134 resultados para Unbiased estimating functions
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Sphingomonas wittichii RW1 is a dibenzofuran and dibenzodioxin-degrading bacterium with potentially interesting properties for bioaugmentation of contaminated sites. In order to understand the capacity of the microorganism to survive in the environment we used a genome-wide transposon scanning approach. RW1 transposon libraries were generated with around 22 000 independent insertions. Libraries were grown for an average of 50 generations (five successive passages in batch liquid medium) with salicylate as sole carbon and energy source in presence or absence of salt stress at -1.5 MPa. Alternatively, libraries were grown in sand with salicylate, at 50% water holding capacity, for 4 and 10 days (equivalent to 7 generations). Library DNA was recovered from the different growth conditions and scanned by ultrahigh throughput sequencing for the positions and numbers of inserted transposed kanamycin resistance gene. No transposon reads were recovered in 579 genes (10% of all annotated genes in the RW1 genome) in any of the libraries, suggesting those to be essential for survival under the used conditions. Libraries recovered from sand differed strongly from those incubated in liquid batch medium. In particular, important functions for survival of cells in sand at the short term concerned nutrient scavenging, energy metabolism and motility. In contrast to this, fatty acid metabolism and oxidative stress response were essential for longer term survival of cells in sand. Comparison to transcriptome data suggested important functions in sand for flagellar movement, pili synthesis, trehalose and polysaccharide synthesis and putative cell surface antigen proteins. Interestingly, a variety of genes were also identified, interruption of which cause significant increase in fitness during growth on salicylate. One of these was an Lrp family transcription regulator and mutants in this gene covered more than 90% of the total library after 50 generations of growth on salicylate. Our results demonstrate the power of genome-wide transposon scanning approaches for analysis of complex traits.
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The SOS screen, as originally described by Perkins et al. (1999) [7], was setup with the aim of identifying Arabidopsis functions that might potentially be involved in the DNA metabolism. Such functions, when expressed in bacteria, are prone to disturb replication and thus trigger the SOS response. Consistently, expression of AtRAD51 and AtDMC1 induced the SOS response in bacteria, even affecting E. coli viability. 100 SOS-inducing cDNAs were isolated from a cDNA library constructed from an Arabidopsis cell suspension that was found to highly express meiotic genes. A large proportion of these SOS(+) candidates are clearly related to the DNA metabolism, others could be involved in the RNA metabolism, while the remaining cDNAs encode either totally unknown proteins or proteins that were considered as irrelevant. Seven SOS(+) candidate genes are induced following gamma irradiation. The in planta function of several of the SOS-inducing clones was investigated using T-DNA insertional mutants or RNA interference. Only one SOS(+) candidate, among those examined, exhibited a defined phenotype: silenced plants for DUT1 were sensitive to 5-fluoro-uracil (5FU), as is the case of the leaky dut-1 mutant in E. coli that are affected in dUTPase activity. dUTPase is essential to prevent uracil incorporation in the course of DNA replication.
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DNA methylation is involved in a diversity of processes in bacteria, including maintenance of genome integrity and regulation of gene expression. Here, using Caulobacter crescentus as a model, we exploit genome-wide experimental methods to uncover the functions of CcrM, a DNA methyltransferase conserved in most Alphaproteobacteria. Using single molecule sequencing, we provide evidence that most CcrM target motifs (GANTC) switch from a fully methylated to a hemi-methylated state when they are replicated, and back to a fully methylated state at the onset of cell division. We show that DNA methylation by CcrM is not required for the control of the initiation of chromosome replication or for DNA mismatch repair. By contrast, our transcriptome analysis shows that >10% of the genes are misexpressed in cells lacking or constitutively over-expressing CcrM. Strikingly, GANTC methylation is needed for the efficient transcription of dozens of genes that are essential for cell cycle progression, in particular for DNA metabolism and cell division. Many of them are controlled by promoters methylated by CcrM and co-regulated by other global cell cycle regulators, demonstrating an extensive cross talk between DNA methylation and the complex regulatory network that controls the cell cycle of C. crescentus and, presumably, of many other Alphaproteobacteria.
