Dynamic Non-parametric Monitoring of Air-Pollution

Air pollution poses a major problem in modern cities, as it has a significant effect in poor quality of life of the general population. Many recent studies link excess levels of major air pollutants with health-related incidents, in particular respiratory-related diseases. This introduces the need for city pollution on-line monitoring to enable quick identification of deviations from “normal” pollution levels, and providing useful information to public authorities for public protection. This article considers dynamic monitoring of pollution data (output of multivariate processes) using Kalman filters and multivariate statistical process control techniques. A state space model is used to define the in-control process dynamics, involving trend and seasonality. Distribution-free monitoring of the residuals of that model is proposed, based on binomial-type and generalised binomial-type statistics as well as on rank statistics. We discuss the general problem of detecting a change in pollutant levels that affects either the entire city (globally) or specific sub-areas (locally). The proposed methodology is illustrated using data, consisting of ozone, nitrogen oxides and sulfur dioxide collected over the air-quality monitoring network of Athens

Incidence and antimicrobial susceptibilities of genital mycoplasmas in outpatient women with clinical vaginitis in Athens, Greece

Objectives: The incidence and antimicrobial susceptibilities of Ureaplasma urealyticum and Mycoplasma hominis, isolated from vaginal and endocervical swabs collected from 369 outpatient women, were determined. Methods: Isolation, identification and typing of the pathogens were performed by means of conventional methods. The antimicrobial susceptibilities of the genital mycoplasmas were determined with commercially available kits and evaluated according to the CLSI. Results and conclusions: In 65 (47.44%) out of the 137 positive specimens, U. urealyticum was grown as a single pathogen, in 0.72% M. hominis was grown as a single pathogen and in 2.92% both urogenital mycoplasmas were grown. In the remaining specimens (48.90%), there was a mixed growth with other microbes. Of the isolated U. urealyticum strains, 87.4% and 98.2% were susceptible to tetracycline and doxycycline, respectively, 79.2% were susceptible to josamycin, 48.6% were susceptible to clarithromycin and 91.8% were susceptible to pristinamycin, while erythromycin, azithromycin, ciprofloxacin and ofloxacin proved to be inactive against most of the strains. M. hominis isolates were 100% susceptible to tetracycline, doxycycline and pristinamycin, while susceptibilities to the other antimicrobial agents varied mainly in the range of 'intermediate' or 'resistant'. As results originating from similar studies from various countries are very controversial, the simplest way to avoid therapeutic failures would be the implementation of rational treatment regimens based on culture isolation and the in vitro determination of the antimicrobial susceptibility of genital mycoplasmas in each clinical case

Discriminating membrane proteins using the joint distribution of length sums of success and failure runs

Discriminating integral membrane proteins from water-soluble ones, has been over the past decades an important goal for computational molecular biology. A major drawback of methods appeared in the literature, is that most of the authors tried to solve the problem using machine learning techniques. Specifically, most of the proposed methods require an appropriate dataset for training, and consequently the results depend heavily on the suitability of the dataset, itself. Motivated by these facts, in this paper we develop a formal discrimination procedure that is based on appropriate theoretical observations on the sequence of hydrophobic and polar residues along the protein sequence and on the exact distribution of a two dimensional runs-related statistic defined on the same sequence. Specifically, for setting up our discrimination procedure, we study thoroughly the exact distribution of a bivariate random variable, which accumulates the exact lengths of both success and failure runs of at least a specific length in a sequence of Bernoulli trials. To investigate the properties of this bivariate random variable, we use the Markov chain embedding technique. Finally, we apply the new procedure to a well-defined dataset of proteins. © 2016, Springer-Verlag Berlin Heidelberg