dataesr/scientific-paragraphs-categorization huggingface dataset:
Small RNA data analysis Total RNA was extracted from 14-d-old B73 shoot apices comprising the SAM and three-or four-leaf primordia using TRIzol reagent (Invitrogen). RNA was treated with DNase I (Promega), and small RNA-seq libraries prepared from 1. 2 µg total RNA using standard Illumina protocols. All libraries were sequenced on Illumina Hi-Seq platforms (for sequencing metrics, see Supplemental Data Set S5). Sequence reads were trimmed using cutadapt version 1. 13  (Martin 2011). Trimmed reads (18-26 nt)  were aligned to the maize B73 RefGen_v3 genome (AGP_v3) using Bowtie version 1. 1. 2 (Langmead et al. 2009). No mismatches and a maximum of 20 alignments per read were allowed. Reads matching known structural RNAs (rRNAs, tRNAs, sn-RNAs, and sno-RNAs) identified from the Rfam and GenBank noncoding RNA databases (https:// www.
sanger. ac. uk/tool/rfam/ and https://www. ncbi. nlm. nih. gov/genbank/, respectively), were removed from further analysis. Remaining sequences were annotated using BEDTools (version 2. 25. 0) (Quinlan and Hall 2010)  based on known miRNAs in miR-Base (v21). For correlation analysis between mature miRNA and precursor abundance, sum expression for all precursors associated with a given mature miRNA isoform across meristem, internode, P1, P2, P3, and vasculature (nonoverlapping tissues in the atlas reflecting the tissue used for small RNA analysis) was calculated. Total precursor levels and miRNA abundance were normalized at logscale, and the Spearman's correlation calculated in R (R Core Team 2018).
Small RNA target identification and PARE analysis Potential targets were predicted using TargetFinder, allowing a maximum score of 5 (https://github. com/carringtonlab/ TargetFinder). Predicted targets were validated using PARE (parallel analysis of RNA ends) sequencing data generated from 14-d-old B73 seedling apices comprising the SAM and three-or four-leaf primordia  (Dotto et al. 2014). PARE captures the 5 ′ ends of noncapped transcripts, including miRNA-directed cleavage products. Mean PARE tag abundance for large (31 nt, W L ) and small (5 nt, W s ) windows around the predicted miRNA cleavage site was calculated from two biological replicates to assess enrichment of signatures at the miRNA cleavage site over chance occurrence, for instance, signatures stemming from RNA degradation  (Dotto et al.
2014). Cleavage sites were filtered to retain only those for which W s /W L ≥ 0. 75 and W s ≥ 4. Potential target genes without PARE signatures were identified using MapMan annotations of the maize filtered gene set (version FGSv5b+) or the paralog search tool in BioMart (https://www. gramene. org) (Tello-Ruiz et al. 2018). Binding site sequences were extracted from TargetFinder. Binding site positions and flanking sequences were annotated based on transcript information for the filtered gene set version FGSv5b+. The relative frequency of PARE signatures was calculated based on the W s PARE value of a target over its mRNA level in the miRNA precursor domain (Supplemental Table  S2 ). Only targets with a mean expression value ≥1 RPM in at least one tissue were considered. Owing to target sequence conservation, select PARE signatures map to more than one target.
Relative PARE signatures were calculated two ways: first, the observed W s signature frequency was assigned to each target independently; second, the signature frequency was distributed to the respective targets according to their relative transcript abundance in the miRNA precursor domain. The two approaches produced analogous results from which the same inferences and conclusions arise. Data using the first approach are presented. Pearson's correlations between the relative number of PARE signatures and TargetFinder score, miRNA level, or miRNA-to-target ratio were calculated in R (R Core Team 2018). miRNA-to-target ratios were calculated as the sum of miRNA precursor expression over the level of target expression in the miRNA precursor domain (Supplemental Table  S2 ). The combined miRNA-to-target ratio was calculated as the sum expression of miRNA precursors over the sum expression of all targets in the miRNA precursor domains (Supplemental Table  S2 ).
Acknowledgments M. C. P. T. thanks the members of the maize  SAM  project, whose fruitful collaboration provided the basis for the work presented here. We also thank current and former members of the  Timmermans laboratory  for their insightful feedback on the project and helpful comments on the manuscript. The authors further acknowledge support by the high performance and cloud computing group at the  University of Tübingen. This work was supported by award  IOS-1238142  and  IOS-1127112  from the  U. S. National Science Foundation Plant Genome Research Program  and by an  Alexander von Humboldt Professorship  to M. C. P. T.
