dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We acknowledge grant  633053  from the  European Union 's  Horizon 2020 research and innovation program , grant  EP/I501045  from the  RCUK   Energy Program , grants  15H04244  and  15K14109  from  JSPS KAKENHI and ' Advanced Characterization Nanotechnology Platform, Nanotechnology Platform Program' of the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan , at the  Research Centre for Ultra-High Voltage Electron Microscopy (Nanotechnology Open Facilities) in Osaka University. The views and opinions expressed herein do not necessarily reflect those of the  European Commission.
2. 2. COI sequence-based species delimitation for the DNA taxonomic analyses We sequenced the mitochondrial cytochrome oxidase 1 (COI) gene commonly used to study the interspecific relationship in bumblebees  (Williams et al. , 2012b). We followed the total DNA extraction protocol, polymerase chain reaction amplification reactions, sequencing procedures, and alignment method described in  Lecocq et al. (2015b). Sequences were deposited in GenBank (Appendix Table  A1 ). The final molecular dataset spanned 849bp. We delimited species based on the COI sequences using the Bayesian implementation of the general mixed Yule-coalescent (bGMYC) method integrating the uncertainty related to phylogenetic inference  (Reid and Carstens, 2012) , recently underlined as an efficient species delimitation method compared to alternative single molecular-based methods (i.
(2015b). The final chemical dataset spanned 68 compounds (Appendix Table  A2 ). We performed statistical analyses in R (R Development Core Team, 2013) to detect CLGS differentiations among taxa. We transformed data (log (x+1)) to reduce the great difference of abundance between compounds in highly and low concentration. We compared the CLGS composition between individuals with a clustering analysis performed with UPGMA (unweighted pair group method with arithmetic mean) clustering method on Pearson r Correlation distances matrix (R-package ape,  Paradis et al. , 2004). We assessed the uncertainty in hierarchical cluster analysis with p-values calculated via multiscale bootstrap resampling with 100 000 number of bootstrap sample size (R-package pvclust, Suzuki and Shimodaira 2011). We assessed CLGS differentiations between groups detected in cluster by performing multiple response permutation procedure (MRPP) (R-package vegan,  Oksanen et al.
Acknowledgements We thank  R. De Jonghe ,  N. E. Raine ,  L. Chittka , and  Biobest bvba  for providing the biological material. TL was post-doc funded by the  Belgian Scientific Politic (BELSPO ,  BR/132/A1/BELBEES ). Financial support was provided by the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement  no244090 ,  STEP  Project (Status and Trends of European Pollinators, www. step-project. net) and the  Czech Science Foundation  (grant No. 14-04291S ). This project was supported by the  network Bibliothèque du Vivant  funded by the  CNRS , the  Muséum National d'Histoire Naturelle  and the  Institut National de la Recherche Agronomique.
Acknowledgements This work was supported by  CNRS  (especially through a visitor position to YL), by the  French research agency  ( ANR-06-BLANC-0195 ), and by the  Korean CCS 2020 Program , the  Brain Pool Program of KOFST  ( 121S-1-3-0380 ,  131S-1-3-0504  and  151S-1-3-1232 ), and the  Grand Challenge Program of KISTI  ( KSC-2012-C3-26 ).
Phylogenetic analyses and species identification Phylogenetic relatedness among surveyed sponge species was assessed using a phylogeny constructed from a partitioned alignment of gene sequences from 40 collected specimens and 44 GenBank sequences coding for the large (28S) nuclear ribosomal subunits, which is a common marker used for molecular identification of sponge species  (Thacker et al. , 2013). Sequences were aligned using the default options of MAFFT 7. 245  (Katoh et al. , 2002). We implemented a relaxed-clock model in MrBayes version 3. 2. 1  (Ronquist et al. , 2012) , using the CIPRES computational resources  (Miller, Pfeiffer & Schwartz, 2010)  and constrained sponges in the class Calcarea as an outgroup using the independent gamma rate relaxed clock model with a birth-death process  (Aris-Brosou & Yang, 2003).
