Search results for "DSI"

showing 10 items of 1457 documents

Enzyme-linked immunosorbent assay for detection of Aeromonas hydrophila serogroup O:19

2006

An enzyme-linked immunosorbent assay has been developed for the detection of Aeromonas hydrophila serogroup O:19 isolated from epizootics in eels. The enzyme-linked immunosorbent assay specificity was confirmed after testing A. hydrophila O:19 and non-O:19 strains from different origins, as well as other Aeromonas species and other fish pathogens such as Vibrio vulnificus biotype 2, V. furnisii, V. damsela, Yersinia ruckerii and Edwardsiella tarda. The detection limits for A. hydrophila O:19 cells were around 10(4)-10(5) cells/well. Artificially infected eels were analyzed and the immunodetection was confirmed by cultural methods. With this methodology A. hydrophila O:19 was successfully de…

Enzyme-Linked Immunosorbent AssayVibrio vulnificusYersiniaMicrobiologyMicrobiologyFish DiseasesBacterial ProteinsAntigenAgglutination TestsGeneticsAnimalsMolecular Biologychemistry.chemical_classificationEelsbiologyEdwardsiella tardabacterial infections and mycosesbiology.organism_classificationMolecular biologyAeromonas hydrophilaAeromonas speciesAeromonas hydrophilaEnzymechemistrybacteriaBacterial outer membraneFEMS Microbiology Letters
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"Table 5" of "In-medium modifications of the pi pi interaction in photon-induced reactions."

2005

Differential cross section for PI0PI+/- production with a Carbon target. Errors are statistical only.

GAMMA C --> PI0 PI-Single Differential Cross SectionGAMMA C --> PI0 PI+PhotoproductionDSIG/DMExclusiveGAMMA NUCLEUS --> PI- PI0GAMMA NUCLEUS --> PI+ PI0
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"Table 3" of "In-medium modifications of the pi pi interaction in photon-induced reactions."

2005

Differential cross section for PI0PI0 production with a Lead target. Errors are statistical only.

GAMMA NUCLEUS --> PI0 PI0Single Differential Cross SectionPhotoproductionDSIG/DMExclusiveGAMMA PB --> PI0 PI0
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"Table 2" of "In-medium modifications of the pi pi interaction in photon-induced reactions."

2005

Differential cross section for PI0PI0 production with a Carbon target. Errors are statistical only.

GAMMA NUCLEUS --> PI0 PI0Single Differential Cross SectionPhotoproductionDSIG/DMGAMMA C --> PI0 PI0Exclusive
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"Table 2" of "Measurement of the g p -> K^0 Sigma^+ reaction with the Crystal Ball/TAPS detectors at the Mainz Microtron"

2014

The differential cross section for photon energies 1275, 1325, 1375 and 1425 MeV.

GAMMA P --> K0 SIGMA+Single Differential Cross SectionPhotoproductionStrange productionNuclear TheoryPhysics::Accelerator PhysicsExclusiveNuclear ExperimentDSIG/DOMEGA
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"Table 1" of "Measurement of the g p -> K^0 Sigma^+ reaction with the Crystal Ball/TAPS detectors at the Mainz Microtron"

2014

The differential cross section for photon energies 1125, 1175 and 1225 MeV.

GAMMA P --> K0 SIGMA+Single Differential Cross SectionPhotoproductionStrange productionNuclear TheoryPhysics::Accelerator PhysicsExclusiveNuclear ExperimentDSIG/DOMEGA
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"Table 7" of "Exclusive rho0 and phi muoproduction at large q**2"

1995

No description provided.

GAMMA* NUCLEON --> PHI NUCLEONSingle Differential Cross SectionPhotoproductionMUON NUCLEUS --> MUON PHI NUCLEUSExclusiveDSIG/DPT**2Transverse Momentum DependenceMuon production
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NUMERICAL ALGORITHMS

2013

For many systems of differential equations modeling problems in science and engineering, there are natural splittings of the right hand side into two parts, one non-stiff or mildly stiff, and the other one stiff. For such systems implicit-explicit (IMEX) integration combines an explicit scheme for the non-stiff part with an implicit scheme for the stiff part. In a recent series of papers two of the authors (Sandu and Zhang) have developed IMEX GLMs, a family of implicit-explicit schemes based on general linear methods. It has been shown that, due to their high stage order, IMEX GLMs require no additional coupling order conditions, and are not marred by order reduction. This work develops a …

General linear methodsMathematical optimizationIMEX methods; general linear methods; error analysis; order conditions; stability analysisIMEX methodsDifferential equationSCHEMESorder conditionsMathematics AppliedExtrapolationStability (learning theory)QUADRATIC STABILITYstability analysisPARABOLIC EQUATIONSSYSTEMSNORDSIECK METHODSFOS: MathematicsApplied mathematicsMathematics - Numerical AnalysisRUNGE-KUTTA METHODSMULTISTEP METHODSerror analysisMathematicsCONSTRUCTIONSeries (mathematics)Applied MathematicsNumerical analysisComputer Science - Numerical AnalysisStability analysisORDEROrder conditionsNumerical Analysis (math.NA)Computer Science::Numerical AnalysisRunge–Kutta methodsGeneral linear methodsError analysisORDINARY DIFFERENTIAL-EQUATIONSOrdinary differential equationgeneral linear methodsMathematics
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Molecular biomarkers in glaucoma

2013

The seventh annual ARVO/Pfizer Ophthalmic Research Institute conference was held Friday and Saturday, April 29 and 30, 2011, at the Fort Lauderdale Hyatt Regency Pier 66, Fort Lauderdale, Florida. The conference, funded by The ARVO Foundation for Eye Research through a grant from Pfizer Ophthalmics, provided an opportunity to gather experts from within and outside ophthalmology to determine the state of knowledge pertaining to molecular biomarkers associated with glaucoma, as well as the methods to identify and validate them to predict (a) those who would be susceptible to development of glaucoma; (b) markers that will enable prediction of glaucoma progression; and (c) markers that will pre…

Genetic MarkersProteomicsmedicine.medical_specialtygenetic structuresGlaucomaHuman healthCellular and Molecular NeuroscienceOphthalmologyMedicineHumansBiomarkers; Genetic Markers; Genomics; Glaucoma; Humans; Metabolome; Proteomics; Ophthalmology; Sensory Systems; Cellular and Molecular Neuroscience; Medicine (all)Biomarker discoveryBlindnessbusiness.industryMedicine (all)GlaucomaArticlesGenomicsmedicine.diseaseMolecular biomarkerseye diseasesBench to bedsideSensory SystemsOphthalmologyMetabolomeOptometrysense organsbusinessBiomarkers
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CloudSim: A fair benchmark for comparison of methods for times series reconstruction from cloud and atmospheric contamination

2015

Cloud contamination of optical data is a constant and annoying feature of time series analyses, whether while using vegetation indices or surface temperatures, since it tends to decrease artificially the values taken by these parameters. Therefore, any time series analysis of optical data needs a previous step for gap-filling reconstruction of the time series. Numerous techniques have been presented in the literature to carry out this preliminary and mandatory step. However, the evaluation and comparison of these techniques is difficult, since no “truth” time series is available. We present here a probabilistic model (CloudSim) to provide global typical annual time series for NDVI (Normaliz…

GeographyMeteorologySeries (mathematics)business.industryBenchmark (surveying)CloudSimCloud computingStatistical modelTime seriesbusinessNormalized Difference Vegetation IndexStandard deviationRemote sensing2015 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp)
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