Patients' prediction of extubation success.

The spontaneous breathing trial (SBT)-relying on objective criteria assessed by the clinician-is the major diagnostic tool to determine if patients can be successfully extubated. However, little is known regarding the patient's subjective perception of autonomous breathing. We performed a prospective observational study in 211 mechanically ventilated adult patients successfully completing a SBT. Patients were randomly assigned to be interviewed during this trial regarding their prediction of extubation success. We compared post-extubation outcomes in three patient groups: patients confident (confidents; n = 115) or not (non-confidents; n = 38) of their extubation success and patients not subjected to interview (control group; n = 58). Extubation success was more frequent in confidents than in non-confidents (90 vs. 45%; p < 0.001/positive likelihood ratio = 2.00) or in the control group (90 vs. 78%; p = 0.04). On the contrary, extubation failure was more common in non-confidents than in confidents (55 vs. 10%; p < 0.001/negative likelihood ratio = 0.19). Logistic regression analysis showed that extubation success was associated with patient's prediction [OR (95% CI): 9.2 (3.74-22.42) for confidents vs.non-confidents] as well as to age [0.72 (0.66-0.78) for age 75 vs. 65 and 1.31 (1.28-1.51) for age 55 vs. 65]. Our data suggest that at the end of a sustained SBT, extubation success might be correlated to the patients' subjective perception of autonomous breathing. The results of this study should be confirmed by a large multicenter trial.

Optimal latent period in a bacteriophage population model structured by infection-age

We study the lysis timing of a bacteriophage population by means of a continuously infection-age-structured population dynamics model. The features of the model are the infection process of bacteria, the natural death process, and the lysis process which means the replication of bacteriophage viruses inside bacteria and the destruction of them. We consider that the length of the lysis timing (or latent period) is distributed according to a general probability distribution function. We have carried out an optimization procedure and we have found the latent period corresponding to the maximal fitness (i.e. maximal growth rate) of the bacteriophage population.

Stochastic demography and the neutral substitution rate in class-structured populations.

The neutral rate of allelic substitution is analyzed for a class-structured population subject to a stationary stochastic demographic process. The substitution rate is shown to be generally equal to the effective mutation rate, and under overlapping generations it can be expressed as the effective mutation rate in newborns when measured in units of average generation time. With uniform mutation rate across classes the substitution rate reduces to the mutation rate.

Oscillations in a molecular structured cell population model

We consider a nonlinear cyclin content structured model of a cell population divided into proliferative and quiescent cells. We show, for particular values of the parameters, existence of solutions that do not depend on the cyclin content. We make numerical simulations for the general case obtaining, for some values of the parameters convergence to the steady state but also oscillations of the population for others.

Psychoses a l'adolescence, les neurosciences ameliorent-elles la prediction? [Adolescent psychosis, can neuroscience improve prediction?]

Developments in the field of neuroscience have created a high level of interest in the subject of adolescent psychosis, particularly in relation to prediction and prevention. As the medical practice of adolescent psychosis and its treatment is characterised by a heterogeneity which is both symptomatic and evolutive, the somewhat poor prognosis of chronic development justifies the research performed: apparent indicators of schizophrenic disorders on the one hand and specific endophenotypes on the other are becoming increasingly important. The significant progresses made on the human genome show that the genetic predetermination in current psychiatric pathologies is complex and subject to moderating effects and there is therefore significant potential for nature-nurture interactions (between the environment and the genes). The road to be followed in researching the phenotypic expression of a psychosis gene is long and winding and is susceptible to many external influences at various levels with different effects. Neurobiological, neurophysiological, neuropsychological and neuroanatomical studies help to identify endophenotypes, which allow researchers to create identifying "markers" along this winding road. The endophenotypes could make it possible to redefine the nosological categories and enhance understanding of the physiopathology of schizophrenia. In a predictive approach, large-scale retrospective and prospective studies make it possible to identify risk factors, which are compatible with the neurodevelopmental hypothesis of schizophrenia. However, the predictive value of such markers or risk indicators is not yet sufficiently developed to offer a reliable early-detection method or possible schizophrenia prevention measures. Nonetheless, new developments show promise against the background of a possible future nosographic revolution, based on a paradigm shift. It is perhaps on the basis of homogeneous endophenotypes in particular that we will be able to understand what protects against, or indeed can trigger, psychosis irrespective of the clinical expression or attempts to isolate the common genetic and biological bases according to homogeneous clinical characteristics, which have to date, proved unsuccessful

Prediction of the economic cost of individual long-term care in the Spanish population

Pensions together with savings and investments during active life are key elements of retirement planning. Motivation for personal choices about the standard of living, bequest and the replacement ratio of pension with respect to last salary income must be considered. This research contributes to the financial planning by helping to quantify long-term care economic needs. We estimate life expectancy from retirement age onwards. The economic cost of care per unit of service is linked to the expected time of needed care and the intensity of required services. The expected individual cost of long-term care from an onset of dependence is estimated separately for men and women. Assumptions on the mortality of the dependent people compared to the general population are introduced. Parameters defining eligibility for various forms of coverage by the universal public social care of the welfare system are addressed. The impact of the intensity of social services on individual predictions is assessed, and a partial coverage by standard private insurance products is also explored. Data were collected by the Spanish Institute of Statistics in two surveys conducted on the general Spanish population in 1999 and in 2008. Official mortality records and life table trends were used to create realistic scenarios for longevity. We find empirical evidence that the public long-term care system in Spain effectively mitigates the risk of incurring huge lifetime costs. We also find that the most vulnerable categories are citizens with moderate disabilities that do not qualify to obtain public social care support. In the Spanish case, the trends between 1999 and 2008 need to be further explored.

