Search results for "C63"

showing 10 items of 58 documents

Anticoagulation in splanchnic and cerebral vein thrombosis: Still groping in the dark.

2020

Cerebral veinsmedicine.medical_specialtybusiness.industrymedicine.drug_classSplanchnic Circulation10031 Clinic for Angiology2720 HematologyAnticoagulantanticoagulantvenous thromboembolism610 Medicine & healthHematologyCerebral vein thrombosisbleedingcerebral veinsplanchnic circulationsInternal medicineCardiologyCommentaryMedicineDiseases of the blood and blood-forming organsRC633-647.5businessSplanchnicVenous thromboembolismResearch and practice in thrombosis and haemostasis
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The Asynchronous Leontief Model

1992

International audience; The traditional dynamic Leontief model is synchronous: every vertex acts simultaneously. A model with delays of action has been proposed, but it still remains synchronous. In this paper we propose an asynchronous version of the model that allows realistic computations. We fiurnish an algorithm and a program.

Discrete mathematicsLeontief modelVertex (graph theory)JEL : C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C67 - Input–Output ModelsEconomics and EconometricsJEL: C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C67 - Input–Output ModelsComputer scienceComputationJEL: D - Microeconomics/D.D5 - General Equilibrium and Disequilibrium/D.D5.D57 - Input–Output Tables and Analysis[SHS.ECO]Humanities and Social Sciences/Economics and FinanceAction (physics)JEL: C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C63 - Computational Techniques • Simulation ModelingJEL : C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C63 - Computational Techniques • Simulation ModelingAsynchronous communicationJEL : D - Microeconomics/D.D5 - General Equilibrium and Disequilibrium/D.D5.D57 - Input–Output Tables and Analysis[ SHS.ECO ] Humanities and Social Sciences/Economies and finances[SHS.ECO] Humanities and Social Sciences/Economics and Finance
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Role of Human Sec63 in Modulating the Steady-State Levels of Multi-Spanning Membrane Proteins

2012

The Sec61 translocon of the endoplasmic reticulum (ER) membrane forms an aqueous pore, allowing polypeptides to be transferred across or integrated into membranes. Protein translocation into the ER can occur co- and posttranslationally. In yeast, posttranslational translocation involves the heptameric translocase complex including its Sec62p and Sec63p subunits. The mammalian ER membrane contains orthologs of yeast Sec62p and Sec63p, but their function is poorly understood. Here, we analyzed the effects of excess and deficit Sec63 on various ER cargoes using human cell culture systems. The overexpression of Sec63 reduces the steady-state levels of viral and cellular multi-spanning membrane …

Gastroenterology and hepatologylcsh:MedicineProtein SynthesisEndoplasmic ReticulumBiochemistryHepatitisViral Envelope ProteinsMolecular Cell BiologyTranslocaseRNA Small Interferinglcsh:ScienceIntegral membrane proteinEndoplasmic Reticulum Chaperone BiPHeat-Shock ProteinsMultidisciplinarybiologyMembrane transport proteinReverse Transcriptase Polymerase Chain ReactionRNA-Binding ProteinsHepatitis BCellular StructuresCell biologyInfectious hepatitisCytochemistryMedicineInfectious diseasesResearch ArticleBlotting WesternViral diseasesReal-Time Polymerase Chain ReactionTransfectionCell LineSEC63Bacterial ProteinsHumansBiologyLiver diseasesDNA PrimersEndoplasmic reticulumlcsh:RCell MembraneMembrane ProteinsMembrane Transport ProteinsProteinsSEC61 TransloconChaperone ProteinsTransmembrane ProteinsLuminescent ProteinsMembrane proteinGene Expression RegulationMicroscopy FluorescenceSubcellular OrganellesChaperone (protein)Mutationbiology.proteinlcsh:QMolecular ChaperonesPLoS ONE
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Analyzing structural change : the biproportional mean filter and the biproportional bimarkovian filter

1998

The biproportional filter was created to analyze structural change between two input-output matrices by removing the effect of differential growth of sectors without predetermining if the model is demand or supply-driven, but with the disadvantage that projecting a first matrix on a second is not the same thing than projecting the second matrix on the first. Here two alternative methods are proposed which has not this last drawback, with the additional advantage for the biproportional bimarkovian filter that effects of sector size are also removed. Methods are compared with an application for France for 1980 and 1996.

