dataesr/scientific-paragraphs-categorization huggingface dataset:
(C) Neutrophils (1 × 10 7 /mL HBSS) were incubated in the absence or presence of different concentrations of juglone or PiB and stimulated by fMLP (10 -7 M) for 1 min. Neutrophils were lysed and Pin1 activity was measured using the Suc-AEPF-p-nitroaniline (pNA) peptide-Glu. The increase of pNA by chemotrypsin was detected at 390 nm. Ctrl corresponds to resting neutrophils. Data are means ± S. E. M. of 3 separate experiments (* p < 0. 05, ** p < 0. 001, *** p < 0. 0001 juglone and PiB as compared to control).
Strain Genotyping The spa screening approach showed that MRSA strains presented three distinct profiles. spa typing showed that these three profiles corresponded to three spa types (t002, t045, t127), and MLST/wgMLST confirmed that these three spa types belonged to 2 STs (ST5 for t002 and t045, ST1 for t127). According to the Ridom SpaServer, the three spa types identified in our study had previously been reported in different countries and their frequencies were 6. 57% for t002 (the third most common spa type in the database), 2. 63% for t127 and 0. 69% for t045 (http://spa. ridom. de/frequencies. shtml, accessed on 20 February 2021). MRSA of t002 were the most identified in both patients, representing 85% of strains in the study (86% in Patient 1 and 98% in Patient 2). Patient 1 presented a co-colonization by MRSA of two related spa types, t002 and t045 with t045 (repeat succession 26-17-20-17-.
Strain Genotyping The spa screening approach showed that MRSA strains presented three distinct profiles. spa typing showed that these three profiles corresponded to three spa types (t002, t045, t127), and MLST/wgMLST confirmed that these three spa types belonged to 2 STs (ST5 for t002 and t045, ST1 for t127). According to the Ridom SpaServer, the three spa types identified in our study had previously been reported in different countries and their frequencies were 6. 57% for t002 (the third most common spa type in the database), 2. 63% for t127 and 0. 69% for t045 (http://spa. ridom. de/frequencies. shtml, accessed on 20 February 2021). MRSA of t002 were the most identified in both patients, representing 85% of strains in the study (86% in Patient 1 and 98% in Patient 2). Patient 1 presented a co-colonization by MRSA of two related spa types, t002 and t045 with t045 (repeat succession  26-17-20-17-12-17-16) , presenting a deletion of three repeats compared with t002 (repeat succession  26-23-17-34-17-20-17-12-17-16).
Identifying of the Genetic Determinant of Linezolid Resistance Four LRSA strains (two strains co-isolated from the last sample included in the study for each of the two patients and showing linezolid MICs of 16, 24, 32 and >256 mg/L) were submitted to whole genome sequencing to determine the genetic determinant(s) of linezolid resistance. Whole genome sequence-based analysis identified a unique mutation (G2576T substitution) in the sequences of the four LRSA strains analyzed, whereas this substitution was not found in any of the 13 WGSs analyzed for susceptible strains, either of t002 or t045 (data not shown). I-CeuI macrorestriction-based analysis of the DNA of seven strains (the three susceptible strains isolated in 2007 and the four LRSA strains isolated in 2016) revealed that all possessed five copies of the 23S rRNA gene (data not shown).
Patients, Bacterial Strains and Data Collection Two CF siblings (Patients P1 and P2) attending the CF center at Montpellier University Hospital, France, were included in this study. Ethical approval was obtained through the Institutional Review Board at Nîmes University hospital (Interface Recherche Bioéthique IRB n 21. 03. 01, 04 March 2021). Both patients are chronically colonized by MRSA and Pseudomonas aeruginosa. These are two sisters whose genotype is F508del/R553X, born in 1991 (P1) and 1996 (P2), respectively. The oldest child had a childhood and adolescence without an antibiotic infusion, without bronchial obstruction and simply the use of oral antibiotics against S. aureus. There was a sharp decline of FEV 1 from the age of 18, concomitant with P. aeruginosa colonization) (Figure  2 ).
