data to that calculated from the methane absorption coefficients derived in situ from DISR/Huygens (Tomasko et al., 2008a; Karkoschka and Tomasko, 2010) and from the band models of Irwin et al. (2006) and Karkoschka and Tomasko (2010). The Irwin et al. (2006) band model clearly underestimates the absorption in the window at temperature-pressure conditions representative of Titan's troposphere, while the Ka…

010504 meteorology & atmospheric sciencesInfraredCASSINI VIMSHUYGENS PROBEMONODEUTERATED METHANEAtmospheric sciences01 natural sciences7. Clean energyMethaneSpectral lineTropospherechemistry.chemical_compoundsymbols.namesake0103 physical sciencesSpectral resolutionSpectroscopy010303 astronomy & astrophysicsCLOUD STRUCTURE0105 earth and related environmental sciencesPhysics[PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph][PHYS.PHYS.PHYS-OPTICS]Physics [physics]/Physics [physics]/Optics [physics.optics][ PHYS.PHYS.PHYS-OPTICS ] Physics [physics]/Physics [physics]/Optics [physics.optics]Astronomy and Astrophysics9500 CM(-1)SPECTROSCOPIC DATABASEM TRANSPARENCY WINDOWComputational physicsAerosolchemistry[ PHYS.PHYS.PHYS-AO-PH ] Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph]TEMPERATURE-DEPENDENCE13. Climate actionSpace and Planetary SciencesymbolsSHIFT COEFFICIENTSOUTER SOLAR-SYSTEMTitan (rocket family)
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The UKC3 regional coupled environmental prediction system

2019

Abstract. This paper describes an updated configuration of the regional coupled research system, termed UKC3, developed and evaluated under the UK Environmental Prediction collaboration. This represents a further step towards a vision of simulating the numerous interactions and feedbacks between different physical and biogeochemical components of the environment across sky, sea and land using more integrated regional coupled prediction systems at km-scale resolution. The UKC3 coupled system incorporates models of the atmosphere (Met Office Unified Model), land surface with river routing (JULES), shelf-sea ocean (NEMO) and ocean surface waves (WAVEWATCH III), coupled together using OASIS3-MC…

010504 meteorology & atmospheric sciencesMeteorology010505 oceanographylcsh:QE1-996.5Forecast skillContext (language use)Unified Model01 natural sciencesWind speedAtmospherelcsh:GeologyCoupling (physics)Meteorology and ClimatologySurface waveRange (statistics)Environmental sciencePhysics::Atmospheric and Oceanic Physics0105 earth and related environmental sciences
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The Making of the New European Wind Atlas - Part 2: production and evaluation

2020

This is the second of two papers that document the creation of the New European Wind Atlas (NEWA). In Part 1, we described the sensitivity experiments and accompanying evaluation done to arrive at the final mesoscale model setup used to produce the mesoscale wind atlas. In this paper, Part 2, we document how we made the final wind atlas product, covering both the production of the mesoscale climatology generated with the Weather Research and Forecasting (WRF) model and the microscale climatology generated with the Wind Atlas Analysis and Applications Program (WAsP). The paper includes a detailed description of the technical and practical aspects that went into running the mesoscale simulati…

010504 meteorology & atmospheric sciencesMeteorology020209 energyMesoscale meteorologyTerrainParameterization02 engineering and technology01 natural sciencesWind speedWind speed0202 electrical engineering electronic engineering information engineeringWind atlasData flow modelSurface wind0105 earth and related environmental sciences:Enginyeria agroalimentària::Ciències de la terra i de la vida::Climatologia i meteorologia [Àrees temàtiques de la UPC]lcsh:QE1-996.5Física atmosféricalcsh:GeologyWeather Research and Forecasting ModelEnvironmental scienceNew European Wind AtlasSimulacio per ordinadorComputational methods in engineeringDownscalingModel
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The making of the New European Wind Atlas - Part 1: Model sensitivity

2020

This is the first of two papers that document the creation of the New European Wind Atlas (NEWA). It describes the sensitivity analysis and evaluation procedures that formed the basis for choosing the final setup of the mesoscale model simulations of the wind atlas. The suitable combination of model setup and parameterizations, bound by practical constraints, was found for simulating the climatology of the wind field at turbine-relevant heights with the Weather Research and Forecasting (WRF) model. Initial WRF model sensitivity experiments compared the wind climate generated by using two commonly used planetary boundary layer schemes and were carried out over several regions in Europe. They…

010504 meteorology & atmospheric sciencesMeteorologyPlanetary boundary layer010505 oceanography020209 energylcsh:QE1-996.5Mesoscale meteorologyFísica atmosférica02 engineering and technology01 natural sciences7. Clean energyWind speedlcsh:GeologyBoundary layerRoughness length/dk/atira/pure/sustainabledevelopmentgoals/climate_action13. Climate actionWeather Research and Forecasting ModelWind resource assessmentWind atlasSDG 13 - Climate Action0202 electrical engineering electronic engineering information engineeringEnvironmental science0105 earth and related environmental sciences
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The Synergistic Impacts of Anthropogenic Stressors and COVID-19 on Aquaculture: A Current Global Perspective

2021

13 pages, 6 figures, 2 tables.-- This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License

010504 meteorology & atmospheric sciencesNatural resource economicsSocio-ecological systemsvulnerabilityVulnerabilitySARS (Disease)01 natural sciencesFood security -- Case studiesStakeholder perceptionsCOVID-19 (Disease)Aquaculturefood insecurityStakeholderPerceptionsClimate changeZoologíastakeholders perceptions2. Zero hunger04 agricultural and veterinary sciencesSARS-COV2-pandemicmultiple stressorsFood insecurityclimate change2019-20 coronavirus outbreakCoronavirus disease 2019 (COVID-19)VulnerabilityClimate changesocio-ecological systemManagement Monitoring Policy and LawAquatic Science14. Life underwaterSARS-CoV-2 pandemic ; supply chain ; food insecurity ; climate change ; multiple stressors ; vulnerability ; stakeholder perceptions ; socioecological systemsMultiple stressorssupply chainEcology Evolution Behavior and Systematics0105 earth and related environmental sciencesFood insecuritybusiness.industryPerspective (graphical)Stressorclimate change food insecurity multiple stressors SARS-CoV-2 pandemic socio-ecological systems stakeholder perceptions supply chain vulnerabilitySocioecological systemsVulnerability model of recoveryClimatic changesSupply chain13. Climate action040102 fisheriesBusiness logistics -- Case studies0401 agriculture forestry and fisheriesEnvironmental scienceSARS-CoV-2 pandemicbusiness
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