dataesr/scientific-paragraphs-categorization huggingface dataset:
" (NIH Metabolomics symposium, August 2013). We demonstrate here that feature degeneracy -the ocean of data -has a great impact on the final annotated peak list information, thus impacting the biological knowledge mined from untargeted metabolomic studies. We estimate, based on analysis of standard mixtures, that feature inflation is close to 95%, in agreement with other studies  33, 11. Our package MS-CleanR, with its a point-and-click software on a Shiny interface, is a new component in the suite of tools comprising the GUI software MS-DIAL and the annotation capabilities of MS-FINDER which altogether provide a comprehensive workflow, from raw data to final annotated peaklist. MS-CleanR can reduce the number of features by 80-90% and keep most unique metabolite signals without compromising the final data structure.
The opportunity to rank the annotation results with reference to in-house databases narrows down the final identification possibilities. Additionally, the package is able to combine both PI and NI mode (A. thaliana experiment) or to treat only one mode (M. truncatula study) depending of the study objectives. We demonstrate the utility of this workflow by analyzing secondary metabolites levels in three M. truncatula strains with different susceptibilities to a pathogenic oomycete. We could annotate 70% of the dataset with 60% at the genus or family level using DBs prioritization. The resulting mass spectral similarity network further supports annotation results since most clusters gathered the same metabolite chemical class. Still, our approach was unable to keep only unique metabolite features regarding the annotation rate comprising between 24 and 45% for standard mixtures.
ASSOCIATED CONTENT The Supporting Information is available free of charge on… (PDF) • Sup Table  S1 : Excel table with cluster annotation, result summary and database for level 2 annotation imported in MS-FINDER for the 51 NPs dataset (XLSX) • Sup Table  S2 : Excel table with cluster annotation, result summary and database for level 2 annotation imported in MS-FINDER for the 167 IROA MS standard library dataset (XLSX) • Sup Table  S3 : Arabidopsis DB used as input for level 2 annotation in MS-FINDER (TXT) • Sup Table  S4 : Brassicaceae DB used as input for level 2 annotation in MS-FINDER (TXT) • Sup Table  S5 : Arabidopsis dataset treated by MS-CleanR and OPLS-DA top ranked features (XLSX) • Sup Table  S6 : Medicago dataset treated by MS-CleanR and OPLS regression coefficient for feature ranking (XLSX) • Sup Table  S7 : Medicago DB used as input for level 2 annotation in MS-FINDER (TXT) • Sup Table  S8 : Fabaceae DB used as input for level 2 annotation in MS-FINDER (TXT) • Raw data from Arabidopsis and Medicago LC-MS profiling available on Zenodo using the DOI: 10.
ACKNOWLEDGMENTS We thank  Dr. Stephane Bertani  for providing us standards from the  IROA-MS Library. Financial support from The  French national infrastructure for metabolomics and fluxomics, MetaboHUB - ANR-11-INBS-0010  and  PSPC Solstice  project supported by  Bpifrance  ( SOLutionS for Integrates Treatments under Environmental Management ). We thank  E Amblard ,  N. Jariais  and  C. Jacquet  for M. truncatula cultures and  A. Haouy  for A. thalina cultures and sample preparations. We also acknowledge  Carol Featherstone of Plume Scientific Communication Services  for professional scientific editing.
Visualization of LN Formation in Whole-Mount Embryos To comprehensively study embryonic LN formation, we first used whole-mount 3D immunofluorescence on E11. 5-E14. 5 RORgt EGFP embryos. We separately quantified LTi 0 and LTi 4 populations within all LN anlagen using the spotfunction tool in Imaris (Figures 1A-1D, S1A, and S1B; Videos S1, S2, and S3). Although we did not observe any LTi cells in E11. 5 RORgt eGFP embryos (data not shown), clusters of LTi 0 cells (RORgt + cells) and LTi 4 (RORgt + CD4 + ) cells started to appear at E12. 5  (Figures 1A and S1A; Video S1). At this stage, small LTi 0 clusters as well as the presumptive cervical LN (CLN) anlagen and mandibular LN (MdLN) anlagen were located very close to each other in the cervical region. A second cluster could also be observed in the axillary region but not yet the separate brachial LN (BrLN) anlagen and axillary LN (AxLN) anlagen.
