dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2017)  as query. Selected contigs were manually inspected, their coding sequences were predicted using BioEdit and S. frugiperda orthologs as a master, and 5' and 3' UTRs were removed. In few cases, when two contigs were overlapping and likely representing the same transcripts based on their alignments with S. frugiperda orthologous genes, they were merged together to create a consensus sequence. Redundant sequences were then identified by iterative generations of maximum-likelihood trees and removed from the dataset. Thus, the final dataset contains a non-redundant list of putatively unique transcripts, which likely correspond to unique genes, although we cannot exclude that some of them might be allelic variants of the same gene. Maximum-likelihood (ML) trees were built with protein sequences annotated from S.
frugiperda  (Gouin et al. , 2017) , S. litura  (Zhu et al. , 2018)  and B. mori genomes  (Forêt et al. , 2007; Tanaka et al. , 2009; van Schooten et al. , 2016; Vogt et al. , 2015; Wanner and Robertson, 2008) , as well as putative proteins deduced from S. littoralis  (Walker et al. , 2019) , S. litura  (Gu et al. , 2015) , Helicoverpa armigera, H. assulta  (Chang et al. , 2017)  and previous S. exigua transcriptomes  (Du et al. , 2018; Zhang et al. , 2018). Trees were built using RAxML  (Stamatakis, 2014)  and amino acid MUSCLE alignments  (Edgar, 2004 ) generated by MEGAX  (Kumar et al. , 2018). Based on the phylogenetic relationships, S. exigua chemosensory-related proteins were named according to the S. frugiperda nomenclature. Web based blastx searches were then run for S. exigua chemosensory-related transcripts whose corresponding proteins had no one-to-one ortholog in any Spodoptera species in order to verify their annotation as chemosensory-related genes.
RNA-seq quantification of chemosensory-related gene expression Expression levels of S. exigua chemosensory-related transcripts in larva head, female adult antenna and male adult antenna (3 replicates for each tissue) were estimated by mapping the trimmed reads to the chemosensory-related genes annotated in this study. Mapping was performed using Bowtie 2 (version 2. 3. 5. 1)  (Langmead and Salzberg, 2012)  and RSEM (version 1. 3. 1) (Li and Dewey, 2011)  with default parameters. Relative abundance of each candidate transcript is reported as TPM (Transcript per Million). For each transcript family, expression data were clustered by hierarchical clustering analysis using the heatmap. 2 function from gplots v3. 0. 1. 1 package of R software. Differential expression analysis between male and female antennae was carried out using EdgeR  (Robinson et al.
Amplifications were run in a StepOnePlus Real-Time PCR system (Applied Biosystems) using 5x HOT FIREpol EvaGreen qPCR Mix Plus (ROX) from Solis BioDyne. The total reaction volume was 20 µl. Forward and reverse primers for all the transcripts were designed using the online software tool Primer3Plus  (Untergasser et al. , 2007). A list of primers used in this experiment is provided in Supplementary Table  1. RT-PCR products were run in a 2% agarose gel to visualize the amplification of a single band of the expected size.
, 2007 ). An endogenous control ATP synthase subunit C housekeeping gene was used in each qPCR to normalize the RNA concentration. A list of the used primers is provided in the Supplementary Table  1. The differences in expression between treatments (control and infected) were calculated using the ∆∆Ct method  (Livak and Schmittgen, 2001). A one-sample t-test was used to search for statistical differences comparing each Log Fold-Change (LogFC) value to the theoretical value of 1. Graphs and the statistical analysis were performed using GraphPad Prism software (v7. 0). Heatmaps were performed using the R packages gplots and RColorBrewer.
Forty-eight S. exigua OBP candidate transcripts were identified (SexiOBPs) (Table  1 ). Most of them are complete (69%), and only 15 had a partial ORF (Supplementary Table  2 ). Sixteen transcripts were newly annotated. All SexiOBPs have a clear one-to-one orthologue in S. frugiperda except SexiOBP46 and SexiOBP47 (Supplementary Figure  4 ). Two of the transcripts described by  Zhang et al. (2018)  (OBP25 and OBPN-3) were not identified in our transcriptome. Compared to Du et al. (2018), our annotation missed 19 putative OBPs. However, a closer look to these missing OBPs revealed that they did not cluster with any Spodoptera OBPs. Twelve of them grouped with Helicoverpa OBPs in our phylogenetic tree whereas the other seven were far distant (Supplementary Figure  4 ). Further analysis with blastx revealed that the best hit of missing OBPs was against sequences from distantly-related species (such as butterflies or Coleoptera species) (Supplementary Table  4 ).
