dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, this solution may be very important for persons with auditory problems. As already stated, the tablet's screen can only display a limited number of options at the same time, and therefore cultural knowledge about the person plays a key role to display those options that are more likely to be selected (the person can browse a menu if the desired option is not among the suggested ones). The second component of the Conversation module is a tool for speech and natural language processing used by the robot to collect requests, statements, and answers based on free-speech interactions with the person. The component relies on the usage of Cloud-based solution for Automatic Speech Recognition (either Google ASR or Nuance Freespeech) to convert an audio file to text, and then feeds it to an on-board system for keyboard extraction and processing (which constitutes the basis for guessing the person's intentions, and to start a corresponding dialogue).
}. Then, if the person pronounces the sentence "I need to clean the dining table, would you come with me?", the robot will be able to recognize a main keyword in the first set and a second-level keyboard in the second set, and respond properly by heading to the kitchen  6. In case that the robot cannot understand what the person says, the system proposes to switch to a tablet-guided dialogue (improving reliability at the price of a reduced naturalness and versatility). The third component of the Conversation Module is the off-the-shelf tool for keyword recognition included in the standard NAOqi API. This tool has limited functionalities with respect to Google ASR and Nuance Freespeech, but it turns out to be sufficiently reliable when the person replies by picking out a keyword in a small set.
ACKNOWLEDGEMENT This work has been supported by the  European Commission H2020 Programme  under grant agreement No. 737858 , and from the  Ministry of Internal Affairs and Communication of Japan. The authors wish to acknowledge all CARESSES researchers and their teams, including  Prof. I. Papadopoulos  ( Middlesex Univ. ),  Prof. A. Saffiotti ,  Prof. F. Pecora  ( Örebro Univ. ),  Prof. N. Y. Chong ,  Prof. Y. Lim  (JAIST),  Prof. H. Kamide  ( Nagoya Univ. ), Dr. J. Lee  ( Chubu Univ. ), Dr. C. Papadopoulos ,  Prof. G. Randhawa  ( Univ. of Bedfordshire ),  Dr. S. Kanoria ,  Mr. L. Merton  ( Advinia Health Care ).
TATFIND does not predict cleavage sites. TatP is also based on the search for a specific motif (RR. [FGAVML][LITMVF]), and integrates two machine learning algorithms (Artificial Neural Networks) for cleavage site recognition and hydrophobicity discrimination. Finally PRED-TAT uses a Hidden Markov Model (HMM) method with sub-modules for the distinguishing between Sec, Tat and TM (transmembrane) regions. Comparisons of these prediction algorithms on test sets tend to show that TatP generates fewer false positives than TATFIND and slightly more false negatives  13. PRED-TAT outperforms both TATFIND and TatP in sensitivity, specificity and in prediction for the precise location of the cleavage site  14. Overall these algorithms have both strengths and weaknesses, and using them in combination can improve the accuracy of signal peptide prediction.
We selected the 39 Tat signal peptides predicted by both algorithms for further analysis (Table  1A  and Fig. 1 ). To avoid dismissing real Tat substrates predicted only by TATFIND or TatP as occurred in Streptomyces coelicolor  22  , we next compared our list of candidates with the candidates obtained with the most recent program, PRED-TAT. This allowed us to select another five candidate signal peptides that were positive with this program (and either TatP or TATFIND) for further analysis (Table  1B  and Fig. 1 ). The final set contained 44 putative Tat signal peptides. Of these, seven out of the eight already known P. aeruginosa Tat signal peptides (PA14_53360/ PlcH, PA14_21110/PlcN, PA14_13330/EddA, PA14_33740/PvdP, PA14_33720/PvdN, PA14_48450/Agu2A'and PA14_10370/PA4140) were present (Fig.
Identification of a new periplasmic Tat substrate with a missassigned start codon. Recently, a comparative proteomic approach allowed us to identify a new Tat substrate in P. aeruginosa with a missassigned start codon, PA2699  29. Our in silico approach misses such substrates because the screening was performed on the P. aeruginosa predicted proteome. Consequently, to identify other putative Tat substrates that may have missassigned start codons we ran a modified TATFIND prediction program on all PA14 open reading frames (instead of the P. aeruginosa protein database) bigger than 150 base pairs starting with an ATG and having two codons encoding RR, RK and KR within the first 120 nucleotides. This analysis led to the identification of 523 potential Tat peptides encoded by the 33416 putative ORFs tested (Table  S1 ).
