dataesr/scientific-paragraphs-categorization huggingface dataset:
This allowed us to perform chromosome painting for each non-recombinant block associated with a particular tree. This approach revealed large-scale haplotypes corresponding to inverted and non-inverted ancestries, while simultaneously showing local ancestry variation at a finer scale. Chromosome painting strongly reflected the patterns observed in local PCA and FST landscapes (see Supplementary Figures  S14, S15a  and S15b  for the full dataset). For lower quality samples, we found evidence for phasing errors in the form of switches between maternal and paternal haplotypes, which were visible in heterokaryotic samples. These errors did not prevent us from locating introgressed tracts in homokaryotes, which spanned up to ~100 kb for B12 and up to ~500 kb for B2.
Acknowledgements The data were partly produced and analysed with the support of the GenSeq genotyping and sequencing platform and the  MBB Montpellier Bioinformatics Biodiversity  platform, both being supported by  ANR program "Investissements d'avenir"  ( ANR-10-LABX-04-01 ). We thank  Rémy Dernat  and  Khalid Belkhir  for.
These events had durations of up to few hours and all followed the characteristic pattern of paroxysmal activity at Mt. Etna. Initial rapid-re Strombolian explosions evolved into nearly continuous lava fountaining, feeding lava ows and volcanic plumes with heights of up to several kilometers above the vent  37. Infrasound and thermal IR data recorded during the paroxysmal activity on 20 June are shown in Figs. 1a-1f. Figures  1c-1d  illustrate early Strombolian activity, consisting of discrete explosions clearly distinguishable in both the waveform (Fig. 1c ) and spectrogram (Fig. 1d ). During the later stages of the paroxysm, the acoustic signal evolves into nearly continuous tremor (Fig. 1e ) and individual explosions are only occasionally discerned (Fig. 1f ). Figure  1b  shows thermal IR images tracking the change from discrete explosions to lava fountains feeding a sustained volcanic plume.
Data Infrasound array. For this study, we used acoustic data recorded by a temporary 6-element infrasound array deployed in June 2021. The array was installed and operated by the University of Liverpool and the Istituto Nazionale di Geo sica e Vulcanologia (INGV sezione di Pisa and INGV-Osservatorio Etneo) at the Mt. Conca site (Fig. 2a , CONC), approximately 6km from the active vent within the South East Crater (SEC) area (Fig. 2b ). The array had an aperture (i. e. , max distance between any two sensors) of 50m (Fig. 2c ) and was instrumented with IST2018 infrasound microphones 38 (full-scale range of 480 Pa peak-to-peak, at response between 0. 1 and 40 Hz). Data were recorded on-site with a sampling frequency of 100 Hz and 24-bit resolution. Thermal IR. Mt. Etna is routinely monitored by INGV-Osservatorio Etneo (INGV-OE) with an extensive multi-parameter sensor network, including thermal IR cameras.
2a ) during paroxysmal activity on 20 June, 2023. The measurements, performed with a time step of 1 minute, can be compared with the 1-minute median jet velocity obtained from acoustic data (Fig. 3c , yellow circles); the two independent time series show excellent agreement throughout the phase of sustained lava fountaining (~ 22:45 - 23:30). After 23:30, concurrent with waning of lava fountaining, thermal IR estimates of velocity decrease while infrasound-derived jet velocities remain high as acoustic sensors continue detecting individual explosions with no signi cant associate lava fountaining. Figure  3d  shows the thermal IR data from the ENT camera plotted as a time series. The gure, produced by plotting Radar-derived height of the plume and plume model. We retrospectively analysed X-band radar data to estimate the temporal evolution of plume height during paroxysmal activity.
Finally, signal attenuation (IL) can be evaluated from numerical modelling of acoustic wave propagation (see 5 in this manuscript for additional details). The acoustic and thermal IR ow velocity time series shown in Fig. 3c  show good agreement until approximately 23:15 on 20 June, 2021. After that time the acoustic data appear to continue tracking eruption while the thermal IR signal decreases abruptly. Infrasound waveforms and array DOAs (Fig. 3a, 3b ) con rm that eruptive activity was still ongoing until approximately 23:45; at the same time the X-band radar detected a well-established plume (Fig. 3e ). A plausible explanation for the divergence between acoustic and thermal IR data starting from 23:15 is a migration of the fragmentation front deeper into the conduit towards the nal stages of paroxysmal activity.
