High resolution genetic and physical mapping of the I-3 region of tomato chromosome 7 reveals almost continuous microsynteny with grape chromosome 12 but interspersed microsynteny with duplications on Arabidopsis chromosomes 1, 2 and 3

The tomato I-3 gene introgressed from the Lycopersicon pennellii accession LA716 confers resistance to race 3 of the fusarium wilt pathogen Fusarium oxysporum f. sp. lycopersici. We have improved the high-resolution map of the I-3 region of tomato chromosome 7 with the development and mapping of 31 new PCR-based markers. Recombinants recovered from L. esculentum cv. M82 × IL7-2 F2 and (IL7-2 × IL7-4) × M82 TC1F2 mapping populations, together with recombinants recovered from a previous M82 × IL7-3 F2 mapping population, were used to position these markers. A significantly higher recombination frequency was observed in the (IL7-2 × IL7-4) × M82 TC1F2 mapping population based on a reconstituted L. pennellii chromosome 7 compared to the other two mapping populations based on smaller segments of L. pennellii chromosome 7. A BAC contig consisting of L. esculentum cv. Heinz 1706 BACs covering the I-3 region has also been established. The new high-resolution map places the I-3 gene within a 0.38 cM interval between the molecular markers RGA332 and bP23/gPT with an estimated physical size of 50-60 kb. The I-3 region was found to display almost continuous microsynteny with grape chromosome 12 but interspersed microsynteny with Arabidopsis thaliana chromosomes 1, 2 and 3. An S-receptor-like kinase gene family present in the I-3 region of tomato chromosome 7 was found to be present in the microsyntenous region of grape chromosome 12 but was absent altogether from the A. thaliana genome.

An evaluation of the apxIVA based PCR-REA method for differentiation of Actinobacillus pleuropneumoniae

A restriction analysis of PCR (PCR-REA) amplified apxIVA gene has been suggested as an alternative method for serotyping of Actinobacillus pleuropneumoniae by Jaglic et al. [Jaglic, Z., Svastova, P., Rychlik, I., Nedbalcova, K., Kucerova, Z., Pavlik, I., Bartos, M., 2004. Differentiation of Actinobacillus pleuropneumoniae by PCR-REA based on sequence variability of the apxIVA gene and by ribotyping. Vet. Microbiol. 103, 63-69]. The current study investigated whether this alternative method could distinguish between the reference strains of serovars 13-15 and the value of the method when applied to 47 field isolates representing serovars 1-3, 5, 7-9, 12 and 15 as well as non-typable isolates. The reference strains of serovars 13 and 14 had the same sized product after the apxIVA PCR, while the product for serovar 15 was of different size compared to all the other serovar reference strains. The CfoI digest profiles of the reference serovars 13 and 14 strains were different from each other and from all other serovars. The HpaII digest profiles of these two serovars were very similar to each other, but both were distinctively different from the other serovar profiles. The CfoI digest profile of serovar 15 strain was very similar to the serovars 3 and 12 strains except for two faint extra bands for serovar 15. The HpaII digest profiles of serovars 12 and 15 reference strains were identical. The PCR-REA method correctly recognized the serovar of 21 of 43 field isolates. It was concluded that the method was a useful additional tool to support, but could not replace, conventional serotyping.

Spatial model of site index based on Y-ray spectrometry and a digital elevation model for two Pinus species in Tuan Toolara State Forest, Queensland, Australia

Site index prediction models are an important aid for forest management and planning activities. This paper introduces a multiple regression model for spatially mapping and comparing site indices for two Pinus species (Pinus elliottii Engelm. and Queensland hybrid, a P. elliottii x Pinus caribaea Morelet hybrid) based on independent variables derived from two major sources: g-ray spectrometry (potassium (K), thorium (Th), and uranium (U)) and a digital elevation model (elevation, slope, curvature, hillshade, flow accumulation, and distance to streams). In addition, interpolated rainfall was tested. Species were coded as a dichotomous dummy variable; interaction effects between species and the g-ray spectrometric and geomorphologic variables were considered. The model explained up to 60% of the variance of site index and the standard error of estimate was 1.9 m. Uranium, elevation, distance to streams, thorium, and flow accumulation significantly correlate to the spatial variation of the site index of both species, and hillshade, curvature, elevation and slope accounted for the extra variability of one species over the other. The predicted site indices varied between 20.0 and 27.3 m for P. elliottii, and between 23.1 and 33.1 m for Queensland hybrid; the advantage of Queensland hybrid over P. elliottii ranged from 1.8 to 6.8 m, with the mean at 4.0 m. This compartment-based prediction and comparison study provides not only an overview of forest productivity of the whole plantation area studied but also a management tool at compartment scale.

Net radiation dynamics: performance of 20 daily net radiation models as related to model structure and intricacy in two climates.

We compared daily net radiation (Rn) estimates from 19 methods with the ASCE-EWRI Rn estimates in two climates: Clay Center, Nebraska (sub-humid) and Davis, California (semi-arid) for the calendar year. The performances of all 20 methods, including the ASCE-EWRI Rn method, were then evaluated against Rn data measured over a non-stressed maize canopy during two growing seasons in 2005 and 2006 at Clay Center. Methods differ in terms of inputs, structure, and equation intricacy. Most methods differ in estimating the cloudiness factor, emissivity (e), and calculating net longwave radiation (Rnl). All methods use albedo (a) of 0.23 for a reference grass/alfalfa surface. When comparing the performance of all 20 Rn methods with measured Rn, we hypothesized that the a values for grass/alfalfa and non-stressed maize canopy were similar enough to only cause minor differences in Rn and grass- and alfalfa-reference evapotranspiration (ETo and ETr) estimates. The measured seasonal average a for the maize canopy was 0.19 in both years. Using a = 0.19 instead of a = 0.23 resulted in 6% overestimation of Rn. Using a = 0.19 instead of a = 0.23 for ETo and ETr estimations, the 6% difference in Rn translated to only 4% and 3% differences in ETo and ETr, respectively, supporting the validity of our hypothesis. Most methods had good correlations with the ASCE-EWRI Rn (r2 > 0.95). The root mean square difference (RMSD) was less than 2 MJ m-2 d-1 between 12 methods and the ASCE-EWRI Rn at Clay Center and between 14 methods and the ASCE-EWRI Rn at Davis. The performance of some methods showed variations between the two climates. In general, r2 values were higher for the semi-arid climate than for the sub-humid climate. Methods that use dynamic e as a function of mean air temperature performed better in both climates than those that calculate e using actual vapor pressure. The ASCE-EWRI-estimated Rn values had one of the best agreements with the measured Rn (r2 = 0.93, RMSD = 1.44 MJ m-2 d-1), and estimates were within 7% of the measured Rn. The Rn estimates from six methods, including the ASCE-EWRI, were not significantly different from measured Rn. Most methods underestimated measured Rn by 6% to 23%. Some of the differences between measured and estimated Rn were attributed to the poor estimation of Rnl. We conducted sensitivity analyses to evaluate the effect of Rnl on Rn, ETo, and ETr. The Rnl effect on Rn was linear and strong, but its effect on ETo and ETr was subsidiary. Results suggest that the Rn data measured over green vegetation (e.g., irrigated maize canopy) can be an alternative Rn data source for ET estimations when measured Rn data over the reference surface are not available. In the absence of measured Rn, another alternative would be using one of the Rn models that we analyzed when all the input variables are not available to solve the ASCE-EWRI Rn equation. Our results can be used to provide practical information on which method to select based on data availability for reliable estimates of daily Rn in climates similar to Clay Center and Davis.