dataesr/scientific-paragraphs-categorization huggingface dataset:
The libraries were sequenced on separate Illumina MiSeq runs (v2 chemistry 2Â 250-bp kit; Illumina). Due to the low DNA concentrations in the ITS2 library, the run was not successful, and data were unavailable. Raw data from the 16S rRNA gene runs were subjected to quality control and read processing, performed with the R package DADA2, version 1. 6. 040 (48). This included forward-reverse-read merging and the removal of reads with conflicting bases and chimeric sequences. Classification of ASVs was performed from the kingdom to the subgenus levels based on EzBioCloud  (49). Further processing of reads, including ASV table processing and metadata annotation, was based on the in-house-developed R package tidyamplicons, publicly available at https://github. com/SWittouck/tidyamplicons. Sequencing data are available at the European Nucleotide Archive under accession number PRJEB33108.
The effect size was measured by the median of all pairwise differences of RAD values between treatment groups (Hodges-Lehmann estimator), while statistical significance was assessed using a Wilcoxon rank sum test on the RAD values. The P value matrix was corrected for multiple testing using the method of Benjamini and Yekutieli  (50). For comparisons of relative and estimated absolute abundances between the placebo and treatment groups, Kruskal-Wallis tests were used. For the evaluation of the temporal dynamics in our study, samples were divided into groups according to their dominant taxa to reduce the complexity of the data (Figure  S2 , Figure  S6 ). A sample was classified as dominated by a taxon if its relative abundance exceeded 40% and it was the most abundant taxon in the sample. As many samples showed high relative abundances of both L.
ACKNOWLEDGMENTS This work was funded by the  Lesaffre Group  (clinical study, analysis of samples, and personnel costs [E. D. , B. R. , N. B. , and, partly, E. O. ]). We also kindly acknowledge the  ERC  (starting grant  Lacto-Be SL) , the  Flanders Fund for Scientific Research (FWO)  ( scholarship 72798  to S. W. and project  45641 ), and the  University of Antwerp  ( DOCPRO 37054  to S. A. ) for funding a large part of the vaginal microbiome research team of the laboratory.
METHODOLOGY Data Collection We used application programming interfaces (APIs) for the two social media platforms. We hashed all usernames and removed URLs through the de-identification process. To further pseudonymize the data, we transformed special characters in the tweets or Reddit posts to lowercase and extracted contractions. The supplementary materials and our previous work describe more details about the data collection and preprocessing steps  [28]. For Twitter searches, we chose relevant hashtags (e. g. , #longcovid, #covidlong, or #longhauler) and words included in tweets (e. g. , long-hauler, chronic symptoms, long-term effects). Re-tweets, replies, quotes, and nullcast were excluded from the dataset, as well as any tweets that were not in English. Our Twitter dataset's average character length and word count were 133 and 23, respectively.
Self-report Extraction While individual posts in our corpus may have had various purposes at the time of posting, such as disseminating news and sharing ideas and experiences, for the purpose of this study, we were primarily interested in posts containing self-reported medical symptoms. We used a combination of the regular expression approach (RegEx) and transformer-based BERT classifier to distinguish between posts of self-reports (explaining personal health status) and other posts, including opinions or news reports. The RegEx approach relies on personal pronouns, as shown in Table  1a. The classifier, which we call MNLI classifier, is a Natural Language Inference model -the COVID-Twitter BERT v2, fine-tuned on the MultiNLI dataset  [25]. The classifier annotates posts as either self-report (annotated as "1") or not (annotated as "0") and poses the candidate labels (e.
g. , "my experience") as either "premise" or "hypothesis". We ran the RegEx model and the combination of RegEx and MNLI classifier (RegEx+MNLI classifier) on the entire Twitter dataset. We saved the scores, with the averaged performances shown in Table  1b. The standalone RegEx filter outperformed the RegEx+MNLI classifier without substantial loss in precision, as reflected by the F1 score (Table  1b ). Although the proportion of self-reports is much higher on Reddit, the same self-filtration approach was applied to Reddit posts to keep consistency.
Location Information Inference for Twitter data For the Twitter dataset, we used the Nominatim API  [29]  to infer detailed location information such as city, region, and country from user-defined location information attached to tweets. The API uses OpenStreetMap data to make predictions. In cases where user-defined location information could allude to multiple locations (e. g. , London is a city in Canada and England), Nominatim API returns the most likely location based on its criteria. For the Reddit data set, location information was not available.