3c ). Infrasound array processing. Infrasound array data were processed following the methodology of De Angelis et al. 43. The processing work ow implements least-squares beamforming to evaluate the DOA of coherent energy and the propagation velocity of acoustic waves across the array. The method implements a simple inversion problem: Where is a data vector of time delay measurements between each pair of sensors within the array, is a model vector of slowness in the East-West and North-South directions, is a matrix of distances between each pair of sensors ( is the number of sensors, is the DOA, is the acoustic wave velocity across the array). Delay times for each data window analysed are measured using cross-correlation between all pairs of sensors within the array. The results in Fig. 3b  were obtained by processing 10-second sliding windows with 50% overlap; a preliminary bandpass lter (0.
9  to estimate the maximum height reached by the volcanic plume. The model is a 3-dimensional description of the rise of volcanic ash columns based on the uid dynamics of turbulent buoyant plumes in a windy atmosphere. This is achieved by combining an integral model of pure plumes in a horizontal wind eld  51  with an integral model of volcanic columns in a static atmosphere  52. PlumeRise also accounts for the thermodynamics of heat transfer between hot pyroclasts and the surrounding atmosphere and the effects of a variable atmosphere on the rise of volcanic plumes. Additional details and the full model formulation can be found in ref. 9. In this study, we used a model of the atmopshere obtained from the Reanalysis v5 (ERA5) dataset. ERA5, produced by the of the European Centre for Medium-Range Weather Forecasts of the Copernicus Climate Change Service, provides hourly estimates of a large number of atmospheric, land and oceanic climate variables.
Acknowledgements SDA was supported by by  NERC  Grant  NE/W004771/1  and project  SINFONIA , progetto  Bando Ricerca Libera   2021-Delibera 214/2021- INGV. LZ was supported by project  SINFONIA , progetto  Bando Ricerca Libera   2021 -Delibera 214/2021- INGV  and by the  INGV-MIUR  project  Pianeta Dinamico -"Working Earth" -Sub- project  VT -DYNAMO -2023. We thank all  INGV-OE  technicians who are involved in the maintenance of the thermal ir and visibile cameras operated by  INGV-OE.
We report the major highlights of the School of Cheminformatics in Latin America, Mexico City, November 24-25, 2022. Six lectures, one workshop, and one roundtable with four editors were presented during an online public event with speakers from academia, big pharma, and public research institutions. One thousand one hundred eighty-one students and academics from seventy-nine countries registered for the meeting. As part of the meeting, advances in enumeration and visualization of chemical space, applications in natural product-based drug discovery, drug discovery for neglected diseases, toxicity prediction, and general guidelines for data analysis were discussed. Experts from ChEMBL presented a workshop on how to use the resources of this major compounds database used in cheminformatics. The school also included a round table with editors of cheminformatics journals.
EDA deals with the entire dataset; splitting into training and test sets is not performed. It also has inherent biases, such as artifacts and correlations, that need to be acknowledged and considered. EDA is used to identify clusters of compounds in physicochemical property spaces. Predictive models are used to assess the suitability of the data, while image analysis is used to reduce noise. Thus, there are important constraints we operate under that need to be acknowledged: • Data can be big or small. • Data is updated or missing. • There are many different data types; they need to be identified and integrated • Integration is driven by the types of questions we want to answer. • Some data types may be regulated. • Usually we need to respond quickly (days to weeks). These constraints imply that the appropriate infrastructure needs to be in place to enable integration and that EDA needs to be efficient, well-executed, and communicated to the scientific team.
Chemography concept in chemical space analysis; speaker Alexandre Varnek Chemography is a tool for "big data" analysis, with approximately 10 9 compounds physically available, up to 10 26 structures stored in proprietary databases, and 10 33 compounds with drug-like properties that could be synthesized. Oprea  [2]  proposed the chemography approach to build maps describing the structure and properties of molecules using cheminformatics techniques. Generative topographic mapping (GTM) is a method that transforms the initial space of n descriptors into a two-dimensional space.
