Chemical characterisation of wild populations of Thymus from different climatic regions in southeast Spain

Thymus is taxonomically a very complex genus with a high frequency of hybridisation and introgression among sympatric species. The variation in accumulation of leaf-surface flavonoids was investigated in 71 wild populations of Thymus front different putative hybrid swarm areas in Andalucia, Spain. Twenty-two flavones, five flavanones, two dihydroflavonols, a flavonol and two unknowns were detected by HPLC-DAD combined with LC-APCI-MS analysis. The majority of compounds were flavones with a lutelin-type substitution of the B-ring, in contrast to previous reports on Macedonian taxa, which predominantly accumulate flavones with apigenin-type substitution of the B-ring. Anatomical and morphometric studies, supported by cluster analysis, identified pure Thymus hyemalis and Thymus baeticus populations, and a large number of putative hybrids. Flavonoid variation was closely related to morphological variation in all populations and is suspected to be a result of genetic polymorphism. Principal component analysis identified the presence of species-specific and geographically linked chemotypes and putative hybrids with mixed morphological and chemical characteristics. Qualitative and quantitative flavonoid accumulation appears to be genetically regulated, while external factors play a secondary role. Flavonoid profiles can thus provide diagnostic markers for the taxonomy of Thymus and are also useful in detecting hybridising taxa. (C) 2007 Elsevier Ltd. All rights reserved.

Evaluation of diagnostic molecular markers for DUS phenotypic assessment in the cereal crop, barley (Hordeum vulgare ssp. vulgare L.)

The deployment of genetic markers is of interest in crop assessment and breeding programmes, due to the potential savings in cost and time afforded. As part of the internationally recognised framework for the awarding of Plant Breeders’ Rights (PBR), new barley variety submissions are evaluated using a suite of morphological traits to ensure they are distinct, uniform and stable (DUS) in comparison to all previous submissions. Increasing knowledge of the genetic control of many of these traits provides the opportunity to assess the potential of deploying diagnostic/perfect genetic markers in place of phenotypic assessment. Here, we identify a suite of 25 genetic markers assaying for 14 DUS traits, and implement them using a single genotyping platform (KASPar). Using a panel of 169 UK barley varieties, we show that phenotypic state at three of these traits can be perfectly predicted by genotype. Predictive values for an additional nine traits ranged from 81 to 99 %. Finally, by comparison of varietal discrimination based on phenotype and genotype resulted in correlation of 0.72, indicating that deployment of molecular markers for varietal discrimination could be feasible in the near future. Due to the flexibility of the genotyping platform used, the genetic markers described here can be used in any number or combination, in-house or by outsourcing, allowing flexible deployment by users. These markers are likely to find application where tracking of specific alleles is required in breeding programmes, or for potential use within national assessment programmes for the awarding of PBRs.

PCR-based markers diagnostic for spring and winter seasonal growth habit in barley

Toward the ultimate goal of replacing field-based evaluation of seasonal growth habit, we describe the design and validation of a multiplex polymerase chain reaction assay diagnostic for allelic status at the barley (Hordeum vulgare ssp. vulgare L.) vernalization locus, VRN-H1 By assaying for the presence of all known insertion–deletion polymorphisms thought to be responsible for the difference between spring and winter alleles, this assay directly tests for the presence of functional polymorphism at VRN-H1 Four of the nine previously recognized VRN-H1 haplotypes (including both winter alleles) give unique profiles using this assay. The remaining five spring haplotypes share a single profile, indicative of function-altering deletions spanning, or adjacent to, the putative “vernalization critical” region of intron 1. When used in conjunction with a previously published PCR-based assay diagnostic for alleles at VRN-H2, it was possible to predict growth habit in all the 100 contemporary UK spring and winter lines analyzed in this study. This assay is likely to find application in instances when seasonal growth habit needs to be determined without the time and cost of phenotypic assessment and during marker-assisted selection using conventional and multicross population analysis.

