dataesr/scientific-paragraphs-categorization huggingface dataset:
Although the dataset we provided is on static gestures, the DVS and the spiking EMG signals provide the capability for lowpower processing using event-based neuromorphic chips and enable embedded systems with online on-site processing without having to send the data to remote sensors. Therefore, this work is an important first step toward edge-computing applications. The static dataset also helps with reducing the noise from the EMG signals as we mentioned in section 2. 2. However, this does not move away from the real application as we have shown in a live demo in  Ceolini et al. (2019a). The selected multi-sensor data fusion, which combines vision and EMG sensors, derives from the need of multiple sources to help the classification in real-scenario cases. Although the results show a small improvement due to the EMG sensors, they still provide some classification in case light conditions or camera occlusions are not ideal.
ACKNOWLEDGMENTS The authors would like to acknowledge the 2019 Capocaccia and  Telluride Neuromorphic Workshops  and all their participants for the fruitful discussions, and  Intel Corporation  for access to Loihi neuromorphic platform. We thank  Prof. B. Miramond ,  Prof. D. Bol ,  Prof. S. Liu ,  Prof. T. Delbruck , and  Prof. G. Indiveri. Finally , we thank  Garrick Orchard  for supporting us with the use of the Loihi platform and the useful comments to the paper.
FUNDING This work was supported by the  EU 's  H2020 MSC-IF grant NEPSpiNN  (Grant No. 753470 ), the  Swiss Forschungskredit  grants  FK-18-103  and  FK-l9-106 , the  Toshiba Corporation , the  SNSF  grant No. 200021_172553 , the  fonds Européen de Développement Régional FEDER , the Wallonia within the  Wallonie-2020. EU program , the  Plan Marshall , the  FRS-FNRS of Belgium , the  EU 's  H2020  project  NEUROTECH  (Grant No. 824103 ), and the  H2020 MC SWITCHBOARD ETN  (Grant No. 674901 ). The authors declare that this study received funding from  Toshiba Corporation. The funder was not involved in this study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.
LC-MS/MS in University of Lyon The Scout-MRM method was performed on a 1290 HPLC system (Agilent Technologies) coupled with a QTRAP 6500® from SCIEX (Concord, Canada) equipped with a Turbo V TM source (SCIEX). Instrument control, data acquisition and processing were performed using modified Analyst 1. 6. 2 software®. Implementing the "Scout-MRM" tool in the Analyst® 1. 6. 2 software required computer developments by David Cox at the Sciex R&D laboratory. Chromatographic separations were performed on a 3. 5µm BEH C18, 100 x2. 1mm (Waters, Milford, MA, USA), at ambient temperature. The flow rate was to set at 300 µl/min and an elution gradient was used. The mobile phase consisted of (A) water containing 0. 05 % formic acid and 2 mM ammonium formate and methanol containing 0. 05% formic acid, 2mM formate ammonium.
LC-MS/MS in Toronto (Sciex) The Scout-MRM method was performed on an HPLC Model-LC 20 AT Prominence, (Shimadzu, Japan) coupled with a QTRAP 5500® from SCIEX (Concord, Canada) equipped with a Turbo V TM source (SCIEX). Instrument control, data acquisition and processing were performed using modified Analyst 1. 6. 2 software®. Implementing the "Scout-MRM" tool in the Analyst® 1. 6. 2 software required computer developments by David Cox at the Sciex R&D laboratory. Chromatographic separations were performed on 3 µm X Bridge C18, 100 x 2. 1mm (Waters, Milford, MA, USA), operated at 50°C. The flow rate was to set at 600 µl/min. An elution gradient was used. The mobile phase consisted of (A) water containing 0. 05 % formic acid and 2 mM ammonium formate and methanol containing 0. 05% formic acid, 2mM formate ammonium.
