High throughput, high resolution selection of polymorphic microsatellite loci for multiplex analysis

Background Large-scale genetic profiling, mapping and genetic association studies require access to a series of well-characterised and polymorphic microsatellite markers with distinct and broad allele ranges. Selection of complementary microsatellite markers with non-overlapping allele ranges has historically proved to be a bottleneck in the development of multiplex microsatellite assays. The characterisation process for each microsatellite locus can be laborious and costly given the need for numerous, locus-specific fluorescent primers. Results Here, we describe a simple and inexpensive approach to select useful microsatellite markers. The system is based on the pooling of multiple unlabelled PCR amplicons and their subsequent ligation into a standard cloning vector. A second round of amplification utilising generic labelled primers targeting the vector and unlabelled locus-specific primers targeting the microsatellite flanking region yield allelic profiles that are representative of all individuals contained within the pool. Suitability of various DNA pool sizes was then tested for this purpose. DNA template pools containing between 8 and 96 individuals were assessed for the determination of allele ranges of individual microsatellite markers across a broad population. This helped resolve the balance between using pools that are large enough to allow the detection of many alleles against the risk of including too many individuals in a pool such that rare alleles are over-diluted and so do not appear in the pooled microsatellite profile. Pools of DNA from 12 individuals allowed the reliable detection of all alleles present in the pool. Conclusion The use of generic vector-specific fluorescent primers and unlabelled locus-specific primers provides a high resolution, rapid and inexpensive approach for the selection of highly polymorphic microsatellite loci that possess non-overlapping allele ranges for use in large-scale multiplex assays.

Neural network basis function center selection using cluster analysis

This paper deals with the selection of centres for radial basis function (RBF) networks. A novel mean-tracking clustering algorithm is described as a way in which centers can be chosen based on a batch of collected data. A direct comparison is made between the mean-tracking algorithm and k-means clustering and it is shown how mean-tracking clustering is significantly better in terms of achieving an RBF network which performs accurate function modelling.

Optimising the analysis of transcript data using high density oligonucleotide arrays and genomic DNA-based probe selection

Background: Affymetrix GeneChip arrays are widely used for transcriptomic studies in a diverse range of species. Each gene is represented on a GeneChip array by a probe- set, consisting of up to 16 probe-pairs. Signal intensities across probe- pairs within a probe-set vary in part due to different physical hybridisation characteristics of individual probes with their target labelled transcripts. We have previously developed a technique to study the transcriptomes of heterologous species based on hybridising genomic DNA (gDNA) to a GeneChip array designed for a different species, and subsequently using only those probes with good homology. Results: Here we have investigated the effects of hybridising homologous species gDNA to study the transcriptomes of species for which the arrays have been designed. Genomic DNA from Arabidopsis thaliana and rice (Oryza sativa) were hybridised to the Affymetrix Arabidopsis ATH1 and Rice Genome GeneChip arrays respectively. Probe selection based on gDNA hybridisation intensity increased the number of genes identified as significantly differentially expressed in two published studies of Arabidopsis development, and optimised the analysis of technical replicates obtained from pooled samples of RNA from rice. Conclusion: This mixed physical and bioinformatics approach can be used to optimise estimates of gene expression when using GeneChip arrays.

Roles in construction projects: analysis and terminology

Standard form contracts are typically developed through a negotiated consensus, unless they are proffered by one specific interest group. Previously published plans of work and other descriptions of the processes in construction projects tend to focus on operational issues, or they tend to be prepared from the point of view of one or other of the dominant interest groups. Legal practice in the UK permits those who draft contracts to define their terms as they choose. There are no definitive rulings from the courts that give an indication as to the detailed responsibilities of project participants. The science of terminology offers useful guidance for discovering and describing terms and their meanings in their practical context, but has never been used for defining terms for responsibilities of participants in the construction project management process. Organizational analysis enables the management task to be deconstructed into its elemental parts in order that effective organizational structures can be developed. Organizational mapping offers a useful technique for reducing text-based descriptions of project management roles and responsibilities to a comparable basis. Research was carried out by means of a desk study, detailed analysis of nine plans of work and focus groups representing all aspects of the construction industry. No published plan of work offers definitive guidance. There is an enormous amount of variety in the way that terms are used for identifying responsibilities of project participants. A catalogue of concepts and terms (a “Terminology”) has been compiled and indexed to enable those who draft contracts to choose the most appropriate titles for project participants. The purpose of this terminology is to enable the selection and justification of appropriate terms in order to help define roles. The terminology brings an unprecedented clarity to the description of roles and responsibilities in construction projects and, as such, will be helpful for anyone seeking to assemble a team and specify roles for project participants.

