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International audience; We have performed a parallel tempering crankshaft motion Monte Carlo simulation on a model of the GABA type A receptor with the aim of exploring a wide variety of local conformational space. We develop a novel method to analyse the protein movements in terms of a correlation tensor and use this to explore the gating process, that is, how agonist binding could cause ion channel opening. We find that simulated binding impulses to varying clusters of GABA binding site residues produce channel opening, and that equivalent impulses to single GABA sites produce partial opening.

International audience; A model and data toolbox is presented to assess risks from combined exposure to multiple chemicals using probabilistic methods. The Monte Carlo Risk Assessment (MCRA) toolbox, also known as the EuroMix toolbox, has more than 40 modules addressing all areas of risk assessment, and includes a data repository with data collected in the EuroMix project. This paper gives an introduction to the toolbox and illustrates its use with examples from the EuroMix project. The toolbox can be used for hazard identification, hazard characterisation, exposure assessment and risk characterisation. Examples for hazard identification are selection of substances relevant for a specific adverse outcome based on adverse outcome pathways and QSAR models. Examples for hazard characterisation are calculation of benchmark doses and relative potency factors with uncertainty from dose response data, and use of kinetic models to perform in vitro to in vivo extrapolation. Examples for exposure assessment are assessing cumulative exposure at external or internal level, where the latter option is needed when dietary and non-dietary routes have to be aggregated. Finally, risk characterisation is illustrated by calculation and display of the margin of exposure for single substances and for the cumulation, including uncertainties derived from exposure and hazard characterisation estimates.

International audience; Proton imaging can be carried out on microscopic samples by focusing the beam to a diameter ranging from a few micrometers down to a few tens of nanometers, depending on the required beam intensity and spatial resolution. Three-dimensional (3D) imaging by tomography is obtained from proton transmission (STIM: Scanning Transmission Ion Microscopy) and/or X-ray emission (PIXE: Particle Induced X-ray Emission). In these experiments, the samples are dehydrated for under vacuum analysis. In situ quantification of nanoparticles has been carried out at CENBG in the frame of nanotoxicology studies, on cells and small organisms used as biological models, especially on Caenorhabditis elegans (C. elegans) nematodes. Tomography experiments reveal the distribution of mass density and chemical content (in g.cm−3) within the analyzed volume. These density values are obtained using an inversion algorithm. To investigate the effect of this data reduction process, we defined different numerical phantoms, including a (dehydrated) C. elegans phantom whose geometry and density were derived from experimental data. A Monte Carlo simulation based on the Geant4 toolkit was developed. Using different simulation and reconstruction conditions, we compared the resulting tomographic images to the initial numerical reference phantom. A study of the relative error between the reconstructed and the reference images lead to the result that 20 protons per shot can be considered as an optimal number for 3D STIM imaging. Preliminary results for PIXE tomography are also presented, showing the interest of such numerical phantoms to produce reference data for future studies on X-ray signal attenuation in thick samples.

International audience; For the high throughput design of protein:peptide binding, one must explore a vast space of amino acid sequences in search of low binding free energies. This complex problem is usually addressed with either simple heuristic scoring or expensive sequence enumeration schemes. Far more efficient than enumeration is a recent Monte Carlo approach that adaptively flattens the energy landscape in sequence space of the unbound peptide and provides formally exact binding free energy differences. The method allows the binding free energy to be used directly as the design criterion. We propose several improvements that allow still more efficient sampling and can address larger design problems. They include the use of Replica Exchange Monte Carlo and landscape flattening for both the unbound and bound peptides. We used the method to design peptides that bind to the PDZ domain of the Tiam1 signaling protein and could serve as inhibitors of its activity. Four peptide positions were allowed to mutate freely. Almost 75 000 peptide variants were processed in two simulations of 109 steps each that used 1 CPU hour on a desktop machine. 96% of the theoretical sequence space was sampled. The relative binding free energies agreed qualitatively with values from experiment. The sampled sequences agreed qualitatively with an experimental library of Tiam1-binding peptides. The main assumption limiting accuracy is the fixed backbone approximation, which could be alleviated in future work by using increased computational resources and multi-backbone designs.

Dengue dynamics are shaped by the complex interplay between several factors, including vector seasonality, interaction between four virus serotypes, and inapparent infections. However, paucity or quality of data do not allow for all of these to be taken into account in mathematical models. In order to explore separately the importance of these factors in models, we combined surveillance data with a local-scale cluster study in the rural province of Kampong Cham (Cambodia), in which serotypes and asymptomatic infections were documented. We formulate several mechanistic models, each one relying on a different set of hypotheses, such as explicit vector dynamics, transmission via asymptomatic infections and coexistence of several virus serotypes. Models are confronted with the observed time series using Bayesian inference, through Markov chain Monte Carlo. Model selection is then performed using statistical information criteria, and the coherence of epidemiological characteristics (reproduction numbers, incidence proportion, dynamics of the susceptible classes) is assessed in each model. Our analyses on transmission dynamics in a rural endemic setting highlight that two-strain models with interacting effects better reproduce the long term data, but they are difficult to parameterize when relying on incidence cases only. On the other hand, considering the available data, incorporating vector and asymptomatic components seems of limited added-value when seasonality and underreporting are already accounted for. Keywords: Model selection, Dengue, Vector borne disease, Mathematical model, Cambodia

International audience; The pencil beam method is commonly used for dose calculations in intensity-modulated radiation therapy (IMRT). In this study, we have proposed a novel pencil model for calculating photon dose distributions in heterogeneous media. To avoid any oblique kernel-related bias and reduce computation time, dose distributions were computed in a spherical coordinate system based on the pencil kernels of different distances from source to surface (DSS). We employed two different dose calculation methods: the superposition method and the fast Fourier transform convolution (FFTC) method. In order to render the superposition method more accurate, we scaled the depth-directed component by moving the position of the entry point and altering the DSS value for a given beamlet. The lateral components were thus directly corrected by the density scaling method along the spherical shell without taking the densities from the previous layers into account. Significant computation time could be saved by performing the FFTC calculations on each spherical shell, disregarding density changes in the lateral direction. The proposed methods were tested on several phantoms, including lung- and bone-type heterogeneities. We compared them with Monte Carlo (MC) simulation for several field sizes with 6 MV photon beams. Our results revealed mean absolute deviations

International audience; Significant alterations in the pharmacokinetics (PK) of antimicrobials have been reported in critically ill patients. We describe PK parameters of imipenem in intensive care unit (ICU) patients with suspected ventilator-associated pneumonia and evaluate several dosage regimens. This French multicentre, prospective, open-label study was conducted in ICU patients with a presumptive diagnosis of ventilator-associated pneumonia caused by Gram-negative bacilli, who empirically received imipenem intravenously every 8 h. Plasma imipenem concentrations were measured during the fourth imipenem infusion using six samples (trough, 0.5, 1, 2, 5 and 8 h). Data were analysed with a population approach using the stochastic approximation expectation maximization algorithm in Monolix 4.2. A Monte Carlo simulation was performed to evaluate the following six dosage regimens: 500, 750 or 1000 mg with administration every 6 or 8 h. The pharmacodynamic target was defined as the probability of achieving a fractional time above the minimal inhibitory concentration (MIC) of >40%. Fifty-one patients were included in the PK analysis. Imipenem concentration data were best described by a two-compartment model with three covariates (creatinine clearance, total bodyweight and serum albumin). Estimated clearance (between-subject variability) was 13.2 l h(-1) (38%) and estimated central volume 20.4 l (31%). At an MIC of 4 μg ml(-1) , the probability of achieving 40% fractional time > MIC was 91.8% for 0.5 h infusions of 750 mg every 6 h, 86.0% for 1000 mg every 8 h and 96.9% for 1000 mg every 6 h. This population PK model accurately estimated imipenem concentrations in ICU patients. The simulation showed that for these patients, the best dosage regimen of imipenem is 750 mg every 6 h and not 1000 mg every 8 h.

