Surface characterization, mechanical properties and corrosion behaviour of ternary based ZneZnOeSiO2composite coating of mild steel

Zinc coatings are obtained either from cyanide, non-cyanide alkaline or acid solutions. Because of the pollution and high cost associated with cyanide, deposition from other baths is gaining importance. In order to develop a bath with additive that could produce a quality coating is the motivation behind this present work which is surface modification of Zne8ZnOeSiO2 nano composite coating on mild steel surface by electrodeposition route. The influence of SiO2 on Zne8ZnO sulphate electrolyte on the properties and microstructure of the produced nano-coatings were investigated. The SiO2 was varied from 0 to 16wt%. The microstructure characteristics of these produced series composites coating were investigated using scanning electron microscopy couple with energy dispersive spectroscopy (SEM/EDS), X-ray diffraction and atomic force microscopy (AFM). The corrosion degradation properties in 3.65% NaCl medium were studied using potentiodynamic polarization technique and characterized by high resolution optical microscope (HR-OPM). The hardness and wear of the composite coating were measured with high diamond microhardness tester and dry abrasive MTR-300 testers respectively. The results showed that average hardness value of 142.5 and 251.2HV and corrosion rate of 0.13088 and 0.00122 mm/yr were obtained for the 0 and 16wt% SiO2 in Zne8ZnO. The work have established that upto 16% SiO2 in Zne8ZnO composite coating on mild steel can be used in improving the microhardness, wear loss and corrosion resistance of mild stee

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<div><p>Gene gain and loss shape both proteomes and the networks they form. The increasing availability of closely related sequenced genomes and of genome-wide network data should enable a better understanding of the evolutionary forces driving gene gain, gene loss and evolutionary network rewiring. Using orthology mappings across 23 ascomycete fungi genomes, we identified proteins that were lost, gained or universally conserved across the tree, enabling us to compare genes across all stages of their life-cycle. Based on a collection of genome-wide network and gene expression datasets from baker’s yeast, as well as a few from fission yeast, we found that gene loss is more strongly associated with network and expression features of closely related species than that of distant species, consistent with the evolutionary modulation of gene loss propensity through network rewiring. We also discovered that lost and gained genes, as compared to universally conserved “core” genes, have more regulators, more complex expression patterns and are much more likely to encode for transcription factors. Finally, we found that the relative rate of network integration of new genes into the different types of networks agrees with experimentally measured rates of network rewiring. This systems-level view of the life-cycle of eukaryotic genes suggests that the gain and loss of genes is tightly coupled to the gain and loss of network interactions, that lineage-specific adaptations drive regulatory complexity and that the relative rates of integration of new genes are consistent with network rewiring rates.</p></div

Independent Effects of Protein Core Size and Expression on Residue-Level Structure-Evolution Relationships

<div><p>Recently, we demonstrated that yeast protein evolutionary rate at the level of individual amino acid residues scales linearly with degree of solvent accessibility. This residue-level structure-evolution relationship is sensitive to protein core size: surface residues from large-core proteins evolve much faster than those from small-core proteins, while buried residues are equally constrained independent of protein core size. In this work, we investigate the joint effects of protein core size and expression on the residue-level structure-evolution relationship. At the whole-protein level, protein expression is a much more dominant determinant of protein evolutionary rate than protein core size. In contrast, at the residue level, protein core size and expression both have major impacts on protein structure-evolution relationships. In addition, protein core size and expression influence residue-level structure-evolution relationships in qualitatively different ways. Protein core size preferentially affects the non-synonymous substitution rates of surface residues compared to buried residues, and has little influence on synonymous substitution rates. In comparison, protein expression uniformly affects all residues independent of degree of solvent accessibility, and affects both non-synonymous and synonymous substitution rates. Protein core size and expression exert largely independent effects on protein evolution at the residue level, and can combine to produce dramatic changes in the slope of the linear relationship between residue evolutionary rate and solvent accessibility. Our residue-level findings demonstrate that protein core size and expression are both important, yet qualitatively different, determinants of protein evolution. These results underscore the complementary nature of residue-level and whole-protein analysis of protein evolution.</p> </div

Thiyl Radical-Based Charge Tagging Enables Sterol Quantitation via Mass Spectrometry

Inspired by the high reactivity and specificity of thiyl radicals toward alkenes, we have developed a new charge derivatization method to enable fast and quantitative analysis of sterols via electrospray ionization-mass spectrometry (ESI-MS). Thioglycolic acid (TGA), a commercially available compound, has been established as a highly efficient tagging reagent. Initiated from photochemical reactions, the thiyl radical derived from TGA abstracts an allylic hydrogen in the B ring of sterols, forming a radical intermediate which rapidly recombines with a second thiyl radical to produce the final tagged product. Because of the incorporation of a carboxylic acid group, TGA tagging not only improves the limit of detection (sub-nM) for sterols but also facilitates their quantitation via characteristic 44 Da neutral loss scan. This radical based derivatization is fast (1 min) and efficient (>90% yield) when conducted in a flow microreactor. The analytical utility of thiyl radical charge tagging method has been demonstrated by quantifying sterols from human plasma and vegetable oil

