The amino acid composition of proteins: a method of analysis.

Four quasiloglinear models are proposed for describing relationships between the amino acid composition of proteins and the structure of the genetic code. The models allow estimation of base frequencies in all three codon positions and can be used to investigate “interactions” between any two codon positions. The estimation procedure proposed by Ohta and Kimura (Genetics64 (1970), 387–395) is discussed and using two of the proposed quasiloglinear models an analysis of the amino acid composition of human cytochrome c is presented. The analysis suggests that of the six codons which code for leucine (CUU, CUC, CUA and CUG) do not occur in human cytochrome c.     

Multivariate assessment of activated sludge stability in lab-scale experiments

In wastewater research, the transfer of activated sludge from large-scale plants to lab-scale reactors induces a transient period. It is crucial to quantitatively assess the stability of activated sludge before starting any experimental procedure. Otherwise, the transient interferes with the experimental results, jeopardizing reproducibility and accuracy. This paper presents a novel multivariate technique to assess activated sludge stability based on the combination of principal component analysis (PCA) with the squared prediction error (SPE) statistic. The proposed method allows for a more accurate estimation of activated sludge stability than existing univariate methods and also eliminates the need to establish thresholds for every single variable. The procedure is validated on experimental data obtained by Van den Broeck et al. [1].     

State estimation of a Uricase production process with Candida Utilis

The present work discusses the implementation of a Kalman filtering procedure in a state estimation of a batch Uricase production process with Candida Utilis. An unstructured model of the process is used for the estimation procedure. The observability is thoroughly investigated and a Kalman filter is applied afterwards as a powerful and precise state estimation tool. The estimates in all cases of observability are presented, compared and discussed.     

Efficient statistical inference procedures for partially nonlinear models and their applications

Summary .   Motivated by an analysis of a real data set in ecology, we consider a class of partially nonlinear models where both a nonparametric component and a parametric component are present. We develop two new estimation procedures to estimate the parameters in the parametric component. Consistency and asymptotic normality of the resulting estimators are established. We further propose an estimation procedure and a generalized F -test procedure for the nonparametric component in the partially nonlinear models. Asymptotic properties of the newly proposed estimation procedure and the test statistic are derived. Finite sample performance of the proposed inference procedures are assessed by Monte Carlo simulation studies. An application in ecology is used to illustrate the proposed methods.     

A sampling design strategy to reduce survey costs in forest monitoring

High-quality data and long-term time series are the basis of any research activity dealing with natural resources analysis. Adequate sampling designs are fundamental to allow a robust statistical analysis to be representative of a relevant set of target variables. In this work, the sampling strategy of ICP-Forests Level II European network has been proposed to define more efficient and cost-effective procedures under the hypothesis that the average value of single-tree growth (increment) is a proxy of forest health. ICP plots have a fixed spatial structure consisting of a square of 50 × 50 m framed into 25 squared sub-plots. To estimate basal area (G) and increase over time (ΔG), two different sub-sampling methods have been implemented based on a measure of (i) the dominant layer only (i.e. a subset of the highest trees in the plot), and (ii) a random sample of squared sub-plots. While the vertical sampling procedure was performed using a progressive threshold, the horizontal sampling followed a bootstrapping procedure with random extraction without replacement. The mean absolute relative error (MARE) was used to evaluate quality of the two sub-sampling methods. Results highlighted a low predictive power with both methodologies, preventing the possibility to reduce the sampling efforts when estimating ΔG directly. In this context, the vertical sampling was strictly related to species-specific ecology, spatial structure and forest age, being influenced by vertical distribution of trees. The use of horizontal sampling for direct ΔG estimation led to systematically high errors. However, the use of horizontal sampling for total G estimation and indirect estimation of ΔG may reveal as a more effective procedure for a coherent representation of horizontal distribution of trees. Estimate ΔG as the difference between G values at time t and t + Δt finally allows for a sensible reduction of costs with a controlled estimation error. An adequate level of MARE should be decided a-priori to select the number of sub-squares to be randomly sampled.     