International audience; Current preclinical dosimetric models often fail to take account of the complex nature of absorbed dose distribution typical of in vitro clonogenic experiments in targeted radionuclide therapy. For this reason, clonogenic survival is often expressed as a function of added activity rather than the absorbed dose delivered to cells/cell nuclei. We designed a multi-cellular dosimetry model that takes into account the realistic distributions of cells in the Petri dish, for the establishment of survival curves as a function of the absorbed dose. General-purpose software tools were used for the generation of realistic, randomised 3D cell culture geometries based on experimentally determined parameters (cell size, cell density, cluster density, average cluster size, cell cumulated activity). A mixture of Monte Carlo and analytical approaches was implemented in order to achieve as accurate as possible results while reducing calculation time. The model was here applied to clonogenic survival experiments carried out to compare the efficacy of Betalutin®, a novel 177Lu-labelled antibody radionuclide conjugate for the treatment of non-Hodgkin lymphoma, to that of 177Lu-labelled CD20-specific (rituximab) and non-specific antibodies (Erbitux) on lymphocyte B cells. The 3D cellular model developed allowed a better understanding of the radiative and non-radiative processes associated with cellular death. Our approach is generic and can also be applied to other radiopharmaceuticals and cell distributions.

International audience; BACKGROUND: Ovarian cancer is the leading cause of gynaecological cancer-related death in Western countries. Intraperitoneal (IP) peroperative chemotherapy is an interesting therapeutic option. However, very few data are available regarding pharmacokinetics and especially population pharmacokinetics. PATIENTS AND METHODS: Thirty-one patients with advanced epithelial cancer classified as International Federation of Gynecology and Obstetrics stage IIIC were included in the study. Blood and IP samples were taken over a 24-hour period during and after IP treatment. Both total and ultrafiltered (Uf) platinum (Pt) concentrations were analysed using a population approach with nonlinear mixed-effects modelling (NONMEM) software. Improvement of the model with covariates was performed as well as assessment of the model using bootstrap and posterior visual predictive methods. RESULTS: Both IP fluid and serum pharmacokinetics were satisfactorily described with a three-compartment model for both Uf Pt and total Pt concentrations. The covariates were bodyweight for the IP volume of distribution in the Uf Pt model, and both IP and serum protein concentrations for the clearance from the central compartment in the total Pt model. A nomogram, based on the results of the Monte Carlo simulations, displays a dose recommendation regarding both the risk of renal toxicity and the potent efficacy of the treatment. A limited sampling strategy (LSS) allowing the estimation of potential risk of renal toxicity is also described. CONCLUSION: The pharmacokinetics of cisplatin during peroperative IP chemotherapy could be successfully fitted with the present model, which allowed a dosing strategy accompanied by an LSS to facilitate the follow-up of patients.

International audience; The activity of a neural network is defined by patterns of spiking and silence from the individual neurons. Because spikes are (relatively) sparse, patterns of activity with increasing numbers of spikes are less probable, but, with more spikes, the number of possible patterns increases. This tradeoff between probability and numerosity is mathematically equivalent to the relationship between entropy and energy in statistical physics. We construct this relationship for populations of up to N = 160 neurons in a small patch of the vertebrate retina, using a combination of direct and model-based analyses of experiments on the response of this network to naturalistic movies. We see signs of a thermodynamic limit, where the entropy per neuron approaches a smooth function of the energy per neuron as N increases. The form of this function corresponds to the distribution of activity being poised near an unusual kind of critical point. We suggest further tests of criticality, and give a brief discussion of its functional significance.

International audience; Significant alterations in the pharmacokinetics (PK) of antimicrobials have been reported in critically ill patients. We describe PK parameters of imipenem in intensive care unit (ICU) patients with suspected ventilator-associated pneumonia and evaluate several dosage regimens. This French multicentre, prospective, open-label study was conducted in ICU patients with a presumptive diagnosis of ventilator-associated pneumonia caused by Gram-negative bacilli, who empirically received imipenem intravenously every 8 h. Plasma imipenem concentrations were measured during the fourth imipenem infusion using six samples (trough, 0.5, 1, 2, 5 and 8 h). Data were analysed with a population approach using the stochastic approximation expectation maximization algorithm in Monolix 4.2. A Monte Carlo simulation was performed to evaluate the following six dosage regimens: 500, 750 or 1000 mg with administration every 6 or 8 h. The pharmacodynamic target was defined as the probability of achieving a fractional time above the minimal inhibitory concentration (MIC) of >40%. Fifty-one patients were included in the PK analysis. Imipenem concentration data were best described by a two-compartment model with three covariates (creatinine clearance, total bodyweight and serum albumin). Estimated clearance (between-subject variability) was 13.2 l h(-1) (38%) and estimated central volume 20.4 l (31%). At an MIC of 4 μg ml(-1) , the probability of achieving 40% fractional time > MIC was 91.8% for 0.5 h infusions of 750 mg every 6 h, 86.0% for 1000 mg every 8 h and 96.9% for 1000 mg every 6 h. This population PK model accurately estimated imipenem concentrations in ICU patients. The simulation showed that for these patients, the best dosage regimen of imipenem is 750 mg every 6 h and not 1000 mg every 8 h.

International audience; Significant alterations in the pharmacokinetics (PK) of antimicrobials have been reported in critically ill patients. We describe PK parameters of imipenem in intensive care unit (ICU) patients with suspected ventilator-associated pneumonia and evaluate several dosage regimens. This French multicentre, prospective, open-label study was conducted in ICU patients with a presumptive diagnosis of ventilator-associated pneumonia caused by Gram-negative bacilli, who empirically received imipenem intravenously every 8 h. Plasma imipenem concentrations were measured during the fourth imipenem infusion using six samples (trough, 0.5, 1, 2, 5 and 8 h). Data were analysed with a population approach using the stochastic approximation expectation maximization algorithm in Monolix 4.2. A Monte Carlo simulation was performed to evaluate the following six dosage regimens: 500, 750 or 1000 mg with administration every 6 or 8 h. The pharmacodynamic target was defined as the probability of achieving a fractional time above the minimal inhibitory concentration (MIC) of >40%. Fifty-one patients were included in the PK analysis. Imipenem concentration data were best described by a two-compartment model with three covariates (creatinine clearance, total bodyweight and serum albumin). Estimated clearance (between-subject variability) was 13.2 l h(-1) (38%) and estimated central volume 20.4 l (31%). At an MIC of 4 μg ml(-1) , the probability of achieving 40% fractional time > MIC was 91.8% for 0.5 h infusions of 750 mg every 6 h, 86.0% for 1000 mg every 8 h and 96.9% for 1000 mg every 6 h. This population PK model accurately estimated imipenem concentrations in ICU patients. The simulation showed that for these patients, the best dosage regimen of imipenem is 750 mg every 6 h and not 1000 mg every 8 h.