Finally, the fact that any unattended retention shift does not translate into a false negative detection strengthens the confidence level of pesticide assays and the resulting results. It should also be noted that routine laboratories running high-throughput pesticide assay are used to introduce few internal standards in each run. This function can be here advantageously fulfilled by the deuterated scout pesticides, hence without any radical change in the procedure. The Scout-MRM provisional software patch is available for academic research on request from SCIEX (please contact yves. leblanc@sciex. com) and will be integrated automatically in the MS controlling software instruments.
ACKNOWLEDGMENTS We thank  Arnaud Martin  for discussions. Funding for this project was provided by  European Research Council  (FP7/2007-2013 Grant Agreement no. 337579 ) to VCO,  ANR-14-ACHN-0003  and  ANR-19-CE45-0009-01  to FD and the  CeMEB LabEx/University of Montpellier  ( ANR-10-LABX-04-01 ) and an  INRAE-SPE starting grant  to NOR. VCO brought the research topic, all authors developed the model, FD did the analysis, implemented numerical solutions, ran stochastic simulations. All authors analysed data and wrote the manuscript. We are grateful to the  INRAE MIGALE bioinformatics facility (MIGALE, INRAE, 2020. Migale bioinformatics Facility , doi:  10. 15454/1. 5572390655343293E12 )for providing computing resources.
Genotyping Revealed High Knock-Down Rate in CCR1 but High Knock-out Rate in IAA9A To reveal the CRISPR/Cas9 system genome editing and characterize the mutation types, we extracted genomic DNA from E. grandis transgenic roots. We then amplified by PCR the target gene regions including the two sgRNA sequences. The PCR amplicons were directly sequenced and analyzed by the web-based tool "Degenerate Sequence Decoding (DSDecode, http://dsdecode. scgene. com). For CCR1-lines the majority of the direct sequencing of PCR amplicons could not be read by the DSDecode program. We then subcloned PCR amplicons into pGEM-T vectors and several subclones were sequenced by Sanger sequencing.
Among the 89 edited alleles, 87 generated amino acid changes, potentially modifying the activity of the corresponding enzyme (Supplementary Table  S1 ). Noteworthily, the majority (69. 7%) of the CCR1-targeted edition introduced significant modifications among which 67. 4% were reading frame shifts (Table  3 ) and 24. 7% were 15 bp or more insertion/deletions. The very highly complex pattern of CCR1 edition explained why the DSDecode online tool failed to run, thereby confirming that this analytic tool is not suitable for chimeric or multiple mutations. We performed three independent transformation batches for CCR1. For the first batch, we collected our samples on 153-day-old roots (109 days of in vitro culture + 44 days in hydroponic culture) in order to obtain roots containing secondary xylem.
The majority (86. 4%) of IAA9A targeted edition introduced significant protein modifications (Table  3 ) including 85. 2% of reading frame shifts with premature stop codons, and 56. 8% of 15 bp or more indels (insertion/deletions). In parallel to subcloning, we used the DSDecode online tool on the 13 PCR amplicon sequences (13 transgenic hairy roots). Nine were successfully treated by DSDecode (Supplementary Table  S5 ). For three of them, we got the same results as our subcloning and sequencing data (highlighted in green, Supplementary Table  S5 ). They included one monoallelic mutation line (IAA9A_5), one biallelic line (IAA9A_15) and a line without any mutation (IAA9A_10). For two of them the results obtained with the two methods were different (highlighted in yellow, Supplementary Table  S5 ).
For the line IAA9A_3, for instance, the analysis of the subcloning results showed that it was a biallelic mutation whereas the DSDecode concluded that it was a homozygote mutation with the allele of 1 bp deletion; for the line IAA9A_11, the two edition types detected by subcloning were different from the one edition type and WT allele detected by DSDecode. For the four others, we observed only partial overlapping of the results between the two methods (Supplementary Table  S5 ). For example, for the line IAA9A_1, the subcloning results showed biallelic mutations (eight out of nine subclones showed a 73 bp deletion and one showed a 6 bp deletion) whereas the DSDecode online tool concluded that it was a homozygote mutation consisting of the allele with a 73 bp deletion. For the four lines failed to be analyzed by DSDecode, the subcloning sequencing revealed one chimeric line (no WT, A1/A2/A3, etc.