A new simple method to determine the diffusion coefficient from Active Enzyme Centrifugation experiments

A simple and accurate procedure to determine the diffusion coefficient from Active Enzyme Centrifugation experiments is presented. Using computer-simulated concentration distributions we demonstrate that the procedure, derived by Vinograd for the conventional band sedimentation, is suitable to exploit Active Enzyme Centrifugation experiments when the Cohen's Difference Curves Method is used. This new empirical method avoids all the difficulties of the rigorous method previously proposed by Cohen et al., without any loss of accuracy. Optimal conditions are described which allow the determination of the enzyme diffusion coefficient with a 5% uncertainty. Such an easy determination of the sedimentation and diffusion coefficients by the AEC technique can provide a good and rapid estimation of the active enzyme molecular weight, either with a low amount of material or in very impure preparations.     

Recursive Random Lasso (RRLasso) for Identifying Anti-Cancer Drug Targets

Uncovering driver genes is crucial for understanding heterogeneity in cancer. L ₁-type regularization approaches have been widely used for uncovering cancer driver genes based on genome-scale data. Although the existing methods have been widely applied in the field of bioinformatics, they possess several drawbacks: subset size limitations, erroneous estimation results, multicollinearity, and heavy time consumption. We introduce a novel statistical strategy, called a Recursive Random Lasso (RRLasso), for high dimensional genomic data analysis and investigation of driver genes. For time-effective analysis, we consider a recursive bootstrap procedure in line with the random lasso. Furthermore, we introduce a parametric statistical test for driver gene selection based on bootstrap regression modeling results. The proposed RRLasso is not only rapid but performs well for high dimensional genomic data analysis. Monte Carlo simulations and analysis of the “Sanger Genomics of Drug Sensitivity in Cancer dataset from the Cancer Genome Project” show that the proposed RRLasso is an effective tool for high dimensional genomic data analysis. The proposed methods provide reliable and biologically relevant results for cancer driver gene selection.     

Electrophoresis of acidic glycosaminoglycans in hydrochloric acid: a micro method for sulfate determination

A rapid and simple method for estimation of the sulfate content of less than 1 μg of acidic glycosaminoglycans is presented. The procedure is based on the fact that the electrophoretic mobility in 0.1 M hydrochloric acid is proportional to the sulfate content of the polysaccharide.     

Estimating Population Size for Continuous-Time Capture-Recapture Models Via Sample Coverage

An estimation procedure using the idea of sample coverage is proposed to estimate population size for capture-recapture experiments in continuous time. The capture rates (intensity) are allowed to vary by time and individuals (heterogeneity). Only capture frequency history are sufficient for estimating population size while capture times and sequential orders of animals caught are irrelevant for the analysis. An example is given for illustration. The performance of the proposed estimation procedure is also investigated by simulation.     

Estimating the proportion of true null hypotheses and adaptive false discovery rate control in discrete paradigm

Storey's estimator for the proportion of true null hypotheses, originally proposed under the continuous framework, has been modified in this work under the discrete framework. The modification results in improved estimation of the parameter of interest. The proposed estimator is used to formulate an adaptive version of the Benjamini–Hochberg procedure. Control over the false discovery rate by the proposed adaptive procedure has been proved analytically. The proposed estimate is also used to formulate an adaptive version of the Benjamini–Hochberg–Heyse procedure. Simulation experiments establish the conservative nature of this new adaptive procedure. Substantial amount of gain in power is observed for the new adaptive procedures over the standard procedures. For demonstration of the proposed method, two important real life gene expression data sets, one related to the study of HIV and the other related to methylation study, are used.     