Gestionjel:C63Economicséconomieeconomic theoryjel:C67[ SHS.ECO ] Humanities and Social Sciences/Economies and finances[SHS.ECO] Humanities and Social Sciences/Economics and Finance[SHS.ECO]Humanities and Social Sciences/Economics and FinanceManagement economicsjel:D57management
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Dynamique de la structure industrielle française

1990

JEL : C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C67 - Input–Output ModelsJEL: C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C63 - Computational Techniques • Simulation ModelingJEL: C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C67 - Input–Output ModelsJEL: C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C61 - Optimization Techniques • Programming Models • Dynamic AnalysisJEL : C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C63 - Computational Techniques • Simulation ModelingJEL: L - Industrial Organization/L.L1 - Market Structure Firm Strategy and Market Performance/L.L1.L16 - Industrial Organization and Macroeconomics: Industrial Structure and Structural Change • Industrial Price IndicesJEL : D - Microeconomics/D.D5 - General Equilibrium and Disequilibrium/D.D5.D57 - Input–Output Tables and AnalysisJEL : C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C61 - Optimization Techniques • Programming Models • Dynamic Analysis[ SHS.ECO ] Humanities and Social Sciences/Economies and financesJEL: D - Microeconomics/D.D5 - General Equilibrium and Disequilibrium/D.D5.D57 - Input–Output Tables and AnalysisJEL : L - Industrial Organization/L.L1 - Market Structure Firm Strategy and Market Performance/L.L1.L16 - Industrial Organization and Macroeconomics: Industrial Structure and Structural Change • Industrial Price Indices[SHS.ECO]Humanities and Social Sciences/Economics and Finance[SHS.ECO] Humanities and Social Sciences/Economics and Finance
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A Note on added information in the RAS Procedure: reexamination of some evidence

2006

International audience; An example in Miernyk (1977) presented a rather counterintuitive result, namely that introducing accurate exogenous information into an RAS matrix estimating procedure could lead to an estimate that was worse than one generated by RAS using no exogenous information at all. This became an oft-cited black mark against RAS. Miller and Blair (1985) included a different (and small) illustration of the same possibility. It was recently pointed out by one of us that the Miller/Blair numerical results are wrong. For that reason, we decided to reexamine all the empirical evidence we could find on the subject. While figures in both Miernyk and Miller/Blair appear to be wrong, …

JEL : C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C67 - Input–Output ModelsJEL: C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C67 - Input–Output ModelsCounterintuitiveClosenessJEL: D - Microeconomics/D.D5 - General Equilibrium and Disequilibrium/D.D5.D57 - Input–Output Tables and AnalysisEnvironmental Science (miscellaneous)Development[SHS.ECO]Humanities and Social Sciences/Economics and FinanceJEL: C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C63 - Computational Techniques • Simulation ModelingJEL : C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C63 - Computational Techniques • Simulation ModelingInput-outputbiproportionEconometricsJEL : D - Microeconomics/D.D5 - General Equilibrium and Disequilibrium/D.D5.D57 - Input–Output Tables and Analysis[ SHS.ECO ] Humanities and Social Sciences/Economies and finances[SHS.ECO] Humanities and Social Sciences/Economics and FinanceEmpirical evidenceMathematical economicsCounterexampleMathematicsRAS
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Normalizing biproportional methods

2002

International audience; Biproportional methods are used to update matrices: the projection of a matrix Z to give it the column and row sums of another matrix is R Z S, where R and S are diagonal and secure the constraints of the problem (R and S have no signification at all because they are not identified). However, normalizing R or S generates important mathematical difficulties: it amounts to put constraints on Lagrange multipliers, non negativity (and so the existence of the solution) is not guaranteed at equilibrium or along the path to equilibrium.

JEL: C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C67 - Input–Output Modelsjel:C63Diagonaljel:C67JEL: D - Microeconomics/D.D5 - General Equilibrium and Disequilibrium/D.D5.D57 - Input–Output Tables and Analysismathematical economicsColumn (database)Projection (linear algebra)Combinatoricssymbols.namesakeMatrix (mathematics)JEL: C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C63 - Computational Techniques • Simulation ModelingmatricesJEL : D - Microeconomics/D.D5 - General Equilibrium and Disequilibrium/D.D5.D57 - Input–Output Tables and Analysis[ SHS.ECO ] Humanities and Social Sciences/Economies and financesNon negativity[SHS.ECO] Humanities and Social Sciences/Economics and FinanceGeneral Environmental ScienceMathematicsJEL : C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C67 - Input–Output ModelsGeneral Social Sciences[SHS.ECO]Humanities and Social Sciences/Economics and Financejel:D57community developmentJEL : C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C63 - Computational Techniques • Simulation ModelingLagrange multiplierPath (graph theory)symbols
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Catastrophic risks and the pricing of catastrophe equity put options

