dataesr/scientific-paragraphs-categorization huggingface dataset:
Study design and data sources In this population-based cohort study, we used data from Programme de M edicalisation des Syst emes d'Information (PMSI), the French national hospital database in which administrative and medical data are systematically collected for all obstetrical, acute and psychiatric hospital care facilities. The PMSI database has a system of coding with strict variable definitions which is the same for all the hospitals in France and a subset of records audited on a regular basis to avoid excessively high rates of coding errors. This database is based on diagnosis-related groups, with all diagnoses coded according to the 10th revision of the International Classification of Diseases (ICD-10). The inclusion and exclusion criteria were as follows: all women who had a singleton live birth, stillbirth or medical abortion between January 1 st 2015 and December 31 st 2019 were included.
Procedure For the purposes of our study, we defined two unmatched cohorts of cases and controls and two matched cohorts of cases and controls. Cases were women who had a diagnosis of schizophrenia according to specific ICD-10 codes (i. e. , F20* schizophrenia, F22* persistent delusional disorder, or F25* schizoaffective disorder) in either the obstetrical, acute or psychiatric hospital care databases in the 4 years preceding the index date (or date of delivery). The severity of the disorder is not captured in this database and all cases were included (stabilized or non-stabilized illnesses). Controls were women without severe mental disorders (who did not have a diagnosis of schizophrenia, bipolar disorders (F30*, F31*) or recurrent major depressive disorder (F33*)). To avoid the selection of 'super-healthy' controls, all other F* ICD-10 codes were kept in both groups (e.
Data collected The following sociodemographic, clinical, and hospital characteristics were collected during pregnancy due to their potential impact on pregnancy, delivery and neonatal outcomes: age, social deprivation, parity, smoking, alcohol and substance addictions, poor maternal nutrition, obesity (body mass index 30), somatic comorbidities, hospital category and levels of maternal care at admission. Psychiatric care (inpatient care, day-hospital or ambulatory care and no care) was collected for women with schizophrenia during pregnancy. Social deprivation was assessed with the FDep09, an index that has been previously validated with French data and that is based on the residential code of the person's address  [41]. The FDep09 index involves four socioeconomic ecological variables: the proportion of residents who graduated from high school, median household income, percentage of residents who are blue-collar workers, and unemployment rate.
Statistical analysis Analyses were performed in two steps. Before matching, sociodemographic, clinical, and hospital characteristics were compared between cases and controls using standardized differences. Standardized differences (SD) use effect size methods to identify meaningful differences between groups that, unlike p-values, are not influenced by sample size  [43]. After exact matching, we performed as many univariate and multivariate analyses as there were outcomes to analyse the association between cases and matched controls for each outcome. We conducted multivariable logistic regression models adjusted for the matching factors to account for any residual confounding and based on the generalized estimating equation (GEE) framework to take into account correlations within matched clusters (SAS GLIMMIX procedure).
Limits. Illness severity, psychotropic drugs, treatment observance, diet, physical activity, marital status, medical follow-up, social isolation, ethnicity and domestic violence were not available, as in most of the previous population-based studies published thus far. Women with schizophrenia have been found to be more frequently single and at risk of domestic violence victimization than those without mental disorders  [79]. Singleness is associated with increased unvoluntary abortion  [63] , and domestic violence can lead to maternal stress and poor foetal outcomes. Ethnicity is not reported in French national databases due to ethical issues, whereas ethnicity can influence pregnancy outcomes and social behaviours  [80]. Antipsychotic drugs are not available in the French national hospital database.
The increased metabolic risk may be explained by second-generation antipsychotic drugs delivered to women with schizophrenia  [61]. A weakness of large hospital databases is the miscoding of diagnoses during hospital stays that can underestimate patients' comorbidities. Some data is known to be insufficiently coded in medico-administrative databases (e. g. , tobacco smoking and obesity)  [81]. Missing data is thus assumed as no presence of disease. Last, we used matching and adjustment based on a high number of patient characteristics and with control for confounders at hospital level to compare the cohorts of mother with and without schizophrenia. The risk of bias is to omit some potential confounders that can alter the comparability of cohorts and therefore threaten the validity of outcome measures.
