Distinguishing effects on tumor multiplicity and growth rate in chemoprevention experiments

In some types of cancer chemoprevention experiments and short-term carcinogenicity bioassays, the data consist of the number of observed tumors per animal and the times at which these tumors were first detected. In such studies, there is interest in distinguishing between treatment effects on the number of tumors induced by a known carcinogen and treatment effects on the tumor growth rate. Since animals may die before all induced tumors reach a detectable size, separation of these effects can be difficult. This paper describes a flexible parametric model for data of this type. Under our model, the tumor detection times are realizations of a delayed Poisson process that is characterized by the age-specific tumor induction rate and a random latency interval between tumor induction and detection. The model accommodates distinct treatment and animal-specific effects on the number of induced tumors (multiplicity) and the time to tumor detection (growth rate). A Gibbs sampler is developed for estimation of the posterior distributions of the parameters. The methods are illustrated through application to data from a breast cancer chemoprevention experiment.     

Improved techniques for sampling complex pedigrees with the Gibbs sampler

Markov chain Monte Carlo (MCMC) methods have been widely used to overcome computational problems in linkage and segregation analyses. Many variants of this approach exist and are practiced; among the most popular is the Gibbs sampler. The Gibbs sampler is simple to implement but has (in its simplest form) mixing and reducibility problems; furthermore in order to initiate a Gibbs sampling chain we need a starting genotypic or allelic configuration which is consistent with the marker data in the pedigree and which has suitable weight in the joint distribution. We outline a procedure for finding such a configuration in pedigrees which have too many loci to allow for exact peeling. We also explain how this technique could be used to implement a blocking Gibbs sampler.     

A personal sampler to monitor airborne particles of biological origin

Aerobiological studies are important in many fields, specifically in medicine as they enable the identification of those airborne particles of biological origin coming into contact with man. These data are useful to evaluate the cause-effect ratio, to prevent allergy, to adjust the dose of drugs. A personal sampler for aerobiological particles (PARTRAP FA 52) was handed over to eleven atopic patients for a monitoring day. The sampling room was placed at about 15 cm from nose and mouth. The patients also underwent the Prick test and RAST test. The personal sampler for aerobiological particles enables a qualitative and quantitative sampling of the air close to the patient. More allergenic particles were found with the aerobiological personal sampling as compared to the sensitisation shown by Prick test and RAST in the same patient. This sampling method can be useful to demonstrate the cause-effect ratio directly near the patient and, moreover and can be indicative for further clinical and immunological investigations.     

Mixed‐Effects State‐Space Models for Analysis of Longitudinal Dynamic Systems

Summary The rapid development of new biotechnologies allows us to deeply understand biomedical dynamic systems in more detail and at a cellular level. Many of the subject‐specific biomedical systems can be described by a set of differential or difference equations that are similar to engineering dynamic systems. In this article, motivated by HIV dynamic studies, we propose a class of mixed‐effects state‐space models based on the longitudinal feature of dynamic systems. State‐space models with mixed‐effects components are very flexible in modeling the serial correlation of within‐subject observations and between‐subject variations. The Bayesian approach and the maximum likelihood method for standard mixed‐effects models and state‐space models are modified and investigated for estimating unknown parameters in the proposed models. In the Bayesian approach, full conditional distributions are derived and the Gibbs sampler is constructed to explore the posterior distributions. For the maximum likelihood method, we develop a Monte Carlo EM algorithm with a Gibbs sampler step to approximate the conditional expectations in the E‐step. Simulation studies are conducted to compare the two proposed methods. We apply the mixed‐effects state‐space model to a data set from an AIDS clinical trial to illustrate the proposed methodologies. The proposed models and methods may also have potential applications in other biomedical system analyses such as tumor dynamics in cancer research and genetic regulatory network modeling.     

Large variation of suction sampling efficiency depending on arthropod groups,species traits,and habitat properties

Suction sampling is widely used to estimate arthropod abundance and diversity. To test the reliability of abundance data derived from suction sampling, we examined sampling efficiency across a wide range of arthropod groups and tested for effects of species traits, vegetation density, and differences between sites. Suction sampling efficiency was quantified by vacuuming an enclosed meadow area and subsequent removal of the turf, which was treated with heat extraction to collect the remaining arthropods. We obtained 250 pairs of suction and turf samples from seven grasslands with variable vegetation density. High suction sampling efficiencies between 49 and 86% were obtained for Auchenorrhyncha, Heteroptera, Araneida, Curculionoidea, Hymenoptera, and Diptera. In contrast, efficiencies were below 30% for Aphidae, Thysanoptera, Staphylinidae and other Coleoptera, and for soil arthropods such as Collembola, Isopoda, Diplopoda, and Formicidae. Efficiency varied significantly among habitats (sites) for most groups, often more than two‐fold. Surprisingly, sampling efficiency for Hymenoptera, Diplopoda, and Collembola increased with vegetation density, probably because aboveground activity of these taxa was higher in dense vegetation. Suction sampling was nearly twice as efficient for spiders living in the vegetation than for spiders living near the soil surface, and cursorial and large‐bodied spider species were more efficiently sampled than web‐builders and small species. Depending on the sampling effort, suction sampling missed between 49% (one sample) and 31% (250 samples) of the spider species present. Suction sampling efficiency varied more strongly among sites and among arthropod groups than previously recognized. Abundance data derived from suction sampling are strongly underestimated, especially for arthropods living near the soil surface. Thus, comparisons of abundance and diversity between sites should be restricted to vegetation‐dwelling species of the most efficiently sampled groups. The positive relationship of sampling efficiency with vegetation density demonstrates that variation in efficiency is mediated by arthropod behaviour.     

