dataesr/scientific-paragraphs-categorization huggingface dataset:
Patients and healthy donor-derived materials All tumors used as samples were excised by surgeons from the Department of Digestive Surgery at the University Hospital of Besançon between May 2016 and July 2019. All patients were enrolled in the Epitopes-CRC02 (NCT02817178) cohort after obtaining informed consent in accordance with French regulations and approval by the local and national ethics committees. Peripheral blood samples were collected from 16 patients in the cohort prior to surgery. Paraffin-embedded tissue specimens from each patient were retrieved from pathology archives and processed/archived in Biobank BB-0033-00024, Tumorothèque Régionale de Franche-Comté, throughout the project (#F1820). Liver metastases and paired primary tumors (when available) from 39 CRC patients treated with chemotherapy were analyzed using RNA next-generation sequencing.
Flow cytometry Before immunostaining, peripheral blood mononuclear cells (PBMCs) and TILs were counted and suspended in FACS buffer (1X PBS, 50 μM EDTA, 0. 2% BSA) before labeling with appropriate antibodies for 30 min in the dark at 4 °C. The cells were then washed with 1X PBS. To determine viability, the cells were stained with eFluor 780 viability dye (eBioscience) according to the manufacturer's instructions. The following antibodies were used: Pacific Blue anti-CD3 (UCHT1), BV510 CD8 (SK1), FITC/PerCp-Cy5. 5 CD226 (11A8), PercP/Cy5 anti-CD4 (clone OKT4), Pe/Cy7 anti-PD1 (clone EH12. 2H7), A700 CD69 (FN50), BV711 CD103 (Ber-ACT8), and PE anti-TIM3 (F38-2E2) (clone MBSA43). Samples were acquired using a FACS Canto II or FACSLyric (BD Biosciences) and analyzed with FACSDiva software (BD Biosciences).
Immunohistochemistry Archival formalin-fixed and paraffin-embedded specimens were sectioned at 4/5 μm. The first section was histopathologically assessed by HES staining to confirm the presence of tumor cells. Immunostaining for CD3 with a polyclonal rabbit anti-human CD3 antibody (A045201, Agilent)  [1:200]  was performed using a Ventana Benchmark (Roche). A polyclonal antibody against CD155 (anti-poliovirus/PVR; Abcam) was utilized to detect CD155. The relevant protocols are described in the Supplementary Material. CD3 slides were digitized with a Nanozoomer HT2. 0 (Hammamatsu) at 20× magnification to generate a whole-slide imaging (WSI) file in ndpi format and imported into QuPath software  [25]  to evaluate densities (cells/mm²) of CD3 in the CT and IM. CD155 expression was defined as membranous staining of tumor and bill duct cells.
Bioinformatic analysis Normalized RNA-seq fragments per kilobase million (FPKM) (GRCh38) of 478 colon adenocarcinoma patients from The Cancer Genome Atlas (TCGA) were retrieved using the R package TCGAbiolinks  [26]. Affymetrix DNA expression sets from the GPL570 platform (GSE14333, GSE17536, GSE33113, GSE37892, and GSE39582) focusing on primary CRC were retrieved from public repositories. Data were merged and linked to patient survival. Mean expression of CD155 was determined for each sample using the four probes with the best variance and similar mean expression. Analyses were discretized into two groups (high and low) based on median expression of CD226 and CD155. Relapse-free survival (RFS) analysis was performed using 1046 patients and overall survival (OS) for 737 patients using the Survminer R package according to the high and low expression groups.
Bulk RNA-seq data from 54 samples (normal colon, primary CRC, and CRC liver metastasis) generated for 18 CRC patients were retrieved from Gene Expression Omnibus (GEO) GSE50760  [27]  using normalized FPKM. Local bulk RNA-seq reads were aligned using STAR v2. 7. 9a to GENCODE Human Release 39 (GRCh38. p13), and the read count matrix was produced with FeatureCounts v2. 0. 1, both by using the default parameters. Immune deconvolution analyses of the datasets were performed using the immunedeconv R package  [28]  and MCPCounter  [29]. Clustering of the CD226 high and CD226 low expression populations was performed by cutting off the median of full CD226 gene expression for the dataset. A gene signature score was calculated using the geometric mean of log2(1+ gene expression matrix (FPKM-normalized)) transformation.