The low level of biallelic mutations and the high level of chimera were unexpected especially when compared to poplar  [20, 22, 29] , or even to hairy roots system in soybean, tomato or chicory  [55] [56] [57] [58] [59] , where high percentage of biallelic mutations and low number of chimera were reported in general. Although in some studies, the percentage of chimera could have been underestimated by the use of the DSDecode software, which is not able to detect complex patterns of mutations such as chimeric or multiple mutations and is less accurate than subcloning to identify chimera as we showed here. There is room in our system to increase the percentage of biallelic knockout and reduce the mono allelic mutation and chimera percentages. Indeed, for many species, it has been observed that monoallelic/chimera mutations predominate in the first-generation (T0) when CRISPR editing efficiencies were low  [19, 23].
CRISPR/Cas9 Target Site Selection and sgRNAs Design We selected two different target sites for each gene with the help of the sgRNA design online tool 'CRISPOR' (http://crispor. tefor. net/), which enables to evaluate the genome wide off-targets and to score on-target efficiency  [54]. We first retrieved the genomic sequences of our two target genes obtained from Eucalyptus grandis genome sequencing database of Phytozome (https://phytozome. jgi. doe. gov). All possible sgRNAs on the genomic sequence were ranked by CRISPOR based on the off-target risk and on target efficiency. Among those, we selected pairs of sgRNAs at around 100 bp interval to increase editing efficiency and to possibly create a large deletion between the two sgRNAs  [62]. The target sites selected had a 'G' as their first base to function as the RNA polymerase III start site (Guide RNA prefix for U6 promoter) and were followed by the Protospacer Adjacent Motif (PAM) sequence 'NGG' given the Streptococcus pyogenes Cas9 as PAM selection preference.
The selected sgRNAs and the off-target risk and editing efficiency prediction were presented in Supplementary Table S7  [66]. Since eucalypts are highly heterozygous, we verified the absence of SNPs in the guide RNAs by blasting them against a large RNAseq data set of E. grandis [67] registered at the NCBI SRA database (PRJNA514408). Unexpectedly, the alignment between the RNAseq and the genomic sequence of CCR1 retrieved from Phytozome (Eucgr. J03114) allowed us to detect a deletion of 14 bp (5′gcttctctcctcgagc-3′) at position 33649876 (Chr. J; Figure  5 ) in the latter and consequently the predicted exon/intron structure and protein sequence were wrong. We corrected manually the sequence and both gene structure and proteic sequence were in perfect agreement with our previous work  [2, 68].
The selected sgRNAs and the off-target risk and editing efficiency prediction were presented in Supplementary Table S7  [66]. Since eucalypts are highly heterozygous, we verified the absence of SNPs in the guide RNAs by blasting them against a large RNAseq data set of E. grandis [67] registered at the NCBI SRA database (PRJNA514408). Unexpectedly, the alignment between the RNAseq and the genomic sequence of CCR1 retrieved from Phytozome (Eucgr. J03114) allowed us to detect a deletion of 14 bp (5 -gcttctctcctcgagc-3 ) at position 33649876 (Chr. J; Figure  5 ) in the latter and consequently the predicted exon/intron structure and protein sequence were wrong. We corrected manually the sequence and both gene structure and proteic sequence were in perfect agreement with our previous work  [2, 68].