Estimation of DNA, RNA and 125 I- and 3 H-labelled DNA in the same sample

Estimation of DNA, RNA, and the specific activity of DNA after labelling with [³H]thymidine and/or [¹²⁵I]iodeoxyuridine has been accomplished using a recently developed procedure for the estimation of DNA with p-nitrophenylhydrazine (pNPH). Samples of the pNPH reaction mixture are used for RNA estimation by th orcinol procedure and for ¹²⁵I and tritium measurement. Correction for ¹²⁵I contribution to the tritium measurement in double labelling experiments is accomplished either by use of a simple calibration curve (for high ${}^3\text{H}{}^{125}\text{I}$ ratios) or by removal of ¹²⁵I by hydrolysis and precipitation as AgI; in the latter procedure the efficiency of removal of ¹²⁵I was greater than 99%.     

Biomass estimation on the basis of yeast cultivation model with morphophysiological parameters

An approach of biomass estimation of yeast fed-batch cultivation is proposed. It is based on the model that takes into account the information about the morphophysiological parameters: size, coefficients of shape and skewness of optical density of yeast cells. Procedure for parameter identification of the model, r ₁-rate of transformation of active cells into weakened cells, r ₂-rate of transformation of weakened cells into dead cells, r ₃-rate of transformation of weakened cells into active cells, r ₄-rate by which the buds of the budding cells grow and transform after their separation into active cells and r ₅-rate of transformation of active cells into budding cells, is described. The procedure is performed in MATLAB environment.     

Efficiency of a Two-Stage Procedure for Survival Studies

A two-stage procedure using occurrences of a surrogate endpoint regarding a true clinical endpoint has been proposed (FLANDRE, O'QUIGLEY and BROOKMEYER, 1994). The procedure allows estimation of the survival function S(t) reliably and comparison of treatment groups. The aim of this paper is to investigate the efficiency of such a two-stage procedure compared to a one stage procedure, i.e. a classical study. The efficiency is estimated by the ratio of the standard derivation of S(t) for the classical procedure to the standard derivation of S(t) for the two-stage procedure and represents the precision on the estimation of S(t). Standard deviation of S(t) is approximate by the delta-method. Using the exponential model. some numerical results are presented.     

A Quantitative Real-time PCR Assay for Quantification of Viable Listeria Monocytogenes Cells After Bacteriocin Injury in Food-First Insights

Quantitative real-time PCR may be a rapid and automated procedure for detection of bacterial pathogens from food samples. Nevertheless, when testing the effects of antimicrobials on the viability of bacterial pathogens in foods, we found that DNA from dead cells interfered greatly in the detection of viable Listeria monocytogenes after treatment with the broad-spectrum bacteriocin enterocin AS-48. To overcome this problem, a quantitative real-time PCR (qRT-PCR) assay based on bacterial mRNA was adapted to quantify viable L. monocytogenes in food after bacteriocin treatments. The procedure allowed a better and faster estimation of viable cells compared to PALCAM viable cell counts when the threshold level was 2 log units/g of food, while PALCAM viable count allowed detection of one log unit/g. This procedure may be useful to verify the efficacy of bacteriocins against L. monocytogenes in foods.     

Estimation for the optimal combination of markers without modeling the censoring distribution

Summary .  In the time-dependent receiver operating characteristic curve analysis with several baseline markers, research interest focuses on seeking appropriate composite markers to enhance the accuracy in predicting the vital status of individuals over time. Based on censored survival data, we proposed a more flexible estimation procedure for the optimal combination of markers under the validity of a time-varying coefficient generalized linear model for the event time without restrictive assumptions on the censoring pattern. The consistency of the proposed estimators is also established in this article. In contrast, the inverse probability weighting (IPW) approach might introduce a bias when the selection probabilities are misspecified in the estimating equations. The performance of both estimation procedures are examined and compared through a class of simulations. It is found from the simulation study that the proposed estimators are far superior to the IPW ones. Applying these methods to an angiography cohort, our estimation procedure is shown to be useful in predicting the time to all-cause and coronary artery disease related death.     