1 2 3 ). One species included several individuals of variable growth forms collected from different locations (an encrusting growth form in Opunohu Bay [OB3# 1-6] and a massive fan-shaped growth form in the Motu Channel [MC# 1-5] and Intercontinental Reef [ICR# 1-5]; Figs. 1  and 2 ) that were shown to all be genetically identical (Fig. 3 ). BLAST results in GenBank revealed that these samples most closely matched Fasciospongia chondrodes (also known as Lendenfeldia chondrodes). Histological comparisons indicated internal fiber structure and thickness consistent with L. chondrodes  (C Diaz, pers. comm. , 2015) , and 16S and ITS cloning of all three morphotypes (C Freeman & C  Easson, 2016, unpublished data)  showed the presence of both Oscillatoria spongeliae and Synechocystis sp. symbionts, a composition unique to L.
, yielding an approximately 650bp fragment. PCR reaction products were gel-purified and cleaned using the Wizard PCR Preps DNA Purification System Dedham, MA, USA) and Geneious (version 6. 1. 8; Biomatters Limited, Auckland, New Zealand), yielding a final consensus sequence for each voucher specimen. A representative sequence for each species is archived in GenBank under accession numbers KU746954, KU746955, KU746956, KU746957, KU746958, KU746959, KU746960 and KU746961.
, yielding an approximately 650bp fragment. PCR reaction products were gel-purified and cleaned using the Wizard PCR Preps DNA Purification System Dedham, MA, USA) and Geneious (version 6. 1. 8; Biomatters Limited, Auckland, New Zealand), yielding a final consensus sequence for each voucher specimen. A representative sequence for each species is archived in GenBank under accession numbers KU746954, KU746955, KU746956, KU746957, KU746958, KU746959, KU746960 and KU746961. (Promega, Madison, WI, USA). Forward and reverse sequencing reactions were performed at the University of Alabama at Birmingham (UAB) Center for AIDS Research (CFAR) DNA Sequencing Core Facility. Forward and reverse sequences were then compared to ensure the accuracy of sequencing reactions in CodonCode Aligner software (CodonCode,.
Materials and Methods Xoo database mining. We accessed 1,922 records of Xoo live cultures maintained in the International Rice Research Institute (IRRI). The accessions were collected across different rice-growing areas in the Philippines between 1972 and 2012. We selected a total of 1,719 records that have consistent passport data and have retained race determination (Supplementary Table  S1 ) assigned based on pathogenicity reactions on differential cultivars, as described in Mew et al. 2. Based on these criteria, the Xoo population was categorized into 10 major races. Most of the strains with the PXO (X. oryzae from the Philippines) designation have been described before in a number of studies  2, 18. Genome sequencing, assembly, annotation and SNP mining. We selected one representative strain for each of the 10 Xoo races present in the Philippines.
The genome sequences of PXO86  20  and PXO99  A 21  were retrieved from the National Center for Biotechnology Information (NCBI) while the remaining eight Xoo strains were sequenced using PacBio single molecule real-time (SMRT) technology. The bacterial DNA of overnight grown Xoo cultures (30 °C) were extracted and purified using the Easy-DNA kit (Invitrogen, USA) following the manufacturer's protocol. For every strain, two SMRT libraries were sequenced through P6-C4 chemistry. De novo assembly was performed as described in Booher et al. 20  using HGAP v. 3 and the PBX toolkit. TALE-containing reads were assembled independently and parsed using the PBX toolkit  20. Annotation of all the assembled genomes was done using the NCBI Prokaryotic Genome Annotation Pipeline (PGAP). The finished genomes were deposited in GenBank under project accession number PRJNA304675.
A summary of the genome statistics of each strain can be found in Supplementary Table  S2. Single nucleotide polymorphisms (SNPs) were obtained from the core whole-genome alignment using the parsnp program implemented in Harvest suite tools  22. For most of the analysis we included the Korean Xoo strain KACC10331 (AE013598), the Japanese Xoo strain MAFF331018 (AP008229), the African Xoo strain AXO1947 (CP013666), and the X. oryzae pv. oryzicola (Xoc) strain BLS256 (CP003057) as out group. Core SNPs were annotated using TRAMS  23.
