Nuclear DNA content of the amphidiploid, G. hirsutum, and two closely related diploid species, G. herbaceum var. africanum and G. raimondii, was ascertained by the reassociation kinetics of 250 nucleotide DNA fragments. Between diploid species the difference in chromosome size is attributed largely to variation in repetitive sequences, where there has been a change in both frequency and complexity. The evolution of single-copy DNA sequences by cross hybridizations among species reveals: (1) a high degree of sequence conservation between diploid species, showing 78% duplex formation under standard criterion and 6% sequence mismatch upon thermal denaturation; and (2) greater than 95% duplex formation between the diploid species and the amphidiploid with less than 1% single-copy sequence mismatch. The latter findings are consistent with an early Pleistocene origin for the tetraploid cottons.

No description available.

Vegetation mapping is often considered the process of identifying landscape patterns of individuals or clusters of species or life forms (LF). At the landscape scale, the larger pattern represented by individuals or clusters represents the conceptualization of "vegetation mapping" and can be used as a building block to describe an ecosystem. To represent these building blocks or LF a "common entity (CE)" concept is introduced to represent the components of Formations as described by the National Vegetation Classification (NVC) system. The NVC has established protocols to consistently represent plant communities and promote coordinated management, particularly across jurisdictional boundaries. However, it is not a universal standard and the methods of producing detailed maps of vegetation CE from very high spatial resolution (VHR) remote sensing data are important research questions.This research addressed how best to understand and represent plant cover in arid regions, the most effective methods of mapping vegetation cover using high spatial resolution data, how to assess the accuracy of these maps, and their value in establishing more standardized mapping protocols across ecosystems. Utilizing VHR products from the IKONOS and QuickBird sensors the study focused on the Coronado National Memorial and Chiricahua National Monument in Arizona and Los Ajos and Pinacate - Grand Desierto Biosphere Reserves in México. Individual CE were semi-automatically mapped incorporating spectral, textural and geostatistical variables. The results were evaluated across sensors, study sites, and input variables. In addition, multiple methods of acquiring field data for accuracy assessment were evaluated and then an evaluation was made of a semi-automatic determination of Formation based on CE.The results of the study suggest consistency across study sites using the IKONOSdata. A comparison between VHR products from the same place is feasible but sensor spectral differences may affect which derived bands would improve classification. CE classification procedures were not significantly different across sensors. The overall accuracy obtained for each Park was 59.5% for Chiricahua using QuickBird and 51.9% using IKONOS; at Pinacate 70.0% using IKONOS, and 55.9% for Ajos. Incorporating the geostatistical semi-variogram variables improved CE accuracy for some CE but not all.

Sorghum downy mildew (SDM) of maize is caused by the oomycete Peronosclerospora sorghi (Weston and Uppal) C. G. Shaw. The disease can cause devastating yield losses in maize (Zea mays L.). Quantitative trait loci (QTLs) mediating resistance to SDM were mapped using both restriction fragment length polymorphisms (RFLPs), and simple sequence repeats (SSRs) in 220 F2 individual maize progeny derived from a cross between two extremes; highly susceptible inbred parent SC-TEP5-19-1-3-1-4-1-1 (white) and highly resistant inbred P345C4S2B46-2-2-1-2-B-B-B (yellow). The phenotypic expression was assessed on F2:3 families in a wide range of environments under natural field infection and in a controlled greenhouse screening method. Heritability estimates of disease reaction ranged from 93.3% in Thailand sit 1 to 48% in Thailand sit 2. One hundred and thirty three polymorphic markers were assigned to the ten chromosomes of maize with LOD scores exceeding 4.9 covering about 1265 cM with an average interval length between markers of 9.5 cM. About 90% of the genome was located within a 10 cM distance to the nearest marker. Three putative QTLs were detected in association with resistance to SDM in different environments using composite interval mapping. Despite environmental and symptom differences, one QTL on chromosome 2 bin 9 had a major effect in all trials and explained up to 70% of the phenotypic variation in Thailand where the highest disease pressure was experienced. Two other QTLs on chromosome 3 bin 5 and chromosome 9 bin 2 had a minor effect, each explaining no more than 4% of the phenotypic variation. These results revealed one major gene and two minor genes that control sorghum downy mildew resistance. These markers should be very useful in breeding programs in facilitating the introgression of the resistance genes into commercial varieties. Marker-assisted selection for these loci should be useful in incorporating SDM resistance genes in maize across environments, even in the absence of the pathogen.

