Characterization of Tajogaite volcanic plumes detected over the Iberian Peninsula from a set of satellite and ground-based remote sensing instrumentation

Three volcanic plumes were detected during the Tajogaite volcano eruptive activity (Canary Islands, Spain, September–December 2021) over the Iberian Peninsula. The spatiotemporal evolution of these events is characterised by combining passive satellite remote sensing and ground-based lidar and sun-photometer systems. The inversion algorithm GRASP is used with a suite of ground-based remote sensing instruments such as lidar/ceilometer and sun-photometer from eight sites at different locations throughout the Iberian Peninsula. Satellite observations showed that the volcanic ash plumes remained nearby the Canary Islands covering a mean area of 120 ± 202 km2 during the whole period of eruptive activity and that sulphur dioxide plumes reached the Iberian Peninsula. Remote sensing observations showed that the three events were mainly composed of sulphates, which were transported from the volcano into the free troposphere. The high backscatter-related Ångström exponents for wavelengths 532–1064 nm (1.17 ± 0.20 to 1.40 ± 0.24) and low particle depolarization ratios (0.08 ± 0.02 to 0.09 ± 0.02), measured by the multi-wavelength Raman lidar, hinted at the presence of spherical small particles. The layer aerosol optical depth at 532 nm (AODL532) obtained from lidar measurements contributed between 49% and 82% to the AERONET total column AOD at 532 nm in event II (11–13 October). According to the GRASP retrievals, the layer aerosol optical depth at 440 nm (AODL440) was higher in all sites during event II with values between 0.097 (Badajoz) and 0.233 (Guadiana-UGR) and lower in event III (19–21 October) varying between 0.003 (Granada) and 0.026 (Évora). Compared with the GRASP retrievals of total column AOD at 440 nm, the AODL440 had contributions between 21% and 52% during event II. In the event I (25–28 September), the mean volume concentrations (VC) varied between 5 ± 4 μm3cm−3 (El-Arenosillo/Huelva) and 17 ± 10 μm3cm−3 (Guadiana-UGR), while in event II this variation was from 11 ± 7 μm3cm−3 (Badajoz) to 27 ± 10 μm3cm−3 (Guadiana-UGR). Due to the impact of volcanic events on atmospheric and economic fields, such as radiative forcing and airspace security, a proper characterization is required. This work undertakes it using advanced instrumentation and methods.PROBE Cost Action - NASA Ra-diation Sciences Program and Earth Observing System UIDB/04683/2020National funds through FCT -Fundacao para a Ciencia e Tecnologia, I.P., in the framework of the ICT project UIDB/04683/2020 UIDP/04683/2020TOMA-QAPA PTDC/CTAMET/29678/2017GRASP-ACE 778349ACTRIS-IMP 871115ATMO-ACCESS 101008004PROBE CA18235HARMONIA CA21119EUMETNET through the E-PROFILE program and REALISTIC 101086690ACTRIS-2 654109Spanish Government PID2019-103886RB-I00/AEI/10.13039/501100011033NTEGRATYON3 PID2020-117825GB-C21 PID2020-117825GB- C22ELPIS PID2020-120015RB-I00CLARIN CGL2016-81092-REPOLAAR RTI2018-097864-B-I00CAMELIA PID2019-104205GB- C21/AEI/10.13039/501100011033ACTRIS-Espa ~na CGL2017- 90884REDTUniversity of Granada Plan Propio through Singular Laboratory LS2022-1Andalusia Autonomous Government projects AEROPRE and ADAPNE P18-RT-3820 P20_00136UGR-FEDER projects DEM3TRIOS A-RNM-524-UGR20MOGATRACO UCE-PP2017-02Scientific Units of Excellence Program RTI 2018-097332-B-C22R+D+i grant MCIN/AEI/ 10.13039/ 501100011033ERDF A Way of Doing EuropeINTA predoctoral contract program A-RNM-430-UGR2

EcoCyc: fusing model organism databases with systems biology.

EcoCyc (http://EcoCyc.org) is a model organism database built on the genome sequence of Escherichia coli K-12 MG1655. Expert manual curation of the functions of individual E. coli gene products in EcoCyc has been based on information found in the experimental literature for E. coli K-12-derived strains. Updates to EcoCyc content continue to improve the comprehensive picture of E. coli biology. The utility of EcoCyc is enhanced by new tools available on the EcoCyc web site, and the development of EcoCyc as a teaching tool is increasing the impact of the knowledge collected in EcoCyc

Characterization of Tajogaite volcanic plumes detected over the Iberian Peninsula from a set of satellite and ground-based remote sensing instrumentation

