dataesr/scientific-paragraphs-categorization huggingface dataset:
3d ). Double immunofluorescence staining of diseased murine AAA sections confirmed the marked presence of netrin-1 protein in CD68 positive macrophages (Fig. 3e , top) as compared to PBS-infused mice (Supplementary Figure  1a ). Similar results were recapitulated in human sections (Fig. 3e , bottom), further validating the data obtained by the single-cell RNA-seq and emphasizing on the translational impact of macrophage-dependent expression of netrin-1 in AAA. Transcriptomic analysis of the macrophage cluster using the Loupe Cell Browser software expanded the set of genes that best discriminate the pools of Ntn1 positive macrophages within the inflamed aortic vessel wall. Of note, we found that Ntn1 enriched macrophages were those that highly expressed pro-inflammatory markers and pro-angiogenic markers, while macrophages that harbored lower levels of Ntn1 exhibited high anti-inflammatory macrophage mannose receptor 1 mRNA (Mrc1) and antiatherogenic repertoire of genes consistent with previous reports  20, 21  (Fig.
All images were acquired using the settings provided by the manufacturer. The probe was first applied on short axis to locate the aorta, and the pulsatile property of the vessel was used to distinguish from the vena cava (PW mode). The aorta was then centered and the probe switched to the long axis. Color mode Doppler was then activated to help localize the two renal arteries, displaying a red blood flow toward the probe on the screen. The probe was moved until the aorta was strictly parallel to the device in order to acquire measurements of the aortic diameter. Three measurements were performed by a blinded investigator on the long axis (M mode): above the upper renal artery, on maximal aortic dilation, and below the lower renal artery. Data for the maximal transverse aortic diameter is shown for individual animals.
RNA sequencing. RNA was extracted from aortas using the RNEasy fibrous tissue kit as per manufacturer's instructions (74704, Qiagen). RNA quality was verified using a Bioanalyzer (G2939BA, Agilent Technologies). The samples were run on a HiSeq (Illumina) as single-end reads, 50 nucleotides in length. FASTQ files were aligned to the MM9 Mus musculus reference genome using Tophat (version 2. 0. 9) with two mismatches allowed. The resulting read counts were extracted using the Featurecounts program from binary alignment map (BAM) files. Differential gene expression analysis was conducted using the DESEQ2 package from the Bioconductor repository using the open source R statistical programming environment. All downstream data manipulation, plotting, and statistical filtering were also performed in the same environment using custom scripts.
Libraries were prepared using the following kits: Chromium Single Cell 3′ Library & Gel Bead Kit v2, PN-120237; Single Cell 3′ Chip Kit v2 PN-120236 and i7 Multiplex Kit PN-120262, 10x Genomics) as described  64. Sequencing was performed on an Illumina HiSeq 4000 as 2 x 150 paired-end reads, one full lane per sample, for approximately >90% sequencing saturation. For alignment, barcode assignment and unique molecular identifiers (UMI) counting, the Cell Ranger Single Cell Software Suite, version 1. 3 was used to perform sample de-multiplexing, barcode and UMI processing, and single-cell 3′ gene counting (https://support. 10xgenomics. com/singlecell-gene). Data analysis was performed using the Loupe Cell Browser software (10x Genomics) on Cloupe files displaying tSNE projections of cell transcriptome.
Cell types were identified by their expression levels of gene provided that at least two copies of the transcript were found in the cell (cutoff: log 2 fold-increased copies = 1 vs 1 copy only per cell) (Supplementary Table  1 ). Loupe Cell Browser was then used to compare the transcriptome of each identified cluster corrected for false discovery rates, as applied in RNA sequencing. The expression of transcripts within macrophage clusters identified as Ntn-1 positive or negative subclusters are provided in supplementary Table  2. Real-time quantitative PCR. Samples were homogenized in TRIzol reagent (15596026, Ambion, Life Technologies) and mRNA was extracted using a Directzol RNA MiniPrep Kit (R2052, Zymo Research). Retro-transcription in cDNA was performed with the iScript cDNA Synthesis Kit (1708890, Bio-Rad).
