dataesr/scientific-paragraphs-categorization huggingface dataset:
Scenario Testing The BLIGHTSIM model was used to investigate two sets of scenarios. AUDPC values were calculated by integrating hourly severity values within BLIGHTSIM using the R code of  [55] , and these were used as the response variable for all scenarios. The purpose of the first scenario set was to investigate if there was an interaction between average temperature and temperature amplitude with respect to the effects on AUDPC and to check if the temperature amplitude was more important than the average temperature or vice versa. First, hourly temperature and RH data were created (1200 h) using the same sine-wave equations as described earlier (see Section 4. 4). Simulations were evaluated for 1200 h time courses for all combinations of seven average temperatures (10 • C, 12 • C, 15 • C, 17 • C, 20 • C, 23 • C and 27 • C) and three temperature amplitudes (0 • C, 5 • C and 10 • C).
The oscillations in RH were so that the maximum RH was 100% at 4:00 am and the minimum 50% at 4:00 pm. Three sets of parameter values of RRR, L0, RLGRx, LPRx and INFR (Infection Rate) that were estimated during calibration of the Cutuglahua data in 1997 (cultivar Gabriella), the La Tola data in 1997 (cultivar Gabriella), and the Cutuglahua data in 1997 (cultivar Bolona) were used in simulations, resulting in three independent replications for each of the average temperatures and temperature amplitudes. For this scenario, the model was run 63 times (seven average temperatures, three amplitudes and three locations) and the AUDPC values for each epidemic were calculated and compared. The aim of the second scenario set was to investigate how the epidemics are affected when the model is initialized with latent or infectious sites and if the starting hour of the simulation has a substantial effect on disease progress.
Statistical analyses Statistical analyses were conducted using R software (version 3. 4. 2) and the packages Rcmdr (version 2. 4-4) and conover. test (version 1. 1. 5), all available from The Comprehensive R Archive Network (CRAN; https://cran. r-project. org). The statistical significance of compound treatments on VSP1p:GUS and PR1p:GUS activation was performed using the Kruskal-Wallis test followed by multiple comparisons using the Conover-Iman test with Benjamini-Hochberg adjustment of P-values for false discovery rate. All proteasome activity tests were performed at least in duplicate and data were statistically analysed with a one-way ANOVA.
Acknowledgements The authors are sincerely grateful to  Erich Kombrink  for valuable discussions. This work was supported by a  "Chaire d'Excellence" FUNAPP grant  ( ANR-12-CHEX-0008-01 ) from the  Agence Nationale de la Recherche  to RJO, and a  Deutsche   Forschungsgemeinschaft grant  ( GA 2419/2-1 ) to DG. The RJO laboratory benefits from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ). The funders had no role in the study design, data collection, analysis, and interpretation, or writing of the manuscript.
Genes Homology Gene prediction was computed using Prodigal software (v2. 6. 3) (Hyatt et al. , 2010). The predicted proteins with a size less than 50 amino acids were discarded and 25 genes between 50 and 99 amino acids analyzed with Phyre2 software as having an abnormal tri-dimensional structure were eliminated of the dataset  (Kelley et al. , 2015). Homology between the two sets of predicted gene sequence was first investigated using ProteinOrtho software (v5. 16b) with default parameters (e-value cut-off of 1. 0e-5; identity threshold of 25% and coverage threshold of 50%)  (Lechner et al. , 2011). Then, a complementary analysis was achieved using the Blast software suite (Blastp, Blastn, and tBlastn) on predicted proteins to refine results  (Altschul et al. , 1990; Camacho et al. , 2009).
Identification of conserved domains of predicted protein unique to either viruses was realized using in combination CD-search tool online, ProDom software online (v2012. 1) and Phyre2 software online  (Servant et al. , 2002; Kelley et al. , 2015; Marchler-Bauer et al. , 2017). SignalP v4. 1 and PSORT online software were used to identify a cellular localization of interesting sequences  (Nakai et al. , 1999; Nielsen, 2017). The structure of interesting sequences was analyzed by multiple alignment. A prediction of 3D structure was made with Phyre2 software online and visualized with BIOVIA Discovery Studio Visualizer 2019 software  (Kelley et al. , 2015; Dassault Systèmes Biovia, 2017).