Extraction and Normalization of Long COVID Symptoms and conditions (SyCo) Transformer-based BERT models were mainly used to extract and normalize long COVID symptoms and conditions, which we refer to as SyCo terms in this study. Specifically, we used a UmlsBERT-clinical model fine-tuned on the n2c2 (2010) dataset  (Bhambhoria et al. , 2022)  and for comparison also used the Stanza clinical NER (Stanza-Clinical) from the Stanford NLP group. Given the BERT models' input length limit, we used rolling windows to segment Reddit posts into smaller chunks with less than 1024 characters. We implemented a two-step normalization task to analyze our findings, reduce inflectional forms, and compare them with existing works. In the first step, we transformed each extracted raw symptom and condition into its common base form.
Data Overview Table  2  lists the statistics of the English-language posts collected from Twitter and Reddit. In addition to posts, the timestamp, geographical coordinates (if available), user's location (user-defined), and user's profile description were gathered for each tweet. A total of 107 countries were represented in our Twitter sample; most respondents tweeted from the United States (50. 7%), followed by the UK (29. 9%) and Canada (6. 9%).
Evaluation of Extraction of SyCo Terms Table  3  compares the performance of UmlsBERT-Clinical and Stanza-Clinical for entity extraction task from Twitter data. Both models' performances are compared against human annotators in terms of the proximity score. As shown in the table, we also included entity extraction results by the data augmentation approach UMLS MetaMap (+AMIA) introduced in our earlier work  [28]. UMLS MetaMap (+AMIA) uses the MetaMapLite tool to extract entities associating with UMLS' Concept Unique Identifiers (CUIs) and augments the results with a manually annotated dataset consisting of clinical concepts and colloquial expressions (e. g. , brain fog) from tweets. Based on the results, UMLS MetaMap (+AMIA) tends to capture more entities than human annotators; however, some may not be as relevant or provide sufficient insight to experts.
Immunologic / Autoimmune, Dermatology, and Reproductive / Genitourinary / Endocrine were the lowest three categories of SyCo terms across all three sources -Twitter, Reddit, and Survey study. Twitter Reddit Figure  1. The occurrence frequency of the most prevailing extracted SyCo terms in Twitter and Reddit data with occurrence frequency greater than 1%. Mapped SyCo terms are the nomalized SyCo terms that were mapped to the unique concepts in the KB derived from  [3]. Mapped SyCo terms were further grouped based on the affected organ system established by  [3] ; Novel SyCo terms are the unmapped normlized SyCo terms.
This analysis's potential value is connecting SyCo terms early in illness onset with prognostic factors, such as the likelihood of developing long COVID, and the severity or duration of the long COVID condition. In addition, in our study, we extracted long COVID novel terms such as 'Infection', 'Hair Loss', and 'Weight Loss' and reported conditions similar to other diseases such as 'Flu', 'Cancer', or 'Lyme Disease'. One strength of the NLP pipeline developed in this study is its scalability leading to high-level adaptive capacity for other social media platforms or different medical conditions. Our NLP pipeline is well-poised to scale the information extraction process from user-generated data and connect vernacular to recognized medical ontology. In addition, it enables exploring the longitudinal evolution of symptoms, which may be correlated with prevalent SARS-CoV-2 variants at the time of onset to guide insights into variation in disease course associated with different source viral strains.
Another limitation of this study relates to our self-report filter. While regular expressions are effective in finding posts containing first-person self-reports, they are also prone to false positives -e. g. , mistakenly keeping a post that voices an opinion or reports symptoms on behalf of someone else -and false negatives -e. g. , mistakenly discarding a tweet that excludes first-person pronouns but would still qualify as a self-report. We, therefore, considered other context-based approaches, including a fine-tuned BERT classifier. These approaches may reduce false positive and false negative rates; however, further work is needed to annotate sufficient datasets for manually fine-tuning, sweeping, and optimizing hyperparameters. The nature of social media data and our NLP pipeline introduces bias to our findings which could impact the reliability of our outcomes.
Acknowledgements The participating companies and medical subject matter experts deserve special credit. Together, over the course of the project Deloitte,  Roche , and  TELUS  provided clinical and machine learning expertise that enabled the pipeline to function. Key contributions by these Vector sponsor companies include original project ideation; review of clinical literature on long COVID; data collection, cleaning, and annotation; filter implementation; normalization; NER modeling; visualization engineering; and interpretation of results. We also thank the contributions of  Jennifer Camaradou  as a patient advocate for her insights into long COVID and potential benefit from this body of work.
