dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We apologize to authors whose work could not be cited due to space constraint. The authors thank  Moaine El  baidouri and  Ariadna Picart-Picolo  for their input. F. P. was supported by the  French Laboratory of Excellence  project  TULIP  ( ANR-10-LABX-41  and  ANR-11-IDEX-0002-02 ), the  ANR NucleoReg  ( ANR-15-CE12-0013-01 ). C. L. was supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 757600 ). C. L. and F. P. are part of the  COST   ACTION CA16212 INDEPTH. References  and recommended reading Papers of particular interest, published within the period of review, have been highlighted as: of special interest of outstanding interest.
Unlike phasing in single individuals, population phasing can be challenging because neither the number of haplotypes nor their frequencies are known in advance. Other studies have yet to apply population phasing using LR data, presumably because such large datasets have not been feasible before. Our strategy involves leveraging naturally occurring haplotype blocks in populations and trading-off linkage against sequencing (read) coverage: first, we reconstruct the set of haplotypes present in the study population by pooling reads from many individuals for maximum coverage (because most segregating haplotypes are common). Next, we infer or impute the two haplotypes carried by each individual across the genome by evaluating the evidence at the haplotype block level (i. e. , using linkage) in addition to raw per-base read coverage.
The increased overlap among molecules often lead to >10-fold increase in molecular coverage (an average of 0. 81 reads overlapping a given position versus an average of 23. 2 molecules here; SI Appendix, Table  S4 ). In a large population, LR data allow both accurate haplotype reconstruction using pooled read depths and accurate imputation by leveraging linkage information, even with input read coverage reduced to 0. 07× (SI Appendix, Fig. S3B ). Bead and Tn5 image modified with permission from Zinkia Entertainment, S. A. /Pocoyo. H. erato and 2. 72× for H. melpomene (samples both individually and molecularly barcoded; see Materials and Methods and SI Appendix, Fig. S2C  and Tables  S3  and S5   S6 ). This dataset represents a qualitative jump in quality and resolution over the state-of-the-art in a natural population study.
3  and SI Appendix, Fig. S10  and Tables  S9  and S10  and Datasets S4 and S5; refs. 36 and 37). Here, the LR data allows us to track the breakdown of haplotypes across the hybrid zone (SI Appendix, Fig. S11 ) and greatly increase the power of the haplotype-based ω-statistic (especially compared to haplotypes reconstructed from short-read only data, SI Appendix, Fig. S8C ). The resolution in these tests is high enough to separate the dennis and ray cis-regulatory regions from the target gene optix in H. erato (SI Appendix, Fig. S8C ). The data can reveal unsuspected molecular details as evidenced by the detection of rare H. melpomene recombinants at the tightly linked A B Fig. 2. Parallel hybrid zones in a pair of Müllerian comimicking Heliconius butterflies. (A) In eastern Ecuador, butterflies of the species H.
However, they are hard to detect using short-read techniques. By contrast, longer LR molecules that span rearranged junctions can systematically reveal insertions, deletions, inversions, and additional chromosome rearrangements. We therefore analyzed beadTag sharing across adjacent 10-kbp windows to detect differences between the physical molecules and the reference assembly (Fig. 4 ; see Materials and Methods for details). We detected 685 and 415 indels and 14 and 19 major inversion/translocation events in H. erato and H. melpomene, respectively (Datasets S2 and S3). Although structural rearrangements occasionally overlap divergent peaks or signatures of selection in single high-or lowland populations in either species, generally speaking, they differ only very little between highland and lowland populations.
See SI Appendix, Fig. S2  and SI Materials and Methods for details on beadTag demultiplexing and handling. Code is available at https:// github. com/evolgenomics/haplotagging. Analysis and Phasing of Molecules. A schematic of the analytical pipeline is shown in SI Appendix, Fig. S2. Data analysis follows mainly standard shortread sequencing processing while retaining molecular information using a BX beadTag. For the human GM12878 and mouse F1(BL6xCAST) hybrid and backcross mice, known SNP sites were used to evaluate molecular accuracy and phasing performances using HAPCUT2  (10). For Longshanks mice, a set of variable sites from ref. 13  was used, and genotypes from the same individuals sequenced previously  (13)  were used to evaluate genotype imputation performance using STITCH  (11). Population Genomics of the Parallel Heliconius Hybrid Zones.
