dataesr/scientific-paragraphs-categorization huggingface dataset:
An adequate database and processing scheme is required in order to isolate the SP response before determining the unknown parameters in the Neel's model (sample specific saturation magnetization and grain dipole moment) with non-linear data inversion procedures. Particle concentration is then estimated from a basic, constitutive relationship relating the model parameters obtained from data inversion. SPCDM is validated by a control experiment, comprising samples with different concentrations of nano-sized synthetic magnetite within a narrow size distribution and then applied to analyse air particulate matter from air-quality monitoring stations installed in the Jânio Quadros tunnel in São Paulo, Brazil.
The MicroMag3900-VSM measures the sample dipole moment S D [Am 2 ], and not the magnetization M [Am -1 ]. The relationship between the two is S D = M V  (Evans & Heller 2003) , where V is the volume [m 3 ] of the sample. Thus, the sample dipole saturation S DS [Am 2 ] is given by S DS = M S V. The procedure devised to provide a database for the proposed technique followed the following steps. Initially, the sample was AF-demagnetized (120 Hz, from 1T to zero in 2 per cent increments) and then subjected to external fields B i of  5, 10, 15, 20, 25, 30, 40, 60, 80, 100, 120, 140, 180, 220, 260, 300 and 340  mT, where i = 1 : 17 during 10 s intervals. Longer exposures of 20 and 40 s were tested but this gave equivalent results. The magnetization M 0 is measured when the external field is on, setting t = 0 s when this field is turned off.
Errors were estimated by repeating the acquisition procedure for the calibrator sample (2853 YIG Sphere) 15 times. The percentage error was then evaluated and applied to measurements with tested samples. Paramagnetic SPCDM procedure 1979 correction was carried out by the approach to saturation method, developed by  Jackson & Sølheid (2010). This correction was implemented through the scripts introduced by  Paterson et al. (2018) , using the estimated paramagnetic susceptibility χ and calculated it for each field Bi as χ B i to remove the paramagnetic response from M 0 i estimates.
Computational aspects The SPCDM codes were implemented in two main steps. Data files from MicroMag3900 were processed according to the procedures described in Section 2. 3, to isolate the SP response. An inverse procedure (as described in Section 2. 2) was then applied to minimize Q(S DS , μ). The minimization of this function was achieved with the FMINCON function of the Matlab Optimization Toolbox, based on the interior point algorithm of  Byrd et al. (2000). As an iterative procedure, FMINCON requires an initial solution, which, in SPCDM, was chosen randomly within a feasibility interval, guaranteeing the parameters positivity. The optimization procedure was applied 10 times for each data set, starting from random initial solutions in order to probe alternative solutions and uncertainty.
SPCDM data were measured with a MicroMag3900-VSM (Lake Shore Cryotonics). To enhance the magnetic response, the set of 10 filtered fractions was grouped into pairs, according to adjacent grain size specifications, and then measured as a single sample. As illustrated in Fig. 2 (c), the magnetic relaxation of the air PM collected in the Jânio Quadros tunnel has a fast SP behaviour and growing IRM acquisition with increasing external fields, which is indicative of samples of mixed carriers with different properties (grain size and/or composition). A magnetic response indicating mixed contributions in air PM was also observed by  Maher (2009). In the Jânio Quadros tunnel, the finer-grained minerals exhibit fast SP decay (Fig. 3b ) and the coarser ones show low coercivity (1. 93-4. 55 mT) in the hysteresis curve; this is indicative of magnetite as a contributor in the IRM response.
1997 ). We incubated cdc13-1 cells expressing FHA1-mCherry reporter for 3 h at 32°C and then either incubated them for an additional 21 h at 32°C or returned them for 2 h to 23°C (Fig. 4D ). As expected, the nuclear mCherry signal increased significantly after 3 h at the restrictive temperature, indicative of checkpoint activation, and then decreased to basal levels after 2 h at 23°C. However, cells remaining for 24 h at 32°C showed sustained elevation of the nuclear mCherry signal upon adaptation compared with basal levels (Fig. 4D ; Supplemental Fig. S4D, E ). These data demonstrate that the FHA1-mCherry reporter can distinguish between recovery and adaptation events in live single cells. We used this reporter to assess checkpoint activation in individual telomerase-negative cell lineages.