The convergence was assessed to be sufficient since changes in the hidden Markov model parameters were negligible over the last 70% of the iterations. The estimated significance of the recombinant segments was determined with a threshold of 5% using 100 permutations. The core SNP dataset on the 10 Xoo strains was subjected to Bayesian analysis using STRUCTURE  27. We chose the admixture model and the correlated allele frequencies between populations as setting option. Five independent runs were performed for each population or K result ranging from 2 to 10. We used 100,000 Markov chain Monte Carlo iterations for each run, with the first half of the run considered as burn-in. To assess the optimal number of population (K), the program estimates the log probability of data Pr (X/K) for each K.
Comparative genomic and positive selection analyses. Whole genome alignment for structural variation was performed in MAUVE 2. 3. 1  29  using the progressiveMAUVE method with default parameters. The pan-genome and core genome were determined using GET_HOMOLOGUES  30  package using the OCML and bidirectional best hit (BDBH) algorithm with sequences having an e-value of 1e-5, a coverage of at least 75%, and a minimum identity of 70%. The core genes were aligned using MUSCLE  31. Dispensable genes were filtered for transposable element and phage-related genes. An estimation of the numbers of synonymous (Ks) and nonsynonymous (Ka) substitutions per site was used to assess the selection  32. The assessment of the Ka/Ks ratio (ω ) for Xoo genes was done using KaKs-Calculator 2. 0  33. The Yn00 model  32  was used for determining the statistically significant ω values.
Comparison of TALE and Xop effector contents were computed using Jaccard similarity coefficient. TALE and Xop analysis. Preliminary TALE classification was based on 80% identity at the repeat variable diresidues (RVDs). Xop classification was based on local alignments to entries of the Xanthomonas database (http://www. xanthomonas. org/). Trees based on alignments of repeat regions and DNA binding probabilities were obtained using DisTAL and FuncTAL, respectively  34. Target predictions for TALE were made using Talvez  35  with default parameters against the promoter region of the rice Nipponbare genome version MSU7 1 Kb upstream the translation start site. The top 200 targets for each TALE were kept, and prediction scores for each TALE were normalized against the highest score obtained. Multiple prediction scores for a gene by TALEs from the same strain were added to obtain strain level predictions.
Pathogenicity tests. We phenotypically characterized 10 Xoo strains as described in Mew et al. 2  with some modifications. Essentially two-day old cultures were suspended in demineralized sterile water and the inoculum concentration was adjusted to 10 8 CFU/ml. Forty five-day old plants were inoculated using the leaf clipping inoculation method 2. Disease was assessed 14 days post inoculation by measuring lesion length in inoculated leaves. In particular, we determined the virulence spectrum of the 10 Xoo strains on 14 near-isogenic lines (NILs) carrying single resistance genes (Supplementary Table  S3 ). A Jaccard similarity coefficient was calculated from a qualitative phenotype dataset with the following designations: 1 = resistant, 2 = moderately resistant, 3 = moderately susceptible, and 4 = susceptible.
Results and Discussion A historic case of emerging Xoo races. The emergence of novel pathogen populations is a common phenomenon in the agricultural landscape that can be associated with drastic events of selection  37. To investigate the historical dynamics of Xoo races, we mined a well-characterized dataset spanning 40 years of bacterial blight collections in the Philippines. The database recorded 10 races and 5 derived subgroups collected between 1972 and 2012 (Supplementary Table  S1 ). We plotted the time distribution of all major races to describe two major population shifts that coincide with the emergence of novel groups during the 70s and 90s (Fig. 1 ). In the first case, Race 2 replaced the prevalent type Race 1 in response to change in rice cultivars across the Philippines. The event was documented by Mew et al.
oryzicola strain BLS256 was included. of Race 2, most members of PX-B (Races 5, 7, and 8) belong to races restricted to a single mountainous region of central Luzon  17. While Race 5 has been mostly isolated from traditional cultivars at high elevation, Races 7 and 8 have been found only in transitional zones  17. Though further studies are under way to determine the presence of additional lineages, our data distinguish at least three lineages of Xoo in the archipelago. Whether PX-A and PX-B lineages are derived from ancestral Xoo populations in Asia or represent native Xoo populations from Philippines remains to be solved.