ACKNOWLEDGMENTS. We thank  Felicity Jones  for input into experimental design, helpful discussion, and improving the manuscript. We thank the  C. R. ,  C. D. J. ,  Y. F. C. , and  Jones laboratory  members for support, insightful scientific discussion, and improving the manuscript. We thank the  C. R. laboratory  members, the  Animal Resource Centre  staff at the  University of Calgary , and  Caroline Schmid  and  Ann-Katrin Geysel  at the  Friedrich Miescher Laboratory  for animal husbandry. We thank  Christa Lanz ,  Rebecca Schwab , and  Ilja Bezrukov  for assistance with high-throughput sequencing and associated data processing and  Andre Noll  and the  MPI Tübingen  information technology team for computational support. We thank  Ben Haller  and  Richard Durbin  for helpful discussions.
Data Availability. Analysis codes are available at the following repositories: https://github. com/evolgenomics/haplotagging and https://github. com/evolgenomics/HeliconiusHaplotagging. Sequence data have been deposited at the National Center for Biotechnology Information Sequence Read Archive under the BioProject accession number PRJNA670070. Phenotypic and collection data for all Heliconius butterfly specimens are given in Dataset S6.
Whole genome sequencing (WGS) is increasingly used for epidemiological investigations of pathogens. While SNP variant calling is currently considered as the most suitable method, the choice of a representative reference genome and the isolate dependency of results limit standardization and affect resolution in an unknown manner. Whole or core genome Multi Locus Sequence Typing (wg-, cg-MLST) represents an attractive alternative. Here, we assess the accuracy of wg-and cg-MLST by comparing results of four Pseudomonas aeruginosa datasets for which epidemiological and genomic data were previously described. Three datasets included 155 isolates from three different sequence types (ST) of P. aeruginosa collected in our ICUs over a 5-year period. The fourth dataset consisted of 10 isolates from an investigation of P.
aeruginosa contaminated hand soap. All isolates were previously analyzed by a core SNP approach. In this study, wg-and cg-MLST were performed in BioNumerics TM using a scheme developed by Applied-Maths. Correlation between SNP calling and wg-or cg-MLST results were evaluated by calculating linear regressions and their coefficient of correlations (R 2 ) between the number of SNPs and the number of allele differences in pairwise comparison of isolates. The number of SNPs and allele difference between isolates with close epidemiological linkage varies between 0-26 and 0-13, respectively. When compared to core-SNP calling, a higher coefficient of correlation was obtained with cgMLST (R 2 of 0. 92-0. 99) than with wgMLST (0. 78-0. 99). In one dataset, a putative homologous recombination of a large DNA fragment (202 loci) was identified among these isolates, affecting its phylogeny, but with no impact on the epidemiological analysis of outbreak isolates.
, 2018). Since a reference genome might not always be available, results are dependent on the chosen genome and the collection of isolates, limiting standardization and influencing the resolution in an unknown fashion  (Pightling et al. , 2014). An alternative approach is a gene-by-gene typing method, indexing core or accessory gene variation due to mutations or recombination events  (Maiden et al. , 2013; Pearce et al. , 2018). This can be considered as an extended MLST scheme. The core genome (cg) MLST includes loci that are present in all isolates of a given population or a subset thereof  (Maiden et al. , 2013) , whereas whole genome (wg) MLST adds a selection of accessory loci as well. An advantage of this approach is that, as with MLST, loci used in the schemes are readily maintained and shared among laboratories using the same or similar online databases  (Maiden et al.
Description of the Four Datasets The first three datasets included 155 isolates from the three predominant P. aeruginosa MLST sequence types (ST) (ST17, 253 and 1076) collected in our ICUs over a 5year surveillance period (2010-2014)  (Magalhães et al. , 2020)  (Table  1 ). In summary, during this period, all consecutive patients hospitalized in the ICU with a clinical sample growing P. aeruginosa at any site were considered. In 2012, the ICU environment was investigated for the presence of P. aeruginosa. Tap water samples and environmental swabs obtained from taps and sink traps of all patient's rooms were analyzed. Thereafter, sink traps were investigated twice a year. All isolates were analyzed with molecular typing and the three predominant MLST STs were further investigated with WGS. The first dataset, ST1076, includes isolates from an outbreak occurring in the burn unit  (Tissot et al.