When information is sparse, individual parameters derived from a non-linear mixed effects model analysis can shrink to the mean. The objective of this work was to predict individual parameter shrinkage from the Bayesian information matrix (M BF ). We 1) Propose and evaluate an approximation of M BF by First-Order linearization (FO), 2) Explore by simulations the relationship between shrinkage and precision of estimates and 3) Evaluate prediction of shrinkage and individual parameter precision. We approximated M BF using FO. From the shrinkage formula in linear mixed effects models, we derived the predicted shrinkage from M BF . Shrinkage values were generated for parameters of two pharmacokinetic models by varying the structure and the magnitude of the random effect and residual error models as well as the design. We then evaluated the approximation of M BF FO and compared it to Monte-Carlo (MC) simulations. We finally compared expected and observed shrinkage as well as the predicted and estimated Standard Errors (SE) of individual parameters. M BF FO was similar to M BF MC. Predicted and observed shrinkages were close . Predicted and estimated SE were similar. M BF FO enables prediction of shrinkage and SE of individual parameters. It can be used for design optimization.

International audience; Nonhuman primates show remarkable variation in several aspects of social structure. One way to characterize this variation in the genus Macaca is through the concept of social style, which is based on the observation that several social traits appear to covary with one another in a linear or at least continuous manner. In practice, macaques are more simply characterized as fitting a four-grade scale in which species range from extremely despotic (grade 1) to extremely tolerant (grade 4). Here, we examine the fit of three core measures of social style-two measures of dominance gradients (hierarchical steepness) and another closely related measure (counter-aggression)-to this scale, controlling for phylogenetic relationships. Although raw scores for both steepness and counter-aggression correlated with social scale in predicted directions, the distributions appeared to vary by measure. Counter-aggression appeared to vary dichotomously with scale, with grade 4 species being distinct from all other grades. Steepness measures appeared more continuous. Species in grades 1 and 4 were distinct from one another on all measures, but those in the intermediate grades varied inconsistently. This confirms previous indications that covariation is more readily observable when comparing species at the extreme ends of the scale than those in intermediate positions. When behavioral measures were mapped onto phylogenetic trees, independent contrasts showed no significant consistent directional changes at nodes below which there were evolutionary changes in scale. Further, contrasts were no greater at these nodes than at neutral nodes. This suggests that correlations with the scale can be attributed largely to species' phylogenetic relationships. This could be due in turn to a structural linkage of social traits based on adaptation to similar ecological conditions in the distant past, or simply to unlinked phylogenetic closeness.

International audience; Errors in the flip angle have to be corrected in many magnetic resonance imaging applications, especially for T1 quantification. However, the existing methods of B1 mapping fail to measure lower values of the flip angle despite the fact that these are extensively used in dynamic acquisition and 3D imaging. In this study, the nonlinearity of the radiofrequency (RF) transmit chain, especially for very low flip angles, is investigated and a simple method is proposed to accurately determine both the gain of the RF transmitter and the B1 field map for low flip angles. The method makes use of the spoiled gradient echo sequence with long repetition time (TR), such as applied in the double-angle method. It uses an image acquired with a flip angle of 90° as a reference image that is robust to B1 inhomogeneity. The ratio of the image at flip angle alpha to the image at a flip angle of 90° enables us to calculate the actual value of alpha. This study was carried out at 1.5 and 4.7 T, showing that the linearity of the RF supply system is highly dependent on the hardware. The method proposed here allows us to measure the flip angle from 1° to 60° with a maximal uncertainty of 10% and to correct T1 maps based on the variable flip angle method.

International audience; We address the issue of jointly detecting brain activity and estimating underlying brain hemodynamics from functional MRI data. We adopt the so-called Joint Detection Estimation (JDE) framework that takes spatial dependencies between voxels into account. We recast the JDE into a missing data framework and derive a Variational Expectation-Maximization (VEM) algorithm for its inference. It follows a new algorithm that has interesting advantages over the previously used intensive simulation methods (Markov Chain Monte Carlo, MCMC): tests on artificial data show that the VEM-JDE is more robust to model mis-specification while additional tests on real data confirm that it achieves similar performance in much less computation time.

International audience; The decomposition of multiunit signals consists of the restoration of spike trains and action potentials in neural or muscular recordings. Because of the complexity of automatic decomposition, semiautomatic procedures are sometimes chosen. The main difficulty in automatic decomposition is the resolution of temporally overlapped potentials. In a previous study , we proposed a Bayesian model coupled with a maximum a posteriori (MAP) estimator for fully automatic decomposition of multiunit recordings and we showed applications to intramuscular EMG signals. In this study, we propose a more complex signal model that includes the variability in amplitude of each unit potential. Moreover, we propose the Markov Chain Monte Carlo (MCMC) simulation and a Bayesian minimum mean square error (MMSE) estimator by averaging on samples that converge in distribution to the joint posterior law. We prove the convergence property of this approach mathematically and we test the method representatively on intramuscular multiunit recordings. The results showed that its average accuracy in spike identification is greater than 90% for intramuscular signals with up to 8 concurrently active units. In addition to intramuscular signals, the method can be applied for spike sorting of other types of multiunit recordings.

Background During the last decade, oral bisphosphonates (BP) became the most widely prescribed pharmacologic class for post-menopausal osteoporosis. However, many surveys revealed the important issue of poor persistence with those drugs resulting in a failure of treatment to reduce fracture risk sufficiently. Using a published Markov model, this study analyses the economic impact of non-persistence with bisphosphonates in the context of the introduction of generics in France. Methods Direct costs of vertebral, hip and wrist fracture were assessed and included in an existing 10-year Markov model developed to analyse consequences of non-persistence. Three alternatives of comparison were set: no treatment, real-world persistence, and ideal persistence. Simulated patients' characteristics matched those from a French observational study and the real-world adherence alternative employed persistence data from published database analysis. The risk of fracture of menopausal women and the risk reduction associated with the drugs were based on results reported in clinical trials. Incremental cost-effectiveness ratios (ICERs) were calculated first between real-world adherence and no treatment alternatives, and second between ideal and real-world persistence alternatives. The cost of non-persistence was defined as the difference between total cost of ideal and real-world persistence alternatives. Results Within fractured women population, mean costs of 10-year management of fracture were significantly different between the three alternatives with €7,239 (± €4,783), €6,711 (± €4,410) and €6,134 (± €3,945) in the no-treatment, the real-world and ideal persistence alternatives, respectively (p < 0.0001). Cost-effectiveness ratio for real-world treatment persistence compared with no-treatment alternative was found dominant and as well, alternative of ideal persistence dominated the former. Each ten percentage point of persistence gain amounted to €58 per patient, and extrapolation resulted in a global annual cost of non-persistence of over €30 million to the French health care system, with a substantial transfer from hospital to pharmacy budgets. Conclusion Within term, improving persistence with oral bisphosphonates should be economically dominant on levels currently known in real-world. Given this potential savings, ambitious adherence-enhancing interventions should be considered in osteoporotic patients.