Three volcanic plumes were detected during the Tajogaite volcano eruptive activity (Canary Islands, Spain, September–December 2021) over the Iberian Peninsula. The spatiotemporal evolution of these events is characterised by combining passive satellite remote sensing and ground-based lidar and sun-photometer systems. The inversion algorithm GRASP is used with a suite of ground-based remote sensing instruments such as lidar/ ceilometer and sun-photometer from eight sites at different locations throughout the Iberian Peninsula. Satellite observations showed that the volcanic ash plumes remained nearby the Canary Islands covering a mean area of 120 ± 202 km2 during the whole period of eruptive activity and that sulphur dioxide plumes reached the Iberian Peninsula

Strategies towards digital and semi-automated curation in RegulonDB

Experimentally generated biological information needs to be organized and structured in order to become meaningful knowledge. However, the rate at which new information is being published makes manual curation increasingly unable to cope. Devising new curation strategies that leverage upon data mining and text analysis is, therefore, a promising avenue to help life science databases to cope with the deluge of novel information. In this article, we describe the integration of text mining technologies in the curation pipeline of the RegulonDB database, and discuss how the process can enhance the productivity of the curators. Specifically, a named entity recognition approach is used to pre-annotate terms referring to a set of domain entities which are potentially relevant for the curation process. The annotated documents are presented to the curator, who, thanks to a custom-designed interface, can select sentences containing specific types of entities, thus restricting the amount of text that needs to be inspected. Additionally, a module capable of computing semantic similarity between sentences across the entire collection of articles to be curated is being integrated in the system. We tested the module using three sets of scientific articles and six domain experts. All these improvements are gradually enabling us to obtain a high throughput curation process with the same quality as manual curation

Sensory systems and transcriptional regulation in escherichia coli

In free-living bacteria, the ability to regulate gene expression is at the core of adapting and interacting with the environment. For these systems to have a logic, a signal must trigger a genetic change that helps the cell to deal with what implies its presence in the environment; briefly, the response is expected to include a feedback to the signal. Thus, it makes sense to think of genetic sensory mechanisms of gene regulation. Escherichia coli K-12 is the bacterium model for which the largest number of regulatory systems and its sensing capabilities have been studied in detail at the molecular level. In this special issue focused on biomolecular sensing systems, we offer an overview of the transcriptional regulatory corpus of knowledge for E. coli that has been gathered in our database, RegulonDB, from the perspective of sensing regulatory systems. Thus, we start with the beginning of the information flux, which is the signal's chemical or physical elements detected by the cell as changes in the environment; these signals are internally transduced to transcription factors and alter their conformation. Signals transduced to effectors bind allosterically to transcription factors, and this defines the dominant sensing mechanism in E. coli. We offer an updated list of the repertoire of known allosteric effectors, as well as a list of the currently known different mechanisms of this sensing capability. Our previous definition of elementary genetic sensory-response units, GENSOR units for short, that integrate signals, transport, gene regulation, and the biochemical response of the regulated gene products of a given transcriptional factor fit perfectly with the purpose of this overview. We summarize the functional heterogeneity of their response, based on our updated collection of GENSORs, and we use them to identify the expected feedback as part of their response. Finally, we address the question of multiple sensing in the regulatory network of E. coli. This overview introduces the architecture of sensing and regulation of native components in E.coli K-12, which might be a source of inspiration to bioengineering applications.Funding for this work came from Universidad Nacional Autónoma de México (UNAM) and by NIGMS-NIH grant numbers 5RO1GM131643 and 2R01GM077678. Funding for open access publication fees comes from NIGMS-NIH grant 5RO1GM131643. We acknowledge funding from Universidad Nacional Autónoma de México (UNAM) and by NIGMS-NIH grant numbers 5RO1GM131643 and 2R01GM07767

Table1_Flexible gold standards for transcription factor regulatory interactions in Escherichia coli K-12: architecture of evidence types.DOCX

Post-genomic implementations have expanded the experimental strategies to identify elements involved in the regulation of transcription initiation. Here, we present for the first time a detailed analysis of the sources of knowledge supporting the collection of transcriptional regulatory interactions (RIs) of Escherichia coli K-12. An RI groups the transcription factor, its effect (positive or negative) and the regulated target, a promoter, a gene or transcription unit. We improved the evidence codes so that specific methods are incorporated and classified into independent groups. On this basis we updated the computation of confidence levels, weak, strong, or confirmed, for the collection of RIs. These updates enabled us to map the RI set to the current collection of HT TF-binding datasets from ChIP-seq, ChIP-exo, gSELEX and DAP-seq in RegulonDB, enriching in this way the evidence of close to one-quarter (1329) of RIs from the current total 5446 RIs. Based on the new computational capabilities of our improved annotation of evidence sources, we can now analyze the internal architecture of evidence, their categories (experimental, classical, HT, computational), and confidence levels. This is how we know that the joint contribution of HT and computational methods increase the overall fraction of reliable RIs (the sum of confirmed and strong evidence) from 49% to 71%. Thus, the current collection has 3912 reliable RIs, with 2718 or 70% of them with classical evidence which can be used to benchmark novel HT methods. Users can selectively exclude the method they want to benchmark, or keep for instance only the confirmed interactions. The recovery of regulatory sites in RegulonDB by the different HT methods ranges between 33% by ChIP-exo to 76% by ChIP-seq although as discussed, many potential confounding factors limit their interpretation. The collection of improvements reported here provides a solid foundation to incorporate new methods and data, and to further integrate the diverse sources of knowledge of the different components of the transcriptional regulatory network. There is no other genomic database that offers this comprehensive high-quality architecture of knowledge supporting a corpus of transcriptional regulatory interactions.</p