A nontargeting siRNA-scrambled sequence was as control (SC-37007, Santacruz Biotechnology). MMP3 activity assay. MMP3 activity in cells and homogenized aortic tissue was assessed using the MMP3 Activity Fluorimetric Assay Kit (K783-100, Biovision), following the manufacturer's instruction. Data were expressed as the amount of enzyme generating 1 µmol of fluorescent substrate per minute per milliliter of sample (nmol/min/ml). Flow cytometry. Aortic cell suspensions were prepared as described above. Digested aortas were then mechanically disrupted and passed through a 70 µm cell strainer (340607, BD Bioscience), centrifuged 10 min at 1000 × g at room temperature and fixed in 300 µl PBS-2%PFA before further staining. A volume of 50 µl of aortic cell suspension was then stained for 20 min with an antibody mix consisting of VioGreen TM anti-CD45 (130-102-412, Miltenyi biotechnology) PE-Vio770 anti-F4/80 (130-102-193, Miltenyi biotechnology) and APC anti-CD11b (130-091-241, Miltenyi biotechnology), 1:50 dilution each, resuspended in 500 µl of PBS-1%PFA, ran on a BD LSRII flow cytometer (Becton Dickinson) recorded using the FACSDiva software (Becton Dickinson) and acquired data were analyzed using the FlowJo software (FlowJo, LLC).
Acknowledgements We are thankful to  Dr Adriana Heguy  from the  New York University Genome Technology Center for RNA sequencing  (a resource partially supported by the  Cancer Center Support Grant ,  P30CA016087 , at the  Laura and Isaac Perlmutter Cancer Center)  and  Zahid Dewan  form the immunohistocore for sectioning assistance. We are grateful to  Akshay Shekhar  for excellent technical support and input for Vevo ultrasound acquisition and measurements. This work was supported by the  US National Institutes of Health  ( R00HL125667 ) to B. R. R01 DK097075 ,  POI-HL114457 ,  R01-HL109233 ,  R01-DK109574 ,  R01-HL119837  and  R01-HL133900  to H. K. E.
Data availability The data and computer codes that support our findings are available from the corresponding author upon reasonable request. All RNA sequencing datasets are deposited in Gene Expression Omnibus (GEO)-accession number GSE118237 (GSM3323357 for the CLOUPE file used to generate Fig. 3a-d  and f  and the Supplementary Tables  1  and 2 ) and GSE118217 (GSM3321219 to GSM3321226 for each individual sample used to generate Fig. 4a  and b , and Supplementary Figure  2a  and b ). A reporting summary for this Article is available as a Supplementary Information file.
Acknowledgements Special thanks to  Adrien Nivoliez , who prepared the Lactobacillus rhamnosus. Our personal recognition is given to  Mikael Skigin  for his generosity, and to  Jean-Cyril Spinetta  for his constant support. We would like to express our gratitude to  Pr. Jeffrey Almond  and  Pr. Marc Van Regenmortel , who reviewed the manuscript, and to  Pr. Claude Guthmann , who adapted the figure.
For CBT, HLA-compatibility requirements followed the current practice of antigen level typing for HLA-A and -B and allelelevel typing of HLA-DRB1. CB units were 4-6/6 HLA-A, -B, and -DRB1 matched to the recipient in all patients and to the other unit in case of double CBT in most patients  (26, 27). HLA disparities between each unit and the recipient and between the two units were not necessarily at the same loci. The choice between single or double CBT was done according to transplant center policy  (26, 28). Generally, double CBT was performed when a single unit with adequate cell dose was not available. Grading of acute and chronic GVHD was performed using established criteria  (29). For the purpose of this study, all necessary data were collected according to EBMT guidelines.
Statistical analyses Data from all patients meeting the inclusion/exclusion criteria were included in the analyses. We did not censor patients who did not engraft. Furthermore, as the comparison of donor type was done in an intent-to-treat principle, we did not censor patients at time of second allo-HCT. The latter was given in 52 MSD, 27 MUD, 5 haplo, and 20 CBT patients, respectively. Patient, disease, and transplant-related characteristics for the 4 cohorts (MSD/UD/CBT/Haplo) were compared by using x 2 statistics for categorical variables and the Kruskal-Wallis test for continuous variables. Start time was date of transplant for all endpoints. As follow-up duration varied significantly between the 4 groups, all survival times were censored 2 years after transplantation. Measured outcomes were leukemia-free survival (LFS), relapse incidence (RI), nonrelapse mortality (NRM), overall survival (OS), acute graftversus-host disease (aGVHD), chronic graft-versus-host-disease (cGVHD), and GVHD and relapse-free survival (GRFS).