CD8+ cytotoxic T lymphocytes (CTLs), natural killer (NK) cells, B cells and target cell limitation have all been suggested to play a role in the control of SIV and HIV-1 infection. However, previous research typically studied each population in isolation leaving the magnitude, relative importance and in vivo relevance of each effect unclear. Here we quantify the relative importance of CTLs, NK cells, B cells and target cell limitation in controlling acute SIV infection in rhesus macaques. Using three different methods, we find that the availability of target cells and CD8+ T cells are important predictors of viral load dynamics. If CTL are assumed to mediate this anti-viral effect via a lytic mechanism then we estimate that CTL killing is responsible for approximately 40% of productively infected cell death, the remaining cell death being attributable to intrinsic, immune (CD8+ T cell, NK cell, B cell) -independent mechanisms. Furthermore, we find that NK cells have little impact on the death rate of infected CD4+ cells and that their net impact is to increase viral load. We hypothesize that NK cells play a detrimental role in SIV infection, possibly by increasing T cell activation., Author Summary The role of the immune response in controlling HIV infection, or its simian counterpart SIV, has not been fully elucidated. Different studies have provided evidence of a role for different components of the immune system. Unfortunately, the different components are usually studied in isolation, making it hard to determine their relative importance. Here we study three main immune cell populations, T, B and NK cells, in parallel and found that cytotoxic T cells play the largest role in the control of SIV infection, but were not responsible for the majority of infected cell death. Interestingly, we found indications that, although NK cells contribute to the killing of infected cells, the net impact of the NK response is to increase viral load.

International audience; Subcortical-cortical interactions in the language network were investigated using dynamic causal modeling of magnetoencephalographic data recorded during auditory comprehension. Participants heard sentences that either were correct or contained violations. Sentences containing violations had syntactic or prosodic violations or both. We show that a hidden source, modeling magnetically silent deep nuclei, is required to explain the data best. This is in line with recent brain imaging studies and intracranial recordings suggesting an involvement of subcortical structures in language processing. Here, the processing of syntactic and prosodic violations elicited a global increase in the amplitude of evoked responses, both at the cortical and subcortical levels. As estimated by Bayesian model averaging, this was accompanied by various changes in cortical-cortical and subcortical-cortical connectivity. The most consistent findings in relation to violations were a decrease of reentrant inputs to Heschl's gyrus (HG) and of transcallosal lateral connections. These results suggest that in conditions where one hemisphere detects a violation, possibly via fast thalamocortical (HG) loops, the intercallosal connectivity is reduced to allow independent processing of syntax (left hemisphere) and of prosody (right hemisphere). This study is the first demonstration in cognitive neuroscience that subcortical-cortical loops can be empirically investigated using noninvasive electrophysiological recordings.

International audience; Subcortical-cortical interactions in the language network were investigated using dynamic causal modeling of magnetoencephalographic data recorded during auditory comprehension. Participants heard sentences that either were correct or contained violations. Sentences containing violations had syntactic or prosodic violations or both. We show that a hidden source, modeling magnetically silent deep nuclei, is required to explain the data best. This is in line with recent brain imaging studies and intracranial recordings suggesting an involvement of subcortical structures in language processing. Here, the processing of syntactic and prosodic violations elicited a global increase in the amplitude of evoked responses, both at the cortical and subcortical levels. As estimated by Bayesian model averaging, this was accompanied by various changes in cortical-cortical and subcortical-cortical connectivity. The most consistent findings in relation to violations were a decrease of reentrant inputs to Heschl's gyrus (HG) and of transcallosal lateral connections. These results suggest that in conditions where one hemisphere detects a violation, possibly via fast thalamocortical (HG) loops, the intercallosal connectivity is reduced to allow independent processing of syntax (left hemisphere) and of prosody (right hemisphere). This study is the first demonstration in cognitive neuroscience that subcortical-cortical loops can be empirically investigated using noninvasive electrophysiological recordings.

International audience; Characterizing the behavior and robustness of enzymatic networks with numerous variables and unknown parameter values is a major challenge in biology, especially when some enzymes have counter-intuitive properties or switch-like behavior between activation and inhibition. In this paper, we propose new methodological and tool-supported contributions, based on the intuitive formalism of temporal logic, to express in a rigorous manner arbitrarily complex dynamical properties. Our multi-step analysis allows efficient sampling of the parameter space in order to define feasible regions in which the model exhibits imposed or experimentally observed behaviors. In a first step, an algorithmic methodology involving sensitivity analysis is conducted to determine bifurcation thresholds for a limited number of model parameters or initial conditions. In a second step, this boundary detection is supplemented by a global robustness analysis, based on quasi-Monte Carlo approach that takes into account all model parameters. We apply this method to a well-documented enzymatic reaction network describing collagen proteolysis by matrix metalloproteinase MMP2 and membrane type 1 metalloproteinase (MT1-MMP) in the presence of tissue inhibitor of metalloproteinase TIMP2. For this model, our method provides an extended analysis and quantification of network robustness toward paradoxical TIMP2 switching activity between activation or inhibition of MMP2 production. Further implication of our approach is illustrated by demonstrating and analyzing the possible existence of oscillatory behaviors when considering an extended open configuration of the enzymatic network. Notably, we construct bifurcation diagrams that specify key parameters values controlling the co-existence of stable steady and non-steady oscillatory proteolytic dynamics.

Background Cellular response to changes in the concentration of different chemical species in the extracellular medium is induced by ligand binding to dedicated transmembrane receptors. Receptor density, distribution, and clustering may be key spatial features that influence effective and proper physical and biochemical cellular responses to many regulatory signals. Classical equations describing this kind of binding kinetics assume the distributions of interacting species to be homogeneous, neglecting by doing so the impact of clustering. As there is experimental evidence that receptors tend to group in clusters inside membrane domains, we investigated the effects of receptor clustering on cellular receptor ligand binding. Results We implemented a model of receptor binding using a Monte-Carlo algorithm to simulate ligand diffusion and binding. In some simple cases, analytic solutions for binding equilibrium of ligand on clusters of receptors are provided, and supported by simulation results. Our simulations show that the so-called "apparent" affinity of the ligand for the receptor decreases with clustering although the microscopic affinity remains constant. Conclusions Changing membrane receptors clustering could be a simple mechanism that allows cells to change and adapt its affinity/sensitivity toward a given stimulus.

Motivation: When comparing the organization of two genomes, it is important not to draw conclusions on their modes of evolution from a single most parsimonious scenario explaining their differences. Better estimations can be obtained by sampling many different genomic rearrangement scenarios. For this problem, the Double Cut and Join (DCJ) model, while less relevant, is computationally easier than the Hannenhalli–Pevzner (HP) model. Indeed, in some special cases, the total number of DCJ sorting scenarios can be analytically calculated, and uniformly distributed random DCJ scenarios can be drawn in polynomial running time, while the complexity of counting the number of HP scenarios and sampling from the uniform distribution of their space is unknown, and conjectured to be #P-complete. Statistical methods, like Markov chain Monte Carlo (MCMC) for sampling from the uniform distribution of the most parsimonious or the Bayesian distribution of all possible HP scenarios are required. Results: We use the computational facilities of the DCJ model to draw a sampling of HP scenarios. It is based on a parallel MCMC method that cools down DCJ scenarios to HP scenarios. We introduce two theorems underlying the theoretical mixing properties of this parallel MCMC method. The method was tested on yeast and mammalian genomic data, and allowed us to provide estimates of the different modes of evolution in diverse lineages. Availability: The program implemented in Java 1.5 programming language is available from http://www.renyi.hu/~miklosi/DCJ2HP/. Contact: miklosi@renyi.hu

