A vacuum benthos sampler suitable for diverse habitats

The vacuum benthos sampler consists of a vacuum chamber equipped with a collecting net connected to the intake of a 12 volt pump, a battery, and a standpipe. Contents of the standpipe are vacuumed while substrate is removed and washed with the exhaust hose. The vacuum chamber is designed for rapid changing of nets during replicate sampling. This sampler is equally efficient in flowing and standing water. It was more effective than a modified Hess sampler for collecting a large variety of benthos from flowing (ca 0.25–0.75 m/s) riffles. Required operation time is variable, but 93% of invertebrates caught in 10 min were captured in the first 5 min during our tests, and there was a 94% mean recovery of released organisms during 10 min of subsequent operation. Advantages over previous suction samplers include interception of organisms before they pass through a pump, return of outlet water to the standpipe, capability of sampling in shallow (20–30 mm) water, and that it can be carried and operated by one person.     

The efficiency of a new lightweight suction sampler for sampling aphids and their predators in arable land

A petrol driven machine, normally used to collect leaf litter, was modified to be used as a suction sampler for polyphagous predators in cereals and grassy habitats and aphids in cereals. Recovery efficiency of Araneae, Carabidae and Staphylinidae did not differ significantly between winter sampling from Agrostis stolonifera, Dactylis glomerata, Festuca rubra, Holcus lanatus or Lolium perenne although the structure of these grasses was very different. Summer sampling of aphids and their predators in winter wheat showed that the new sampler captured significantly more target organisms per unit area than did a traditional suction sampler. The machine was also lighter, cheaper and much easier to use than a traditional machine.     

A hierarchical Bayesian model for combining multiple 2 x 2 tables using conditional likelihoods

This paper introduces a hierarchical Bayesian model for combining multiple 2 x 2 tables that allows the flexibility of different odds ratio estimates for different tables and at the same time allows the tables to borrow information from each other. The proposed model, however, is different from a full Bayesian model in that the nuisance parameters are eliminated by conditioning instead of integration. The motivation is a more robust model and a faster and more stable Gibbs algorithm. We work out a Gibbs scheme using the adaptive rejection sampling for log concave density and an algorithm for the mean and variance of the noncentral hypergeometric distribution. The model is applied to a multicenter ulcer clinical trial.     

Exact tests of goodness of fit of log-linear models for rates

We propose Metropolis-Hastings sampling methods for estimating the exact conditional p-value for tests of goodness of fit of log-linear models for mortality rates and standardized mortality ratios. We focus on two-way tables, where the required conditional distribution is a multivariate noncentral hypergeometric distribution with known noncentrality parameter. Two examples are presented: a 2 x 3 table, where the exact results, obtained by enumeration, are available for comparison, and a 9 x 7 table, where Monte Carlo methods provide the only feasible approach for exact inference.     

A multi-purpose modular limnological sampler

This paper describes a new type of multi-purpose limnological sampler, based on several modules which, when fitted together in various ways, and with accessory items, can be used to sample in many different fashions. The main advantages of the sampler are that, in addition to its ability to perform a variety of functions, it is both cheap and light. It is particularly suitable for multi-purpose work on remote waters to which transport of heavy or bulky equipment is difficult or expensive. The complete package described, best operated by two people, currently costs £ 75 and weighs 20 kg. It enables the operators to sample several aspects of water, sediments, phytoplankton, macrophytes, zooplankton, zoobenthos and fish in several ways, both qualitatively and quantitatively, from a boat or from the shore.     

A simple and inexpensive trap-tube sampler for zooplankton collection in shallow waters

The paper describes a new type of zooplankton sampler, which combines the concepts of the Pennak core sampler and the Schindler-Patalas plankton trap. The new sampler, called Trap Tube Sampler, consists of a PVC water pipe (1.5–2.0 m, long; 10 cm diameter) provided, at the bottom end, of a filtering unit and closing mechanism which alternatively closes the mouth of the tube and the mouth of the filtering unit. The new device is particularly suitable for collecting samples from the entire water column in shallow vegetated water bodies, fish ponds and mesocosm tanks.     

Multibreed analysis by splitting the breeding values

An equivalent model for multibreed variance covariance estimation is presented. It considers the additive case including or not the segregation variances. The model is based on splitting the additive genetic values in several independent parts depending on their genetic origin. For each part, it expresses the covariance between relatives as a partial numerator relationship matrix times the corresponding variance component. Estimation of fixed effects, random effects or variance components provided by the model are as simple as any model including several random factors. We present a small example describing the mixed model equations for genetic evaluations and two simulated examples to illustrate the Bayesian variance component estimation.     

Prediction of MHC binding peptide using Gibbs motif sampler,weight matrix and artificial neural network

The identification of MHC restricted epitopes is an important goal in peptide based vaccine and diagnostic development. As wet lab experiments for identification of MHC binding peptide are expensive and time consuming, in silico tools have been developed as fast alternatives, however with low performance. In the present study, we used IEDB training and blind validation datasets for the prediction of peptide binding to fourteen human MHC class I and II molecules using Gibbs motif sampler, weight matrix and artificial neural network methods. As compare to MHC class I predictor based on sequence weighting (Aroc=0.95 and CC=0.56) and artificial neural network (Aroc=0.73 and CC=0.25), MHC class II predictor based on Gibbs sampler did not perform well (Aroc=0.62 and CC=0.19). The predictive accuracy of Gibbs motif sampler in identifying the 9-mer cores of a binding peptide to DRB1 alleles are also limited (40¢), however above the random prediction (14¢). Therefore, the size of dataset (training and validation) and the correct identification of the binding core are the two main factors limiting the performance of MHC class-II binding peptide prediction. Overall, these data suggest that there is substantial room to improve the quality of the core predictions using novel approaches that capture distinct features of MHC-peptide interactions than the current approaches.     

Exact two-sample inference with missing data

When comparing follow-up measurements from two independent populations, missing records may arise due to censoring by events whose occurrence is associated with baseline covariates. In these situations, inferences based only on the completely followed observations may be biased if the follow-up measurements and the covariates are correlated. This article describes exact inference for a class of modified U-statistics under covariate-dependent dropouts. The method involves weighing each permutation according to the retention probabilities, and thus requires estimation of the missing data mechanism. The proposed procedure is nonparametric in that no distributional assumption is necessary for the outcome variables and the missingness patterns. Monte Carlo approximation by the Gibbs sampler is proposed, and is shown to be fast and accurate via simulation. The method is illustrated in two small data sets for which asymptotic inferential procedures may not be appropriate.