Atmospheric Models For the atmosphere, the Meso-NH research model (http://mesonh. aero. obs-mip. fr/, accessed on 27 May 2021)  [37]  or the AROME French operational model  [38, 39]  can be used. Meso-NH is the non-hydrostatic mesoscale atmospheric model of the French research community. It incorporates a non-hydrostatic system of equations that enables dealing with a large range of scales (from synoptic to large eddy). It has a complete set of modules and parameterizations for the representation of clouds and precipitation, aerosol, and chemistry but also fires, volcanic eruptions, and atmospheric electricity  [37]. Meso-NH is designed to perform both real case simulations and academic cases. It also has a complete set of observation operators to compare model output directly with observations (radar, lidar, GPS, satellites).
AROME is a kilometer-scale NWP model operational at Météo-France  [38]. While the dynamic core comes from ALADIN [40], the main physical parameterizations mostly come from Meso-NH. Over the SWIO, AROME operates with a 2. 5 km grid mesh over a 1600 × 900 points domain and 90 stretched vertical levels. In its operational version, it is coupled to a 1D ocean mixed layer model  [41] , and at initialization, an Analysis Incremental Update (IUA) scheme  [42]  is used to combine ECMWF large-scale analysis and AROME-OI forecast in order to reduce spin up time  [39]. In its research version, it can be equipped with a 3D-Var assimilation scheme and it has the capability to be coupled to a full 3D ocean. Herein, the AROME model is used in its research version, but the 3D-Var data assimilation scheme is not used.
Some Examples of Coupled Ocean-Atmosphere Modeling of TCs in the SWIO The coupled system described in Section 2 has been used to simulate several TCs that evolved in the SWIO with the aim to progress on the physical processes involved in TC development and intensification and to produce realistic wind, precipitation, and swell fields over the SWIO territories. The objective of this section is to illustrate the OWA coupled system and its modularity and the consistence of the models and physical parameterizations. Several observational data obtained in the framework of the RNR-CYC program  [15]  are used to validate the coupled model. The best-track database from RSMC La Réunion is used to evaluate the track and intensity of each modelled TC. In the SWIO, the maximum wind speed (V MAX ) used to classify the storm intensity is calculated over a 10-min period.
When V MAX is between 14 and 16 m s -1 , a storm is called a Tropical Depression (TD). Although it has not been given a name at this stage, it is registered in operational TC databases and starts to be tracked by RSMC La Réunion. A storm is given a name when it reaches the stage of Moderate Tropical Storm (MTS, V MAX ≥ 17 m s -1 ), which is generally considered as the time corresponding to the completion of the cyclogenesis. Storms reaching MTS intensity, hereafter referred to as Cyclogenetic Storms (CS), are classified into four more categories according to their lifetime maximum intensity (LMI): Strong Tropical Storms (STS, 28 ≤ V MAX ≤ 32 m s -1 ), Tropical Cyclones (TC, 33 ≤ V MAX ≤ 45 m s -1 ), Intense Tropical Cyclones (ITC, 46 ≤ V MAX ≤ 59 m s -1 ), and Very Intense Tropical Cyclones (VITC, V MAX > 60 m s -1 ).
As shown in Bousquet et al. [15] , one important achievement of RNR-CYC lied in the collection of numerous space-borne synthetic aperture radar (SAR) observations within TC developing in the SWIO. As shown in the following, such data can be particularly useful to evaluate TC simulations made in the frame of the project but also to improve RSMC La Réunion real-time or post-event analyses of TC intensity and improve model forecast from assimilating SAR observations into high-resolution NWP systems  [91]. During TC Idai, two exploitable SAR images were collected directly within the core of the storm, including one slightly before its landfall in Beira, on 14 March 2019 at 16:05 UTC. This SAR image (Figure  6a ) locates the center of TC Idai 90 km to the east of Beira. The retrieved surface wind field shows wind speed values in the eyewall between 35 and 46 m s -1 , with a reduced region with maximum wind speed of 46 m s -1 in the northeastern part of the eyewall.