Chemosensory-related transcripts in S. exigua adults and larvae Transcript levels were evaluated mapping RNA-seq reads obtained from larval head, female and male antennae to our manually curated chemosensory-related gene dataset. Since data from larvae and adults were not directly comparable because they proceeded from different tissues (isolated antennae in adults versus whole heads from larvae), we could not relate expression levels between adults and larvae. Instead, we used these data to 1) establish the larval chemosensory-related gene set by detecting the ones that had transcription signals in larval head samples, 2) identify which genes were differentially expressed between male and female antennae. Mapping of reads obtained from larval head to the manually curated chemosensory-related transcript dataset only gave us a hint about expressed genes without providing any information of transcripts not expressed at all, since RNA-seq data from composite structures (such as head) often lead to false negatives  (Johnson et al.
DISCUSSION In Lepidoptera, the study of olfaction has been mainly limited to adult stage, and focused on the understanding of sex-linked behaviours such as sex pheromone detection and egglaying substrate selection  (Allison and Carde, 2016; García-Robledo and Horvitz, 2012; Haverkamp et al. , 2018). In contrast, the molecular and physiological mechanisms that underlie olfactory behaviours in larvae are poorly understood and only few reports have described the chemosensory gene set expressed at the larval stage  (Chang et al. , 2017; Di et al. , 2017; McCormick et al. , 2017; Poivet et al. , 2013; Tanaka et al. , 2009; Walker et al. , 2016)  compared to the plethora of adult transcriptomes available  (Montagné et al. , 2015). Here, we identified chemosensory-related genes expressed in S. exigua larval heads using RNA-seq and RT-PCR data, expanding previous datasets built from adult data  (Du et al.
exigua have been limited to the main adult olfactory tissues, antenna and maxillary palps  (Du et al. , 2018; Liu et al. , 2015b; Wan et al. , 2015; Zhang et al. , 2018). Here we provide the first comprehensive dataset of chemosensory-related transcripts expressed in the larval stage. We combined RNA-seq and RT-PCR as RNA-seq data from composite tissues (such as head) often delivers false negative results  (Johnson et al. , 2013) , especially for genes expressed in few cells and at a low level, which is the case of chemosensory receptors. Altogether, we found 50 out of 63 ORs expressed in larvae, including the four sex pheromone receptors  (Liu et al. , 2013). Notably we did not observe any larval specific OR. Similarly, no larval-specific ORs have been found in transcriptomic data from S.
Acknowledgements We thank  Rosa María González-Martínez  and  Óscar Marín-Vázquez  at  University of Valencia (Spain)  for their excellent help with insect rearing and laboratory management. We thank  Marie-Christine François ,  Christelle Monsempès  and  Gabriela Caballero-Vidal  at  iEES-Paris (Versailles, France ) for help in chemosensory gene annotation.
LC-MS analysis Samples were analyzed without prior purification of the peptide using an Ultimate 3000 nanoHPLC and a Vanquish UHPLC systems by LC-MS connected to a Q Exactive Focus mass spectrometer (Thermo Fisher Scientific). Acetonitrile gradients between 10-30% and 15-25% in formic acid (0. 1%) were used. Mass analysis was performed at a resolution of 35,000 (m/z 200) with a MS range of 500-1300 and MS/MS analysis. Data were processed using Xtract tools included in the Freestyle software suite version 1. 3 (Thermo Fisher Scientific). All daughter ions were verified and annotated manually.
EPR analysis X-band EPR spectra were recorded on a Bruker ELEXSYS 500 spectrometer equipped with a Bruker ER 4116DM X-band resonator, an Oxford Instrument continuous flow ESR 900 cryostat, and an Oxford ITC 503 temperature control system. Conditions: Microwave frequency = 9. 636 GHz, microwave power = 1. 0 milliwatt, modulation amplitude = 8 Gauss, modulation frequency = 100 KHz, Gain = 40 db, temperature =10 K. Simulations were done using the Bruker software XSophe.
SFV4 was produced from plasmid pCMV-SFV4, as described previously  (73). Briefly, the plasmid was transfected using Lipofectamine 2000 (Thermo Fisher Scientific) into BHK-21 cells grown in GMEM with 2% FBS and 10% TPB at 37°C with 5% CO 2. The virus was titrated by a plaque assay on BHK-21 cells, as described above. Plasmids. The pIZ-FLuc and pAcIE1-RLuc plasmids have been previously described  (74, 75). The plasmid pPUb-V5MBP was synthesized by subcloning maltose-binding protein (MBP) from pcDNA-DEST40-MBP-hDVR (75) into the mosquito expression vector pPUb  (29) , based on an expression construct containing the A. aegypti polyubiquitin promoter  (76). The plasmid pUC57-p400 was synthesized (GenScript) and contains the A. aegypti coding sequence, as indicated in VectorBase with accession number AAEL001440 (assembly AaegL1).