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Modeling concentration-response function became extremely popular in ecotoxicology during the last decade. Indeed, modeling allows determining the total response pattern of a given substance. However, reliable modeling is consuming in term of data, which is in contradiction with the current trend in ecotoxicology, which aims to reduce, for cost and ethical reasons, the number of data produced during an experiment. It is therefore crucial to determine experimental design in a cost-effective manner. In this paper, we propose to use the theory of locally D-optimal designs to determine the set of concentrations to be tested so that the parameters of the concentration-response function can be estimated with high precision. We illustrated this approach by determining the locally D-optimal designs to estimate the toxicity of the herbicide dinoseb on daphnids and algae. The results show that the number of concentrations to be tested is often equal to the number of parameters and often related to the their meaning, i.e. they are located close to the parameters. Furthermore, the results show that the locally D-optimal design often has the minimal number of support points and is not much sensitive to small changes in nominal values of the parameters. In order to reduce the experimental cost and the use of test organisms, especially in case of long-term studies, reliable nominal values may therefore be fixed based on prior knowledge and literature research instead of on preliminary experiments
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Bone marrow hematopoietic stem cells (HSCs) are responsible for both lifelong daily maintenance of all blood cells and for repair after cell loss. Until recently the cellular mechanisms by which HSCs accomplish these two very different tasks remained an open question. Biological evidence has now been found for the existence of two related mouse HSC populations. First, a dormant HSC (d-HSC) population which harbors the highest self-renewal potential of all blood cells but is only induced into active self-renewal in response to hematopoietic stress. And second, an active HSC (a-HSC) subset that by and large produces the progenitors and mature cells required for maintenance of day-to-day hematopoiesis. Here we present computational analyses further supporting the d-HSC concept through extensive modeling of experimental DNA label-retaining cell (LRC) data. Our conclusion that the presence of a slowly dividing subpopulation of HSCs is the most likely explanation (amongst the various possible causes including stochastic cellular variation) of the observed long term Bromodeoxyuridine (BrdU) retention, is confirmed by the deterministic and stochastic models presented here. Moreover, modeling both HSC BrdU uptake and dilution in three stages and careful treatment of the BrdU detection sensitivity permitted improved estimates of HSC turnover rates. This analysis predicts that d-HSCs cycle about once every 149-193 days and a-HSCs about once every 28-36 days. We further predict that, using LRC assays, a 75%-92.5% purification of d-HSCs can be achieved after 59-130 days of chase. Interestingly, the d-HSC proportion is now estimated to be around 30-45% of total HSCs - more than twice that of our previous estimate.
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DNA-binding proteins mediate a variety of crucial molecular functions, such as transcriptional regulation and chromosome maintenance, replication and repair, which in turn control cell division and differentiation. The roles of these proteins in disease are currently being investigated using microarray-based approaches. However, these assays can be difficult to adapt to routine diagnosis of complex diseases such as cancer. Here, we review promising alternative approaches involving protein-binding microarrays (PBMs) that probe the interaction of proteins from crude cell or tissue extracts with large collections of synthetic or natural DNA sequences. Recent studies have demonstrated the use of these novel PBM approaches to provide rapid and unbiased characterization of DNA-binding proteins as molecular markers of disease, for example cancer progression or infectious diseases.
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Estimating the time since discharge of a spent cartridge or a firearm can be useful in criminal situa-tions involving firearms. The analysis of volatile gunshot residue remaining after shooting using solid-phase microextraction (SPME) followed by gas chromatography (GC) was proposed to meet this objective. However, current interpretative models suffer from several conceptual drawbacks which render them inadequate to assess the evidential value of a given measurement. This paper aims to fill this gap by proposing a logical approach based on the assessment of likelihood ratios. A probabilistic model was thus developed and applied to a hypothetical scenario where alternative hy-potheses about the discharge time of a spent cartridge found on a crime scene were forwarded. In order to estimate the parameters required to implement this solution, a non-linear regression model was proposed and applied to real published data. The proposed approach proved to be a valuable method for interpreting aging-related data.
