dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS. We thank  Carl-Henrik Heldin  and  Aristidis Moustakas  for valuable comments and  Chunheng Mo  for technical assistance. The study was supported by the  Knut and Alice Wallenberg Foundation  (to L. A. ),  Vetenskapsrådet  (to L. A. ),  Research Council of Norway  project  254774 ,  GEN-SINC  (to A. F. , F. B. and L. A. ), and  AquaCRISPR  ( ANR-16-COFA-0004-01 ) (to A. H. ). The  National Genomics Infrastructure (NGI)/Uppsala Genome Center  and  UPPMAX (Uppsala Multidisciplinary Center for Advanced Computational Science)  provided service in massive parallel sequencing and computational infrastructure. Work performed at  NGI/Uppsala Genome Center  has been funded by  RFI/VR  ( Council for Research Infrastructure/Vetenskapsrådet ) and  Science for Life Laboratory, Sweden.
The volume of the field of view (volume 500 x 500 x 250 µm) was reconstructed with Imaris 8. 0 (Bitplane) from z-stack images (512 x 512 pixels). Mural cells skeleton were traced using the filaments tracer module of Imaris, which calculated the surface of vessels covered by mural cells. Sets of coordinates of the 3 different imaging sessions were extracted with the Edlund method in order to align the coordinates with a stable reference point and further point-by-point analysis using MatLab (Mathworks). Mural cells extensions and retractions on vessel walls were measured by subtraction of signal between consecutive images.
To measure velocities of red blood cells (RBCs) in individual vessels, retro-orbital injections of a solution of 70-kDa Dextran-Texas red (Sigma-Aldrich) were performed to fluorescently label the plasma but not red cells permitting identification of their circulation. Fluorescence excitation was delivered by a Lambda LS xenon arc lamp (300W; Sutter Instruments) fitted with a fast-rotating filter wheel (27 milliseconds lag) and linked to the stereomicroscope with an optical fiber. Fluorescence emission was captured with a sCmos camera (C11440 Orca-Flash 4. 0, Hamamatsu Photonics) capturing images at 100 to 800 Hz. To allow accurate quantification of flow velocities in vessels with widely varying diameters (between 5 to 100 µm) such as brain microvasculature, several movies were acquired with MetaMorph software (Molecular Devices) in the same field at different focus and different magnifications and resolution (512 x 512 pixels).
With the 20x objective, the speed limit was up to 5 mm/s if images were taken at 100 Hz, 9 mm/s at 200 Hz, 14 mm/s at 400 Hz and up to 17 mm/s at 800 Hz. RBC flow was analyzed from 10,000 images per field using an in-house Matlab sofware based on an algorithm as described  (Kim et al. , 2012). Matlab code for LS-PIV (Line-Scanning Particle Image Velocimetry) available at: https://sourceforge. net/projects/lspivsupplement/files). Briefly, one line was traced along the center lumen of the vessel. Parallel line-scans were automatically traced from the reference line and the emission fluorescence along this ribbon along the vessel was averaged and instantaneous red blood cell flow was measured. Two Fast Fourier Transform (FFT) were sequentially applied, a cross-correlation on the spatial frequency domain was performed, and inverse FFT on cross-correlation gave access to a correlation peak.
Acknowledgements This work is supported by  Ligue Française  contre l'epilepsie (MA-L),  Fondation pour la Recherche sur le Cerveau (FJ) ,  France Alzheimer  (FJ),  IPAM-BioCampus Montpellier  (PF, CL, PM) and FranceBioImaging  ANR-10-INSB-04  (PM).
Pan-cancer analysis of RNA expression in relation to genetic and DNA methylome aberrations of ERGs The second approach in our analysis focused on RNA expression deregulation (by RNA-seq) of ERGs across cancer types. For each cancer type, the analysis consisted of two parts: expression variation across tumor samples relative to one another (independent of the corresponding normal tissue) and expression changes in tumor relative to adjacent normal tissue. By integrating genetic and transcriptomic information matched to the same samples (using the TCGA database) a higher proportion of tumor samples showed significantly increased ERG expression (Z-score > 2) relative to down-regulation (Z-score < -2) (Fig. 3A ), in line with the observed higher proportion of samples with ERG amplifications than with deletions (Figs.
