Constructing High-Active Surface of Plasmonic Tungsten Oxide for Photocatalytic Alcohol Dehydration.

Plasmonic semiconductors with broad spectral response hold significant promise for sustainable solar energy utilization. However, the surface inertness limits the photocatalytic activity. Herein, a novel approach is proposed to improve the body crystallinity and increase the surface oxygen vacancies of plasmonic tungsten oxide by the combination of hydrochloric acid (HCl) regulation and light irradiation, which can promote the adsorption of tert-butyl alcohol (TBA) on plasmonic tungsten oxide and overcome the hindrance of the surface depletion layer in photocatalytic alcohol dehydration. Additionally, this process can concentrate electrons for strong plasmonic electron oscillation on the near surface, facilitating rapid electron transfer within the adsorbed TBA molecules for C-O bond cleavage. As a result, the activation barrier for TBA dehydration is significantly reduced by 93% to 6.0 kJ mol-1, much lower than that of thermocatalysis (91 kJ mol-1). Therefore, an optimal isobutylene generation rate of 1.8 mol g-1 h-1 (selectivity of 99.9%) is achieved. A small flow reaction system is further constructed, which shows an isobutylene generation rate of 12 mmol h-1 under natural sunlight irradiation. This work highlights the potential of plasmonic semiconductors for efficient photocatalytic alcohol dehydration, thereby promoting the sustainable utilization of solar energy.

Assembly of gold nanorods functionalized by zirconium-based metal-organic frameworks for surface enhanced Raman scattering.

Surface-enhanced Raman scattering (SERS) is a promising detection technique providing outstanding molecular fingerprint identification and high sensitivity of analytes. Developing sensitive and stable SERS substrates is highly desirable but remains a challenge. We herein report a wet-chemistry approach for the preparation of (Au nanorod core)@(Zr-based metal-organic framework shell) (Au nanorod@Zr-MOF) nanostructures with the Zr-MOF shell thickness ranging from 3 nm to 90 nm. The stacked Au nanorod@Zr-MOF composites exhibit remarkably improved SERS sensitivity because the MOF shell enriches the molecules to the abundant plasmonic hotspots between the Au nanorod cores. The optimized Au nanorod@Zr-MOF structures exhibit superior SERS activity for detecting 4'-mercaptobiphenylcarbonitrile molecules at a concentration as low as 2 × 10-10 M, with the SERS enhancement factor 2 and 8 times as high as that of ordered bare Au nanorod arrays and random stacking bare Au nanorods, respectively. This study enriches the library of hybrid nanostructures of plasmonic nanocrystals and MOFs, providing an integrated SERS platform with molecular enrichment capability for the realization of sensitive and quantitative analyte identification.

Yeast Phenomics: An Experimental Approach for Modeling Gene Interaction Networks that Buffer Disease.

The genome project increased appreciation of genetic complexity underlying disease phenotypes: many genes contribute each phenotype and each gene contributes multiple phenotypes. The aspiration of predicting common disease in individuals has evolved from seeking primary loci to marginal risk assignments based on many genes. Genetic interaction, defined as contributions to a phenotype that are dependent upon particular digenic allele combinations, could improve prediction of phenotype from complex genotype, but it is difficult to study in human populations. High throughput, systematic analysis of S. cerevisiae gene knockouts or knockdowns in the context of disease-relevant phenotypic perturbations provides a tractable experimental approach to derive gene interaction networks, in order to deduce by cross-species gene homology how phenotype is buffered against disease-risk genotypes. Yeast gene interaction network analysis to date has revealed biology more complex than previously imagined. This has motivated the development of more powerful yeast cell array phenotyping methods to globally model the role of gene interaction networks in modulating phenotypes (which we call yeast phenomic analysis). The article illustrates yeast phenomic technology, which is applied here to quantify gene X media interaction at higher resolution and supports use of a human-like media for future applications of yeast phenomics for modeling human disease.

Clinical research on using acupuncture to treat female adult abdominal obesity with spleen deficiency and exuberant dampness.

OBJECTIVE: To observe the curative effect of acupuncture at hour-prescriptive points, a method of midnight-noon ebb-flow, to treat female adult abdominal obesity with spleen deficiency and exuberant dampness. METHODS: Seventy-two patients with adult abdominal obesity with spleen deficiency and exuberant dampness were randomly divided into a treatment group and a control group with 36 patients in each group. Patients in the treatment group were treated with acupuncture at hour-prescriptive points from 9 to 11 AM every day on the principle of taking points along channels in time. Patients in the control group were treated with acupuncture at any time beyond 9 to 11 AM. Patients in both groups were treated for three courses of treatment. RESULTS: The total effective rate was 87.5% in the treatment group and 78.8% in the control group. The total curative effect in the treatment group was significantly better than that in the control group in reducing body weight, body mass index, waistline, obesity level, and clinical symptoms (P < 0.05). After treatment, t-test was used on two independent samples to analyze the ratio of waistline to hipline and hipline. A value of 0.01 < P < 0.05 expressed a weaker outcome and similar curative effect between the two groups in reducing ratio of waistline to hipline and hipline of patients. This value indicates that the treatment group has no obvious superiority to that of the control group for curative effect. CONCLUSION: Because it was superior in reducing waistline and body weight of female adult patients suffering from abdominal obesity with spleen deficiency and exuberant dampness, acupuncture at hour-prescriptive points, a method of midnight-noon ebb-flow, is an effective method to treat obesity.