The single-cell RNA-seq dataset was collected from Gene Expression Omnibus accession number GSE178318  [30]. Quality control and downstream analysis were performed in Seurat v4. 0. 6  [31]  to remove cells with a low number of uniquely detected genes (< 500) and discard cells in which the proportion of the UMI count attributable to mitochondrial genes was greater than 10%, resulting in an overall of 107820 remaining cells and 23289 genes. Batch integration of the samples was performed with Harmony  [32]  using 2000 highly variable genes. The annotation of each cell cluster was realized using a list of gene markers delivered in the source data article, and our analysis focused on T cells from liver metastases and primary tumors expressing the following gene markers: CD3G, CD3D, CD8A, and CD226.
Differentially expressed gene analysis was performed using the Seurat integrated function 'Find Markers' to compare cells expressing the CD226 marker and those not expressing it among CD8 cells. We selected the most significant genes with an adjusted p value below or equal to 0. 05 and a Log2FoldChange below -0. 25 and above 0. 25. TCR analysis was performed on the bulk RNA-seq data using MiXCR v3. 0. 3, with the single command parameter "shotgun" for the entire pipeline. Computations were performed at the supercomputer facilities of Mésocenter de calcul de Franche-Comté. Raw data from the bulk RNA-seq analysis were deposited in NCBI Gene Expression Omnibus (GEO) under accession number GSE207194.
Statistical analysis Median (interquartile range) and frequency (percentage) values are used to describe continuous and categorical variables, respectively. Medians and proportions were compared using Student's t test and the chi-square test (or Fisher's exact test, if appropriate), respectively. OS was calculated from the date of liver metastasis surgery to the date of death from any cause. RFS was calculated from the date of surgery for liver metastasis to the date of relapse or death from any cause or the date of the last follow-up, at which point the data were censored. Survival data were censored at the last follow-up visit. OS and RFS were estimated using the Kaplan-Meier method and are described using the median or rate at specific time points with 95% confidence intervals (95%CI). The Cox proportional hazard model was employed to estimate the hazard ratio (HR) and 95% confidence interval (CI) for factors associated with RFS.
The association of parameters with RFS was first assessed using univariate Cox analyses, and then parameters with p < 0. 05 were entered into a final multivariate Cox regression model. All analyses were performed using R software (version 3. 6. 1; R Development Core Team; http://www. r-project. org) and GraphPad Prism v9. 3. 1. Statistical significance was set at P < 0. 05, and all tests were twosided.
RESULTS CD226 is associated with a potent immune contexture in CRC leading to better prognosis for liver metastatic diseases Although the role of CD226 in TCR signaling and CD8 + T cell functions has been recently established  [18] , the role of this receptor in the CRC immune context remains elusive. We first sought to gain insight into the contribution of CD226 to immunerelated gene expression in both primary tumors and liver metastases from CRC. We first examined the association of CD226 with the immune environment using public RNA-seq datasets. Bulk gene expression analysis revealed low expression of CD226 in both the normal colon and liver (transcript per million (TPM) < 1). In contrast, CD155 was highly expressed (TPM > 14) in these organs. CD226 expression levels were similar in the colon and in the liver, whereas CD155 expression was higher in normal liver than in normal colon (Supplementary Fig.
S1A ). CD155 RNA was expressed at twice the rate in CRC tissues as in normal tissues. In contrast, CD226 displayed similar expression patterns in primary CRC and normal tissues (Supplementary Fig. S1B ). Next, we explored the correlation between ligands and receptors of the CD226/TIGIT axis using a dataset from TCGA. Receptors (CD96, CD226, TIGIT, and PVRIG) correlated positively with each other. Ligands correlated negatively with receptors, with the best association between CD226 and CD155 (Pearson's correlation coefficient = -0. 3). Some nonsignificant associations between CD112 and TIGIT were also observed (Supplementary Fig. S1C ). These findings were also validated in an independent cohort. The RNA-seq dataset from TCGA was then used to validate the correlation between CD226 expression and immune cell infiltration transcriptomic profiles in primary CRC.
