dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding  NetSARC (IRSCA & DGOS)  and  RREPS (IRSCA & DGOS) ,  RESOS (IRSCA & DGOS) ,  EURACAN  ( EC 739521 ), LYRICAN ( IRSCA-DGOS-INSERM 12563 ),  LabEx DEvweCAN  ( ANR-10-LABX-0061 ),  Institut Convergence PLASCAN  ( 17-CONV-0002 ),  RHU4   DEPGYN  ( ANR-18-RHUS-0009 , Association  DAM's, Ensemble contre Le GIST ,  Eurosarc  ( FP7-278742 ), la  Fondation ARC, Infosarcome ,  InterSARC (IRSCA) ,  Ligue de L'Ain  contre le Cancer,  La Ligue  contre le Cancer funded this study.
Climate change will exacerbate environmental threats, which will, in turn, affect agricultural production patterns. This study addresses the feasibility of using Sentinel-2 (S2) optical remote sensing data to map the consequences of heatwaves on vineyard plots. The proposed method to map damaged and undamaged vineyards is based on the use of an inter-annual (C1) and an intra-annual (C2) criterion derived from the Normalized Difference Vegetation Index (NDVI) calculated on S2 data. While the inter-annual criterion compares the NDVI of the heatwave year to the average NDVI of the previous years with no heatwave, the intra-annual criterion compares the NDVI values before and after the heatwave in the same year. Predictions from either criteria or both combined were tested against two datasets collected during two field surveys performed in 2020 and 2021, with different ways of recording the damage caused on the grapevine by the 2019 heatwave in southeastern France.
Multi-temporal S2 images and field data have been collected within the area to evaluate the potential of S2 imagery to observe the impact of this heatwave event on the vines. After describing the studied area and the dataset used in Section 2, Section 3 presents the methodology employed to spatially characterise the heatwave consequences on vineyard plots. The analysis of the heatwave impact on vine growing areas from Sentinel-2 data is described in Section 4, followed by a discussion in Section 5. Finally, the main conclusions are presented in Section 6.
Sentinel-2 images One hundred and two Sentinel-2A (S2A) and Sentinel-2B (S2B) optical images acquired over our study site from March to October 2016 to 2019 were used. Currently, S2A and S2B sensors provide images with a revisit time of 5 days and a spatial resolution of 10 m for the blue, green, red and near infra-red bands. The S2 images have been downloaded from the Theia website, a French open-source data and service centre (https://www. theia-land. fr/). Theia provides S2 images of Level-2A corrected for atmospheric effects and ortho-rectified.
Observations on reference vineyards The reference dataset is composed of 141 vineyard plots with observed damage caused by the heatwave that took place between 24 and 28 June 2019 (Figure  2 ). In these 141 plots, red wine varieties are the most common, comprising mainly Syrah, Mourvèdre and Grenache. The vineyard management differs largely among winegrowers. For example, the spacing between rows in the studied vineyard is 2 meters for 22 % of the reference dataset plots and 2. 5 meters for 74. 5 %. Only 3. 5 % of the 141 vineyard plots have a row spacing between 2. 5 and 3 meters. Moreover, 66 % of the reference plots were found to apply the cover cropping technique. Cover cropping consists of covering the bare soil between or under the vine rows by different types of plants. Referring to the 141 reference surveyed plots, 62 % of the plots have a surface area greater than 0.
5 ha. The width of the plots varies between 11. 33 m and 221. 75 m, and the length ranges between 22. 39 m and 413. 11 m. The reference dataset, estimated at the whole farm level, derives from winegrowers' declarations of yield loss registered by French institutions in the summer of 2019, just after the heatwave. Then, winegrowers were selected in 2020, covering the whole area affected by the 2019 heatwave, to conduct personal interviews and field surveys. This resulted in 141 reference plots that were geo-referenced and delineated where the winegrowers confirmed the damage declared in 2019. Reference plots described as damaged could include only desiccated berries or both burned leaves and desiccated berries on all or part of the plot. These different damage types caused by the 2019 heatwave and their location within plots were detailed for 18 of the 141 reference plots during a second field survey carried out in June 2021 with six winegrowers.
For the 18 reference plots, which were further characterised in 2021, Table  1  summarises both the global declaration collected in 2020 at plot level and the percent area of the plot declared in 2021 as damaged on leaves or berries by the 2019 heatwave. Sixteen out of the eighteen detailed surveyed plots in 2021 present damage symptoms at the leaf and berry levels, simultaneously. As the reference dataset is not extensive for each vine variety and plot management type, the relationship between these vineyard parameters and the consequent impacts of the heatwave could not be evaluated in this study. Therefore, all reference plots were analysed without distinguishing vine variety or plot management type.
