dataesr/scientific-paragraphs-categorization huggingface dataset:
Improving the exploration of Regulatory Circuits's biological data and networks In a previous work  [14]  we showed that the Regulatory Circuits workflow can be described using Semantic Web technologies thus increasing its reproducibility. This new implementation, including not only the input data but also the TF-gene interaction networks resulting from the in-silico integration of source biological data, allows a more flexible (re)use of Regulatory Circuits. The implementation we propose allows a fine-grained exploration: the user can select portions of the network, for example excluding regions at a lower distance than the Regulatory Circuits threshold, or excluding one type of region (e. g. for taking into account that promoters relations are more reliable).
Improving the reusability and the enrichment of the source biological data with SPARQL queries The net- works available in the Regulatory Circuits website are static and cannot be updated when the biological datasets it was based upon evolve. A major advantage of our approach is that TF-gene interaction networks for samples and tissues are generated with SPARQL queries from the source biological data before being inserted in the resource. In addition, this implementation also allows the user to easily change some parts of the pipeline that generates the network, such as new calculation of the ranks, to add new genes or transcription factors in the networks, or to remove some of them. All these changes can easily be implemented by adapting the few available queries used to generate the network but necessitate to re-compute the new networks.
This would require to pre-compute rankings for transcripts and regulatory regions which are at the moment provided by the Regulatory Circuits resource and cannot be recomputed. Similarly, introducing a new TFs would require to introduce new confidence values for their binding to regulatory regions. Improving interoperability Among the 217 articles citing Regulatory Circuits, at least 42 either use directly the resulting networks for biological data explanation or use them as comparison for regulatory network inference. In 10 of these, Regulatory Circuits was used in combination with one or several other databases. Other resources on TF-genes relations exist  [21, 22]  but are complementary of Regulatory Circuits, the latter being the only one categorizing tissue-specific networks. By representing Regulatory Circuits as an RDF graph we therefore improve its interoperability with resources of similar scope already based on Semantic Web technologies, which facilitates its reuse in combination with other already existing RDF resources.
The requirements are included separately for "High Thermosphere" (200 to ∼600 km) and "Low Thermosphere" (100-200 km) because temporal variations are more rapid and vertical gradients are stronger in the latter region. The gap assessment uses the following criteria: • Poor-minimum observing requirements not met, no or limited quality observations provided only by scientific instruments without plans for continuity; • Marginal-minimum requirements met, can be provided by research instruments with existing plans to convert them to operational; and • Acceptable-better than minimum user requirements but less than optimum; operational quality data, with identified risk of discontinuity in data flow. The gap associated with the thermospheric variables is summarized in Table  2. It is clear that the thermosphere is currently poorly observed by WMO standards.
SSWRF Missions to Address Space Weather Research and Operational Prototyping The WMO requirements provide guidelines to help inform designs of SmallSat missions targeted at space weather research or operations. Even missions intended for other applications, such as solar or terrestrial research, could provide data useful for investigating space weather, and thus can also be considered in the context of the recommended WMO requirements. Here, we discuss several current missions and developed mission concepts that were presented at SSWRF and can be used to address open scientific questions in space weather research and gaps in space weather operations. These missions represent only a small sampling of the many current and future pathfinding SmallSats relevant to space weather (e. g. , ELFIN: many others) but highlight a broad cross section of WMO-relevant measurements.
Mission Implementation The 8 small satellites of the CYGNSS constellation were deployed from a single deployment module. To maximize the coverage of tropical cyclones, the observatories need to be evenly spaced out along the orbit. Since the spacecraft do not include thrusters, passive techniques have been implemented to control their trajectories and position them at equal distances along the orbit. These maneuvers, called differential drag maneuvers, consist of pitching the satellites by ∼78° to increase the cross-sectional area with respect to the velocity vector. By doing this, the drag force acting on the maneuvered satellite is multiplied by a factor ∼6, altering the in-track velocity, and thus its spacing from the neighboring satellite. The technique of controlling the small satellite trajectories using atmospheric drag can be used to improve the accuracy of algorithms in determining the thermospheric density from the satellite ephemerides.