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Mitochondrial dysfunction is one of the possible mechanisms by which azole resistance can occur in Candida glabrata. Cells with mitochondrial DNA deficiency (so-called "petite mutants") upregulate ATP binding cassette (ABC) transporter genes and thus display increased resistance to azoles. Isolation of such C. glabrata mutants from patients receiving antifungal therapy or prophylaxis has been rarely reported. In this study, we characterized two sequential and related C. glabrata isolates recovered from the same patient undergoing azole therapy. The first isolate (BPY40) was azole susceptible (fluconazole MIC, 4 μg/ml), and the second (BPY41) was azole resistant (fluconazole MIC, >256 μg/ml). BPY41 exhibited mitochondrial dysfunction and upregulation of the ABC transporter genes C. glabrata CDR1 (CgCDR1), CgCDR2, and CgSNQ2. We next assessed whether mitochondrial dysfunction conferred a selective advantage during host infection by testing the virulence of BPY40 and BPY41 in mice. Surprisingly, even with in vitro growth deficiency compared to BPY40, BPY41 was more virulent (as judged by mortality and fungal tissue burden) than BPY40 in both systemic and vaginal murine infection models. The increased virulence of the petite mutant correlated with a drastic gain of fitness in mice compared to that of its parental isolate. To understand this unexpected feature, genome-wide changes in gene expression driven by the petite mutation were analyzed by use of microarrays during in vitro growth. Enrichment of specific biological processes (oxido-reductive metabolism and the stress response) was observed in BPY41, all of which was consistent with mitochondrial dysfunction. Finally, some genes involved in cell wall remodelling were upregulated in BPY41 compared to BPY40, which may partially explain the enhanced virulence of BPY41. In conclusion, this study shows for the first time that mitochondrial dysfunction selected in vivo under azole therapy, even if strongly affecting in vitro growth characteristics, can confer a selective advantage under host conditions, allowing the C. glabrata mutant to be more virulent than wild-type isolates.
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Preface The starting point for this work and eventually the subject of the whole thesis was the question: how to estimate parameters of the affine stochastic volatility jump-diffusion models. These models are very important for contingent claim pricing. Their major advantage, availability T of analytical solutions for characteristic functions, made them the models of choice for many theoretical constructions and practical applications. At the same time, estimation of parameters of stochastic volatility jump-diffusion models is not a straightforward task. The problem is coming from the variance process, which is non-observable. There are several estimation methodologies that deal with estimation problems of latent variables. One appeared to be particularly interesting. It proposes the estimator that in contrast to the other methods requires neither discretization nor simulation of the process: the Continuous Empirical Characteristic function estimator (EGF) based on the unconditional characteristic function. However, the procedure was derived only for the stochastic volatility models without jumps. Thus, it has become the subject of my research. This thesis consists of three parts. Each one is written as independent and self contained article. At the same time, questions that are answered by the second and third parts of this Work arise naturally from the issues investigated and results obtained in the first one. The first chapter is the theoretical foundation of the thesis. It proposes an estimation procedure for the stochastic volatility models with jumps both in the asset price and variance processes. The estimation procedure is based on the joint unconditional characteristic function for the stochastic process. The major analytical result of this part as well as of the whole thesis is the closed form expression for the joint unconditional characteristic function for the stochastic volatility jump-diffusion models. The empirical part of the chapter suggests that besides a stochastic volatility, jumps both in the mean and the volatility equation are relevant for modelling returns of the S&P500 index, which has been chosen as a general representative of the stock asset class. Hence, the next question is: what jump process to use to model returns of the S&P500. The decision about the jump process in the framework of the affine jump- diffusion models boils down to defining the intensity of the compound Poisson process, a constant or some function of state variables, and to choosing the distribution of the jump size. While the jump in the variance process is usually assumed to be exponential, there are at least three distributions of the jump size which are currently used for the asset log-prices: normal, exponential and double exponential. The second part of this thesis shows that normal jumps in the asset log-returns should be used if we are to model S&P500 index by a stochastic volatility jump-diffusion model. This is a surprising result. Exponential distribution has fatter tails and for this reason either exponential or double exponential jump size was expected to provide the best it of the stochastic volatility jump-diffusion models to the data. The idea of testing the efficiency of the Continuous