Adhesion of Candida albicans and Candida dubliniensis to acrylic and hydroxyapatite

The aim of this work was to compare the ability of strains of Candida albicans and Candida dubliniensis to adhere to acrylic and hydroxyapatite (HAP). In order to interpret the adhesion results, the surface properties of cells and materials were determined. Surface tension components (polar and apolar) and hydrophobicity were calculated through contact angle measurement and the elemental composition was determined by X-ray photoelectron spectroscopy (XPS). The results showed no significant differences in the number of adhered cells of both species to acrylic and hydroxyapatite. This was corroborated by the similarities in their surface properties and elemental composition. For both species, the adhesion to acrylic increased in the presence of artificial saliva due to the increase in the electron-donor capacity of this material. In the absence of artificial saliva, the number of adhered cells to HAP was greater than to acrylic, on account of the higher number of electron-donor groups of HAP. Hydrophobicity played a minor role in the adhesion process of both candidal species. Conversely, Lewis acid–base interactions seamed to govern this phenomenon.Fundação para a Ciência e a Tecnologia (FCT) - BD3195/2000, Programa Operacional “Ciência, Tecnologia, Inovação” (POCTI) POCTI/BIO/42638/2001

Susceptibility of Candida albicans filamentation-defective mutants to clinical biocides

Letters to the Editorlink_to_subscribed_fulltex

A Functional Data Analysis Approach to Traffic Volume Forecasting

Traffic volume forecasts are used by many transportation analysis and management systems to better characterize and react to fluctuating traffic patterns. Most current forecasting methods do not take advantage of the underlying functional characteristics of the time series to make predictions. This paper presents a methodology that uses functional principal components analysis to create high-quality online traffic volume forecasts. The methodology is validated with a data set of 1755 days of 15 min aggregated traffic volume time series. Compared with 365 randomly selected days, the functional forecasts are found to outperform traditional seasonal autoregressive integrated moving average-based methods in both count deviation and root mean squared error. In addition, through the functional data analysis approach the full exploitation of the continuous nature of the data can be achieved

CXXC Domain of Human DNMT1 Is Essential for Enzymatic Activity

DNA cytosine methylation is one of the major epigenetic gene silencing marks in the human genome facilitated by DNA methyltransferases. DNA cytosine-5 methyltransferase 1 (DNMT1) performs maintenance methylation in somatic cells. In cancer cells, DNMT1 is responsible for the aberrant hypermethylation of CpG islands and the silencing of tumor suppressor genes. Here we show that the catalytically active recombinant DNMT1, lacking 580 amino acids from the amino terminus, binds to unmethylated DNA with higher affinity than hemimethylated or methylated DNA. To further understand the binding domain of enzyme, we have used gel shift assay. We have demonstrated that the CXXC region (C is cysteine; X is any amino acid) of DNMT1 bound specifically to unmethylated CpG dinucleotides. Furthermore, mutation of the conserved cysteines abolished CXXC mediated DNA binding. In transfected COS-7 cells, CXXC deleted DNMT1 (DNMT1^ΔCXXC) localized on replication foci. Both point mutant and DNMT1^ΔCXXC enzyme displayed significant reduction in catalytic activity, confirming that this domain is crucial for enzymatic activity. A permanent cell line with DNMT1^ΔCXXC displayed partial loss of genomic methylation on rDNA loci, despite the presence of endogenous wild-type enzyme. Thus, the CXXC domain encompassing the amino terminus region of DNMT1 cooperates with the catalytic domain for DNA methyltransferase activity