Phylogeny of the Targeted RNAse T2 Sequence First, when a target sequence was found, the phylogeny was performed using BLASTP against the non-redundant database with default parameters. All sequences identified by BLAST were retrieved, such as sequences originate from different amoeba species and already studied in the literature corresponding to the target sequence. We aligned the sequences with MUSCLE on MEGA 7 software and a phylogenetic tree was made using the Maximum Likelihood method (ML) with JTT as substitution matrix with 1000 bootstraps  (Kumar et al. , 2016).
Evolutionary Analyses Evolutionary analyses were performed as previously described  (Levasseur et al. , 2006 (Levasseur et al. , , 2010)). Briefly, protein and DNA sequences were retrieved from the National Center for Biotechnology Information. Protein sequences were aligned using MUSCLE  (Edgar, 2004). Correspondence between protein alignment and each DNA sequence was established using Wise2 software followed by manual adjustments  (Birney et al. , 2004). The codeml program of the PAML (Phylogenetic Analysis by Maximum Likelihood) software package was applied to test for positive selection  (Yang, 1997). PAML uses a maximum likelihood algorithm to assign likelihood scores to different models for selection. If a model incorporating positive selection gave a higher likelihood score than a null model without positive selection, this constitutes evidence for positive selection.
Gain and Loss of Genes in Both TPV Genes prediction resulted in 1251 and 1339 predicted genes for TPV-DO and TPV-SL, respectively. A total of 1178 clusters of orthologous genes (COGs) were detected between both strains. 73 and 138 genes were unique in TPV-DO and TPV-SL, respectively (Figure  2 ). Those unique genes are distributed along all both genomes through the three LCB described overhead. In the small block in which occurs inversion, one gene was found for TPV-DO (TPV-DO_1250) and two for TPV-SL (TPV-SL_1008 and TPV-SL_1011). TPV-SL also possess 23 paralogous genes, all identified as hypothetical protein or putative orfan. The 73 putative proteins unique of TPV-DO were analyzed and the results indicate that 38 are ORFans, 19 are annotated as hypothetical protein and 16 had a hit in NCBI non-redundant database (Supplementary Table  1 ).
Acknowledgments The first author would like to acknowledge the financial support of the  China Scholarship Council (CSC)  and the  Norman Fraser Design Trust. The third author would like to acknowledge the grant  GRF/16212818  funded by the  Hong Kong Research Grant Council  (RGC) as well as the grant no. 51922112  provided by the  National Natural Science Foundation of China (NSFC)  for the time spent on this work. Gary Callon  is also gratefully acknowledged for 3D root printing, as are  Mark Truswell  and  Grant Kydd  for their assistance in centrifuge tests at the  University of Dundee.
Thermal conditions across seasons Data sampling was carried out during the 2013 dry season, from June 13th (when the elephants had settled in their dry season home-range) until October 23 rd , when the elephants dispersed again after the first significant storm. We focused on the elephants' foraging trips that occurred during three periods of the dry season as defined based on changes in water availability  (Valls-Fox et al. 2018). The first period covered the early cold-dry season (13 June to 7 July; mean daily temperature ± SE: 13. 1 ± 0. 4°C, from 105 water pans at the start of the period to 97 at the end). The following periods covered the mid-dry season (5 August to 10 September; 18. 0 ± 0. 3°C, from 72 to 63 pans), and the hot-dry season (11 September to 23 October; 23. 7 ± 0. 2°C, from 62 to 57 pans).
We excluded the end of the cold-dry season because of the rapid decrease of the number of water pans during that period of time (from 92 to 73 pans;  Valls-Fox et al. 2018). Cold-dry season was included as a matter of comparison with warm seasons during which we expect thermal constraints to be the highest (Supplementary Data SD1). At the same time, air temperature also was measured every hour at the Hwange LTER-CNRS weather station (26. 952°E 18. 731°S; Supplementary Data SD2). Whatever the season, temperatures were maximal around 14:00h -15:00h and reached up to 27. 6°C during the cold-dry season, 32. 3°C during the mid-dry season, and 38. 1°C during the hot-dry season (Supplementary Data SD2).