As expected, amongst these we found all the putative Tat signal peptides starting with an ATG that were identified with the previous analysis. Next we manually curated the remaining hits to identify putative Tat signal peptides by removing (i) sequences in the opposite orientation to an annotated gene, (ii) sequences not directly in frame with an annotated gene and (iii) sequences further than 150 nucleotides from the start codon of an annotated gene. The remaining sequences were then analyzed for the presence of signal peptides using the SignalP, TatP and PRED-TAT algorithms and scrutinized for the presence of a putative SD sequence upstream of the putative missassigned start codon. This analysis led to the identification of one more Tat candidate, PA14_70330 or SphC. Of note PA2699, which we identified previously with a missassigned initiation codon, was not identified in this analysis because the TATFIND program does not recognize the PA2699 Tat signal peptide  29.
coli AmiA with an His 6 epitope-tag). Strains were grown aerobically and 5 μl were spotted on LB agar plate containing 2% (w/v) SDS as indicated in the material and methods section (B) Microscopic observation of ΔssamiAC strains carrying the empty vector or pssAmiA-AmiAH. Scale: the white bar represents 10 μm (C) Immunoblot analysis of whole cells prepared from wild type, ΔtatC and ΔssamiAC strains carrying the empty vector or pssAmiA-AmiAH. The predicted sizes of AmiAH precursor (p) and mature (m) forms are 34 kDa and 28. 9 kDa respectively. A form corresponding to AmiAH degradation product is indicated with an asterisk in the ΔtatC strain. The molecular masses (in kilodaltons) are indicated on the left of the gels. Full-length blots are presented in Supplementary Fig. 4 (D)  The immunoblot bands from panel C were quantified from 6 experiments by densitometry analysis using the ImageQuant software.
Material and Methods In silico genome and sequence analysis. A modified version of TATFIND was used to screen the P. aeruginosa PA14 proteome for the presence of Tat signal peptides (File S1). For the identification of P. aeruginosa Tat substrates with TatP (http://www. cbs. dtu. dk/services/TatP) we specified the regular expression [RK] [KR]. [FGAVML][LITMVF] for output filtering. Identifiers with at least 1 "Yes" amongst the five scores represented in the output of TatP were selected as candidates Tat signal peptides. For the identification of P. aeruginosa Tat substrates with PRED-TAT we ran the PRED-TAT (HMMER) program (available http://www. compgen. org/tools/PRED-TAT/supplement) on all PA14 predicted proteins then submitted the hits we obtained to the PRED-TAT webserver (http://www. compgen.
org/tools/PRED-TAT/) using both the "original" and the "new" models. This analysis predicted 32 Tat substrates (Table  S1 ). For the identification of P. aeruginosa Tat substrates which might have a missassigned start codon, all PA14 ORFs of 50 amino acids starting with an ATG and having RR or RK or KR within the first 20 amino acids were extracted from a genbank file downloaded from https://www. ncbi. nlm. nih. gov/nuccore/NC_008463. 1/ leading to 33416 sequences. The obtained amino acid sequences were then used as input to the modified TATFIND program. For manual signal peptides prediction and analysis, SignalP3. 0 (http://www. cbs. dtu. dk/services/SignalP-3. 0/), TATFINDv1. 4 (http://signalfind. org/TATFIND. html), TatP1. 0 and PRED-TAT algorithms were used on the first 70 residues of P.
Microscopy and outer membrane integrity assay. For phase-contrast microscopy, cells were grown to mid-exponential phase at 37 °C in LB medium and mounted on microscope slides covered with Poly-L-Lysine (Sigma-Aldrich) to immobilized cells. Fixed cells were imaged with a Zeiss AxioImager m2 equipped with a Hamamatsu OrcaR2 camera with a 100 × phase-contrast objective. The images were acquired with AxioVision software. For viability measurements, overnight cultures were adjusted to an OD 600 of 1, serial dilutions from 10 -1 to 10 -6 were prepared in LB, and 5 µl of each dilution were spotted onto LB agar or LB agar + 2% (w/v) SDS. Plates were incubated overnight at 37 °C and photographed.
Peroxidase-conjugated anti-Mouse or anti-Rabbit IgGs (Sigma, dilution 1:5000) were used as secondary antibodies. Nitrocellulose membranes were developed with homemade enhanced chemiluminescence and exposed to X-ray film for the appropriate time or were scanned using ImageQuant LAS4000 analysis software (GE Healthcare Life sciences). Protein samples equivalent to 0. 1 OD 600 units were loaded for whole cell and spheroplasts analysis while protein samples equivalent to 0. 2 OD 600 units (anti-DsbA and anti-EF-Tu immunoblots) or 1 OD 600 units (anti-His 6 immunoblot) were used for periplasm analysis and protein samples equivalent to 1 OD 600 units were used for supernatant analysis.