Technical details GTM maps can be built with different descriptors, such as ISIDA fragment descriptors  [3, 4] , which can vary by topology, size, and atom labels. Two scenarios can be considered to profile a chemical library with respect to different activities: preparation of single-task models for particular activities or construction of one universal map able to predict the entire pharmacological profile in a multitasking manner. A prototype of the universal map was constructed for the ChEMBL database containing about 1. 7 million compounds, which delineates the biological relevant chemical space and is able to predict > 700 biological activities. This gave rise to the ChemSpace Atlas  [5]  tool which contains approximately 1. 5 billion compounds, > 40,000 hierarchically related maps and > 1.
Moving forward, an interesting transition happened  [9]  when researchers were looking to create small fragments by mapping potential small molecules as reactants for the reaction and describe all the plausible chemical reactions available in the library. So, this method gives them much higher reliability on the synthetic accessibility of compounds. Recently, in 2019 Marcus Gastreich  [10]  summarized advances in modeling chemical space classification and their recent growth rates. Basically, all those databases were created by using reaction rules and certain fragments. Pharmaceutical companies extended this concept even further by taking more and more potential reactants and additional reactions to make these spaces much larger. The large number of molecules and possible reactions makes the enumeration of molecules from fragments almost infeasible and will consume a lot of time to process.
They covered six major areas of application: data curation, analysis, visualization, navigation, and comparison of the chemical space; quantification of natural product-likeness; prediction of bioactivities; ADME and safety profiles; natural-products-inspired de novo design; and prediction of natural products prone to interfere with biological assays. Natural products databases such as the SuperNatural 3. 0  [17]  database offer big data downloads, and the BIOFACQUIM database  [18] , developed at the National Autonomous University of Mexico (UNAM) which collects natural products isolated and characterized in Mexico offers big downloads and virtual screening. Several other natural product databases in the public domain have been reviewed  [16]. A machine-learning approach, reported in 2019  [19] , identifies natural products with high accuracy and can be used to quantify drug-likeness in large molecular databases.
Workshop. ChEMBL-accessing big molecular data via the web interface and API; led by Barbara Zdrazil and Fiona Hunter The first launch of the ChEMBL database (www. ebi. ac. uk/ chembl) in 2009  [1]  was a milestone in the recent history of chemical biology and drug discovery because it provided unprecedented free access to large amounts of high-quality, curated data on bioactive molecules. ChEMBL has grown significantly since then and now impacts a wide range of areas that include drug discovery, data science, and the development and validation of AI, machine learning, and other in silico methods. The ChEMBL database links drug-like compounds to their biological targets via experimental bioactivity data. UniChem is a database that produces cross-references between chemical structure identifiers from different databases (www.
ebi. ac. uk/ unich em). Both ChEMBL and UniChem can be accessed through an interactive web interface as well as programmatically via web services. A general introduction to ChEMBL and UniChem was presented, and the following topics were discussed: (i) What is ChEMBL and how is it structured? (ii) What types of data does ChEMBL contain? (iii) The process to extract and curate bioactivity data (iv) Sources of drug data for ChEMBL, and its curation and integration (v) What is UniChem? (vi) A demonstration of ChEMBL web interface (vii) Other methods to access ChEMBL data (via download, sematic web, or API) (viii) Worked examples to access ChEMBL programmatically using the API endpoints. Further information is available via free training resources: • ChEMBL webinar: https:// www. ebi. ac. uk/ train ing/ events/ guide-explo re-drug-compo unds-and-theirbiolo gical-targe ts-using-chembl/.
The research group then turned to the COVID-AI project  [22, 23]  which focuses on the discovery of anti-COVID-19 agents. The workflow consisted of nine steps: in silico assays, target-based assays, cytotoxicity assays, cell-based SARS-CoV-2 assays, chemical synthesis, in vitro ADMET, in vivo DMPK, development of drug-targeted nanoparticles, and in vivo SARS-CoV-2 models. The best model for cytopathic effect assays was developed using available information from PubChem, MACCS key descriptors, and the random forest machine learning algorithm. For the main protease, a virtual screening campaign of compound databases was performed. The first compounds obtained showed mediumto-high potency.