The anti-inflammatory and antioxidant capabilities of ursolic acid thus make it a potent candidate drug for improving epileptic sequelae. Two azepine-indole alkaloids, nemerosine and fargesine from Psychotria nemorosa were investigated in the study of Kirchweger et al. for their ability to modulate serotonergic signaling (human 5-HT 2A receptor) and to afford protection against the in vivo toxicity of the neurotoxic peptides Aβ and α-synuclein, both involved in Alzheimer (AD) and Parkinson (PD) diseases' pathogeneses, respectively. A primary in silico approach using ChEMBL database reported a modulation of the human 5-HT 2A receptor by these two alkaloids. Moreover, this ability to regulate the serotonergic pathway was demonstrated to confer a protection against protein toxicity in the worm, Caenorhabditis elegans.
Tissue Samples and Patient Characteristics Ninety-seven patients, aged 18 years or older with a tissue diagnosis of WHO grade II, III, and IV diffuse gliomas made between September 2001 and March 2012, were identified from the brain tumor registry of Caen University Hospital, France. Characteristics, treatment history, and pathologic data from these patients are summarized in Table  S1. For 7 patients, a blood sample was also available. The 8 non-tumoral brain tissues were from patients without glioma who underwent brain surgery. All patients provided informed consent regarding the collection of tumor specimens and their molecular evaluation, as required by French law. The study was approved by the institutional ethics committee of Caen University Hospital, France (DC-2008-588). All tumor specimens were reviewed by a neuropathologist (ELZ) to confirm the diagnosis and grade according to the new classification system adopted by the World Health Organization (WHO) in 2016, as described in  [10].
Quantitative Real-Time Reverse Transcription: PCR The miRNAs were retrotranscribed and amplified (PCR) using the TaqMan MiRNA Reverse transcription kit (Applied Biosystem, Birchwood, United Kingdom). An amount of 5 ng of miRNA was used for every miRNA that we tested. MiRNA expression was analyzed using the following TaqMan MiRNA assays (Applied Biosystem Birchwood, United Kingdom): has-miR-200b-3p (Assay ID: 002251), has-miR-200c-3p (002300), has-miR-210-3p (000512), has-miR-100-5p (000437), has-miR-126-5p (000451), has-mir-132-3p (000457), hsa-miR-221-3p (000524), has-miR-424-5p (000604), has-miR-128-3p (002216), and has-miR-451-5p (001141) (targets for each miRNA are listed in Table  S2 ). The RT-PCR data were normalized to the small nucleolar house-keeping RNA, RNAS RNU48 (SNORD48) (assay ID 001006).
BFU-EÀderived colonies in the presence of depleted and non-depleted sera were counted at day 14. Binding assays. UT 7 cell line were a gift from Dr Patrick Mayeux, at Cochin institute. 12 Briefly, UT7 cells (0. 25 £ 10 6 ) were pre-incubated with 1 mg/mL of human IgG for 30 min on ice to block IgG receptors. IgA1 binding was examined by FACS by an indirect immunofluorescence assay, using a biotinylated anti human IgA1 (Southern Biotech, ref 2052-08), followed by a streptavidin-APC (BD pharmigen ref 554067). 13 or inhibition studies, cells were pre-incubated with sTfR1 (Bio-Techne, 2474-TR-050). Data were analyzed with FlowJo software (FlowJo LLC, Ashland, Oregon) Biochemical assays. Quantification of IgA in serum was performed using Architect c16000 Ò analyzer (Abbott) with an immunoturbidimetric method.
Contributors CC, SC, KB, MD, KEK, ICM and OH designed the study, collected the data, verified the data and performed in vitro experiments. CC and AN performed the statistical analysis. MBLS, GF, MF, FV, AH, BK, LM, ER, TTM, FF, NC, TNK, SM, CL, RCM, collected the data. CC, SC, MB, OH, KEK and ICM drafted the manuscript and made the figures. All authors approved the final version of the manuscript. We dedicate this work to Ivan C Moura, whose scientific contribution has greatly improved the comprehension of pathophysiology of IgA-related diseases in humans.
fib. captured in our study and immune cells from our NSCLC immune cell dataset  (32). This analysis showed that MHCII expression in CCL19 + CAF is orders of magnitude lower than in EC, as well as in the classic MHCII-expressing cells, type 1 dendritic cells (DC1). Nevertheless, the high density of CCL19 + CAF in TLS may contribute to effective antigen presentation in these areas (Fig. 2D ). Taken together, our combined IHC and single-cell analysis has defined diverse fibroblast populations with distinct molecular and spatial patterns in human NSCLC. By enriching for stromal cells from a large NSCLC cohort, we achieved highly granular scRNA-seq characterization and uncovered CAF populations undescribed to date, including a single layer of MYH11 + αSMA + CAF bordering tumor cells in a fraction of NSCLC lesions.