Elephant movement data In November 2012, 13 adult female elephants belonging to different family herds were equipped with GPS collars recording one location every 30 min (every 5 min from 11 October to 14 October). We retained data from 8 collars for which the occasional failures in acquiring locations occurred when the elephant was too far away from water to have been able to reach water and be back at the next acquired location. This enabled us to reliably identify visits to water. Note that GPS collars were programmed to collect one location every 30 minutes and the average time spent by elephant family herds at a waterhole is 20 min. Trips were removed from the dataset if two conditions were fulfilled: (a) the gap lasted more than 1h and (2) the gap occurred when the elephant was sufficiently close to water for it to travel back and forth in a straight line at a speed below 1m.
s -1. By using a speed criterion, trips with small gaps in the GPS sequence occurring far away from waterholes were maintained in the dataset. An individual was considered to have visited a waterhole if its GPS track intersected a buffer of 200m radius, or if an individual could have reached water between two successive GPS locations based on its speed and direction (see  Valls-Fox et al. 2018  appendices for methods and validation). A foraging trip was defined as the movement path covered between two consecutive visits to a waterhole (see  Valls-Fox et al. 2018  for further information about the extraction of trips from trajectory data). During the 2013 dry season, we identified 901 trips. In this study, direct observations and camera trap records of the studied herds suggest that they were made of 5-17 individuals, typically comprising about 2-8 adult females and their immature offspring, and occasionally one or a few adult males.
Due to the strong multimodality in trip duration (Fig. 1A ), and as in previous studies  (Chamaillé-Jammes et al. 2013; H. Valls-Fox et al. 2018) , we divided the trips into four categories named after their average values (Fig. 1A ): trips that lasted less than 12 hours (4. 5 hours in average in our data); 24 h long trips (12 -36 h long); 48 hours long trips (from 36 -60 h long); and 72 h (≥ 60 h). We focused on trips of the 24h, 48h, and 72h, categories, as we considered that trips lasting 12h or less were too short to be constrained by the risk of overheating or dehydration, or that adjustments would occur at a smaller scale than the one considered here. Ultimately, we analyzed data from 535 trips (Fig. 1A ). We analyzed how elephants timed their departure according to sunset by fitting the linear regression between the duration of the trip and time elapsed since sunset.
Departure time was 0 hour if it occurred at sunset, positive if departure occurred within 12h after sunset and negative if departure occurred within 12h before sunset. Sunset time has been estimated for the mean coordinates of Hwange National Park (26. 9°E 18. 8°S) using the algorithms provided by the National Oceanic and Atmospheric Administration (NOAA) with the "maptools" package in R  (Bivand and Lewin-Koh 2019).
ACKNOWLEDGEMENTS We thank the Director General of the Zimbabwe Parks and Wildlife Management Authority and the  Research Council of Zimbabwe  for providing the opportunity to carry out this research. Funding was provided by grant  SAVARID ANR-11-CEPS-003  and  LANDTHIRST ANR-16 CE02-0001-01  of the  French 'Agence National de la Recherche '. This work was carried out within the framework of the  Zone Atelier Hwange-Hwange LTSER  (www. zahwange. cnrs. fr)and under the auspices of the  Research Platform ''Production and Conservation in Partnership''  (www. rp-pcp. org). We thank  R. Reyna-Hurtado ,  A. Shrader , and two anonymous reviewers, for their comments.
Fluorescent protein expression was analyzed on a FACSCalibur (BD Biosciences, USA) using argon-ion laser excitation (488 nm) and detected using the FL1 parameter (emission filter: 530 ± 15 nm). Data acquisition was carried out by analyzing 10,000 events/sample using Cell Quest software version 5. 1 (BD Biosciences, Australia). CHO-K1 cells expressing high levels of GFP (CHO-AstR stable) were sorted, maintained in T175 cm 2 and used for cell-based assays.
In the second surgery, 14 ± 4 days after the NDG injections, BP telemeters were implanted into the abdominal aorta of SHR anesthetized as described above. Following surgery, the rats were returned to their home cages for recovery for 2 weeks before commencement of control BP recordings. Telemetry devices for recording blood pressure (BP; TA11PA-C40 telemeters; Data sciences International, USA) were implanted following the manufacturer's protocol. Briefly, the abdominal cavity was opened in the midline and the pressure sensing catheter inserted into the abdominal aorta distal to the renal arteries. Vetbond was applied to seal the vascular opening and ensure attachment to the abdominal aorta. The transmitter probe was placed in the abdominal cavity and sutured to the abdominal muscle layer. After 2 weeks, 3 baseline BP recordings were taken for each rat on separate days.
