The influence of mixing methods and disinfectant on the physical properties of alginate impression materials.

The aims of this in vitro study were to quantify the effect of manual versus automatic mixing and of using a disinfectant on mechanical properties of three different alginate impression materials. Two of the three alginates tested were especially developed for orthodontic use: Orthotrace® and Orthofine® while the third was a conventional alginate CA37FS®. Alginates were mixed by hand or automatically using a Cavex alginate mixer II®. Mixing was performed at room temperature using tap water. The material was allowed to set in a water bath at 35°C (±1°C), simulating intra-oral setting conditions, and half of the samples were disinfected before testing. For each tested material, 10 standardized samples were used. The disinfectant used was the CavexImpreSafe® that has a bactericide, virucide, and fungicide function. The specimens were exposed for 3 minutes in a 3% solution and were then tested according to the ISO 1563: 1990 (E) standard specifications. Descriptive statistics and three-way analysis of variance were performed, and a 5% significance level was used for statistical analysis. Evaluation of tensile strength and elastic recovery of different alginate samples, hand versus automatical mixing or disinfected versus not disinfected, resulted in significant differences for all materials except for Orthofine®. Considering detail reproduction, all three alginates evaluated reproduced the 50-µm line successfully without interruption. The mixing method can significantly affect the elastic recovery and tensile strength of the alginates tested while the effect of using a disinfectant is less explicit.

Identifying differentially expressed genes in the absence of replication.

In microarray data analysis, the comparison of gene expression levels in different conditions and selection of biologically relevant genes are essential tasks. In this study, we propose a novel statistical procedure based on standardised conditional residuals from a linear mixed-effects model which allows comparison of conditions, even if only one replicate per experimental condition is available. We illustrate this method by using three publicly available datasets. We show that this method can be extended to handle more complex designs. Finally, simulations show that the tests developed have good statistical power to detect true differences among conditions at the gene level.

The use of semiparametric mixed models to analyze PamChip(R) peptide array data: an application to an oncology experiment.

MOTIVATION: Phosphorylation by protein kinases is a central theme in biological systems. Aberrant protein kinase activity has been implicated in a variety of human diseases (e.g. cancer). Therefore, modulation of kinase activity represents an attractive therapeutic approach for the treatment of human illnesses. Thus, identification of signature peptides is crucial for protein kinase targeting and can be achieved by using PamChip(®) microarray technology. We propose a flexible semiparametric mixed model for analyzing PamChip(®) data. This approach enables the estimation of the phosphorylation rate (Velocity) as a function of time together with pointwise confidence intervals. RESULTS: Using a publicly available dataset, we show that our model is capable of adequately fitting the kinase activity profiles and provides velocity estimates over time. Moreover, it allows to test for differences in the velocity of kinase inhibition between responding and non-responding cell lines. This can be done at individual time point as well as for the entire velocity profile. CONTACT: pushpike@med.kuleuven.be SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Modeling the asymmetric evolution of a mouse and rat-specific microRNA gene cluster intron 10 of the Sfmbt2 gene.

BACKGROUND: The total number of miRNA genes in a genome, expression of which is responsible for the miRNA repertoire of an organism, is not precisely known. Moreover, the question of how new miRNA genes arise during evolution is incompletely understood. Recent data in humans and opossum indicate that retrotranspons of the class of short interspersed nuclear elements have contributed to the growth of microRNA gene clusters. METHOD: We studied a large miRNA gene cluster in intron 10 of the mouse Sfmbt2 gene using bioinformatic tools. RESULTS: Mice and rats are unique to harbor a 55-65 Kb DNA sequence in intron 10 of the Sfmbt2 gene. This intronic region is rich in regularly repeated B1 retrotransposons together with inverted self-complementary CA/TG microsatellites. The smallest repeats unit, called MSHORT1 in the mouse, was duplicated 9 times in a tandem head-to-tail array to form 2.5 Kb MLONG1 units. The center of the mouse miRNA gene cluster consists of 13 copies of MLONG1. BLAST analysis of MSHORT1 in the mouse shows that the repeat unit is unique for intron 10 of the Sfmbt2 gene and suggest a dual phase model for growth of the miRNA gene cluster:arrangement [corrected] of 10 MSHORT1 units into MLONG1 and further duplication of 13 head-to-tail MLONG1 units in the center of the miRNA gene cluster. Rats have a similar arrangement [corrected] of repeat units in intron 10 of the Sfmbt2 gene. The discrepancy between 65 miRNA genes in the mouse cluster as compared to only 1 miRNA gene in the corresponding rat repeat cluster is ascribed to sequence differences between MSHORT1 and RSHORT1 that result in lateral-shifted, less-stable miRNA precursor hairpins for RSHORT1. CONCLUSION: Our data provides new evidence for the emerging concept that lineage-specific retroposons have played an important role in the birth of new miRNA genes during evolution. The large difference in the number of miRNA genes in two closely related species (65 versus 1, mice versus rats) indicates that this species-specific evolution can be a rapid process.

Evidence for co-evolution between human microRNAs and Alu-repeats.

This paper connects Alu repeats, the most abundant repetitive elements in the human genome and microRNAs, small RNAs that alter gene expression at the post-transcriptional level. Base-pair complementarity could be demonstrated between the seed sequence of a subset of human microRNAs and Alu repeats that are integrated parallel (sense) in mRNAs. The most common target site coincides with the evolutionary most conserved part of Alu. A primate-specific gene cluster on chromosome 19 encodes the majority of miRNAs that target the most conserved sense Alu site. The individual miRNA genes within this cluster are flanked by an Alu-LINE signature, which has been duplicated with the clustered miRNA genes. Gene duplication events in this locus are supported by comparing repeat length variations of the LINE elements within the cluster with those in the rest of the chromosome. Thus, a dual relationship exists between an evolutionary young miRNA cluster and their Alu targets that may have evolved in the same time window. One hypothesis for this dual relationship is that these miRNAs could protect against too high rates of duplicative transposition, which would destroy the genome.

A nonlinear mixed-effects model for estimating calibration intervals for unknown concentrations in two-color microarray data with spike-ins.

In this study, we propose a calibration method for preprocessing spiked-in microarray experiments based on nonlinear mixed-effects models. This method uses a spike-in calibration curve to estimate normalized absolute expression values. Moreover, using the asymptotic properties of the calibration estimate, 100(1-alpha)% confidence intervals for the estimated expression values can be constructed. Simulations are used to show that the approximations on which the construction of the confidence intervals are based are sufficiently accurate to reach the desired coverage probabilities. We illustrate applicability of our method, by estimating the normalized absolute expression values together with the corresponding confidence intervals for two publicly available cDNA microarray experiments (Hilson et al., 2004; Smets et al., 2008). This method can easily be adapted to preprocess one-color oligonucleotide microarray data with a slight adjustment to the mixed model.