8% of the total cell population was recombined (Figure  S8D ). In line with this, 4OHT administration between E8 and E9 induced only residual microglia labeling (Figure  5B ), whereas we observed about 63% recombination within the LTi population using the same approach (Figure  5B ). To confirm recombination of LTi cells in situ within the LN anlagen, we analyzed sections from iCdh5 tdT and iCxcr4 tdT embryos obtained from dams injected with 4OHT between E8. 5 and E10. 5. Indeed, the presence of tdTomato + CD4 + LTi 4 cells within the LN anlagen followed the results obtained by flow cytometry (Figure  5C ). Collectively, the fate mapping data from the iCdh5 tdT or iCxcr4 tdT models revealed that embryonic LTi cells are derived from early embryonic hematopoietic activity, most likely from the AGM region, but not from YS hematopoiesis.
Data and Code Availability Sequencing data and codes were deposited in online databases. All RNA-sequencing data were deposited in the Gene Expression Omnibus public database as superseries under GSE153771. In-house-made code, original data, and corresponding docker images are available from the project github repository (https://github. com/CIML-bioinformatic/SPlab_BecomingLTi) both with a readme explaining the detailed procedure to reproduce the results.
RNA was isolated using a RNA purification kit (Norgen Biotek Corp. 51800) and the quality and concentration of the RNA was determined with the bioanalyzer using RNA 6000 pico kit (Agilent 5067-1513) and Qubit fluorometer (ThermoFisher) using the Qubit dsDNA HS Assay Kit (ThermoFisher Q32854). Only those samples with a RIN value above 8 were used for subsequent library preparation. Samples were pre-amplified with the SMART-Seq v4 Ultra Low Input RNA Kit (Clontech). Libraries were generated with sample barcode and molecular indexing, the latter being used in order to provide an absolute digital measurement of gene expression levels. Sequencing was performed using a NextSeq 500, in 2 runs of 14 muLTiPlexed samples. NextSeq 500 data were demuLTiPlexed individually per sample. Furthermore, the raw fastq files were processed to trim the molecular barcodes, the end of reads depending on quality, and the potential remaining illumina adapters.
Cells were subsequently stained and alive CD45 + F4/80 -IL7Ra + LTi cells were sort-purified using the BD FACS ARIA III sorter. Sorted-purified cells were subsequently prepared for single-cell sequencing using the Chromium Single Cell 3 0 kit from 10X genomics (v2 for periphery and v3 for FL (120267 and 1000092 respectively-California, USA) for the library preparation and the NextSeq 500/550 High Output Kit v2 (75 cycles) cartridges from Illumina for the sequencing. The quality and concentration of the cDNA was determined with the bioanalyzer using the High Sensitivity DNA Analysis Kit (Agilent 5067-4626) and Qubit fluorometer using the Qubit dsDNA HS Assay Kit. FASTQ raw files were processed using Cell Ranger software (v3, default parameters), which performs alignment, filtering, barcode counting and unique molecular identifier (UMI) counting.
Reads were aligned to the mouse mm10 genome. Quality control and cell filtering: Quality control (QC) was performed to remove poor quality cells thanks to in-house developed R scripts. Using Seurat R package CreateSeuratObject function, cells with less than 200 or more than 5000 (potential doublets) detected genes, cells with a too low or too high number of UMI (nUMI < median -3MAD or nUMI > media + 3MAD) and cells with more than 40% mitochondrial gene expression (apoptotic cells) were removed. After quality control, for E13. 5 FL 248 cells were taken out, leaving 10533 for analysis; for E13. 5 periphery, 322 cells were taken out, leaving 4209 for analysis; for E14. 5 FL, 678 cells were taken out, leaving 11410 for analysis; for E14. 5 periphery 707 cells were taken out, leaving 4301 for analysis.
ACKNOWLEDGMENTS This work was supported by  FRM Amorcage de jeunes equipes  ( AJE20150633331 ),  ANR ACHN  ( ANR-16-ACHN-0011 ),  ANR   PRCI  ( ANR-17-CE13-0029-01 ), and A*midex Chaire d'excellence (to S. A. v. d. P. ) and institutional grants from  INSERM ,  CNRS , and  Aix-Marseille University  (to the CIML). We acknowledge the PICSL imaging facility of the  CIML (ImagImm) , a member of the national infrastructure  France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ), notably  Mathieu Fallet  and  Sebastian Mailfert. We thank  Catherine Robin  for fruitful discussions;  Pierre Milpied ,  Laurine Gil , and the genomic core platform for help with single-cell sequencing; LABO'M for social support; the animal facility, notably  Toufik Guelmami  and  Michel Pontier ; the flow cytometry core facility, notably  Atika Zouine ,  Sylvain Bigot , and  Marc Barad ;  Lionel Chasson  of the histology platform;  Ralf Adams  for use of Cdh5-CreERT2; Gerard Eberl for the kind donation of the RORgt eGFP mice;  Shinichi Nishikawa  for the IL7Ra antibody clone; and  Reina Mebius  for past input.