27 June was chosen as it corresponds to the date of the first S2 image acquired in 2019 during the heat event. Averaging the NDVI over two months enabled minimising the slight variations in NDVI due to the uncertainties in the reflectance calculation and smoothing small variations of NDVI due, for example, to mild water stress. In addition, the use of averaged NDVI over a two-month period allowed overcoming the problem of missing data on a given date due to cloud cover. For a year with no heatwave (2016, 2017 or 2018) and over the chosen period (27 June to 5 September), NDVI in vineyards is at (or close to) its maximum with slight temporal variations. We hypothesise that the NDVI average over this period with a significantly lower value in 2019 than in the preceding years is likely due to an alteration of plant health owing to the heatwave.
RESULTS In this section, first, we built a confusion matrix to assess the performance of C1 and C2 classifications, either separately or in combination, using the data collected for the 18 detailed surveyed plots (intra-plot scale). Then, the classification methods were applied to 141 reference plots where damage caused by the 2019 heatwave were surveyed in 2020 at the whole plot scale. A sensitivity analysis was also reported to test the influence of changing the threshold values of the criteria C1 and C2 on the results.
CONCLUSION This study aimed to address the feasibility of using Sentinel-2 optical remote sensing data to map heatwave consequences on vineyards. The vegetative vine cycle is highly dependent on the climate, so much of its variability affects vineyard development. Accordingly, different levels of vine damage were reported by winegrowers following the June 2019 heatwave in southeast France. Part of the vineyard damage could be detected using NDVI derived from Sentinel-2 images at 10 meters resolution. Three methods were tested based on two criteria to predict the impacts of a heatwave on NDVI. Within 18 plots where the heatwave damage was recorded in detail, only 46 % of their total area was predicted as damaged by the inter-annual NDVIbased method (C1) and 62 % by the intra-annual method (C2).
Barton (2006)  studies governments' legitimacy. Sheftell et al. (2007)  identify the headache episodes found in the books as symptomatic of migraine. Woeste (2010) revisits the debate over free will and determinism. As of June 6, 2022, Amazon listed over 40 000 items with 'Harry Potter' in the title, over 20 000 of them under books. In addition to books, there are also hundreds of papers, published and unpublished. For example, SSRN lists 63 Harry Potter studies in law, administrative science, political science, philosophy, history, marketing, etc. SciVerse-Scopus lists 46 items in engineering, neuroscience, psychology, etc. (Source: estrip. org/articles/read/tiny pliny/45318/Harry_Potter_in_Scientific_Literature. html, accessed June 6, 2022. ). We also found 51 articles in PubMed. Finally, the Jstor database includes 159 papers, with 'Harry Potter' in their title  (accessed June 6, 2022).
Acknowledgments We thank an anonymous reviewer for helpful comments and the editor  Amrita Dhillon  for guidance. Many colleagues and students have commented on earlier versions of this paper. In particular, we would like to thank  Zurab Abramishvili ,  Markus Brückner ,  Roger Congleton  and  Pierre-Guillaume Méon , the discussants at the  Silvaplana Workshop on Political Economy. In addition, we thank  Anat Alexandron ,  Sima Amram ,  Paul Anglin ,  Katrina Babb ,  Anindya Banerjee ,  Bob Barsky ,  Yoram Bauman ,  Mark Bergen ,  Sabrina Artinger ,  Helen Casey ,  Allan Chen ,  Ong EeCheng ,  Raphael Franck ,  Max Gillman ,  Allen Goodman ,  Danielle Gurevitch ,  Yuval Heller ,  Arye Hillman ,  Daniel Houser ,  Miriam Krausz ,  Christoph Kuzmics ,  Frank Lechner ,  Steven Levitt ,  Sarit Levy ,  Lorence Maimony ,  Asher Meir ,  Hugo Mialon ,  Igal Milchtaich ,  Lavinia Moldovan ,  Shmuel Nitzan ,  Chryssa Papathanassiou ,  Ron Peretz ,  Paul Rubin ,  Adi Schnytzer ,  Matthew Shapiro ,  Ainit Snir , the late  Heinrich Ursprung ,  Timothy Wong , and the seminar participants at  Bar-Ilan University , at the  Silvaplana Workshop on Political Economy , at the  International School of Economics Tbilisi (ISET) , at the  National University of Singapore , at the  American Economic Association Conference on Teaching and Research in Economic Education , and at the  American Economic Association 's annual conference, for comments, suggestions and conversations about the economics of magic.
