Search results for "Monte Carlo method."

2016

We determine knotting probabilities and typical sizes of knots in double-stranded DNA for chains of up to half a million base pairs with computer simulations of a coarse-grained bead-stick model: Single trefoil knots and composite knots which include at least one trefoil as a prime factor are shown to be common in DNA chains exceeding 250,000 base pairs, assuming physiologically relevant salt conditions. The analysis is motivated by the emergence of DNA nanopore sequencing technology, as knots are a potential cause of erroneous nucleotide reads in nanopore sequencing devices and may severely limit read lengths in the foreseeable future. Even though our coarse-grained model is only based on …

Population pharmacokinetic meta-analysis of individual data to design the first randomized efficacy trial of vancomycin in neonates and young infants.

2019

Abstract Objectives In the absence of consensus, the present meta-analysis was performed to determine an optimal dosing regimen of vancomycin for neonates. Methods A ‘meta-model’ with 4894 concentrations from 1631 neonates was built using NONMEM, and Monte Carlo simulations were performed to design an optimal intermittent infusion, aiming to reach a target AUC0–24 of 400 mg·h/L at steady-state in at least 80% of neonates. Results A two-compartment model best fitted the data. Current weight, postmenstrual age (PMA) and serum creatinine were the significant covariates for CL. After model validation, simulations showed that a loading dose (25 mg/kg) and a maintenance dose (15 mg/kg q12h if &lt…

State transition identification in multivariate time series (STIMTS) applied to rotational jump trajectories from single molecules

2018

Time resolved data from single molecule experiments often suffer from contamination with noise due to a low signal level. Identifying a proper model to describe the data thus requires an approach with sufficient model parameters without misinterpreting the noise as relevant data. Here, we report on a generalized data evaluation process to extract states with piecewise constant signal level from simultaneously recorded multivariate data, typical for multichannel single molecule experiments. The method employs the minimum description length principle to avoid overfitting the data by using an objective function, which is based on a tradeoff between fitting accuracy and model complexity. We val…

Coarse-grained models of double-stranded DNA based on experimentally determined knotting probabilities

2018

Abstract To accurately model double-stranded DNA in a manner that is computationally efficient, coarse-grained models of DNA are introduced, where model parameters are selected by fitting the spectrum of observable DNA knots: We develop a general method to fit free parameters of coarse-grained chain models by comparing experimentally obtained knotting probabilities of short DNA chains to knotting probabilities that are computed in Monte Carlo simulations, resulting in coarse-grained DNA models which are tailored to reflect DNA topology in the best possible way. The method is exemplified by fitting ideal chain models as well as a bead-spring model with excluded volume interactions, to model …

Uncertainty quantification on a spatial Markov-chain model for the progression of skin cancer

2019

AbstractA spatial Markov-chain model is formulated for the progression of skin cancer. The model is based on the division of the computational domain into nodal points, that can be in a binary state: either in ‘cancer state’ or in ‘non-cancer state’. The model assigns probabilities for the non-reversible transition from ‘non-cancer’ state to the ‘cancer state’ that depend on the states of the neighbouring nodes. The likelihood of transition further depends on the life burden intensity of the UV-rays that the skin is exposed to. The probabilistic nature of the process and the uncertainty in the input data is assessed by the use of Monte Carlo simulations. A good fit between experiments on mi…

Distributed channel prediction for multi-agent systems

2017

Los sistemas multiagente (MAS) se comunican a través de una red inalámbrica para coordinar sus acciones e informar sobre el estado de su misión. La conectividad y el rendimiento del sistema pueden mejorarse mediante la predicción de la ganancia del canal. Presentamos un esquema basado en regresión de procesos gaussianos (GPR) distribuidos para predecir el canal inalámbrico en términos de la potencia recibida en el MAS. El esquema combina una máquina de comité bayesiano con un esquema de consenso medio, distribuyendo así no sólo la memoria sino también la carga computacional y de comunicación. A través de simulaciones de Monte Carlo, demostramos el rendimiento del GPR propuesto. RACHEL TEC20…

Search for a Dark Leptophilic Scalar in e(+) e(-) Collisions

2020

Many scenarios of physics beyond the standard model predict the existence of new gauge singlets, which might be substantially lighter than the weak scale. The experimental constraints on additional scalars with masses in the MeV to GeV range could be significantly weakened if they interact predominantly with leptons rather than quarks. At an e+e- collider, such a leptophilic scalar (φL) would be produced predominantly through radiation from a τ lepton. We report herein a search for e+e-→τ+τ-φL, φL→ℓ+ℓ- (ℓ=e, μ) using data collected by the BABAR experiment at SLAC. No significant signal is observed, and we set limits on the φL coupling to leptons in the range 0.04<mφL<7.0 GeV. These bounds s…

Particle identification in ALICE: a Bayesian approach

2016

We present a Bayesian approach to particle identification (PID) within the ALICE experiment. The aim is to more effectively combine the particle identification capabilities of its various detectors. After a brief explanation of the adopted methodology and formalism, the performance of the Bayesian PID approach for charged pions, kaons and protons in the central barrel of ALICE is studied. PID is performed via measurements of specific energy loss ($\mathrm{d}E/\mathrm{d}x$) and time-of-flight. PID efficiencies and misidentification probabilities are extracted and compared with Monte Carlo simulations using high-purity samples of identified particles in the decay channels ${\rm K}^0_S \righta…

Measurements of underlying-event properties using neutral and charged particles in pp collisions at root s=900 GeV and root s=7 TeV with the ATLAS de…

2011

We present first measurements of charged and neutral particle-flow correlations in pp collisions using the ATLAS calorimeters. Data were collected in 2009 and 2010 at centre-of-mass energies of 900 GeV and 7 TeV. Events were selected using a minimum-bias trigger which required a charged particle in scintillation counters on either side of the interaction point. Particle flows, sensitive to the underlying event, are measured using clusters of energy in the ATLAS calorimeters, taking advantage of their fine granularity. No Monte Carlo generator used in this analysis can accurately describe the measurements. The results are independent of those based on charged particles measured by the ATLAS …

Luminosity determination in pp collisions at s=7 TeV using the ATLAS detector at the LHC

2011

Measurements of luminosity obtained using the ATLAS detector during early running of the Large Hadron Collider (LHC) at s√=7 TeV are presented. The luminosity is independently determined using several detectors and multiple algorithms, each having different acceptances, systematic uncertainties and sensitivity to background. The ratios of the luminosities obtained from these methods are monitored as a function of time and of μ, the average number of inelastic interactions per bunch crossing. Residual time- and μ-dependence between the methods is less than 2% for 0<μ<2.5. Absolute luminosity calibrations, performed using beam separation scans, have a common systematic uncertainty of ±11%, do…