Finally, it remains unclear whether failures to induce antibody responses in patients under immunomodulatory regimens such as abatacept, mycophenolate mofetil (MMF), CD-20 inhibitors, calcineurin inhibitors  5 19  are also associated, or not, with an absence of vaccine-induced SARS-CoV-2-specific T cell responses. Here, we report post-vaccination disease activity data in 126 patients with SLE, prospectively followed during the completion of a two-dose mRNA Pfizer/BioNTech (BNT162b2) vaccination regimen. SARS-CoV-2-specific humoral and cellular responses were monitored against not only the SARS-CoV-2 historical strain but also against SARS-CoV-2 variants of concern (VOCs).
[9] [10] [11] [12] [13] [14]  However, our group showed that Raman peak fitting features have better predictive performances than PCA for cancer/benign brain tissue classification. 15  In our previous study, features consisted of individual intensities of Raman bands, and the machine learning algorithm was a linear support vector machine (L1-SVM) for feature selection and an SVM with a radial basis function kernel (RBF-SVM) for classification. In this new analysis, the same dataset, features, and machine learning architecture were kept, but a new set of features was added. Those features were obtained by fitting a Gaussian function on Raman peaks and extracting their heights and widths. Individual intensities of Raman bands can be any band within a Raman peak, whereas fitted heights capture only the maximum of peaks, but they differ since they are much less prone to stochastic noise.
The algorithm used to extract these new features is an improved version of the Raman peak fitting algorithm used in Ref. 15. This algorithm was initially designed to extract height and width features of Raman peaks to improve the interpretability of Raman signal in brain tissue by fitting only Raman peaks that are constantly present in the brain Raman literature. This present study aims to demonstrate that these two sets of features are complementary and thus improve the classifying results of three PC tissue classification tasks: benign versus cancer, cancer versus IDC-P, and high-grade prostatic intraepithelial neoplasia (HGPIN) versus IDC-P, where the cancer dataset does not contain any IDC-P spectra and the benign dataset does not contain any HGPIN spectra. Major modifications to the algorithm consisted of implementing an algorithm that the finds most common peaks without requiring a previously established list of Raman peaks, and improving the algorithm that identifies inflection points of peaks to obtain more accurate width of peaks especially in signal regions with stochastic noise.
Patient Samples and Imaging Method The dataset consisted of 483 PC patients from three different institutions: Centre hospitalier de l'Université de Montréal (CHUM), University Health Network (UHN), and Centre hospitalier universitaire de Québec-Université Laval (CHUQc-UL). The characteristics of the dataset are shown in Table  1. Tumor stages defines where cancer is present in prostate tissue; there are three tumors stages: pT2 (organ confined), pT3a (extraprostatic extension or microscopic invasion of bladder neck), and pT3b (seminal vesicle muscle wall invasion). The Gleason score details the arrangement and pattern of cancer cells. There are five patterns, and the Gleason score is equal to the sum of the most and second most common pattern present prostate tissues. Confocal RμS measurements on formalin-fixed paraffin embedded tissue microarrays (TMAs) of PC and IDC-P tissue were acquired as described in our previous work.
At the beginning and end of a spectrum, half of this structuring element extends past the range of spectra and causes distortions in the extracted Raman signals due to this border effect. The magnitude of this effect depends on the smoothness of the signal baseline. In this particular dataset, the baseline shows a step non-smooth gradient at the beginning of the signal, which increases the distortion, and a smooth baseline at higher wavelengths. It was determined, empirically, that to remove any residue due to this effect, signal regions between 602 and 669 cm -1 as well as between 1707 and 1726 cm -1 had to be removed, i. e. , a portion of the beginning and end of each spectrum. The next preprocessing step is to fit Gaussian functions over the most common Raman peaks present in the dataset.