Field campaign and observational data sets The in situ measurements used in this study are aircraft data from the StratoClim campaign, which took place over the Indian subcontinent from 27 July to 10 August 2017. The M55-Geophysica high-flying research aircraft deployed in Kathmandu, Nepal, was equipped with a comprehensive set of instruments to capture high-quality data sets, which include, but are not limited to, water vapor, number densities, size distributions of ice particles, and meteorological parameters in the subtropical upper troposphere-lower stratosphere (UTLS) region  (Khaykin et al. , 2022). In this study, we use in situ measurements of temperature and pressure made by TDC (ThermoDynamic Complex)  (Shur et al. , 2007)  and measurements of the cloud particle number size distribution in the size range of 3-937 µm diameter at a time resolution of 1 Hz  (Krämer at al.
Therefore, ice habit could have a significant impact on the cirrus life cycle and the (de)hydration of the TTL by regulating the sedimentation rate and the freeze-drying process. -Ice habit also influences in-cloud radiative and latent heating rates, which are known to influence cloud dynamics at a variety of scales  (Gasparini et al. , 2022; Ramaswamy and Ramanathan, 1989; Sherwood et al. , 1994). These results demonstrate the importance of ice habit in shaping the properties and evolution of tropical cirrus clouds. is supported by NASA FINESST (grant no. 80NSSC20K1613). Martina Krämer and Armin Afchine are supported by the Seventh Framework Programme (STRATOCLIM; grant no. 603557). Sergey Khaykin was supported by the Agence Nationale de la Recherche TTL-Xing project (ANR-17-CE01-0015). Financial support.
This research has been supported by the Office of International Science and Engineering (grant no. OISE-1743753), the Division of Atmospheric and Geospace Sciences (grant no. AGS-1759255), NASA FINESST (grant no. 80NSSC20K1613), the Seventh Framework Programme (STRATOCLIM; grant no. 603557) and the Agence Nationale de la Recherche TTL-Xing project (grant no. ANR-17-CE01-0015). Review statement. This paper was edited by Jianzhong Ma and reviewed by Colin Tully and one anonymous referee.
Geologic Background and Previous Xenolith Work Most of the Siberian craton is overlain by thick Phanerozoic sedimentary and volcanic cover  (Cherepanova et al. , 2013) , but information about crust formation and structure is gleaned from limited basement outcrops and kimberlite-hosted xenoliths (Figure  1 ). Based on geophysical data and outcrops in the Anabar shield, the craton is divided into five major terranes: Tungus, Magan, Daldyn-Markha, Olenek, and Aldan (see summaries in  Rosen, 2002). Basement gneisses and granulites exposed in the Anabar shield reveal that the central craton is composed of Archean crust (ca. 3. 6-2. 7 Ga) reworked throughout the Archean and Proterozoic as well as juvenile Proterozoic crust (2. 1-1. 8 Ga) (Figure  1 ;  Moyen et al. , 2017; Paquette et al. , 2017; Rosen, 1989; Smelov et al.
Metamorphic Histories In order to link the thermal histories of the xenoliths to heat fluxes in the lithosphere, it is necessary to evaluate what portions of the xenoliths' P-T history correspond to thermal conditions shortly prior to eruption (ca. 360 Ma) versus older episodes of metamorphism (e. g. , during Proterozoic craton amalgamation). The tonalite and amphibolite xenoliths contain zircon with concordant Archean U-Pb dates and apatite with solely Paleoproterozoic U-Pb dates (Figure  3 ). The data suggest that these rocks formed in the Archean and were later heated to temperatures high enough to fully reset the apatite (e. g. , >700°C for >1 Myr) or recrystallized apatite during ca. 1. 8 Ga amalgamation of the Siberian craton  (Moyen et al. , 2017; Paquette et al. , 2017; Rosen et al.