We observed a strong association between CD226 high expression and an inflamed immune context (Fig. 1A ). All immune cell signatures identified with MCPcounter were found to be overexpressed in the CD226 high -enriched cluster. Analysis of primary CRC immune activation using T cells, interferon-gamma secretion, and cytotoxicity score signatures revealed a correlation with CD226 gene expression (Pearson's correlation coefficients were 0. 56, 0. 74, and 0. 68, respectively; p < 0. 05) (Fig. 1B ). Therefore, CD226 is associated with a potent immune response in primary CRC. To investigate CD226 expression in liver metastases, the paired primary cancers and liver metastases of 18 patients from GSE50760 and 39 patients from the University Hospital of Besancon (UHB cohort) were analyzed. CD226 expression was enhanced in CRC metastases compared to primary tumors and correlated with an inflamed immune environment (Fig.
1F ), suggesting that CD226 is associated with an immune context in which T cell polyclonal amplification is possible. We then took advantage of a single-cell RNA-seq dataset to investigate gene expression levels according to CD226 expression in TILs isolated from CRC. We first explored differential gene expression by analyzing T cells from primary and liver metastases. Ten genes were upregulated (IL7R, CD40LG, and CXCL13) and five downregulated (GZMK) (Supplementary Fig. S1D ). The same differential analyses were performed for T cell-infiltrating liver metastases from patients with CRC. Five genes showed downregulated expression (DNAJB1, SERTAD1, GZMH, CCL4, and ITM2C) and twelve upregulated (CXCL13, NR3C1, RBPJ, S100A4, S100A11, CCR7, TXNIP, GPR171, ARPC1B, TSPO, LGALS1, CD82) in TILs  -- Number at risk OJ67C BC71C RM62F BC72F PG42F JMP74F CM37C AS75C MJ3C CN40F PJ80F CA26F MJ4F MJP56F BF8F OJ68F SRR975591 CJ52F SRR975600 AS76F GD32F SRR975587 RD84F JPC20F MA60F SRR975553 SRR975563 SRR975588 HB85F SRR975558 SRR975557 CN39C SRR975603 SRR975567 SRR975589 SRR975604 GD31C RN21C HP16F SRR975602 SRR975599 SRR975596 SRR975562 SRR975598 OP81C GR18F SRR975595 JPC19C CA25C CF14F HP15C SRR975564 SRR975565 SM23C SRR975561 GR69C RD83C TJ9C CF13C AE1C SA49C SRR975560 PC35C SRR975552 SRR975556 BC86F OA11C MA44F TL28F TL27C SRR975568 PC36F BF7C RC48F SRR975594 GS54F PJP64F BP66F OP82F SRR975601 AE2F TJ10F GR70F SRR975555 OA12F SRR975551 SRR975566 SRR975554 SRR975559 SRR975593 SRR975590 SRR975597 MA43C SM78F SRR975592 CM38F.
Mean centered MCP-counter score Fig. 1  CD226 is associated with a potent immune contexture in CRC, leading to a better prognosis in liver metastases. A Immune deconvolution using MCPcounter for RNA-seq data for primary CRC from TCGA. Annotation of the CD226 expression level discretized from median expression in the whole cohort. B Correlation of CD226 and signature scores for T cells and cytotoxicity using MCPcounter for RNAseq data for primary CRC from TCGA and the interferon gamma secretion signature: IFNG, CD274, LAG3, CXCL9. C Immune deconvolution using MCPcounter for RNA-seq data for paired primary and liver metastasis from 39 patients from University Hospital of Besancon and 18 patients from public database GSE50760/SRP029880. Annotation according to sample site, CD226 expression level discretized from median expression in the whole cohort and CD226 + T cell signature from single-cell RNA-seq described in G.
D CD226 normalized expression level (TMM) from previous cohorts for normal adjacent colon tissue, primary CRC and CRC liver metastasis. E Correlation between CD226 expression level and number of distinct beta chains of the TCR calculated for each sample with MiXCR. The number of beta chains of the TCR is a reflection of TCR diversity. F Frequency of clone counts from the beta chain of the TCR according to the level of CD226 discretized from median expression in the whole cohort and sample site. G Volcano plot of differential expression of T cells in liver metastases from CRC of public single-cell RNA-seq GSE178318 according to CD226 + versus CD226 -. Log2-fold-change cutoff of 1 and adjusted p cutoff of 0. 05. H Estimated RFS since surgery according to the CD226 signature created from previous differential analysis in a "home curated" database of primary CRC.