Additionally, one strain from early infection episodes in each patient (2007) was included for the purpose of comparing them with strains isolated during the study period (these strains were the first ones available for P1 and P2). Both strains were previously analyzed by the microarray ADN Alere ® Staphylococcus aureus genotyping kit 2. 0 (Alere Technologies, Jena, Germany)  [41]  showing them to belong to the CC5-MRSA-I Geraldine clone, described as agr-2, ST5, spa type t002 or related, and SCCmec type I, and harboring the tst gene  [28]. Data regarding age at first MRSA isolation, co-colonizing pathogens and linezolid courses received by patients P1 and P2 were collected retrospectively.
Acknowledgments: The authors gratefully acknowledge  Teresa Sawyers , English medical writer at  Nîmes University Hospital , for editing this paper,  Nassim Boutouchent  for his help with the therapeutic data collection and  Agnès Masnou  for performing I-CeuI fragment analysis. We thank the  Nîmes University Hospital  for its structural, human, and financial support through the award obtained by our team during the internal call for tenders "Thématiques phares". The authors belong to the  FHU InCh (Fédération Hospitalo-Universitaire Infections Chroniques, Aviesan ).
In 2019 the BESIII collaboration measured the Born cross section of the e + e -→ p p process and the proton FFs at 22 center-of-mass (CM) energy points from q 2 = 4 (GeV/c) 2 to q 2 = 9. 5 (GeV/c) 2  [41]. The FF ratio was determined with total uncertainties around 10%, comparable to the data in the space-like region at the same |q 2 | values. Proton FFs in the space-like and time-like regions are connected via dispersion relations. Therefore, a precise determination of the time-like FFs over a large q 2 range using different electromagnetic processes can help to constrain the theoretical models and shed light on the discrepancies which have been found in the space-like region. The situation in the time-like region will be improved even more in the future by the data which will be collected with the PANDA (antiProton ANnihilation at DArmstadt) detector.
The PANDA experiment at FAIR The PANDA experiment  [49]  will be located at the Facility for Antiproton and Ion Research (FAIR), which is currently under construction in Darmstadt (Germany). The PANDA experiment will measure annihilation reactions induced by a high-intensity antiproton beam covering a wide range of momenta between 1. 5 GeV/c and 15 GeV/c. The physics program includes hadron spectroscopy in the charmonium, hyperon and light quark sectors, hypernuclear physics, and studies of hadron properties in a nuclear medium. An important part of the PANDA physics program will be dedicated to the investigation of the nucleon structure. It is planned to measure nucleon-to-meson transition distribution amplitudes (TDAs) through the measurements of the exclusive processes p p → γ * π 0 → e + e -π 0  [50]  and p p → J/Ψ π 0 → e + e -π 0  [51].
The generalized distribution amplitudes (GDAs) of the proton can be also accessed Fig. 1  The proposed PANDA detector with the large angle production of the neutral states γ γ and π 0 γ  [49]. In addition, a Drell-Yan physics program to access transverse momentum dependent (TMD) parton distribution functions (PDFs), using the inclusive production of lepton pairs in proton-antiproton annihilations, is also foreseen  [49].
Finally, the antiprotons will be injected in the high energy storage ring (HESR), where the induced antiproton annihilation reactions will be studied by the PANDA fixedtarget experiment. This setup is designed to provide a beam of up to 10 11 antiprotons per filling and peak instantaneous luminosities up to 2×10 32 cm -2 s -1. However, in the initial start-up phase of the FAIR operation without the RESR, the HESR will be used as an accumulator, resulting in a luminosity about a factor of 20 lower than the nominal design value. In the present study, the results are obtained assuming an integrated luminosity of 2 fb -1 , which can be accumulated in 4 to 5 months of data taking at the maximum design luminosity.
Monte Carlo simulation with PandaRoot The offline software for the PANDA detector simulation and analysis, PandaRoot  [70] , has been developed within the framework for the future FAIR experiments, FairRoot  [71]. The PandaRoot software encompasses full detector simulation and event reconstruction. In order to investigate the feasibility to use the p p → μ + μ -process for the measurement of the proton time-like FFs at PANDA, Monte Carlo (MC) simulation studies based on PandaRoot are performed.
Optimizing the μ/π separation by using boosted decision trees The analysis of the simulated data aims to achieve the best possible background suppression while keeping a sufficient signal efficiency. Multivariate data classification is used to optimize the signal-background separation. Signal efficiency and background suppression studies are based on the high statistics μ + μ -sample (S1) and the high statistics π + π - sample (B1). After the event reconstruction, the full analysis based on multivariate data analysis (MVA) is carried out. Dif-ferent methods of multivariate data classification are investigated using the Root-integrated software package Toolkit For Multivariate Data Analysis with ROOT (TMVA)  [82, 83]. For MVA, a set of input variables is needed. The most important detector related to the μ/π separation is the MD, as discussed in the previous section.