Regulatory Network Structure as a Dominant Determinant of Transcription Factor Evolutionary Rate

<div><p>The evolution of transcriptional regulatory networks has thus far mostly been studied at the level of <em>cis</em>-regulatory elements. To gain a complete understanding of regulatory network evolution we must also study the evolutionary role of <em>trans</em>-factors, such as transcription factors (TFs). Here, we systematically assess genomic and network-level determinants of TF evolutionary rate in yeast, and how they compare to those of generic proteins, while carefully controlling for differences of the TF protein set, such as expression level. We found significantly distinct trends relating TF evolutionary rate to mRNA expression level, codon adaptation index, the evolutionary rate of physical interaction partners, and, confirming previous reports, to protein-protein interaction degree and regulatory in-degree. We discovered that for TFs, the dominant determinants of evolutionary rate lie in the structure of the regulatory network, such as the median evolutionary rate of target genes and the fraction of species-specific target genes. Decomposing the regulatory network by edge sign, we found that this modular evolution of TFs and their targets is limited to activating regulatory relationships. We show that fast evolving TFs tend to regulate other TFs and niche-specific processes and that their targets show larger evolutionary expression changes than targets of other TFs. We also show that the positive trend relating TF regulatory in-degree and evolutionary rate is likely related to the species-specificity of the transcriptional regulation modules. Finally, we discuss likely causes for TFs' different evolutionary relationship to the physical interaction network, such as the prevalence of transient interactions in the TF subnetwork. This work suggests that positive and negative regulatory networks follow very different evolutionary rules, and that transcription factor evolution is best understood at a network- or systems-level.</p> </div

TFs co-evolve with activated targets, but not with repressed targets.

<p>Edge signs are inferred from TF knock-out expression data. Each data point is based on a TF with 5 or more targets regulated in the same direction. (A) Median K_a/K_s of activated target genes as a function of TF K_a/K_s. (B) Median K_a/K_s of repressed target genes as a function of TF K_a/K_s. (C) Fraction of activated targets missing an ortholog in <i>S. paradoxus</i> as a function of TF K_a/K_s. (D) Fraction of repressed targets missing an ortholog in <i>S. paradoxus</i> as a function of TF K_a/K_s. Numbers above the bars represent the number of TFs in the bin.</p

Independent effects of protein core size and expression on residue evolution.

<p>The dN/dS versus solvent accessibility relationship for four different protein groups: (<b>A</b>) small-core and high-expression, (<b>B</b>) large-core and high-expression, (<b>C</b>) small-core and low-expression, and (<b>D</b>) large-core and low-expression. Increasing core size and decreasing expression level simultaneously in (D) results in significant increases to the slope of the trend relative to either separate change. The best-fit lines for all four groups of proteins are replicated in each panel for comparison.</p

Results of multi-variate logistic regression for predicting local gene loss.

<p>Results of multi-variate logistic regression for predicting local gene loss.</p

Residue-level structure-evolution relationships.

<p>(<b>A</b>) A cartoon diagram of a protein shown in cross section, highlighting three residues in different relative solvent accessibility (RSA) microenvironments. (<b>B</b>) Evolutionary rate (as measured by dN/dS) scales in a strong, positive, linear manner with RSA, as previously demonstrated <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0046602#pone.0046602-Franzosa1" target="_blank">[7]</a>.</p

Properties in <i>S</i>. <i>pombe</i> of genes lost along different parts of the tree.

<p>A-C) Comparison of genes lost in species close to <i>S</i>. <i>pombe</i> (<i>S</i>. <i>octosporus</i> or <i>S</i>. <i>japonicus</i>) to genes lost along other branches, showing (A) median mRNA expression in <i>S</i>. <i>pombe</i>, (B) median genetic interaction degree in <i>S</i>. <i>pombe</i> and (C) mean PPI degree in <i>S</i>. <i>pombe</i>. D-F) Comparison of <i>S</i>. <i>pombe</i> orthologs of genes lost in species close to <i>S</i>. <i>cerevisiae</i> (after the divergence from <i>K</i>. <i>waltii</i>) to those of genes lost along other branches, showing (D) median mRNA expression in <i>S</i>. <i>pombe</i>, (E) median genetic interaction degree in <i>S</i>. <i>pombe</i> and (F) mean PPI degree in <i>S</i>.<i>pombe</i>. Error bars around medians show the bootstrapped standard error of the median based on 100 resamplings.</p