dataesr/scientific-paragraphs-categorization huggingface dataset:
FUNDING This work was funded by a  Monsanto's Beachell-Borlaug International Scholars Program Ph. D. fellowship awarded to HD and a ' Chercheur d'Avenir ' grant from the  Region Languedoc-Roussillon  attributed to SC. AP-Q and GR were supported by a doctoral fellowships awarded by the  Erasmus Mundus Action 2 PANACEA, PRECIOSA program  of the  European Community  and the  MESR (Ministére de l'Enseignement Supérieur et de la Recherche ), respectively. The authors are grateful to IRD for the financial support provided to the  JEAI CoANA.
Study Area and Sites This study uses a dataset from the Norwegian Institute for Nature Research, consisting of adult Scots pine radial growth series collected along a 670-km longitudinal section of the northernmost European boreal forest margin, i. e. northern Norway and western Kola Peninsula in Russia (Figure  1 ). The area is characterized by coniferous forests (mainly Scots pine), birch forests, mixed pine and birch forests, and open tundra. Although the natural dynamics and distribution of these cold-marginal forests are primarily determined by climate, herbivores may locally play an important role (Sto ¨cklin and Ko ¨rner 1999; Kuuluvainen and others 2017). However, the impact by herbivores on adult Scots pine is limited. In addition, snow distribution and quality (for example, density and water content) are important factors regulating growth and stand dynamics of Scots pine at its northernmost locations (Sto ¨cklin and Ko ¨rner 1999).
Chronology Construction and Dendrochronological Analyses Two sets of site-specific chronologies were built using the ARSTAN program  (Holmes 1992). The first set was used to analyse decadal to centuryscale growth patterns, i. e. low-frequency signals, across bioclimatic gradients of the study area. To construct this first set, individual tree series were first power-transformed to stabilize variance  (Cook and Peters 1997) , and then detrended to remove age-related growth trends by fitting either a negative exponential or a negative slope linear model  (Fritts 1976 ). Ring-width indices were computed as residuals from detrending. Residual series were finally averaged by robust mean to produce six sitespecific low-pass chronologies. The second set of chronologies was designed to analyse climate-growth relationships.
Climate-Growth Analyses As the distance between nearest climate stations and sampling sites varied among sites (Table  1 ), gridded climate data (CRU TS 3. 22; Harris and others 2014) was used for climate-growth analyses. Site-specific gridded climate data for the 1901-2006 period (that is, common period between climate data and radial growth data) were retrieved from Climate Explorer  (Trouet and Van Oldenborgh 2013 , http://climexp. knmi. nl) using a 0. 5°9 1°( lat. 9 long) grid. The 0. 5°9 1°format was used to compensate for the spatial distortion in the curvature of latitude and longitude lines at high latitudes and obtain square-like site-specific climate cells. Correlations between station data and grid data are given in Table  S1. Relationships between high-pass chronologies (dependent variable) and monthly mean temperature and monthly total precipitation (predictors) were examined by correlation and response function analyses.
The significance of coefficients (P < 0. 05) was estimated by bootstrapping. Analyses were performed using the bootRes package in R  (Zang and Biondi 2013). As analyses included the effects of previous year monthly predictors on current year growth, the first result year was 1902. Local weather in our study area is strongly influenced by atmospheric dynamics (Figure  S3 ), in particularly through the alternating dominance of air masses of Atlantic, Arctic and Russian origin. Air masses are characterized by spatially homogeneous temperature and precipitation properties. The spatial localization and extent of a significant climategrowth relationship is a good indicator of climate homogeneity through space, and therefore of air mass origin  (Fritts 1991). To examine the link between temperature-growth relationships and air mass circulation dynamics, we computed spatial correlations between high-pass chronologies and monthly (from June t-1 to August t ) temperature fields (precipitation gave no interpretable results).
sequencing methods  (14). We determined the 5′-terminal sequences by means of a RACE kit (Roche, Munich, Germany). We deposited viral genomes in GenBank (accession numbers in Table ) and submitted sequence alignments under BioProject PRJNA422891. We aligned sequences using ClustalW software (http://www. clustal. org). To investigate the genetic relationship between Africa and global EV-A71 isolates, we constructed subgenomic phylogenetic trees based on the P1, P2, and P3 regions of the genome (Figure  1 ). We identified viral isolates showing related sequences in 1 of these 3 regions by BLAST search (http://www. ncbi. nlm. nih. gov/BLAST) and included them in the corresponding datasets used for analyses. We completed these datasets with a representative global set of EV-A71 sequences available in GenBank and belonging to the different EV-A71 genogroups (https:///wwwnc.