4F ; Supplemental Fig. S7A, B ), further supporting a driver role of ERGs in tumorigenesis and characterizing the nature of biological pathways in which ERGs play a functional role in cancer. These four hallmarks were also topmost significant in the 39 ConsensusDriver (P < 0. 05), top 39 Pan-Cancer Driver (P < 0. 1), and 42 multi-omic driver (P < 0. 05) ERGs (Supplemental Fig. 7C ). Orthogonal CRISPR-Cas9 screen to assess the driver potential of ERGs in epithelial-to-mesenchymal transition (EMT) We conducted a CRISPR-Cas9 screen using a custom-made lentiviral CRISPR library consisting of 1649 gRNAs targeting all 426 ERGs (Fig. 5A ; Supplemental Fig. S8 ) and A549 lung cancer cells stably expressing Cas9 (Fig. 5B, C ), aimed at an orthogonal in vitro assessment of driver potential of ERGs. The screen was conducted in A549 lung cancer cells, considered a gold standard for studying the epithelial-to-mesenchymal transition (EMT), a cellular program conferring on cancer cells multiple traits associated with higher-grade malignancy, which may have an underlying epigenetic mechanism  (Tam and Weinberg 2013).
Pan-cancer analysis of genetic alterations in ERGs The proportions of each of the CNAs and SNAs detected in ERGs were calculated among tumor samples within each of the 33 cancer types. The raw SNA data set contained somatic, nonsynonymous mutations, which were transformed into data with mutation status indicating the number of SNAs for a gene in each sample. The raw CNA data set was used to characterize CNAs by genomic position and amplitude as follows: CNA = 0 indicates diploid with no alteration; amplification = 1 indicates a shallow gain (a few additional copies, often broad); deep amplification = 2 indicates a high-level amplification (more copies, often focal); shallow deletion = -1 indicates a shallow loss, possibly a heterozygous deletion; deep deletion= -2 indicates a deep loss, possibly a homozygous deletion (https://www.
Multi-omics analysis of genomic and transcriptomic aberrations in ERGs To effectively visualize multidimensional data sets of the deregulation of ERGs across different cancer types, we integrated SNAs, CNAs, and RNA expression alterations. For each ERG, the proportions of CNAs versus SNAs were calculated among tumor samples within each of 33 cancer types. The analysis included only genetically deregulated genes that show SNAs and CNAs (amplification = 1, 2, and deletion = -1, -2) in at least 1% and 10% of the samples in any cancer, respectively. ERGs were then classified based on their mutation profiles such that those harboring mostly SNAs, with CNAs not passing the threshold of 10%, were considered as only mutated; whereas genes with CNAs in at least 10% of the samples were considered as amplified or deleted.
Differential expression analysis of ERGs in tumor samples and adjacent normal tissues We downloaded HTSeq count files from the Genomic Data Commons Data Portal (https://portal. gdc. cancer. gov) for each cancer type and divided adjacent normal samples and tumor samples based on the ID annotation of TCGA samples. Of the total of 33 cancer types, we focused on 18 cancer types that had available ID annotation for adjacent normal samples. We used DESeq2 analysis to calculate expression changes comparing tumor samples with adjacent normal tissues. Genes with coverage of fewer than 10 reads were excluded. Only ERGs with absolute values of log FC > 1 and FDR < 0. 05 were considered to be significantly differentially expressed.
ERG driver prediction models The characterization of potential driver roles for ERGs was based on ConsensusDriver, a novel approach that provides a systematic way to integrate the strengths of various driver prediction algorithms  (Bertrand et al. 2018). ConsensusDriver scores for ERGs overlapping with the ConsensusDriver genes  (Bailey et al. 2018)  were shown as a heatmap. The Pan-Cancer Driver score was generated for each of the 426 ERGs using a ranking method that accounts for SNAs, CNAs, and RNA expression aberrations within each cancer type and across multiple cancer types (the script provided on GitHub: https://github. com/IARCbioinfo/EPIDRIVE R2020 and as Supplemental Code). First, in the SNA and CNA data, we focused on ERGs that had a SNA or CNA (sharing the same direction) in at least 5% of samples in any cancer type.
Then, for each gene, we counted the number of cancer types in which that gene had a SNA or CNA; the number of cancer types was used as a primary ranking method, and the percentage of samples showing alterations was used as the secondary ranking for genes having identical primary ranks. The gene with the lowest rank was given a score of 1, the second rank a score of 2, and so on. Genes (n = x) with equal ranks "y" (based on both the primary and secondary methods) were all assigned the same ranking score "y"; a subsequent gene with rank "y + 1" was then assigned a ranking score of "y + x. " Two rankings were obtained, one for SNA and, independently, another for CNA. For expression data, we used both the Z-score and log 10 FC data and similarly calculated the ranking by counting for each gene the number of cancer types in which that gene had a |Z-score| > 2 or |log 10 FC| > 2 (and then using a secondary ranking based on the exact proportion of samples with |Z-score| > 2 or on the exact values of |log 10 FC|).
