Substrate co-doping modulates electronic metal-support interactions and significantly enhances single-atom catalysis

Transitional metal nanoparticles or atoms deposited on appropriate substrates can lead to highly economical, efficient, and selective catalysis. One of the greatest challenges is to control the electronic metal–support interactions (EMSI) between the supported metal atoms and the substrate so as to optimize their catalytic performance. Here, from first-principles calculations, we show that an otherwise inactive Pd single adatom on TiO2(110) can be tuned into a highly effective catalyst, e.g. for O2 adsorption and CO oxidation, by purposefully selected metal–nonmetal co-dopant pairs in the substrate. Such an effect is proved here to result unambiguously from a significantly enhanced EMSI. A nearly linear correlation is noted between the strength of the EMSI and the activation of the adsorbed O2 molecule, as well as the energy barrier for CO oxidation. Particularly, the enhanced EMSI shifts the frontier orbital of the deposited Pd atom upward and largely enhances the hybridization and charge transfer between the O2 molecule and the Pd atom. Upon co-doping, the activation barrier for CO oxidation on the Pd monomer is also reduced to a level comparable to that on the Pd dimer which was experimentally reported to be highly efficient for CO oxidation. The present findings provide new insights into the understanding of the EMSI in heterogeneous catalysis and can open new avenues to design and fabricate cost-effective single-atom-sized and/or nanometer-sized catalysts

Resampling methods to reduce the selection bias in genetic effect estimation in genome-wide scans

Using the simulated data of Problem 2 for Genetic Analysis Workshop 14 (GAW14), we investigated the ability of three bootstrap-based resampling estimators (a shrinkage, an out-of-sample, and a weighted estimator) to reduce the selection bias for genetic effect estimation in genome-wide linkage scans. For the given marker density in the preliminary genome scans (7 cM for microsatellite and 3 cM for SNP), we found that the two sets of markers produce comparable results in terms of power to detect linkage, localization accuracy, and magnitude of test statistic at the peak location. At the locations detected in the scan, application of the three bootstrap-based estimators substantially reduced the upward selection bias in genetic effect estimation for both true and false positives. The relative effectiveness of the estimators depended on the true genetic effect size and the inherent power to detect it. The shrinkage estimator is recommended when the power to detect the disease locus is low. Otherwise, the weighted estimator is recommended

Comparison of family-based association tests in chromosome regions selected by linkage-based confidence intervals

We use the Genetic Analysis Workshop 14 simulated data to explore the effectiveness of a two-stage strategy for mapping complex disease loci consisting of an initial genome scan with confidence interval construction for gene location, followed by fine mapping with family-based tests of association on a dense set of single-nucleotide polymorphisms. We considered four types of intervals: the 1-LOD interval, a basic percentile bootstrap confidence interval based on the position of the maximum Zlr score, and asymptotic and bootstrap confidence intervals based on a generalized estimating equations method. For fine mapping we considered two family-based tests of association: a test based on a likelihood ratio statistic and a transmission-disequilibrium-type test implemented in the software FBAT. In two of the simulation replicates, we found that the bootstrap confidence intervals based on the peak Zlr and the 1-LOD support interval always contained the true disease loci and that the likelihood ratio test provided further strong confirmatory evidence of the presence of disease loci in these regions

Mildly suppressed star formation in central regions of MaNGA Seyfert galaxies

Negative feedback from accretion onto super-massive black holes (SMBHs), that is to remove gas and suppress star formation in galaxies, has been widely suggested. However, for Seyfert galaxies which harbor less active, moderately accreting SMBHs in the local universe, the feedback capability of their black hole activity is elusive. We present spatially-resolved H$\alpha$ measurements to trace ongoing star formation in Seyfert galaxies and compare their specific star formation rate with a sample of star-forming galaxies whose global galaxy properties are controlled to be the same as the Seyferts. From the comparison we find that the star formation rates within central kpc of Seyfert galaxies are mildly suppressed as compared to the matched normal star forming galaxies. This suggests that the feedback of moderate SMBH accretion could, to some extent, regulate the ongoing star formation in these intermediate to late type galaxies under secular evolution.STFC ER

The ALMaQUEST Survey: The Molecular Gas Main Sequence and the Origin of the Star-forming Main Sequence

The origin of the star forming main sequence ( i.e., the relation between star formation rate and stellar mass, globally or on kpc-scales; hereafter SFMS) remains a hotly debated topic in galaxy evolution. Using the ALMA-MaNGA QUEnching and STar formation (ALMaQUEST) survey, we show that for star forming spaxels in the main sequence galaxies, the three local quantities, star-formation rate surface density (\sigsfr), stellar mass surface density (\sigsm), and the \h2~mass surface density (\sigh2), are strongly correlated with one another and form a 3D linear (in log) relation with dispersion. In addition to the two well known scaling relations, the resolved SFMS (\sigsfr~ vs. \sigsm) and the Schmidt-Kennicutt relation (\sigsfr~ vs. \sigh2; SK relation), there is a third scaling relation between \sigh2~ and \sigsm, which we refer to as the `molecular gas main sequence' (MGMS). The latter indicates that either the local gas mass traces the gravitational potential set by the local stellar mass or both quantities follow the underlying total mass distributions. The scatter of the resolved SFMS ($\sigma \sim 0.25$ dex) is the largest compared to those of the SK and MGMS relations ($\sigma \sim$ 0.2 dex). A Pearson correlation test also indicates that the SK and MGMS relations are more strongly correlated than the resolved SFMS. Our result suggests a scenario in which the resolved SFMS is the least physically fundamental and is the consequence of the combination of the SK and the MGMS relations