Image analysis and single-cell lineage tracking Custom software written in Matlab, phyloCell 2. 1 was used to segment and track cells and assign mother-daughter links. Time-lapse images were exported as high-resolution TIF files and analyzed with the graphical user interface of phyloCell. For details about the routines and algorithms implemented in phylo-Cell, see  Fehrmann et al. (2013). In contrast to  Fehrmann et al. (2013) , the cell at the tip of the cavity would frequently be replaced by its daughter cell, which was intended in our approach. To efficiently track such lineages, in which we frequently switched focus from a given cell to its daughter cell, the timelapse images were retrospectively analyzed starting from the last image. This avoided tracking of lineages in which the cells were ejected from the microcavity.
In experiments involving the FHA1-mCherry/Hta2-yECFP reporter, the nucleus was segmented using the CFP channel with an Otsu threshold method, and the cell was segmented with either phyloCell for microfluidic experiments or a custom watershed-based method for phase contrast images. The nuclear mCherry signal was measured in the overlap of the cellular and nuclear masks and averaged over the measured area of the nucleus. For the experiment using the single inducible HO cut, image analysis was performed on the median slice of each image. Nuclei were segmented based on the CFP signal (automatic Otsu threshold), and the mean mCherry intensity was measured for each nucleus using Fiji software  (Schindelin et al. 2012).
Computational and statistical analyses All computational and statistical analyses were performed with the statistical toolbox of Matlab R2013a. Data are represented as the median, first and third quartiles, 99th percentiles (box plots), the mean ± SD, or the mean and 95% confidence interval, as indicated in the legends. Differences were compared using Student's t-test, the Mann-Whiney U test, and two-sample Kolmogorov-Smirnov test as indicated.
Acknowledgments We thank  David Toczyski ,  Jim Haber , and  Naïma Belgareh Touzé  for yeast strains and plasmids;  Pascale Jolivet ,  Aubin Fleiss , and  Gilles Fischer  for technical help; and  Eric Vandjour  for medium preparation. We also thank members of  M. T. T. 's laboratory ,  Miguel Ferreira ,  Claus Azzalin ,  Ted Weinert ,  Titia de Lange ,  Joachim Lingner ,  Jim Haber , and  Damien D'Amours  for fruitful discussions. K. D. thanks the  European Research Council  under the European Community's  Seventh Framework Program  (FP7/2007 2013/ European Research Council  grant agreement  281287 ) for funding. S. P. thanks the  Swiss National Science Foundation and the  University of Lausanne  for funding, and  Nadim Mira  for technical assistance. This work was supported by  "Fondation de la Recherche Medicale" ("MTT Équipe Labellisée")  and the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" Program (LabEx Dynamo)  and  ANR-16-CE12-0026  to M.
T. T. and G. C. H. C. has been supported by a doctoral grant from the  Paris Sciences et Lettres  ( PSL) Idex program  implemented by the  ANR  ( ANR-10-IDEX-0001-02 PSL ). Author contributions: Z. X. and M. T. T. conceived the study. H. C. , Z. X. , S. P. , G. C. , K. D. , and M. T. T. performed the methodology. Z. X. and G. C. were responsible for the software used. H. C. and Z. X. performed the formal analysis. H. C. , Z. X. , M. C. M. , M. L. -A. , S. P. , and K. D. performed the investigations. H. C. , Z. X. , and M. T. T. wrote the original draft of the manuscript. H. C. and Z. X. visualized the study. Z. X. and M. T. T. supervised the study.
Ethics and consent The protocol was approved by an independent ethics committee [Comité de Protection des Personnes (CPP) Sud Méditerranée. IV (N°ID-RCB: 2015-A01084-45; Protocol version 1: 06/23/2015)] and was conducted in accordance with the Declaration of Helsinki (clinicaltrials. gov: NCT02760446). Written consent was required from the patient or the legally authorized representative or a proxy/surrogate decision maker (patient's next of kin) who gave consent on the patient's behalf, followed by the patient's consent as soon they could communicate.
Demographic and medical data Age, gender, comorbidities and the reason for ICU admission were recorded. The Simplified Acute Physiological Score II (SAPS-II) score  [21]  and the Sequential Organ Failure Assessment (SOFA) score  [22]  were calculated within 24 h after ICU admission and upon enrollment. In case enrollment occurred before 24 h, the SAPS-II score took into account the worst value available during the 24 h preceding enrollment. Therapeutics such as sedation, mechanical ventilation and the use of vasopressors were collected upon enrollment.