The PCR amplifications were performed using the High-Fidelity Phusion DNA Polymerase (Thermo Fisher (Illkirch-Graffenstaden, France)). Two methods were used to identify mutations. The PCR amplicons were either directly sequenced and analyzed by the web-based tool "Degenerate Sequence Decoding (DSDecode, http://skl. scau. edu. cn/sadsdecode/#), and/or were subcloned into pGEM-T vectors after adding 'A' tail by GoTaq polymerase (Promega (Charbonnières-les-Bains, France)) and up to 22 colonies were sequenced by Sanger sequencing. For the first method, instead of using the primers for previous amplification, two nested primers were used for PCR amplicon directly sequencing followed the instruction of the DSDecode online tool: nested primer CCR1_R (located 220 bp downstream of gRNA2) and nested primer IAA9A_F (located 191 bp upstream of gRNA1; Supplementary Table  S9 ) to avoid the noise signals of sequencing results.
Funding: This research was funded by the  Centre National pour la Recherche Scientifique (CNRS) ; the  University Paul Sabatier Toulouse III (UPS) ; the  French Laboratory of Excellence  (project ' TULIP'  ( ANR-10-LABX-41 ;  ANR-11-IDEX-0002-02 )); the  China Scholarship Council  (a PhD grant to Y. D. ), the  National Council for Scientific and Technological Development of Brazil (CNPq ; a PostDoc grant to L. M. ).
idtdna. com/calc/analyzer (last accessed October 24, 2020). Primers (0. 5mM each) were mixed with the KAPA2G Robust HotStart ReadyMix (Kapa Biosystems) inside a DNA/ RNA UV-Cleaner cabinet (UVC/T-AR). PCR was performed using the following general profile: 95 C for 5 min, (95 C for 30 s, T m C for 15 s, 72 C for 1 min) Â 35, 72 C for 5 min. Annealing temperature (T m ) was set up for each primer set according to Primer3 predictions (supplementary table 2, Supplementary Material online). When required, the temperature was adjusted trying 5 C above or below of the predicted T m. PCR product size was confirmed by electrophoresis using 1% agarose gel, purified with DNA Clean & Concentrator 5 (Zymo Research), and sequenced by Sanger technology in both directions at Macrogen Europe. For each amplicon, sequences quality screening/clipping and consensus alignment was performed using the Staden Package  (Bonfield and Whitwham 2010).
Phylogenetics, Dating, and Ancestral State Reconstruction of Portiera Lineages To infer the phylogenetic relationship and divergence time of Portiera from the studied whitefly species, two data sets were used. The first data set incorporated sequences of Portiera 16S and 23S rRNA genes amplified in this study, 16S and 23S rRNA gene sequences generated by  Thao and Baumann (2004) , as well as 16S and 23S rRNA gene sequences extracted from downloaded published transcriptomes/ genomes (details in the following sections). The final data set contained 59 sequences from 45 different species (including six belonging to the B. tabaci species complex). The second data set integrated the sequences of the 16S and 23S rRNA genes with those of the three protein coding genes: dnaK, rpoD, and groEL. It contained 32 sequences from 29 whitefly species, mostly obtained in this study plus few that were acquired from public transcriptomes/genomes.
Orthologous genes extracted from Chromohalobacter salexigens DSM3043 (NC_007963. 1) were used as outgroups in the phylogenetic analysis of both data sets (described below). The 16S and 23S rRNA genes were aligned with R-Coffee v11. 00. 8cbe486 (-mode ¼ rmcoffee -iterate ¼ 100)  (Notredame et al. 2000)  and pruned with Gblocks v0. 91b allowing half of gap positions (-t ¼ d -b5¼ h)  (Castresana 2000). The three coding genes (dnaK, rpoD, and groEL) were codon aligned with MACSE v2. 03 (-prog alignSequences -gc_def 11)  (Ranwez et al. 2018 ) and pruned with Gblocks v0. 91b (-t ¼ c -b5 ¼ h). The 19 obtained mtCOI gene sequences (5 0 region) were aligned in the same way but using the invertebrate mitochondrial code in MACSE v2. 03 and no gaps allowed in Gblocks v0. 91b. Substitution saturation was assessed using the pruned alignments as an input for Xia's test implemented in DAMBE v7.