5 GeV/c and 37% at 3. 3 GeV/c. Since the measurement of the muon channel is very challenging due to the overwhelming pion background, the precision of the proton FFs determined in these feasibility studies provides an exciting opportunity for the experiment and the measurement of this channel will offer a very promising contribution to the rich PANDA physics program. or the data will not be deposited. [Authors' comment: All Data are available to members of the PANDA collaboration. The PANDA collaboration is open to researchers at scientific research institutions in the field of hadron physics and particle physics. The ability to analyse the data requires the PandaRoot simulation and analysis software package]. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
While several forms (genetic, epigenetic, transcriptomic, and phenotypic) of ITH have been explored for various types of cancers, genetic ITH has led to more extensive studies  (8). In particular genetic ITH was shown to be frequent and associated to poorer prognosis in several cancer types  (9, 10) , including colon cancer  (11, 12). Concerning transcriptomic ITH, it has been studied in relation to molecular subtypes in different tumor types. In colon cancer, it was notably investigated through intratumor multi-sampling  (13)  or single-cell RNA sequencing (RNA-seq; refs. 14, 15) ; however, in small series. In other cancer types, transcriptome deconvolution was recently shown as a powerful method to evaluate ITH and its prognostic value in large series  (16). Here we used transcriptome deconvolution to assess ITH and its possible prognostic impact in a large multicentric colon cancer cohort collected in the frame of a prospective phase III trial (PETACC8, n ¼ 1779, NCT00265811).
Transcriptome data To promote the clinical use of CMS, our aim was to develop a classifier allowing to study ITH in terms of CMS in FFPE tumor samples, and we chose the NanoString technology for this purpose. The full transcriptome profiles and CMS labels of the 566 samples of the CIT initial series  (17)  were publicly available (GSE39582). For targeted transcriptomic profiling, we selected 196 transcriptomic markers, including 10 housekeeping genes, 81 CMS specific markers identified using the CIT initial series, and 105 other markers, mainly related to microenvironment cell populations  (19) , immune modulators  (20) , and oncogenic pathways  (5) , as recapitulated in extended data Supplementary Table  S1. The transcriptomic profile of these 196 genes was acquired using NanoString nCounter Analysis System using 150 ng of total RNA from 1,779 FFPE samples from the PETACC8 series, 144 samples of the CIT initial series [133 fresh frozen (FF) and 11 FFPE], and 90 samples from the CIT extension series (corresponding to 45 patients with paired FF and FFPE tumor samples).
We may note that after testing for several cutoffs ranging from 10% to 30%, which all yielded similar results, we arbitrarily chose a 20% cutoff. For any sample, the CMS with the highest weight was designated as major CMS, while the CMS with second highest weight (if > 20%) was designated as minor CMS. The major CMS weight varies from 32% to 100% across the 1779 Petacc8 samples (mean: 74%, median 73%). A sample is defined as pure for its major CMS if all other CMS have weights below 20%. The application of the WISP algorithm to public datasets is described in Supplementary Materials and Methods.
Intratumor CMS heterogeneity measures in colon cancer cell lines To probe the existence of intrasample CMS heterogeneity in colorectal cancer cell lines, we used a series  (22)  of 155 cell lines profiled using HT12 Illumina gene expression platform (GSE59857), of which 55 were also profiled  (23)  using Affymetrix HGU133Plus2 gene expression platform [Cancer Cell Line Encyclopedia (CCLE) CC series: GSE36133]. On the basis of the 55 common samples between these two series, we calculated gene-level linear models of the deviation between the two platforms (Affymetrix and Illumina), to derive an "Affymetrix-like" version of the GSE59857 Illumina series  (22). Then, we selected genes with highest coefficients of variation in the cell line series to calculate CMS centroids on the CIT colon cancer dataset (GSE39582; ref.