Analysis of Phytochemical Composition and Chemoprotective Capacity of Rocket (Eruca sativa and Diplotaxis tenuifolia) Leafy Salad Following Cultivation in Different Environments

Consumption of green leafy vegetables is associated with reduced risk of several types of cancer and cardiovascular disease. These beneficial effects are attributed to a range of phytochemicals including flavonoids and glucosinolates, both of which are found in high levels in Brassicaceous crops. Rocket is the general name attributed to cultivars of Eruca sativa and Diplotaxis tenufolia, known as salad rocket and wild rocket, respectively. We have shown that different light levels during the cultivation period of these crops have a significant impact on the levels of flavonoids present in the crop at harvest, with over 15-fold increase achieved in quercetin, isorhamnetin, and cyanidin in high light conditions. Postharvest storage further affects the levels of both flavonoids and glucosinolates, with cyanidin increasing during shelf life and some glucosinolates, such as glucoiberverin, being reduced over the same storage period. In vitro assays using human colon cell lines demonstrate that glucosinolate-rich extracts of Eruca sativa cv. Sky, but not Diplotaxis tenufolia cv. Voyager, confer significant resistance to oxidative stress on the cells, which is indicative of the chemoprotective properties of the leaves from this species. Our findings indicate that both pre and postharvest environment and genotypic selection, when developing new lines of Brassicaceous vegetables, are important considerations with the goal of improving human nutrition and health.

Simulating current global river runoff with a global hydrological model: model revisions, validation, and sensitivity analysis

Global hydrological models (GHMs) model the land surface hydrologic dynamics of continental-scale river basins. Here we describe one such GHM, the Macro-scale - Probability-Distributed Moisture model.09 (Mac-PDM.09). The model has undergone a number of revisions since it was last applied in the hydrological literature. This paper serves to provide a detailed description of the latest version of the model. The main revisions include the following: (1) the ability for the model to be run for n repetitions, which provides more robust estimates of extreme hydrological behaviour, (2) the ability of the model to use a gridded field of coefficient of variation (CV) of daily rainfall for the stochastic disaggregation of monthly precipitation to daily precipitation, and (3) the model can now be forced with daily input climate data as well as monthly input climate data. We demonstrate the effects that each of these three revisions has on simulated runoff relative to before the revisions were applied. Importantly, we show that when Mac-PDM.09 is forced with monthly input data, it results in a negative runoff bias relative to when daily forcings are applied, for regions of the globe where the day-to-day variability in relative humidity is high. The runoff bias can be up to - 80% for a small selection of catchments but the absolute magnitude of the bias may be small. As such, we recommend future applications of Mac-PDM.09 that use monthly climate forcings acknowledge the bias as a limitation of the model. The performance of Mac-PDM.09 is evaluated by validating simulated runoff against observed runoff for 50 catchments. We also present a sensitivity analysis that demonstrates that simulated runoff is considerably more sensitive to method of PE calculation than to perturbations in soil moisture and field capacity parameters.

Genetic parameters and evaluations from single- and multiple-trait analysis of dairy cow fertility and milk production

Genetic parameters and breeding values for dairy cow fertility were estimated from 62 443 lactation records. Two-trait analysis of fertility and milk yield was investigated as a method to estimate fertility breeding values when culling or selection based on milk yield in early lactation determines presence or absence of fertility observations in later lactations. Fertility traits were calving interval, intervals from calving to first service, calving to conception and first to last service, conception success to first service and number of services per conception. Milk production traits were 305-day milk, fat and protein yield. For fertility traits, range of estimates of heritability (h(2)) was 0.012 to 0.028 and of permanent environmental variance (c(2)) was 0.016 to 0.032. Genetic correlations (r(g)) among fertility traits were generally high ( > 0.70). Genetic correlations of fertility with milk production traits were unfavourable (range -0.11 to 0.46). Single and two-trait analyses of fertility were compared using the same data set. The estimates of h(2) and c(2) were similar for two types of analyses. However, there were differences between estimated breeding values and rankings for the same trait from single versus multi-trait analyses. The range for rank correlation was 0.69-0.83 for all animals in the pedigree and 0.89-0.96 for sires with more than 25 daughters. As single-trait method is biased due to selection on milk yield, a multi-trait evaluation of fertility with milk yield is recommended. (C) 2002 Elsevier Science B.V. All rights reserved.

