Search results for "Signal"

showing 10 items of 6924 documents

Relationships between Staphylococcus aureus genetic background, virulence factors, agr groups (alleles), and human disease

2002

ABSTRACT The expression of most Staphylococcus aureus virulence factors is controlled by the agr locus, which encodes a two-component signaling pathway whose activating ligand is an agr -encoded autoinducing peptide (AIP). A polymorphism in the amino acid sequence of the AIP and of its corresponding receptor divides S. aureus strains into four major groups. Within a given group, each strain produces a peptide that can activate the agr response in the other member strains, whereas the AIPs belonging to different groups are usually mutually inhibitory. We investigated a possible relationship between agr groups and human S. aureus disease by studying 198 S. aureus strains isolated from 14 asym…

[SDE] Environmental SciencesStaphylococcus aureus[SDV.OT]Life Sciences [q-bio]/Other [q-bio.OT][SDV]Life Sciences [q-bio]Bacterial ToxinsImmunologyVirulenceLocus (genetics)Biologymedicine.disease_causeMicrobiologylaw.inventionMicrobiology03 medical and health sciencesBacterial ProteinslawPhylogeneticsmedicineHumansAllelePeptide sequenceComputingMilieux_MISCELLANEOUSAllelesPhylogenyPolymerase chain reaction030304 developmental biologyGenetics0303 health sciencesVirulence030306 microbiologyBacterial InfectionsStaphylococcal Infectionsbiochemical phenomena metabolism and nutritionbacterial infections and mycoses[SDV] Life Sciences [q-bio]Infectious DiseasesPOUVOIR PATHOGENEStaphylococcus aureus[SDE]Environmental SciencesTrans-ActivatorsbacteriaFemaleParasitologyAmplified fragment length polymorphismSignal Transduction
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Capteurs et images aériennes pour l’évaluation du peuplement de mauvaises herbes

2013

AIRINOV is specialized in use of UAV for precision agriculture. Thanks to a high spatial resolution up to 1.5 cm/pixel in RGB images, discrimination between vegetation (crop row, weed) and soil can be done. Variability can be detected in weed density inside the whole field. The detection of weeds in the inter-row of hoed row crops was tested on RGB images. The methodology developed is based on Hough transform, and is composed of three main steps: image segmentation, soil/vegetation discrimination and crop rows localization. First results are promising but need complementary measures for validation.

[SDE] Environmental Sciences[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingTransformée de Hough[SDV]Life Sciences [q-bio][ SDV.SA.STA ] Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculturedrone[SDV] Life Sciences [q-bio]images RGB THR[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[SDV.SA.STA]Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture[SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal Biologyadventices
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Mesure de netteté basée sur les descripteurs généralisés de Fourier appliquée à la reconstruction 3D par Shape from Focus

2013

National audience; L'étape principale de la méthode de reconstruction 3D " Shape from Focus " est l'utilisation d'un opérateur de mesure de netteté de chaque pixel de la séquence d'image. Le choix de l'opérateur de mesure de netteté est une étape cruciale pour une reconstruction 3D de qualité. La précision de la mesure de netteté dépend de la taille du voisinage autour du pixel choisi et de la présence ou non de bruit additif dans la séquence d'images. Dans cet article, nous présentons deux nouveaux opérateurs de mesure de netteté basés sur les Descripteurs Généralisés de Fourier. Une nouvelle étude comparative des différents opérateurs est présentée. Cette comparaison est basée sur un plan…

[SDE] Environmental Sciences[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[SDV]Life Sciences [q-bio][ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing??[SDV] Life Sciences [q-bio]Mesure de netteté[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingDescripteurs généralisés de Fourier[SDE]Environmental SciencesShape from Focus[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal Biology[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
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3D ACQUISITION SYSTEM APPLIED TO AGRONOMIC SCENES

2012

International audience; To improve results in automatic wheat ear counting by proxy-detection for early yield prediction, we need depth information of the scene. In this paper, we describe our 3D acquisition system dedicated to reconstruction of agronomic scenes. This system is composed of a camera mounted on a linear displacement driven by a microcontroller. The linear displacement allows acquiring a set of images in different distances to the scene. This image stack is used to apply shape from focus technique which is a passive and monocular 3D reconstruction method. This technique consists in the application of a focus measure for every pixel in the stack. An approximation method is used…

[SDE] Environmental Sciences[SDV.SA.AGRO] Life Sciences [q-bio]/Agricultural sciences/Agronomy[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[SDV]Life Sciences [q-bio][SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/AgronomyComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONagronomic scenes[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingcrop analysis[SDV] Life Sciences [q-bio][INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[ SDV.SA.AGRO ] Life Sciences [q-bio]/Agricultural sciences/Agronomyacquisition system[SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal Biology3D reconstruction[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingComputingMilieux_MISCELLANEOUS[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingComputingMethodologies_COMPUTERGRAPHICS
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Spray droplet characteristics measured using high speed imaging techniques

2016

Presented at Conference International Advances in Pesticide Application (IAPA), Barcelone, ESP (2016-01-13 - 2016-01-15).; International audience; Spray droplet characteristics are important features of an agricultural spray. The objective of this study is to measure the droplet size for different types of hydraulic spray nozzles using a developed backlighted image acquisition system and image processing technique. An in-focus droplet criterion was established to decide whether a droplet is in focus and can be measured in an accurate way. Tests included five different nozzles (Albuz ATR orange and red, TeeJet XR 110 01, XR 110 04 and Al 110 04).