Since the Gaussian function is strictly positive, the minimum value of SNV normalized spectra was subtracted from the entire dataset to obtain only positive values. The peak fitting algorithm, which is an improvement of the algorithm first used in Ref. 15, considers a peak only when it appears in at least 50% of spectra at the same location or within AE2 cm -1 and if its height is above a threshold height previously determined empirically. For every peak, both inflection points are determined as well as the height of the maximum and its position. The distance between inflection points is used to compute an approximate standard deviation (σ) and to determine the fitting domain for a nonlinear least-squares regression algorithm. 18  This standard deviation, and the maximum and position of the peak determine the three starting points given to the regression algorithm to fit a Gaussian over the wavelength domain of a peak to obtain fitted heights and widths (standard deviation, σ).
Statistical Analysis A machine learning model with several hyperparameters was trained on the CHUM dataset and tested on two independent hospital cohorts: CHUQc-UL and UHN. Patient clinicopathological characteristics and number of spectra of each classification task are shown in Table  1. The model uses two types of features: individual features; intensity set, and peak features; peak set. To determine the features in each set, two separate feature selection algorithms are used. The selected features are then combined in a single feature vector and passed to the RBF kernel SVM classifier. Before training a model on the CHUM dataset, cross validation (CV) is required to determine the optimal value of the hyperparameters. For each classification task, the CV identifies the set of hyperparameters, through a grid search, that maximizes the area under the curve (AUC) of the receiver-operator-characteristic (ROC) curve of the CHUM dataset for three independent models: one model using only the intensity features, one using only the peak features, and a combined model that used both feature sets; intensity þ peak.
Results Raman spectra of the CHUM cohort were designated as the training set, due to its larger number of patients compared to the other two cohorts and to obtain a sufficiently large dataset for training purposes, and the CHUQc-UL and UHN cohorts as testing sets. The five highest ranked features of both feature sets along with their associated main vibrational mode and molecule are discussed in the following subsections for each classification task. The Raman shift of each feature and of its associated Raman peak, found in the literature, are both reported. Table  2  presents the classification performances for all three classification tasks for both testing sets. The threshold A double nested five-fold CV is performed to determine the hyperparameters through a randomized grid search. Feature selections on peak and intensity features are done separately using linear kernel L1 SVM.
One hypothesis that could explain the higher AUC improvement for the classification of IDC-P versus cancer is the presence of four width features within the five highest ranked features in the IDC-P/cancer classification task and their absence in benign/cancer task (see Tables  3  and 4 , respectively). In the results section (Sec. 3) of this analysis, peak centers of individual intensity of Raman bands and heights were reported but also compared to the most important features of our previous study. In this particular case, it is the first time the widths of Raman peaks are identified as being useful for classification purposes in a machine learning setting. Therefore, for now, this observation is only factual and there is no interpretation. Raman spectra datasets on various tissues for various classification tasks are required to learn if the width of peaks are also relevant features for other applications.
Tentative molecular assignment of prostate Raman peaks based on the literature. The Raman shift of the features (feature column) and of its associated Raman peak (peak, center column) are reported. The results obtained in this analysis using only the individual intensity features also yielded improved results compared to those obtained in our previous analysis for two classification tasks: cancer/IDP-C and HGPIN/IDC-P. This is likely the result of a combination of several differences such as using the SVN normalized individual intensities as input for the SVM L1 feature selection algorithm instead of using SNV normalized spectra as in the PLUS study. The addition of the n neighbor hyperparameter might also have contributed to improving the classification results. A probable limiting factor for the classification performance of benign/cancer could be the presence of different tumor stages and Gleason scores distributions in the training and testing dataset (see Table  1 ).
Acknowledgments We thank  Guillaume Sheehy ,  Michael Jermyn ,  Catherine St-Pierre ,  Karl St-Arnaud ,  Marion Pineau ,  Claire Asimada , and  Maria Claudia Vladoiu  for their contribution to the project. We further thank  Véronique Ouellet  and  Nathalie Delvoye  for extracting data from the  CHUM tissue biorepository , and the  Molecular Pathology Core Facility of the Centre de recherche du Centre hospitalier de l'Université de Montréal  for creating the TMAs. This work was supported by the  Mitacs, Natural Sciences and Engineering Research Council of Canada , and  Canadian Institutes of Health Research.