Estimation of Parameters of Rotatory Dispersion Curves of Proteins

Estimation of the constants a, b, and λ₀ by means of the standard Moffitt-Yang plot is evaluated. It is found that the method is very insensitive as an estimation procedure and that large errors in b may be expected. Expressions for the maximum-likelihood estimates of the constants are derived.     

A two-stage stepwise estimation procedure

Summary .   This article proposes a two-stage simultaneous confidence procedure for the comparisons of k pairs of population means, without using multiplicity adjustment of more than two populations. The proposed procedure can be broadly applied to parametric or nonparametric models. It is robust and versatile because its derivation only utilizes a partitioning approach in conjunction with a bivariate adjustment, without any assumption on the underlying distribution. To elucidate the application, the proposed procedure is intertwined with the estimation of the therapeutic window of a drug. It provides confidence limits for the efficacy and the toxicity of the effective doses, highest ineffective dose, safe doses, and lowest unsafe dose, simultaneously. Such estimation information facilitates follow-up studies in clinical trials. As an illustrative example, the new procedure is applied to analyze a data set on molecular cancer therapeutics regarding the apoptotic killing effects of different chemical compounds on two leukemia cell lines.     

REML estimation of variance parameters in nonlinear mixed effects models using the SAEM algorithm

Nonlinear mixed effects models are now widely used in biometrical studies, especially in pharmacokinetic research or for the analysis of growth traits for agricultural and laboratory species. Most of these studies, however, are often based on ML estimation procedures, which are known to be biased downwards. A few REML extensions have been proposed, but only for approximated methods. The aim of this paper is to present a REML implementation for nonlinear mixed effects models within an exact estimation scheme, based on an integration of the fixed effects and a stochastic estimation procedure. This method was implemented via a stochastic EM, namely the SAEM algorithm. The simulation study showed that the proposed REML estimation procedure considerably reduced the bias observed with the ML estimation, as well as the residual mean squared error of the variance parameter estimations, especially in the unbalanced cases. ML and REML based estimators of fixed effects were also compared via simulation. Although the two kinds of estimates were very close in terms of bias and mean square error, predictions of individual profiles were clearly improved when using REML vs. ML. An application of this estimation procedure is presented for the modelling of growth in lines of chicken.     

Reproducible reference frame for in vitro testing of the human vertebrae

Definition of an anatomical reference frame is necessary for in vitro biomechanical testing. Nevertheless, there is neither a clear recommendation, nor consensus in the literature concerning an anatomical reference frame for in vitro testing of the human vertebrae. The scope of this work is to define a reference frame for the human vertebrae for in vitro applications. The proposed anatomical reference frame relies on alignment of well-defined points on the endplates, and on two landmarks on the posterior wall. The repeatability of the proposed alignment procedure has been tested in vitro by 5 operators, on 7 specimens. Furthermore, the feasibility and repeatability of the proposed procedure was assessed in silico, using CT-scans of the same specimens.     

Microbial production of enzymes: Nonlinear state and kinetic reaction rates estimation

The nonlinearity of the biotechnological processes and the absence of cheap and reliable instrumentation require an enhanced modelling effort and estimation strategies for the state and the kinetic parameters. This work approaches nonlinear estimation strategies for microbial production of enzymes, exemplified by using a process of lipase production from olive oil by Candida rugosa. First, by using a dynamical mathematical model of this process, an asymptotic observer which reconstructs the unavailable state variables is proposed. The design of this kind of observers is based on mass and energy balances without the knowledge of kinetics being necessary; only minimal information concerning the measured concentrations is used. Second, a nonlinear high-gain observer is designed for the estimation of imprecisely known kinetics of the bioprocess. An important advantage of this high-gain estimator is that the tuning is reduced to the calibration of a single parameter. Numerical simulations in various scenarios are provided in order to test the behaviour and performances of the proposed nonlinear estimation strategies.