The acid/base properties of proteins are essential in biochemistry, and proton binding is a valuable reporter on electrostatic interactions. We propose a constant-pH Monte Carlo strategy to compute protonation free energies and pKas. The solvent is described implicitly, through a generalized Born model. The electronic polarizability and backbone motions of the protein are included through the protein dielectric constant. Side chain motions are described explicitly, by the Monte Carlo scheme. An efficient computational algorithm is described, which allows us to treat the fluctuating shape of the protein/solvent boundary in a way that is numerically exact (within the GB framework) this contrasts with several previous constant-pH approaches. For a test set of six proteins and 78 titratable groups, the model performs well, with an rms error of 1.2 pH units. While this is slightly greater than a simple Null model (rms error of 1.1) and a fully empirical model (rms error of 0.9), it is obtained using physically meaningful model parameters, including a low protein dielectric of four. Importantly, similar performance is obtained for side chains with large and small pKa shifts (relative to a standard model compound). The titration curve slopes and the conformations sampled are reasonable. Several directions to improve the method further are discussed. © 2010 American Chemical Society. 114 32 10634 10648 Cited By :20

International audience; In (18)F-FDG PET, tumors are often characterized by their metabolically active volume and standardized uptake value (SUV). However, many approaches have been proposed to estimate tumor volume and SUV from (18)F-FDG PET images, none of them being widely agreed upon. We assessed the accuracy and robustness of 5 methods for tumor volume estimates and of 10 methods for SUV estimates in a large variety of configurations. METHODS: PET acquisitions of an anthropomorphic phantom containing 17 spheres (volumes between 0.43 and 97 mL, sphere-to-surrounding-activity concentration ratios between 2 and 68) were used. Forty-one nonspheric tumors (volumes between 0.6 and 92 mL, SUV of 2, 4, and 8) were also simulated and inserted in a real patient (18)F-FDG PET scan. Four threshold-based methods (including one, T(bgd), accounting for background activity) and a model-based method (Fit) described in the literature were used for tumor volume measurements. The mean SUV in the resulting volumes were calculated, without and with partial-volume effect (PVE) correction, as well as the maximum SUV (SUV(max)). The parameters involved in the tumor segmentation and SUV estimation methods were optimized using 3 approaches, corresponding to getting the best of each method or testing each method in more realistic situations in which the parameters cannot be perfectly optimized. RESULTS: In the phantom and simulated data, the T(bgd) and Fit methods yielded the most accurate volume estimates, with mean errors of 2% +/- 11% and -8% +/- 21% in the most realistic situations. Considering the simulated data, all SUV not corrected for PVE had a mean bias between -31% and -46%, much larger than the bias observed with SUV(max) (-11% +/- 23%) or with the PVE-corrected SUV based on T(bgd) and Fit (-2% +/- 10% and 3% +/- 24%). CONCLUSION: The method used to estimate tumor volume and SUV greatly affects the reliability of the estimates. The T(bgd) and Fit methods yielded low errors in volume estimates in a broad range of situations. The PVE-corrected SUV based on T(bgd) and Fit were more accurate and reproducible than SUV(max).

International audience; PURPOSE: Synchrotron microbeam radiation therapy (MRT) relies on spatial fractionation of the incident photon beam into parallel micron-wide beams. Our aim was to analyze the effects of MRT on normal brain and 9L gliosarcoma tissues, particularly on blood vessels. METHODS AND MATERIALS: Responses to MRT (two arrays, one lateral, one anteroposterior (2 × 400 Gy), intersecting orthogonally in the tumor region) were studied during 6 weeks using MRI, immunohistochemistry, and vascular endothelial growth factor Western blot. RESULTS: MRT increased the median survival time of irradiated rats (×3.25), significantly increased blood vessel permeability, and inhibited tumor growth; a cytotoxic effect on 9L cells was detected 5 days after irradiation. Significant decreases in tumoral blood volume fraction and vessel diameter were measured from 8 days after irradiation, due to loss of endothelial cells in tumors as detected by immunochemistry. Edema was observed in the normal brain exposed to both crossfired arrays about 6 weeks after irradiation. This edema was associated with changes in blood vessel morphology and an overexpression of vascular endothelial growth factor. Conversely, vascular parameters and vessel morphology in brain regions exposed to one of the two arrays were not damaged, and there was no loss of vascular endothelia. CONCLUSIONS: We show for the first time that preferential damage of MRT to tumor vessels versus preservation of radioresistant normal brain vessels contributes to the efficient palliation of 9L gliosarcomas in rats. Molecular pathways of repair mechanisms in normal and tumoral vascular networks after MRT may be essential for the improvement of such differential effects on the vasculature.

Background Biological networks are highly dynamic in response to environmental and physiological cues. This variability is in contrast to conventional analyses of biological networks, which have overwhelmingly employed static graph models which stay constant over time to describe biological systems and their underlying molecular interactions. Methods To overcome these limitations, we propose here a new statistical modelling framework, the ARTIVA formalism (Auto Regressive TIme VArying models), and an associated inferential procedure that allows us to learn temporally varying gene-regulation networks from biological time-course expression data. ARTIVA simultaneously infers the topology of a regulatory network and how it changes over time. It allows us to recover the chronology of regulatory associations for individual genes involved in a specific biological process (development, stress response, etc.). Results We demonstrate that the ARTIVA approach generates detailed insights into the function and dynamics of complex biological systems and exploits efficiently time-course data in systems biology. In particular, two biological scenarios are analyzed: the developmental stages of Drosophila melanogaster and the response of Saccharomyces cerevisiae to benomyl poisoning. Conclusions ARTIVA does recover essential temporal dependencies in biological systems from transcriptional data, and provide a natural starting point to learn and investigate their dynamics in greater detail.

Corresponding author: m.navascues@gmail.com; International audience; The increasing ability to extract and sequence DNA from noncontemporaneous tissue offers biologists the opportunity to analyse ancient DNA (aDNA) together with modern DNA (mDNA) to address the taxonomy of extinct species, evolutionary origins, historical phylogeography and biogeography. Perhaps more exciting are recent developments in coalescence-based Bayesian inference that offer the potential to use temporal information from aDNA and mDNA for the estimation of substitution rates and divergence dates as an alternative to fossil and geological calibration. This comes at a time of growing interest in the possibility of time dependency for molecular rate estimates. In this study, we provide a critical assessment of Bayesian Markov chain Monte Carlo (MCMC) analysis for the estimation of substitution rate using simulated samples of aDNA and mDNA. We conclude that the current models and priors employed in Bayesian MCMC analysis of heterochronous mtDNA are susceptible to an upward bias in the estimation of substitution rates because of model misspecification when the data come from populations with less than simple demographic histories, including sudden short-lived population bottlenecks or pronounced population structure. However, when model misspecification is only mild, then the 95% highest posterior density intervals provide adequate frequentist coverage of the true rates.

The extent to which self-adopted or intervention-related changes in behaviors affect the course of epidemics remains a key issue for outbreak control. This study attempted to quantify the effect of such changes on the risk of infection in different settings, i.e., the community and hospitals. The 2002-2003 severe acute respiratory syndrome (SARS) outbreak in Hong Kong, where 27% of cases were healthcare workers, was used as an example. A stochastic compartmental SEIR (susceptible-exposed-infectious-removed) model was used: the population was split into healthcare workers, hospitalized people and general population. Super spreading events (SSEs) were taken into account in the model. The temporal evolutions of the daily effective contact rates in the community and hospitals were modeled with smooth functions. Data augmentation techniques and Markov chain Monte Carlo (MCMC) methods were applied to estimate SARS epidemiological parameters. In particular, estimates of daily reproduction numbers were provided for each subpopulation. The average duration of the SARS infectious period was estimated to be 9.3 days (±0.3 days). The model was able to disentangle the impact of the two SSEs from background transmission rates. The effective contact rates, which were estimated on a daily basis, decreased with time, reaching zero inside hospitals. This observation suggests that public health measures and possible changes in individual behaviors effectively reduced transmission, especially in hospitals. The temporal patterns of reproduction numbers were similar for healthcare workers and the general population, indicating that on average, an infectious healthcare worker did not infect more people than any other infectious person. We provide a general method to estimate time dependence of parameters in structured epidemic models, which enables investigation of the impact of control measures and behavioral changes in different settings. © 2009 Cori et al., published_or_final_version