ECF estimator on the simulated data has already appeared when the first estimation results of the first chapter were obtained. In the absence of a benchmark or any ground for comparison it is unreasonable to be sure that our parameter estimates and the true parameters of the models coincide. The conclusion of the second chapter provides one more reason to do that kind of test. Thus, the third part of this thesis concentrates on the estimation of parameters of stochastic volatility jump- diffusion models on the basis of the asset price time-series simulated from various "true" parameter sets. The goal is to show that the Continuous ECF estimator based on the joint unconditional characteristic function is capable of finding the true parameters. And, the third chapter proves that our estimator indeed has the ability to do so. Once it is clear that the Continuous ECF estimator based on the unconditional characteristic function is working, the next question does not wait to appear. The question is whether the computation effort can be reduced without affecting the efficiency of the estimator, or whether the efficiency of the estimator can be improved without dramatically increasing the computational burden. The efficiency of the Continuous ECF estimator depends on the number of dimensions of the joint unconditional characteristic function which is used for its construction. Theoretically, the more dimensions there are, the more efficient is the estimation procedure. In practice, however, this relationship is not so straightforward due to the increasing computational difficulties. The second chapter, for example, in addition to the choice of the jump process, discusses the possibility of using the marginal, i.e. one-dimensional, unconditional characteristic function in the estimation instead of the joint, bi-dimensional, unconditional characteristic function. As result, the preference for one or the other depends on the model to be estimated. Thus, the computational effort can be reduced in some cases without affecting the efficiency of the estimator. The improvement of the estimator s efficiency by increasing its dimensionality faces more difficulties. The third chapter of this thesis, in addition to what was discussed above, compares the performance of the estimators with bi- and three-dimensional unconditional characteristic functions on the simulated data. It shows that the theoretical efficiency of the Continuous ECF estimator based on the three-dimensional unconditional characteristic function is not attainable in practice, at least for the moment, due to the limitations on the computer power and optimization toolboxes available to the general public. Thus, the Continuous ECF estimator based on the joint, bi-dimensional, unconditional characteristic function has all the reasons to exist and to be used for the estimation of parameters of the stochastic volatility jump-diffusion models.
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Low socioeconomic status has been reported to be associated with head and neck cancer risk. However, previous studies have been too small to examine the associations by cancer subsite, age, sex, global region, and calendar time, and to explain the association in terms of behavioural risk factors. Individual participant data of 23,964 cases with head and neck cancer and 31,954 controls from 31 studies in 27 countries pooled with random effects models. Overall, low education was associated with an increased risk of head and neck cancer (OR = 2·50; 95%CI 2·02- 3·09). Overall one-third of the increased risk was not explained by differences in the distribution of cigarette smoking and alcohol behaviours; and it remained elevated among never users of tobacco and non-drinkers (OR = 1·61; 95%CI 1·13 - 2·31). More of the estimated education effect was not explained by cigarette smoking and alcohol behaviours: in women than in men, in older than younger groups, in the oropharynx than in other sites, in South/Central America than in Europe/North America, and was strongest in countries with greater income inequality. Similar findings were observed for the estimated effect of low vs high household income. The lowest levels of income and educational attainment were associated with more than 2-fold increased risk of head and neck cancer, which is not entirely explained by differences in the distributions of behavioural risk factors for these cancers, and which varies across cancer sites, sexes, countries, and country income inequality levels. © 2014 Wiley Periodicals, Inc.
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A major issue in the application of waveform inversion methods to crosshole georadar data is the accurate estimation of the source wavelet. Here, we explore the viability and robustness of incorporating this step into a time-domain waveform inversion procedure through an iterative deconvolution approach. Our results indicate that, at least in non-dispersive electrical environments, such an approach provides remarkably accurate and robust estimates of the source wavelet even in the presence of strong heterogeneity in both the dielectric permittivity and electrical conductivity. Our results also indicate that the proposed source wavelet estimation approach is relatively insensitive to ambient noise and to the phase characteristics of the starting wavelet. Finally, there appears to be little-to-no trade-off between the wavelet estimation and the tomographic imaging procedures.