, 2016). With one exception, epidemiological links were found between these patients, either through environmental contamination or through patient-to-patient transmission. Regarding the second dataset, ST17, epidemiological links were found between five patients hospitalized in the burn unit in 2010. For the other 16 patients, no epidemiological link was suspected, as patients were not hospitalized in the same ICU during overlapping periods and had no link with environmental samples. Within the third dataset, ST253, epidemiological links were identified only between two patients hospitalized in the same ICU and an environmental sample retrieved from a sink trap. The remaining patients were dispersed throughout the six ICUs during the study period, suggesting they were not involved in an outbreak.
The fourth dataset consisted of 10 isolates of ST155 from an epidemiological investigation following the contamination of hand soap with P. aeruginosa  (Blanc et al. , 2016)  (Table  1 ). All datasets were previously analyzed with a SNP variant calling approach with exclusion of putative recombinant segments  (Tissot et al. , 2016; Magalhães et al. , 2020). The read data of the first three datasets have been deposited at the Sequence Read Archive (SRA) 1 under the accession project number PRJNA503802. The read data of the fourth dataset have been deposited with the European Nucleotide Archives (ENA) 2 under the accession study number PRJEB36387.
Positions in the assembly covered by less than 3 reads, 1 forward or 1 reverse read were replaced by N. Briefly, reference alleles were blasted against the resulting consensus with megablast, using an identity threshold of 85%. Blast hits were compared with known high quality alleles. If no match was found in this set and the hit contains a valid start and stop within 18 bp of the start and stop of the reference and no internal stop, the allele is assigned a new ID. Finally, 1 https://www. ncbi. nlm. nih. gov/sra 2 www. ebi. ac. uk/ena a consensus allele call is performed with both the assembly free and the de novo assembly based calling, results found with both algorithms or with only one algorithm are maintained, discrepant results are removed. cgMLST consisted in the selection of loci that were present in >90% of all isolates in the analyzed dataset (see Supplementary Material).
RESULTS The ST1076 dataset was composed of 74 isolates from a well characterized outbreak  (Tissot et al. , 2016) , during which the first patient (two isolates) was excluded based on genotyping results  (Tissot et al. , 2016; Magalhães et al. , 2020). wgMLST identified 6066 loci that were present in at least one of these 74 isolates. Among these, 153 loci were variable (Supplementary Table  S1 ). The identification of genetically highly related isolates was concordant with previously acquired epidemiological information: differences of 0-16 SNPs and 0-12 alleles were observed among outbreak' isolates, whereas differences of 108-120 SNPs and 74-101 alleles were observed between these isolates and the two non-outbreak isolates of patient #1. The linear regression between the pairwise SNP distances and the pairwise allele differences is shown in Figure  1A  and a high coefficient of correlation was observed (R 2 = 0.
99). The clustering obtained by the SNP variant calling and the wgMLST approaches were highly similar (Supplementary Figure  S1 ). The ST17 dataset is composed of 50 isolates that originated from the previously mentioned surveillance in the ICU over a 5 year-period. Epidemiological links were found only among five patients hospitalized in the same ICU over a short period. For the other 16 patients, no epidemiological link was suspected. wgMLST identified 1114/6678 variable loci among these 50 isolates (Supplementary Table  S2 ). Differences of 0-13 SNPs and 0-17 alleles were observed among epidemiologically linked isolates. The clustering obtained by the SNP variant calling and the wgMLST approaches were highly similar (Supplementary Figure  S2 ). The correlation between SNPs and alleles pairwise differences (Figure  1B ) was lower than in the first dataset (R 2 = 0.
89). One explanation for the low correlation is that each distance between two isolates obtained with the wgMLST analysis is based on the pairwise comparison of all loci present in both isolates (core and accessory loci), whereas the SNP analysis takes into consideration only positions where a nucleotide is present in all isolates of the dataset (core genome). This means that a larger percentage of the genome is considered in the wgMLST analysis vs. the core-SNP approach. To test this hypothesis, we performed a cgMLST analysis in Bionumerics TM considering only loci with alleles present in at least 90% of isolates of the dataset (see Supplementary Material for more details). This cgMLST identified 838/5932 variable loci (Supplementary Table  S2 ). The newly obtained distance matrix was compared to the original SNP matrix (Figure  2A ) and the plot shows a much better correlation (R 2 = 0.