Microbiome profiling by Illumina sequencing of combinatorial sequence-tagged PCR products

We developed a low-cost, high-throughput microbiome profiling method that uses combinatorial sequence tags attached to PCR primers that amplify the rRNA V6 region. Amplified PCR products are sequenced using an Illumina paired-end protocol to generate millions of overlapping reads. Combinatorial sequence tagging can be used to examine hundreds of samples with far fewer primers than is required when sequence tags are incorporated at only a single end. The number of reads generated permitted saturating or near-saturating analysis of samples of the vaginal microbiome. The large number of reads al- lowed an in-depth analysis of errors, and we found that PCR-induced errors composed the vast majority of non-organism derived species variants, an ob- servation that has significant implications for sequence clustering of similar high-throughput data. We show that the short reads are sufficient to assign organisms to the genus or species level in most cases. We suggest that this method will be useful for the deep sequencing of any short nucleotide region that is taxonomically informative; these include the V3, V5 regions of the bac- terial 16S rRNA genes and the eukaryotic V9 region that is gaining popularity for sampling protist diversity.Comment: 28 pages, 13 figure

Proceedings of the Second Annual Conference of the MidSouth Computational Biology and Bioinformatics Society

The MCBIOS 2004 conference brought together regional researchers and students in biology, computer science and bioinformatics on October 7th-9th 2004 to present their latest work. This editorial describes the conference itself and introduces the twelve peer-reviewed manuscripts accepted for publication in the Proceedings of the MCBIOS 2004 Conference. These manuscripts included new methods for analysis of high-throughput gene expression experiments, EST clustering, analysis of mass spectrometry data and genomic analysi

Considering scores between unrelated proteins in the search database improves profile comparison

<p>Abstract</p> <p>Background</p> <p>Profile-based comparison of multiple sequence alignments is a powerful methodology for the detection remote protein sequence similarity, which is essential for the inference and analysis of protein structure, function, and evolution. Accurate estimation of statistical significance of detected profile similarities is essential for further development of this methodology. Here we analyze a novel approach to estimate the statistical significance of profile similarity: the explicit consideration of background score distributions for each database template (subject).</p> <p>Results</p> <p>Using a simple scheme to combine and analytically approximate query- and subject-based distributions, we show that (i) inclusion of background distributions for the subjects increases the quality of homology detection; (ii) this increase is higher when the distributions are based on the scores to all known non-homologs of the subject rather than a small calibration subset of the database representatives; and (iii) these all known non-homolog distributions of scores for the subject make the dominant contribution to the improved performance: adding the calibration distribution of the query has a negligible additional effect.</p> <p>Conclusion</p> <p>The construction of distributions based on the complete sets of non-homologs for each subject is particularly relevant in the setting of structure prediction where the database consists of proteins with solved 3D structure (PDB, SCOP, CATH, etc.) and therefore structural relationships between proteins are known. These results point to a potential new direction in the development of more powerful methods for remote homology detection.</p

Light-Driven H2 Evolution and C═C or C═O Bond Hydrogenation by Shewanella oneidensis : A Versatile Strategy for Photocatalysis by Nonphotosynthetic Microorganisms

Photocatalytic chemical synthesis by coupling abiotic photosensitizers to purified enzymes provides an effective way to overcome the low conversion efficiencies of natural photosynthesis while exploiting the high catalytic rates and selectivity of enzymes as renewable, earth-abundant electrocatalysts. However, the selective synthesis of multiple products requires more versatile approaches and should avoid the time-consuming and costly processes of enzyme purification. Here we demonstrate a cell-based strategy supporting light-driven H2 evolution or the hydrogenation of C═C and C═O bonds in a nonphotosynthetic microorganism. Methylviologen shuttles photoenergized electrons from water-soluble photosensitizers to enzymes that catalyze H2 evolution and the reduction of fumarate, pyruvate, and CO2 in Shewanella oneidensis. The predominant reaction is selected by the experimental conditions, and the results allow rational development of cell-based strategies to harness nature’s intrinsic catalytic diversity for selective light-driven synthesis of a wide range of products

An oxidized magnetic Au single atom on doped TiO2(110) becomes a high performance CO oxidation catalyst due to the charge effect

Catalysis using gold nanoparticles supported on oxides has been under extensive investigation for many important application processes. However, how to tune the charge state of a given Au species to perform a specific chemical reaction, e.g. CO oxidation, remains elusive. Here, using first-principles calculations, we show clearly that an intrinsically inert Au anion deposited on oxygen-deficient TiO2(110) (Au@TiO2(110)) can be tuned and optimized into a highly effective single atom catalyst (SAC), due to the depletion of the d-orbital by substrate doping. Particularly, Ni- and Cu-doped Au@TiO2 complexes undergo a reconstruction driven by one of the two dissociated O atoms upon CO oxidation. The remaining O atom heals the surface oxygen vacancy and results in a stable bow-shaped surface “O–Au–O” species; thereby the highly oxidized Au single atom now exhibits magnetism and dramatically enhanced activity and stability for O2 activation and CO oxidation, due to the emergence of high density of states near the Fermi level. Based on further extensive calculations, we establish the “charge selection rule” for O2 activation and CO oxidation on Au: the positively charged Au SAC is more active than its negatively charged counterpart for O2 activation, and the more positively charged the Au, the more active it is