Total RNA extraction and RT-qPCR from cells. For analysis of p400 knockdown efficiency, cells were lysed in TRIzol reagent (Thermo Fisher Scientific) at 24 hpt and extracted according to the manufacturer's instructions. Reverse transcription (RT) was performed using the SuperScript III enzyme kit (Thermo Fisher Scientific) from 1 g of RNA in a final volume of 20 l. Quantitative PCR (qPCR) was carried out using Fast SYBR green master mix (Applied Biosystems) on a 7500 Fast machine (Applied Biosystems). The primers used are listed in Table  S1. Data were analyzed with the 7500 Software v2. 0. 6, and transcript expression relative to the ribosomal S7 as a reference was calculated according to the 2 ϪΔΔCT method (77). The dseGFP control was set to 1, and dsp400 samples were normalized to dseGFP.
The hemocyte sample was set to 1, and other samples were normalized to the hemocyte sample. Data were obtained from 3 independent biological replicates. For analysis of p400 knockdown efficiency and the effect on ago-2 levels in whole NBF females, transcript expression relative to the ribosomal S7 transcript as a reference was calculated according to the 2 ϪΔΔCT method (77). The dsLacZ control was set to 1, and dsp400 samples were normalized to dsLacZ. Data from 3 independent biological replicates (pool of 10 females per replicate) were analyzed by a one-sample t test (Prism software). For analysis of p400 knockdown and the effect on ago-2 levels and SFV infection, data were analyzed as described by Taylor et al. (78)  in order to obtain normalized expression values, relative to the ribosomal S7 transcript as a reference, with a geometric mean (geomean) of 1 for the dsLacZ control group (two independent experiments; n ϭ 73 females for dsLacZ and 66 for dsp400 in Fig.
3A  and B and 6A; n ϭ 39 females for dsLacZ and 43 for dsp400 in Fig. S1 ). Log 2 -normalized expression values were analyzed using a Mann-Whitney test (with the Prism software). Technology-Science Education Institute, and the University of the Philippines Visayas and delivered by the British Council. For further information, please visit http://www. newtonfund. ac. uk/.
ACKNOWLEDGMENTS We acknowledge  A. Merits  ( University of Tartu, Estonia ) and the  Drosophila Genomics Resource Center (DGRC ; supported by  NIH  grant  2P40OD010949 ) for reagents, as well as  M. Chase-Topping  ( University of Edinburgh, UK ) for help with data analysis. We declare no conflicts of interest. Conceptualization, M. M. , F. A. , E. P. , E. S. , A. -B. F. , and A. K. ; methodology, M. M. , F. A. , C. L. D. ,  M. Varjak ,  M. Vazeille , A. -B. F. , E. P. , E. S. , A. K. ; formal analysis, M. M. , F. A. ,  M. Varjak , E. P. , E. S, A. K. , and B. W. ; ivestigation, M. M. , F. A. , E. P. , C. L. D. , M. L. , J. K. , C. L. , S. T. , A. M. ,  M. Varjak ,  M. Vazeille , and R. J. G. ; validation, M. M. , F. A. , E. P. , C. L. D. , M. L. , J. K. , A. M. , and  M. Varjak ; resources,  M.
Vazeille , A. -B. F. , I. D. , and R. J. G. ; writing-original draft preparation, M. M. , F. A. , E. P. , and A. K. ; writing-review and editing, M. M. , F. A. , C. L. D. ,  M. Varjak ,  M. Vazeille , M. L. , A. M. , A. -B. F. , J. K. , E. S. , E. P. , and A. K. ; visualization, M. M. , F. A. , E. P. , and A. K. ; data curation, M. M. , F. A. , E. P. , and A. K. ; supervision, A. K. , E. P. , and E. S. ; project administration, A. K. , E. P. , and E. S. ; funding acquisition, A. K. , E. S. , and E. P. This work was funded by the  UK Medical Research Council  (grant  MC_UU_12014/8  to A. K. , E. S. , and E. P. ),  Institut Pasteur  (to  M. Vazeille  and A. -B. F. ), and the  French Government 's  Investissements d'Avenir program ,  Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases " (grant  ANR-10-LABX-62-IBEID  to A.