983), confirming that the lower correlation was due to the absence of loci in some isolates of the dataset. The ST253 dataset was composed of 31 isolates also originating from surveillance in the ICU. wgMLST identified 667/7077 variable loci (Supplementary Table  S3 ). Epidemiological links were identified only between two hospitalized patients and an environmental sample; differences of 0-1 SNP and zero alleles were observed between the isolates. The remaining patients were dispersed throughout the different ICUs during the study period, suggesting there was no transmission. The linear regression between SNPs and alleles pairwise differences shows a low correlation (R 2 = 0. 439) due to the presence of two clouds of dots; one showing a possible good correlation between the two variables and a second suggesting a higher number of alleles compared to the number of SNPs (Figure  1C ).
The clustering obtained by the SNP variant calling and the wgMLST approaches were similar, except a group of three environmental isolates that clustered distantly to the others in the wgMLST tree (Supplementary Figure  S3 ). Similarly to the second dataset, a cgMLST was performed and 545/5795 variable loci were identified. The newly obtained distance matrix was compared to the original SNP matrix and the plot shows the persistence of the second cloud (Figure  2B ). We hypothesized the occurrence of a recombinant segment that was included in the wgMLST analysis, but excluded in the core SNP variant calling analysis. To test this hypothesis, we visually inspected the allele value of the dataset in order to identify polymorph adjacent loci suggesting a homologous recombination. Such a segment of 196 loci was identified (PSEUAE03725 to PSEUAE03927, Supplementary Table  S3 ).
These loci were unselected from the cgMLST scheme and a new comparison performed showing 358/5599 variable loci. The distance matrix was compared to the original SNP matrix and the results were plotted in Figure  2C. The second cloud was no longer present and a high level of correlation was observed between SNPs and alleles pairwise distances (R 2 = 0. 927). These loci are found on the reference genome N15-01092 (accession number CP012901) between 4. 75 and 5 Mbp (Applied Maths, personal communication). Lastly, the ST155 dataset is composed of 10 isolates from an investigation of contaminated hand soap with P. aeruginosa  (Blanc et al. , 2016). Two isolates originated from the soap and the remaining isolates from patients. wgMLST identified 656/6920 variable loci (Supplementary Table  S4 ).
Both MLST and wgMLST analyses consider a recombination as a single mutational even, whereas the SNP calling analysis considers each SNP in the recombinant segment as an additional mutation. However, as the wgMLST scheme includes accessory genes, the presence/absence of these genes might have an impact on the resulting phylogeny. This is shown in our study using three datasets of P. aeruginosa isolates where the correlation between the number of core-SNP differences and the number of allele differences was higher for a cgMLST rather than a wgMLST scheme. We defined a cgMLST consisting of loci with alleles present in at least 90% of isolates of the dataset. As expected, this was accompanied with a loss of discrimination as the loss of variable loci varies from 2 to 38% between wgMLST and cgMLST; but had no influence on the number of allele differences among our strongly clonal outbreak isolates (Supplementary Table  S1 ).
In our study, all isolates with epidemiological links showed less than 26 SNPs or 13 allele differences, suggesting that recombination did not took place during the short evolution of P. aeruginosa isolates. Nevertheless, it is important to assess whether such recombination events occurred within outbreak isolates  (Parcell et al. , 2018). In conclusion, we showed with four P. aeruginosa datasets that epidemiologically linked isolates were genetically highly related with both a core-SNPs calling approach and the wgMLST approach in BioNumerics. We also showed that epidemiological linked isolates showed less than 26 SNPs or 13 allele differences. These are important figures for the distinction between clonal and non-clonal isolates when interpreting WGS results in outbreak investigation. As P.
aeruginosa has an epidemicpanmicitic population structure, a cgMLST approach was found to be more adapted than wgMLST, as it limits the effect of the presence/absence of accessory genes or homologous recombination of small DNA segment. However, homologous recombination of large DNA segments can still affect the phylogeny obtained with cgMLST analyses. Despite such an event was not observed within outbreak isolates of our datasets, it is important to be cautious and to use protocols that enable their recognition. S1  | wg-and cgMLST loci and alleles of clusters of ST1076 isolates. The locus tag corresponds to NCBI nomenclature. A value of "-1" means the absence of an allele at this locus. TABLE  S2  | wg-and cgMLST loci and alleles of clusters of ST17 isolates. The locus tag corresponds to NCBI nomenclature.
