Sensitivity of positive ion mode electrospray ionization mass spectrometry (ESI MS) in the analysis of purine bases in ESI MS and on-line electrochemistry ESI MS (EC/ESI MS)

A cone-shaped MS inlet and on-line electrochemistry (EC) were used to enhance the ionization efficiency in electrospray ionization mass spectrometry (ESI MS) of purine bases. A pathway of positive ion mode ESI may involve oxidation of purine bases, guanine, adenine, xanthine and hypoxanthine, by 1e⁻, 1H⁺ processes. The electrospray process generates dimers of purine bases that are detected in ESI MS as protonated ions, except for xanthine, for which a protonated radical dimer is detected. Thus electrochemical oxidation of purine bases during ESI may generate reactive radicals that can subsequently dimerize. Dimer formation is facilitated in ESI MS when the carrier solution pH is high. The positive ion mode ESI MS ionization is consistent with the reactivity of the bases toward oxidation. Furthermore, the formation of the protonated ions, and Na⁺ and K⁺ adducts of the bases, expected in positive ion ESI MS, are observed. In addition, unusual H-bonding of purine bases guanine and xanthine is confirmed by ESI MS. Application of low EC voltage to the on-line EC cell in EC/ESI MS improves the sensitivity and correlates with the decrease of the intensity of the dimers, possibly as a result of their further oxidation.     

Catalytic partial oxidation of ethane to ethylene and syngas over Rh and Pt coated monoliths: Spatial profiles of temperature and composition

The catalytic partial oxidation of C₂H₆ over Pt and Rh coated monolithic supports (4.7 wt% M/α-Al₂O₃ 45 PPI) was investigated with a capillary sampling technique for a range of C₂H₆/air ratios at constant inlet flow (～8 ms contact time), with and without H₂ addition. Effluent data clearly indicate the differences in product distribution between catalysts and equilibrium. Rh effectively converts the reactant mixtures to syngas with ～80% selectivity, whereas Pt produces C₂H₄ with ～55% C-atom selectivity, while neither produces thermodynamically favored C. Spatially resolved measurements provide direct evidence of the multi-zone nature of the reactors. With Rh, complete conversion of O₂ occurs to produce mostly CO, H₂ and H₂O within the first 3 mm of catalyst, followed by a reforming zone to produce additional syngas. Pt consumes O₂ more slowly, which results in a steady increase in temperature along the reactor. Ethylene formation correlates to reactor temperatures >750 °C, regardless of C/O, in line with the onset of homogeneous reactions. Hydrogen addition tests (C₂H₆/O₂/H₂=2/1/2) clearly exhibit preferential oxidation of H₂ with O₂ over Pt, which shifts the maximum in temperature upstream while preserving a portion of the C₂H₆ for C₂H₄ production. H₂ addition modifies the concentration and temperature profiles minimally on Rh. The main differences between catalysts are the high reforming and O₂ consumption activity with Rh compared to Pt, which are likely responsible for differences in C₂H₄ yields.     

Injectable Human Hair Keratin–Fibrinogen Hydrogels for Engineering 3D Microenvironments to Accelerate Oral Tissue Regeneration

Traumatic injury of the oral cavity is atypical and often accompanied by uncontrolled bleeding and inflammation. Injectable hydrogels have been considered to be promising candidates for the treatment of oral injuries because of their simple formulation, minimally invasive application technique, and site-specific delivery. Fibrinogen-based hydrogels have been widely explored as effective materials for wound healing in tissue engineering due to their uniqueness. Recently, an injectable foam has taken the spotlight. However, the fibrin component of this biomaterial is relatively stiff. To address these challenges, we created keratin-conjugated fibrinogen (KRT-FIB). This study aimed to develop a novel keratin biomaterial and assess cell–biomaterial interactions. Consequently, a novel injectable KRT-FIB hydrogel was optimized through rheological measurements, and its injection performance, swelling behavior, and surface morphology were investigated. We observed an excellent cell viability, proliferation, and migration/cell–cell interaction, indicating that the novel KRT-FIB-injectable hydrogel is a promising platform for oral tissue regeneration with a high clinical applicability.     