The FastQC data generated by MiSeq were first analyzed in Galaxy using the BLASTN tool (2. 5. 0+ Package: blast 2. 5. 0). To obtain read counts, we performed mapping of sgRNA against all sgRNA sequences present in the custom-made CRISPR library. To identify the enriched and depleted sgRNAs in EMT epidriver screening, we performed paired analysis comparing the sorted VIM + populations at different time points (days 14, 21, and 28) with cells collected at day 0. The differential sgRNA abundance of the read counts was analyzed using CRISPRAnalyzeR software (DKFZ, Version: 1. 50)  (Winter et al. 2016). The list of enriched and depleted sgRNAs for each time point was defined by hit candidate overlapping the list of hit candidates identified by DESeq2  (Love et al. 2014 ) and edgeR  (Robinson et al.
Scratch wound healing assay A549 Vim Cas9 R6 parental cell clone and all generated A549 single targeted knockout clones were analyzed by scratch wound healing assay using a standard protocol. Briefly, cells were plated in 12-well plates (2 × 10 5 cells/well); after reaching 90%-100% confluence (∼24 h after plating), two scratches per well were performed (in the form of a cross) using a 200-μL tip. Experiments were performed in duplicates. To measure cell migration and wound healing capacity, four different pictures were taken per well using a Zeiss TELAVAL 31 microscope and a Nikon D40 camera, both at 0-h (time of scratch) and 24-h time points. Closure of the wound by cell migration was calculated by comparing the scratched areas at both time points, using the ImageJ software (version 1. 52b).
Assessing disruption of EMT-specific driver candidates in clinical samples and pathway enrichment To assess the disruption of EMT-specific driver candidates identified in the CRISPR-Cas9 screen in clinical samples, we analyzed the TCGA data using the Genomic Data Commons Data Portal (https://portal. gdc. cancer. gov/). We divided samples into nonmetastatic (M0) and metastatic (M1) subsets based on the American Joint Committee on Cancer metastasis stage code. For more robust analysis, we further selected samples based on tumor stage using the American Joint Committee on Cancer neoplasm disease stage code. Only stage I and IV were considered to be M0 and M1, respectively. Overall, we collected 873 M0 and 371 M1 cases across 21 different cancer types available in TCGA (Supplemental Table  S9 ).
Based on these data sets, we calculated the mean percentage of mutations of each of the EMT-specific ERGs in the M1 versus M0 subsets. We performed the pathway enrichment analyses using bioinformatics mapping tools of different databases, including the NCI-Nature 2016, Panther 2016, KEGG 2016, and Reactome 2016 databases. We used Enrichr  (Kuleshov et al. 2016 ) score of each database to define the pathway enrichment of EMT-specific epidrivers. The network of EMT-specific ERGs was constructed with the GeneMANIA package (https://genemania. org/) (Warde-Farley et al. 2010 ).
Acknowledgments We thank  Dr. Maria Ouzounova  for her help with flow cytometry and  Mr. Cyrille Cuenin  for his excellent technical support with lentiviral library preparation and high-throughput sequencing. We also thank  Drs. Hector Hernandez-Vargas ,  Davide Degli-Esposti ,  Srikant Ambatipudi ,  Fazlur R. Talukdar , and  Nora Fernandez Jimenez  for their valuable input to initial stages of the project. We thank  Dr. Claire Renard  for her help with depositing the raw sequencing data on the NCBI database. We thank  Drs. Michael Korenjak ,  Magali Olivier ,  Vlatka Zoldoš ,  Aleksandar Vojta ,  Rabih Murr ,  Lynnette Fernandez-Cuesta , and  Nino Sincǐcf  or critical reading of the manuscript; and  Karen Müller  for editing the manuscript. This work was partially supported by the grant from the  Institut National du Cancer (INCa, France) ,  La direction générale de l'offre de soins (DGOS) , and  Institut National de la Santé et de la Recherche Médicale (INSERM)  ( SIRIC LYriCAN, INCa-DGOS-Inserm_12563 ).