As a result, for further recordings we sampled waveform BP data at 2000 Hz, using an analog-to-digital data acquisition card (PCI-8024E; National Instruments, Australia), imported the signal into Spike2 (Cambridge Electronic Design, UK) and analyzed the data offline. All points in excess of 300 mmHg were removed and replaced with the average of the preceding 1 s of data. We compared the resultant data based on this method with the automatic average data and determined that such interference results in random shifts, both up and down, in average BP readings that can be of a magnitude of 10-15 mmHg over a 2 h recording period. As a result, the data included in the manuscript is from a 2h recording period, excluding the 30 min immediately after handling to turn on the BP telemeter, and is only based upon those SH rats in the second and third cohort which had noise readings removed (SHR Experimental (n = 6); SHR Control (n = 5)).
The measurements were normalized to the control period for each animal and then presented as mean ± 95% confidence intervals for the groups, with each individual animal also shown. For completeness of the dataset, all raw BP data from all SH rats (SHR Experimental (n = 9); SHR Control (n = 8)) are shown (Figure  S2B ). Analysis of the data which included all animals shows the same overall response, with just an alteration in the number of days that were significantly different between groups. Systolic, mean and diastolic BP and heart rate (HR) were derived from the pulsatile arterial BP trace. The systolic BP was used as a surrogate measure of the cardiac R-wave and the time between successive pressure maxima as the interbeat interval (IBI). The standard deviation of BP and IBI were derived and plotted for each animal.
The baroreceptor sensitivity (BRS) was determined with the sequence method  (Kuusela, 2013) , and defined as the relationship between a change in the IBI following a unitary change in BP (ms/ mmHg). Individual points for systolic BP and IBI were examined in 10-20 min sections during quiet, inactive periods between 11:00 h and 13:00 h. The data were analyzed using a custom-made MATLAB script that identified periods in which systolic BP and IBI rise or fall monotonically in the same direction for at least three beats, exceeding or meeting a defined threshold of a change of 0. 5 mmHg and 1 ms respectively (Figure  S3A ). The IBI series was advanced by one beat to adjust for the delay between BP and corresponding changes in IBI. A minimum correlation co-efficient (> 0. 8) between systolic BP and IBI had to be achieved for the script to consider an actual BRS sequence.
For input frequency curve comparisons (step protocols) a twoway ANOVA was used. Synaptic jitter was defined as the standard deviation of the latency ST-EPSCs (shock 1 of 5) over 10-20 trials (electric shocks). Frequency-dependent depression was defined as paired pulse ratios less than 1. 0 as calculated from ST-EPSC 2-5 as a ratio of ST-EPSC 1. Data are presented as mean ± SEM. For in vivo studies all data are presented as mean ± 95% confidence intervals (CI). Differences in measured parameters between the groups prior to surgery were analyzed using an unpaired Student's t test. Cardiovascular measurements (systolic BP, diastolic BP, mean BP and HR), baroreceptor reflex sensitivity, quantification of the number of sequences detected during rises and falls in systolic BP and bodyweight data following each surgery were analyzed by a two-way repeated-measures analysis of variance, with a Bonferroni post-test.
) bilateral microinjections of rAAV1/2-HA-Ast-3-mCherry into the NDG; (2) implantation of BP telemeters into the abdominal aorta; and (3) microinjection of Lv-PRSx8-AstR-GFP (SHR experimental) or Lv-PRSx8-GFP (SHR control) into the cNTS. Experiments conducted in the nine SHR experimental and eight SHR control are shown in Figure S2. However, as outlined in detail in the STAR Methods, three rats of each group were excluded from full analysis due to a sampling error during recordings. Both datasets show identical trends. Prior to NTS microinjections, SHR experimental and SHR control groups had similar cardiovascular function (Figures 4 and S2) (SHR experimental: systolic blood pressure [sBP]: 167 ± 4 mm Hg; diastolic blood pressure [dBP]: 114 ± 4 mm Hg; mean blood pressure [mBP]: 131 ± 3 mm Hg; heart rate [HR]: 282 ± 5 beats per minute [bpm]; SHR control: sBP: 161 ± 3 mm Hg; dBP: 118 ± 3 mm Hg; mBP: 132 ± 2 mm Hg;.
ACKNOWLEDGMENTS We are very grateful to  Tania Ferraro  for assistance with assays and  Jairus Bowne  for generating schematic diagrams. Grant funding was provided by the  Australian NHMRC  (APP# 1102477  and APP# 1156727 ), the  Australian Research Council  ( DP170104582 ), and the  State Government of Victoria Operational Infrastructure Support Program  to the  Florey Institute. R. A. D. B. is an  NHMRC Senior Research Fellow.
