Beryllium fastener technology

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

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 μmol 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 μmol 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

<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

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

SLC19A1 transports immunoreactive cyclic dinucleotides.

The accumulation of DNA in the cytosol serves as a key immunostimulatory signal associated with infections, cancer and genomic damage1,2. Cytosolic DNA triggers immune responses by activating the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway3. The binding of DNA to cGAS activates its enzymatic activity, leading to the synthesis of a second messenger, cyclic guanosine monophosphate-adenosine monophosphate (2'3'-cGAMP)4-7. This cyclic dinucleotide (CDN) activates STING8, which in turn activates the transcription factors interferon regulatory factor 3 (IRF3) and nuclear factor κ-light-chain-enhancer of activated B cells (NF-κB), promoting the transcription of genes encoding type I interferons and other cytokines and mediators that stimulate a broader immune response. Exogenous 2'3'-cGAMP produced by malignant cells9 and other CDNs, including those produced by bacteria10-12 and synthetic CDNs used in cancer immunotherapy13,14, must traverse the cell membrane to activate STING in target cells. How these charged CDNs pass through the lipid bilayer is unknown. Here we used a genome-wide CRISPR-interference screen to identify the reduced folate carrier SLC19A1, a folate-organic phosphate antiporter, as the major transporter of CDNs. Depleting SLC19A1 in human cells inhibits CDN uptake and functional responses, and overexpressing SLC19A1 increases both uptake and functional responses. In human cell lines and primary cells ex vivo, CDN uptake is inhibited by folates as well as two medications approved for treatment of inflammatory diseases, sulfasalazine and the antifolate methotrexate. The identification of SLC19A1 as the major transporter of CDNs into cells has implications for the immunotherapeutic treatment of cancer13, host responsiveness to CDN-producing pathogenic microorganisms11 and-potentially-for some inflammatory diseases

Gap Detection for Genome-Scale Constraint-Based Models

Constraint-based metabolic models are currently the most comprehensive system-wide models of cellular metabolism. Several challenges arise when building an in silico constraint-based model of an organism that need to be addressed before flux balance analysis (FBA) can be applied for simulations. An algorithm called FBA-Gap is presented here that aids the construction of a working model based on plausible modifications to a given list of reactions that are known to occur in the organism. When applied to a working model, the algorithm gives a hypothesis concerning a minimal medium for sustaining the cell in culture. The utility of the algorithm is demonstrated in creating a new model organism and is applied to four existing working models for generating hypotheses about culture media. In modifying a partial metabolic reconstruction so that biomass may be produced using FBA, the proposed method is more efficient than a previously proposed method in that fewer new reactions are added to complete the model. The proposed method is also more accurate than other approaches in that only biologically plausible reactions and exchange reactions are used