Parallel analyses using STRING (23) (Fig. 2A ) and the molecular signature database MSigDB (24) (Fig. 2B  and table S1 ) were run on the enriched protein lists from the T0-15 differential analysis. These methods were used to highlight protein clusters and biological processes associated to neosynthesized TfR trafficking. The pathways "intracellular transport," "cellular macromolecular localization," "intracellular protein transport," and "secretion" were highly enriched compared to T0 as shown by the low false discovery rate (FDR) values, an expected result due to the nature of the assay (Fig. 2B ). Moreover, the pathways associated to "exocytosis" (FDR = 4. 75 × 10 -23 ) and "Golgi vesicle transport" (FDR = 2 × 10 -15 ) were also significantly enriched to a lower extent. As a proof of concept, we confirmed that TMED10, a protein identified as enriched in our proteomics analyses, was recruited to TfR secretory vesicles (table S1 and fig.
S2B ). By live-cell imaging, we visualized some rare events of mitochondria "associated" with vesicles containing TfR-eRUSH that seemed to bud off the ER, but the resolution achieved with our spinning disk confocal microscope does not allow us to draw a significant conclusion (fig. S2C  and movie S2 ). Proteins regulating intracellular trafficking may be differentially recruited on vesicular membranes to activate a specific trafficking route. Therefore, we chose to further investigate the role of Rab    16  proteins as they are well-known small GTPase regulators of intracellular membrane traffic. In our AP-LC-MS/MS dataset, we detected a total of 20 Rab proteins (table  S2 ). No Rab proteins were enriched in TfR-containing membranes at T15-T30, but 10 Rab proteins were significantly enriched at T0-T15 with a fold change above 1.
3B ). These data were reminiscent of a recent work nicely demonstrating that post-Golgi vesicles were positive for Rab6  (14) , and in our model, TfR-eRUSH was also trafficking through Rab6 (fig. S3A ). Quantification of TfR-eRUSH association with indicated Rabs was then performed using antibody staining on TfR-eRUSH cells fixed at 15 min after biotin addition. As expected, the percentage of non-Golgi TfR-eRUSH signal associated to Rab5 was very low (9. 3 ± 1. 3%), while association with Rab6 and Rab7 was relatively high (31. 7 ± 2. 5% and 42. 3 ± 3. 3%, respectively; Fig. 3, C  and D ). However, TfR-eRUSH vesicles would harbor either Rab7A or Rab6, but no post-Golgi TfR-eRUSH-Rab6-Rab7A triple colocalization was seen (fig. S3B ). Together, our proteomics analysis revealed that several Rabs are enriched onto secretory TfR-containing vesicles and that Rab7A rep-resents an unexpected protein recruited in the neosynthetic secretory pathway.
4, B  and C ). TfR-eRUSH cells were transfected with Ruby3-Rab7A and visualized by live imaging at 10 min after biotin addition. LysoTracker was used as a readout for relative pH acidity (a brighter signal corresponding to a lower pH). Our data show that TfR-eRUSH vesicles harboring Rab7A had little to no LysoTracker signal (Fig. 4B , white arrows), indicating that these vesicles do not exhibit features of classical proteolytic compartments. To assess for actual degradative properties of these vesicles, we preincubated the cells with DQ-BSA, a bovine serum albumin (BSA) protein that contains self-quenched fluorescent dyes that fluoresce only when the BSA is cleaved, and stained the cells with an anti-Rab7 antibody (Fig. 4C ). Quantification of the percentage of TfR-eRUSH colocalizing with Rab7 or DQ-BSA demonstrated that the TfRs were mainly found in Rab7 vesicles devoid of degraded DQ-BSA (Fig.
Overexpression assays In 24-well plates, 50,000 TfR-eRUSH cells were seeded in complete medium in the presence of avidin (2 g/ml). The next day, 0. 5 g of DNA containing the different plasmids (GFP, GFP-Rab7, or GFP-Rab7 T22N) was transfected using jetPRIME (Polyplus) according to the manufacturer's instructions. Cells were incubated for 24 hours in complete medium containing avidin (2 g/ml). The day of the experiment, the cytometry-based assay was carried out. Data were   16  analyzed on the GFP cell population of high intensity corresponding to the transfected cells. S C I E N C E A D V A N C E S | R E S E A R C H A R T I C L E 11 of.
