Predicting a cyclic Poisson process

We construct and investigate a (1−α)-upper prediction bound for a future observation of a cyclic Poisson process using past data. A normal based confidence interval for our upper prediction bound is established. A comparison of the new prediction bound with a simpler nonparametric prediction bound is also given.     

Estimating the intensity of a cyclic Poisson process in the presence of linear trend

We construct and investigate a consistent kernel-type nonparametric estimator of the intensity function of a cyclic Poisson process in the presence of linear trend. It is assumed that only a single realization of the Poisson process is observed in a bounded window. We prove that the proposed estimator is consistent when the size of the window indefinitely expands. The asymptotic bias, variance, and the mean-squared error of the proposed estimator are also computed. A simulation study shows that the first order asymptotic approximations to the bias and variance of the estimator are not accurate enough. Second order terms for bias and variance were derived in order to be able to predict the numerical results in the simulation. Bias reduction of our estimator is also proposed.     

From PCA's to equilibrium systems and back

Stationary measures for probabilistic cellular automata (PCA's) ind dimensions give rise to space-time histories whose statistics may naturally be described by Gibbs states ind+1 dimensions for an interaction energy ? obtained from the PCA. In this note we study the converse question: Do all Gibbs states for this ? correspond to statistical space-time histories for the PCA? Our main result states that the answer is yes, at least for translation invariant or periodic Gibbs states. Thus ergodicity questions for PCA's can, at least partially, be formulated as questions of uniqueness of Gibbs states.     

Development of bacteria-based bioassays for arsenic detection in natural waters

Arsenic contamination of natural waters is a worldwide concern, as the drinking water supplies for large populations can have high concentrations of arsenic. Traditional techniques to detect arsenic in natural water samples can be costly and time-consuming; therefore, robust and inexpensive methods to detect arsenic in water are highly desirable. Additionally, methods for detecting arsenic in the field have been greatly sought after. This article focuses on the use of bacteria-based assays as an emerging method that is both robust and inexpensive for the detection of arsenic in groundwater both in the field and in the laboratory. The arsenic detection elements in bacteria-based bioassays are biosensor–reporter strains; genetically modified strains of, e.g., Escherichia coli, Bacillus subtilis, Staphylococcus aureus, and Rhodopseudomonas palustris. In response to the presence of arsenic, such bacteria produce a reporter protein, the amount or activity of which is measured in the bioassay. Some of these bacterial biosensor–reporters have been successfully utilized for comparative in-field analyses through the use of simple solution-based assays, but future methods may concentrate on miniaturization using fiberoptics or microfluidics platforms. Additionally, there are other potential emerging bioassays for the detection of arsenic in natural waters including nematodes and clams.     

SAR study of 1-aryl-4-(phenylarylmethyl)piperazines as ligands for both dopamine D2 and serotonin 5-HT1A receptors showing varying degrees of (Ant)agonism. Selection of a potential atypical antipsychotic

The syntheses of several 1-aryl-4-(arylpyridylmethyl)piperazines (4) and their affinities for dopamine D(2) and serotonin 5-HT(1A) receptors are described. The compounds were evaluated both in vitro and in vivo, resulting in the identification of the drug candidate SLV313 (4e) with equipotent and full D(2) receptor antagonism and 5-HT(1A) receptor agonism. Minor structural modifications in SLV313 revealed the possibility of designing compounds possessing varying degrees of partial agonism on one or both target receptors.     

Response Characteristics of Arsenic-Sensitive Bioreporters Expressing the gfp Reporter Gene

This paper describes the development of an analytical technique for arsenic analyses that is based on genetically-modified bioreporter bacteria bearing a gene encoding for the production of a green fluorescent protein (gfp). Upon exposure to arsenic (in the aqueous form of arsenite), the bioreporter production of the fluorescent reporter molecule is monitored spectroscopically. We compared the response measured as a function of time and concentration by steady-state fluorimetry (SSF) to that measured by epi-fluorescent microscopy (EFM). SSF is a bulk technique; as such it inherently yields less information, whereas EFM monitors the response of many individual cells simultaneously and data can be processed in terms of population averages or subpopulations. For the bioreporter strain used here, as well as for the literature we cite, the two techniques exhibit similar performance characteristics. The results presented here show that the EFM technique can compete with SSF and shows substantially more promise for future improvement; it is a matter of research interest to develop optimized methods of EFM image analysis and statistical data treatment. EFM is a conduit for understanding the dynamics of individual cell response vs. population response, which is not only a matter of research interest, but is also promising in the practical terms of developing micro-scale analysis.     

