Baseline data on the oceanography of Cook Inlet, Alaska

Regional relationships between river hydrology, sediment transport, circulation and coastal processes were analyzed utilizing aircraft, ERTS-1 and N.O.A.A. -2 and -3 imagery and corroborative ground truth data. The use of satellite and aircraft imagery provides a means of acquiring synoptic information for analyzing the dynamic processes of Cook Inlet in a fashion not previously possible

Land use/vegetation mapping in reservoir management. Merrimack River basin

This report consists of an analysis of: ERTS-1 Multispectral Scanner imagery obtained 10 August 1973; Skylab 3 S190A and S190B photography, track 29, taken 21 September 1973; and RB-57 high-altitude aircraft photography acquired 26 September 1973. These data products were acquired on three cloud-free days within a 47-day period. The objectives of this study were: (1) to make quantitative comparisons between high-altitude aircraft photography and satellite imagery, and (2) to demonstrate the extent to which high resolution (S190A and B) space-acquired data can be used for land use/vegetation mapping and management of drainage basins

Arctic and subarctic environmental analyses utilizing ERTS-1 imagery

The author has identified the following significant results. ERTS-1 imagery provides a means of distinguishing and monitoring estuarine surface water circulation patterns and changes in the relative sediment load of discharging rivers on a regional basis. Physical boundaries mapped from ERTS-1 imagery in combination with ground truth obtained from existing small scale maps and other sources resulted in improved and more detailed maps of permafrost terrain and vegetation for the same area. Snowpack cover within a research watershed has been analyzed and compared to ground data. Large river icings along the proposed Alaska pipeline route from Prudhoe Bay to the Brooks Range have been monitored. Sea ice deformation and drift northeast of Point Barrow, Alaska have been measured during a four day period in March and shore-fast ice accumulation and ablation along the west coast of Alaska have been mapped for the spring and early summer seasons

Skylab imagery: Application to reservoir management in New England

The author has identified the following significant results. S190B imagery is superior to the LANDSAT imagery for land use mapping and is as useful for level 1 and 2 land use mapping as the RB-57/RC8 high altitude imagery. Detailed land use mapping at levels 3 and finer from satellite imagery requires better resolution. For evaluating factors that are required to determine volume runoff potentials in a watershed, the S190B imagery was found to be as useful as the RB-57/RC8 high altitude aircraft imagery

Thermal tools to evaluation of decayed and weathered wood polymer composites prepared by in situ polymerization.

This study aims to apply thermal tools in the evaluation of decayed and weathered wood polymer composites prepared by in situ polymerization of methyl methacrylate (MMA) using glycidyl methacrylate (GMA) and methacrylic acid (MAA) as cross-linkers. The pine wood samples were impregnated in a vacuum/pressure system and polymerized in an oven at 90°C for 10h, using benzoyl peroxide at 1.5 wt% as catalyst. The untreated wood and composites were exposed to in vitro decay tests with Trametes versicolor and Gloeophyllum trabeum fungi, and to artificial weathering. The weight loss after tests was measured, and the characterization was performed by thermogravimetric (TGA) and differential scanning calorimetry (DSC) analysis. The mass loss caused by exposure to fungi was evidently higher in untreated wood in relation to the composites, ~2.5 to 10 times - the composites with GMA and MAA showed the highest resistance to both fungi. The composites without cross-linkers showed the higher mass loss in the artificial weathering tests (>11%), due to the leaching of part of poly(MMA) formed inside wood. By TGA and DSC analysis, we observed shifting in the temperature of thermal events related to polysaccharides and lignin after exposed to decays tests ? more significant changes were for Trametes versicolor tests. The thermograms related to weathered samples showed different results for each composite. The untreated wood and the composite without cross-linker presented loss in lignin, meanwhile the composites with cross-linkers presented degradation in the copolymer formed onto surface of wood. Keywords: TGA, DSC, pinewood, methacrylate, additivesCBRATEC