, 2016) , Kaapvaal craton  (Jollands et al. , 2018)  and Tibetan Plateau  (Hacker et al. , 2000). If deep crustal heating was depth-dependent, then samples with greater U-Pb resetting resided at greater depths relative to those with minimal overprinting (e. g. , the mafic granulites were deeper than the felsic granulites; Figure  4 ). However, there is no correlation between the extent of rutile U-Pb resetting and peak pressure, and advective heating may have been heterogeneous. This inference is supported by data on Udachnaya mantle xenoliths that suggest similar heterogeneous heating in the mantle lithosphere (e. g. ,  Liu et al. , 2022). Finally, if the U-Pb heterogeneity was caused by processes that operated immediately preceding kimberlite eruption, the preservation of Proterozoic rutile and apatite dates indicates that the deep crust of the Siberian craton generally resided at temperatures cool enough that Pb was not actively diffusing out of the rutile and apatite crystal lattice prior to this late-stage heating.
Conclusions New laser ablation split-stream U-Pb and trace-element data from accessory phases in xenoliths from the ca. 360 Ma Udachnaya kimberlite, Siberian craton, show that the U-Pb system is sensitive to transient heating of the lower crust and not just slow cooling. Length-scales of U-Pb and trace element gradients revealed by depth profiling of rutile suggest that depth-dependent heating of the deep crust occurred for <1 Myr before eruption. Preserved Mesoproterozoic dates in both apatite and rutile long-term lower-crustal temperatures were cool enough that Pb was not actively diffusing out of the both phases (<400°C). The lower-crustal temperature bounds imparted by these data are consistent with P-T arrays of Udachnaya peridotite xenoliths that suggest relatively cool geothermal gradients, signifying that the mantle xenoliths accurately capture the thermal state of the lithosphere prior to eruption.
Combined, the xenolith data imply low crustal heat production for the Siberian craton (∼0. 3 μW/m 3 ). Although exceedingly low, such values still produce surface heat flow values higher than those measured around Udachnaya (average 19 mW/m 2 ), suggesting that the surface heat flow measurements are too low (possible compromised by thick sedimentary cover and deep regional aquifers). Collectively, the xenolith data support low crustal heat production in the Siberian craton, implying a relatively mafic bulk crust composition or a HPE-depleted felsic bulk crust.
in xenolith studies of the Siberian craton. He generously provided some of the new xenoliths described here (79-XX samples). The authors thank Gareth Seward and Andrew Kylander-Clark for assistance with SEM/electron probe micro analyzer and LASS analyses, respectively. Thanks also go to Bradley Hacker, Joshua Garber, Matthew Rioux, and Zachary Eilon for productive discussions throughout the course of the study; and editor Peter van der Beek and reviewers Andrew Smye, Terrence Blackburn, and David Chew for constructive comments that improved the manuscript. Funded by the University of California, Santa Barbara, NSF-EAR-1650260 to Rudnick and Cottle, an NSF graduate research fellowship to Apen, and an INSU-CNRS grant to Ionov. Golovin acknowledges support from the Russian Federation state assignment project of IGM SB RAS; Korsakov acknowledges Russian Science Foundation Grant 18-17-00186.
However, the Mojiang henipavirus (MojV), suspected to have caused human disease in China, was considered a possible exception due to its association with rodents of the Rattus flavipectus species  [13]. Recently, a novel rodent-borne henipavirus sequence, closely related to MojV, from a Apodemus agrarius host became available at the NCBI in September 2021  [14]. Furthermore, novel henipaviruses were described or had sequences submitted to GenBank database that described being found in shrews, mostly of the genus Crocidura from China, South Korea, Belgium, and Guinea  [14] [15] [16]. Moreover, a presumably shrewborne novel henipavirus, phylogenetically related to MojV, referred as Langya henipavirus, was associated with a febrile illness in patients from China, mostly farm workers. The limited epidemiological data analysis indicated that human-to-human transmission was unlikely, and the human cases probably emerged from multiple spillover events  [17].
Sequencing and Sequence Assembly The cDNA library was prepared with the shotgun methodology using the Nextera XT DNA Library Preparation Kit (Illumina, San Diego, CA, USA) according to the manufacturer's protocol. Quantification of cDNA was assessed using Qubit 2. 0 Fluorometer (Thermo Fischer Scientific) and fragments size range was evaluated using 2100 Bioanalyzer Instrument (Agilent Technologies, Santa Clara, CA, USA). Sequencing was performed on the NextSeq 500 System (Illumina) based on 150 bp paired-end technology  [20]. The generated results were assembled de novo by IDBA-UD (k-mers 20, 40, 60, 80 and 100)  [21]  and SPAdes (k-mers, 21, 33, 55 and 77)  [22] , and aligned against the non-redundant protein database by DIAMOND  [23]  with a 10 -3 e-value threshold. The contigs were inspected at MEGAN6  [24]  to identify those corresponding to viral sequences.