Mission Objectives The CubeSat Imaging X-ray Solar Spectrometer (CubIXSS;  Caspi et al. , 2020 ) is a 6U Sun-pointed CubeSat selected through NASA's HFORT program in November 2021. Its measurements of the soft X-ray (SXR: ∼0. 025-5. 5 nm; ∼0. 23-50 keV) solar spectral irradiance have distinct space weather applications  (Caspi et al. , 2015). In particular, the 1-5 nm wavelength band is highly variable with solar activity  (Rodgers et al. , 2006)  and also drives significant dynamics in the ionosphere D-and E-regions  (Sojka et al. , 2013 (Sojka et al. , , 2014)). The specific details of the dynamics depend strongly on the spectral distribution, which to date has never been fully measured well, with prior observations being severely limited in some combination of spectral resolution, passband, cadence, or overall duration; this significantly limits understanding of how ionospheric dynamics are driven by solar SXR forcing, both on minute-to-hour timescales from solar flares and on few-day timescales from active region evolution and solar rotation.
The largest single benefit of the SmallSat approach to space missions is reduced cost-from development to launch, and potentially to operations-which in turn enables new types of flexible mission and capability development that would be too costly to realistically implement via a traditional high-reliability, low-risk, centralized or monolithic mission design. Some concepts (e. g. , CubIXSS, AMICal Sat, ATISE) benefit from reduced cost of a single observatory or instrument that implements a centralized mission at lower cost than traditional larger missions. Many copies could be built and launched-perhaps even with iteratively improved designs-at a rapid pace to provide continuous temporal coverage and/or reduced data latency. Other concepts (e. g. , CYGNSS, PUNCH, RICCI, and S 3 T) leverage the reduced cost profile of SmallSats to implement constellations to perform coordinated measurements that could not otherwise be feasibly carried out.
High-speed RF communications solutions for SmallSats in S-and X-band already exist, but these still rely on visibility of ground stations and, due to the limited gain possible within the small footprint available to an on-board SmallSat antenna, are of limited utility for intra-constellation use. LEO-to-MEO or LEO-to-GEO solutions could enable real-time communication without concern for ground-station visibility, but CubeSat-compatible solutions are still rare and currently limited to very restricted data rates. Optical (laser) communication terminals are being developed for SmallSats and present an enticing solution for low-power, high-bandwidth satellite-to-ground communications, including from deep space (e. g. , the fractionated L5 or S 3 T concepts), as well as for intra-constellation communications.
Data from such missions could be used to enhance forecasting models and/or to test and validate data ingestion into forecasting pipelines. These initial missions can then serve as testbeds for dedicated, operationally oriented SmallSat missions and associated infrastructure that enable new space weather measurements at lower cost than, with more flexibility than, and potentially in parameter space not explorable by, the current traditional operational strategy of large, monolithic, redundant platforms. With proper direction from and investment by the cognizant funding agencies, the next few years could bring significant new capabilities online.
by NASA grants NNX14AH54G, NNX15AQ68G, NNX17AI71G, and 80NSSC19K0287. M. Barthelemy was partially supported by CNES and by the Programme National PNST of CNRS/INSU, co-funded by CNES. C. Bussy-Virat was supported by NASA grant NNL13AQ00C. I. Cohen was funded through internal investment by the APL Space Exploration Sector. A. Vourlidas was supported by NASA grant 80NSSC19K1261. We thank the organizers of the first International Workshop on SmallSats for Space Weather Research and Forecasting (SSWRF), and NSF award 1712718 for workshop funding support.
The implementation of more sophisticated models that include detailed radiative transfer schemes may provide MuSA the capability of ingesting new remotely sensed information such as surface reflectances or radar backscatter. In addition, as FSM2 has support for different temporal resolutions, the future versions of MuSA will support different timesteps, allowing to reduce the computational cost both in terms of run time and data storage requirements. As such, there is room for reducing the computational cost of MuSA if necessary, which would open up the possibility of implementing it even at continental and hemispheric scales.
Acknowledgements. Esteban Alonso-González  has been funded by the  CNES postdoctoral fellowship. Data from Izas experimental catchment was collected in the frame of HIDROIBERNIEVE-CGL2017-82216-R funded by the  Spanish Ministry of Economy and Competitiveness project. Kristoffer Aalstad  was funded by the  Research Council of Norway  through the  Spot-On  project (# 301552 ), and acknowledges.