International audience; Analysis of count data from clinical trials using mixed effect analysis has recently become widely used. However, algorithms available for the parameter estimation, including LAPLACE and Gaussian quadrature (GQ), are associated with certain limitations, including bias in parameter estimates and the long analysis runtime. The stochastic approximation expectation maximization (SAEM) algorithm has proven to be a very efficient and powerful tool in the analysis of continuous data. The aim of this study was to implement and investigate the performance of a new SAEM algorithm for application to count data. A new SAEM algorithm was implemented in MATLAB for estimation of both, parameters and the Fisher information matrix. Stochastic Monte Carlo simulations followed by re-estimation were performed according to scenarios used in previous studies (part I) to investigate properties of alternative algorithms (Plan et al., 2008, Abstr 1372 [ http://wwwpage-meetingorg/?abstract=1372 ]). A single scenario was used to explore six probability distribution models. For parameter estimation, the relative bias was less than 0.92% and 4.13% for fixed and random effects, for all models studied including ones accounting for over- or under-dispersion. Empirical and estimated relative standard errors were similar, with distance between them being

International audience; This article focuses on parameter estimation of multilevel nonlinear mixed-effects models (MNLMEMs). These models are used to analyze data presenting multiple hierarchical levels of grouping (cluster data, clinical trials with several observation periods, ...). The variability of the individual parameters of the regression function is thus decomposed as a between-subject variability and higher levels of variability (e.g. within-subject variability). We propose maximum likelihood estimates of parameters of those MNLMEMs with 2 levels of random effects, using an extension of the stochastic approximation version of expectation-maximization (SAEM)-Monte Carlo Markov chain algorithm. The extended SAEM algorithm is split into an explicit direct expectation-maximization (EM) algorithm and a stochastic EM part. Compared to the original algorithm, additional sufficient statistics have to be approximated by relying on the conditional distribution of the second level of random effects. This estimation method is evaluated on pharmacokinetic crossover simulated trials, mimicking theophylline concentration data. Results obtained on those data sets with either the SAEM algorithm or the first-order conditional estimates (FOCE) algorithm (implemented in the nlme function of R software) are compared: biases and root mean square errors of almost all the SAEM estimates are smaller than the FOCE ones. Finally, we apply the extended SAEM algorithm to analyze the pharmacokinetic interaction of tenofovir on atazanavir, a novel protease inhibitor, from the Agence Nationale de Recherche sur le Sida 107-Puzzle 2 study. A significant decrease of the area under the curve of atazanavir is found in patients receiving both treatments.

International audience; PURPOSE: We propose a new method for efficient particle transportation in voxelized geometry for Monte Carlo simulations. We describe its use for calculating dose distribution in CT images for radiation therapy. MATERIAL AND METHODS: The proposed approach, based on an implicit volume representation named segmented volume, coupled with an adapted segmentation procedure and a distance map, allows us to minimize the number of boundary crossings, which slows down simulation. The method was implemented with the GEANT4 toolkit and compared to four other methods: One box per voxel, parameterized volumes, octree-based volumes, and nested parameterized volumes. For each representation, we compared dose distribution, time, and memory consumption. RESULTS: The proposed method allows us to decrease computational time by up to a factor of 15, while keeping memory consumption low, and without any modification of the transportation engine. Speeding up is related to the geometry complexity and the number of different materials used. We obtained an optimal number of steps with removal of all unnecessary steps between adjacent voxels sharing a similar material. However, the cost of each step is increased. When the number of steps cannot be decreased enough, due for example, to the large number of material boundaries, such a method is not considered suitable. CONCLUSION: This feasibility study shows that optimizing the representation of an image in memory potentially increases computing efficiency. We used the GEANT4 toolkit, but we could potentially use other Monte Carlo simulation codes. The method introduces a tradeoff between speed and geometry accuracy, allowing computational time gain. However, simulations with GEANT4 remain slow and further work is needed to speed up the procedure while preserving the desired accuracy.

International audience; GATE Monte Carlo simulations are performed to verify sampling schemes in multislice mode PET. Two types of scanner were simulated. The effect of attenuation and scatter were investigated by simulating a realistic phantom. The results are in accordance with the theoretical expectations.

In nonlinear mixed-effects models, estimation methods based on a linearization of the likelihood are widely used although they have several methodological drawbacks. Kuhn and Lavielle (Comput. Statist. Data Anal. 49:1020-1038 (2005)) developed an estimation method which combines the SAEM (Stochastic Approximation EM) algorithm, with a MCMC (Markov Chain Monte Carlo) procedure for maximum likelihood estimation in nonlinear mixed-effects models without linearization. This method is implemented in the Matlab software MONOLIX which is available at http://www.math.u-psud.fr/~lavielle/monolix/logiciels. In this paper we apply MONOLIX to the analysis of the pharmacokinetics of saquinavir, a protease inhibitor, from concentrations measured after single dose administration in 100 HIV patients, some with advance disease. We also illustrate how to use MONOLIX to build the covariate model using the Bayesian Information Criterion. Saquinavir oral clearance (CL/F) was estimated to be 1.26 L/h and to increase with body mass index, the inter-patient variability for CL/F being 120%. Several methodological developments are ongoing to extend SAEM which is a very promising estimation method for population pharmacockinetic/pharmacodynamic analyses.

International audience; Spontaneous calcium activity can be detected in embryonic mouse cortical slices as fluorescence intensity variations, in the presence of a fluorescent calcium indicator. Current methods to detect and quantify these variations depend heavily on experimenters whose judgement may interfere with measurement. In the present work, we developed new software called CalSignal for automatic detection and tracking of cellular bodies and quantification of spontaneous calcium activity on time-series of confocal fluorescence images. Analysis of 28 neocortical slices revealed that 21.0% of detected cells displayed peaks of fluorescence corresponding to spontaneous activity, with a mean frequency of one peak per 4 min. This activity was blocked in the absence of extracellular calcium but was not modified after depletion of calcium stores with thapsigargin or blockade of voltage-gated calcium channels with Ni2+. Further, statistical analysis of calcium activity revealed concomitant activation of distant cells in 24 slices, and the existence of a significant network of synchrony based on such coactivations in 17 slices out of 28. These networks enclosed 84.3% of active cells, scattered throughout the neocortical wall (mean distance between cellular bodies, 111.7 microm). Finally, it was possible to identify specific cells which were synchronously active with more neighbouring cells than others. The identity of these nodal cells remains to be investigated to fully comprehend the role of spontaneous calcium activity, before synaptogenesis, in shaping cortical neurogenesis.