Predicting feed quality - chemical analysis and in vitro evaluation

As the ideal method of assessing the nutritive value of a feedstuff, namely offering it to the appropriate class of animal and recording the production response obtained, is neither practical nor cost effective a range of feed evaluation techniques have been developed. Each of these balances some degree of compromise with the practical situation against data generation. However, due to the impact of animal-feed interactions over and above that of feed composition, the target animal remains the ultimate arbitrator of nutritional value. In this review current in vitro feed evaluation techniques are examined according to the degree of animal-feed interaction. Chemical analysis provides absolute values and therefore differs from the majority of in vitro methods that simply rank feeds. However, with no host animal involvement, estimates of nutritional value are inferred by statistical association. In addition given the costs involved, the practical value of many analyses conducted should be reviewed. The in sacco technique has made a substantial contribution to both understanding rumen microbial degradative processes and the rapid evaluation of feeds, especially in developing countries. However, the numerous shortfalls of the technique, common to many in vitro methods, the desire to eliminate the use of surgically modified animals for routine feed evaluation, paralleled with improvements in in vitro techniques, will see this technique increasingly replaced. The majority of in vitro systems use substrate disappearance to assess degradation, however, this provides no information regarding the quantity of derived end-products available to the host animal. As measurement of volatile fatty acids or microbial biomass production greatly increases analytical costs, fermentation gas release, a simple and non-destructive measurement, has been used as an alternative. However, as gas release alone is of little use, gas-based systems, where both degradation and fermentation gas release are measured simultaneously, are attracting considerable interest. Alternative microbial inocula are being considered, as is the potential of using multi-enzyme systems to examine degradation dynamics. It is concluded that while chemical analysis will continue to form an indispensable part of feed evaluation, enhanced use will be made of increasingly complex in vitro systems. It is vital, however, the function and limitations of each methodology are fully understood and that the temptation to over-interpret the data is avoided so as to draw the appropriate conclusions. With careful selection and correct application in vitro systems offer powerful research tools with which to evaluate feedstuffs. (C) 2003 Elsevier B.V. All rights reserved.

Point estimates and confidence regions for sequential trials involving selection

A number of authors have proposed clinical trial designs involving the comparison of several experimental treatments with a control treatment in two or more stages. At the end of the first stage, the most promising experimental treatment is selected, and all other experimental treatments are dropped from the trial. Provided it is good enough, the selected experimental treatment is then compared with the control treatment in one or more subsequent stages. The analysis of data from such a trial is problematic because of the treatment selection and the possibility of stopping at interim analyses. These aspects lead to bias in the maximum-likelihood estimate of the advantage of the selected experimental treatment over the control and to inaccurate coverage for the associated confidence interval. In this paper, we evaluate the bias of the maximum-likelihood estimate and propose a bias-adjusted estimate. We also propose an approach to the construction of a confidence region for the vector of advantages of the experimental treatments over the control based on an ordering of the sample space. These regions are shown to have accurate coverage, although they are also shown to be necessarily unbounded. Confidence intervals for the advantage of the selected treatment are obtained from the confidence regions and are shown to have more accurate coverage than the standard confidence interval based upon the maximum-likelihood estimate and its asymptotic standard error.

Sequential designs for phase III clinical trials incorporating treatment selection

Most statistical methodology for phase III clinical trials focuses on the comparison of a single experimental treatment with a control. An increasing desire to reduce the time before regulatory approval of a new drug is sought has led to development of two-stage or sequential designs for trials that combine the definitive analysis associated with phase III with the treatment selection element of a phase II study. In this paper we consider a trial in which the most promising of a number of experimental treatments is selected at the first interim analysis. This considerably reduces the computational load associated with the construction of stopping boundaries compared to the approach proposed by Follman, Proschan and Geller (Biometrics 1994; 50: 325-336). The computational requirement does not exceed that for the sequential comparison of a single experimental treatment with a control. Existing methods are extended in two ways. First, the use of the efficient score as a test statistic makes the analysis of binary, normal or failure-time data, as well as adjustment for covariates or stratification straightforward. Second, the question of trial power is also considered, enabling the determination of sample size required to give specified power. Copyright © 2003 John Wiley & Sons, Ltd.