Even bevacizumab, which is considered as one of the most effective anti-angiogenic drugs to date, yields only modest improvements, adding only around 5 months of progression-free survival (PFS) in patients with metastatic colorectal cancer  (98). With these limited results, researchers have also looked targets further downstream of these pathways. There are MEK and mTOR inhibitors that have been approved or are undergoing trials for clinical use by the US Food and Drug Administration (FDA), though it appears that their impact only adds a few months to PFS. Trametinib and Dabrafenib are MEK inhibitors used in combination for metastatic melanoma that presented a BRAF V600 activating-mutation, adding around 2. 2 months of PFS when compared to dabrafenib alone (Clinical Trial NCT01584648)  (171).
mTOR inhibitors such as Everolimus were found to increase PFS by around 2. 1 months for patients with metastatic renal cell carcinoma that has progressed from receptor-targeting drugs such as sunitinib (Clinical Trial NCT00510068)  (172). As characterized in previous reviews, there are no FDA-approved selective agents that specifically suppress lymphangiogenesis  (173) , despite the growing evidence on their contributions to cancer and other diseases. Antilymphangiogenesis therapies are following the same approach as anti-angiogenesis therapies, targeting the key receptors and growth factors of the lymphatic system. A phase I study was completed for anti-VEGFR-3 monoclonal antibody, IMC-3C5, against solid tumors and colorectal cancer. They were able to set a tolerable dose, but only saw minimal anti-tumor activity so far (Clinical Trial NCT01288989)  (99).
Here, we introduce murine Microenvironment Cell Populations counter (mMCP-counter), the adaptation of the MCP-counter method to murine samples (Fig. 1 ), which was made possible thanks to the release of large datasets of microarray-measured gene expression of murine sorted immune populations by the Immunological Genome Project (ImmGen)  [7, 8]. mMCPcounter can be accessed as an R package (https://github. com/cit-bioinfo/mMCP-counter)  [9]. It takes a gene expression profiles matrix as input and returns the abundance of RNA originating from 16 defined cell populations present in the heterogeneous sample. We compared the performance of mMCP-counter with other previously published methods on simulated mixtures generated using two datasets that are independent from the discovery ImmGen microarrays datasets, and we validated our approach on samples from peripheral blood, peritoneum, spleen, and several grafted tumors that were analyzed by both RNA-sequencing (RNA-Seq) and flow cytometry (Fig.
Public data accession, curation, and normalization Data included in the discovery dataset included the two micro-array releases of ImmGen (Gene Expression Omnibus (GEO) accession numbers GSE15907 and GSE37448), and parts of several datasets for: epithelial cells (GSE27456 and GSE74317), breast cancer (GSE25525, GSE54626, and GSE78698), hypothalamic cell line (GSE61402), melanoma B16F10 cells (GSE84155), pancreatic ductal adenocarcinoma (GSE48643), myoblasts (GSE26764), and hepatocytes (GSE18614). Raw CEL files were used and data was normalized through frozen robust multi-array analysis  [10]  with the R package fRMA. Batch effect was correcting using ComBat  [11]  from the R package sva. Consistency within the data was verified using principal components analysis with the R package FactoMineR  [12]  and outliers were discarded.
Signatures discovery For signature discovery, only populations for which all subsets were present in the dataset and that have appropriate negative samples were taken into account. All probes were screened for (log2) fold-change (FC), specific fold change (sFC), and area under the ROC curve (AUC). FC and sFC are defined as follows: FC ¼ X -X sFC ¼ X -X min X max -X min where X is the centroid (i. e. , the mean over all samples) for the population of interest, X the centroid of all other samples, and X max and X min denote, respectively, the maximum and the minimum of cell-type-specific centroids for population different from the population of interest. Signatures were built using the following cut-offs: FC > 2. 1, sFC > 2. 1, and AUC > 0. 97. After this automated screening, all retained probed were manually curated to verify the accuracy of the selection.