As expected, we found that genomes in the same lineage share similar transposases (Supplementary Fig. S5 ), which catalyze the movement of the transposons inside the genome. However, a number of strain-specific genes were also identified, accounting for 24. 42% of the compared Xoo genomes, which suggests unique capabilities of gaining and losing genetic material among individual genomes. Overall, our data are consistent with the idea that members in the same lineage went through a similar adaptation process.
To understand the composition and distribution of effector genes, we first classified all 181 TALE protein sequences, including 15 pseudogenes, into 30 TALE families (TEFs) based on RVD configuration (Supplementary Table  S5 ). Based on this classification, each TEF displayed up to 6 alleles with half of the TEFs carrying only one allele (Supplementary Table  S5 ). Classification using the program DisTAL, which performs alignments based on repeat sequences  34  , largely reveals the same groupings (Supplementary Fig. S7 ). We also defined a set of TEFs representing the core (20%), dispensable (60%), and unique (20%) genes (Fig. 3 ). Xop families were more conserved across genomes, with core and dispensable families accounting for 86% and 14%, respectively. However, Xop sequences showed 1-7 alleles per family.
S4 ). However, other forms of variation such as recombination, repeat shuffling, or horizontal gene transfer with other taxa cannot be excluded as alternative ways to acquire effector families. The activation of target genes by TALEs is guided by cognate effector binding elements (EBEs) in the host genome  6. Based on DNA-binding specificities, TEF clustering followed the same pattern of classification as described using RVD distances (Supplementary Fig. S7 ). This indicates that alleles within each TEF are likely to have similar host targets. To investigate if members from the same lineage have functionally equivalent TEFs, we analyzed predicted target genes in the rice genome using the program Talvez  35  and found that lineage members share more predicted targets that non-members (Fig. 4 ).
Results are presented from a search for new physics in final states containing a photon and missing transverse momentum. The data correspond to an integrated luminosity of 19. 6 fb -1 collected in proton-proton collisions at √ s = 8 TeV with the CMS experiment at the LHC. No deviation from the standard model predictions is observed for these final states. New, improved limits are set on dark matter production and on parameters of models with large extra dimensions. In particular, the first limits from the LHC on branon production are found and significantly extend previous limits from LEP and the Tevatron. An upper limit of 14. 0 fb on the cross section is set at the 95% confidence level for events with a monophoton final state with photon transverse momentum greater than 145 GeV and missing transverse momentum greater than 140 GeV.
e. , the final state is required to contain a reconstructed charged lepton. After this selection, 104 events are observed and 126 ± 23 are expected. Electrons misidentified as photons arise mainly from highly off-shell W boson (W * → eν) events. These backgrounds are inclusively estimated from data. The efficiency, pix , of matching electron showers in the calorimeter to pixel seeds is estimated using a tag-and-probe technique  [38]  on Z → ee events in data, verified with simulated events. The efficiency is found to be pix = 0. 984 ± 0. 002 for electrons with E T > 100 GeV. A control sample of W * → eν events is also obtained from data through use of all the standard candidate selections, with the exception of the pixel seed, which is inverted. The number of events in this sample is scaled by the value of (1pix )/ pix resulting in an inclusive estimate of 60 ± 6 W * → eν events in the signal region.