To this end, we start our work by systematically searching for articles in literature in Scopus, Web of Science and IEEE Xplore databases with the following keywords: "holonic traffic", "holonic transport", "holonic transportation", "holon traffic", "holon transport", "holon transportation", "multilevel traffic", "multilevel transport", "multilevel transportation", "hierarchical traffic", "hierarchical transport", "hierarchical transportation". We double checked the literature by using the same keywords in the Google Scholar database as presented by Figure  1. Papers focusing on either traffic or/and transportation using holonic paradigm were retained. After reading the abstracts of the papers and skimming them, we observed that two main different holonic paradigms were used to model traffic and transportation: Holonic Multi-Agent System and Holonic Manufacturing System (more details are provided at Section 3).
CRIO is used by Gaud et al. [46]  and Rodriguez et al. [88]  to design their traffic models. CRIO metamodel has been extended to model human activities, named K-CRIO  [65]. K-CRIO uses a semantic layer for collaborative software to enhance knowledge management within the targeted organizations. CRIO has also been extended to consider the norms named NCRIO  [72]. ASPECS  [23]  is an agent-oriented software process for complex engineering systems. ASPECS allows the development of a HMAS. ASPECS is based on a holonic organizational metamodel and provides a step-by-step guide from requirements to code allowing the modeling of a system at different levels of detail using a set of refinement methods. ASPECS integrates design models and philosophies from both object-oriented and agent-oriented software engineering.
The process described by ASPECS was followed by Galland and Gaud  [40] , Galland et al. [41]  in their environment traffic model. SARL  [85, 96]  is a general statically-typed agent-programming language with emphasis on holonic agents. SARL aims at providing the fundamental abstractions for dealing with concurrency, distribution, interaction, decentralization, reactivity, autonomy and dynamic reconfiguration. SARL can be used for agent-based simulations because SARL provides features for agent execution and direct communication. The particularity of SARL language is that, agents are holons. SARL provides therefore a complete support for holons allowing the development of HMAS. SARL is generally coupled with Janus  [42]  as an execution platform. Tchappi et al. [100]  use the SARL programming language in their road traffic implementation model.
, M (T m , P)], where m is the last chord in the progression P. M results from an adaptation of Lerdahl's tonal indicators  [1]  calculated in the Tonal Interval Space (TIS)  [31]. The next section briefly summarizes the most important features of the TIS. Refer to  [31]  for further information. The implementation of the model can be found at https://github. com/merismeris/tonal-tension-TIS.
We analyze different configurations of chord progressions, considering the consonance of the chords, the position of the notes for the voice leading, and the hierarchical structures that they represent. The final chord progressions that we used in our experiments are shown in Appendix A (Figure  A1  and the MIDI files can be accessed in https://github. com/merismeris/tonal-tension-TIS). Our hypothesis is that these five global tonal tension profiles are able to represent the prototypical curves categories associated with the selected chord progressions. Option (f) None was included among the options in Experiment 1 to test this hypothesis. We further hypothesize that longer musical passages may have more intricate associated tonal tension profiles that may be expressed as combinations of these basic categories.
The main difference between the two lies in knee flexion-extension (Figure  8 ): thanks to its reference height going up after the first step, the VHIP pattern brings the robot to knee extension while the LIP one bends down then raises again (Figure  7 ). In the horizontal plane, DCM-ZMP tracking looks similar to a regular capture-point walking controller (Figure  9 ) since the variations of ω(t) required to lift the CoM up are very small: in this stair climbing scenario, ω ranges from 3. 536 Hz to 3. 550 Hz (Figure  10 ), as opposed e. g. to the elliptic staircase scenario (Figure  4a ) where ω ranges from 3. 4 Hz to 3. 8 Hz. We release the code of this controller as open source software. 16.