Additionally, a CD226-related gene expression signature was applied to a bulk RNA-seq dataset derived from the liver metastases of CRC patients (UHB cohort). A dataset of 737 patients with primary CRC from five Affymetrix DNA expression sets of GPL570 was manually curated to obtain survival data. There was no difference in RFS probability in localized CRC according to CD226-related gene expression signature level (Cox regression: HR = 0. 957, 95% CI = 0. 848-1. 066, p = 0. 691) (Fig. 1H ). In addition, we observed no differences in OS (p = 0. 47) (Supplementary Fig. S1E ). In contrast, the CD226 signature discriminated RFS in the exploratory UHB cohort dataset, which included CRC liver metastases. Low expression of the CD226-related signature correlated with decreased RFS (9. 66 months [95% CI = 4.
CD226 prognostic value is not CD155 dependent CD155-expressing CRC leads to better OS in primary colorectal (data not shown from TCGA) and breast cancers  [34]. CD155 binds to CD226 and TIGIT to activate T cells and inhibit T cell function. The prognostic role of CD155 expression n CRC liver metastases has not yet been investigated and may be different from that suggested by primary CRC samples. Therefore, we investigated whether the presence of CD155 influences the prognostic value of CD226. For this purpose, CD155 expression was evaluated by immunohistochemistry and scored as 0 in the absence of expression. A score ranging from 1+ to 3+ according to staining intensity was used (Fig. 4A-C ). CD155 expression in liver metastases did not correlate with CD8 + CD226 + T cell levels among TILs (Fig.
In turn, little can be said about differences in ERN amplitudes between groups that behave similarly on average, but that include individuals with varying degrees of performance. Instead, we argue that performance matching, which uses a procedure designed to equate performance between each participant is necessary to assess the specificity of electrophysiological markers of performance monitoring in SSD. In the present preregistered study (https://gitlab. com/nfaivre/meta_scz_public), task performance was controlled via a staircase procedure adapting the amount of sensory evidence (motion coherence) according to individual perceptual abilities. This procedure enabled us to match performance between groups and individuals, and therefore to discuss further whether the typical blunted ERN in individuals with SSD is dependent on task-performance or not.
Data analysis Behavioral analysis. Behavioral analyses were performed using R (2020), to ensure that our behavioral conclusions in the original study  21  were still valid on this subset of participants. In particular, we assessed whether our groups of participants were comparable both in terms of demographic and neuropsychological characteristics, and metacognitive performance (i. e. how well participants were able to calibrate their confidence judgments on performance, by computing an index of metacognitive efficiency or M-ratio  33  , in a Bayesian framework  34  (See pre-registered plan for more details). EEG recording and preprocessing. During the task, EEG activity was continuously recorded using a 64-channels HIamp system (g. tec, Schiedlberg, Austria), sampled at 1200 Hz. Electrodes were positioned according to the international 10-10 system with AFz as the reference site.
Impedance of electrodes was kept below 5kΩ. Pre-processing was conducted with Matlab  35  scripts including functions from the EEGLAB toolbox (v2021. 0, EEGLAB  36  ). EEG signal was downsampled at 128 Hz, then high-pass filtered at 0. 5 Hz and low-pass filtered at 45 Hz. Bad channels were removed manually through visual inspection. Continuous EEG data were locked on mouse movement onset and epoched between -1s pre-movement to 2 s post-movement. All channels were re-referenced to the common average, i. e. average over all scalp channels. Horizontal and vertical electro-oculograms were estimated by subtracting AF7 from AF8, and AFz from FPz for later identification and correction of eye-induced artifacts. At this point, noisy epochs with non stereotypical patterns of activity (as opposed to identified nonneural artifactual activity) were excluded through visual inspection.
Other priors were by default according to the brms package in R. For model (5) we specified the following priors: 1) we assumed that voltage amplitude would be higher (resp. lower) for higher (resp. lower) confidence ratings with a mildly informative Gaussian prior (Mean = 0. 5, SD = 1); 2) we assumed no difference of voltage amplitude between groups with a mildly informative prior (Mean = 0, SD = 1); 3) Because we expected a compensatory electrophysiological signal related to confidence among patients with SSD, we assumed an increased confidence ERP among patients with SSD, leading to an interaction effect of group x confidence on voltage amplitude (Gaussian prior Mean = 0. 5, SD = 1); 4) We assumed a decreasing amplitude as a function of movement onset times (Mean = -1, SD = 1); and 5) we assumed an interaction effect on amplitude between movement onset times and groups, such that movement onset times from patients with SSD would correlate less with voltage amplitude compared to healthy controls (Mean = 0.