, 2014). Additionally, the humidity-dependent N 2 O 5 wall loss rate must be accurately characterized to model the performance of a specific OFR-iN 2 O 5 configuration. Future applications of OFR-iN 2 O 5 will investigate the NO 3 -initiated OVOC and SOA formation potential of simple and complex precursors in laboratory and field studies. Code and data availability. Data and KinSim mechanisms presented in this paper are available upon request. The KinSim kinetic solver is freely available at http://tinyurl. com/kinsim-release (Peng and Jimenez, 2020). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/amt-13-2397-2020-supplement.
Acknowledgements. Andrew T. Lambe  thanks  Christopher Boyd  and  Sally Ng  ( Georgia Tech ) for sharing AMS data obtained in their environmental chamber, and the following colleagues for helpful discussions:  Megan Claflin ,  Manjula Canagaratna ,  John Jayne , and  Douglas Worsnop  from  ARI ;  William Brune  from  Pennsylvania State University ;  Manfred Winnewisser  from  Ohio State University ;  Karl Christe  from the  University of Southern California ; and  Robert Woodward-Massey ,  Youfeng Wang , and  Chunxiang Ye  from  Peking University. The authors thank the ACMCC and participants of the ACMCC pON experiment in December 2018. Financial support. The ACMCC pON experiment was supported by the  French Ministry of Environment  and part of the  COST Action CA16109 COLOSSAL  and the  Aerosol, Clouds , and  Trace gases Research InfraStructure (ACTRIS).
Because of the complexity of the computations involved, the execution of these tools may become tricky when the datasets to analyse contain more than a few thousands queries. Because rapidly evolving sequencing technologies enable researchers and clinicians to routinely produce increasingly large sequence datasets -potentially containing millions of viral reads-we have developed a fast alignment-free method to detect inter-strain recombinants within large collections of queries, based on the use of phylo-k-mers  (Linard et al. , 2019)  (see  Sec. 2. 2). The new tool, called SHERPAS (Screening Historical Events of Recombination in a Phylogeny via Ancestral Sequences) is able to process thousands of long queries (potentially covering whole viral genomes) within minutes or seconds. It can be used as a tool to screen large sequence datasets for novel recombinants.
If necessary, the putative recombinants found by SHERPAS can be subsequently re-analysed with more precise methods such as REGA, SCUEAL or jpHMM. Beside being orders of magnitude faster than available tools for the discovery of novel recombinants, SHERPAS presents other points of interest. Unlike some popular web interfaces, the code of SHERPAS is distributed freely, which may be an advantage when, for privacy reasons, it is important to process the data in-house (e. g. in a clinical setting). This also makes SHERPAS very flexible: users can choose their own reference alignments, update them as new highquality sequences become available, and most importantly adapt SHERPAS to any virus for which a reference alignment of su cient quality can be obtained. Moreover, SHERPAS appears to be relatively robust to the high error rates that characterize Oxford Nanopore sequencers.
Preprocessing and overview At a preprocessing stage, SHERPAS needs a collection of aligned reference sequences for the virus of interest and a phylogenetic tree built from this alignment. Each reference sequence must be annotated as belonging to exactly one strain, via a. csv file. In the Suppl. Materials (Sec. 3), we discuss a number of properties that we would ideally expect the references (alignment, tree and strains) to satisfy, such as the monophyly of strains and the absence of widespread recombination within the reference alignment. From the reference alignment and tree, a database of phylo-k-mers (the pkDB) is then constructed using the pkDB construction step currently implemented in the RAPPAS software  (Linard et al. , 2019)  (see next section). The pkDB construction is a heavy computational step, but it only needs to be executed when a new reference alignment is employed, or when it is updated.
Once these preprocessing steps have been carried out, large datasets of unaligned DNA sequences can be analyzed with the pkDB, as they become available. These sequences -which we refer to as queries-can be genomic fragments of moderate size (a few hundreds bp at least) up to entire genomes, including error-prone long reads generated by third-generation sequencing technologies. The output of SHERPAS is a text file classifying continuous regions within the queries as either unassigned ("N/A") or as belonging to one of the strains. The same format used by jpHMM is adopted. what strains may look like in a realistic phylogeny (adapted from part of the reference tree for the HIV-pol dataset). Bottom: Illustration of the composition of a query and of the outputs of two programs. The query combines a small segment of a sequence annotated as A1, and a larger segment of a sequence annotated as B.