Bayesian Estimation of Density via Multiple Sequential Inversions of Two-Dimensional Images With Application to Electron Microscopy

We present a new Bayesian methodology to learn the unknown material density of a given sample by inverting its two-dimensional images that are taken with a scanning electron microscope. An image results from a sequence of projections of the convolution of the density function with the unknown microscopy correction function that we also learn from the data; thus, learning of the unknowns demands multiple inversions. We invoke a novel design of experiment, involving imaging at multiple values of the parameter that controls the subsurface depth from which information about the density structure is carried, to result in the image. Real-life material density functions are characterized by high-density contrasts and are highly discontinuous, implying that they exhibit correlation structures that do not vary smoothly. In the absence of training data, modeling such correlation structures of real material density functions is not possible. So we discretize the material sample and treat values of the density function at chosen locations inside it as independent and distribution-free parameters. Resolution of the available image dictates the discretization length of the model; three models pertaining to distinct resolution classes (at micrometer to nanometer scale lengths) are developed. We develop priors on the material density, such that these priors adapt to the sparsity inherent in the density function. The likelihood is defined in terms of the distance between the convolution of the unknown functions and the image data. The posterior probability density of the unknowns given the data is expressed using the developed priors on the density and priors on the microscopy correction function as elicited from the microscopy literature. We achieve posterior samples using an adaptive Metropolis-within-Gibbs inference scheme. The method is applied to learn the material density of a three-dimensional sample of a nano-structure, using real image data. Illustrations on simulated image data of alloy samples are also included.     

Growth of low temperature silicon nano-structures for electronic and electrical energy generation applications

This paper represents the lowest growth temperature for silicon nano-wires (SiNWs) via a vapour-liquid–solid method, which has ever been reported in the literature. The nano-wires were grown using plasma-enhanced chemical vapour deposition technique at temperatures as low as 150°C using gallium as the catalyst. This study investigates the structure and the size of the grown silicon nano-structure as functions of growth temperature and catalyst layer thickness. Moreover, the choice of the growth temperature determines the thickness of the catalyst layer to be used.The electrical and optical characteristics of the nano-wires were tested by incorporating them in photovoltaic solar cells, two terminal bistable memory devices and Schottky diode. With further optimisation of the growth parameters, SiNWs, grown by our method, have promising future for incorporation into high performance electronic and optical devices.     

Leishmania major pteridine reductase 1 belongs to the short chain dehydrogenase family: stereochemical and kinetic evidence

Pteridine reductase 1 (PTR1) is a novel broad spectrum enzyme of pterin and folate metabolism in the protozoan parasite Leishmania. Overexpression of PTR1 confers methotrexate resistance to these protozoa, arising from the enzyme's ability to reduce dihydrofolate and its relative insensitivity to methotrexate. The kinetic mechanism and stereochemical course for the catalyzed reaction confirm PTR1's membership within the short chain dehydrogenase/reductase (SDR) family. With folate as a substrate, PTR1 catalyzes two rounds of reduction, yielding 5,6,7, 8-tetrahydrofolate and oxidizing 2 equiv of NADPH. Dihydrofolate accumulates transiently during folate reduction and is both a substrate and an inhibitor of PTR1. PTR1 transfers the pro-S hydride of NADPH to carbon 6 on the si face of dihydrofolate, producing the same stereoisomer of THF as does dihydrofolate reductase. Product inhibition and isotope partitioning studies support an ordered ternary complex mechanism, with NADPH binding first and NADP+ dissociating after the reduced pteridine. Identical kinetic mechanisms and NAD(P)H hydride chirality preferences are seen with other SDRs. An observed tritium effect upon V/K for reduction of dihydrofolate arising from isotopic substitution of the transferred hydride was suppressed at a high concentration of dihydrofolate, consistent with a steady-state ordered kinetic mechanism. Interestingly, half of the binary enzyme-NADPH complex appears to be incapable of rapid turnover. Fluorescence quenching results also indicate the existence of a nonproductive binary enzyme-dihydrofolate complex. The nonproductive complexes observed between PTR1 and its substrates are unique among members of the SDR family and may provide leads for developing antileishmanial therapeutics.     

The impact of ignored attributes on a CMS design

The formation of part families and machine cells is an important design step in implementing a Cellular Manufacturing System (CMS). The effectiveness of this design step, for a practical CMS, depends heavily on the proper consideration of the relevant set of attributes. Generally, the relevant attribute set consists of both geometrical and manufacturing attributes of parts. Most published CMS design procedures use a very small number of manufacturing attributes for two reasons. First, precise information about relevant attributes may not be available, and second, considering an adequate number of attributes causes computational intractability. This paper identifies the sources of imprecision and offers an efficient sensitivity analysis procedure for examining the impact of ignored attributes on a CMS design. The sensitivity analysis is based on the theory of Rough Sets and assumes that an initial design has been obtained by using a computationally efficient CMS design procedure. In addition, this procedure can be used to revalidate the configuration of an existing CMS when product mix or parts design has changed. Background on the theory of Rough Sets is provided and appropriate Rough Sets measures are discussed. A numerical example illustrates the proposed use of Rough Sets theory in CMS design.