955 hPa on 17 March at 06 UTC). For the last run (HR4, yellow line), the intensity at initialization time is even more erroneous, with an error in initial MSLP of nearly -35 hPa with respect to best-track data. This strong initial bias is nevertheless quickly cancelled, with modeled intensities beginning to precisely match best-track data after 18 h of simulation. The initial positions of the storm in the two latter runs are also slightly shifted southwards (∼20 km) with respect to best-track data with positioning errors slightly increasing over time to reach ∼80 km at the end of the simulations. Despite these errors, the simulation of TC Herold by AROME/NEMO can be considered as satisfactory. Hence, the coupled model is shown to simulate the cyclogenesis and the first smooth intensification phase of the TC with little discrepancies (runs HR1 and HR2).
While surface temperature data collected by ST India during this period can be used to investigate the temporal evolution of the SST field in the vicinity of TCs (see Figure  10  in  [15] ), vertical profiles of ocean temperature reconstructed from sea turtles observations also provide a rare opportunity to investigate the performance of coupled ocean-atmosphere models in cyclonic conditions. Figure  9  presents comparisons between temperature profiles simulated by the AROME/ NEMO coupled system and observed by ST India from 13 to 16 March at 15 UTC. In order to reconstruct ocean temperature profiles from ST-borne observations, temperature-depth measurement pairs collected every 5 min by ST India were aggregated over a period of 3 h centered on 15 UTC. NEMO temperature profiles forecasted from the three model runs encompassing this 4-day period (HR1-3) were extracted at the same time at the grid point closest to the observations.
These model outputs will be made available to the community as open access through the Geosur database development  [14]  (https://geosur. univ-reunion. fr/web/, accessed on 27 May 2021). These TCs simulations have also underlined several important results. In addition to the need for a 3D ocean-atmosphere coupling to correctly describe the intensity and the structure of ocean surface cooling in the wake of the TC  [66] , the significant role of sea salt aerosols (as the main source of CCN in TCs) in representing the track, intensity, and structure of TCs has been underlined  [33]. The importance of coupling the ocean-atmosphere system with a wave model has also been highlighted to produce sea state coherent with the atmospheric and oceanic fields, which is crucial for sea salt aerosol emission  [23]  and for certain air-sea flux parameterizations.
) on a large number of systems would enable one to propose an optimal configuration of the OWA platform for TC forecasting. As shown in Duong et al. [91] , SAR data obtained during RNR-CYC have started being implemented in the 3D-Var assimilation scheme of AROME in its research version. Since AROME analysis can be used as initial and lateral boundary conditions for Meso-NH, improved AROME analysis would also be a benefit to the OWA coupled simulations involving Meso-NH through a better position, intensity, and structure of the vortex in the initial state. Several improvements are already planned concerning ice microphysics and sea sprays. A key issue concerns the role of sea sprays in air-sea flux and in cloud microphysics and more particularly the sea spray emission function in extreme conditions.
We will rely on Boreal unmanned airborne system flights in the environment of TC Joaninha (2019)  [15]. Measurements of aerosol concentration, wind, wave height and sea state, SST, and meteorological parameters will enable a better evaluation and calibration of the sea salt aerosol flux. Moreover, new observations in ocean-atmosphere interactions in strong winds and high waves conditions will arise from the Marion Dufresne Atmospheric program (MAP-IO; http://www. mapio. re/, accessed on 27 May 2021) during which numerous instruments loaded onboard the Marion Dufresne vessel will collect data along its routes in the Indian and Southern oceans.
Acknowledgments: Numerical modeling was made possible thanks to the computer resources of Météo-France. This study contains modified Copernicus Sentinel-1 (2019-20) data. Sentinel-1 is part of the European space component of the Copernicus European program. L1 Data are free of charge and available on the  Copernicus Open Access Hub  (https://scihub. copernicus. eu/,accessed on 27 May 2021) and their specific planning and processing to tropical cyclone wind products is supported by the  ESA  Sentinel-1 mission ground segment team and the  ESA -funded  CYMS  project (Contract No. 4000129822/19/I-DT ).