[SDE] Environmental Sciences[SDV.SA]Life Sciences [q-bio]/Agricultural sciences[SDV.SA] Life Sciences [q-bio]/Agricultural sciences[SDV]Life Sciences [q-bio]droplet generatorspray characterisationdroplet size[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[SDV] Life Sciences [q-bio][SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal Biologyhigh speed imaging[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
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The Medicago truncatula hypermycorrhizal B9 mutant displays an altered response to phosphate and is more susceptible to Aphanomyces euteiches.

2014

SPE IPM; National audience; Inorganic phosphate (Pi) plays a key role in the development of arbuscular mycorrhizal (AM) symbiosis, which is favoured when Pi is limiting in the environment. We have characterized the Medicago truncatula hypermycorrhizal B9 mutant for its response to limiting (P/10) and replete (P2) Pi. On P2, mycorrhization was significantly higher in B9 plants than in wild-type (WT). The B9 mutant displayed hallmarks of Pi-limited plants, including higher levels of anthocyanins and lower concentrations of Pi in shoots than WT plants. Transcriptome analyses of roots of WT and B9 plants cultivated on P2 or on P/10 confirmed the Pi-limited profile of the mutant on P2 and highli…

[SDE] Environmental Sciencesarbuscular mycorrhiza[SDV]Life Sciences [q-bio]fungifood and beveragessymbiosis[SDV] Life Sciences [q-bio]Aphanomyces euteichesnutrientsMedicago truncatula[SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal Biologysignallingtranscriptomephosphate
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Estimation de l’indice foliaire et de la biomasse du blé et des adventices par imagerie visible et machine learning : vers un nouvel indicateur non d…

2019

National audience; Cette étude propose d’estimer précocement par imagerie deux variables clés dans la gestion des cultures et dans la compétition culture-adventices : l’indice foliaire (LAI) et la biomasse aérienne sèche (BM). Une expérimentation a été conduite au champ pendant la phase végétative d’une culture de blé. Pour chaque peuplement (culture de blé, adventices), les taux de couverture du sol par la végétation (TCc, TCw) ont été déduits du traitement d’image basé sur une technique de machine learning. LAI et BM ont été mesurés de façon destructive. Puis, une calibration a été réalisée entre TC et LAI d’une part et entre TC et BM d’autre part. Ce travail pourrait, à terme, faciliter …

[SDE] Environmental Sciencesnuisibilitéharmfulnessbiomass[SDV]Life Sciences [q-bio][SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/AgronomyAdventiceImagerie visibleimage visible[SDV] Life Sciences [q-bio]Indice foliairemachine learning[SDE]Environmental Sciencesbiomasse[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal Biologyadventicesvisible image[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingweed
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Comment stimuler l’immunité de la vigne avec des éliciteurs

2013

[SDE] Environmental Sciencesphytoalexinsplant innate immunity[SDV]Life Sciences [q-bio]Microbe Associated Molecular Patterns (MAMP)defense signalinginduced resistance[SDV] Life Sciences [q-bio]Pattern Recognition Receptors (PRR)elicitorsVitis vinifera[SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal Biology
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Observation of the suppression of the flux of cosmic rays above 4x10^19eV

2008

The energy spectrum of cosmic rays above 2.5 × 10¹⁸ eV, derived from 20,000 events recorded at the Pierre Auger Observatory, is described. The spectral index γ of the particle flux, J ∝ E-γ, at energies between 4 × 10¹⁸ eV and 4 × 10¹⁹ eV is 2.69 ± 0.02(stat) ± 0.06(syst), steepening to 4.2 ± 0.4(stat) ± 0.06(syst) at higher energies. The hypothesis of a single power law is rejected with a significance greater than 6 standard deviations. The data are consistent with the prediction by Greisen and by Zatsepin and Kuz'min.

[SDU.ASTR.CO]Sciences of the Universe [physics]/Astrophysics [astro-ph]/Cosmology and Extra-Galactic Astrophysics [astro-ph.CO]Astrophysics::High Energy Astrophysical Phenomenaenergy spectrumFOS: Physical sciencesGeneral Physics and AstronomyFluxOsservatorio Pierre Augerspectral indexCosmic rayparticle fluxAstrophysicsAstrophysics::Cosmology and Extragalactic AstrophysicsEXTENSIVE AIR-SHOWERSAstrophysicsUPPER LIMIT01 natural sciencesPower lawAugerNuclear physicsENERGY[PHYS.ASTR.CO]Physics [physics]/Astrophysics [astro-ph]/Cosmology and Extra-Galactic Astrophysics [astro-ph.CO]Raggi cosmicicosmic rays0103 physical sciencesddc:550Particle flux010303 astronomy & astrophysicsCiencias ExactasPhysicsPierre Auger ObservatorySpectral indexSPECTRUM[SDU.ASTR]Sciences of the Universe [physics]/Astrophysics [astro-ph]010308 nuclear & particles physicsAstrophysics (astro-ph)Settore FIS/01 - Fisica SperimentaleAstrophysics::Instrumentation and Methods for AstrophysicsFísicaEnergia ultra altaARRAYHigh Energy Physics::ExperimentSciami atmosferici estesiEnergy (signal processing)
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Biotransformation of odorants modifies the olfactory signal

2009

International audience

[SDV.AEN] Life Sciences [q-bio]/Food and Nutrition[SDV.AEN]Life Sciences [q-bio]/Food and NutritionComputingMilieux_MISCELLANEOUSolfactory signal
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