To minimize carryover, a solvent blank injection was performed after each sample. A sample pool comprising equal amounts of all protein extracts was constituted and regularly injected four times during the course of the experiment, as an additional quality control (QC). Protein identification rates and coefficient of variation (CV) monitoring of this QC sample revealed very good stability of the system: 2207 of the 2271 (97%) identified proteins showed a CV value lower than 20% considering all four injections. Data interpretation Raw MS data processing was performed using MaxQuant software [v1. 5. 8. 3 (53) ]. Peak lists were searched against a composite database including all Homo sapiens protein sequences extracted from UniProtKB-SwissProt (version April 2019; taxonomy ID: 9606) using the MSDA (Mass Spectrometry Data Analysis) software suite  (54).
MaxQuant parameters were set as follows: MS tolerance set to 20 parts per million (ppm) for the first search and 5 ppm for the main search, MS/MS tolerance set to 40 ppm, maximum number of missed cleavages set to 1, carbamidomethyl (C) set as fixed modification, and acetyl (protein N terminus) and oxidation (M) set as variable modifications. FDRs were estimated on the basis of the number of hits after searching a reverse database and were set to 5% for both peptide spectrum matches (minimum length of seven amino acids) and proteins. Data normalization and protein quantification were performed using the label-free quantification option implemented in MaxQuant (53) using a "minimal ratio count" of two. The "match between runs" option was enabled using a 2-min time window after retention time alignment.
All other MaxQuant parameters were set as default. To be validated, proteins must be identified in all four replicates of one condition at least. The imputation of the missing values and differential data analysis were performed using the open-source ProStaR software  (55). Two runs of imputation were applied: The "SLSA" mode was applied for the POVs (partially observed values) and the "del quantile" for the MEC (missing in the entire condition). Pairwise comparisons were performed using a Limma t test on protein intensities. P value calibration was performed using the pound calibration method, and the FDR threshold was set at 5%. The complete proteomics dataset is available via ProteomeXchange (56, 57) with identifier PXD010576.
Gene ontology analysis Gene set enrichment analysis (GSEA) was run on the protein lists found to be significantly enriched at least 1. 5 times in T0-T15, T0-T30, and T15-T30 using the online molecular signature database MSigDB v6. 2  (24). Significantly enriched gene ontology (GO) pathways related to relevant "biological process" were extracted with their FDR. Table  S1  summarizes the relevant GO pathways associated to the T0-T15 time points. No significant enrichment was found at T0-T30 and T15-T30.
45 NA) oil objective (Zeiss) controlled by VisiView v. 3. 3. 0 software (Visitron Systems). For TIRF microscopy, live imaging was performed with a TIRF PALM STORM microscope from Nikon using a back-illuminated EMCCD camera (Evolve 512, Photometrics) and a 100× APO (1. 49 NA) oil objective controlled by MetaMorph and an iLas 2 FRAP/TIRF module (BioVision Technologies). The TIRF angle was chosen to obtain a calculated evanescent field depth of <100 nm. Acquisition was performed from 5 to 25 min after biotin addition. Images were processed with FiJi software and presented as single z-stack for visualization of the co-distribution.
Software analysis Image processing was performed using either the Fiji upgrade of ImageJ  (58)  or Imaris software v9. 2 (Bitplane, Oxford Instruments). Quantifications for colocalization measurements were performed using Imaris software v9. 2 (Bitplane, Oxford Instruments). Statistical analyses were performed with Microsoft Excel 2016 and Prism v7. 04 (GraphPad). Flow cytometry analysis was done using FlowJo software v10. 4. 2 (FlowJo LLC). Raw MS data were first analyzed using MaxQuant v1. 6. 0. 16. Differential proteomics data analysis was performed using DAPAR v1. 10. 3 and ProStaR v1. 10. 4.