Data Availability Statement: The numerical models used can be downloaded at the following websites: http://mesonh. aero. obs-mip. fr/ (accessed on 27 May 2021) for Meso-NH, https://www. croco-ocean. org/ (accessed on 27 May 2021) for CROCO, https://github. com/NOAA-EMC/WW3/ (accessed on 27 May 2021) for WW3, https://verc. enes. org/oasis/download (accessed on 27 May 2021) for OASIS3-MCT and http://www. nemo-ocean. eu/ (accessed on 27 May 2021) for NEMO. AROME is not available in open source. Public data used in this study are available in the following repositories: https://cyclobs. ifremer. fr/ (accessed on 27 May 2021) (S1 and RS2 SAR data); http://www. meteo. fr/temps/domtom/La_Reunion/webcmrs9. 0/anglais/index. htm (accessed on 27 May 2021) (best-track data). Other data presented in this study are not publicly available due to temporary use restriction by data owners (these data, available on request from the corresponding author, will be soon deposited in the project data repository).
lepbarcoding. org/Sphingidae/index. php]). In total, the DNA barcode dataset used comprises 90 sequences (Table  1 ) and includes the DNA barcodes of the holotypes and several paratypes of the species X. alexandrei  Haxaire & Vaglia, 2009 , X. huloti Haxaire & Vaglia, 2008 and X. vagliai Haxaire, 2003 , as well as a partial (552 bp) sequence from the pre-1875 lectotype of X. virescens  (Butler, 1875) , which was obtained using the protocol in  Lees et al. (2010). In total, all species in the X. crotonis species-group are represented in this dataset. All specimen and sequence data for the records listed in Table  1  are available in the public dataset DS-XYLOCOL1 (dx. doi. org/ 10. 5883/ DS-XYLOCOL1) in BOLD (Barcode of Life  Datasystems, www. boldsystems. org; Ratnasingham & Hebert, 2007).
DNA sequences are also publicly accessible on GenBank with accession numbers as listed in Table  1. (the term "species-group" is used here to indicate an informal grouping of species that are considered to be each other's closest relatives, and which may or may not have an explicit phylogenetic justifi cation). The species of this group are large hawkmoths characterized by a green upperside ground coloration (but which frequently fades to brown with age; Xylophanes kiefferi Cadiou, 1995 is a notable exception in being brown even when fresh) and a yellow to orange underside ground colour; a forewing upperside with parallel, oblique lines that cross the wing from the posterior margin towards the apex, the number of which varies according to the species; and a black hindwing upperside with a series of pale-yellow triangular spots.
MATERIAL AND METHODS After study of all the species within the X. crotonis speciesgroup, we considered that the Colombian specimens we suspected to represent new species most closely resembled the three species X. aristor, X. crotonis and X. huloti in their habitus (wing patterns and coloration). These species were then considered thoroughly for our comparative morphological analysis. However, because preliminary analyses of DNA barcode data revealed that morphologically similar species were not necessarily the nearest genetically, our genetic distances analyses included all representatives of the X. crotonis species-group for which DNA barcodes were available to us.
DNA barcodes of primary type specimens are emphasized in bold, those of the two new species with DNA barcodes are in red. The codes for each terminal refer to BOLD SampleID codes as listed in Table  1. All records are available on BOLD (www. boldsystems. org) in the dataset DS-XYLOCOL1. We ran a genetic distance analysis in BOLD (Ratnasingham & Hebert, 2007), using the built-in analytical tools. We used MEGA X  (Kumar et al. , 2018)  for tree representation of distances. We built a Neighbour-Joining (NJ) tree using uncorrected p-distances, uniform rates of substitution among sites and pairwise deletion option, and midpoint-rooted. Support for nodes was evaluated with 1000 bootstrap replications. The tree was exported in Newick format and further edited using the interactive Tree of Life (iTOL) web application  (Letunic & Bork, 2019).
, 2012; Hundsdoerfer et al. , 2019; Hundsdoerfer & Kitching, 2020)  that could result in incongruence between morphological and mtDNA-based species discrimination. This supports the reliability of DNA barcoding for the identifi cation of species in the X. crotonis species-group (X. pijao sp. n. currently lacks a DNA barcode). As such our dataset re presents an important contribution to future studies of this diffi cult group. Colombia is one of the most diverse countries on earth, containing an array of biogeographical provinces, each with their own unique geological features. Among them, the Andean region can be considered the most diverse because of its mountainous complexes and the Páramo ecosystem, which exhibits a high level of endemism  (Rangel, 2006; Morrone, 2014). Additionally, the Sierra Nevada de Santa Marta (SNSM) is the highest non-Andean mountain range in the Neotropics, and is an important component of the Tropical Andes biodiversity hotspot  (Mittermeier et al.