Conversely, in the noncapsid region, sequence similarity between Africa EV-A71-C2 isolates and classical subgenogroup C2 isolates (e. g. , GenBank accession no. HQ647175) was much lower (66%-77%). This finding confirmed a recombination event of the Africa EV-A71 C2 lineage with an unknown enterovirus, the most likely breakpoint being located between nt 3596 and 3740, within the 2A gene. Sequence identity of EV-A71-C2 study strains with their closest related viruses (coxsackievirus A10 [CV-A10], CV-A5, EV-A120, and EV-A71 genogroup E strains) in the 3′ half of the genome was <87. 7%. Of note, we found much higher sequence identity with the full-genome sequence of CV-A14 isolate in our database, obtained in 2014 from a patient with AFP in Senegal  (15). This strain features a high similarity value (>97%) with the 3′ half of the genomes of EV-A71-C2 West Africa strains (Figure  2 ), indicating that their P3 regions share a recent common ancestor.
For clarity, CV-A10, CV-A5, and EV-A71 subgenogroups C3, C4, and C5 have been collapsed. Study strains are indicated by laboratory code, country of origin, and year of isolation; previously published strains are indicated by GenBank accession number, isolate code, country of origin, and year of isolation. Black triangles indicate EV-A71 strains from this study; black square indicates the CV-A14 strain from this study. Strains gathered in brackets belong to EV-A71 genogroups or subgenogroups; strains marked in blue color belong to other species of EV-A. Scale bars indicate nucleotide substitutions per site. CV, coxsackievirus; EV, enterovirus.
Figure2. Identification of recombinant sequences in the genome of EV-A71 C2 isolates from patients with acute flaccid paralysis in Africa (14-157, 14-250, 13-365, 15-355) by similarity plot against potential parent genomes (CV-A14 strain 14-254; EV-A71 genogroup E strains 13-194, 08-146, and 03-008) and from GenBank (CV-A10, CV-A5, EV-A120). Similarity plot analysis was performed using SimPlot version 3. 5. 1 (http://sray. med. som. jhmi. edu/SCRoftware/simplot) on the basis of full-length genomes. For the analysis, we used a window of 600 nt moving in 20-nt steps. Approximate nt positions in the enterovirus genome are indicated. The enterovirus genetic map is shown in the top panel. We used the genome of EV-A71 study strain 14-157 as a query sequence. UTR, untranslated region.
Acknowledgments We thank  Karla Prieto  and  Catherine Pratt , who assisted in obtaining nearly complete genomes of West Africa strains, and  Joseph Chitty  for analysis of the next-generation sequencing data. The next-generation sequencing equipment at  Institut Pasteur of Dakar  was provided by the  Defense Biological Product Assurance Office  under the Targeted Acquisition of Reference Materials Augmenting Capabilities Initiative. This work was supported by the  IPD , the  Pasteur Institute 's  Transverse Research Program   PTR484 ,  Actions Concertées Inter-Pasteuriennes   A22-16 ,  Fondation Total  Grant  S-CM15010-05B ,  Roux Howard Cantarini postdoctoral fellowship , and  Grant Calmette  and  Yersin  from the  International Directorate of the Institut Pasteur.
Table. Description of enterovirus isolates from patients with acute flaccid paralysis in Africa that were sequenced for characterization of enterovirus A71* Patient age at Genogroup or Strain (reference) Country of isolation diagnosis, y Year Virus subgenogroup Genbank accession no. 14-157 (9) Senegal 3 2014 EV-A71 C2 MG672480 14-250 (9) Mauritania 1. 6 2014 EV-A71 C2 MG672481 13-365 (9) Guinea 1. 7 2013 EV-A71 C2 MG672479 15-355 (this study) Senegal 2. 4 2015 EV-A71 C2 MG013988 13-194 (9) Niger 1. 3 2013 EV-A71 E MG672478 03-008 (7) Central African Republic 1. 9 2003 EV-A71 E LT719068 08-146 (8) Cameroon 2. 6 2008 EV-A71 E LT719066 08-041 (8) Cameroon 1. 7 2008 EV-A71 C1 LT719067 14-254 (15) Senegal 3 2014 CV-A14 NA MG672482 *NA, not available.