Of these cells, 9,437 (65%) passed the quality check filters: (i) at least 1,000 genes with at least one count each, and (ii) less than 20% of counts related to mitochondrial genes. To normalize the read counts within each cell, we divided them by the total number of reads and multiplied them by 100,000; then we log 2 transformed the obtained values. To recover cells, respectively, corresponding to Lovo and Mdst8 cell lines within each mixture, we first selected Lovo-specific and Mdst8-specific markers comparing the bulk transcriptome of these two cell lines (GSE36133) and, retaining markers with a log 2 fold change above 7, then we used these two sets of markers to calculate their mean expression within each cell: cells showing both a mean expression of Lovo (respectively Mdst8) markers above 0.
Statistical tests and survival analyses Analyses were performed using R software (v3. 6. 1). Association between CMS and qualitative variables were assessed using x 2 tests. Association between CMS and quantitative variables were assessed with ANOVA. The survival R package was used to perform log-rank tests (for difference between Kaplan-Meier survival curves) and Cox univariate and multivariate models. The significant variables in the univariate analyses (P < 0. 05) were kept in multivariate analyses. In multivariate analyses, we used recoded variables, based on grouping of modalities. Because major endpoint of PETACC8 was DFS, this grouping was based on DFS univariate analyses and was kept for overall survival (OS) analyses. Because it was an exploratory and descriptive study, no prespecified hypothesis was made.
Validation of ITH measures in cell lines through single-cell analyses The aforementioned analyses of bulk transcriptomics do not resolve the fundamental doubt as to whether each tumor represents a mixture of CMS types, because each tumor cell, one by one, could represent a mixed CMS type, or whether colon cancers would be composed of cells falling into distinct CMS categories. To solve this problem, we first analyzed the bulk transcriptome of 155 distinct colon cancer cell lines (GSE59857; ref. 22). We discovered that very few of them represented a "pure" CMS type, meaning that the overwhelming majority of them (73%) could not be classified in one single CMS category and thus have an ITH score >1 (Fig. 3A ; extended data Supplementary Fig. S3A  and B ). Again, this result did not completely solve the issue, because each cell line might contain an ensemble of cells that are very similar among each other, each reflecting a mixed CMS phenotype, oralternatively-a heterogeneous m elange of cells corresponding to various CMS types.
This observation confirms the validity of the WISP-based measure of ITH on bulk transcriptomic analyses insofar as it reveals a true intercellular heterogeneity. In addition, reanalysis of a recent colon cancer single-cell RNA-seq series (GSE144735) of freshly obtained colon cancer samples revealed that 4 of 6 tumors had a mixed ITH score, further supporting that ITH is common in colon cancer tumors (Fig. 3D ). In conclusion, it appears that the ITH score obtained by bulk transcriptomic analyses that averages mRNA expression across all cells in the tissue reflects an intercellular colon cancer heterogeneity that can be confirmed by single-cell transcriptomics.
Finally, we assessed the potential added prognostic value of the CMS-based ITH, through multivariable DFS and OS Cox modelling. We first built a DFS multivariable reference model, treatment-arm-stratified, including all prognostic factors shown to be significant in univariate analysis, that is, age, gender, obstruction, and/or perforation, T and N stages, tumor differentiation grade, World Health Organization performance status, KRAS/ NRAS mutation, and an mRNA-based surrogate of the Immunoscore (extended data Supplementary Tables  S2  and S3 ). Then, using a DFS Cox univariate model, we selected the CMS combinations related to the poorest survival (CMS1-CMS4, CMS4-CMS1, CMS3-CMS4 and CMS1-CMS3, P ≤ 0. 002; extended data Supplementary Table  S2 ) to define a variable set to "high risk" for these subgroups, and to "low risk" for the remaining subgroups.
E D A B Disease-free survival, % survival, % Disease-free survival, % Disease-free survival, %    (14)  or multi-region bulk transcriptomic analyses  (13) , in small series (n < 30 patients). While these three studies highlighted the existence of transcriptomic ITH in colon cancer, the limited number of patient samples precluded conclusions regarding its prognostic impact. We recently explored transcriptomic ITH through supervised deconvolution of bulk transcriptome profiles from 450 mesotheliomas, with the WISP software, and described its major impact on prognosis  (16). Here, we used the WISP method to analyze transcriptomic ITH in 1,779 samples from the stage III colon cancer PETACC8 trial. Based on the strength of the international consensus classification of colon cancer initially proposed by Guinney and colleagues (5), we explored the hypothesis that the four corresponding consensus molecular subtypes (CMS1.