Conclusions The virtual School of Cheminformatics in Latin America, Mexico City, November 24, 25, 2022, gave an overview of recent developments of chemical space enumeration, applications of cheminformatics to natural products research with focus on drug discovery, drug discovery for neglected diseases, toxicity prediction, and data analysis. The school also featured a workshop on using of the broadly available resources from ChEMBL, focusing on how to use the resources of this major compounds database used in cheminformatics. The school included a round table to discuss topics related to scientific publishing. The event was part of a continued effort in Mexico to contribute to developing the rigorous practice of cheminformatics in Latin America  [26]  in an open format available to the scientific community worldwide that, in this school, was attended by participants from seventy-nine countries.
Methods In this longitudinal analysis, we analysed MASK-air Ò data from patients with self-reported asthma who reported complete weeks and at least one day of ICS use. We compared adherence levels in patients using ICS+F versus those using ICS+other LABA. In addition, we compared these groups on reported symptoms and symptom-medication scores, performing stratified analyses according to weekly adherence levels. Finally, we built regression models to assess whether the weekly use of ICS+F or ICS+other LABA was associated with the weekly use of short-acting beta-agonists (SABA).
Data analysis When responding to the MASK-air Ò daily monitoring questionnaire, it is not possible to skip any of the questions. Data are saved to the dataset only after the final answer, which precludes any missing data. All analyses were performed using the software R. We computed effect size measures for all comparisons between weeks from patients under ICS+F versus ICS+other LABA (effect size helps to understand the magnitude of differences, whereas statistical significance examines whether the findings are likely to be due to chance. With large samples, p-values are very often significant, rendering it important to use the effect size). Values >0. 2 were considered to represent meaningful differences (i. e. , sufficiently large differences to be potentially relevant from a clinical point of view).
Funding information and role of the sponsors MASK-air Ò has been supported by  EU  grants (POLLAR,  EIT Health ;  Structural and Development Funds, Twinning, EIP on AHA ,  H2020  and  Horizon Europe)  and by educational grants from  Mylan-Viatris, ALK, GSK ,  Novartis and Uriach. There was no specific funding for this paper.
Funding We would like to acknowledge the  INDIGO Consortium  (https://indigo-vaccines. eu/) in the generation of data used in this publication. The  INDIGO  project " Effective and Affordable flu Vaccine  for the World" is funded by the  European Union   Horizon 2020 programme  under grant agreement No. H2020-SC1-2019-874653-INDIGO  and the  Department of Biotechnology, Ministry of Science and Technology, Government of India  ( BT/IN/EU-INF/17/GK/19-20 ). Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the  Government of India. Funds received for open access publication fees, from the current grant.
The measured conditions were for K, Na, Ba, Ca, Sr, Mn, Mg, and Fe with standards of Guaymas Data. This analysis provides data on the chemical composition of carbonates. The analysis focused on the oxides content of Fe carbonates, magnetite, and clays. The results were normalized for CO 2. Bulk-rock chemical analysis of 10 samples was performed at Gmas Lab. (Bogotá) by x-ray fluorescence in fused LiBO 2 /Li 2 B 4 O 7 disks using a BrukerS4-Explorer X-ray fluorescence spectrometer with an Rh-anode X-ray tube as a radiation source. The semiquantitative and quantitative analysis was carried out using the QUANT-EXPRESS method (fundamental parameters) before calibration of the equipment with certificated USGS standard samples (AGV1, BCR2, DST2, QLO1, G2, and W2A). Additionally, chemical analysis of 5 bulk-rock samples was performed at Activation Laboratories Ltd (Actlabs) in Canada, with the Ultratrace 5 analytical package (Instrumental Neutron Activation Analysis, INAA + Inductively coupled plasma mass spectrometry, ICP-MS) in a Thermo Finnigan-High Resolution mass spectrometer.