Arctic and subarctic environmental analyses utilizing ERTS-1 imagery

There are no author-identified significant results in this report

New England reservoir management: Land use/vegetation mapping in reservoir management (Merrimack River Basin)

The author has identified the following significant results. It is evident from this comparison that for land use/vegetation mapping the S190B Skylab photography compares favorably with the RB-57 photography and is much superior to the ERTS-1 and Skylab 190A imagery. For most purposes the 12.5 meter resolution of the S190B imagery is sufficient to permit extraction of the information required for rapid land use and vegetation surveys necessary in the management of reservoir or watershed. The ERTS-1 and S190A data products are not considered adequate for this purpose, although they are useful for rapid regional surveys at the level 1 category of the land use/vegetation classification system

Visualization of proteomics data using R and bioconductor.

Data visualization plays a key role in high-throughput biology. It is an essential tool for data exploration allowing to shed light on data structure and patterns of interest. Visualization is also of paramount importance as a form of communicating data to a broad audience. Here, we provided a short overview of the application of the R software to the visualization of proteomics data. We present a summary of R's plotting systems and how they are used to visualize and understand raw and processed MS-based proteomics data.LG was supported by the European Union 7th Framework Program (PRIME-XS project, grant agreement number 262067) and a BBSRC Strategic Longer and Larger grant (Award BB/L002817/1). LMB was supported by a BBSRC Tools and Resources Development Fund (Award BB/K00137X/1). TN was supported by a ERASMUS Placement scholarship.This is the final published version of the article. It was originally published in Proteomics (PROTEOMICS Special Issue: Proteomics Data Visualisation Volume 15, Issue 8, pages 1375–1389, April 2015. DOI: 10.1002/pmic.201400392). The final version is available at http://onlinelibrary.wiley.com/doi/10.1002/pmic.201400392/abstract

The effect of organelle discovery upon sub-cellular protein localisation.

Prediction of protein sub-cellular localisation by employing quantitative mass spectrometry experiments is an expanding field. Several methods have led to the assignment of proteins to specific subcellular localisations by partial separation of organelles across a fractionation scheme coupled with computational analysis. Methods developed to analyse organelle data have largely employed supervised machine learning algorithms to map unannotated abundance profiles to known protein–organelle associations. Such approaches are likely to make association errors if organelle-related groupings present in experimental output are not included in data used to create a protein–organelle classifier. Currently, there is no automated way to detect organelle-specific clusters within such datasets. In order to address the above issues we adapted a phenotype discovery algorithm, originally created to filter image-based output for RNAi screens, to identify putative subcellular groupings in organelle proteomics experiments. We were able to mine datasets to a deeper level and extract interesting phenotype clusters for more comprehensive evaluation in an unbiased fashion upon application of this approach. Organelle-related protein clusters were identified beyond those sufficiently annotated for use as training data. Furthermore, we propose avenues for the incorporation of observations made into general practice for the classification of protein–organelle membership from quantitative MS experiments. Biological significance Protein sub-cellular localisation plays an important role in molecular interactions, signalling and transport mechanisms. The prediction of protein localisation by quantitative mass-spectrometry (MS) proteomics is a growing field and an important endeavour in improving protein annotation. Several such approaches use gradient-based separation of cellular organelle content to measure relative protein abundance across distinct gradient fractions. The distribution profiles are commonly mapped in silico to known protein–organelle associations via supervised machine learning algorithms, to create classifiers that associate unannotated proteins to specific organelles. These strategies are prone to error, however, if organelle-related groupings present in experimental output are not represented, for example owing to the lack of existing annotation, when creating the protein–organelle mapping. Here, the application of a phenotype discovery approach to LOPIT gradient-based MS data identifies candidate organelle phenotypes for further evaluation in an unbiased fashion. Software implementation and usage guidelines are provided for application to wider protein–organelle association experiments. In the wider context, semi-supervised organelle discovery is discussed as a paradigm with which to generate new protein annotations from MS-based organelle proteomics experiments. This article is part of a Special Issue entitled: New Horizons and Applications for Proteomics [EuPA 2012]