Virus Isolation and Phylogenetic Analysis We collected 1,374 oropharyngeal and cloacal swab specimens from poultry during surveillance of farms for avian influenza viruses in Pakistan during 2014-2016. Of these specimens, 78 (5. 7%) were confirmed by hemagglutinin inhibition (HI) assay to be positive for H9, and sequencing data for 55 virus isolates were generated. Next-generation sequencing of isolates yielded 43 complete genomes and 12 partial genomes. No other avian influenza virus subtypes were detected in this study. Similar to previous results for H9N2 viruses from Pakistan, phylogenetic analysis showed that all HA and NA genes from specimens collected during 2014-2016 continued to belong to the G1 lineage, and all HA genes clustered within the Middle East B clade. Viruses isolated during this study show continued evolution, as shown by phylogenies that demonstrated drift of subclades away from previous isolates (Appendix Figures  1 2 3 ).
BLASTn (https://blast. ncbi. nlm. nih. gov/Blast. cgi) comparisons for each gene segment of the 2014-2016 viruses against National Center for Biotechnology Information (https://www. ncbi. nlm. nih. gov) and GISAID (https://www. gisaid. org) databases showed the closest related viruses to be previous isolates from Pakistan, which had >95% nucleotide homology, suggesting continuous in situ evolution. Comparison of maximum pairwise nucleotide differences between viruses isolated during 2014-2016 showed variable genetic diversity within each gene segment. Segment 8 (nonstructural) showed the greatest diversity (7% nucleotide difference), and segment 5 (nucleoprotein) showed the least diversity (2% nucleotide difference). In many instances, this diversity could be attributed to a small subset of sequenced viruses: when these segments were removed from the pairwise comparison, virus diversity was reduced.
Acknowledgments We thank the veterinary staff and students at the  University of Veterinary and Animal Sciences in Lahore, Pakistan , for their sample collection from poultry farms in Pakistan and  Steve Martin  for allowing us to use his Octet analysis software. This study was supported by  Biotechnology and Biological Sciences Research Council (BBSRC) Zoonoses and Emerging Livestock Systems (ZELS) Zoonoses and Emerging Livestock Systems (ZELS)  award no. BB/L018853/1 ,  BBSRC ZELS-AS  award no. BB/N503563/1 ,  BBSRC  award no. BBS/E/I/0000198 ,  BBSRC ISPG  award no. BBS/E/I/00007034 ,  BBS/E/I/00007035 ,  BBS/E/I/00007038 , and  BBS/E/I/00007039 ;  Cancer Research UK  ( FC001030 ); the  Medical Research Council  ( FC001030 ); and the  Wellcome Trust  ( FC001030 ).
Materials and methods To ground the semantics of greetings we used 5 terms as seeds: 'good morning', 'good afternoon', 'good evening', 'good night' and 'hello' (a timeunspecific greeting used for comparison). We translated them to 53 languages and variants using Bing translator. 1  We use italics to refer to greet-ings irrespective of the language. 172,802,620 tweets were collected from Sept. 2 to Dec. 7 2016. For some languages (e. g. Spanish), there is no differentiation between 'good evening' and 'good night', and they both are translated to the same expression. For some others, some expressions cannot be considered equivalent, e. g. 'good morning' is translated to 'bonjour' in French, which is however commonly used as 'hello', or simply as 'good day'. Text preprocessing is not necessary: we rely on metadata, not on the tweet itself, and only the seed words are needed to categorize tweets within a part of day.
To clean up the data, we removed retweets, as they last for hours, biasing the temporal analysis. Duplicate tweets were kept, as similar messages from different days and users (e. g. 'good night!') are needed for the task at hand. Tweets need to be associated with a timestamp and country-level geolocation. Tweets have a creation time, composed of a UTC time and a UTC offset that varies depending on the time zone. However, most tweets are not geolocated and we must rely on the data provided by the user. This may be fake or incomplete, e. g. specifying only a village. We used fine-grained databases 2  to do the mapping to the country level location and performed a sanity check, comparing the Twitter offset to the valid set of offsets for that country 3  , to reduce the amount of wrongly geolocated tweets.
4 Comparing the solar and standard time could provide more insights, but this requires a fine-grained geolocation of the tweets. We obtained a dataset of 10,523,349 elements, available at https://github. com/aghie/ peoples2018_grounding: 4,503,077 good morning's, 599,586 good afternoon's, 214,231 good evening's, 880,003 good night's and 4,359,797 hello's. 5.
Acknowledgments DV and CGR receive funding from the  European Research Council (ERC) , under the European Union's  Horizon 2020 research and innovation programme (FASTPARSE , grant agreement No  714150 ), from the  TELEPARES-UDC  project ( FFI2014-51978-C2-2-R ) and the  ANSWER-ASAP  project ( TIN2017-85160-C2-1-R ) from  MINECO , and from  Xunta de Galicia  ( ED431B 2017/01 ).