Proportionality assumption was significantly violated when studying GRFS, relapse, and nonrelapse mortality. We thus split the follow-up of these endpoints at day 100. This cut-off point was chosen based on a clinical rationale, after occurrence of acute GVHD and before occurrence of chronic GVHD. All tests were two sided. The type I error rate was fixed at 0. 017 for determination of factors associated with time to event outcomes after using the Bonferroni correction for three comparisons. Statistical analyses were performed with SPSS 24. 0 (SPSS Inc) and R 3. 4. 1 (https://www. R-project. org/).
Patients and donors Data from all 1,715 patients meeting the inclusion/exclusion criteria were included in the analyses. They received grafts from either MSD (n ¼ 701), MUD (n ¼ 611), or haplo (n ¼ 112) donors or were given single or double CBT (n ¼ 291; Table  1 ). The proportion of patients transplanted in CR1 was 86%, 80%, 66%, and 61% in MSD, MUD, haplo, and CBT groups, respectively (P < 0. 001). Median patient age at transplantation was 58, 62, 58, and 55 years, respectively (global P < 0. 001). The proportion of patients with secondary AML was 15%, 20%, 14%, and 18% in MSD, MUD, haplo, and CBT groups (global.
Acknowledgments We thank  Emmanuelle Polge  and  Audrey Mailhol  from the office of the  ALWP of EBMT, Chantal Kenzey  and  Fernanda Volt  from the Eurocord study office. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U. S. C. Section 1734 solely to indicate this fact. Received December 5, 2017; revised February 2, 2018; accepted March 13, 2018; published first March 19, 2018.
The presence of an haploblock structure in the RNASE3 gene was investigated and a genetic segment  appeared to be part of a haploblock structure composed of 3 'successive' RNASE3 SNPs, for which the confidence interval for D′ was 0. 9-1 (+371G/C, +499G/C and +577A/T). Table  3  summarizes the frequency of each polymorphism in the SM, UM and CTR groups. Allelic frequencies obtained in our study are similar to the data provided by the NCBI dbSNP database with regard to African populations except +577A/T. The SNP RNASE3 +371G/C introduces a non-synonymous change that results in an Arg/Thr mutation in protein sequence NP_002926. 2, whereas +577A/T and +499G/C are located in 3′UTR.
Acknowledgements The authors are grateful to all the patients and medical staff who have generously collaborated in the  Malaria Genomics Project Clayton Dedonder 2014  ( Institut Pasteur de Dakar ). Authors thanks to  Professor Jean-Francois Zagury  and  Dr Cedric Coulonges  ( CNAM, Paris, GEA Bioinformatic ) for lecture and corrections. The authors also wish to thank  Cécile Dulary  from the  Centre National de Genotypage (CNG)  at the CEA for her technical assistance in performing the genotyping. The  French Ministry of Education and Research  supported the  CNG-CEA.
As a consequence, MSI-1436 is being tested in a Phase 1 clinical trial in metastatic breast cancer patients (ClinicalTrials. gov: NCT025 24951). Nevertheless, MSI-1436 is also a charged molecule with limited oral bioavailability, which restricts the indications in which it can be applied therapeutically. In our efforts to generate more potent analogs of MSI-1436, we identified DPM-1001. In this study, we present the characterization of DPM-1001. Furthermore, using an animal model of diet-induced obesity, we have demonstrated that the compound is an orally bioavailable inhibitor of PTP1B. Overall, this study illustrates a novel mechanism for inhibiting PTP1B that may reinvigorate interest in this phosphatase as a therapeutic target for diabetes and obesity.
75 milliunits/g, i. p. ) for 15 min. E, representative immunoblots (IB) showing leptin-induced tyrosine phosphorylation of JAK2 and total JAK2 levels in hypothalamus tissue lysates from 14-week-old mice treated with saline or DPM-1001, either orally or intraperitoneally (IP). For leptin stimulation, animals were treated with leptin (1 mg/kg, s. c. ) for 15 min. D and E, all immunoblots are representative of experiments performed independently three times. Immunoblots were quantified using ImageJ software. The quantitation includes the data from all three experiments, presented as the mean Ϯ S. E. Statistical analysis was performed using one-way ANOVA (**, p Ͻ 0. 01). mice helped to cement the regulatory link between PTP1B and signaling through insulin and leptin receptors. These mice displayed enhanced glucose tolerance with increased insulin sensitivity, and they were lean, resistant to diet-induced obesity, and displayed hypersensitivity to leptin  (15, 22).