Power analysis The study sample size was determined in relation to the primary endpoint. For expected values  [8]  of sensitivity and specificity at 85 and 75%, respectively, a desired level of precision set at 10% and the prevalence of delirium set at 50%, the number of patient inclusions required for achieving appropriate power would be 95. Taking into account possible post-enrollment exclusions, 115 patients needed to be enrolled in the study. The prevalence of delirium ranged from 30 to 90% in the literature  [1]. Thus, we set the prevalence of delirium at 50% which is conservative regarding the number of patients needed to be analyzed, in order to maximize the power. Data presentation Quantitative data are shown as medians and 25th-75th percentiles. Data were analyzed using SAS version 9.
137%). Overall CV is 0. 122% for total iTFA. CVs for rTFAs are also good, mainly for vaccenic acid, the main iTFA isomer (CV = 0. 282%). CV for total iTFA is 0. 123%. Because the TFA levels in the new-born blood also reflects the TFA status of the mother at the end of pregnancy, measurement of iTFA levels in DBS will furthermore serve as validation for the effect of trans-fat elimination for incident cases. To take into account different cohort effects or changes in life-style in the period 1988 to 2008, we will merge the cohort data with information from the Danish Medical Birth Registry on trends in co-variates which might have changed during the time period, e. g. smoking during pregnancy and age of the mother.
This number can be linked, on an individual level, to Danish birth, patient and medical registries; social and ethnic registries; and clinical and other national databases. The comprehensive linkage of datasets for both aims makes it possible to study mediation, confounding and/or modification of effects of iTFA for childhood haematopoietic neoplasms. For the first objective we do not collect any individual data on iTFA intake. In contrast, all individuals are unselected in relation to iTFA exposure and later disease occurrence. This may be considered a very strong feature of the present study, as neither confounding from external or internal factors, nor the lack of individual data on iTFA intake, is a prerequisite for the study, as the influences of lifestyle differences (e. g. use of infant formula, individual diet intake, use of supplements), or biological (adipose tissue stores, weight changes during pregnancy and post-partum) which we would not be able to adjust for, are all independent of the iTFA legislation.
The existence of the four CAF sub-populations was validated by IHC on serial sections of HGSOC samples (Fig. 2a ), using five out of the six fibroblast markers listed above, except caveolin (see Supplementary Table  1  for list of antibody references and Supplementary Fig. 1a  for isotype controls). We evaluated histological scores (H score) for each marker in the stromal compartment. We applied a decision tree algorithm (Fig. 2b ) to determine the global CAF subset enrichment per tumor. In brief, this decision tree was based on marker intensity thresholds, first defined according to FACS data (intensities of each CAF marker in each CAF subset) from fresh HGSOC decision tree algorithm for prediction of CAF subset identity). HGSOC were mainly enriched in activated CAF-S1 and CAF-S4 subsets (Fig.
2c ). Interestingly, mesenchymal HGSOC accumulated more CAF-S1 fibroblasts than non-mesenchymal tumors (Fig. 2d ). In parallel, we developed a method combining R script and Fiji plugins to stack IHC staining of serial HGSOC sections. This method provided maps, where each square corresponded on average to one cell. Histological scores of the five CAF markers in each square defined CAF subset identity at cellular level and allowed to visualize their geographic repartition within the tumor (Fig. 2e  and Supplementary Fig. 1b ). In addition, we observed on these maps that there was a significant enrichment of CAF-S1 cells at close proximity of cancer cells (Fig. 2f ), suggesting a mutual potential benefit between CAF-S1 fibroblasts and cancer cells, as recently shown in pancreatic cancers  26.
Finally, we confirmed the identity of CAF-S1 and CAFS4 cells by using triple immunofluorescence staining of CD29, SMA, and FAP markers on the same HGSOC sections (Fig. 2g ). Thus, IHC data (obtained from a retrospective cohort of HGSOC patients) confirm FACS observations described above on fresh HGSOC samples and demonstrate for the first time the existence of four distinct CAF sub-populations in HGSOC, with strong accumulation of the CAF-S1 subset in mesenchymal HGSOC. 1-If (FAP ≥ Q3) then CAF-S1 2-Else if(CD29 ≥ Q1 & FSP1≤ median) then CAF-S4 3-Else if(CD29 ≥ Q1 & < median) then CAF-S3 if(CD29 ≥median) then CAF-S4 4-Else if(CD29<Q1 and FOXP3 + T lymphocytes accumulate in HGSOC enriched in CAF-S1. By looking at several HGSOC tumor bed sections, we observed that the stromal compartment accumulated more lymphocytes than the epithelial compartment (see representative hematoxylin-eosin sections, Fig.