These study areas are different as to their configuration and composition of functional habitats (same colour code as in Fig. 2 ): breeding (red), foraging (dark-grey), roosting (grey) and unsuitable habitats (white). The smaller habitat maps were modified from the Biological Value Map V2. 0 under open data access of INBO; maps were created using QGIS 2. 12 Lyon (Open Source Geospatial Foundation Project, http://qgis. osgeo. org) and edited using Adobe Illustrator CC www. adobe. com. bodies). Most breeding habitats are included as Special Protected Areas in Natura 2000 (75-80% Bosland, 100% Meeuwen-Gruitrode and 100% Mechelse Heide). The study sites are different as to their configuration of breeding habitats (fragmented vs. continuous) and as to area of unsuitable habitats that separate breeding sites from foraging sites (high vs.
The other tags were able to log a bird's movement from three up to six nights, depending on the sampling interval (3 or 4 minutes for Biotrack Ltd. PinPoint-40 V2 and Pathtrack nanoFix V1 & V2) and the type of vegetation cover, as shown by Forin-Wiart et al. 34. We performed attempts to recapture the birds one week after deployment. Upon successful recapture, tracking data were downloaded and imported in Quantum GIS V2. 12. Environmental data were derived from nearest (approximately ten kilometres) online weather stations in Hechtel (Bosland), Bree (Meeuwen-Gruitrode) and Maaseik (NPHK). Foraging ecology. We followed seven steps to calculate foraging distance, flight speed and foraging time (see Supplementary Methods M1 for full details of these calculations). For each night and each bird we defined foraging events with initial foraging flights as a measure of foraging distance.
Results We recaptured 31 nightjars carrying GPS-loggers and 30 loggers (Fig. 3 ) contained data on foraging behaviour (Bosland: 20; Meeuwen-Gruitrode: 4; NPHK: 6; Supplementary Table  T1 ). We collected 16385 GPS-observations (30 individuals; 829 ± 298 observations; max = 1145, min = 40) comprising 463 flight paths. We identified 133 complete foraging tracks, 210 initial foraging flights (Fig. 3 ), compared foraging duration for 160 foraging flights and examined habitat-specific flight speed for 199 flight observations. We collected and analysed 58 plasma samples of adult nightjars and analysed food availability using 448 samples. The results of the statistical analyses are summarized in Table  1.
Discussion The use of GPS-loggers yielded detailed information on the movement of 30 nightjars within their respective home ranges. Tracking data allowed us to study foraging behaviour and actual movement paths of 210 foraging flights collected in three research areas. Our findings indicate that not only landscape composition, as already indicated by Evens et al. 5  , but also landscape configuration influences the connectivity between two complementary resources for nightjars: nest sites and foraging areas. On a daily basis, nightjars connect breeding and foraging sites by rapidly crossing unsuitable habitats in order to exploit a higher prey biomass in foraging sites. Increased stress levels were found in birds occupying large, attractive, yet sub-optimal breeding areas. These birds travel longer distances over unsuitable habitat and compensate for longer travel time by spending more time in foraging habitats (i.
Acknowledgements RE is funded by a  BOF-mandate (BSFFCMKDK)  at  Hasselt University. NW is funded as a postdoctoral researcher by  FWO Flanders. Part of this research was funded by another  FWO-grant  ( N1522015 ). We thank  Eddy Ulenaers ,  Luc Lens ,  Dries Gorissen ,  Guido Winters ,  Koen Thijs ,  Lise Hendrick ,  Karen Vanmarcke ,  Fien Evens ,  Albert Geuens ,  Lyndon Kearsley ,  Greg Conway ,  Ian Henderson  and  Nikki Watson  for their support and relevant comments on this manuscript. Research equipment was funded by the  Agency for Nature and Forest (ANB; Belgium). Permissions were granted by the  ANB  and  Royal Belgian Institute of Natural Sciences (Belgium).
Here we present Nematostella vectensis Embryogenesis and Regeneration Transcriptomics -NvERTx, the first platform for comparing gene expression during embryogenesis and regeneration. NvERTx is comprised of close to 50 RNAseq datasets spanning embryogenesis and regeneration in Nematostella. These data were used to perform a robust de novo transcriptome assembly which users can search, BLAST and plot expression of multiple genes during these two developmental processes. The site is also home to the results of gene clustering analyses, to further mine the data and identify groups of co-expressed genes. The site can be accessed at http://nvertx. kahikai. org.