We identified the binding sites for MSI-1436 in PTP1B, including a primary site in the C-terminal segment, and we demonstrated that the compound targeted PTP1B in vivo. Although MSI-1436 is an exciting therapeutic candidate, currently being tested in a Phase 1 clinical trial in metastatic breast cancer patients (ClinicalTrials. gov: NCT02524951), it is also charged and limited in oral bioavailability. Now, as presented here, we have identified DPM-1001, an analog of MSI-1436 that was an orally bioavailable inhibitor of PTP1B with anti-diabetic effects. Like MSI-1436, DPM-1001 is a sterol; however, unlike MSI-1436, it is not a charged molecule, which likely contributes to its oral bioavailability. This and the increased potency relative to MSI-1436 are major steps forward in the quest for druggable inhibitors of PTP1B.
Edited by This work was supported in part by  National Institutes of Health  Grants  CA53840  and  GM55989  (to N. K. T. ),  Cold Spring Harbor Laboratory Cancer Centre Support  Grant  CA45508  from the  National Institutes of Health , the  CSHL-Northwell Alliance , and the " Investissements d'Avenir" Program  launched by the  French Government  and implemented by the  ANR  with reference  ANR-10-IDEX-0001-02 PSL  (to G. G. ). N. K. and N. K. T. serve on the  Scientific Advisory Board of DepYmed Inc. , a company that focuses on inhibitors of PTP1B in various disease indications. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This article contains Figs. S1-S3  and supporting Methods. 1 Recipient of a Swiss Confederation for a Government Excellence Scholarship.
Signatures of selection in the Tasmanian devil genome Our analysis resulted in the identification of 97 signatures of selection dispersed throughout the chromosomes, accounting for about 0. 3% of the genome. Most signatures of selection were found to be population-specific, and only one was common to the three investigated populations (Fig  1A ). The West Pencil Pine (WP), Freycinet (FN) and Narawntapu (NP) populations displayed 54, 38 and 37 signatures of selection, respectively (Fig  1B ). The ability to detect signatures of selection from the dataset was influenced by the experimental sampling protocol and SNP detection, which was specific to each population (Table  1 ). In the WP population, characterized by an adequate time-series sampling but with a low SNP density, a large number of small signatures of selection were detected.
The year of sampling, t i , is provided between brackets. N. B. Data were made publicly available in  [13]. More information about the dataset can be found in  [14]. https://doi. org/10. 1371/journal. pone. 0201838. t001 https://doi. org/10. 1371/journal. pone. 0201838. g002 [23, 24]. Other candidate genes are known to be involved in oncogenic RTK pathways, such as SHC4  [25]  and ST5/DENND2B  [26]. The CEP131 gene is involved in cell proliferation and migration through the activation of the phosphoinositide 3-kinase (PI3K/Akt) signaling  [27]  and the SMAD3 gene is a key mediator of the transforming growth factor-β (TGF-β) signaling pathway involved in cancer progression  [28]. The candidate FOXN3 is a key gene of the Wnt/ β-Catenin pathway that is altered in cancers  [29]. Other candidates, such as the SPTBN1, NSD3 and CDK14 genes, are able to influence cancer progression through Wnt signaling  [18, 30, 31].
Discussion The Tasmanian devil dataset investigated here was initially presented and analyzed by Epstein et al. [14] , who reported two putatively selected regions harboring seven candidate genes. These two regions were also detected in our analysis and correspond to signatures of selection located in scaffolds GL841593 (nucleotides 4,501,785 to 4,979,756) and GL849657 (nucleotides 283,671 to 283,701). Epstein et al. [14]  performed their analysis with a composite test statistic that took into account temporal changes in both allele frequency and integrated extended haplotype homozygosity of an individual SNP site (iES)  [72]. Such an approach is based on the idea that an SNP undergoing strong selection would rapidly rise in frequency, while the haplotypic diversity of the surrounding region would suddenly decrease.
Tasmanian devil dataset The data analyzed in the present article were initially reported in  [14]  and made publicly available as a Dryad data package  [13]. This data package consists of SNP genotyping data produced by Stacks  [96]  following RAD-seq (Restriction-site Associated DNA sequencing) assays  [97]  from tissue samples collected in 360 Tasmanian devils across Tasmania. Samples were collected at different time points between 1999 and 2014, allowing the analysis of genomic time-series that take into account the impact of DFTD through time on Tasmanian devil populations. We restricted our analysis to the same samples as in  [14] , from the localities of Freycinet (FN), Narawntapu (NP) and West Pencil Pine (WP). We reproduced the SNP filtering strategy reported in  [14]. In brief, we performed filtering according to (i) MAF computed over the whole dataset (SNP with MAF less than 0.