The presence of dnaQ on the internal nodes of both data sets was estimated using a maximum likelihood approach as a discrete character and a model assuming only gene losses. Phylogenetic trees with dnaQ presence probabilities were plotted with ape. Whole-Genome Shotgun Sequencing, Genome Assembly, and Annotation of the S. simplex and P. mori Joint Sample In order to obtain Portiera of S. simplex genome, a wholegenome shotgun sequencing strategy was applied. NovaSeq sequencing produced 75,274,888 raw reads that were quality screened with Trimmomatic v0. 33 (TruSeq2-PE. fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:25 MINLEN:98). Possible polyGs produced by the NovaSeq platform were trimmed with fastp v0. 19. 7 (-g)  (Chen et al. 2018). Cleaned reads were classified with Kraken v2. 0. 6-beta using a custom database which included several RefSeq genome databases  (archaea, bacteria, viral, fungi, and protozoa) , all sequenced endosymbionts from whiteflies, the genomes of B.
mori. Smaller contigs with coverage of $600Â had perfect match to the amplified Portiera genes from S. simplex. This confirmed that some P. mori individuals were collected together with S. simplex, probably due to the ability of both whitefly species to exploit Ficus benjamina as a host-tree. As a result, the Kraken2 database was rebuilt to include the obtained contigs, and cleaned reads were reclassified. Portiera reads were reassembled separately according to their whitefly host with SPAdes v3. 13. 0 (-sc -careful). SSPACE v3 (-k 20 -n 35 -g 3)  (Boetzer et al. 2011 ) and GapFiller v1. 10 (-m 50 -o 10 -r 0. 6 -n 50 -t 50)  (Boetzer and Pirovano 2012)  were used for scaffolding and gap-filling the obtained reassembly, respectively. Gap5 from the Staden package was used not only to evaluate the quality of the assemblies but also to detect the presence of chimeras and misassemblies, to join contings manually (when possible), and to check for circular contigs.
simplex. The reads were mapped to the contigs with MIRA v4. 9. 6  (Chevreux et al. 1999) , and then imported to Gap5 for manual joining/gap closure. Both final assemblies were corrected with Pilon v1. 23 (-fix all, amb)  (Walker et al. 2014 ) and the clean classified reads. Finally, the annotation of the genomes was performed with prokka v1. 14. 5  (Seemann 2014) , using all available Portiera genomes for building the protein database of primary annotation (-proteins). Obtained annotations were manually inspected and curated in Artemis v1. 5  (Rutherford et al. 2000). Singhiella simplex and P. mori mitogenomes assembly and annotation procedures can be found at supplementary Material and Methods, Supplementary Material online.
Portiera Lineages Comparative Genomics Proteomes of Portiera from S. simplex (ERZ1272841), P. mori (ERZ1272840), B. tabaci species-MEAM1 (NC_018677. 1), MED (NC_018676. 1), and Asia II 3 (NZ_CP016327. 1), T. vaporariorum (LN649236. 1), A. dispersus (LN649255. 1), and A. floccissimus (LN734649. 1) were extracted with a custom python script. Orthologous clusters of proteins (OCPs) were calculated with OrthoFinder v2. 3. 3 (-M msa -S mmseqs -T iqtree)  (Emms and Kelly 2019). Obtained OCPs were manually curated based on protein annotations. Shared and specific OCPs were plotted with UpsetR  (Conway et al. 2017). Synteny between Portiera genomes, based on 230 singlecopy core OCPs (from 235), was plotted with genoPlotR  (Guy et al. 2010 ). Finally, metabolic potential comparisons were performed with Pathway Tools v23.
5  (Karp et al. 2002). Curated OCPs were converted into a binary matrix (presence/absence) and species-specific OCPs annotated as hypothetical proteins were discarded (21 OCPs). The binary matrix and the species tree obtained with OrthoFinder v2. 3. 3 were used as inputs for COUNT v10. 04  (Csuos 2010)  to reconstruct the gene losses history during Portiera evolution. The reconstruction was performed under a posterior algorithm and allowed only gene losses. SEED profiles were computed for each Portiera using diamond (BlastP -e 1e-9 -f 100), the nonredundant NCBI database (August 08, 2020 release), and MEGAN6  (Buchfink et al. 2015; Huson et al. 2016).
