Ontology highlight
ABSTRACT:
PROVIDER: PRJNA656259 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Drinking water distribution systems Targeted Locus (Loci)
| PRJNA184833 | ENA
Project description:Microbial communities in real drinking water distribution systems
| PRJNA347544 | ENA
Project description:Flushing drinking water distribution systems Targeted Locus (Loci)
| PRJNA241433 | ENA
Project description:Although drinking water disinfection has proved to be an effective strategy to eliminate most waterborne pathogens, bacterial pathogens can still show disinfection tolerance in drinking water distribution systems (DWDSs), posing a great threat to drinking water safety and human health. Despite stress signals such as starvation and low temperature were reported to increase disinfection tolerance of E. coli, it is unclear whether the stress-induced disinfection tolerance was conserved in different bacterial species.
2020-10-10 | PXD021907 | iProX
Project description:Biofilms / Bacteria in drinking water distribution systems (DWDS) Metagenome
| PRJNA202966 | ENA
Project description:Perfluoroalkyl acid carboxylates and sulfonates (PFAAs) have many consumer and industrial applications. The persistence and widespread distribution of these compounds in humans have brought them under intense scrutiny. Limited pharmacokinetic data is available in humans; however, human data exists for two communities with drinking water contaminated by PFAAs. Also, there is toxicological and pharmacokinetic data for monkeys, which can be quite useful for cross-species extrapolation to humans. The goal of this research was to develop a physiologically-based pharmacokinetic (PBPK) model for PFOA and PFOS for monkeys and then scale this model to humans in order to describe available human drinking water data. The monkey model simulations were consistent with available PK data for monkeys. The monkey model was then extrapolated to the human and then used to successfully simulate the data collected from residents of two communities exposed to PFOA in drinking water. Human PFOS data is minimal; however, using the half-life estimated from occupational exposure, our model exhibits reasonable agreement with the available human serum PFOS data. It is envisioned that our PBPK model will be useful in supporting human health risk assessments for PFOA and PFOS by aiding in understanding of human pharmacokinetics.
Model is encoded by Ruby and submitted to BioModels by Ahmad Zyoud
2020-03-20 | MODEL2003190002 | BioModels
Project description:We characterized the bacterial diversity of chlorinated drinking water from three surface water treatment plants supplying the city of Paris, France. For this purpose, we used serial analysis of V6 ribosomal sequence tag (SARST-V6) to produce concatemers of PCR-amplified ribosomal sequence tags (RSTs) from the V6 hypervariable region of the 16S rRNA gene for sequence analysis. Using SARST-V6, we obtained bacterial profiles for each drinking water sample, demonstrating a strikingly high degree of biodiversity dominated by a large collection of low-abundance phylotypes. In all water samples, between 57.2-77.4% of the sequences obtained indicated bacteria belonging to the Proteobacteria phylum. Full-length 16S rDNA sequences were also generated for each sample, and comparison of the RSTs with these sequences confirmed the accurate assignment for several abundant bacterial phyla identified by SARST-V6 analysis, including members of unclassified bacteria, which account for 6.3-36.5% of all V6 sequences. These results suggest that these bacteria may correspond to a common group adapted to drinking water systems. The V6 primers used were subsequently evaluated with a computer algorithm to assess their hybridization efficiency. Potential errors associated with primer-template mismatches and their impacts on taxonomic group detection were investigated. The biodiversity present in all three drinking water samples suggests that the bacterial load of the drinking water leaving treatment plants may play an important role in determining the downstream community dynamics of water distribution networks.
2009-01-08 | GSE14318 | GEO