High samples, 59% of CMS1 samples were related to dMMR versus 0% to 1% in any other CMS; while for RF. low samples, the range of dMMR across the four CMSs ranged between 1% and 9%. Regarding the known prognostic differences across the four CMSs, they were clearly observed for RF. High samples but not for RF. Low samples. We further found that the RF probability of CMS assignment, as a continuous variable, was by itself related to prognosis. Altogether these data support the existence of ITH in colon cancer PETACC8 samples and brings up its possible impact on prognosis. Class prediction approaches such as RF essentially suppose that each sample belongs to a unique class; in addition, some of these approaches, in particular RF, assess the certainty of class assignment through a probabilistic approach.
A supervised deconvolution approach, such as WISP, is totally different, as WISP intrinsically considers that each sample is composed of a mixture of classes and estimates the relative proportions of these classes in the sample  (29). As a consequence, supervised deconvolution algorithms are perfectly fit to measure ITH from bulk transcriptome data, contrary to class prediction approaches. Here we choose to use the WISP-supervised deconvolution algorithm, given its unique ability during the training phase to automatically remove poorly informative samples from the training set (https://github. com/cit-bioinfo/WISP; ref. 16 ). We trained WISP on the CMS labels obtained from the RF classifier, thus obtaining for each of the 1,779 PETACC8 samples the intrasample relative proportions of the four CMS.
This kind of (hypothetical) cooperation among colon cancer cells belonging to different CMS categories might explain the new biological properties acquired by these mixed tumors. Finally, we assessed the possible value for stage III colon cancer prognostication of ITH, in the frame of a multivariable Cox model including all the established prognostic factors. Having pooled the CMS combinations related to shorter survival, the corresponding variable contributed to 13. 4% of the explained DFS variance in the multivariable Cox model. Of note, this model already incorporates a transcriptomic surrogate of the Immunoscore, previously shown to be relevant for colon cancer prognostication  (20, 36). One limitation of our study is the restriction to stage III colon cancer. To address this issue, we leveraged public databases to investigate the ITH score, the enrichment in CMS4 and the enrichment of the CMS1.
C, Heatmaps showing the mean scores for eight immune and two stromal cell populations infiltration (based on MCP-counter estimates), a surrogate of the Immunoscore (Immunoscore-like), and three pathways are shown according to CMS with either high (left) or low RF probability (right). The P values of the t test comparing the distribution of values of each variable between RF > 50% and RF < 50% samples are displayed in the extended data Supplementary TableS3. DFS curves according to RF-predicted CMS in PETACC8 samples, based on RF probability of class assignment above 50% (D) or below 50% (E). Kaplan-Meier curves of DFS (F) and OS (G) are shown according to low (<50%) versus high (>50%) RF probability (whatever the predicted CMS). CMS color codes: CMS1 (orange), CMS2 (blue), CMS3 (purple), CMS4 (green).
Acknowledgments We thank  Guido Kroemer  for fruitful discussion. This study was supported by the program " Cartes d'Identit e des Tumeurs" (CIT)  from the  Ligue Nationale Contre le Cancer (LNCC) , by the  Institut National du Cancer (INCa)  with the program Heterogeneity of tumors and ecosystem ( HTE program ) within the  sub program  "deciphering the heterogeneous genomemicroenvironment interplay in colon and hepatocellular carcinomas (HETCOLI)", by the  ITMO CANCER  through the program single cell COCAHEMISCLE, by the integrated site of cancer research "cancer research for personalized medicine" ( SIRIC CARPEM, INCa-DGOS-Inserm_12561 ) and the  F ed eration Francophone de Canc erologie Digestive (FFCD). We thank the biological resource center EPIGENETEC ( BB-0033-00055 ) for their support.
Between 18 and 25 years old, predicted values are based on an age fixed at 25 years old. Before 18 years old, several equations sets can be applied including those of Polgar and Promadhat published in 1971  [5]. Besides the discontinuity problem between adults and children, the methods employed to produce those equations are far from current standards. In 2017, the Global Lung Function Initiative (GLI) published new reference values for D LCO , alveolar volume (V A ) and transfer coefficient of the lung for carbon monoxide (K CO ) for Caucasians aged from 5 to 85 years old (GLI-2017)  [6]. Data derived from measured values in 9170 subjects from 14 countries excluding France due to French ethical laws. Before using a set of equations on daily basis, the most appropriate should be chosen after comparison to the different available options  [7, 8].