Immobilization of Delftia tsuruhatensis in macro-porous cellulose and biodegradation of phenolic compounds in repeated batch process

Delftia tsuruhatensis BM90, previously isolated from Tyrrhenian Sea and selected for its ability to degrade a wide array of phenolic compounds, was immobilized in chemically modified macro porous cellulose. The development of bacterial adhesion on the selected carrier was monitored by scanning electron microscopy. Evident colonization started already after 8 h of incubation. After 72 h, almost all the carrier surface was covered by the bacterial cells. Extracellular bacterial structures, such as pili or fimbriae, contributed to carrier colonization and cell attachment. Immobilized cells of D. tsuruhatensis were tested for their ability to biodegrade a pool of 20 phenols in repeated batch process. During the first activation batch (72 h), 90% of phenols degradation was obtained already in 48 h. In the subsequent batches (up to 360 h), same degradation was obtained after 24 h only. By contrast, free cells were slower: to obtain almost same degradation, 48 h were needed. Thus, process productivity, achieved by the immobilized cells, was double than that of free cells. Specific activity was also higher suggesting that the use of immobilized D. tsuruhatensis BM90 could be considered very promising in order to obtain an efficient reusable biocatalyst for long-term treatment of phenols containing effluents.     

On Efficiency of Constrained Longitudinal Data Analysis Versus Longitudinal Analysis of Covariance

Summary : In randomized clinical trials, measurements are often collected on each subject at a baseline visit and several post‐randomization time points. The longitudinal analysis of covariance in which the postbaseline values form the response vector and the baseline value is treated as a covariate can be used to evaluate the treatment differences at the postbaseline time points. Liang and Zeger (2000, Sankhyā: The Indian Journal of Statistics, Series B 62, 134–148) propose a constrained longitudinal data analysis in which the baseline value is included in the response vector together with the postbaseline values and a constraint of a common baseline mean across treatment groups is imposed on the model as a result of randomization. If the baseline value is subject to missingness, the constrained longitudinal data analysis is shown to be more efficient for estimating the treatment differences at postbaseline time points than the longitudinal analysis of covariance. The efficiency gain increases with the number of subjects missing baseline and the number of subjects missing all postbaseline values, and, for the pre–post design, decreases with the absolute correlation between baseline and postbaseline values.     

Batch and repeated batch production of l(+)-lactic acid by Enterococcus faecalis RKY1 using wood hydrolyzate and corn steep liquor

Lactic acid production was investigated for batch and repeated batch cultures of Enterococcus faecalis RKY1, using wood hydrolyzate and corn steep liquor. When wood hydrolyzate (equivalent to 50 g l⁻¹ glucose) supplemented with 15–60 g l⁻¹ corn steep liquor was used as a raw material for fermentation, up to 48.6 g l⁻¹ of lactic acid was produced with, volumetric productivities ranging between 0.8 and 1.4 g l⁻¹ h⁻¹. When a medium containing wood hydrolyzate and 15 g l⁻¹ corn steep liquor was supplemented with 1.5 g l⁻¹ yeast extract, we observed 1.9-fold and 1.6-fold increases in lactic acid productivity and cell growth, respectively. In this case, the nitrogen source cost for producing 1 kg lactic acid can be reduced to 23% of that for fermentation from wood hydrolyzate using 15 g l⁻¹ yeast extract as a single nitrogen source. In addition, lactic acid productivity could be maximized by conducting a cell-recycle repeated batch culture of E. faecalis RKY1. The maximum productivity for this process was determined to be 4.0 g l⁻¹ h⁻¹.     

Likelihood methods for incomplete longitudinal binary responses with incomplete categorical covariates

We consider longitudinal studies in which the outcome observed over time is binary and the covariates of interest are categorical. With no missing responses or covariates, one specifies a multinomial model for the responses given the covariates and uses maximum likelihood to estimate the parameters. Unfortunately, incomplete data in the responses and covariates are a common occurrence in longitudinal studies. Here we assume the missing data are missing at random (Rubin, 1976, Biometrika 63, 581-592). Since all of the missing data (responses and covariates) are categorical, a useful technique for obtaining maximum likelihood parameter estimates is the EM algorithm by the method of weights proposed in Ibrahim (1990, Journal of the American Statistical Association 85, 765-769). In using the EM algorithm with missing responses and covariates, one specifies the joint distribution of the responses and covariates. Here we consider the parameters of the covariate distribution as a nuisance. In data sets where the percentage of missing data is high, the estimates of the nuisance parameters can lead to highly unstable estimates of the parameters of interest. We propose a conditional model for the covariate distribution that has several modeling advantages for the EM algorithm and provides a reduction in the number of nuisance parameters, thus providing more stable estimates in finite samples.     

