pinecone/core-2020-05-10-deduplication huggingface dataset:
XPS study of Ni-base alloys oxide films formed in primary conditions of pressurized water reactor

International audienceThe oxide scales formed on alloy 690 during its exposure to pressurized water reactor primary simulated media at 325 °C for different periods, ranging between 24 and 858 h have been characterized through XPS analysis. These results have been compared with the data obtained on Cr<sub>2</sub>O<sub>3</sub>, NiCr<sub>2</sub>O<sub>4</sub>, NiFe<sub>2</sub>O<sub>4</sub>, and Ni(OH)<sub>2</sub> reference compounds. This comparison leads to the following conclusions about the scale formed on alloy 690: (i) the deconvolution of Cr-2<i>p</i><sub>3/2</sub> core level spectra seems to show that the internal oxide scale is made of a mix of chromite and chromia; (ii) the evolution of the height ratio between satellite and principal peaks, on the Ni-2<i>p</i><sub>3/2</sub> core level, has highlighted the presence of nickel hydroxide for all corrosion durations; and (iii) XPS analysis can be used to study the kinetics of nickel ferrite precipitation. In order to obtain a more complete description of such oxide scales based upon XPS measurements, reference data, obtained on nickel ferrites and chromites in which Fe is partially substituted to Ni, are needed. The comparison of published data and reference spectra performed in this work also leads to conclude that core level fits are dependent on the energy resolution, which can be achieved with specific spectrometer and analysis procedures. Therefore, reference compound spectra need to be recorded in the very identical analytical configuration before dealing with results obtained on corroded specimen
Growth, characterization and interfacial reaction of magnetron sputtered Pt/CeO2 thin films on Si and Si3N4 substrates

Pt/CeO2 thin films were deposited on Si and Si3N4 substrates by magnetron sputtering at room temperature. Growth of the films on Si and Si3N4 were characterized by XRD, FESEM and AFM. Interaction of Pt/CeO2 films with Si in Si and Si3N4 substrates was extensively investigated by XPS. XRD studies show that films are oriented preferentially to (200) direction of CeO2. XPS results show that Pt is mainly present in +2 oxidation state in Pt/CeO2/Si film, whereas Pt4+ predominates in Pt/CeO2/Si3N4 film. Ce is present as both +4 and +3 oxidation states in Pt/CeO2 films deposited on both Si and Si3N4 substrates, but concentration of Ce3+ species is observed to be more in Pt/CeO2/Si film. Interfacial reaction between CeO2 and Si\ud
substrate is controlled in presence of Pt. Pt/Ce concentration ratio decreases in Pt/CeO2/Si3N4 film upon successive sputtering, whereas this ratio decreases initially and then increases in Pt/CeO2/Si film. Pt is segregated at the interface in Pt/CeO2/Si film, whereas Pt is diffused outwards in Pt/CeO2/Si3N4 film as observed from depth profiling studies
Energetic mixing of anti-SNAP25 on lipid monolayers: degree of saturation of C18 fatty acids

In our study, various mixtures of C18 fatty acids with different degrees of saturation in their hydrocarbon chain, namely stearic acid (SA), oleic acid (L1), linoleic acid (L2), and linolenic acid (L3), and a polyclonal antibody, anti-synaptosome-associated protein of 25 kDa (SNAP25) (AS25), have been investigated using the Langmuir–Blodgett (LB) technique accompanied by atomic force microscopy (AFM) imaging. The cis-double bonds in unsaturated lipids (L1, L2, and L3) have kinks in their molecular conformation and thus could not pack as tightly and uniformly as SA. The bends and kinks in the molecular structure may interfere with the packing of the lipid monolayer which will promote fluidity as shown in the analyzed compressibility modulus (Cs−1) data. The negative values of Gibbs free energy of mixing (ΔGmix) of C18 fatty acids/AS25 confirm the spontaneity interaction of AS25 molecules on the monolayers. The amount of AS25 incorporated into the monolayer strongly affected the thermodynamic properties of the lipid monolayers. AFM surface roughness analyses also indicate that AS25 molecules are strongly bounded on the surface membrane as predicted by the obtained energetic data. In comparison to all C18 fatty acids studied, the strongest intermolecular interaction is observed in L1 at the investigated ranges. In particular, at mole ratio of 26:1, the most negative ΔGmix is observed at L1. Thus, we can draw the conclusion that AS25 is best mixed with L1. This L1/AS25 ratio mimicking a half bilayer membrane serves as a very useful reference in preparing fatty-acid nanoimmunoliposomes as the targeted drug-delivery vehicles for cancer therap
Spectroscopy on photografted polyethylene surfaces using a perfluorophenyl azide : evidence for covalent attachment