, 2019). Over continental SWA and during the monsoon period, LLC are formed during the night and break up in the afternoon, however, the variability of the LLC cover is only partly understood, which is mainly due to the lack of measurements in this region  (Knippertz et al. , 2015a). One of the goals of the recent DACCIWA project (Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa) was to address this issue, using the important database collected during June and July 2016  (Knippertz et al. , 2015b (Knippertz et al. , , 2017)). The interactions between the nocturnal low-level jet and LLC were studied during the DACCIWA campaign deployed in SWA, using radiosondes, aircraft  (Flamant et al. , 2018b) , and three supersites  (Kalthoff et al. , 2018). During the months of June and July, the maritime air from the Atlantic reaches Savè (Benin), one of the DACCIWA super-sites about 185 km north of the coast, between 16:00 and 21:00 UTC associated with the occurrence of the nocturnal low-level jet and the LLC  (Dione et al.
Moreover,  Deroubaix et al. (2019)  have shown that pollutants are accumulated along the coast during the "daytime drying" phase and transported inland during the "maritime inflow" phase, which suggests that LLC and precipitation could be especially modified by anthropogenic aerosol during a specific phase. This article aims at understanding the influence of the anthropogenic aerosol emissions on the diurnal cycle of LLC and precipitation in SWA. The DACCIWA campaign provides a unique chance to analyze and validate online coupled models. This study presents numerical modeling experiments conducted with WRF-CHIMERE in combination with aerosol and cloud observational datasets from the DACCIWA campaign, which are described in Sect. 2. An analysis of the location and intensity of the modeled anthropogenic plumes is given in Sect.
Modelling set-up and evaluation datasets In this section, we briefly present the coupling of the WRF (Weather Research and Forecast) model and the CHIMERE chemistry-transport model (Sect. 2. 1), and the two experiments conducted using two different anthropogenic emission scenarios (Sect. 2. 2). We then detail the observational datasets acquired during the DACCIWA campaign used to evaluate the two experiments and analyze LLC and precipitation (Sect. 2. 3).
3 dataset, DOI: https://doi. org/10. 5065/D65Q4T4Z, National Centers for Environmental Prediction/National Weather Service/NOAA/U. S. Department of  Commerce, 2015). These fields are used to nudge hourly fields of pressure, temperature, humidity, and wind in the WRF simulations, with spectral nudging, which has been evaluated for regional models by von  Storch et al. (2000). In order to enable the PBL variability to be resolved by WRF, low frequency spectral nudging is used only above the 12th vertical level. This set-up has already been used over SWA by  Deroubaix et al. (2018 Deroubaix et al. ( , 2019)). CHIMERE (version 2017) is a regional chemistrytransport model, which is fully described in  Menut et al. (2013)  and  Mailler et al. (2017)  for its offline mode. Bessagnet et al. (2004)  describe the calculation of gaseous species in the MELCHIOR-2 (reduced) scheme and the aerosol scheme, which takes into account species such as sulfate, nitrate, ammonium, primary organic matter, black carbon, secondary organic aerosols, sea salt, dust, and water.
All aerosols are represented using 10 bins, from 40 nm to 40 µm in diameter. Menut et al. (2016)  have validated and analyzed aerosol speciation and size distribution as well as the chemical mechanisms used in the CHIMERE model over Europe and North Africa. Boundary conditions at a 6 h time step are taken from the Copernicus Atmosphere Monitoring Service made by the European Centre for Medium-Range Weather Forecasts  (Inness et al. , 2019)  for both experiments. These include the biomass burning aerosols from Central Africa (and mineral dust aerosols from the Sahel-Sahara), thanks to assimilation of aerosol optical depth retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) in this dataset (Fig. A1 ). The coupled WRF-CHIMERE model integrates the direct (and semi-direct) and indirect effects between CHIMERE and WRF through exchanges via an external coupling software developed primarily for use in the climate community, namely OASIS3-MCT  (Craig et al.
1 ). The Hemispheric Transport of Air Pollution version 2 (HTAPv2) inventory provides the anthropogenic emission flux  (Janssens-Maenhout et al. , 2015). This dataset is relevant for the year 2010 and, given that the population is rapidly increasing in this region, it should underestimate the 2016 emissions. Therefore, two experiments are proposed (Table  1 ): (i) a simulation with HTAPv2 designed to underestimate anthropogenic emission (AE), which is called AE1, and (ii) a simulation designed to overestimate the AE obtained by multiplying the emissions of HTAPv2 by a factor of 10, called AE10. This factor may seem huge but is not unrealistic, as anthropogenic emissions are estimated to increase very rapidly as "explosive growth"  (Liousse et al. , 2014).
