1,277 research outputs found

    Economics of wood vs. natural gas and coal energy in Ohio

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    Economics of wood vs. natural gas and coal energy in Ohio

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    Beryllium fastener technology

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    Program was conducted to develop, produce, and test optimum-configuration, beryllium prestressed and blind fasteners. The program was carried out in four phases - phase 1, feasibility study, phase 2, development, phase 3, evaluation of beryllium alloys, and phase 4, fabrication and testing

    Nitrogen dynamics in the Irish Sea and adjacent shelf waters: An exploration of dissolved organic nitrogen

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    AbstractRelatively little is known about dissolved organic nitrogen (DON) in the marine environment because research has historically focused on dissolved inorganic nitrogen (DIN). In this study we combine measurements of dissolved organic matter (DOM), DIN, particulate organic nitrogen (PON), dissolved inorganic phosphorus (DIP) and silicon (DIS), with temperature and salinity data from the western shelf region of the UK and Ireland, and with inorganic and organic nitrogen (N) data from the western Irish Sea to develop an understanding of N dynamics in the Irish Sea and adjacent shelf waters, and investigate the role of DON in the nitrogen budget of the seasonally stratifying western Irish Sea. In January 2013, the sampling area was divided by density fronts into 4 regions of distinct oceanography and homogeneous chemistry. DON concentrations accounted for 25.3 ± 1.8% of total dissolved N (TDN) across all regions. DOM concentrations generally decreased from the freshwater influenced water of Liverpool Bay to the oceanic waters of the Celtic Sea and Malin Shelf. Urea and dissolved free amino acids (DFAA) together made up 27.3 ± 3.1% of DON. Estimated concentrations in the rivers discharging into Liverpool Bay were 8.0 and 2.1 Όmol N L−1 respectively: at the high end of reported riverine concentrations. Oceanic nutrient inputs to the Irish Sea only have a small influence on N concentrations. Riverine N inputs to the Irish Sea are substantial but are likely removed by natural N cycling processes. In the western Irish Sea, DON and PON concentrations reached maxima and minima in midsummer and early spring respectively. DIN followed the opposite trend. DON accounted for 38% of the yearly internal N cycling and we estimated that as much as 1.4 ± 1.2 Όmol N L−1 of labile DON was available as an N source at the start of the spring bloom. Our study supports the view that DON plays an important role in N cycling in temperate shelf and coastal seas and should be included more often in biogeochemical measurements if we are to have a complete understanding of N dynamics in a changing world

    Genome-scale metabolic analysis of Clostridium thermocellum for bioethanol production

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    <p>Abstract</p> <p>Background</p> <p>Microorganisms possess diverse metabolic capabilities that can potentially be leveraged for efficient production of biofuels. <it>Clostridium thermocellum </it>(ATCC 27405) is a thermophilic anaerobe that is both cellulolytic and ethanologenic, meaning that it can directly use the plant sugar, cellulose, and biochemically convert it to ethanol. A major challenge in using microorganisms for chemical production is the need to modify the organism to increase production efficiency. The process of properly engineering an organism is typically arduous.</p> <p>Results</p> <p>Here we present a genome-scale model of <it>C. thermocellum </it>metabolism, <it>i</it>SR432, for the purpose of establishing a computational tool to study the metabolic network of <it>C. thermocellum </it>and facilitate efforts to engineer <it>C. thermocellum </it>for biofuel production. The model consists of 577 reactions involving 525 intracellular metabolites, 432 genes, and a proteomic-based representation of a cellulosome. The process of constructing this metabolic model led to suggested annotation refinements for 27 genes and identification of areas of metabolism requiring further study. The accuracy of the <it>i</it>SR432 model was tested using experimental growth and by-product secretion data for growth on cellobiose and fructose. Analysis using this model captures the relationship between the reduction-oxidation state of the cell and ethanol secretion and allowed for prediction of gene deletions and environmental conditions that would increase ethanol production.</p> <p>Conclusions</p> <p>By incorporating genomic sequence data, network topology, and experimental measurements of enzyme activities and metabolite fluxes, we have generated a model that is reasonably accurate at predicting the cellular phenotype of <it>C. thermocellum </it>and establish a strong foundation for rational strain design. In addition, we are able to draw some important conclusions regarding the underlying metabolic mechanisms for observed behaviors of <it>C. thermocellum </it>and highlight remaining gaps in the existing genome annotations.</p

    Hyperspectral chemical imaging reveals spatially varied degradation of polycarbonate urethane (PCU) biomaterials

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    Hyperspectral chemical imaging (HCI) is an emerging technique which combines spectroscopy with imaging. Unlike traditional point spectroscopy, which is used in the majority of polymer biomaterial degradation studies, HCI enables the acquisition of spatially localised spectra across the surface of a material in an objective manner. Here, we demonstrate that attenuated total reflectance Fourier transform infra-red (ATR-FTIR) HCI reveals spatial variation in the degradation of implantable polycarbonate urethane (PCU) biomaterials. It is also shown that HCI can detect possible defects in biomaterial formulation or specimen production; these spatially resolved images reveal regional or scattered spatial heterogeneity. Further, we demonstrate a map sampling method, which can be used in time-sensitive scenarios, allowing for the investigation of degradation across a larger component or component area. Unlike imaging, mapping does not produce a contiguous image, yet grants an insight into the spatial heterogeneity of the biomaterial across a larger area. These novel applications of HCI demonstrate its ability to assist in the detection of defective manufacturing components and lead to a deeper understanding of how a biomaterial’s chemical structure changes due to implantation. Statement of Signifance The human body is an aggressive environment for implantable devices and their biomaterial components. Polycarbonate urethane (PCU) biomaterials in particular were investigated in this study. Traditionally one or a few points on the PCU surface are analysed using ATR-FTIR spectroscopy. However the selection of acquisition points is susceptible to operator bias and critical information can be lost. This study utilises hyperspectral chemical imaging (HCI) to demonstrate that the degradation of a biomaterial varies spatially. Further, HCI revealed spatial variations of biomaterials that were not subjected to oxidative degradation leading to the possibility of HCI being used in the assessment of biomaterial formulation and/or component production

    Filamentation Involves Two Overlapping, but Distinct, Programs of Filamentation in the Pathogenic Fungus Candida albicans

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    The ability of the human pathogenic fungus Candida albicans to switch between yeast-like and filamentous forms of growth has long been linked to pathogenesis. Numerous environmental conditions, including growth at high temperatures, nutrient limitation, and exposure to serum, can trigger this morphological switch and are frequently used in in vitro models to identify genes with roles in filamentation. Previous work has suggested that differences exist between the various in vitro models both in the genetic requirements for filamentation and transcriptional responses to distinct filamentation-inducing media, but these differences had not been analyzed in detail. We compared 10 in vitro models for filamentation and found broad genetic and transcriptomic differences between model systems. The comparative analysis enabled the discovery of novel media-independent genetic requirements for filamentation as well as a core filamentation transcriptional profile. Our data also suggest that the physical environment drives distinct programs of filamentation in C. albicans, which has significant implications for filamentation in vivo
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