3m ). Altogether, these observations show that HGSOC enriched in CAF-S1 fibroblasts are highly infiltrated in particular by FOXP3 + T lymphocytes. CXCL12β expression discriminates CAF-S1 from CAF-S4. In order to uncover CAF-S1-mediated functions in T-cell recruitment in HGSOC, we compared CAF-S1 and CAF-S4 transcriptomic profiles by performing RNA sequencing (RNA-Seq) on sorted cells from fresh HGSOC using the gating strategy shown in Fig. 1  (CAF-S1 and CAF-S4 RNAseq data from HGSOC are available using EBI accession number: EGAS00001002184). Unsupervised principal component analysis (PCA) (Fig. 4a ) and hierarchical clustering (HC) (Fig. 4b ) of the 500 most variant transcripts revealed molecular differences between CAF-S1 and CAF-S4 subsets in HGSOC. DAVID analysis (https://david. ncifcrf.
gov), using GO (Gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) databases, of the differentially expressed genes (CAF-S1-specific gene signature provided in Supplementary Data 1) showed that CAF-S1 cells were enriched in genes involved in biological adhesion, wound healing response, extracellular matrix (ECM) protein remodeling, and skeletal system (Table  2 ), while CAF-S4 gene signature pinpointed muscle contraction and blood vessel development (Table  3 ). Interestingly, genes expressed in CAF-S1 cells included many genes enriched in the mesenchymal HGSOC subtype (Fig. 4c ), thereby confirming that CAF-S1 fibroblasts could be key components in this HGSOC molecular subtype. The common genes between CAF-S1 and mesenchymal signatures encoded ECM components and proteins involved in immune regulation, such as complement factors (C1S and CFH), cytokines (TNFSF4), and chemokines (CXCL12β) that could be involved in T-cell recruitment.
4a-c ). As expected, we confirmed that CXCL12 proteins were mainly detected in the stroma (Fig. 4j ), and its receptor CXCR4 at the surface of epithelial cells (Fig. 4k , arrows). CXCR4 was also detected in endothelial and immune cells (Fig. 4k , arrowheads), underlying the role of the CXCL12/CXCR4 axis in HGSOC microenvironment. We observed a huge diversity in CXCL12 histological scoring in HGSOC (Fig. 4l ). Still, CXCL12 protein significantly accumulated in mesenchymal HGSOC (Fig. 4l ), thereby The first three columns recapitulate clinical parameters from cohorts used for transcriptomic data analyses. TCGA, AOCS and Curie cohorts have previously been described  [6] [7] [8]. The last column concerns samples used for immunohistochemistry analyses. Tumor samples were obtained from a cohort of consecutive ovarian carcinoma patients, treated at the Institut Curie between 1989 and 2012.
All the subjects underwent surgery, 82 of them have a partial debulking and 32 subjects have a full debulking confirming transcriptomic data. In contrast, no difference was observed for CXCR4 protein levels between mesenchymal and non-mesenchymal HGSOC (Fig. 4l , right). Finally, by performing in situ hybridization using the RNAscope ® technology, we observed that CXCL12 mRNA was mainly expressed by stromal cells (Fig. 4m ), observation that was confirmed by using publicly available data set from primary cell lines (GSE49910 from Gene Expression Omnibus resources) (Supplementary Fig. 5 ). Taken as a whole, these data indicate that the CXCL12β isoform accumulates more than CXCL12α in mesenchymal HGSOC, consistent with the accumulation of CAF-S1 fibroblasts in these tumors. All these features, i.
In contrast, none of the markers characterizing MDSC, such as CD11b + or CD33 + , are detected in CAF-S1 cells (EBI study accession number: EGAS00001002184). Furthermore, we show that the CXCL12 chemokine is a key player involved in attraction of regulatory T cells in human HGSOC, confirming previous studies showing the role of CXCL12 in immunosuppression  37, 38. These effects are independent of CXCL12-proangiogenic function  27, 58, 59  , as we detected them using functional assays in vitro. Accordingly, CXCL12/CXCR4 blockade reduces recruitment of regulatory T lymphocytes in immuno-competent mouse models of cancers  28, 29, 37, 38. In contrast, we did not observe tumor growth inhibition upon CXCR4 inhibitor (AMD3100) treatment in immuno-deficient patient-derived xenograft (PDX) models of ovarian cancer (Supplementary Fig.
