Search results for "POPULATIONS"

showing 10 items of 493 documents

"Table 11" of "Search for magnetic monopoles and stable high-electric-charge objects in 13 TeV proton-proton collisions with the ATLAS detector"

2019

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=1g_\textrm{D}$ monopoles of mass 3000 GeV.

Monopole13000.0Computer Science::Information RetrievalQuantitative Biology::Populations and EvolutionEFFComputer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing)High Energy Physics::ExperimentMEfficiencyNuclear Experiment
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"Table 8" of "Search for magnetic monopoles and stable high-electric-charge objects in 13 TeV proton-proton collisions with the ATLAS detector"

2019

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=1g_\textrm{D}$ monopoles of mass 1500 GeV.

Monopole13000.0Computer Science::Information RetrievalQuantitative Biology::Populations and EvolutionEFFComputer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing)High Energy Physics::ExperimentMEfficiencyNuclear Experiment
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"Table 18" of "Search for magnetic monopoles and stable high-electric-charge objects in 13 TeV proton-proton collisions with the ATLAS detector"

2019

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=2g_\textrm{D}$ monopoles of mass 2500 GeV.

Monopole13000.0Computer Science::Information RetrievalQuantitative Biology::Populations and EvolutionEFFComputer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing)High Energy Physics::ExperimentMEfficiencyNuclear Experiment
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"Table 9" of "Search for magnetic monopoles and stable high-electric-charge objects in 13 TeV proton-proton collisions with the ATLAS detector"

2019

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=1g_\textrm{D}$ monopoles of mass 2000 GeV.

Monopole13000.0Computer Science::Information RetrievalQuantitative Biology::Populations and EvolutionEFFComputer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing)High Energy Physics::ExperimentMEfficiencyNuclear Experiment
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"Table 5" of "Search for magnetic monopoles and stable high-electric-charge objects in 13 TeV proton-proton collisions with the ATLAS detector"

2019

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=1g_\textrm{D}$ monopoles of mass 200 GeV.

Monopole13000.0Computer Science::Information RetrievalQuantitative Biology::Populations and EvolutionEFFComputer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing)High Energy Physics::ExperimentMEfficiencyNuclear Experiment
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Evolutionary dynamics of imatinib-treated leukemic cells by stochastic approach

2008

The evolutionary dynamics of a system of cancerous cells in a model of chronic myeloid leukemia (CML) is investigated by a statistical approach. Cancer progression is explored by applying a Monte Carlo method to simulate the stochastic behavior of cell reproduction and death in a population of blood cells which can experience genetic mutations. In CML front line therapy is represented by the tyrosine kinase inhibitor imatinib which strongly affects the reproduction of leukemic cells only. In this work, we analyze the effects of a targeted therapy on the evolutionary dynamics of normal, first-mutant and cancerous cell populations. Several scenarios of the evolutionary dynamics of imatinib-tr…

Monte Carlo simulation stochastic approach Evolutionary dynamicsMutation rate87.23.kgmedicine.drug_classQC1-999medicine.medical_treatmentPopulationGeneral Physics and AstronomyBiologyTyrosine-kinase inhibitorTargeted therapyhemic and lymphatic diseasesmedicine87.10.mncomplex systemsQuantitative Biology - Populations and EvolutioneducationEvolutionary dynamicseducation.field_of_studycancer evolutionPhysicsstochastic dynamics87.19.xjPopulations and Evolution (q-bio.PE)Myeloid leukemiaImatinibSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)FOS: Biological sciencesCancer cellCancer research87.10.rtmedicine.drugOpen Physics
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Analysis of DNA sequence variation within marine species using Beta-coalescents

2013

We apply recently developed inference methods based on general coalescent processes to DNA sequence data obtained from various marine species. Several of these species are believed to exhibit so-called shallow gene genealogies, potentially due to extreme reproductive behaviour, e.g. via Hedgecock's "reproduction sweepstakes". Besides the data analysis, in particular the inference of mutation rates and the estimation of the (real) time to the most recent common ancestor, we briefly address the question whether the genealogies might be adequately described by so-called Beta coalescents (as opposed to Kingman's coalescent), allowing multiple mergers of genealogies. The choice of the underlying…

Most recent common ancestorMutation ratePopulation geneticsInferenceMarine Biology62F99 (Primary) 62P10 92D10 92D20 (Secondary)Biology01 natural sciencesArticleDNA sequencingCoalescent theory010104 statistics & probability03 medical and health sciencesFOS: MathematicsAnimals0101 mathematicsQuantitative Biology - Populations and EvolutionEcology Evolution Behavior and Systematics030304 developmental biologycomputer.programming_languageMarine biology0303 health sciencesBETA (programming language)Probability (math.PR)Populations and Evolution (q-bio.PE)Sequence Analysis DNAOstreidaeEvolutionary biologyFOS: Biological sciencescomputerMathematics - Probability
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The logic of forms in the light of developmental biology and palaeontology.

2010

11 pages; If you ask palaeontologists, and indeed anyone interested in the theory of evolution, for the key words that encapsulate it, you will obtain the following results: adaptation, natural selection, speciation, but also ontogeny and phylogeny. The first three key words apply to the future of the individual and by extension to the future of the species: we are therefore dealing with adults of a reproductive age. The two other key words concern (i) the evolution of the morphology from the egg to the adult (individual ontogeny: short timescale) and especially what goes on in the black box called embryogenesis, and (ii) the modification of ontogenetic sequences over time, resulting in cha…

Natural selectionExtension (metaphysics)Body plan[ SDV.BID.EVO ] Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE]Modern evolutionary synthesisEvolutionary biologyPhylogeneticsOntogenyMorphology (biology)BiologyAdaptation[ SDU.STU.PG ] Sciences of the Universe [physics]/Earth Sciences/Paleontology
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Stochastic 0-dimensional Biogeochemical Flux Model: Effect of temperature fluctuations on the dynamics of the biogeochemical properties in a marine e…

2021

Abstract We present a new stochastic model, based on a 0-dimensional version of the well known biogeochemical flux model (BFM), which allows to take into account the temperature random fluctuations present in natural systems and therefore to describe more realistically the dynamics of real marine ecosystems. The study presents a detailed analysis of the effects of randomly varying temperature on the lower trophic levels of the food web and ocean biogeochemical processes. More in detail, the temperature is described as a stochastic process driven by an additive self-correlated Gaussian noise. Varying both correlation time and intensity of the noise source, the predominance of different plank…

Numerical AnalysisBiogeochemical cycleStatistical Mechanics (cond-mat.stat-mech)Stochastic modellingStochastic processApplied MathematicsRandom processesFluxFOS: Physical sciencesPlanktonAtmospheric sciencesNoise (electronics)symbols.namesakeGaussian noiseModeling and SimulationPlankton dynamicsStochastic differential equationssymbolsEnvironmental scienceQuantitative Biology::Populations and EvolutionMarine ecosystemCondensed Matter - Statistical MechanicsMarine ecosystems
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A Note about Eigenvalues, SVD and PCA

2013

Notes on eigen-decomposition, PCA, SVD and connexions.

PCA[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Computer Science::Computer Vision and Pattern RecognitionComputer Science::MultimediaQuantitative Biology::Populations and Evolution[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]SVDComputer Science::Numerical Analysis
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