Identification of Quantitative Trait Loci Determining Vegetative Growth Traits in Coffea Canephor

Recently the use of molecular markers has been successfully applied for some crops. For coffee, new opportunities have been opened since Nestlé R&D Centre in collaboration with ICCRI completed the first genetic map of Coffea canephora. This study was aimed both to evaluate the phenotypic trait and also to identify the quantitative trait loci (QTLs) controlling the vegetative growth in Robusta coffee. Present study used three C. canephora populations and six genetic maps developed based on these populations using simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs) markers. A total of 17 different quantitative data were used for the detection of QTLs on each of three populations. Present result showed that most of these traits were not heritable. The nine vegetative traits have been identified and distributed over seven different linkage groups. Due to some QTLs determining one given trait were overlapping on the same linkage group and were coming from the same favourable parent, a total of 19 QTLs detected for vegetative traits might finally be considered as only 12 QTLs involved. However, only two of them were shared for different traits. One involved for the number/length of primary branches and width of the canopy while the other for length of internodes and width of canopy. These two QTLs might determine the size of the tree canopy in this species

Blending methodologies in talc operations

The problem posed by Western Mining Corporation involves finding a way of improving or optimising the utilisation of batches of lower grade talc when making up orders for products of different grades. During the MISG a number of Linear Programming models were developed. These models addressed the problems of blending batches of talc for a single order and of blending to meet a series of orders for different products over a specified planning horizon. Preliminary versions of the models were tested using data supplied by Western Mining Corporation

Snowpack monitoring in North America and Eurasia using passive microwave satellite data

Areas of the Canadian high plains, the Montana and North Dakota high plains, and the steppes of central Russia were studied in an effort to determine the utility of spaceborne electrical scanning microwave radiometers (ESMR) for monitoring snow depths in different geographic areas. Significant regression relationships between snow depth and microwave brightness temperatures were developed for each of these homogeneous areas. In the areas investigated, Nimbus 6 (.081 cm) ESMR data produced higher correlations than Nimbus 5 (1.55 cm) ESMR data in relating microwave brightness temperature and snow depth from one area to another because different geographic areas are likely to have different snowpack conditions

Passive microwave applications to snowpack monitoring using satellite data

Nimbus-5 Electrically Scanned Microwave Radiometer data were analyzed for the fall of 1975 and winter and summer of 1976 over the Arctic Coastal Plain of Alaska to determine the applicability of those data to snowpack monitoring. It was found that when the snow depth remained constant at 12.7 cm, the brightness temperatures T sub B varied with air temperature. During April and May the production of ice lenses and layers within the snow, and possibly wet ground beneath the snow contribute to the T sub B variations also. Comparison of March T sub B values of three areas with the same (12.7 cm) snow depth showed that air temperature is the predominant factor controlling the T sub B differences among the three areas, but underlying surface conditions and individual snowpack characteristics are also significant factors

Application of thermal imagery to the development of a Great Lakes ice information system

Recent measurements and analysis have shown that thermal infrared imagery (wavelength, 8-14 microns) can be employed to delineate the relative thicknesses of various regions of freshwater ice, as well as, differentiate new ice from both open water areas and thicker (young)ice. Thermal imagery was observed to be generally superior to visual (0.4 - 0.7 microns) and our SLAR (3.3 cm) imagery for estimating relative ice thicknesses and delineating open water from new ice growth. In a real-time Great Lakes Ice Information System, thermal imagery can not only provide supplementary imagery but also aid in developing interpretative methods for all-weather SLAR imagery, as well as, establishing the areal extent of spot thickness measurements

Earth Resources: A continuing bibliography with indexes, issue 10, August 1976

This bibliography lists 506 reports, articles, and other documents introduced into the NASA scientific and technical information system between April 1976 and June 1976. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Analysis of Coal Samples from the Southwestern District, Kentucky (Clay, Jackson, Knox, Laurel, Lee, McCreary, Owsley, Whitley, and Parts of Bell, Clinton, Estill, Madison, Pulaski, Rockcastle, and Wayne Counties)

Chemical and petrographic data ore presented for 119 samples of cool collected in the Southwestern District, eastern Kentucky. The data include sample-site locations, sampling conditions, stratigraphic position, megoscopic description of the cool, air-drying loss, proximate and ultimate analyses, Btu content, forms of sulfur, initial deformation temperature, softening temperature, fluid temperature, free-swelling index, concentration of major- and minor-oxides and trace elements, and petrographic analyses

Improved Aircraft Environmental Impact Segmentation via Metric Learning

Accurate modeling of aircraft environmental impact is pivotal to the design of operational procedures and policies to mitigate negative aviation environmental impact. Aircraft environmental impact segmentation is a process which clusters aircraft types that have similar environmental impact characteristics based on a set of aircraft features. This practice helps model a large population of aircraft types with insufficient aircraft noise and performance models and contributes to better understanding of aviation environmental impact. Through measuring the similarity between aircraft types, distance metric is the kernel of aircraft segmentation. Traditional ways of aircraft segmentation use plain distance metrics and assign equal weight to all features in an unsupervised clustering process. In this work, we utilize weakly-supervised metric learning and partial information on aircraft fuel burn, emissions, and noise to learn weighted distance metrics for aircraft environmental impact segmentation. We show in a comprehensive case study that the tailored distance metrics can indeed make aircraft segmentation better reflect the actual environmental impact of aircraft. The metric learning approach can help refine a number of similar data-driven analytical studies in aviation.Comment: 32 pages, 11 figure

Multi-task multiple kernel learning reveals relevant frequency bands for critical areas localization in focal epilepsy

The localization of epileptic zone in pharmacoresistant focal epileptic patients is a daunting task, typically performed by medical experts through visual inspection over highly sampled neural recordings. For a finer localization of the epileptogenic areas and a deeper understanding of the pathology both the identification of pathogenical biomarkers and the automatic characterization of epileptic signals are desirable. In this work we present a data integration learning method based on multi-level representation of stereo-electroencephalography recordings and multiple kernel learning. To the best of our knowledge, this is the first attempt to tackle both aspects simultaneously, as our approach is devised to classify critical vs. non-critical recordings while detecting the most discriminative frequency bands. The learning pipeline is applied to a data set of 18 patients for a total of 2347 neural recordings analyzed by medical experts. Without any prior knowledge assumption, the data-driven method reveals the most discriminative frequency bands for the localization of epileptic areas in the high-frequency spectrum (>=80 Hz) while showing high performance metric scores (mean balanced accuracy of 0.89 +- 0.03). The promising results may represent a starting point for the automatic search of clinical biomarkers of epileptogenicity