Model evaluation is an important issue in population analyses. We aimed to perform a systematic review of all population pharmacokinetic and/or pharmacodynamic analyses published between 2002 and 2004 to survey the current methods used to evaluate models and to assess whether those models were adequately evaluated. We selected 324 articles in MEDLINE using defined key words and built a data abstraction form composed of a checklist of items to extract the relevant information from these articles with respect to model evaluation. In the data abstraction form, evaluation methods were divided into three subsections: basic internal methods (goodness-of-fit [GOF] plots, uncertainty in parameter estimates and model sensitivity), advanced internal methods (data splitting, resampling techniques and Monte Carlo simulations) and external model evaluation. Basic internal evaluation was the most frequently described method in the reports: 65% of the models involved GOF evaluation. Standard errors or confidence intervals were reported for 50% of fixed effects but only for 22% of random effects. Advanced internal methods were used in approximately 25% of models: data splitting was more often used than bootstrap and cross-validation; simulations were used in 6% of models to evaluate models by a visual predictive check or by a posterior predictive check. External evaluation was performed in only 7% of models. Using the subjective synthesis of model evaluation for each article, we judged the models to be adequately evaluated in 28% of pharmacokinetic models and 26% of pharmacodynamic models. Basic internal evaluation was preferred to more advanced methods, probably because the former is performed easily with most software. We also noticed that when the aim of modelling was predictive, advanced internal methods or more stringent methods were more often used.

Model evaluation is an important issue in population analyses. We aimed to perform a systematic review of all population pharmacokinetic and/or pharmacodynamic analyses published between 2002 and 2004 to survey the current methods used to evaluate models and to assess whether those models were adequately evaluated. We selected 324 articles in MEDLINE using defined key words and built a data abstraction form composed of a checklist of items to extract the relevant information from these articles with respect to model evaluation. In the data abstraction form, evaluation methods were divided into three subsections: basic internal methods (goodness-of-fit [GOF] plots, uncertainty in parameter estimates and model sensitivity), advanced internal methods (data splitting, resampling techniques and Monte Carlo simulations) and external model evaluation. Basic internal evaluation was the most frequently described method in the reports: 65% of the models involved GOF evaluation. Standard errors or confidence intervals were reported for 50% of fixed effects but only for 22% of random effects. Advanced internal methods were used in approximately 25% of models: data splitting was more often used than bootstrap and cross-validation; simulations were used in 6% of models to evaluate models by a visual predictive check or by a posterior predictive check. External evaluation was performed in only 7% of models. Using the subjective synthesis of model evaluation for each article, we judged the models to be adequately evaluated in 28% of pharmacokinetic models and 26% of pharmacodynamic models. Basic internal evaluation was preferred to more advanced methods, probably because the former is performed easily with most software. We also noticed that when the aim of modelling was predictive, advanced internal methods or more stringent methods were more often used.

International audience; In SPECT, simultaneous (99m)Tc/(123)I acquisitions allow comparison of the distribution of two radiotracers in the same physiological state, without any image misregistration, but images can be severely distorted due to cross-talk between the two isotopes. We propose a generalized spectral factor analysis (GSFA) method for solving the cross-talk issue in simultaneous (99m)Tc/(123)I SPECT. In GSFA, the energy spectrum of the photons in any pixel is expressed as a linear combination of five common spectra: (99m)Tc and (123)I photopeaks and three scatter spectra. These basis spectra are estimated from a factor analysis of all spectra using physical priors (e.g. Klein-Nishina distributions). GSFA was evaluated on (99m)Tc/(123)I Monte Carlo simulated data and compared to images obtained using recommended spectral windows (WIN) and to the gold standard (GS) images (scatter-free, cross-talk-free and noise-free). Using GSFA, activity concentration differed by less than 9% compared to GS values against differences from -23% to 110% with WIN in the (123)I and (99m)Tc images respectively. Using GSFA, simultaneous (99m)Tc/(123)I imaging can yield images of similar quantitative accuracy as when using sequential and scatter-free (99m)Tc/(123)I imaging in brain SPECT.

In single photon emission computed tomography (SPECT) with parallel hole collimation, image reconstruction is usually performed as a set of bidimensional (2D) analytical or iterative reconstructions. This approach ignores the tridimensional (3D) nature of scatter and detector response function that affects the detected signal. To deal with the 3D nature of the image formation process, iterative reconstruction can be used by considering a 3D projector modelling the 3D spread of photons. In this paper, we investigate the value of using accurate Monte Carlo simulations to determine the 3D projector used in a fully 3D Monte Carlo (F3DMC) reconstruction approach. Given the 3D projector modelling all physical effects affecting the imaging process, the reconstruction problem is solved using the maximum likelihood expectation maximization (MLEM) algorithm. To validate the concept, three data sets were simulated and F3DMC was compared with two other 3D reconstruction strategies using analytical corrections for attenuation, scatter and camera point spread function. Results suggest that F3DMC improves spatial resolution, relative and absolute quantitation and signal-to-noise ratio. The practical feasibility of the approach on real data sets is discussed.

International audience; Exposure to lead is a special problem in children, because they are more highly exposed than adults and because this pollutant, which accumulates in the body, induces neurobehavioral and cognitive effects. The objective of this study was to determine the probability density of the lead exposure dose of a 2-year-old child around an old mine site and to analyze its uncertainties, especially those associated with the bioavailability of lead in soil. Children's exposure was estimated indirectly from environmental samples (soils, domestic dust, water, air) and parameters (volume inhaled, body weight, soil intake rate, water intake, dietary intake) from the literature. Uncertainty and variability were analyzed separately in a two-dimensional Monte Carlo simulation with Crystal Ball software. Exposure doses were simulated with different methods for accessing the bioavailability of lead in soil. The exposure dose per kilogram of body weight varied from 2 microg/kgday at the 5th percentile to 5.5 microg/kgday at the 95th percentile (and from 2 to 10 microg/kgday, respectively, when ignoring bioavailability). The principal factors of variation were dietary intake, soil concentrations, and soil ingestion. The principal uncertainties were associated with the level of soil ingestion and the bioavailability of lead. Reducing uncertainty about the bioavailability of lead in soil by taking into account information about the type of mineral made it possible to increase our degree of confidence (from 25% to more than 95%) that the median exposure dose does not exceed the Tolerable Daily Intake. Knowledge of the mineral very substantially increases the degree of confidence in estimates of children's lead exposure around an old mining site by reducing the uncertainty associated with lead's bioavailability.

International audience; Collision-induced dissociation, laser-induced dissociation and electron-capture dissociation are compared on a singly and doubly protonated pentapeptide. The dissociation spectrum depends on the excitation mechanism and on the charge state of the peptide. The comparison of these results with the conformations obtained from Monte Carlo simulations suggests that the de-excitation mechanism following a laser or an electron-capture excitation is related to the initial geometry of the peptide.

Reliable estimation methods for non-linear mixed-effects models are now available and, although these models are increasingly used, only a limited number of statistical developments for their evaluation have been reported. We develop a criterion and a test to evaluate nonlinear mixed-effects models based on the whole predictive distribution. For each observation, we define the prediction discrepancy (pd) as the percentile of the observation in the whole marginal predictive distribution under H(0). We propose to compute prediction discrepancies using Monte Carlo integration which does not require model approximation. If the model is valid, these pd should be uniformly distributed over (0, 1) which can be tested by a Kolmogorov-Smirnov test. In a simulation study based on a standard population pharmacokinetic model, we compare and show the interest of this criterion with respect to the one most frequently used to evaluate nonlinear mixed-effects models: standardized prediction errors (spe) which are evaluated using a first order approximation of the model. Trends in pd can also be evaluated via several plots to check for specific departures from the model.