, 2015; DuBuc et al. , 2014). This process is known to use several developmental signaling pathways originally deployed during embryogenesis  (DuBuc et al. , 2014; Passamaneck et al. , 2012; Schaffer et al. , 2016; Trevino et al. , 2011). It remains unclear, however, if these pathways are deployed the same way, i. e. with similar or divergent regulatory logic. One way to address this question is to systematically compare gene expression profiles during embryonic development and regeneration to identify groups of genes originally used during embryogenesis that are re-used during regeneration. To facilitate these studies we created Nematostella vectensis Embryogenesis and Regeneration Transcriptomics -NvERTx: A quantitative gene expression database for comparing embryogenesis and regeneration in the sea anemone Nematostella vectensis.
NvERTx is comprised of several datasets spanning embryogenesis  (Helm et al. , 2013; Tulin et al. , 2013 )(this study) and regeneration (this study). We used pooled RNAseq data from Nematostella embryogensis and regeneration to generate a de novo transcriptome assembly. Using this assembly, we then quantified each of the RNAseq datasets. We then clustered the transcripts to discover groups of genes that share similar expression. All of these data can be found in a searchable database that is accessible at www. nvertx. kahikai. org. This tool can be used to find transcript sequences, identify co-expressed genes, and directly compare expression profiles during embryogenesis and regeneration.
Results and Discussion: NvERTx Database: de novo assembly, annotation, and data quantification NvERTx is a quantitative gene expression database for embryogenesis and regeneration (Fig. 1C ) comprised of several RNAseq datasets. It includes previously published datasets spanning very early embryogenesis to polyp from  Helm et al. (sampled 2, 7, 12, 24, 120  and 240 hours post fertilization (hpf))  (Helm et al. , 2013) and Fisher et al. (sampled 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, and 19 hpf)   (Fischer et al. , 2014). To complement these, and sample timepoints during tentaclegenesis and pharynx formation, we generated an additional dataset sampled at  24, 48, 72, 96, 120, 144, 168, 192  hpf. Together these datasets cover the major hallmarks of Nematostella development including blastula stages (12-24 hpf), gastrula stages (24-48hpf), planula stages (48-120hpf) and juvenile stages (120-244hpf).
The regeneration RNAseq data were sampled from six week old juveniles after sub-pharyngeal amputation at uncut,  0, 2, 4, 8, 12, 16, 20, 24, 36, 48, 60, 72, 96, 120  and 144 hours post amputation (hpa) (see Fig. 1B  for sampling strategy). We chose these timepoints because they span the most important events of regeneration including wound healing (0-6hpa), pharynx reformation (24-72hpa) and tentacle reformation (72-120hpa)(see Fig. 1A  for Nematostella anatomy)  (Amiel et al. , 2015) , all stages for which we have embryonic data spanning the initial development of these structures. For each of these datasets, we obtained the raw sequencing reads and used these as input into our quantitative workflow. To quantify the RNAseq data, we performed a de novo transcriptome assembly, which we term NvERTx.
We assembled this transcriptome using the short read assembler Trinity with combined paired-end reads from our regeneration dataset and additional embryonic data from  Tulin et al. sampled at 0, 6, 12, 18, 24 hpf (Haas et al. , 2013; Tulin et al. , 2013)  as input (see materials and methods for complete workflow). In the resulting assembly we identified 234381 transcripts and designated each a unique NvERTx. 4 number. We then annotated this transcriptome by extracting the predicted coding sequences using OrfPredictor  (Min et al. , 2005)  and comparing the resultant protein sequences to NCBI's non-redundant protein database (nr) and the Uniprot database using the blast-like tool PLAST  (Nguyen and Lavenier, 2009). From these analyses we identified 85475 transcripts with a significant hit to nr (e value < 5e-5) and 69335 transcripts with a significant hit to the Uniprot databases (e value <5e-5).
Additionally we compared each transcript to the current Nemve1 gene predictions (https://genome. jgi. doe. gov/Nemve1/Nemve1. home. html) using BLASTn and identified 102581 transcripts with a significant hit (e value < 5e-5)  (Kent, 2002). For each of the 234381 NvERTx transcripts we provide all available annotations. We then used the transcriptome assembly to quantify each of the RNAseq datasets. We did this by aligning the reads using Bowtie2  (Langmead et al. , 2009)  and quantifying each transcript using RSEM and edgeR  (Li and Dewey, 2011; Robinson et al. , 2010). Transcripts with the same best Nemve1 hit were combined to obtain 'gene-level' quantification. Finally, the three embryonic datasets were corrected for batch effects using the sva R package with time-point as a categorical covariate  (Leek et al.