2021

In this paper, after a review of the most common financial strategies and products that insurance companies use to hedge catastrophic risks, we study an option pricing model based on processes with jumps where the catastrophic event is captured by a compound Poisson process with negative jumps. Given the importance that catastrophe equity put options (CatEPuts) have in this context, we introduce a pricing approach that provides not only a theoretical contribution whose applicability remains confined to purely numerical examples and experiments, but which can be implemented starting from real data and applied to the evaluation of real CatEPuts. We propose a calibration framework based on his…

Market capitalizationSettore SECS-P/11 - Economia degli Intermediari Finanziari0211 other engineering and technologiesContext (language use)02 engineering and technologyBlack–Scholes modelImplied volatilityManagement Information SystemsCompound Poisson processG1Economics021108 energyVariance gammaG12Hedge (finance)C2Original Paper021103 operations researchActuarial scienceCompound PoissonCatastrophe equity put options · Variance gamma · Compound Poisson · Double-calibrationEquity (finance)Double-calibrationVariance-gamma distributionCatastrophe equity put options · Variance gamma · Compound Poisson ·Double-calibrationC63G22Catastrophe equity put optionsInformation SystemsComputational Management Science
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Granulocyte–Colony Stimulating Factor plus Plerixafor in Patients with β-thalassemia Major Results in the Effective Mobilization of Primitive CD34+ C…

2017

Successful gene therapy for β-thalassemia requires optimal numbers of autologous gene-transduced hematopoietic stem and progenitor cells (HSPCs) with high repopulating capacity. Previous studies suggested superior mobilization in these patients by the combination of granulocyte–colony stimulating factor (G-CSF) plus plerixafor over single agents. We mobilized four adult patients using G-CSF+plerixafor to assess the intra-individual variation of the circulating CD34+ cells number and subtypes preand post-plerixafor administration. The procedure was well-tolerated and the target cell dose of ≥8×10 6 CD34+ cells/kg was achieved in three of them with one apheresis procedure. The addition of ple…

Mobilizationbusiness.industryCD34+ cells expression profilingCd34 cellsPlerixaforGenetic enhancementβ-thalassemia; CD34 cells expression profiling; G-CSF plerixafor mobilization; gene therapygene therapySettore MED/15 - Malattie Del SangueGranulocyte colony-stimulating factorSettore BIO/18 - Geneticagene therapy.β-thalassemiaGene expressionImmunologyCancer researchG-CSF+plerixafor mobilizationMedicineDiseases of the blood and blood-forming organsIn patientβ-thalassemia; CD34+ cells expression profiling; G-CSF+plerixafor mobilization; gene therapyRC633-647.5businessβ thalassemia majormedicine.drugThalassemia Reports
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Biproportional methods of structural change analysis: A typological survey

2004

International audience; Analysts often are interested in learning how much an exchange system has changed over time or how two different exchange systems differ. Identifying structural difference in exchange matrices can be performed using either 'directed' or 'undirected' methods. Directed methods are based on the computation and comparison of column- or row-normalizations of the matrices. The choice of row or column for the normalization implies a specific direction of the exchanges, so that the column-wise normalized results should not be compared to the row-wise normalized results. In this category fall the simple comparison of coefficient matrices and the causative method. Undirected m…

Normalization (statistics)JEL : C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C67 - Input–Output ModelsEconomics and EconometricsJEL: C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C67 - Input–Output Modelscausative matrixComputationJEL: D - Microeconomics/D.D5 - General Equilibrium and Disequilibrium/D.D5.D57 - Input–Output Tables and AnalysisStructural difference[SHS.ECO]Humanities and Social Sciences/Economics and Financemathematical economicsinput-output analysisJEL: C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C63 - Computational Techniques • Simulation ModelingJEL : C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C63 - Computational Techniques • Simulation ModelingbiproportionMedian filterJEL : D - Microeconomics/D.D5 - General Equilibrium and Disequilibrium/D.D5.D57 - Input–Output Tables and Analysis[ SHS.ECO ] Humanities and Social Sciences/Economies and finances[SHS.ECO] Humanities and Social Sciences/Economics and FinanceAlgorithmMathematicsRAS
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