Digital Elevation Models have been in use for more than three decades and have become a major component of geographic information processing. The intensive use of DEMs has given rise to many accuracy investigations. The accuracy estimate is usually given in a form of a global measure such as root-mean-square error (RMSE), mostly from a producer's point of view. Seldom are the errors described in terms of their spatial distribution or how the resolution of the DEM interacts with the variability of terrain. There is a wide range of topographic variation present in different terrain surfaces. Thus, in defining the accuracy of a DEM, one needs ultimately to know the global and local characteristics of the terrain and how the resolution interacts with them. In this thesis, DEMs of various resolutions (i.e., 10 arc-minutes, 5 arc-minutes, 2 km, 1 km, and 50 m) in the study area (Prince George, British Columbia) were compared to each other and their mismatches were examined. Based on the preliminary test results, some observations were made regarding the relations among the spatial distribution of DEM errors, DEM resolution and the roughness of terrain. A hypothesis was proposed that knowledge of the landscape characteristics might provide some insights into the nature of the inherent error (or uncertainty) in a DEM. To test this statistically, the global characteristics of the study area surfaces were first examined by measures such as grain and those derived from spectral analysis, nested analysis of variance and fractal analysis of DEMs. Some important scale breaks were identified for each surface and this information on the surface global characteristics was then used to guide the selection of the moving window sizes for the extraction of the local roughness measures. The spatial variation and complexity of various study area surfaces was characterized by means of seven local geomorphometric parameters. The local measures were extracted from DEMs with different resolutions and using different moving window sizes. Then the multivariate cluster analysis was used for automated terrain classification in which relatively homogeneous terrain types at different scale levels were identified. Several different variable groups were used in the cluster analysis and the different classification results were compared to each other and interpreted in relation to each roughness measure. Finally, the correlations between the DEM errors and each of the local roughness measures were examined and the variation of DEM errors within various terrain clusters resulting from multivariate classifications were statistically evaluated. The effectiveness of using different moving window sizes for the extraction of the local measures and the appropriateness of different variable groups for terrain classification were also evaluated. The major conclusion of this study is that knowledge of topographic characteristics does provide some insights into the nature of the inherent error (or uncertainty) in a DEM and can be useful for DEM error modelling. The measures of topographic complexity are related to the observed patterns of discrepancy between DEMs of differing resolution, but there are variations from case to case. Several patterns can be identified in terms of relation between DEM errors and the roughness of terrain. First of all, the DEM errors (or elevation differences) do show certain consistent correlations with each of the various local roughness variables. With most variables, the general pattern is that the higher the roughness measure, the more points with higher absolute elevation differences (i.e., horn-shaped scatter of points indicating heteroscedasticity). Further statistical test results indicate that various DEM errors in the study area do show significant variation between different clusters resulting from terrain classifications based on different variable groups and window sizes. Cluster analysis was considered successful in grouping the areas according to their overall roughness and useful in DEM error modelling. In general, the rougher the cluster, the larger the DEM error (measured with either the standard deviation of the elevation differences or the mean of the absolute elevation differences in each cluster). However, there is still some of the total variation of various DEM errors that could not be accounted for by the cluster structure derived from multivariate classification. This could be attributed to the random errors inherent in any of the DEMs and the errors introduced in the interpolation process. Another conclusion is that the multivariate approach to the classification of topographic surfaces for DEM error modelling is not necessarily more successful than using only a single roughness measure in characterizing the overall roughness of terrain. When comparing the DEM error modelling results for surfaces with different global characteristics, the size of the moving window used in geomorphometric parameter abstraction also has certain impact on the modelling results. It shows that some understanding of the global characteristics of the surface is useful in the selection of appropriate/optimal window sizes for the extraction of local measures for DEM error modelling. Finally, directions for further research are suggested.

