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Author (up) Chen, C.-H.; Tu, C.-C.; Kuo, H.-Y.; Zeng, R.-F.; Yu, C.-S.; Lu, H.H.-S.; Liou, M.-L. file  url
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  Title Dynamic change of surface microbiota with different environmental cleaning methods between two wards in a hospital Type Journal Article
  Year 2017 Publication Applied Microbiology and Biotechnology Abbreviated Journal Appl Microbiol Biotechnol  
  Volume 101 Issue 2 Pages 771-781  
  Keywords Bacteria/*classification/genetics/*isolation & purification; Cluster Analysis; DNA, Bacterial/chemistry/genetics; DNA, Ribosomal/chemistry/genetics; Disinfection/*methods; *Environmental Microbiology; *Hospitals; Housekeeping, Hospital/*methods; Humans; Intensive Care Units; Metagenomics; Phylogeny; RNA, Ribosomal, 16S/genetics; Sequence Analysis, DNA; Taiwan; 16S rRNA metagenomics; Acinetobacter; Environmental cleaning methods; Healthcare-associated infection; Medical intensive care unit; Respiratory care centre  
  Abstract Terminal disinfection and daily cleaning have been performed in hospitals in Taiwan for many years to reduce the risks of healthcare-associated infections. However, the effectiveness of these cleaning approaches and dynamic changes of surface microbiota upon cleaning remain unclear. Here, we report the surface changes of bacterial communities with terminal disinfection and daily cleaning in a medical intensive care unit (MICU) and only terminal disinfection in a respiratory care center (RCC) using 16s ribosomal RNA (rRNA) metagenomics. A total of 36 samples, including 9 samples per sampling time, from each ward were analysed. The clinical isolates were recorded during the sampling time. A large amount of microbial diversity was detected, and human skin microbiota (HSM) was predominant in both wards. In addition, the colonization rate of the HSM in the MICU was higher than that in the RCC, especially for Moraxellaceae. A higher alpha-diversity (p = 0.005519) and a lower UniFrac distance was shown in the RCC due to the lack of daily cleaning. Moreover, a significantly higher abundance among Acinetobacter sp., Streptococcus sp. and Pseudomonas sp. was shown in the RCC compared to the MICU using the paired t test. We concluded that cleaning changes might contribute to the difference in diversity between two wards.  
  Call Number Serial 2098  
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Author (up) Kim, D.S.; Cho, D.S.; Park, W.-M.; Na, H.J.; Nam, H.G. file  url
openurl 
  Title Proteomic pattern-based analyses of light responses in Arabidopsis thaliana wild-type and photoreceptor mutants Type Journal Article
  Year 2006 Publication Proteomics Abbreviated Journal Proteomics  
  Volume 6 Issue 10 Pages 3040-3049  
  Keywords Arabidopsis/metabolism/*radiation effects; Arabidopsis Proteins/*biosynthesis; Cluster Analysis; Electrophoresis, Gel, Two-Dimensional; *Light; Photosynthetic Reaction Center Complex Proteins/*genetics/physiology; Phytochrome/genetics/physiology; Proteome/*biosynthesis; Signal Transduction  
  Abstract Light critically affects the physiology of plants. Using two-dimensional gel electrophoresis, we used a proteomics approach to analyze the responses of Arabidopsis thaliana to red (660 nm), far-red (730 nm) and blue (450 nm) light, which are utilized by type II and type I phytochromes, and blue light receptors, respectively. Under specific light treatments, the proteomic profiles of 49 protein spots exhibited over 1.8-fold difference in protein abundance, significant at p <0.05. Most of these proteins were metabolic enzymes, indicating metabolic changes induced by light of specific wavelengths. The differentially-expressed proteins formed seven clusters, reflecting co-regulation. We used the 49 differentially-regulated proteins as molecular markers for plant responses to light, and by developing a procedure that calculates the Pearson correlation distance of cluster-to-cluster similarity in expression changes, we assessed the proteome-based relatedness of light responses for wild-type and phytochrome mutant plants. Overall, this assessment was consistent with the known physiological responses of plants to light. However, we also observed a number of novel responses at the proteomic level, which were not predicted from known physiological changes.  
  Call Number Serial 275  
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Author (up) Parsons, A.B.; Brost, R.L.; Ding, H.; Li, Z.; Zhang, C.; Sheikh, B.; Brown, G.W.; Kane, P.M.; Hughes, T.R.; Boone, C. file  url
doi  openurl
  Title Integration of chemical-genetic and genetic interaction data links bioactive compounds to cellular target pathways Type Journal Article
  Year 2004 Publication Nature Biotechnology Abbreviated Journal Nat Biotechnol  
  Volume 22 Issue 1 Pages 62-69  
  Keywords Biotechnology/*methods; Cluster Analysis; Drug Industry/*methods; *Drug Resistance; Fungal Proteins/metabolism; Gene Deletion; *Gene Expression Regulation; Mutation; Pharmacogenetics; Proton-Translocating ATPases/metabolism; Saccharomyces cerevisiae/*genetics; Software  
  Abstract Bioactive compounds can be valuable research tools and drug leads, but it is often difficult to identify their mechanism of action or cellular target. Here we investigate the potential for integration of chemical-genetic and genetic interaction data to reveal information about the pathways and targets of inhibitory compounds. Taking advantage of the existing complete set of yeast haploid deletion mutants, we generated drug-hypersensitivity (chemical-genetic) profiles for 12 compounds. In addition to a set of compound-specific interactions, the chemical-genetic profiles identified a large group of genes required for multidrug resistance. In particular, yeast mutants lacking a functional vacuolar H(+)-ATPase show multidrug sensitivity, a phenomenon that may be conserved in mammalian cells. By filtering chemical-genetic profiles for the multidrug-resistant genes and then clustering the compound-specific profiles with a compendium of large-scale genetic interaction profiles, we were able to identify target pathways or proteins. This method thus provides a powerful means for inferring mechanism of action.  
  Call Number Serial 339  
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