The contamination from jets misidentified as photons is estimated in data using a control sample with E T / < 30 GeV, dominated by QCD events. This sample is used to measure the ratio of the number of objects that pass photon identification criteria to the number that fail at least one of the isolation requirements. The control sample also contains objects from QCD direct photon production that must be removed from the numerator of the ratio. This contribution is estimated by fitting the shower shape distribution with template distributions. For true photons, a template for the shower width is formed using simulated γ+jets events. For jets misidentified as photons, the template is formed using a separate control sample, where the objects are required to fail charged hadron isolation. This corrected ratio is used to scale a set of data events that pass the denominator selection of the fake ratio and all other candidate requirements, providing an inclusive estimate for all backgrounds in which jets are misidentified as photons of 45 ± 14 events.
Noncollision backgrounds are estimated from data by examining the shower width of the EM cluster and the time-of-arrival of the signal in the crystal containing the largest deposition of energy. Templates for anomalous signals, cosmic ray muons, and beam halo events are obtained by inverting the shower shape and beam halo tag requirements, and are fitted to the timing distribution of the candidate sample. The only nonnegligible residual contribution to the candidate sample is found to arise from the beam halo, with an estimated 25 ± 6 events.
Results Table  1  shows the estimated number of events and associated uncertainty from each background process along with the total number of events observed in the data, for the entire data set, which corresponds to 19. 6 fb -1. The number of events observed in data agrees with the expectation from SM background. The photon E T and E T / distributions for the selected candidates and estimated backgrounds are shown in Fig. 1. The spectra expected from the ADD model for M D = 2 TeV and n = 3 are also shown for comparison. Limits are set for the DM, ADD, and branon models using the E γ T spectrum. The product of the acceptance and the efficiency (A ) is estimated by calculating A MC from the simulation, and multiplying it by the F to account for the difference in efficiency between simulation and data.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMWFW  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  MoER ,  ERC IUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  OTKA  and  NIH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  NRF  and  WCU (Republic of Korea) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  CINVESTAV ,  CONACYT ,  SEP , and  UASLP-FAI (Mexico) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom, RAS  and  RFBR (Russia) ;  MESTD (Serbia) ;  SEIDI  and  CPAN (Spain) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ;  ThEPCenter, IPST, STAR  and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU  and  SFFR (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
Acknowledgments We thank  Fabrice Vavre  and  Sylvain Charlat  for useful discussion and  Hélène Henri  for help in molecular biology. We thank  Adriana Escudero  (Spain),  Gabrijel Seljak  (Slovenia),  Marco Valerio Rossi Stacconi  (Italy),  Darren Obbard (UK) ,  Traude Kehrer  (Austria),  Heidrum Vogt ,  Serge Fischer ,  Lionel Delbac ,  Nicolas Ris ,  Christophe Plantamp  and the  CTIFL from Lanxade and Balandran  for providing us D. suzukii from the field. We are also grateful to the  INRA centre of Gotheron  and to the landowners who allowed us to collect insects in their orchards.
Identification, drawing, and measurement of the larval instars A minimum of 20 PBS-rinsed larvae from each 4-hr sample were placed in depression slides and observed with an optical microscope to identify the larval instar. We measured naupliar instars from one brood of male larvae and one brood of female larvae. Dimensions of the larvae digitally photographed at 10× magnification were measured using AnalySIS Work v. 5 software (Olympus SZX7 associated with DP 20 camera). Nauplii larvae were measured for maximal length and for width without the frontal horns. The furcae were also measured for length and for maximal width at their base. Cypris larvae were measured for maximal length. Measurements were calibrated with a micrometric slide. To produce larval drawings, larvae rinsed in PBS were slightly crushed between a slide and coverslip to observe the antennulary setae, and traced at 400× magnification with Lucida camera (Wild M20).
Acknowledgements We are thankful to the  SEM facility of the Direction des Collections  located at the  Muséum National d'Histoire Naturelle (Paris)  for all the SEM images used in this article and  Sylvain Pont  for training in SEM. The authors also thank  Dr. Adrien Bussard  and  Dr. Marc Eléaume  for their advice in statistics and on R, and  Dr. Kurt Liittschwager  for reviewing the English text.