, 2017). Since GIs carry so many genes of interest, countless methods have been published to detect and analyze them  (Langille et al. , 2008; Lu and Leong, 2016; Bertelli et al. , 2018). These methods are often grouped into two categories: compositionbased methods and comparative genomic-based methods. A recent review describes many of the methods that were developed in this field  (Bertelli et al. , 2018)  and benchmarks them using a curated dataset  (Langille et al. , 2008). Nowadays, a deluge of microbial genomes is available in public databanks with more than half a million prokaryotic genomes in Genbank (last accessed 2nd March 2020)  (Sayers et al. , 2019). In parallel, environmental data made of Metagenome Assembled Genomes (MAGs) or Single-cell Assembled Genomes (SAGs) are increasing dramatically.
We will use the term spot throughout this paper and we do not assume that genes in a same spot have related functions. In the present paper, we propose a new method called panRGP which detects RGPs and gathers them into spots of insertion to study the dynamics of GIs. It is a comparative genomic-based method that uses a pangenome graph reconstructed from hundreds to thousands of genomes of the same species. We benchmarked panRGP along a selection of other tools against a previously published dataset on GIs  (Langille et al. , 2008). Finally, we illustrated its use on incomplete and fragmented genomes such as MAGs in the context of the analysis of an insertion spot in Escherichia coli.
Benchmark protocol To assess the reliability of GI detection by panRGP, we used two previously published reference datasets  (Langille et al. , 2008)  that were recently updated  (Bertelli et al. , 2018). The C-dataset is made of 104 genomes among 53 species from which GIs have been automatically predicted using a comparative genomic method based on IslandPick  (Langille et al. , 2008). The L-dataset contains 6 genomes whose GIs have been curated. While it does not cover as much microbial diversity, it contains literature-curated GIs rather than automatically detected ones which makes it a much more reliable source of information. Genomes of the L-dataset are also present in the C-dataset but it should be noted that the GIs of C-dataset only partly cover those of the L-dataset  (Bertelli et al.
, 2018). For each dataset and in each genome, 'positive regions' correspond to regions that are potential GIs and 'negative regions' to those that are not considered to be GIs. A prerequisite for the execution of panRGP is the computation of pangenomes for each studied species. All available NCBI RefSeq genomes (downloaded the 21st November 2019)  (Haft et al. , 2017)  were used as PPanGGOLiN input to obtain a partionned pangenome graph. However, only species with at least 15 RefSeq genomes could be analyzed due to the statistical constraint of the PPanGGOLiN method. Among the 54 species present in the GI datasets, 14 of them did not meet this condition. Furthermore, Prochlorococcus marinus was not considered in the analysis as this group of organisms does not seem to be a relevant single species at the genomic level  (Parks et al.
, 2018). An additional 3 had to be removed as their assemblies did not match the version which were originally used to predict their GIs. A total of 81 genomes from 36 species from the reference datasets could be analyzed. The list of RefSeq genomes with their identifiers that were used to build pangenomes is available from https://github. com/axbazin/panrgp_supdata. The panRGP results were obtained with the PPanGGOLiN software version 1. 1. 72 with default parameters. The results of panRGP were compared to other tools on the same reference datasets. We included tools that were found to correctly predict GIs in a recent study  (Bertelli et al. , 2018) : Islandpath-dimob  (Bertelli and Brinkman, 2018) , SigiCFR  (Waack et al. , 2006) , SigiHMM  (Waack et al. , 2006) , Alien Hunter  (Vernikos and Parkhill, 2006) , PredictBias  (Pundhir et al.
, 2008) , ZislandExplorer  (Wei et al. , 2017)  and IslandViewer4  (Bertelli et al. , 2017) , which combines results from Islander  (Hudson et al. , 2014) , Islandpath-dimob, SigiHMM and IslandPick  (Langille et al. , 2008). In addition, three recent tools for GI detection were included in the benchmark: GI-cluster  (Lu and Leong, 2018) , XenoGI  (Bush et al. , 2018)  and IslandCafe  (Jani and Azad, 2019). GI-Cluster relies on sequence composition and functional annotation to cluster regions. XenoGI uses bidirectional best hits with the information of a phylogenetic tree to identify gene families that arose from the same HGT. IslandCafe uses sequence composition and functional annotation with a custom database of hidden Markov models including genes that are often associated with HGT. All these tools were run with default parameters.