Acknowledgements This work was supported by a joint grant from the  Vaincre la Mucoviscidose  and the Grégory Lemarchal associations (RF20140501138) and recurrent funding from the  CNRS  and  Aix-Marseille Université. M. R. G. was supported by a PhD studentship and ATER position from  Aix-Marseille Université. We thank  J. Delisle  and  T. Doan  for their help with microscopy, and  M. Ba ,  I. Bringer ,  A. Brun  and  O. Uderso  for technical assistance. We are grateful to  S. Bigot ,  E. Cascales ,  B. Field ,  L. Houot , members of the Bleves/Voulhoux teams and all the members of the LISM for helpful discussion and support. We also thank  B. Field  for critical reading and comments about the manuscript.
The 500-bp and 1. 5-kb insert shotgun libraries were prepared with Illumina TruSeq DNAseq Sample Prep kit. The 3-and 5-kb mate-pair libraries were prepared similarly except a custom linker was ligated between the fragment ends to facilitate mate-pair recovery. All libraries were sequenced individually in single lanes for at least 100 cycles on Illumina GAIIx or HiSeq2000 machines using Illumina TruSeq SBS Sequencing kit v3. Bases were called with Casava v1. 6 or 1. 82. The custom 3-and 5-kb matepair libraries were filtered for properly oriented reads of the appropriate insert size and uniqueness using custom pipeline scripts (available from ftp://ccb. jhu. edu/pub/ dpuiu/Bees/scripts/). Raw Illumina reads were 5 0 -and 3 0trimmed for nucleotide-bias and low-quality bases using the FASTX Toolkit (http://hannonlab.
The RNAseq libraries were prepared from an average cDNA fragment size of 250 bases using the Illumina TruSeq Stranded RNAseq Sample Prep kits. The libraries were individually barcoded and quantitated using qPCR before pooling and sequencing from both ends with TruSeq SBS Sequencing kits v2 or 3 for 100 cycles on a HiSeq2000 or with a HiSeq4000 Sequencing kit v1 for 150 cycles on a HiSeq4000 instrument. Bases were called with Casava v1. 6 or 1. 82 or bcl2fastq v2. 17. 1. 14. Reads were trimmed as for DNA sequencing. The resulting trimmed reads from the male and female 2011 and the male and female antennal 2013 samples were assembled using Trinity (Release 2014-04-13) (http://trinityrnaseq. github. io/) (Haas et al. 2013).
Transposable and Repeat Element Annotation Although a repeat analysis for masking purposes is part of the NCBI gene annotation steps, we undertook a dedicated analysis using the REPET package  (Quesneville et al. 2005; Flutre et al. 2011). The computational pipeline to predict and classify transposable element (TE) integrations used both de novo-(TEdenovo) and homology-based methods (TEannot). A summary of the REPET pipeline can be found at https://urgi. versailles. inra. fr/Tools/REPET and detailed description of the applications and methods are in the Supplementary Material online.
Expression Analysis Levels of gene expression were determined by aligning the trimmed RNAseq reads from each library against the chemoreceptor and OBP transcripts from the i5k Workspace@NAL, occasionally truncated to avoid overlap with the UTRs of wellexpressed neighboring genes, using the Burrows-Wheeler Aligner (BWA)  (Li and Durbin 2009)  and Read1 of each read pair. Samtools  (Li et al. 2009 ) was used to sort, index, and summarize the BWA. Read counts were standardized as Read counts Per Kilobase transcript per Million mapped reads in each library (RPKM) to facilitate comparisons across the 17 samples (supplementary table S1, Supplementary Material online).
available from the National Center for Biotechnology Information (NCBI) as BioProjects PRJNA297591 and PRJNA168335 (NCBI GCF_000341935. 1). It is of a size and quality comparable to those of other Hymenoptera in GenBank (supplementary table  S2 , Supplementary Material online). We assessed the completeness of this assembly using BUSCO (Benchmarking Universal Single-Copy Orthologs)  (Waterhouse et al. 2018) , which revealed high completeness scores of 97. 6-99. 5%, with few duplicated (0. 3-0. 7%), fragmented (0. 3-1. 7%), or missing (0. 2-0. 7%) genes. These assembly completeness estimates are on a par with or marginally better than for the other assessed hymenopteran genomes (supplementary table  S4 , Supplementary Material online). In addition, 86-94% of the ILLUMINA RNAseq reads described below mapped to the assembly (NCBI C.
