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  • Rong, Ruichen  (11)
Type of Medium
Language
Year
  • 1
    Language: English
    In: PLoS One, San Francisco: Public Library of Science
    Description: Article discussing the bacterial composition of subgingival plaque among diabetic and non-diabetic subjects to determine the effect that diabetes mellitus has on dental health.
    Keywords: Periodontiitis ; Bacteria ; Diabetes ; Pyrosequencing
    ISSN: 19326203
    E-ISSN: 19326203
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  • 2
    Language: English
    In: PLoS One, San Francisco: Public Library of Science
    Description: Article discussing a study that was conducted to understand the basis of a bacterial infection that is common among dairy cows.
    Keywords: Dairy ; Cows ; Pyrosequencing ; Amplicons ; Bacteria ; Microbiology
    ISSN: 19326203
    E-ISSN: 19326203
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  • 3
    Language: English
    In: PLoS ONE, 2012, Vol.7(5), p.e36298
    Description: Lactobacillus- dominated vaginal microbiotas are associated with reproductive health and STI resistance in women, whereas altered microbiotas are associated with bacterial vaginosis (BV), STI risk and poor reproductive outcomes. Putative vaginal taxa have been observed in male first-catch urine, urethral swab and coronal sulcus (CS) specimens but the significance of these observations is unclear. We used 16 S rRNA sequencing to characterize the microbiota of the CS and urine collected from 18 adolescent men over three consecutive months. CS microbiotas of most participants were more stable than their urine microbiotas and the composition of CS microbiotas were strongly influenced by circumcision. BV-associated taxa, including Atopobium , Megasphaera , Mobiluncus , Prevotella and Gemella , were detected in CS specimens from sexually experienced and inexperienced participants. In contrast, urine primarily contained taxa that were not abundant in CS specimens. Lactobacilllus and Streptococcus were major urine taxa but their abundance was inversely correlated. In contrast, Sneathia , Mycoplasma and Ureaplasma were only found in urine from sexually active participants. Thus, the CS and urine support stable and distinct bacterial communities. Finally, our results suggest that the penis and the urethra can be colonized by a variety of BV-associated taxa and that some of these colonizations result from partnered sexual activity.
    Keywords: Research Article ; Biology ; Medicine ; Public Health And Epidemiology ; Infectious Diseases ; Microbiology ; Urology
    E-ISSN: 1932-6203
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  • 4
    Language: English
    In: Journal of Clinical Microbiology, pp. 1376-1383
    Description: Article on evidence of uncultivated bacteria in the adult female bladder.
    Keywords: Uropathogens ; Clinical Cultivation ; Uncultivated Bacteria ; Urinary Tract Conditions
    ISSN: 1098660X
    E-ISSN: 1098660X
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  • 5
    In: The ISME Journal, 2012, Vol.7(1), p.221
    Description: Bacterial community composition in blood-sucking arthropods can shift dramatically across time and space. We used 16S rRNA gene amplification and pyrosequencing to investigate the relative impact of vertebrate host-related, arthropod-related and environmental factors on bacterial community composition in fleas and ticks collected from rodents in southern Indiana (USA). Bacterial community composition was largely affected by arthropod identity, but not by the rodent host or environmental conditions. Specifically, the arthropod group (fleas vs ticks) determined the community composition of bacteria, where bacterial communities of ticks were less diverse and more dependent on arthropod traits--especially tick species and life stage--than bacterial communities of fleas. Our data suggest that both arthropod life histories and the presence of arthropod-specific endosymbionts may mask the effects of the vertebrate host and its environment.
    Keywords: Biology;
    ISSN: 1751-7362
    E-ISSN: 17517370
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  • 6
    Language: English
    In: PLoS One, San Francisco: Public Library of Science
    Description: Article on bacterial communities of the coronal sulcus and distal urethra of adolescent males.
    Keywords: Reproductive Health ; Coronal Sulcus ; Distal Urethra ; Bacterial Communities
    Source: University of North Texas
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  • 7
    Language: English
    In: Annals of the American Thoracic Society, 01/2014, Vol.11(Supplement 1), pp.S72-S73
    Description: The ability to determine the respiratory microbiome from bronchoalveolar lavage (BAL) is controversial due to oral contamination during bronchoscopy. To determine if processing BAL improved the ability to distinguish lung from oral wash microbiomes. Oral wash samples and bronchoscopy...