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In developmental research, the family has mainly been studied through dyadic interaction. Three-way interactions have received less attention, partly because of their complexity. This difficulty may be overcome by distinguishing between four hierarchically embedded functions in three-way interactions: (1) participation (inclusion of all participants), (2) organization (partners keeping to their roles), (3) focalization (sharing a common focus) and (4) affective contact (being in tune). We document this hierarchical model on a sample of 80 families observed in the Lausanne Trilogue Play situation across four different sites. Hierarchy between functions was demonstrated by means of Guttman scalability coefficient. Given the importance of the child's development in a threesome, the pertinence of this model for family assessment is discussed.
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This paper introduces a nonlinear measure of dependence between random variables in the context of remote sensing data analysis. The Hilbert-Schmidt Independence Criterion (HSIC) is a kernel method for evaluating statistical dependence. HSIC is based on computing the Hilbert-Schmidt norm of the cross-covariance operator of mapped samples in the corresponding Hilbert spaces. The HSIC empirical estimator is very easy to compute and has good theoretical and practical properties. We exploit the capabilities of HSIC to explain nonlinear dependences in two remote sensing problems: temperature estimation and chlorophyll concentration prediction from spectra. Results show that, when the relationship between random variables is nonlinear or when few data are available, the HSIC criterion outperforms other standard methods, such as the linear correlation or mutual information.
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The recently discovered epithelial sodium channel (ENaC)/degenerin (DEG) gene family encodes sodium channels involved in various cell functions in metazoans. Subfamilies found in invertebrates or mammals are functionally distinct. The degenerins in Caenorhabditis elegans participate in mechanotransduction in neuronal cells, FaNaC in snails is a ligand-gated channel activated by neuropeptides, and the Drosophila subfamily is expressed in gonads and neurons. In mammals, ENaC mediates Na+ transport in epithelia and is essential for sodium homeostasis. The ASIC genes encode proton-gated cation channels in both the central and peripheral nervous system that could be involved in pain transduction. This review summarizes the physiological roles of the different channels belonging to this family, their biophysical and pharmacological characteristics, and the emerging knowledge of their molecular structure. Although functionally different, the ENaC/DEG family members share functional domains that are involved in the control of channel activity and in the formation of the pore. The functional heterogeneity among the members of the ENaC/DEG channel family provides a unique opportunity to address the molecular basis of basic channel functions such as activation by ligands, mechanotransduction, ionic selectivity, or block by pharmacological ligands.
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Natural Killer (NK) cells are of special interest in solid organ transplantation (SOT) because classical immunosuppressive drugs could enhance NK cells activity.We studied NK cells after kidney transplantation in three different situations. First, we analysed the peripheral repertoire reconstitution and function of NK cells after a polyclonal rabbit anti-thymocytes globulin (rATG) induction therapy, in 20 patients transplanted with living donor and with a low immunological risk. Second, we analysed the influence of KIR genes on the risk of CMV primo-infection or reactivation in 224 transplanted patients during the first year. Finally, we studied the risk of rejection and graft function during the first 5 years according to the KIR genes. Our study demonstrates that after an intial drop, NK cell reconstitution is fast with a ratio of CD56+/CD3− cells versus CD3+ cells that remains identical. The fraction of NK cells expressing the inhibitory receptor NKG2A significantly increases and the activating receptor NKG2D decreases after transplantation to retrieve the pretransplantation value after one year. The secretion of INF-f × and the cytotoxicity is maintained over time after transplantation. Then, we demonstrated that the presence of 2 KIR missing ligands and a large number of activating KIR gene protected against CMV primo-infection or reactivation during the first year post transplantation. Finally, the KIR genes and their HLA ligands do not influence the long term graft function after univariate and multivariate analysis. Our data suggest that despite the modification of the receptor repertoire, NK cell activity is preserved. NK cells are an important player of the immune response in the first year after transplantation mainly thanks to their anti-infectious activity.