KTI Projekt: Kommission für Technologie und Innovation,
Grant/Award Number: 17132.1 PFNM‐NMThe present study was conducted in order to confirm C―H insertion of a perfluorophenyl nitrene,
produced by UV‐irradiation of a perfluorophenyl azide, to polyethylene surfaces.
It was shown previously that water‐repelling, oil‐repelling, and dirt‐repelling polyethylene surfaces
can be created by “grafting to” of perfluoroalkanes using a photoreactive surface modifier
based on azide/nitrene chemistry. The abrasion resistance of the new surfaces was enhanced
compared with a coating using a simple, long‐chain perfluoroalkane. However, covalent binding
of the surface modifier was not unequivocally demonstrated.
Here, spectroscopic information is presented suggesting that, indeed, a monomolecular, covalently
bound grafted layer is formed from the photodecomposition of a perfluorophenyl azide
on polyethylene surfaces.
Infrared spectroscopy showed that the peak from the azide moiety disappeared upon UV‐irradiation,
and the light dose for completion of the photo decomposition was determined to be
approximately 322 mJ/cm2.
A model compound mimicking the grafted nitrene species was synthesized, having a λmax of
281 nm in hexane. The photografted and washed layer had a λmax of 286 nm, indicating a good
conformity with the model compound.
X‐ray photoelectron spectroscopy of the nitrogen species from the photografted layer showed a
peak at 400.0 eV. The model compound had a N 1s binding energy of 399.7 eV, thus being
comparable
Estimating Risk and Rate Levels, Ratios and Differences
in Case-Control Studies

Classic (or ‘cumulative’) case-control sampling designs do not admit inferences about quantities of interest other than risk ratios, and then only by making the rare events assumption. Probabilities, risk
di erences and other quantities cannot be computed without knowledge of the population incidence fraction. Similarly, density (or ‘risk set’) case-control sampling designs do not allow inferences about
quantities other than the rate ratio. Rates, rate di erences, cumulative rates, risks, and other quantities cannot be estimated unless auxiliary information about the underlying cohort such as the number of controls in each full risk set is available. Most scholars who have considered the issue recommend reporting more than just risk and rate ratios, but auxiliary population information needed to do this is not usually available. We address this problem by developing methods that allow valid inferences about all relevant quantities of interest from either type of case-control study when completely ignorant of or
only partially knowledgeable about relevant auxiliary population information.Governmen
Analysis of Pregnancy and Other Factors on Detection of Human Papilloma Virus (HPV) Infection Using Weighted Estimating Equations for Follow-Up Data

Generalised estimating equations have been well established to draw inference for the marginal mean from follow-up data. Many studies suffer from missing data that may result in biased parameter estimates if the data are not missing completely at random. Robins and coworkers proposed to use weighted estimating equations (WEE) in estimating the mean structure if drop-out occurs missing at random. We illustrate the differences between the WEE and the commonly applied available case analysis in a simulation study. We apply the WEE and re-analyse data on pregnancy and HPV infection. We estimate the response probabilities and demonstrate that the data are not missing completely at random. Upon use of the WEE, we are able to show that pregnant women have an increased odds for an HPV infection compared with study subjects after delivery (p = 0.027). We conclude that the WEE are useful in analysing follow-up data with drop-outs.
Flexible Modelling of Discrete Failure Time Including Time-Varying Smooth Effects

Discrete survival models have been extended in several ways. More flexible models are obtained by including time-varying coefficients and covariates which determine the hazard rate in an additive but not further specified form. In this paper a general model is considered which comprises both types of covariate effects. An additional extension is the incorporation of smooth interaction between time and covariates. Thus in the linear predictor smooth effects of covariates which may vary across time are allowed. It is shown how simple duration models produce artefacts which may be avoided by flexible models. For the general model which includes parametric terms, time-varying coefficients in parametric terms and time-varying smooth effects estimation procedures are derived which are based on the regularized expansion of smooth effects in basis functions.
Analyzing Child Mortality in Nigeria with Geoadditive Survival Models

Child mortality reflects a country's level of socio-economic development and quality of life. In developing countries, mortality rates are not only influenced by socio-economic, demographic and health variables but they also vary considerably across regions and districts. In this paper, we analyze child mortality in Nigeria with flexible geoadditive survival models. This class of models allows to measure small-area district-specific spatial effects simultaneously with possibly nonlinear or time-varying effects of other factors. Inference is fully Bayesian and uses recent Markov chain Monte Carlo (MCMC) simulation. The application is based on the 1999 Nigeria Demographic and Health Survey. Our method assesses effects at a high level of temporal and spatial resolution not available with traditional parametric models.
Generating Survival Times to Simulate Cox Proportional Hazards Models