Gene expression changes in phosphorus deficient potato (Solanum tuberosum L.) leaves and the potential for diagnostic gene expression markers

Background: There are compelling economic and environmental reasons to reduce our reliance on inorganic phosphate (Pi) fertilisers. Better management of Pi fertiliser applications is one option to improve the efficiency of Pi fertiliser use, whilst maintaining crop yields. Application rates of Pi fertilisers are traditionally determined from analyses of soil or plant tissues. Alternatively, diagnostic genes with altered expression under Pi limiting conditions that suggest a physiological requirement for Pi fertilisation, could be used to manage Pifertiliser applications, and might be more precise than indirect measurements of soil or tissue samples. Results: We grew potato (Solanum tuberosum L.) plants hydroponically, under glasshouse conditions, to control their nutrient status accurately. Samples of total leaf RNA taken periodically after Pi was removed from the nutrient solution were labelled and hybridised to potato oligonucleotide arrays. A total of 1,659 genes were significantly differentially expressed following Pi withdrawal. These included genes that encode proteins involved in lipid, protein, and carbohydrate metabolism, characteristic of Pi deficient leaves and included potential novel roles for genes encoding patatin like proteins in potatoes. The array data were analysed using a support vector machine algorithm to identify groups of genes that could predict the Pi status of the crop. These groups of diagnostic genes were tested using field grown potatoes that had either been fertilised or unfertilised. A group of 200 genes could correctly predict the Pi status of field grown potatoes. Conclusions: This paper provides a proof-of-concept demonstration for using microarrays and class prediction tools to predict the Pi status of a field grown potato crop. There is potential to develop this technology for other biotic and abiotic stresses in field grown crops. Ultimately, a better understanding of crop stresses may improve our management of the crop, improving the sustainability of agriculture.

Diagnostic analysis of atmospheric moisture and clear-sky radiative feedback in the Hadley Centre and Geophysical Fluid Dynamics Laboratory (GFDL) climate models

The interannual variability of the hydrological cycle is diagnosed from the Hadley Centre and Geophysical Fluid Dynamics Laboratory (GFDL) climate models, both of which are forced by observed sea surface temperatures. The models produce a similar sensitivity of clear-sky outgoing longwave radiation to surface temperature of ∼2 W m−2 K−1, indicating a consistent and positive clear-sky radiative feedback. However, differences between changes in the temperature lapse-rate and the height dependence of moisture fluctuations suggest that contrasting mechanisms bring about this result. The GFDL model appears to give a weaker water vapor feedback (i.e., changes in specific humidity). This is counteracted by a smaller upper tropospheric temperature response to surface warming, which implies a compensating positive lapse-rate feedback.

A capture–recapture approach for screening using two diagnostic tests with availability of disease status for the test positives only

The article considers screening human populations with two screening tests. If any of the two tests is positive, then full evaluation of the disease status is undertaken; however, if both diagnostic tests are negative, then disease status remains unknown. This procedure leads to a data constellation in which, for each disease status, the 2 × 2 table associated with the two diagnostic tests used in screening has exactly one empty, unknown cell. To estimate the unobserved cell counts, previous approaches assume independence of the two diagnostic tests and use specific models, including the special mixture model of Walter or unconstrained capture–recapture estimates. Often, as is also demonstrated in this article by means of a simple test, the independence of the two screening tests is not supported by the data. Two new estimators are suggested that allow associations of the screening test, although the form of association must be assumed to be homogeneous over disease status. These estimators are modifications of the simple capture–recapture estimator and easy to construct. The estimators are investigated for several screening studies with fully evaluated disease status in which the superior behavior of the new estimators compared to the previous conventional ones can be shown. Finally, the performance of the new estimators is compared with maximum likelihood estimators, which are more difficult to obtain in these models. The results indicate the loss of efficiency as minor.