2013 ) was also employed. Gene modeling was performed by NCBI using their GNOMON pipeline, and yielded 11,210 protein-coding genes and 41 pseudogenes, as well as 1,307 noncoding RNA genes. A total of 25,937 transcripts were modeled, with 25,189 of those having support from the RNAseq and/or homology with other arthropod proteins. The mean number of transcripts per gene is 2. 3 (range 1-50) and the mean number of exons per gene is 8. 9 (range 1-108). The protein-coding gene set is comparable in completeness (as estimated by alignment to the REFSEQ proteins from D. melanogaster) to that of the eusymphytan sawfly Athalia rosae and the woodwasp Orussus abietinus (NCBI C. cinctus Annotation Release 100-https://www. ncbi. nlm. nih. gov/genome/annotation_euk/Cephus_cinctus/ 100/). BUSCO analysis of the quality of this automated annotation in terms of expected gene content reflected the high BUSCO completeness scores for the genome assembly, identifying 98.
Acknowledgments We thank  Alvaro Hernandez  and the  W. M. Keck Center for Comparative and Functional Genomics  at the  University of Illinois at Urbana-Champaign  for genomic and RNA library construction and sequencing;  Daniel Ence  and  Mark Yandell  ( University of Utah ) for annotation of an earlier version of the genome assembly;  Terence Murphy  for assistance with the NCBI annotation;  Monica Poelchau  and  Chris Childers  for assistance with the i5k Workspace@NAL browser and official gene set v1. 1;  Chris Elsik  for an Apollo browser at NasoniaBase;  Masatsugu Hatakeyama ,  Bernhard Misof ,  Oliver Niehuis , and  Jan Philip Oeyen  for granting prepublication access to gene annotations of Athalia rosae and Orussus abietinus; and  Megan Hofland  and  Norma Irish  for preparing figure 1.
Driven by the recent experimental progress on the path to a potential four-top signal observation  [1] , we investigate in this letter how the analysis of 35. 9 fb -1 of LHC proton-proton collisions at a centre-of-mass energy of 13 TeV could constrain light pseudoscalar sgluons, such as those predicted in supersymmetric models featuring Dirac gauginos. To this aim, we have implemented the above-mentioned CMS four-top study in the MadAnalysis 5 framework  [33, 34]  and made it publicly available through the MadAnalysis 5 Public Analysis Database  [35]  and InSpire  [36]. While this offers a possibility to test any given new physics model against the results of this particular search, we focus on the pseudoscalar sgluon case and use state-of-the-art Monte Carlo simulations  [26]  to constrain the associated four-top signal.
Our C++ code is additionally publicly available from InSpire  [36]  through its digital object identifier (DOI) 10. 7484/IN-SPIREHEP. DATA. BBBC. 6732. The main object reconstruction features and baseline selection criteria of the considered search are summarised in Table  I. Jets are reconstructed by means of the antik T algorithm  [62]  with a radius parameter R = 0. 4, and only those jets with a transverse momentum p T and pseudorapidity η satisfying the criteria indicated in the top panel of the table are retained, a slighter selection being imposed on b-tagged jets. Furthermore, electron and muon candidates are required to be central and rather hard (see again the top panel of the table), and the various reconstructed objects are imposed to be isolated as described in Ref. [39]. The baseline selection first includes constraints on the hadronic activity H T , defined as the scalar sum of the p T of all reconstructed jets, and on the missing transverse energy p miss T.
ACKNOWLEDGMENTS We are grateful to  J. Andrea ,  F. Blekman  and  D. Lontkovskyi  for their help in understanding some details of the CMS-TOP-17-009 analysis. This work has been supported in part by  French  state funds managed by the  Agence Nationale de la Recherche (ANR)  in the context of the  LABEX ILP  ( ANR-11-IDEX-0004-02 ,  ANR-10-LABX-63 ). MDG acknowledges support from the  Agence Nationale de Recherche  grant  ANR-15-CE31-0002  " Hig-gsAutomator. "LD is supported in part by the  National Science Centre (NCN)  research grant No. 2015-18-A-ST2-00748. The use of the CIS computer cluster at the  National Centre for Nuclear Research in Warsaw  is gratefully acknowledged.