ACKNOWLEDGMENT The author would like to thank  Boris van Hofslot ,  Alain Micaelli  and  Vincent Thomasset  for their feedback on preliminary versions of this paper. Our thanks also go to  Pierre Gergondet  for developing and helping us with the mc rtc framework. This research was supported in part by  H2020  EU project COMANOID http://www. comanoid. eu/,RIA No  645097 , and the  CNRS-AIST-AIRBUS Group Joint Research Program.
Contribution In this paper, we propose a complete survey of long-read error correction tools. Previous papers already covered this topic. On the one hand,  [24]  and  [70]  focused on hybrid correction. The former proposed a benchmark of ten tools on five datasets, whereas the latter studied performances of graph-based and alignment-based methods. On the other hand,  [50]  covered both hybrid and self-correction, but focused on RNA-sequencing data. Here, we focus on DNA-sequencing, and draw-up a summary of every single approach described in the literature, both for hybrid and self-correction. In addition, we also provide a list of all the available methods, and describe the strategies they rely on. We thus propose the most complete survey on long-read correction up to date. Additionally, long reads characteristics, such as the sequencing depth, the length, the error rate, and the sequencing technology, can impact how well a given tool or strategy performs.
As a result, a given tool performing the best on a given dataset does not mean this same tool will perform as well on other datasets, especially if their characteristics fluctuate from one another. Therefore, we also present an in-depth benchmark of all available long-read correction tools, on a total of 19 different datasets, displaying diverse characteristics. In particular, we assess both simulated and real data, and rely on datasets having varying read lengths, error rates, and sequencing depths, ranging from smaller bacterial to large mammal genomes. Compared to previous papers, we thus propose the most extensive benchmark up to date.
State-of-the-art As mentioned in Section 1, the literature describes two main approaches to tackle long-read error correction. On the one hand, hybrid correction makes use of complementary, high quality, short reads to perform correction. On the other hand, self-correction attempts to correct the long reads solely using the information contained in their sequences. One of the major interests of hybrid correction is that error correction is mainly guided by the short read data. As a result, the sequencing depth of the long reads has no impact on this strategy whatsoever. This way, datasets composed of a very low coverage of long reads can still be efficiently corrected using a hybrid approach, as long as the sequencing depth of the short reads remains sufficient, i. e. around 50x. Contrariwise, self-correction is purely based on the information contained in the long reads.
As a result, deeper long read coverages are usually required, and self-correction can thus prove to be inefficient when dealing with datasets displaying low coverages. The required sequencing depth to allow for an efficient self-correction is however reasonable, as it has been shown that from a coverage of 30x, self-correction methods are able to provide satisfying results  [66]. In this section, we present the state-of-the-art of available long-read error correction methods. More particularly, we describe the various methodologies adopted by the different tools, and list the tools relying on each methodology, both for hybrid and self-correction. Details about performances, both in terms of resource consumption and quality of the results, are however not discussed here. Experimental results of a subset of available correction tools, on various datasets displaying diverse characteristics, are presented in Section 3.
PBDAGCon (the correction module used in the HGAP assembler)  [15] , PBcR-BLASR  [36] , Sprai 4   [26] , PBcR-MHAP  [9] , FalconSense (the correction module used in the assembler Falcon)  [14] , Sparc  [74] , the correction module used in the assembler Canu  [38] , MECAT  [73]  and FLAS  [7]  rely on this approach. 2. Use of de Bruijn graphs. In a similar manner as the hybrid correction approach using de Bruijn graphs, described in Section 2. 1. 3, once the graph is built, it can be used to anchor the reads. The graph can then be traversed, in order to find paths allowing to link together anchored regions of the long reads, and thus correct unanchored, erroneous regions. LoRMA  [64]  and Daccord  [67]  are based on this approach. As for hybrid correction, other methods, such as CONSENT  [58] , combine these two approaches, in order to counterbalance their advantages and their drawbacks.
To this aim, it makes use of poaV2  [44, 43] , an algorithm that computes MSA with the help of partial order graphs. These graphs are DAGs, and are used as data structures containing all the MSA information. For a given MSA, the graph is thus enriched at each step, by aligning the new sequences to it, and updating it with additional vertices and edges, if necessary. Unlike other methods, this strategy thus allows CONSENT to both compute MSA and built the DAG used for consensus computation at the same time. However, poaV2 is resource consuming, even on windows of a few hundreds bps. To address this issue, CONSENT relies on an efficient segmentation strategy, that allows to compute MSA and consensus on much shorter sequences, thus greatly reducing computational costs. First, common k-mers between the sequences included in the window being processed are computed.