The very negative CO 2 generated contributes to lower the residual isotopic δ 13 C values of marine waters. The Fe/Ti vs Al/(Al + Fe + Mn) ratios and maximum Al/(Al + Fe + Mn) ratio (<0. 4) point out that the Cerro Matoso mantle rocks proceed from the Pacific mid-ocean ridge, where hosted hydrothermal vent plumes responsible for the enrichment of associated metals. Boletín de Geología -vol. 45, n. ° 1 de Geofísica Ambiental from the Universidad Nacional Autónoma de México. Datasets for this research are included in this paper and supplementary information files can be found in. Castrillón, Andres (2020) , "Open systems fractionation factor CM", Mendeley Data, v1. https://doi. org/10. 17632/8fr82mx34x. 1#file-.
Acknowledgments This paper shows part of the Doctoral dissertation of the first author ( A. Castrillón ), who gratefully acknowledges a fellowship from  Colciencias (National Doctorate , scholarship 647) and  Universidad Nacional de Colombia. Isotope analyses were done in the  Instituto Andaluz de Ciencias de la Tierra, Granada, Spain. The authors express their gratitude to  Dr. Dario Barrero , for his comments, and to  Dr. Brian Fry  for his selfless collaboration and valuable contributions to isotope modeling, and thanks to  Cerro Matoso. This work also was possible thanks to the support of  Dr. Augusto Rodriguez  and the  Instituto de Geología and Instituto.
| DISCUSSION This expert-derived list of 65 factors to consider in site selection for native oyster habitat restoration in Europe provides an overview for all emerging restoration efforts, upon which to develop their restoration planning. The list serves several aspects of site selection: (1) it may be used to guide habitat suitability models by providing a short list of measurable factors which should be included; (2) it provides new projects with an overview of the relevant data to collect when undertaking the site selection process; (3) it provides an overview of factors that should be discussed during participatory mapping processes, and guidance as to which information may be of interest from the existing body of LEK; and (4) it highlights that early establishment of whether the site is located within a supportive or facilitating regulatory framework can be helpful when engaging both with funders and regulators, which may be important for scaling up the spatial and temporal scale of restoration efforts.
Abiotic factors (Figure  3c ) remain crucial in site selection as they bound the ecological suitability of the site. For O. edulis, these factors are well documented in habitat suitability models that have been developed for offshore areas in Europe  (Smaal et al. , 2017; Kamermans et al. , 2018; Pogoda et al. , 2020b). The expert-derived lists of abiotic factors and measurable threats support this process, by providing a starting point for scoping out which datasets should be sought when undertaking habitat suitability modelling and mapping. The list stops short of providing thresholds for abiotic factors related to the growth and survival of oysters, as it was clear from participating experts that there is little agreement on threshold values. Possible values associated with many factors were deemed to be context specific, or synergistic, such that a single value could not be agreed upon.
, 2020). Site selection can take place at a range of scales. Initially a broader 'search area' might be considered. For example, in offshore areas in Belgium and the Netherlands, habitat suitability surveys were conducted over up to 100 km 2 , in order to narrow down potential areas suitable for O. edulis habitat restoration within windfarms. In nearshore areas, initial site selection within a broader geography, such as a country or region, may take place over a similar scale, to identify individual estuaries or stretches of coastline most amenable to restoration efforts. In these cases, the 'Essential' factors shown in Figures 3a-e should be assessed. It is also recommended that all 'Desirable' factors be considered at both the large and the small scale, although a critical eye should be cast over the factors to determine which are relevant to the aims of the restoration project, and which data are accessible at the scale relevant to support the process.
ACKNOWLEDGEMENTS This work was the product of the Site Selection Working Group within the  Native Oyster Restoration Alliance (NORA). The NORA Secretariat are funded by the  Federal Ministry for the Environment, Nature Conservation and Nuclear Safety  ( Bundesministerium für Umwelt, Naturschutz und Reaktorsicherheit ) and the  German Federal Agency for Nature Conservation (Bundesamt für Naturschutz)  through the  Federal Program for Biodiversity  and the  Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research  within the project  PROCEED  ( FKZ 3517685013 ). PSEzE was supported by The  Nature Conservancy, Global Ocean Team. The authors thank  Andreas Essenberger  of  Essenberger Designs  for creating the infographic of essential factors, and the anonymous reviewers whose constructive feedback greatly increased the clarity and utility of this manuscript.