In vivo expression technology (IVET) selection of genes of Rhizobium leguminosarum biovar viciae A34 expressed in the rhizosphere

IVET was used to identify genes that are specifically expressed in the rhizosphere of the pea-nodulating bacterium Rhizobium leguminosarum A34. A library of R. leguminosarum A34 cloned in the integration vector pIE1, with inserts upstream of a promoter-less purN:gfp:gusA, was conjugated into purN host RU2249 and recombined into the genome. After removal of colonies that expressed the reporter genes of the vector under laboratory conditions, the library was inoculated into a nonsterile pea rhizosphere. The key result is that 29 rhizosphere-induced loci were identified. Sequence analysis of these clones showed that a wide variety of R. leguminosarum A34 genes are expressed specifically in the rhizosphere including those encoding proteins involved in environmental sensing, control of gene expression, metabolic reactions and membrane transport. These genes are likely to be important for survival and colonization of the pea rhizosphere.

Estimation following selection of the largest of two normal means

This paper considers the problem of estimation when one of a number of populations, assumed normal with known common variance, is selected on the basis of it having the largest observed mean. Conditional on selection of the population, the observed mean is a biased estimate of the true mean. This problem arises in the analysis of clinical trials in which selection is made between a number of experimental treatments that are compared with each other either with or without an additional control treatment. Attempts to obtain approximately unbiased estimates in this setting have been proposed by Shen [2001. An improved method of evaluating drug effect in a multiple dose clinical trial. Statist. Medicine 20, 1913–1929] and Stallard and Todd [2005. Point estimates and confidence regions for sequential trials involving selection. J. Statist. Plann. Inference 135, 402–419]. This paper explores the problem in the simple setting in which two experimental treatments are compared in a single analysis. It is shown that in this case the estimate of Stallard and Todd is the maximum-likelihood estimate (m.l.e.), and this is compared with the estimate proposed by Shen. In particular, it is shown that the m.l.e. has infinite expectation whatever the true value of the mean being estimated. We show that there is no conditionally unbiased estimator, and propose a new family of approximately conditionally unbiased estimators, comparing these with the estimators suggested by Shen.

Urban mammals: what does the future hold? An analysis of the factors affecting patterns of use of residential gardens in Great Britain

1Urban areas are predicted to grow significantly in the foreseeable future because of increasing human population growth. Predicting the impact of urban development and expansion on mammal populations is of considerable interest due to possible effects on biodiversity and human-wildlife conflict. 2The British government has recently announced a substantial housing programme to meet the demands of its growing population and changing socio-economic profile. This is likely to result in the construction of high-density, low-cost housing with small residential gardens. To assess the potential effects of this programme, we analysed the factors affecting the current pattern of use of residential gardens by a range of mammal species using a questionnaire distributed in wildlife and gardening magazines and via The Mammal Society. 3Twenty-two species/species groups were recorded. However, the pattern of garden use by individual species was limited, with only six species/species groups (bats, red fox Vulpes vulpes, grey squirrel Sciurus carolinensis, hedgehog Erinaceus europaeus, mice, voles) recorded as frequent visitors to > 20% of gardens in the survey. 4There was a high degree of association between the variables recorded in the study, such that it was difficult to quantify the effects of individual variables. However, all species/species groups appeared to be negatively affected by the increased fragmentation and reduced proximity of natural and semi-natural habitats, decreasing garden size and garden structure, but to differing degrees. Patterns of garden use were most clearly affected by house location (city, town, village, rural), with garden use declining with increasing urbanization for the majority of species/species groups, except red foxes and grey squirrels. Increasing urbanization is likely to be related to a wide range of interrelated factors, any or all of which may affect a range of mammal species. 5Overall, the probable effects of the planned housing development programme in Britain are not likely to be beneficial to mammal populations, although the pattern of use examined in this study may represent patterns of habitat selection by species rather than differences in distribution or abundance. Consequently, additional data are required on the factors affecting the density of species within urban environments.