DNA Sequencing We sequenced 56 individuals representing 18 species or 87. 5% of the species diversity within the genus. A total of 15. 4 million reads were generated and mapped to the reference exons belonging to 176 genes of the Heyduk et al. [26]  baiting kit. Across all Raphia and outgroup individuals, the average coverage depth was 139. 6x. We identified 102 genes for which 75% of the exon length was recovered in at least 25% of individuals. Twenty loci were flagged by Hybpiper as paralogs because multiple assembled contigs matched a single reference locus. Of these 20, those that occurred in the 75/25 set were removed, resulting in a final dataset of 85 supercontigs equaling 162 kb of sequence data. Our SNP calling approach applied filters on mapping quality (>40%) depth (>25), quality by depth (>2), minimum depth across individuals (>10) minor allele frequency (>0.
01), and we excluded monomorphic sites. This ultimately yielded 915 and 1627 high-quality, biallelic SNPs for the R. hookeri and R. zamiana species complexes, respectively (see below). The fastq (R1 and R2) sequences for all individuals are available in Genbank's Sequence Read Archive (SRA) under Bioproject number PRJNA615688 (http://www. ncbi. nlm. nih. gov/bioproject/615688).
Fine Scale Structure in Two Species-Complexes To further explore genetic structure among our two main species complex, namely the "zamiana" and "hookeri" complexes (marked with a black square and a black star in Figure  1 , respectively), we used SNPs extracted from the sequence data to look at the variation among individuals. The "hookeri" complex showed little evidence of clustering, with most individuals evenly spread out on the first two principal component (PCA) axes (Figure  3a ). We observed two major groups of >8 individuals in the "zamiana" complex along PCA1 (Figure  3b ), separating all of the R. laurentii and R. monbuttorum from the rest of the individuals. The first two PCAs in both analyses explained 7-10% of the variance in the dataset. This is a still less than a quarter of the total variance in each case, but this may be expected from our relatively complex dataset of almost 100 genes with independent histories.
Paralog Identification HybPiper flags potential paralogs when multiple contigs are discovered mapping well to a single reference sequence. We ran hybpiper on the 837 exons that made up the baiting kit  [26] , identified flagged loci, and constructed exon trees using RAxML (v8. 2. 9)  [54]. We examined each tree to determine whether putative paralogs formed a species clade. When sequences concerning more than three individuals were flagged for a locus, we examined whether the 'main' and alternative sequences formed separate clades. If so, this locus was classified as a paralog and discarded from the dataset. For each gene, we then calculated at the proportion of exons that we confirmed as paralogs after inspection. If this proportion was <50%, we removed the entire gene from our analyses.
Coalescent Phylogenetic Inference Individual gene trees were constructed with 100 bootstraps and the GTRGAMMA model using RAxML (v8. 2. 9)  [54]  (option "-f a"). If, after inference, branches had bootsrap support values >10, they were collapsed using the program nw_ed  [55]  because this approach has been shown to improve the accuracy of ASTRAL  [31]. We used the selected 75/25 gene trees as our input to run ASTRAL-III (v5. 5. 11)  [31]  using the default options.
Maximum-Likelihood Phylogenetic Inference After suitable loci were identified, we filled any missing individuals in each alignment with an empty sequence. We then concatenated all aligned loci using the pxcat function in the program phyx  [58]. We used IQ-TREE (v1. 6. 8; [59] ) to infer a maximum likelihood tree of all individuals. We partitioned our dataset so that each supercontig had a separate substitution model and used the following options when running the program: "-m MFP + MERGE -rcluster 10 -bb 1000 -alrt 1000". We selected the optimal partitioning scheme using ModelFinder  [60] , choosing the best model based on Bayesian Information Criterion (BIC) score and merging genes until model fit stopped increasing. We also used rcluster  [61]  to decrease computational load. We made use of the ultrafast bootstrapping (  [62] ; 1000 replicates) and the SH-like approximate likelihood ratio test (  [63] ; 1000 replicates) to assess branch support in the tree.
SNP Calling To call SNPs, we first used SeCaPr (v1. 1. 4; [64] ) to build a psuedoreference. After filtering out low coverage and paralogous loci, consensus sequences are built and combined to form a reference file that is closer to the study group than the original, and will recover more data. We mapped our cleaned, paired reads to this new, dataset-specific reference using BWA (v0. 7. 12;  [65] ). Duplicates were removed and we called SNPs using the program HaplotypeCaller in GATK (v4. 0;  [66] ). We applied thresholds to mapping quality (>40%) depth (>25), quality by depth (>2), minimum quality across all individuals (>10) and minor allele frequency (>0. 01) to filter SNPs using bcftools (v1. 8;  [67] ). We kept only biallelic SNPs and excluded monomorphic sites.