Repeats and Intergenic Regions Comparisons, Molecular Evolution Analysis, Transcriptomes Assembly, and Genomic Data Retrieval Repeats and intergenic regions were compared between Portiera lineages and different obligatory endosymbionts to correlate dnaQ presence/absence and genome instability. Synonymous (dS) and nonsynonymous (dN) substitution ratios, nucleotide substitutions per site per year (dS/t and dN/t), and omega (x) values were used to compare evolutionary trends in Portiera lineages and whitefly mitogenomes. These values were calculated as previously described (Santos-Garcia et al. 2015) using Codeml from PAML v4. 7 package  (Yang 2007). Dialeurodes citri (SRR2856996) and B. tabaci SSA1 (SRR5109958) transcriptomes were assembled de novo and several Portiera and whiteflies mitochondrial genomes were downloaded to increase our data set.
Results Using Portiera Gene Sequences to Establish Phylogenetic Relationships and Estimate Divergence Time in Whiteflies gDNA was extracted from 26 of the 29 collected samples, standing for 22 whitefly species from 17 genera (supplementary table 1, Supplementary Material online). The five sets of primers that target the Portiera genes 16S and 23S rRNAs, dnaK, rpoD, and groEL, successfully amplified in 25 samples. One sample, Bemisia reyesi JHM 7496, was excluded from further analysis because we could not amplify the target regions of the 23S rRNA and rpoD genes. We failed to obtain gDNA from three NHM collection exsiccate samples (supplementary table 1, Supplementary Material online), even when applying a whole-genome amplification approach. Two phylogenetic trees (chronograms) were obtained using two different data sets.
Replication and repair (L category) gene losses are accumulated in the lineage leading to Singhiella simplex and Bemisia tabaci (MRCA D). MRCAs nodes are the same as in figure  4. The genes (rows) dendrogram was computed using a binary distance and the ward. D2 clustering method. *WP_180824853: NCBI accession number of a hypothetical protein shared between all Portiera with the exception of S. simplex and B. tabaci. Portiera Gets Wild: On the Origin of Genome Instability GBE Genome Biol. Evol. 12(11):2107-2124 doi:10. 1093/gbe/evaa216 Advance Access publication 13 October 2020 to a relatively high number of inaccuracies and misassignments in the group taxonomy  (Manzari and Quicke 2006). For example, an extensive cladistic analysis suggested that around half of the 117 Aleyrodinae genera analyzed are not monophyletic (excluding monobasic genera)  (Manzari and Quicke 2006).
Acknowledgments The authors of this work would like to honor the memory of  Prof. Dan Gerling  ( 1936-2016 ), a pioneering and passionate researcher of the whitefly superfamily and its natural enemies. This work was supported by the  Israel Science Foundation  (Grant Nos. 1039/12  to S. M. and 484/17 to E. Z. -F. ). D. S. -G. was a recipient of the  Golda Meir Postdoctoral Fellowship  from the  Hebrew University of Jerusalem. We wish to acknowledge  Dr Estrella Hern andez Su  arez and  Dr H elene Delate  for supplying whitefly samples.
, 2010). Although AOPs are chemical-agnostic, it is useful to link chemicals to AOP events in order to identify their targets and their putative toxicities. Novel and innovative approaches have been developed to analyze and identify key information from the very large number of published scientific papers and from high throughput screening studies (HTS). For example, a recent study combined information on toxic effects from manual literature searches with information from the ToxCast and AOP-Wiki databases to establish connections between environmental chemicals and toxic effects  (Bajard et al. , 2019). Frequent itemset mining has also been used to decipher relevant information by integrating multiple data sources from HTS studies to establish predictive toxicological models  (Oki et al. , 2016).