Hand-operated box sampler for shallow water

The construction and operation of a hand-operated box sampler is described. The sampler covers an area of 25 × 25 cm and penetrates into the sediment to the depth of 15 cm. It was successfully operated on a sandy bottom, and on sand mixed with gravel up to a water depth of 1 meter.IAEC, Yavne 70600, Israel     

The Stone Brusher, a New Sampler for Submerged Epilithic Material in Shallow Streams and Lakes

The Stone Brusher is designed to take qualitative or semi-quantitative samples of material attached to stones at 7–50 cm depth in running or stagnant waters. The epilithic material is dislodged from the stone surface with a rotating brush enclosed in a chamber and the material is drawn up directly into the sample bottle with an air-cylinder. The operator takes a sample quickly and without putting hands into the water. The sampling area is about 28 cm². The sampler is made of plastic, stainless steel and aluminium and weighs 3.1 kg. The equipment is robust and easily handled and it is constructed to meet the demand for standardized sampling for research and environmental monitoring and to improve working conditions for sampling personnel. The equipment allows sampling from bedrock and large stones that cannot be lifted from the bottom and it can be used for reliable sampling also in fast-flowing streams where the dislodged material is easily flushed away. Using Near-Infrared Spectroscopy and diatom analyses, this new sampler is evaluated in comparison to the recognized toothbrush method, which indicates that the Stone Brusher reduces sampling variability compared with the toothbrush method.     

Bayesian Modeling of ChIP‐chip Data Through a High‐Order Ising Model

Summary ChIP‐chip experiments are procedures that combine chromatin immunoprecipitation (ChIP) and DNA microarray (chip) technology to study a variety of biological problems, including protein–DNA interaction, histone modification, and DNA methylation. The most important feature of ChIP‐chip data is that the intensity measurements of probes are spatially correlated because the DNA fragments are hybridized to neighboring probes in the experiments. We propose a simple, but powerful Bayesian hierarchical approach to ChIP‐chip data through an Ising model with high‐order interactions. The proposed method naturally takes into account the intrinsic spatial structure of the data and can be used to analyze data from multiple platforms with different genomic resolutions. The model parameters are estimated using the Gibbs sampler. The proposed method is illustrated using two publicly available data sets from Affymetrix and Agilent platforms, and compared with three alternative Bayesian methods, namely, Bayesian hierarchical model, hierarchical gamma mixture model, and Tilemap hidden Markov model. The numerical results indicate that the proposed method performs as well as the other three methods for the data from Affymetrix tiling arrays, but significantly outperforms the other three methods for the data from Agilent promoter arrays. In addition, we find that the proposed method has better operating characteristics in terms of sensitivities and false discovery rates under various scenarios.     

A new adhesive for airborne pollen sampling in Spain

The use of a double-sided self-adhesive tapefor pollen trapping could offer some advantagesover traditional adhesives, manually applied tothe Melinex tape of a Hirst-type spore trap,since variation in terms of adhesiveapplication is nil and additional effort inapplying the adhesive is avoided. Nevertheless,its efficiency with respect to the standardadhesives must be tested. In Cordoba (southwestSpain), the Spanish Aerobiology Network Centreperformed a series of comparative studies andrecommended the use of a silicone fluid as anadhesive. This paper compares the efficiency ofa double-sided self-adhesive tape (3M) withsilicone fluid as adhesive. Sampling wascarried out using a Hirst type 7-day spore traplocated 15 m above ground level. A Melinex tapecoated with silicone fluid covered one half ofthe weekly drum; the other half was coveredwith 3M tape that included the adhesive. It iswidely reported that the physicalcharacteristics of silicone fluid do not varywith temperature (Galán andDomínguez-Vilches, 1997), but this studyshowed that 3M efficiency did vary withtemperature. The results revealed that 3M tapewas less efficacious at low temperatures, whileat high temperatures it becomes stickier. Incontrast, silicone fluid does not vary withtemperature. The efficiency of 3M tape alsoseems to be negatively influenced by rain.     

Bayesian estimation in animal breeding using the Dirichlet process prior for correlated random effects

In the case of the mixed linear model the random effects are usually assumed to be normally distributed in both the Bayesian and classical frameworks. In this paper, the Dirichlet process prior was used to provide nonparametric Bayesian estimates for correlated random effects. This goal was achieved by providing a Gibbs sampler algorithm that allows these correlated random effects to have a nonparametric prior distribution. A sampling based method is illustrated. This method which is employed by transforming the genetic covariance matrix to an identity matrix so that the random effects are uncorrelated, is an extension of the theory and the results of previous researchers. Also by using Gibbs sampling and data augmentation a simulation procedure was derived for estimating the precision parameter M associated with the Dirichlet process prior. All needed conditional posterior distributions are given. To illustrate the application, data from the Elsenburg Dormer sheep stud were analysed. A total of 3325 weaning weight records from the progeny of 101 sires were used.