International audience; A radiation dose enhancement can be obtained in brain tumours after infusion of an iodinated contrast agent and irradiation with kilovoltage x-rays in tomography mode. The aim of this study was to assess dosimetric properties of the synchrotron stereotactic radiotherapy technique applied to humans (SSR) for preparing clinical trials. We designed an interface for dose computation based on a Monte Carlo code (MCNPX). A patient head was constructed from computed tomography (CT) data and a tumour volume was modelled. Dose distributions were calculated in SSR configuration for various energy beam and iodine content in the target volume. From the calculations, it appears that the iodine-filled target (10 mg ml(-1)) can be efficiently irradiated by a monochromatic beam of energy ranging from 50 to 85 keV. This paper demonstrates the feasibility of stereotactic radiotherapy for treating deep-seated brain tumours with monoenergetic x-rays from a synchrotron.

Most studies of neighborhood effects on health have used the multilevel approach. However, since this methodology does not incorporate any notion of space, it may not provide optimal epidemiologic information when modeling variations or when investigating associations between contextual factors and health. Investigating mental disorders due to psychoactive substance use among all 65,830 individuals aged 40-59 years in 2001 in Malmö, Sweden, geolocated at their place of residence, the authors compared a spatial analytical perspective, which builds notions of space into hypotheses and methods, with the multilevel approach. Geoadditive models provided precise cartographic information on spatial variations in prevalence independent of administrative boundaries. The multilevel model showed significant neighborhood variations in the prevalence of substance-related disorders. However, hierarchical geostatistical models provided information on not only the magnitude but also the scale of neighborhood variations, indicating a significant correlation between neighborhoods in close proximity to each other. The prevalence of disorders increased with neighborhood deprivation. Far stronger associations were observed when using indicators measured in spatially adaptive areas, centered on residences of individuals, smaller in size than administrative neighborhoods. In neighborhood studies, building notions of space into analytical procedures may yield more comprehensive information than heretofore has been gathered on the spatial distribution of outcomes.

PURPOSE: In single-photon emission computed tomography (SPECT) of the dopaminergic system, measurements of striatal uptake are useful for diagnosis and patient follow-up but are strongly biased by the partial volume effect (PVE). We studied whether PVE correction might improve patient classification based on binding potential (BP) measurements. METHODS: Patients with a probable diagnosis of dementia with Lewy bodies (DLB, 10 patients) or Alzheimer's disease (AD, 13 patients) were studied by( 123)I-FP-CIT SPECT. SPECT images were reconstructed with and without PVE correction. Each patient SPECT scan was also simulated to obtain SPECT data whose characteristics were fully known. In addition, 17 SPECT scans were simulated with striatal uptake values mimicking pre-symptomatic cases of DLB. RESULTS: Without PVE correction, mean putamen BP values were 2.9+/-0.4 and 0.9+/-0.2 for AD and DLB patients respectively, while with PVE correction, they were 8.6+/-1.5 and 1.9+/-0.5 respectively. All patients were properly identified as having AD or DLB when considering mean putamen BP measured on their real or simulated SPECT scan, with and without PVE correction. All 30 simulations mimicking pre-symptomatic DLB and AD patients were accurately classified with PVE correction, but without PVE correction 15 mean putamen BP values were in a range where AD and DLB could not be distinguished. CONCLUSION: We conclude that putamen BP values measured without PVE correction can be used to differentiate probable DLB and AD due to the already severe reduction in dopamine transporter levels. PVE correction appeared useful for accurate differential diagnosis between AD and pre-symptomatic DLB.

Recently, regression analysis of the cumulative incidence function has gained interest in competing risks data analysis, through the model proposed by Fine and Gray (JASA 1999; 94: 496-509). In this note, we point out that inclusion of time-dependent covariates in this model can lead to serious bias. We illustrate the problems arising in such a context, using bone marrow transplant data as a working example and numerical simulations. Practical advices are given, preventing the misuse of this model.

Background In order to detect potential disease clusters where a putative source cannot be specified, classical procedures scan the geographical area with circular windows through a specified grid imposed to the map. However, the choice of the windows' shapes, sizes and centers is critical and different choices may not provide exactly the same results. The aim of our work was to use an Oblique Decision Tree model (ODT) which provides potential clusters without pre-specifying shapes, sizes or centers. For this purpose, we have developed an ODT-algorithm to find an oblique partition of the space defined by the geographic coordinates. Methods ODT is based on the classification and regression tree (CART). As CART finds out rectangular partitions of the covariate space, ODT provides oblique partitions maximizing the interclass variance of the independent variable. Since it is a NP-Hard problem in RN, classical ODT-algorithms use evolutionary procedures or heuristics. We have developed an optimal ODT-algorithm in R2, based on the directions defined by each couple of point locations. This partition provided potential clusters which can be tested with Monte-Carlo inference. We applied the ODT-model to a dataset in order to identify potential high risk clusters of malaria in a village in Western Africa during the dry season. The ODT results were compared with those of the Kulldorff' s SaTScan™. Results The ODT procedure provided four classes of risk of infection. In the first high risk class 60%, 95% confidence interval (CI95%) [52.22–67.55], of the children was infected. Monte-Carlo inference showed that the spatial pattern issued from the ODT-model was significant (p < 0.0001). Satscan results yielded one significant cluster where the risk of disease was high with an infectious rate of 54.21%, CI95% [47.51–60.75]. Obviously, his center was located within the first high risk ODT class. Both procedures provided similar results identifying a high risk cluster in the western part of the village where a mosquito breeding point was located. Conclusion ODT-models improve the classical scanning procedures by detecting potential disease clusters independently of any specification of the shapes, sizes or centers of the clusters.

International audience; Synchrotron stereotactic radiotherapy (SSR) consists in loading the tumour with a high atomic number element (Z), and exposing it to monochromatic x-rays from a synchrotron source (50-100 keV), in stereotactic conditions. The dose distribution results from both the stereotactic monochromatic x-ray irradiation and the presence of the high Z element. The purpose of this preliminary study was to evaluate the two-dimensional dose distribution resulting solely from the irradiation geometry, using Monte Carlo simulations and a Fricke gel dosimeter. The verification of a Monte Carlo-based dosimetry was first assessed by depth dose measurements in a water tank. We thereafter used a Fricke dosimeter to compare Monte Carlo simulations with dose measurements. The Fricke dosimeter is a solution containing ferrous ions which are oxidized to ferric ions under ionizing radiation, proportionally to the absorbed dose. A cylindrical phantom filled with Fricke gel was irradiated in stereotactic conditions over several slices with a continuous beam (beam section = 0.1 x 1 cm2). The phantom and calibration vessels were then imaged by nuclear magnetic resonance. The measured doses were fairly consistent with those predicted by Monte Carlo simulations. However, the measured maximum absolute dose was 10% underestimated regarding calculation. The loss of information in the higher region of dose is explained by the diffusion of ferric ions. Monte Carlo simulation is the most accurate tool for dosimetry including complex geometries made of heterogeneous materials. Although the technique requires improvements, gel dosimetry remains an essential tool for the experimental verification of dose distribution in SSR with millimetre precision.

BACKGROUND: Maternal age is an important factor in reproduction. Can assisted reproduction technologies (ART) fully compensate for the decline in fertility with age? METHODS: We used a computer simulation (Monte Carlo) model of reproduction, combining the monthly probabilities of conceiving, the risk of miscarriage and the probability of becoming age-dependently permanently sterile. RESULTS: Under natural conditions, 75% of women starting to try to conceive at age 30 years will have a conception ending in a live birth within 1 year, 66% at age 35 years and 44% at age 40 years. Within 4 years the success rates will be respectively 91, 84 and 64%. If women turn to ART after 4, 3 or 2 years respectively without conception, and if the rate of success is as observed after two cycles of insemination in IVF, ART makes up for only half of the births lost by postponing a first attempt of pregnancy from age 30 to 35 years, and