Recursive expectation-maximization clustering: a method for identifying buffering mechanisms composed of phenomic modules.

Interactions between genetic and/or environmental factors are ubiquitous, affecting the phenotypes of organisms in complex ways. Knowledge about such interactions is becoming rate-limiting for our understanding of human disease and other biological phenomena. Phenomics refers to the integrative analysis of how all genes contribute to phenotype variation, entailing genome and organism level information. A systems biology view of gene interactions is critical for phenomics. Unfortunately the problem is intractable in humans; however, it can be addressed in simpler genetic model systems. Our research group has focused on the concept of genetic buffering of phenotypic variation, in studies employing the single-cell eukaryotic organism, S. cerevisiae. We have developed a methodology, quantitative high throughput cellular phenotyping (Q-HTCP), for high-resolution measurements of gene-gene and gene-environment interactions on a genome-wide scale. Q-HTCP is being applied to the complete set of S. cerevisiae gene deletion strains, a unique resource for systematically mapping gene interactions. Genetic buffering is the idea that comprehensive and quantitative knowledge about how genes interact with respect to phenotypes will lead to an appreciation of how genes and pathways are functionally connected at a systems level to maintain homeostasis. However, extracting biologically useful information from Q-HTCP data is challenging, due to the multidimensional and nonlinear nature of gene interactions, together with a relative lack of prior biological information. Here we describe a new approach for mining quantitative genetic interaction data called recursive expectation-maximization clustering (REMc). We developed REMc to help discover phenomic modules, defined as sets of genes with similar patterns of interaction across a series of genetic or environmental perturbations. Such modules are reflective of buffering mechanisms, i.e., genes that play a related role in the maintenance of physiological homeostasis. To develop the method, 297 gene deletion strains were selected based on gene-drug interactions with hydroxyurea, an inhibitor of ribonucleotide reductase enzyme activity, which is critical for DNA synthesis. To partition the gene functions, these 297 deletion strains were challenged with growth inhibitory drugs known to target different genes and cellular pathways. Q-HTCP-derived growth curves were used to quantify all gene interactions, and the data were used to test the performance of REMc. Fundamental advantages of REMc include objective assessment of total number of clusters and assignment to each cluster a log-likelihood value, which can be considered an indicator of statistical quality of clusters. To assess the biological quality of clusters, we developed a method called gene ontology information divergence z-score (GOid_z). GOid_z summarizes total enrichment of GO attributes within individual clusters. Using these and other criteria, we compared the performance of REMc to hierarchical and K-means clustering. The main conclusion is that REMc provides distinct efficiencies for mining Q-HTCP data. It facilitates identification of phenomic modules, which contribute to buffering mechanisms that underlie cellular homeostasis and the regulation of phenotypic expression.

Capturing the spectrum of interaction effects in genetic association studies by simulated evaporative cooling network analysis.

Evidence from human genetic studies of several disorders suggests that interactions between alleles at multiple genes play an important role in influencing phenotypic expression. Analytical methods for identifying Mendelian disease genes are not appropriate when applied to common multigenic diseases, because such methods investigate association with the phenotype only one genetic locus at a time. New strategies are needed that can capture the spectrum of genetic effects, from Mendelian to multifactorial epistasis. Random Forests (RF) and Relief-F are two powerful machine-learning methods that have been studied as filters for genetic case-control data due to their ability to account for the context of alleles at multiple genes when scoring the relevance of individual genetic variants to the phenotype. However, when variants interact strongly, the independence assumption of RF in the tree node-splitting criterion leads to diminished importance scores for relevant variants. Relief-F, on the other hand, was designed to detect strong interactions but is sensitive to large backgrounds of variants that are irrelevant to classification of the phenotype, which is an acute problem in genome-wide association studies. To overcome the weaknesses of these data mining approaches, we develop Evaporative Cooling (EC) feature selection, a flexible machine learning method that can integrate multiple importance scores while removing irrelevant genetic variants. To characterize detailed interactions, we construct a genetic-association interaction network (GAIN), whose edges quantify the synergy between variants with respect to the phenotype. We use simulation analysis to show that EC is able to identify a wide range of interaction effects in genetic association data. We apply the EC filter to a smallpox vaccine cohort study of single nucleotide polymorphisms (SNPs) and infer a GAIN for a collection of SNPs associated with adverse events. Our results suggest an important role for hubs in SNP disease susceptibility networks. The software is available at (http://sites.google.com/site/McKinneyLab/software).