Tone mapping is a computational task of significance in the context of displaying high dynamic range images on low dynamic range devices. While a number of tone mapping algorithms have been proposed and are in common use, there is no single operator that yields optimal results under all conditions. Moreover, obtaining satisfactory mappings often requires the manual tweaking of parameters. This thesis proposes interactive evolution as a computational tool for tone mapping. An evolution strategy that blends the results from several tone mapping operators while at the same time adapting their parameters is proposed. As well, the results are adapted such that such that approximately uniform perceptual distances between offspring candidate solutions and the parent are ensured. The introduction of a perceptually based step size adaptation technique enhances the control of the variability between newly generated offspring, when compared to parameter space step size adaptation.

Dry beans (Phaseolus vulgaris L.) contain high levels of folates. These compounds are essential vitamins and folate deficiencies may lead to a number of health problems. The objectives of this study were to examine the mode of inheritance of folate content and identify quantitative trait loci (QTL) associated with folate content in dry beans. Inheritance of folate content was studied in the F1 hybrids of one-way diallel crosses among Othello, AC Elk, Redhawk and Taylor, and an F2 population of the cross between Redhawk and Othello. Total folate content and 5 methyltetrahydrofolate (5MTHF) were measured twice within a one hour interval. Significant variation in folate content was observed among the parental genotypes, their F1 hybrids, and the F2 individuals of a cross between Redhawk and Othello, ranging from 147 to 345 µg/100g. Reductions in the 5MTHF content and total folate content values in the second measurement from samples were highly variable for all four parental lines ranging from 5 to 30% and 7 to 33%, respectively. A single marker QTL analysis identified at least three QTL for folate content in the F2 population. For the majority of identified QTL, dominance effects appeared to be the major genetic effect.

The Houston Lightning Mapping Array (LMA) is a lightning detection network providing total lightning mapping for the Houston metropolitan area and southeast Texas. The network is comprised of twelve Very High Frequency (VHF) time-of-arrival total lightning mapping sensors built by New Mexico Institute of Mining and Technology and purchased by Texas A&M University. The sensors, installed in April 2012, are of the latest, modular design and built to be independent stations that utilize a solar panel for electricity and cellular data modems for communication. Each sensor detects the time of arrival of a VHF impulse emitted as part of the electrical breakdown and lightning propagation process. Data from each sensor are processed on a central LMA server to provide three-dimensional mapping of these impulses, also called LMA sources. This processing facilitates the analysis of variations in thunderstorm structure and the associated changes in both space and time. The primary objectives for the installation of the Houston LMA network are twofold: first, to provide a dataset enabling research into thunderstorm electrification in the context of a coastal, urban, polluted environment; and second, to enable improvements in operational forecasting and public safety by providing total lightning data to partners including the National Weather Service (NWS). A workflow was established to create and share real-time data to these partners, while simultaneously maintaining a full, research-quality dataset. Data are retrieved from the field sensors and backed up to a central LMA server for processing and storage. Archived network data are available from July 2012 through the present. The network measures 150 km from north to south, with stations in College Station and Galveston complementing the ten sites surrounding downtown Houston. This extends the region constrained by the network beyond the immediate metropolitan Houston area, resulting in increased accuracy in locating sources further from the network center. Based on initial analyses, the effective range of the Houston LMA is 75 km for three-dimensional mapping and approximately 250 km for two-dimension mapping.

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No description available.