Methods Cohorts of HGSOC patients. 8] 12. Main clinical features of these cohorts have also been summarized in Table  1  and its corresponding legend. In total, 107 ovarian tumor samples from the Institut Curie cohort have been considered only if they were enriched in at least in 55% of epithelial cancer cells. RNA has been subsequently extracted and analyzed using Human Genome U133 Plus 2. 0 microarray (Affymetrix), as described in ref. 7. Microarray data from Institut Curie cohort are freely accessible in the Gene Expression Omibus under the accession number GSE26193. For the TCGA cohort, freely available transcriptomic data (L2 level) obtained using Human Genome U133A microarray (Affymetrix)  8  have been downloaded from the following portal: http://cancergenome. nih. gov/. The microarray data set of the AOCS cohort has been obtained with U133 Plus 2 microarrays (Affymetrix) and is freely accessible under the accession number GSE9899  6.
The project developed here studying samples from the Institut Curie is based on surgical tumor tissues available after histo-pathological analyses that are not needed for diagnostic purposes. There is no interference with the clinical practice. Analysis of tumor samples was performed according to the relevant national law on the protection of people taking part in the biomedical research. Their referring oncologist informed all patients included in our study that their biological samples could be used for research purposes and patients signed an informed consent of non-opposition. In case of patient refusal, which could be either orally expressed or written, residual tumor samples were not included in our study. The Institutional Review Board and Ethics committee of the Institut Curie Hospital Group approved all analyses realized in this study, as well as the CNIL (Commission Nationale de l'Informatique et des Libertés), the National Commission for Data Processing and Liberties (N°approval: 1674356 delivered on March 30, 2013).
DAPI (3 μm) (ThermoFisher Scientific #D1306) was added just before flow cytometry analysis. In the two conditions (surface and intracellular staining), cells were analyzed on the LSRFORTESSA analyzer (BD Biosciences) for flow cytometry analysis. At least 5×10 5 events were recorded. Compensations were performed using single staining on anti-mouse IgG and negative control beads (BD Biosciences #552843) for each antibody and on ArC reactive beads (Molecular Probes #A10346) for Live/Dead staining. Data analysis was performed using FlowJo version X. Cells were first gated based on forward (FSC-A) and side (SSC-A) scatters (measuring cell size and granulosity, respectively) to exclude debris. Single cells were next selected based on SSC-A versus SSC-W parameters. Dead cells were excluded based on their positive staining for Live/Dead (fixed conditions) or DAPI (surface staining).
Reads were mapped on reference human genome (hg19/GRCh37 from UCSC genome release) using Tophat_2. 0. 6 algorithm with the following parameters: global alignment, no mismatch in seed alignment (of size 22), three mismatches in read length. Quality control was performed using FastQC software and duplicates were removed using Samtools rmdup. Quantifications of expression at both gene and transcript levels were performed using HTSeq-count and featureCouts (implemented in Bioconductor R package Rsubread). Only genes with one read in at least 5% of all samples were kept for further analyses. Normalization was performed using method implemented in DESeq2 R package. Analysis strategy includes unsupervised analysis such as PCA and HC, as well as differential expression analysis (done with DESeq2 bioconductor package).
Biological interpretations of the genes (or transcripts) identified were assessed by computational functional analyses based on several bioinformatics resources (Gene Ontology, KEGG, Reactome, Ingenuity Pathway Analysis). Accession number: RNAseq data from CAF-S1 and CAF-S4 fibroblast sorted from HGSOC samples have been submitted at the European Genome-Phenome Archive (EGA) at EMBL-EBI (EBI study accession number: EGAS00001002184). Immunohistochemistry staining on HGSOC sections. A total of 118 HGSOC samples have been selected by pathologists, as representative of the pathology. Consecutive sections of paraffin-embedded HGSOC tissues (3 μm) were stained using streptavidin-peroxidase protocol (Vectastain ABC kit; Vector Labs #PK-6101) on the Autostainer Labvision (Thermoscientific). In brief, paraffin-embedded sections were incubated with specific antibodies recognizing CXCL12, CXCR4, CAF (CD29, FAP, PDGFRβ, SMA, FSP1) and T-lymphocytes markers (CD3, CD8, FOXP3) (Supplementary Table  1  for antibody references and dilutions used for immunohistochemistry (IHC)) in PBS solution containing 0.