Spherical molecularly imprinted polymer particles: a promising tool for molecular recognition in capillary electrokinetic separations

Spherical molecularly imprinted polymer particles obtained via precipitation polymerization, were introduced as a pseudostationary phase in capillary electrophoresis (CE) to study molecular recognition. Analyses were performed via a partial filling technique using (+)-ephedrine-imprinted microspheres (100-200 nm) which were polymerized from methacrylic acid and 1,1,1-Tris(hydroxymethyl)propanetrimethacrylate using acetonitrile as the solvent. The influence of pH and the modifier content on the separation was investigated. A 0.1% w/v suspension in an aqueous 10 mM phosphate buffer (pH 2.5 with 40% acetonitrile) was hydrodynamically injected into the CE system (80% of the effective capillary length) and led to full baseline separation of racemic ephedrine within 10 min.     

Development of a microfluidics biosensor for agarose-bead immobilized Escherichia coli bioreporter cells for arsenite detection in aqueous samples

Contamination with arsenic is a recurring problem in both industrialized and developing countries. Drinking water supplies for large populations can have concentrations much higher than the permissible levels (for most European countries and the United States, 10 μg As per L; elsewhere, 50 μg As per L). Arsenic analysis requires high-end instruments, which are largely unavailable in developing countries. Bioassays based on genetically engineered bacteria have been proposed as suitable alternatives but such tests would profit from better standardization and direct incorporation into sensing devices. The goal of this work was to develop and test microfluidic devices in which bacterial bioreporters could be embedded, exposed and reporter signals detected, as a further step towards a complete miniaturized bacterial biosensor. The signal element in the biosensor is a nonpathogenic laboratory strain of Escherichia coli, which produces a variant of the green fluorescent protein after contact to arsenite and arsenate. E. coli bioreporter cells were encapsulated in agarose beads and incorporated into a microfluidic device where they were captured in 500 × 500 μm(2) cages and exposed to aqueous samples containing arsenic. Cell-beads frozen at -20 °C in the microfluidic chip retained inducibility for up to a month and arsenic samples with 10 or 50 μg L(-1) could be reproducibly discriminated from the blank. In the 0-50 μg L(-1) range and with an exposure time of 200 minutes, the rate of signal increase was linearly proportional to the arsenic concentration. The time needed to reliably and reproducibly detect a concentration of 50 μg L(-1) was 75-120 minutes, and 120-180 minutes for a concentration of 10 μg L(-1).     

Effect of Groundwater Composition on Arsenic Detection by Bacterial Biosensors

A luminescent bacterial biosensor was used to quantify bioavailable arsenic in artificial groundwater. Its light production above the background emission was proportional to the arsenite concentration in the toxicologically relevant range of 0 to 0.5 μM. Effects of the inorganic solutes phosphate, Fe(II) and silicate on the biosensor signal were studied. Phosphate at a concentration of 0.25 g L⁻¹ phosphate slightly stimulated the light emission, but much less than toxicologically relevant concentrations of the much stronger inducer arsenite. No effect of phosphate was oberved in the presence of arsenite. Freshly prepared sodium silicate solution at a concentration of 10 mg L⁻¹ Si reduced the arsenite-induced light production by roughly 37%, which can be explained by transient polymerization leading to sequestration of some arsenic. After three days of incubation, silicate did not have this effect anymore, probably because depolymerization occurred. In the presence of 0.4 mg L⁻¹ Fe(II), the arsenite-induced light emission was reduced by up to 90%, probably due to iron oxidation followed by arsenite adsorption on the less soluble Fe(III) possibly along with some oxidation to the stronger adsorbing As(V). Addition of 100 μM EDTA was capable of releasing all arsenic from the precipitate and to transform it into the biologically measurable, dissolved state. The biosensor also proved valuable for monitoring the effectiveness of an arsenic removal procedure based on water filtration through a mixture of sand and iron granules.