Revisiting youden’s index as a useful measure of the misclassification error in meta-analysis of diagnostic studies

The paper considers meta-analysis of diagnostic studies that use a continuous score for classification of study participants into healthy or diseased groups. Classification is often done on the basis of a threshold or cut-off value, which might vary between studies. Consequently, conventional meta-analysis methodology focusing solely on separate analysis of sensitivity and specificity might be confounded by a potentially unknown variation of the cut-off value. To cope with this phenomena it is suggested to use, instead, an overall estimate of the misclassification error previously suggested and used as Youden’s index and; furthermore, it is argued that this index is less prone to between-study variation of cut-off values. A simple Mantel–Haenszel estimator as a summary measure of the overall misclassification error is suggested, which adjusts for a potential study effect. The measure of the misclassification error based on Youden’s index is advantageous in that it easily allows an extension to a likelihood approach, which is then able to cope with unobserved heterogeneity via a nonparametric mixture model. All methods are illustrated at hand of an example on a diagnostic meta-analysis on duplex doppler ultrasound, with angiography as the standard for stroke prevention.

Chlorophyll fluorescence technique as a rapid diagnostic test of the effects of the photosynthetic inhibitor chlorotoluron on two winter wheat cultivars

A modified chlorophyll fluorescence technique was evaluated as a rapid diagnostic test of the susceptibility of wheat cultivars to chlorotoluron. Two winter wheat cultivars (Maris Huntsman and Mercia) exhibited differential response to the herbicide. All of the parameters of chlorophyll fluorescence examined were strongly influenced by herbicide concentration. Additionally, the procedure adopted here for the examination of winter wheat cultivar sensitivity to herbicide indicated that the area above the fluorescence induction curve and the ratio F-V/F-M are appropriate chlorophyll fluorescence parameters for detection of differential herbicide response between wheat cultivars. The potential use of this technique as an alternative to traditional methods of screening new winter wheat cultivars for their response to photosynthetic inhibitor herbicide is demonstrated here.

Segregation of morphological and isozyme markers in a cross of Capsicum baccatum and Capsicum cardenasii

The genus Capsicum has 20-30 species, of which only a few are cultivated. Capsicum annuum L. is the best known Capsicum all around the world, while the other species are not common outside Latin America. Since it is the best known and commercially the most valuable species, many breeding programs have been conducted on C annuum L., especially on the non-pungent vegetable types. Breeding of other species has received less attention. Therefore, this work was conducted on two species other than C. annuum that are rarely studied-C. baccatum and C. cardenasii. Other results concern linkage groups and association of the marker genes or linkage groups with the chromosomes involved in an interchange. Linkage was detected for two pairs of genes only; these were between Got-1 and Idh-1, and between Pgi-2 and Est-5. No gene was found to show a statistically significant association with chromosomes with interchanged segments.

Influence-matrix diagnostic of a data assimilation system

The influence matrix is used in ordinary least-squares applications for monitoring statistical multiple-regression analyses. Concepts related to the influence matrix provide diagnostics on the influence of individual data on the analysis - the analysis change that would occur by leaving one observation out, and the effective information content (degrees of freedom for signal) in any sub-set of the analysed data. In this paper, the corresponding concepts have been derived in the context of linear statistical data assimilation in numerical weather prediction. An approximate method to compute the diagonal elements of the influence matrix (the self-sensitivities) has been developed for a large-dimension variational data assimilation system (the four-dimensional variational system of the European Centre for Medium-Range Weather Forecasts). Results show that, in the boreal spring 2003 operational system, 15% of the global influence is due to the assimilated observations in any one analysis, and the complementary 85% is the influence of the prior (background) information, a short-range forecast containing information from earlier assimilated observations. About 25% of the observational information is currently provided by surface-based observing systems, and 75% by satellite systems. Low-influence data points usually occur in data-rich areas, while high-influence data points are in data-sparse areas or in dynamically active regions. Background-error correlations also play an important role: high correlation diminishes the observation influence and amplifies the importance of the surrounding real and pseudo observations (prior information in observation space). Incorrect specifications of background and observation-error covariance matrices can be identified, interpreted and better understood by the use of influence-matrix diagnostics for the variety of observation types and observed variables used in the data assimilation system. Copyright © 2004 Royal Meteorological Society

Characterization of microsatellite markers for Sycoscapter nonpollinating fig wasps

We isolated 18 microsatellites from Sycoscapter australis, a nonpollinating fig wasp that develops in figs of Ficus macrophylla, and assessed their variability in 20 wasps. We further optimized nine of these loci for use in three other Sycoscapter species that develop in Ficus rubiginosa figs and assessed their variability in 47-140 wasps per species. These are the first microsatellites developed for nonpollinating fig wasps and show sufficient polymorphism to become important tools in evolutionary and genetical studies of Sycoscapter wasps.