Importantly, our fi ndings emphasize the uniqueness of the Sierra Nevada de Santa Marta, a centre of endemism  (Cadena et al. , 2015; Sanín, 2015) , whose entomofauna remains largely unexplored, and where further discoveries surely await entomologists. ACKNOWLEDGEMENTS. Sequencing of DNA barcodes for the Sphingidae global campaign were supported by grants from the Ontario Ministry of Research, Innovation and Science, the Canada Foundation for Innovation and Genome Canada to the Centre for Biodiversity genomics (University of Guelph, Ontario, Canada). The ANR grant SPHINX (ANR-16-CE02-0011-01) funded sequencing of newly collected Colombian specimens at the MNHN "Service de Systématique Moléculaire (SSM)", part of the Service Unit "Acquisition et Analyse de Données (2AD, UMS2700)". RR acknowledges the invaluable support by both C.
Type material. Holotype ♂, Colombia, Magdalena, Cerro Kennedy, Minca to Cerro Kennedy, 11. 095°N 74. 0784°W, 1600 m, 01. xii. 2016, BOLD SampleID 2CO-1355, Rougerie et al. leg. , access. no. IAvH-E 186517, in coll. IAVH. Paratypes: 1♂, Colo mbia, Magdalena, Cerro Kennedy, Minca to Cerro Kennedy, antenna station El Ramo, 11. 1041°N 74. 0591°W, 2500 m, 19. 30 h, 28. xi. 2016, BOLD SampleID 2CO-0222, Rougerie et al. leg. , in MNHN (under accession number IAvH-E 186499). 3♂, Colombia, Magdalena, Cerro Kennedy, Minca to Cerro Kennedy, antenna station El Ramo, 11. 104°N 74. 056°W, 2500 m, 19. 30 h, 28. xi. 2016, Rodolphe Rougerie leg. , two of these in MNHN (under access. no IAvH-E186694, and IAvH-E186491), and 1 in IAVH (access. no. IAvH-E186431). 1♂, Colombia: Magdalena, Sierra Nevada de Santa Marta, 1400 m, 10°54´N, 74°03´W, 3.
Data were collected by a team of researchers from IPGP/OVPF and INGV who carried out surveys on the two islands between 2017 and 2020. In addition, we included data from older measurement campaigns which were undertaken for different purposes between 2005 and 2014. The results of the geochemical investigation highlight the differences in outgassing characteristics between the two volcanic islands and their link with the recent volcanic activity. We perform a comparison with the gas geochemistry of La Réunion island, where a deep and plume-like undegassed mantle contribution has long been identified. Grande Comore and Mayotte are densely populated islands and in view of the high level of seismic and volcanic activity and the related hazards, these first results represent a significant contribution to pave the way for future activities on geochemical monitoring and hazard mitigation.
In our survey strategy, we aimed at characterizing the CO 2 fluxes from the soil on the territory and linking them with known or hidden tectonic structures and with the main degassing areas (summit of Karthala in Grande Comore and Dziani lake and Airport tidal flat in Mayotte). Measurements on Mayotte tidal flat were performed at low tide, when the sandy/muddy flat is wet but without a continuous water cover, excepted some large bubbling pools. Samples of soil gas were taken alongside the soil CO 2 flux measurements to investigate the isotopic signature of carbon in CO 2 and thus constrain the sources of the soil CO 2 emission. As previously mentioned in Section 3. 1, as the soil CO 2 data set was acquired using two different methods and at different times, it is not uniform and therefore our choice in data analysis was to consider each area separately.
Under these assumptions, equilibrium temperatures range between around 381 and 460°C at Karthala (Figures S2) which is consistent with data from  Benavente and Brotheridge (2015). At Mayotte temperature, vary between 314 and 339°C (excluding MAN 1 and 2 which are recognized as affected by a severe dissolution of CO 2 in water). Interestingly, we do not record a change neither in equilibrium temperature nor in outlet temperature (in equilibrium with sea water temperature) in bubbling gases of the BAS Mayotte tidal flat in the period 2005-2019 in spite of the large magmatic event occurring at ca. 50 km from its coast. To explore possible evidences of recent input of deep fluids in Mayotte hydrothermal system we evaluated the thermal equilibrium in combination with their isotopic signatures on the basis of their δ 13 C isotopic fractionation factor between CO 2 and CH 4.
An exception is the sample Ka-Su-01 which is significantly different from the present Karthala database, where only the samples SKM182 and SKM183 (fumarole 6 -survey 2014; Benavente & Brotheridge, 2015)  show some similarity in low 40 Ar and He/N 2 ratio. Another exception is sample SKM70, (sampled in 2010 by Benavente and Brotheridge [2015].