In silico prediction of human carboxylesterase-1 (hCES1) metabolism combining docking analyses and MD simulations.

Metabolic problems lead to numerous failures during clinical trials, and much effort is now devoted in developing in silico models predicting metabolic stability and metabolites. Such models are well known for cytochromes P450 and some transferases, whereas little has been done to predict the hydrolytic activity of human hydrolases. The present study was undertaken to develop a computational approach able to predict the hydrolysis of novel esters by human carboxylesterase hCES1. The study involves both docking analyses of known substrates to develop predictive models, and molecular dynamics (MD) simulations to reveal the in situ behavior of substrates and products, with particular attention being paid to the influence of their ionization state. The results emphasize some crucial properties of the hCES1 catalytic cavity, confirming that as a trend with several exceptions, hCES1 prefers substrates with relatively smaller and somewhat polar alkyl/aryl groups and larger hydrophobic acyl moieties. The docking results underline the usefulness of the hydrophobic interaction score proposed here, which allows a robust prediction of hCES1 catalysis, while the MD simulations show the different behavior of substrates and products in the enzyme cavity, suggesting in particular that basic substrates interact with the enzyme in their unprotonated form.

High-resolution numerical modelling of flow-vegetation interactions

In this paper, we present and apply a new three-dimensional model for the prediction of canopy-flow and turbulence dynamics in open-channel flow. The approach uses a dynamic immersed boundary technique that is coupled in a sequentially staggered manner to a large eddy simulation. Two different biomechanical models are developed depending on whether the vegetation is dominated by bending or tensile forces. For bending plants, a model structured on the Euler-Bernoulli beam equation has been developed, whilst for tensile plants, an N-pendula model has been developed. Validation against flume data shows good agreement and demonstrates that for a given stem density, the models are able to simulate the extraction of energy from the mean flow at the stem-scale which leads to the drag discontinuity and associated mixing layer.

Structural prediction of peptides bound to MHC class I.

An ab initio structure prediction approach adapted to the peptide-major histocompatibility complex (MHC) class I system is presented. Based on structure comparisons of a large set of peptide-MHC class I complexes, a molecular dynamics protocol is proposed using simulated annealing (SA) cycles to sample the conformational space of the peptide in its fixed MHC environment. A set of 14 peptide-human leukocyte antigen (HLA) A0201 and 27 peptide-non-HLA A0201 complexes for which X-ray structures are available is used to test the accuracy of the prediction method. For each complex, 1000 peptide conformers are obtained from the SA sampling. A graph theory clustering algorithm based on heavy atom root-mean-square deviation (RMSD) values is applied to the sampled conformers. The clusters are ranked using cluster size, mean effective or conformational free energies, with solvation free energies computed using Generalized Born MV 2 (GB-MV2) and Poisson-Boltzmann (PB) continuum models. The final conformation is chosen as the center of the best-ranked cluster. With conformational free energies, the overall prediction success is 83% using a 1.00 Angstroms crystal RMSD criterion for main-chain atoms, and 76% using a 1.50 Angstroms RMSD criterion for heavy atoms. The prediction success is even higher for the set of 14 peptide-HLA A0201 complexes: 100% of the peptides have main-chain RMSD values &lt; or =1.00 Angstroms and 93% of the peptides have heavy atom RMSD values &lt; or =1.50 Angstroms. This structure prediction method can be applied to complexes of natural or modified antigenic peptides in their MHC environment with the aim to perform rational structure-based optimizations of tumor vaccines.

Structured output SVM for remote sensing image classification

In the recent years, kernel methods have revealed very powerful tools in many application domains in general and in remote sensing image classification in particular. The special characteristics of remote sensing images (high dimension, few labeled samples and different noise sources) are efficiently dealt with kernel machines. In this paper, we propose the use of structured output learning to improve remote sensing image classification based on kernels. Structured output learning is concerned with the design of machine learning algorithms that not only implement input-output mapping, but also take into account the relations between output labels, thus generalizing unstructured kernel methods. We analyze the framework and introduce it to the remote sensing community. Output similarity is here encoded into SVM classifiers by modifying the model loss function and the kernel function either independently or jointly. Experiments on a very high resolution (VHR) image classification problem shows promising results and opens a wide field of research with structured output kernel methods.