, 2018b). Mass concentrations of four aerosol types are analyzed, namely black carbon (BC), organic carbon (OC), ammonium, and nitrate, as well as two trace gases, CO and NO 2 , and meteorological variables. We use measurements of a SP2 (Single Particle Soot Photometer) for black carbon, and of a C-ToF-AMS (Compact Time-of-Flight Aerosol Mass Spectrometer) instruments for inorganic and organic aerosol concentration. We compare with modeled aerosol size less than 1 µm, which is compatible to the aerosol instruments used here. iii. At Savè, one of the three super-sites of the DACCIWA project, meteorological measurements were performed. Savè is a village located downstream of several large coastal cities, due to the prevailing low-level southwesterlies during the WAM season. For Savè, priority is given to the CHM15k ceilometer data providing a robust value of the cloud base height  (Adler et al.
Data availability. The aircraft measurements taken during the DACCIWA field campaign can be obtained from https://baobab. sedoo. fr/ (last access: 7 March 2022) after (free) registration or https://doi. org/10. 6096/dacciwa. 1618 (Derrien and Bezombes, 2016). Author contributions. AD designed the study, performed the data analysis and produced the figures. AD, CF and LM wrote the initial version of the paper. All co-authors contributed to the interpretation of the results and edited the final version of the paper.
2012). Genotyping-by-sequencing (GBS) is able to generate thousands of Single Nucleotide Polymorphisms (SNPs) markers by applying massively parallel sequencing and multiplexing methods  (Elshire et al. 2011). GBS-generated SNP markers are useful to explore the genetic diversity and structure of population in order to better define phylogeny, adaptation of plants to their environment or domestication  (Favre et al. 2021). Previous studies validated the efficiency of GBS to characterize Vanilla genetic diversity and to identify hybrids  (Hu et al. 2019; Alomia et al. 2021). Herein, we developed SNP markers derived from GBS data to study intraspecific diversity and enlighten the evolutionary history of cultivated vanilla in its introduction areas. Our study focused on genetic diversity of Vanilla resources conserved in the Biological Resources Centers (BRCs) Vatel and Etablissement Vanille de Tahiti (EVT) (Roux-Cuvelier et al.
Library preparation and sequencing Library preparation was performed by the Regional genotyping technology platform (UMR AGAP, CIRAD, Montpellier, France) as described by  Elshire et al. (2011). Two kinds of adapters are used for constructing GBS libraries, a common adapter and a sample-specific barcode adapter. Both adapters are designed to fit with Illumina sequencing. Adapters were mixed together in a 1:1 ratio and plated into two 96-well plates, so that each well contained one specific barcode. Extracted DNA was added into the 96-well plates and digested with PstI methylation-sensitive restriction enzyme (New England Biolabs, Ipswich, Massachusetts, USA). Adapters were ligated to the ends of the DNA fragments using a T4 ligase (New England Biolabs, Ipswich, Massachusetts, USA). Samples were then pooled together, amplified by Polymerase Chain Reaction (PCR) and purified to remove unreacted adapters.
Sequence analysis and SNP calling Sequence quality was checked with FastQC  (Andrews 2010). Low-quality reads, reads with uncalled bases and reads with Illumina adapter sequences were removed using the Cutadapt software  (Martin 2011). The remaining reads were assigned to each sample using the GBS barcode splitter tool (https:// sourc eforge. net/ proje cts/ gbsba rcode/). Demultiplexed sequences were trimmed to 140 bp to normalize the length between individuals. SNP calling was performed using STACKS de novo pipeline  (Catchen et al. 2013 ) and identified SNPs were converted into Variant Call Format file (VCF;  Danecek et al. 2011). Low-quality SNPs were filtered out with vcfR package 1. 11. 0  (Knaus and Grünwald 2017)  from Rstudio (version 3. 6. 3) (R Development Core Team 2010) and using successive filters: minimum minor allele frequency < 10%, missing data per site > 30% and up to 3 SNPs per locus.