01 were discarded), (ii) observed heterozygosity computed over the whole dataset (SNP with heterozygosity over 0. 5 were discarded), (iii) the proportion of missing genotypes (SNP with less than one-third of genotypes either in the whole dataset or in a sample were discarded, except for the two smallest samples in which SNP with less than half genotypes were removed), (iv) the linkage disequilibrium between neighboring SNP (using PLINK  [98] , we removed one SNP from pairs of SNP harboring R 2 > 0. 99 over 20 successive SNP and 50 kb of distance in any sample). We obtained filtered datasets of 16978, 27173 and 5401 SNP for the FN, NP and WP populations, respectively. Relevant information about the dataset for the present work is summarized in Table  1. Further information about sample collection, genotyping and data processing can be found in  [14].
Signatures of selection We submitted the genomic time-series of the three investigated Tasmanian devil populations to a customized method for detecting footprints of selection (available at https://github. com/ hubert-pop/signasel). This method was initially described and successfully implemented to detect genomic regions targeted by short-term selection in experimental wheat populations  [15]. Briefly, this method compares two Wright-Fisher models, one including drift and the other including drift plus selection, in a maximum-likelihood framework. The model that best fits the SNP frequency variation observed over time is identified through a Likelihood Ratio Test (LRT). Each SNP is individually tested and associated with a p-value that quantifies to what extent the temporal variation in SNP frequency may be due to selection, under a null hypothesis postulating an effect of drift only.
A strength of this method is to model and disentangle the effects of drift plus selection as opposed to those of drift alone in a population-specific manner. This has proven to be efficient for detecting SNP under selection from genomic temporal samples separated by a few generations in small populations undergoing intense selection, as in the Tasmanian devil. For example, we performed simulations mimicking a strong selection (applying a constant selection coefficient of 0. 5) in the FN population (considering a constant effective population size, N e , of 34, and a sampling interval of 6 generations). In such a case, the power to detect a truly selected SNP was 61%, while the false-positive rate was about 1%, suggesting that our method would provide enough power to find signatures of selection from the Tasmanian devil dataset.
Candidate gene identification We identified as candidate genes all the protein coding genes standing at less than 100 kb from the detected signatures of selection and having a human orthologue according to the Ensembl genome browser 90 (https://www. ensembl. org). Ensembl stable ID of candidate genes and corresponding orthologues were retrieved using the Ensembl Genes 90 database and the Devil_ref v7. 0 Tasmanian devil reference assembly  [101]  obtained from BioMart.
Ingenuity pathway analysis We used the manually curated database IPA1 (Ingenuity Pathway Analysis, QIAGEN Inc. , https://www. qiagenbioinformatics. com/products/ingenuity-pathway-analysis) to identify the diseases and developmental disorder as well as the molecular and cellular functions associated with our candidate genes. We submitted the list of 147 human orthologues of our candidate genes to a "Core Analysis" with the "Ingenuity Knowledge Base" reference set to find overrepresented functions and diseases in our gene set. This is achieved by IPA by applying a righttailed Fisher Exact test to estimate the likelihood that the overlap between the set of genes and a given function or disease is due to random chance.
V. B2. Comorbidity programs for daily practice EULAR has provided recommendations (points to consider) in order to standardize the way of collecting the information related to the reporting, screening and preventing of the most frequently observed comorbidities in chronic inflammatory rheumatic disorders. [72]  Several initiatives are ongoing in order to evaluate the impact of programs led by rheumatologists or nurses. For example, a recent clinical trial (COMEDSPA , NCT02374749) has recently been conducted in 500 spondyloarthritis patients in order to evaluate a nurse led program. As an example, the percentage of patients who have received an influenza or a pneumococcal vaccination during the 12 months follow-up of the study was 28. 6% vs. 9. 9% (influenza) and 40. 0% vs. 21. 1% (pneumococcal) in the group of patients who have visited a nurse at baseline checking for comorbidities versus the group of patients who have visited a nurse without specific attention to comorbidities  [73].