Ontology highlight
ABSTRACT:
PROVIDER: PRJNA770855 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Climate change forecasts increase the susceptibility of forest due to longer drier seasons. The adaptive management protocols have highlighted the reduction of the forest densification to improve their vulnerability to extreme climate events (i.g. drought). One of this sensitive woody species to climate change is the Abies pinsapo, a relic conifer tree endemic from the southern Spain. Previous works have shown changes in their trends because of the climate change action, being carried out experimental thinning management in their lowest distribution limit, in Sierra de las Nieves Natural Park (Malaga). Our objective is to evaluate the water improvements of thinned trees in terms of light availability by means of a shading treatment in those thinned trees. To do that we have evaluated the synergic effect of ecophysiology, metabolomics and transcriptomics in control, thinning and thinning+shading plots in wet and dry seasons for two years. The results showed strong differences between summer and spring seasons at the three studied levels. The water deficit shows a greater influence than light exposure in the ecophysiology and metabolomics tree response. And the transcriptomics suggested an improvement of thinned trees when light exposure was reduced. Our results support the necessity of adaptive forest management in order to improve the conservation status of A. pinsapo forest. The combination of different levels of tree response is paramount to understand and predict the tree physiology under water and light stress conditions.
2022-07-17 | GSE208073 | GEO
Project description:Drinking water distribution systems Targeted Locus (Loci)
| PRJNA184833 | ENA
Project description:Flushing drinking water distribution systems Targeted Locus (Loci)
| PRJNA241433 | ENA
Project description:Microbial communities in real drinking water distribution systems
| PRJNA347544 | 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 |
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