Cell Counting and Statistical Analysis The cells were counted manually using graphic tools of the Zeiss computer software (Zen 2012). The fraction of immuno+ cells among the total number of cells identified by Hoechst staining was determined in ten fields per coverslip in each in vitro condition and for each replicate experiment. Each field to analyze was randomly chosen to avoid any experimental bias. Three independent experiments were conducted for each determination and data were expressed as mean ± SEM (standard error of the mean). Throughout the study, the ''N'' represents the number of experiments performed independently, at different time, using different cell passaging with the same protocol (technical replicates). In addition, and for each experiment, every condition was run in triplicate for Fluidigm (biological replicates with two independent lines).
Beside the generation of enriched human OSPCs in these conditions, the method reveals a level of heterogeneity that may be useful in identifying important additional modifications in in vitro conditions to selectively stabilize the developmental otic sensory trajectory lineage. Advances in knowledge of stages and time course of OSPC differentiation offers an opportunity to study fundamental questions of human inner ear development, in addition to the use of their derivatives for testing drugs and cell therapy for deafness and balance disorders caused by HC loss. (A) Gene interaction networks were constructed using the IPA software. Nodes shaded in pink represent genes that are upregulated in day 6 and day 13, and green nodes are genes that are downregulated in day 6 and day 13 cultures. These networks assembled by up-and down-regulated genes include genes linked to Notch signaling pathway.
Article reSeArcH Supplementary Data 1). Analysis of near-universal single-copy orthologues using BUSCO  6  revealed a slight increase in complete singlecopy orthologues and a reduction in fragmented and missing genes compared to previous assemblies (see Supplementary Methods and Supplementary Discussion). AaegL5 is markedly more contiguous than AaegL3 and AaegL4 assemblies  2, 4  (Fig. 1a , b, e and Table  1 ). Using the TEfam, Repbase and de novo identified repeat databases, we found that 65% of AaegL5 was composed of transposable elements and other repetitive sequences (Fig. 1f  and Supplementary Data 2, 3). Complete and correct gene models are essential for studying all aspects of mosquito biology. We used the NCBI RefSeq annotation pipeline to produce annotation version 101 (AaegL5. 0; Extended Data Fig.
Two rounds of contig polishing were performed. For the first round, as part of the FALCON-Unzip pipeline, primary contigs and secondary haplotigs were polished using haplotype-phased reads and the Quiver consensus caller  45. For the second round of polishing we used the 'resequencing' pipeline in SMRT Link v. 3. 1, with primary contigs and haplotigs concatenated into a single reference. Resequencing maps all raw reads to the combined assembly reference with BLASR (v. 3. 1. 0)  46 , followed by consensus calling with Arrow (https://github. com/PacificBiosciences/ GenomicConsensus)  46. Hi-C sample preparation and analysis. Library preparation. In brief, insect tissue was crosslinked and homogenized. The nuclei were then extracted and permeabilized, and libraries were prepared using a modified version of the in situ Hi-C protocol that we optimized for insect tissue  47.
Separate libraries were prepared for samples derived from three individual male pupae. The resulting libraries were sequenced to yield 118 million, 249 million and 114 million reads (coverage: 120×) and these were processed using Juicer  48. Hi-C approach. Using the results of FALCON-Unzip as input, we used Hi-C to correct misjoins, to order and orient contigs, and to merge overlaps (Extended Data Fig. 1c-e ). The Hi-C based assembly procedure that we used is described in detail in the Supplementary Methods and Supplementary Discussion. Notably, both primary contigs and haplotigs were used as input. This was essential, because Hi-C data identified genomic loci in which the corresponding sequence was absent in the primary FALCON-Unzip contigs, and present only in the haplotigs; the loci would have led to gaps, instead of contiguous sequence, if the haplotigs were excluded from the Hi-C assembly process (Extended Data Fig.
After PBJelly, the liftover file was used to translate the renamed scaffolds to their original identifiers. For this final polishing step (run with SMRT Link v3. 1 resequencing), the reference sequence included the scaffolded, gap-filled reference, as well as all contigs and contig fragments not included in the final scaffolds (https://github. com/skingan/AaegL5_FinalPolish). This reduces the likelihood that reads map to the wrong haplotype, by providing both haplotypes as targets for read mapping. For submission to NCBI, two scaffolds identified as mitochondrial in origin were removed (see below), and all remaining gaps on scaffolds were standardized to a length of 100 Ns to indicate a gap of unknown size. The assembly quality value was estimated using independent Illumina sequencing data from a single individual male pupa (library H2NJHADXY_1/2).