5, SD = 1). Other priors were by default according to the brms package in R. Operationalised hypotheses were as follows: 1. The presence of an ERN is indicated by a significant main effect of correctness over frontocentral sites following movement onset, 2. The presence of ERN-blunting is indicated by a correctness x group interaction within the above mentioned cluster of electrodes, characterized by a dampened difference of voltage amplitude between correct and incorrect trials among individuals with SSD compared to healthy controls, 3. The presence of confidence-related compensatory mechanisms in patients is indicated by a confidence x group interaction, characterized by an increased difference of voltage amplitude between confidence levels in patients compared to healthy controls. We did not expect any particular localization or time-window for this effect.
ACKNOWLEDGEMENTS M. P. is supported by a  Postdoc. Mobility fellowship  from the  Swiss National Science Foundation  ( P400PM_199251 ). N. F. has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  803122 ). We thank  Vincent de Gardelle  and  Jean-Christophe Vergnaud  for their support during data acquisition.
As usually recommended, only transitions corresponding to Y type fragment ions were retained to build the testing methods, owing to their propensity to induce the most intense and less interfered signals. No more than 50 peptide candidates, i. e. 150 transitions, were included in each MRM method to ensure that a minimum of 8 data points were collected per reconstructed ion chromatogram. The MRM methods were used to screen among all peptide reporter candidates designed in-silico those which were effective in the detection of the protein targets. This screening was performed from a detectability set of 20 S. aureus strains selected from the whole genome-sequenced biobank of the French National Reference Centre for Staphylococci (Table  S2 ) among positive representative isolates of each of the virulence factors and resistance markers.
Strains and data of the clinical test set Clinical isolates and accompanying clinical data were obtained from a previously published French cohort of severe CAP  (Gillet et al. , 2021). The clinical features retained for the present study were those representing severity and outcome in the cohort study: death, leukopenia (defined as total leukocyte count< 3 G/L), and hemoptysis extracted from the study by  Gillet et al. (2021)  (Table  S4 ). The age-adapted Charlson Comorbidity Index score was retained as a control covariate to adjust prediction models to the baseline characteristics of the patients. Patients aged less than 3y (n=20), who were previously found to present very distinct clinical presentations and outcomes compared to older patients, were excluded. Patients co-infected with several S.
Targeted mass spectrometry parameters The scheduled MRM experiments were deployed on a hybrid triple quadrupole/linear ion trap mass spectrometer 6500 QTRAP (AB Sciex, Toronto) equipped with an ESI Turbo VTM ion source operating at 550°C and using an ion spray voltage of 5500 volts. The settings of curtain gas and nebulizing gas 1 and gas 2 were adjusted to 50, 70, and 60 psi, respectively. The instrument and the data acquisition and processing were controlled by the Analyst 1. 6. 2 software. Unit mass resolution was fixed for Q1 and Q3 in MRM mode. The tandem mass spectrometer (MS) was hyphenated to a liquid chromatography (LC) set-up comprising 2 binary pumps (1290 series, Agilent technologies). For each experiment, 80 μL of the trypsin digest was injected and submitted to on-line solid phase extraction on a PLRP-S, 12.
Peptide and protein detection validation MultiQuant TM 2. 1 software (AB Sciex) was used to process the raw data leading to automated chromatographic peak detection for each transition signal as well as the calculation of their absolute area (MQ4 algorithm). Peak detection parameters were set as follows: a Gaussian smooth width of 2. 0 point, a retention time half window of 35 seconds, a baseline subtraction window of 2 minutes, and a 50% noise level for baseline. A reference transition ratio (TR) was calculated for each peptide from the synthetic form (Equation  1 ), which validated effective detection of peptide surrogates during the analyses of the S. aureus strain collection. Only the peptides with a TR standard deviation below 20% relative to the mean TR were ultimately validated.
Statistical analysis We used visualization, correlation, and regression procedures to assess the relationships between the expression level of virulence factors, the genetic background of the isolates, and the clinical outcome of the infected patient. The anonymized data and software codes required to reproduce the results are available at [https:// github. com/Mpvrd/Proteomic]. Relative protein quantities were log2-transformed before analysis to improve the interpretation of model coefficients, as this transformation allows to express coefficients as the change of the response variable per doubling of the protein quantity. To avoid obtaining infinitely negative values after transformation, relative protein quantities equal to zero were replaced with the smallest non-zero quantity for the same protein and divided by 2 before being log2-transformed.