The IDBA-UD contig was mapped against the raw data using Geneious Mapper at Geneious v. 9. 1. 8 [25]. Then, the analyzed sequence was mapped to reference sequence (NiV; NC_002728) by Geneious Mapper. The raw metagenomic data are available at SRA/NCBI database under the accession number SRR21710685, BioProject PRJNA882858.
Alignment and Identity/Divergence Analysis The multiple alignment of 86 sequences was performed using Clustal W  [26]  for both nucleotide (nt) and amino acid (aa) analysis. The PBV partial genome obtained was compared with 72 RefSeq genomes from Paramyxoviridae family currently available, nine complete genomes of novel rodent and shrew-borne henipaviruses, and four sequences of Brazilian jeilongviruses. All sequences included in the analysis are available at Table  S2. The partial genome sequence of PBV was deposited in GenBank under the accession number MZ615319. The identity and divergence matrixes were generated using Geneious v. 9. 1. 8 and MEGA X  [27]. The amino acid identity matrix generated by MEGA X using the amino acid substitution p-distances was converted by R programming language in a boxplot graph of intra and intergroups distances/divergence among members of the same genus against each other, and between them and PBV, respectively.
29%/55. 84% and 55. 29%/55. 2% for HeV. The low identities in the L gene among distinct henipavirus species and the two original henipaviruses is a common feature, described for the rodent/shrew-borne clade as in MojV  [13] , Gamak virus and Daeryong virus  [15] , and observed even in the bat-borne clade as for GhV and Cedar henipavirus. PBV translated amino acid sequence was aligned to a set with all reference genomes of the Paramyxoviridae viral family deposited in the RefSeq database. Genomes of novel rodent and shrew-borne henipaviruses  [14] [15] [16] , and jeilongviruses recovered from Brazilian bats (Amazon and Atlantic Forest)  [32]  were also included in the alignment, the latter due to their proximity with Henipavirus clade and their Brazilian origin. Both nucleotide and amino acid phylogenetic analysis showed similar topology and the amino acid tree was chosen to represent the phylogeny (Figures  3  and S1 ).
The pictures were obtained and analyzed using the NIS Element AR software (version 5. 01. 00) (Nikon). The results represent the mean of biofilm volume (mm 3 ) of three independent experimental values, and P values were determined using a variance Student's t test. Biofilm formation was also detected using the crystal violet staining method previously described  (70). Overnight cultures of S. lugdunensis N920143 and DagrA mutant were diluted to obtain OD 600 of 0. 1 in BHI. Sterile polypropylene microplates (Eppendorf, Montesson, France) were loaded with 200 ml of bacterial suspensions and incubated for 24 h at 37°C. Adherent cells were stained with 0. 1% crystal violet for 15 min and, after three washes with sterile water, wells were air-dried. For quantitative estimation of the biofilm density, bound crystal violet was solubilized with 70% ethanol and the absorbance of the solubilized dye was read at 600 nm with the microtiter plate reader.
The procedure was repeated three times, and the results were analyzed using a one-way analysis of variance with a Bonferroni correction posttest with SPSS statistical software (SPSS, Chicago, IL, USA). All statistical analyses were performed using Prism software (version 5. 00) for Windows (GraphPad Software, San Diego, CA, USA). For all comparisons, a P value of less than 0. 05 was considered significant. The experiments were performed under a protocol approved by the Institutional Animal Use and Care Committee at Università Cattolica del S. Cuore, Rome, Italy (Permit number: Z21, 11/01/2010) and authorized by the Italian Ministry of Health, according to Legislative Decree 116/92, which implemented the European Directive 86/609/EEC on laboratory animal protection in Italy. Animal welfare was routinely checked by veterinarians of the Service for Animal Welfare.
Proteins were prepared from three independent biological samples and the quantification was realized with the Pierce BCA protein assay kit (ThermoFisher, Waltham, MA, USA). Five mg of each protein extract were digested with trypsin/Lys-C overnight at 37°C. After protein or peptide desalting and concentration, the chromatography step was performed on a NanoElute (Bruker Daltonics, Billerica, MA, USA) ultrahigh-pressure Nano flow chromatography system. Mass spectrometry experiments were carried out on a TIMS-TOF pro mass spectrometer (Bruker Daltonics) and spectra were acquired in the positive mode in the mass range from 100 to 1700 m/z. These data were processed with MaxQuant version 1. 6. 7. 0 and the mass spectrometry (MS)/MS spectra were searched by the Andromeda search engine against the Uniprot S.