    Keywords: Bronchus ; Alveoli ; Plant Diseases;
    ISSN: 2325-6621
    ISSN: 23296933
    Source: CrossRef
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  • 8
    Dissertation
    Dissertation
    University of North Texas
    Language: English
    Description: Metagenomics is the study of the totality of the complete genetic elements discovered from a defined environment. Different from traditional microbiology study, which only analyzes a small percent of microbes that could survive in laboratory, metagenomics allows researchers to get entire genetic information from all the samples in the communities. So metagenomics enables understanding of the target environments and the hidden relationships between bacteria and diseases. In order to efficiently analyze the metagenomics data, cutting-edge technologies for analyzing the relationships among microbes and communities are required. To overcome the challenges brought by rapid growth in metagenomics datasets, advances in novel methodologies for interpreting metagenomics data are clearly needed. The first two chapters of this dissertation summarize and compare the widely-used methods in metagenomics and integrate these methods into pipelines. Properly analyzing metagenomics data requires a variety of bioinformatcis and statistical approaches to deal with different situations. The raw reads from sequencing centers need to be processed and denoised by several steps and then be further interpreted by ecological and statistical analysis. So understanding these algorithms and combining different approaches could potentially reduce the influence of noises and biases at different steps. And an efficient and accurate pipeline is important to robustly decipher the differences and functionality of bacteria in communities. Traditional statistical analysis and machine learning algorithms have their limitations on analyzing metagenomics data. Thus, rest three chapters describe a new phylogeny based machine learning and feature selection algorithm to overcome these problems. The new method outperforms traditional algorithms and can provide more robust candidate microbes for further analysis. With the frowing sample size, deep neural network could potentially describe more complicated characteristic of data and thus improve model accuracy. So a deep learning framework is designed on top of the shallow learning algorithm stated above in order to further improve the prediction and selection accuracy. The present dissertation work provides a powerful tool that utilizes machine learning techniques to identify signature bacteria and key information from huge amount of metagenomics data.
    Keywords: Metagenomics ; Machine Learning
    Source: University of North Texas
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  • 9
    Dissertation
    Dissertation
    University of North Texas
    Description: Metagenomics is the study of the totality of the complete genetic elements discovered from a defined environment. Different from traditional microbiology study, which only analyzes a small percent of microbes that could survive in laboratory, metagenomics allows researchers to get entire genetic information from all the samples in the communities. So metagenomics enables understanding of the target environments and the hidden relationships between bacteria and diseases. In order to efficiently analyze the metagenomics data, cutting-edge technologies for analyzing the relationships among microbes and communities are required. To overcome the challenges brought by rapid growth in metagenomics datasets, advances in novel methodologies for interpreting metagenomics data are clearly needed. The first two chapters of this dissertation summarize and compare the widely-used methods in metagenomics and integrate these methods into pipelines. Properly analyzing metagenomics data requires a variety of bioinformatcis and statistical approaches to deal with different situations. The raw reads from sequencing centers need to be processed and denoised by several steps and then be further interpreted by ecological and statistical analysis. So understanding these algorithms and combining different approaches could potentially reduce the influence of noises and biases at different steps. And an efficient and accurate pipeline is important to robustly decipher the differences and functionality of bacteria in communities. Traditional statistical analysis and machine learning algorithms have their limitations on analyzing metagenomics data. Thus, rest three chapters describe a new phylogeny based machine learning and feature selection algorithm to overcome these problems. The new method outperforms traditional algorithms and can provide more robust candidate microbes for further analysis. With the frowing sample size, deep neural network could potentially describe more complicated characteristic of data and thus improve model accuracy. So a deep learning framework is designed on top of the shallow learning algorithm stated above in order to further improve the prediction and selection accuracy. The present dissertation work provides a powerful tool that utilizes machine learning techniques to identify signature bacteria and key information from huge amount of metagenomics data.
    Keywords: Metagenomics ; Machine Learning
    Source: Networked Digital Library of Theses and Dissertations
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  • 10
    Description: Inflammation and infection of bovine mammary glands, commonly known as mastitis, imposes significant losses each year in the dairy industry worldwide. While several different bacterial species have been identified as causative agents of mastitis, many clinical mastitis cases remain culture negative, even after enrichment for bacterial growth. To understand the basis for this increasingly common phenomenon, the composition of bacterial communities from milk samples was analyzed using culture independent pyrosequencing of amplicons of 16S ribosomal RNA genes (16S rDNA). Comparisons were made of the microbial community composition of culture negative milk samples from mastitic quarters with that of non-mastitic quarters from the same animals. Genomic DNA from culture-negative clinical and healthy quarter sample pairs was isolated, and amplicon libraries were prepared using indexed primers specific to the V1–V2 region of bacterial 16S rRNA genes and sequenced using the Roche 454 GS FLX with titanium chemistry. Evaluation of the taxonomic composition of these samples revealed significant differences in the microbiota in milk from mastitic and healthy quarters. Statistical analysis identified seven bacterial genera that may be mainly responsible for the observed microbial community differences between mastitic and healthy quarters. Collectively, these results provide evidence that cases of culture negative mastitis can be associated with bacterial species that may be present below culture detection thresholds used here. The application of cultureindependent bacterial community profiling represents a powerful approach to understand long-standing questions in animal health and disease.
    Keywords: Milk ; Mastitis ; Bovine Mastitis ; Microbiome ; Sequence Databases ; Dna Isolation ; Bacterial Pathogens ; Mycoplasma ; Large Or Food Animal And Equine Medicine ; Veterinary Infectious Diseases ; Veterinary Microbiology And Immunobiology
    Source: Iowa State University
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