1,720,973 research outputs found
Data associated with the publication: Influence of simplified microbial community biofilms on bacterial retention in porous media under conditions of stormwater biofiltration
No full textPorous media filters are used widely to remove bacteria from contaminated water, such as stormwater run-off. Biofilms that colonize filter media during normal function can significantly alter performance, but it is not clear how characteristics of individual populations colonizing porous media combine to affect bacterial retention. We assess how four bacterial strains isolated from stormwater and a laboratory strain, Pseudomonas aeruginosa PAO1, alter Escherichia coli retention in experimental sand columns under conditions of stormwater filtration relative to a clean-bed control. Our results demonstrate that these strains differentially affect E. coli retention, as was previously shown for a model colloid. To determine whether E. coli retention could be influenced by changes in relative abundance of strains within a microbial community, we selected two pairs of biofilm strains with the largest observed differences in E. coli retention and tested how changes in relative abundance of strain pairs in the biofilm affected E. coli retention. The results demonstrate that E. coli retention efficiency is influenced by the retention characteristics of the strains within biofilm microbial community, but individual strain characteristics influence retention in a manner that cannot be determined from changes in their relative abundance alone. This study demonstrates that changes in the relative abundance of specific members of a biofilm community can significantly alter filter performance, but these changes are not a simple function of strain-specific retention and the relative abundance. Our results suggest that the microbial community composition of biofilms should be considered when evaluating factors that influence filter performance
Data associated with the publication: Abundant and Persistent Sulfide-oxidizing Microbial Populations are Responsive to Hypoxia in the Chesapeake Bay
No full textThe number, size and severity of aquatic low oxygen dead-zones are increasing worldwide. Microbial processes in low oxygen environments have important ecosystem-level consequences, such as denitrification, greenhouse gas production and acidification. To identify key microbial processes occurring in low oxygen bottom waters of the Chesapeake Bay, we sequenced both 16S rRNA genes and shotgun metagenomic libraries to determine the identity, functional potential and spatiotemporal distribution of microbial populations in the water column. Unsupervised clustering algorithms grouped samples into three clusters using water chemistry or microbial communities, with extensive overlap of cluster composition between methods. Clusters were strongly differentiated by temperature, salinity, and oxygen. Sulfide-oxidizing microorganisms were found to be enriched in the low-oxygen bottom water and predictive of hypoxic conditions. Metagenome assembled genomes demonstrate that some of these sulfide-oxidizing populations are capable of partial denitrification, and transcriptionally active in a prior study. These results establish the importance of sulfide-oxidizing microorganisms in the microbial response to low oxygen in the Chesapeake Bay and suggest ties between the sulfur, nitrogen and oxygen cycles that could be important to capture when predicting the ecosystem response to remediation efforts or climate change
Assessing genetic predictors of antibiotic resistance phenotypes in wastewater Pseudomonas isolates
No full textThe escalating threat of antimicrobial resistance (AMR), recognized by the World Health Organization as a major global health challenge, underscores the urgency of enhancing resistance detection methods. Advances in sequencing technology have not only improved the identification of AMR genes but also provided the possibility of streamlining the diagnosis and treatment of infections without the need for extensive culturing or isolation of microbes. These advancements also facilitate the global monitoring of AMR genes through the analysis of environmental isolates. However, the performance of gene identification tools for resistance phenotype prediction remains underexplored.
This study investigates the genotype-phenotype relationship in Pseudomonas strains collected from wastewater influent in the Baltimore region, a source of AMR genes relevant to human health. To determine whether drug resistance pattern is affected by evolutionary distance, we correlated the 16S rRNA gene sequence distance of 220 Pseudomonas with the similarity of resistance profiles against 13 antibiotics. Linear regression analysis revealed a significant, although very weak, negative relationship between phenotype similarity and evolutionary distance. This suggests that organisms with greater evolutionary distances exhibit slightly less antibiotic phenotype similarity, although the relationship is not robust enough to predict resistance profiles based solely on evolutionary distance. Analysis of very closely related isolates and their short evolutionary distances could slightly diminish this relationship.
Subsequently, we examined the concordance between in silico predictions of phenotypic resistance and the presence of resistance genes in the genome using metrics such as precision, specificity, accuracy, and the Matthews Correlation Coefficient. Our analysis revealed that higher gene similarity and longer sequence overlap with the database generally lead to better performance. Notably, gene identities above 94% and percentage lengths of reference sequence above 72% yields the highest levels of precision and specificity, which are statistically significant. These findings aid clinicians in making informed decisions about medication choices, including which antibiotics to use for susceptible strains and which to avoid for resistant strains. However, the translation from gene presence to gene expression and phenotype adds complexity to these predictions. The prevalence of efflux pumps, which confer resistance to multiple antibiotics, further complicates the determination of drug-specific phenotypes
Assessing genetic predictors of antibiotic resistance phenotypes in wastewater Pseudomonas isolates
No full textThe escalating threat of antimicrobial resistance (AMR), recognized by the World Health Organization as a major global health challenge, underscores the urgency of enhancing resistance detection methods. Advances in sequencing technology have not only improved the identification of AMR genes but also provided the possibility of streamlining the diagnosis and treatment of infections without the need for extensive culturing or isolation of microbes. These advancements also facilitate the global monitoring of AMR genes through the analysis of environmental isolates. However, the performance of gene identification tools for resistance phenotype prediction remains underexplored.
This study investigates the genotype-phenotype relationship in Pseudomonas strains collected from wastewater influent in the Baltimore region, a source of AMR genes relevant to human health. To determine whether drug resistance pattern is affected by evolutionary distance, we correlated the 16S rRNA gene sequence distance of 220 Pseudomonas with the similarity of resistance profiles against 13 antibiotics. Linear regression analysis revealed a significant, although very weak, negative relationship between phenotype similarity and evolutionary distance. This suggests that organisms with greater evolutionary distances exhibit slightly less antibiotic phenotype similarity, although the relationship is not robust enough to predict resistance profiles based solely on evolutionary distance. Analysis of very closely related isolates and their short evolutionary distances could slightly diminish this relationship.
Subsequently, we examined the concordance between in silico predictions of phenotypic resistance and the presence of resistance genes in the genome using metrics such as precision, specificity, accuracy, and the Matthews Correlation Coefficient. Our analysis revealed that higher gene similarity and longer sequence overlap with the database generally lead to better performance. Notably, gene identities above 94% and percentage lengths of reference sequence above 72% yields the highest levels of precision and specificity, which are statistically significant. These findings aid clinicians in making informed decisions about medication choices, including which antibiotics to use for susceptible strains and which to avoid for resistant strains. However, the translation from gene presence to gene expression and phenotype adds complexity to these predictions. The prevalence of efflux pumps, which confer resistance to multiple antibiotics, further complicates the determination of drug-specific phenotypes
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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