Experimental protocol overview Dataset construction. We evaluated the performance of SHERPAS on four datasets of synthetic recombinants, that is, query sequences that are constructed by concatenating fragments of real-world viral sequences. The first three datasets were obtained following the same general procedure: Each dataset is constructed from a di erent pair of alignments containing real-world sequences reliably annotated as belonging to known strains of a virus of interest (details in Sec. 3. 3 to 3. 5). One of these alignments is used as the reference alignment for SHERPAS. The sequences in the other alignment are called pre-queries. We ensure the two alignments contain no sequence in common. The pre-queries are used to build a large collection of queries by (  1 ) drawing random recombination breakpoints in the alignment containing the pre-queries, (  2 ) cutting the pre-queries at those breakpoints and (3) concatenating the resulting fragments.
The fourth dataset was obtained by simulating long-read sequencing errors over the queries of one of the other datasets (Sec. 3. 6). For each of the queries, we record the positions of the breakpoints, and the strain of origin of the fragments that are separated by those breakpoints. This recorded information is used as "ground truth" to evaluate the accuracy of the tested methods (see  Sec. 3. 2). Software comparison. We compare the performance (accuracy and running times) of SHERPAS over these datasets against that of jpHMM  (Schultz et al. , 2006 (Schultz et al. , , 2009)) , a natural choice because (1) it is the only tool whose main stated goal is the same as that of SHERPAS (detect inter-strain recombinants and partition them according to the strain of origin). Moreover, (2) jpHMM is not specialized for any single virus species, and is distributed with its own reference alignments for a number of viruses, which allows us to compare it to SHERPAS using the same reference alignments.
HIV-pol dataset To evaluate the performances of SCUEAL, Kosakovsky Pond et al. (2009) generated 10,000 synthetic recombinant queries, combining fragments from 863 pre-queries from the HIV-1 pol gene. We used this dataset without modification. The queries are about 1. 6 kbp long. To run SHERPAS on these queries, we built the pkDB using the same reference alignment as SCUEAL. This alignment contains 167 HIV pol sequences distributed into 17 strains, which correspond to groups, types, subtypes, chimpanzee SIV sequences, and the circulating recombinant form CRF01_AE. These strains are named A, A1, A2, A3, AE, B, C, D, F1, F2, G, H, J, K, N, O, CPZ. (The inclusion of CPZ and AE is discussed in the Suppl. Materials, Secs. 3. 3 and 3. 4, respectively. ) The output of SCUEAL on these queries is distributed along with the software, so we did not re-run SCUEAL on this dataset.
(Also because SCUEAL is a non-deterministic algorithm. ) The queries include intra-strain recombinants and SCUEAL's output includes the detection of intra-strain recombination. In order to make this information comparable to the output of SHERPAS, we ignored intrastrain recombination, and only retained inter-strain recombination information. As a consequence, the mosaic-based accuracy measures that we obtain for SCUEAL (Table  2 ) are much better than those reported by  Kosakovsky Pond et al. (2009)  (e. g. 93. 2% matches vs. 46. 6%). In order to interpret the results for jpHMM on this dataset, we note that strains A and N cannot be recognized by jpHMM, which negatively impacts its accuracy measures on this dataset. The impact, however, is limited. (See the Suppl. Materials, Sec. 5. 2 for more detail.
HBV-genome dataset Both SHERPAS and the latest version of jpHMM are able to analyze data from viruses with circular genomes, such as the hepatitis B virus (HBV)  (Schultz et al. , 2012). To experiment with HBV data, we used the reference alignment that is distributed with jpHMM. It contains 339 whole-genome sequences classified into strains A, B, C, D, E, F, G, H (known as genotypes). Prior to the construction of the pkDB for SHERPAS, we extended this reference alignment by copying the first 9 columns of the alignment to the end of the alignment. This allows the construction of phylo-k-mers (with k = 10) from positions that overlap with the artificial end of the alignment. To build a collection of queries, we started with a collection of pre-queries extracted from the database of aligned whole-genome HBV sequences available at the HBVdb website  (HBVdb contributors, 2019; Hayer et al.
HIV-genome dataset This dataset consist of whole-genome sequences from HIV. Again, we used the reference alignment of jpHMM for HIV to build the pkDB database for SHERPAS. This alignment contains 881 whole-genome sequences, classified in the following 14 strains: A1, A2, AE, B, C, D, F1, F2, G, H, J, K, O, CPZ. To construct a collection of 3000 synthetic queries, we used prequeries extracted from Los Alamos HIV sequence database (the "complete Web alignment 2018"). In brief, the main di erence with the procedure for the HBV-genome queries is that the number of parental pre-queries and the number of breakpoints are both drawn from (shifted) geometric distributions. Again, the construction procedure was designed to reflect the broad characteristics of known recombinant forms, those listed in the Los Alamos HIV sequence database.