3E ) with some cells coexpressing both markers, suggesting that ADH1B + CAF represent a range of lowly activated fibroblasts. ADH1B + CAF also shared genes with both PI16 + fib. and alv. fib. , which may point toward both lung fibroblast types as their potential cellular sources (Fig. 3D ; Supplementary Fig. S5A ). Interestingly, the scRNA-seq data showed that ADH1B + CAF cells express a gradient of FAP + CAF and PI16 + fib. genes from cells with high expression of PI16 genes and low FAP genes to cells with low PI16 genes and high FAP genes (Fig. 3F ). This further supports the hypothesis that    ADH1B + CAF are a lowly activated form of fibroblast and may derive from PI16 + fib. Leveraging our scRNA-seq datasets, we created gene signatures for ADH1B + CAF and FAP + CAF by selecting for genes with highly specific expression in their corresponding CAF populations in contrast with all other cell types.
3H ). LUAD across tumor stages confirmed our observation that ADH1B + CAF and FAP + CAF were correlated with lower-and higher-stage tumors, respectively. Similar associations were observed in our in-house FFPE cohort (Supplementary Table  S1 ). Altogether, we showed that ADH1B + and FAP + CAF phenotypes were correlated with tumor stage and clinically relevant histologic subtypes  (46, 47) , suggesting that molecular characterization of fibroblasts could refine the clinical categorization of NSCLC tumors. MFAP5 SFRP4 IGFBP6 CCDC80 GSN CST3 ASPN TAGLN ACTA2 BGN POSTN SPARC CTHRC1 COL1A1 COL3A1 COL1A2 MMP11 CTSK VCAN ABCA8 SLIT2 ABCA10 ADH1B GPC3 FMO2 COL6A5 TCF21 LRRN3 WISP1 TWIST1 GJB2 GREM1 PLPP4 P4HA3 LRRC15 COL11A1 DIO2 ADAM12 To validate the diverse transcriptional programs of CAF observed in our dataset, we studied their expression in four additional public scRNA-seq datasets in NSCLC  (23) (24) (25) (26).
This analysis identified two fibroblast subsets expressing ADH1B + and FAP + CAF genes, confirming their enrichment in early-and late-stage NSCLC tumors, respectively (Supplementary Fig. S6A -S6C; Supplementary Table  S8 ). FAP + cells display variable expression levels of the FAP + αSMA + CAF program (Supplementary Fig. S6D ), reflecting the observed heterogeneity within FAP + cells (Supplementary Fig. S4D ). In Kim et al. (24) , we identified a population resembling MYH11 + αSMA + CAF expressing multiple MYH11 + αSMA + CAF genes including MYH11, COL4A1, and COL4A2 (Supplementary Fig. S6E ). As expected, these cells were not found in the other studies, which were predominantly composed of stage 2+ tumors and squamous cell carcinomas. Altogether, the external datasets examined validated the dominant CAF populations in NSCLC.
Next, we sought to determine if ADH1B + and FAP + CAF were present across different tumor types. To search for ADH1B + and FAP + CAF in other cancers, we returned to TCGA to analyze the data available for breast, colon, pancreatic, prostate, and ovarian cancers. Unsupervised hierarchical clustering grouped ADH1B + and FAP + CAF genes by their coexpression in each dataset separately (Supplementary Fig. S7 ). In pancreatic, breast, and colon cancers, we observed a significant separation of the two gene groups, suggesting that other cancer types also harbor lowly and highly activated CAF with similar transcriptional profiles as ADH1B + and FAP + CAF found in NSCLC.
Sequencing Data Analysis and Unsupervised Batch-Aware Clustering Transcriptomic library reads were aligned to the GRCh38 reference genome and quantified using Cell Ranger (v3. 1. 0). Stromal cells isolated from tumor and adjacent lung samples were analyzed using an unsupervised batch-aware clustering method we recently described  (33). First, stromal cells were filtered for cell barcodes recording >800 UMI, with <25% mitochondrial gene expression, and with less than defined thresholds of expression for genes associated with red blood cells, epithelial cells, macrophages, T cells, and plasma cells (Supplementary Table  S17 ). Thirteen tumor and 11 adjacent samples were clustered jointly. This expectation-maximization-like algorithm iteratively updates both cluster assignments and sample-wise noise estimates until it converges, using a multinomial mixture model capturing the transcriptional profiles of the different cell states and sample-specific fractions of background noise.