SNPs with an allele frequency below 10% were discarded because these very rare variants probably resulted from genotyping errors, while retaining rare alleles that are associated to under-represented samples in the dataset. The V. planifolia accessions were used to study structure within such a clonally propagated vanilla, and only polymorphic SNP markers were kept. Loci with unfiltered SNP markers and filtered SNPs were mapped over the Daphna V. planifolia chromosomes  (Hasing et al. 2020)  to check SNP distribution and density. The genotyping data sets were converted into biallelic tables by vcfR package  (Knaus and Grünwald 2017) , which were used for both phylogenetic relationships and population structure analysis.
Genetic diversity and phylogenetic analysis based on SNP markers The number of effective alleles (Ne), Shannon's information index (I), observed heterozygosity (Ho) and percentage of polymorphic loci (P) were calculated using GenAIEx (version 6. 502)  (Peakall and Smouse 2012). The values were compared across the nine species and interspecific hybrids using the complete genotyping data set, and across different types of V. planifolia cultivars. From the complete genotyping dataset, a dissimilarity coefficient was calculated with DarWIN software  (Perrier and Jacquemoud-Collet 2006)  using the simple matching index  (Sokal and Michener 1958) : d ij = 1 -1 L ∑ L l=1 m l where d ij is the dissimilarity between units i and j; L the number of loci; m l the number of matching alleles for locus l; and π the ploidy.
The data set obtained consisted of 2040 high-quality filtered SNPs. The accession V. planifolia CR0844 was removed due to a missing data rate > 45%. The final data set consisted of 133 genotyped vanilla samples using 2004 filtered SNPs, with a mean heterozygosity of 19. 4% (± 16. 7%) and a mean missing data rate of 19. 5% (± 6. 3%). A specific subset of 84 V. planifolia individuals using 1129 SNPs was produced from the complete genotyping matrix using the same filters, with a mean heterozygosity of 43. 6% (± 14. 9%) and a mean missing data rate of 15. 2% (± 7. 5%). Among the 2004 and 1129 SNPs, 1916 (95. 6%) and 1081 (95. 8%) mapped onto the 14 chromosomes of the V. planifolia cv Daphna genome. For the 2004 SNP matrix, the density of SNPs per chromosome ranged from one SNP every 527 kb (chromosome 2) to one SNP every 230 kb (chromosome 1), with an average of one SNP every 359 kb (Fig.
Genetic relationships between V. planifolia and several of its hybrids In the light of our GBS-generated SNPs we explored the relationships between several species and derived interspecific hybrids. A Bayesian clustering analysis based on 2004 SNPs (Fig. 3a ) was performed on a reduced dataset including six hybrids, two V. pompona (Fig. 3b ), seven V. odorata (Fig. 3c ), seven V. × tahitensis (Fig. 3d ), four V. sotoarenasii (Fig. 3e ), and a representative subset of 12 V. planifolia accessions (Fig. 3f ) selected among subgroups identified in Fig. 2. The estimated likelihood was greatest for K = 4, suggesting the presence of 4 clusters corresponding to the 4 species V. odorata, V. planifolia, V. pompona and V. sotoarenasii. For supposed first-generation hybrids, we were expecting a probability of 0.
The first and second coordinates of the PCoA of V. planifolia accessions explained 13. 92% and 8. 29% of the genetic variability, respectively (Supporting information Fig. S3a ). These values were lower than those observed from the complete dataset PCoA (Supporting information Fig. S2 ), indicating a low structuration within the V. planifolia group. Individuals obtained by vegetative propagation were clustered along the first axis, while individuals derived from sexual reproduction were much more dispersed throughout the plan. No structuration related to geographic origin or the ploidy level of cultivars could be evidenced by PCoA (Supporting information Fig. S3b  and c ). Genetic structure was explored by a Bayesian clustering analysis with 84 V. planifolia accessions using the 1129 SNP matrix (Supporting information Fig.
Acknowledgements We are grateful to  Katia Jade  for preparing tissue cultured plant material and to the  Plant Protection Platform  ( 3P ,  IBISA ) for lab facilities and plant resources (BRC Vatel) access. We would like to thank the  SouthGreen Bioinformatics Platform  (http:// www. south green. fr/) for computational resources and the  GeT-PlaGe platform  ( INRAE, Toulouse, France ) for Illumina sequencing. KeyGene N. V. owns patents and patent applications protecting its Sequence Based Genotyping technologies.