Exact inference for growth curves with intraclass correlation structure

We consider repeated observations taken over time for each of several subjects. For example, one might consider the growth curve of a cohort of babies over time. We assume a simple linear growth curve model. Exact results based on sufficient statistics (exact tests of the null hypothesis that a coefficient is zero, or exact confidence intervals for coefficients) are not available to make inference on regression coefficients when an intraclass correlation structure is assumed. This paper will demonstrate that such exact inference is possible using generalized inference.     

Modeling a categorical variable allowing arbitrarily many category choices

This article discusses the modeling of a categorical variable for which subjects can select any number of categories. For c categories, the response variable consists of a cross-classification of c binary components, one pertaining to each category. Using data from a survey (Loughin, T. M. and Scherer, P. N., 1998, Biometrics, 54, 630 637) in which Kansas farmers indicated their primary sources of veterinary information, we discuss simultaneous logit modeling of the binary components of the multivariate response. The use of maximum likelihood or quasi-likelihood fitting provides chi-squared tests with degrees of freedom df = c(r - 1) for testing the independence between each of the c response components and an explanatory variable with r categories. These tests are alternatives to the weighted chi-squared test and the bootstrap test proposed by Loughin and Scherer for this hypothesis.     

Analysis of times to repeated events in two-arm randomized trials with noncompliance and dependent censoring

This article develops randomization-based methods for times to repeated events in two-arm randomized trials with noncompliance and dependent censoring. Structural accelerated failure time models are assumed to capture causal effects on repeated event times and dependent censoring time, but the dependence structure among repeated event times and dependent censoring time is unspecified. Artificial censoring techniques to accommodate nonrandom noncompliance and dependent censoring are proposed. Estimation of the acceleration parameters are based on rank-based estimating functions. A simulation study is conducted to evaluate the performance of the developed methods. An illustration of the methods using data from an acute myeloid leukemia trial is provided.     

Dynamic analysis of multivariate failure time data

We present an approach for analyzing internal dependencies in counting processes. This covers the case with repeated events on each of a number of individuals, and more generally, the situation where several processes are observed for each individual. We define dynamic covariates, i.e., covariates depending on the past of the processes. The statistical analysis is performed mainly by the nonparametric additive approach. This yields a method for analyzing multivariate survival data, which is an alternative to the frailty approach. We present cumulative regression plots, statistical tests, residual plots, and a hat matrix plot for studying outliers. A program in R and S-PLUS for analyzing survival data with the additive regression model is available on the web site http://www.med.uio.no/imb/stat/addreg. The program has been developed to fit the counting process framework.     

Estimating causal treatment effects from longitudinal HIV natural history studies using marginal structural models

Several recently completed and ongoing studies of the natural history of HIV infection have generated a wealth of information about its clinical progression and how this progression is altered by therepeutic interventions and environmental factors. Natural history studies typically follow prospective cohort designs, and enroll large numbers of participants for long-term prospective follow-up (up to several years). Using data from the HIV Epidemiology Research Study (HERS), a six-year natural history study that enrolled 871 HIV-infected women starting in 1993, we investigate the therapeutic effect of highly active antiretroviral therapy regimens (HAART) on CD4 cell count using the marginal structural modeling framework and associated estimation procedures based on inverse-probability weighting (developed by Robins and colleagues). To evaluate treatment effects from a natural history study, specialized methods are needed because treatments are not randomly prescribed and, in particular, the treatment-response relationship can be confounded by variables that are time-varying. Our analysis uses CD4 data on all follow-up visits over a two-year period, and includes sensitivity analyses to investigate potential biases attributable to unmeasured confounding. Strategies for selecting ranges of a sensitivity parameter are given, as are intervals for treatment effect that reflect uncertainty attributable both to sampling and to lack of knowledge about the nature and existence of unmeasured confounding. To our knowledge, this is the first use in "real data" of Robins's sensitivity analysis for unmeasured confounding (Robins, 1999a, Synthese 121, 151-179). The findings from our analysis are consistent with recent treatment guidelines set by the U.S. Panel of the International AIDS Society (Carpenter et al., 2000, Journal of the American Medical Association 280, 381-391).