DataSheet3_Flexible gold standards for transcription factor regulatory interactions in Escherichia coli K-12: architecture of evidence types.xlsx

Post-genomic implementations have expanded the experimental strategies to identify elements involved in the regulation of transcription initiation. Here, we present for the first time a detailed analysis of the sources of knowledge supporting the collection of transcriptional regulatory interactions (RIs) of Escherichia coli K-12. An RI groups the transcription factor, its effect (positive or negative) and the regulated target, a promoter, a gene or transcription unit. We improved the evidence codes so that specific methods are incorporated and classified into independent groups. On this basis we updated the computation of confidence levels, weak, strong, or confirmed, for the collection of RIs. These updates enabled us to map the RI set to the current collection of HT TF-binding datasets from ChIP-seq, ChIP-exo, gSELEX and DAP-seq in RegulonDB, enriching in this way the evidence of close to one-quarter (1329) of RIs from the current total 5446 RIs. Based on the new computational capabilities of our improved annotation of evidence sources, we can now analyze the internal architecture of evidence, their categories (experimental, classical, HT, computational), and confidence levels. This is how we know that the joint contribution of HT and computational methods increase the overall fraction of reliable RIs (the sum of confirmed and strong evidence) from 49% to 71%. Thus, the current collection has 3912 reliable RIs, with 2718 or 70% of them with classical evidence which can be used to benchmark novel HT methods. Users can selectively exclude the method they want to benchmark, or keep for instance only the confirmed interactions. The recovery of regulatory sites in RegulonDB by the different HT methods ranges between 33% by ChIP-exo to 76% by ChIP-seq although as discussed, many potential confounding factors limit their interpretation. The collection of improvements reported here provides a solid foundation to incorporate new methods and data, and to further integrate the diverse sources of knowledge of the different components of the transcriptional regulatory network. There is no other genomic database that offers this comprehensive high-quality architecture of knowledge supporting a corpus of transcriptional regulatory interactions.</p

DataSheet2_Flexible gold standards for transcription factor regulatory interactions in Escherichia coli K-12: architecture of evidence types.xlsx

Post-genomic implementations have expanded the experimental strategies to identify elements involved in the regulation of transcription initiation. Here, we present for the first time a detailed analysis of the sources of knowledge supporting the collection of transcriptional regulatory interactions (RIs) of Escherichia coli K-12. An RI groups the transcription factor, its effect (positive or negative) and the regulated target, a promoter, a gene or transcription unit. We improved the evidence codes so that specific methods are incorporated and classified into independent groups. On this basis we updated the computation of confidence levels, weak, strong, or confirmed, for the collection of RIs. These updates enabled us to map the RI set to the current collection of HT TF-binding datasets from ChIP-seq, ChIP-exo, gSELEX and DAP-seq in RegulonDB, enriching in this way the evidence of close to one-quarter (1329) of RIs from the current total 5446 RIs. Based on the new computational capabilities of our improved annotation of evidence sources, we can now analyze the internal architecture of evidence, their categories (experimental, classical, HT, computational), and confidence levels. This is how we know that the joint contribution of HT and computational methods increase the overall fraction of reliable RIs (the sum of confirmed and strong evidence) from 49% to 71%. Thus, the current collection has 3912 reliable RIs, with 2718 or 70% of them with classical evidence which can be used to benchmark novel HT methods. Users can selectively exclude the method they want to benchmark, or keep for instance only the confirmed interactions. The recovery of regulatory sites in RegulonDB by the different HT methods ranges between 33% by ChIP-exo to 76% by ChIP-seq although as discussed, many potential confounding factors limit their interpretation. The collection of improvements reported here provides a solid foundation to incorporate new methods and data, and to further integrate the diverse sources of knowledge of the different components of the transcriptional regulatory network. There is no other genomic database that offers this comprehensive high-quality architecture of knowledge supporting a corpus of transcriptional regulatory interactions.</p