Revisiting Youden's index as a useful measure of the misclassification error in meta-analysis of diagnostic studies

The paper considers meta-analysis of diagnostic studies that use a continuous Score for classification of study participants into healthy, or diseased groups. Classification is often done on the basis of a threshold or cut-off value, which might vary between Studies. Consequently, conventional meta-analysis methodology focusing solely on separate analysis of sensitivity and specificity might he confounded by a potentially unknown variation of the cut-off Value. To cope with this phenomena it is suggested to use, instead an overall estimate of the misclassification error previously suggested and used as Youden's index and; furthermore, it is argued that this index is less prone to between-study variation of cut-off values. A simple Mantel-Haenszel estimator as a summary measure of the overall misclassification error is suggested, which adjusts for a potential study effect. The measure of the misclassification error based on Youden's index is advantageous in that it easily allows an extension to a likelihood approach, which is then able to cope with unobserved heterogeneity via a nonparametric mixture model. All methods are illustrated at hand of an example on a diagnostic meta-analysis on duplex doppler ultrasound, with angiography as the standard for stroke prevention.

A capture-recapture approach for screening using two diagnostic tests with availability of disease status for the test positives only

The article considers screening human populations with two screening tests. If any of the two tests is positive, then full evaluation of the disease status is undertaken; however, if both diagnostic tests are negative, then disease status remains unknown. This procedure leads to a data constellation in which, for each disease status, the 2 x 2 table associated with the two diagnostic tests used in screening has exactly one empty, unknown cell. To estimate the unobserved cell counts, previous approaches assume independence of the two diagnostic tests and use specific models, including the special mixture model of Walter or unconstrained capture-recapture estimates. Often, as is also demonstrated in this article by means of a simple test, the independence of the two screening tests is not supported by the data. Two new estimators are suggested that allow associations of the screening test, although the form of association must be assumed to be homogeneous over disease status. These estimators are modifications of the simple capture-recapture estimator and easy to construct. The estimators are investigated for several screening studies with fully evaluated disease status in which the superior behavior of the new estimators compared to the previous conventional ones can be shown. Finally, the performance of the new estimators is compared with maximum likelihood estimators, which are more difficult to obtain in these models. The results indicate the loss of efficiency as minor.

Headspace volatile markers for sensitivity of cocoa (Theobroma cacao L.) somatic embryos to cryopreservation

The mechanisms that reduce the viability of plant somatic embryos following cryopreservation are not known. The objective of the present study was to evaluate the sensitivity of cocoa (Theobroma cacao L.) somatic embryos at different stages of an encapsulation-dehydration protocol using stress-related volatile hydrocarbons as markers of injury and recovery. The plant stress hormone ethylene and volatile hydrocarbons derived from hydroxyl radicals (methane) and lipid peroxidation (ethane) were determined using gas chromatography headspace analysis. Ethylene and methane were the only volatiles detected, with both being produced after each step of the cryogenic protocol. Ethylene production was significantly reduced following exposure to liquid nitrogen, but then increased in parallel with embryo recovery. In contrast, the production of methane was cyclic during recovery, with the first cycle occurring earlier for embryos recovered from liquid nitrogen and desiccation than those recovered from earlier steps in the protocol. These results suggest that loss of somatic embryo viability during cryopreservation may be related to the oxidative status of the tissue, and its capacity to produce ethylene. This study has demonstrated that headspace volatile analysis provides a robust non-destructive analytical approach for assessing the survival and recovery of plant somatic embryos following cryopreservation.