Gene Module Analysis Gene correlation modules were generated using a similar method as previously described in  (33). Briefly, cells are downsampled to 2,000 total UMIs and highly variable genes are isolated. A gene-gene correlation matrix for the isolated gene set is computed for each sample over the cell population(s) of interest, and correlation matrices are averaged following a Fisher Z-transformation. Applying the inverse transformation then results in the best-estimate correlation coefficients of gene-gene interactions across the dataset. Genes are clustered into modules using complete linkage hierarchical clustering over correlation distance. Ribosomal, mitochondrial, HLA, and immunoglobulin genes were removed from the analysis prior to the creation of gene modules, as these genes were not of interest in this study, reflected patient genomic variability, or were heavily influenced by contaminating plasma cells.
Acquisition of TCGA Dataset and Histologic Subtypes The TCGA-LUAD RNA-seq data were downloaded using the GDCquery and GDCdownload functions from the TCGAbiolinks R package. GDCquery options included: project = "TCGA-LUAD," data. category = "Transcriptome Profiling," data. type = "Gene Expression Quantification," workflow. type = "HTSeq-FPKM," experimental. strategy = "RNA-Seq, and legacy = F. Whole-exome sequencing data were downloaded using the GDCquery_Maf function with the arguments tumor = "TCGA-LUAD" and pipelines = "mutect2. " Clinical data were downloaded using the GDCquery_clinic function with the arguments project = "TCGA-LUAD" and type = "clinical. " The dominant histologic subtype for each TCGA tumor was sourced from ref. 46.
CAF Gene Signatures and LCAM in Bulk Analysis Our initial gene list was acquired from gene module analysis, as described above. To define cell type-specific gene signatures, we first excluded genes well expressed in nonfibroblast lineages, such as EC and PvC within our dataset. Next, we utilized the datasets from refs. 32 and 23 to exclude genes found in epithelial and immune cells. For each dataset, we performed in-house clustering and identified stromal clusters, and then excluded any genes from our signatures if they showed higher expression in the nonstromal clusters. We then compared the expression of the genes between fibroblast subsets and kept genes only if they showed high expression in the cluster of interest relative to other clusters. Due to their similarity, FAP + CAF and FAP + αSMA + CAF were treated as one group, and due to ADH1B + CAF similarity to adjacent fibroblast clusters, they were not contrasted with alv.
fib. and PI16 + fib. Signatures were further refined by manually checking that expression was consistently enriched in the cell type of interest across at least three patients. Bulk RNA samples were scored in the following method: Mitochondrial genes, hemoglobins, and Ig variants genes were removed from the data tables. Next, genes were converted to a percent expression of the total reads and regularized with a constant value, 1e-8, added to each gene and log-transformed. Z-scores were then calculated for each gene across all samples. Finally, the overall cell type score was calculated by taking the average of all genes within the signature. A complete list containing the signature and removed genes can be found in Supplementary Table  S17. The derivation of LCAM hi or LCAM lo scores is described in  (32).
External scRNA-seq Dataset Analysis Based on our scRNA-seq data we, defined separate gene signatures for pan-fibroblast cells, PvC, alv. fib. , meso. fib. , MYH11 + αSMA + CAF, ADH1B + CAF, FAP + CAF, and FAP + αSMA + CAF (Supplementary Table  S17 ). Fibroblasts were isolated by selecting those above our pan-fibroblast score threshold and below the PvC score threshold (Supplementary Fig. S6A ). Then, alv. fib. , meso. fib. , and MYH11 + αSMA + CAF were identified and removed from subsequent plots (Supplementary Fig. S6A ). The remaining cells were then compared against the ADH1B + , FAP + , and FAP + αSMA + CAF gene signatures (Supplementary Fig. S6B-S6D ). The datasets can be found at the following locations:.
Histology Analysis and Overlays All histology analyses were performed using the open-source image analysis QuPath software (QuPath-0. 2. 3, https://qupath. github. io/; ref. 69)  and ImageJ/Fiji  (70, 71). To create overlayed images, scans were exported from QuPath as OME. TIFF and then imported into ImageJ using the BioFormats plugin  (72). Alignment was done using the "Linear Stack Alignment with SIFT" plug-in  (73). The AEC and hematoxylin stains were extracted from individual scans using "colour deconvolution" and colored as desired.