Qualitative comparison In this section, we present an in-depth benchmark of available hybrid and self-correction tools described in Section 2, and summarized in Table  1  and in Table  2. The following methods are however excluded from the benchmark, for the reasons mentioned below: • FalconSense: this tool could not be installed on any of the computers used in our experiments; • HECIL: this tool did not manage to correct any read, and produced a set of corrected read identical to the set of uncorrected reads, for all the datasets; • LSCplus: this tool is not available for download anymore; • MiRCA: this tool stopped and reported an error during the correction of all the datasets; • ParLECH: this tool could not be installed on any of the computers used in our experiments; • PBcR: this tool stopped and reported an error during the correction of all the datasets; • PBcR-BLASR and PBcR-MHAP: this two tools are preliminary versions of the correction module from the assembler Canu.
All tools were ran with default or recommended parameters. The command lines that were used for each correction tool are detailed in Supplementary Materials. We present results in various subsections, according to the characteristics of the assessed datasets. In particular, we distinguish seven different categories of datasets. 1. Datasets with high error rate and high coverage. This corresponds to the A. baylyi real and S. cerevisiae real datasets of Table  3. We present these results in Section 3. 3, Table  4. 2. Datasets with high error rate and low coverage. This corresponds to the C. elegans real dataset of Table  3. We present these results in Section 3. 4, Table  5. 3. Datasets with medium error rate and low coverage. This corresponds to the A. baylyi 20x, E. coli 20x, S. cerevisiae 20x, C.
elegans 20x and E. coli real datasets of Table  3. We present these results in Section 3. 5, Tables  6  and 7. 4. Datasets with medium error rate and medium coverage. This corresponds to the A. baylyi 60x (medium), E. coli 60x (medium), S. cerevisiae 60x (medium) and C. elegans 60x (medium) datasets of Table  3. We present these results in Section 3. 6, Table  8.
5. Datasets with low error rate and low coverage. This corresponds to the E. coli 30x, S. cerevisiae 30x and C. elegans 30x datasets of Table  3. We present these results in Section 3. 7, Table  9. 6. Datasets with low error rate and medium coverage. This corresponds to the E. coli 60x (low), S. cerevisiae 60x (low) and C. elegans 60x (low) datasets of Table  3. We present these results in Section 3. 8, Table  10. 7. Datasets containing ONT ultra-long reads. This corresponds to the H. sapiens dataset of Table  3. We present these results in Section 3. 9, Table  11. Additionally, for the sake of clarity, the names of hybrid correction tools are reported in normal font, while the names of self-correction tools are reported in italic. Moreover, in tables, we always report the results of hybrid correction tools first, and then the results of self-correction tools.
High error rate, high coverage Here, we present results on the A. baylyi real and S. cerevisiae real datasets of Table  3. For these datasets, when possible, we evaluated the split corrected long reads. When split reads were not available, we evaluated the trimmed corrected long reads. Finally, when both split and trimmed reads were not available, we evaluated the complete corrected long reads. These results are summarized in Table  4  and commented below. Most hybrid correction tools performed well and managed to reduce the error rates of the reads to around 1% or less, even on the highly noisy S. cerevisiae dataset, displaying an initial error rate of 44. 51%. Actually, only LSC, and Hercules displayed poor performances, reaching error rates of 6. 7% for the former, and up to 28. 5% for the latter, on the S.
cerevisiae dataset. Moreover, even on the A. baylyi dataset, containing a smaller proportion of errors, Hercules still displayed an error rate of 18. 6%. In terms of assembly, all hybrid tools displayed a fairly low number of contigs, and covered the reference genomes quite well, except for ECTools, which only reached around 50% of genome coverage on the two datasets. A few additional tools yielded low quality assemblies on the S. cerevisiae dataset, namely Hercules, which only reached 29% of genome coverage, Jabba, which reached 72%, and especially LSC, which could not yield a proper assembly. Conversely, self-correction tools displayed poor performances, and did not manage to greatly reduce the error rates of the reads for any of the datasets. Indeed, the lowest error rate is reached by LoRMA, and is mostly caused by the fact it aggressively splits the reads, as suggested by their average length, reaching around 230 bp on the two datasets.