Automated Author ProfilePicazo, Antonio
Universitat de València
Picazo, Antonio
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 9.2 (sum of 7 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
No description available
Authors
- Morant, Daniel ;
- Rochera, Carlos ;
- Picazo, Antonio ;
- Miralles-Lorenzo, Javier ;
- Camacho-Santamans, Alba ;
- Camacho, Antonio
No description available
Authors
- Morant, Daniel ;
- Rochera, Carlos ;
- Picazo, Antonio ;
- Miralles-Lorenzo, Javier ;
- Camacho-Santamans, Alba ;
- Camacho, Antonio
No description available
Authors
- Morant, Daniel ;
- Rochera, Carlos ;
- Picazo, Antonio ;
- Miralles-Lorenzo, Javier ;
- Aguirre, Ernesto ;
- Sánchez-Ortega, Vanessa ;
- Camacho, Antonio
No description available
Authors
- Morant, Daniel ;
- Rochera, Carlos ;
- Picazo, Antonio ;
- Miralles-Lorenzo, Javier ;
- Aguirre, Ernesto ;
- Sánchez-Ortega, Vanessa ;
- Camacho, Antonio
No description available
Authors
- Morant, Daniel ;
- Rochera, Carlos ;
- Picazo, Antonio ;
- Miralles-Lorenzo, Javier ;
- Camacho, Antonio
No description available
Authors
- Morant, Daniel ;
- Rochera, Carlos ;
- Picazo, Antonio ;
- Miralles-Lorenzo, Javier ;
- Camacho, Antonio
Freshwaters ecosystems are critical but fragile environments directly affecting society and its welfare. However, our understanding of genuinely freshwater microbial communities, constrained by our capacity to manipulate its prokaryotic participants in axenic cultures, remains very rudimentary. Even the most abundant components, freshwater Actinobacteria, remain largely unknown. Here, applying deep metagenomic sequencing to the microbial community of a freshwater reservoir, we were able to circumvent this traditional bottleneck and reconstruct de novo seven distinct streamlined actinobacterial genomes. These genomes represent three new groups of photoheterotrophic, planktonic Actinobacteria. We describe for the first time genomes of two novel clades, acMicro (Micrococcineae, related to Luna2,) and acAMD (Actinomycetales, related to acTH1). Besides, an aggregate of contigs belonged to a new branch of the Acidimicrobiales. All are estimated to have small genomes (~1.2 Mb) and their GC content varied from 40-61%. One of the Micrococcineae genomes encodes a proteorhodopsin, a rhodopsin type reported for the first time in Actinobacteria.The remarkable potential capacity of some of these genomes to transform recalcitrant plant detrital material, particularly lignin derived compounds, suggests close linkages between the terrestrial and aquatic realms. Moreover, abundances of Actinobacteria correlate inversely to those of Cyanobacteria that are responsible for prolonged and frequently irretrievable damage to freshwater ecosystems. This suggests that they might serve as sentinels of impending ecological catastrophes.
Authors
- Ghai, Rohit ;
- Mizuno, Carolina M. ;
- Picazo, Antonio ;
- Camacho, Antonio ;
- Rodriguez-Valera, Francisco