Genetic Clustering We performed Discriminant Analysis of Principal Components (DAPC)  [68]  to identify genetic clusters in two species complexes of Raphia. We used the function find. clusters in the R package 'adegenet'  [69]  to infer the number of clusters using successive K-means with 100,000 iterations per value of k up to k = 20. We used BIC to identify the best-fitting number of clusters. We then used the function dapc [68] to define the diversity among the clusters identified. We chose the optimum number of axes to use with the function optim. a. score.
It is unclear whether this difference can be attributed to the smaller |y| range for the ALICE point. Comparing the theory predictions for the ratio of cross sections for Λ ± c and D 0 production shown in Figs. 3, 6  and 9 , we can see that they are very similar; there is neither an indication for a strong dependence on rapidity, nor on the range of p T values which have been investigated by the three experiments, LHCb (2 < p T < 8 GeV), ALICE (1 < p T < 8 GeV) and CMS (5 < p T < 20 GeV). We should expect that also the measured Λ + c /D 0 ratios in the three experiments should be equal. This is, however, not the case. The data for the Λ + c /D 0 ratio measured by LHCb and CMS are almost equal inside their large experimental uncertainties, 0. 3 for both experiments. In contrast, the values found by ALICE are larger.
Here we describe a new fit including Belle data at √ S = 10. 52 GeV  [19]. The Belle data set is much more precise and contains more points. Only 35 of the available total number of 42 points will be used in our fit, since we have to exclude data at small values of the scaling variable x, where theory is not reliable without taking resummation of soft-gluon logarithms into account. The combination of old OPAL data with the more recent data from Belle requires special care, since the two experiments have based their analyses on the observation of the decay Λ + c → π + K -p for which different branching ratios have been used. OPAL has used the 1996 value Br(Λ + c → π + K -p) = 0. 044  [8] , whereas the analysis of the Belle measurements relies on the world average Br(Λ + c → π + K -p) = 0. 0635 from the 2016 Review of Particle Physics  [29].
We use these data and thus do not have to apply radiative corrections ourselves, as described in Ref. [4]. Similarly to the work in Ref. [4] , we perform three different fits. First, FFs are determined using separately the B-factory data (Belle fit) and the rescaled Z-factory data (OPAL fit). Then we obtain a common fit (global fit) combining the two data sets. We start by updating the FF fit to the OPAL data using the rescaled cross sections to match the up-to-date value of Br(Λ + c → π + K -p). f = M Z. OPAL  [8] : one sample includes only Λ ± c baryons produced in the decays of b hadrons from Z → b b (denoted b-tagged); a second sample includes in addition Λ ± c baryons from the direct production in Z → cc events and from light-quark and gluon fragmentation (denoted total). We determine the FFs for c → Λ + c and b → Λ + c in a common fit.
The corresponding values for the fragmentation and average energy fractions are given in Tables  2  and 3 , respectively. Finally, in our global fit, we use all available data for inclusive Λ + c + c. c. production in e + e - annihilation from Belle and OPAL. The resulting values of the fit parameters and of χ 2 are included in Table  1 , and the resulting fragmentation and average energy fractions are found in Tables  2  and 3 , respectively. The result of the global fit is compared with OPAL   11. The quality of the fit is obviously not perfect. This might be connected to the fact that the Bowler ansatz for the heavy-quark FFs contains only three free parameters and, therefore, is not flexible enough. It is clear that the Belle data dominate the fit, since this data set contains many more data points with smaller uncertainties.
Discussion and summary In Ref. [31] , an attempt was made to explain the large Λ + c /D 0 ratio seen in the ALICE measurements as arising from processes where Λ + c is produced from the decay of excited charm baryon states, which are not seen in the Belle data of e + e -annihilation. Actually, in the Belle data, only about 50% of the Λ ± c production rate originates from direct c → Λ + c fragmentation; the other half is due to decays of excited charm baryon states. However, it is difficult to understand that this fraction should be larger in pp collisions by a large factor, or that the production strength of the higher resonances discovered already in the Belle experiment should be so much larger in the ALICE pp collision experiment. This is an experimental question which could be answered only in the respective experiments.
Acknowledgment We thank  M. Niiyama  for providing us with data of the Belle experiment in numerical form. I. S. would like to acknowledge the  Mainz Institute of Theoretical Physics (MITP)  for its hospitality and support. The work of B. A. K. was supported in part by the  German Federal Ministry for Education and Research BMBF  through Grant No. 05H18GUCC1  and by the  German Research Foundation DFG through Research Unit   FOR 2926  " Next Generation Perturbative QCD for Hadron Structure: Preparing for the Electron-Ion Collider " with Grant No. KN 365/14-1.