This paper discusses techniques to generate survival times for simulation studies regarding Cox proportional hazards models. In linear regression models, the response variable is directly connected with the considered covariates, the regression coefficients and the simulated random errors. Thus, the response variable can be generated from the regression function, once the regression coefficients and the error distribution are specified. However, in the Cox model, which is formulated via the hazard function, the effect of the covariates have to be translated from the hazards to the survival times, because the usual software packages for estimation of Cox models require the individual survival time data. A general formula describing the relation between the hazard and the corresponding survival time of the Cox model is derived. It is shown how the exponential, the Weibull and the Gompertz distribution can be used to generate appropriate survival times for simulation studies. Additionally, the general relation between hazard and survival time can be used to develop own distributions for special situations and to handle flexibly parameterized proportional hazards models. The use of other distributions than the exponential distribution only is indispensable to investigate the characteristics of the Cox proportional hazards model, especially in non-standard situations, where the partial likelihood depends on the baseline hazard.
Estimation of the false negative fraction of a diagnostic kit through Bayesian regression model averaging

In modelling we usually endeavour to find a single 'best' model that explains the relationship between independent and dependent variables. Selection of a single model fails to take into account the prior uncertainty in the model space. The Bayesian model averaging (BMA) approach tackles this problem by considering the set of all possible models. We apply BMA approach to the estimation of the false negative fraction (FNF) in a particular case of a two-stage multiple screening test for bowel cancer. We find that after taking model uncertainty into consideration the estimate of the FNF obtained is largely dependent on the covariance structure of the priors. Results obtained when the Zellner g-prior for the prior variance is used is largely influenced by the magnitude of g
Impact of modelling intra-subject variability on tests based on non-linear mixed-effects models in cross-over pharmacokinetic trials with application to the interaction of tenofovir on atazanavir in HIV patients.

We evaluated the impact of modelling intra-subject variability on the likelihood ratio test (LRT) and the Wald test based on non-linear mixed effects models in pharmacokinetic interaction and bioequivalence cross-over trials. These tests were previously found to achieve a good power but an inflated type I error when intra-subject variability was not taken into account. Trials were simulated under H0 and several H1 and analysed with the NLME function. Different configurations of the number of subjects n and of the number of samples per subject J were evaluated for pharmacokinetic interaction and bioequivalence trials. Assuming intra-subject variability in the model dramatically improved the type I error of both interaction tests. For the Wald test, the type I error decreased from 22, 14 and 7.7 per cent for the original (n = 12, J = 10), intermediate (n = 24, J = 5) and sparse (n = 40, J = 3) designs, respectively, down to 7.5, 6.4 and 3.5 per cent when intra-subject variability was modelled. The LRT achieved very similar results. This improvement seemed mostly due to a better estimation of the standard error of the treatment effect. For J = 10, the type I error was found to be closer to 5 per cent when n increased when modelling intra-subject variability. Power was satisfactory for both tests. For bioequivalence trials, the type I error of the Wald test was 6.4, 5.7 and 4.2 per cent for the original, intermediate and sparse designs, respectively, when modelling intra-subject variability. We applied the Wald test to the pharmacokinetic interaction of tenofovir on atazanavir, a novel protease inhibitor. A significant decrease of the area under the curve of atazanavir was found when patients received tenofovir
Methods for comparing center‐specific survival outcomes using direct standardization

The evaluation of center‐specific outcomes is often through survival analysis methods. Such evaluations must account for differences in the distribution of patient characteristics across centers. In the context of censored event times, it is also important that the measure chosen to evaluate centers not be influenced by imbalances in the center‐specific censoring distributions. The practice of using center indicators in a hazard regression model is often invalid, inconvenient, or undesirable to carry out. We propose a semiparametric version of the standardized rate ratio (SRR) useful for the evaluation of centers with respect to a right‐censored event time. The SRR for center  j  can be interpreted as the ratio of the expected number of deaths in the total population (if the total population were in fact subject to the center  j  mortality hazard) to the observed number of events. The proposed measure is not affected by differences in center‐specific covariate or censoring distributions. Asymptotic properties of the proposed estimators are derived, with finite‐sample properties examined through simulation studies. The proposed methods are applied to national kidney transplant data. Copyright © 2014 John Wiley & Sons, Ltd
Flexible semiparametric mixed models

In linear mixed models the influence of covariates is restricted to a strictly parametric form. With the rise of semi- and nonparametric regression also the mixed model has been expanded to allow for additive predictors. The common approach uses the representation of additive models as mixed models. An alternative approach that is proposed in the present paper is likelihood based boosting. Boosting originates in the machine learning community where it has been proposed as a technique to improve classification procedures by combining estimates with reweighted observations. Likelihood based boosting is a general method which may be seen as an extension of L2 boost. In additive mixed models the advantage of boosting techniques in the form of componentwise boosting is that it is suitable for high dimensional settings where many influence variables are present. It allows to fit additive models for many covariates with implicit selection of relevant variables and automatic selection of smoothing parameters. Moreover, boosting techniques may be used to incorporate the subject-specific variation of smooth influence functions by specifying  random slopes  on smooth e ects. This results in flexible semiparametric mixed models which are appropriate in cases where a simple random intercept is unable to capture the variation of e ects across subjects.
The SAEM algorithm for group comparison tests in longitudinal data analysis based on non-linear mixed-effects model.

International audienceNon-linear mixed-effects models (NLMEMs) are used to improve information gathering from longitudinal studies and are applied to treatment evaluation in disease-evolution studies, such as human immunodeficiency virus (HIV) infection. The estimation of parameters and the statistical tests are critical issues in NLMEMs since the likelihood and the Fisher information matrix have no closed form. An alternative method to numerical integrations, in which convergence is slow, and to methods based on linearization, in which asymptotic convergence has not been proved, is the Stochastic Approximation Expectation-Maximization (SAEM) algorithm. For the Wald test and the likelihood ratio test, we propose estimating the Fisher information matrix by stochastic approximation and the likelihood by importance sampling. We evaluate these SAEM-based tests in a simulation study in the context of HIV viral load decrease after initiation of an antiretroviral treatment. The results from this simulation illustrate the theoretical convergence properties of SAEM. We also propose a method based on the SAEM algorithm to compute the minimum sample size required to perform a Wald test of a given power for a covariate effect in NLMEMs. Lastly, we illustrate these tests on the evaluation of the effect of ritonavir on the indinavir pharmacokinetics in HIV patients and compare the results with those obtained using the adaptative Gaussian quadrature method implemented in the SAS procedure NLMIXED. Copyright (c) 2007 John Wiley & Sons, Ltd
Flexible parametric models for relative survival, with application in coronary heart disease

Relative survival is frequently used in population-based studies as a method for estimating disease-related mortality without the need for information on cause of death. We propose an extension to relative survival of a flexible parametric model proposed by Royston and Parmar for censored survival data. The model provides smooth estimates of the relative survival and excess mortality rates by using restricted cubic splines on the log cumulative excess hazard scale. The approach has several advantages over some of the more standard relative survival models, which adopt a piecewise approach, the main being the ability to model time on a continuous scale, the survival and hazard functions are obtained analytically and it does not use split-time data.Post prin
Comparing Syndromic Surveillance Detection Methods: EARS' Versus a CUSUM-Based Methodology

Statistics in Medicine, 27, 3407-3429.The article of record as published may be located at http://dx.doi.org/10.1002/sim.3197This paper compares the performance of three detection methods, entitled C1, C2, and C3, that are
implemented in the early aberration reporting system (EARS) and other syndromic surveillance systems
versus the CUSUM applied to model-based prediction errors. The cumulative sum (CUSUM) performed
signiﬁcantly better than the EARS’ methods across all of the scenarios we evaluated. These scenarios
consisted of various combinations of large and small background disease incidence rates, seasonal cycles
from large to small (as well as no cycle), daily effects, and various types and levels of random daily
variation. This leads us to recommend replacing the C1, C2, and C3 methods in existing syndromic
surveillance systems with an appropriately implemented CUSUM method. Published in 2008 by John
Wiley & Sons, Ltd
Disparities in Defining Disparities: Statistical Conceptual Frameworks

Motivated by the need to meaningfully implement the Institute of Medicine's (IOM's) definition of health care disparity, this paper proposes statistical frameworks that lay out explicitly the needed causal assumptions for defining disparity measures. Our key emphasis is that a scientifically defensible disparity measure must take into account the direction of the causal relationship between <i>allowable covariates</i> that are not considered to be contributors to disparity and <i>non-allowable covariates</i> that are considered to be contributors to disparity, to avoid flawed disparity measures based on implausible populations that are not relevant for clinical or policy decisions. However, these causal relationships are usually unknown and undetectable from observed data. Consequently, we must make strong causal assumptions in order to proceed. Two frameworks are proposed in this paper, one is the <i>conditional disparity</i> framework under the assumption that allowable covariates impact non-allowable covariates  but not <i>vice versa</i>. The other is the <i>marginal disparity</i> framework under the assumption that non-allowable covariates impact allowable ones but not <i>vice versa</i>. We establish theoretical conditions under which the two disparity measures are the same and present a theoretical example showing that the difference between the two disparity measures can be arbitrarily large. Using data from the Collaborative Psychiatric Epidemiology Survey, we also provide an example where the conditional disparity is misled by Simpson's paradox, whereas the marginal disparity approach handles it correctly.Statistic