dataesr/scientific-paragraphs-categorization huggingface dataset:
Consideration of additional larger introns/ciRNAs In the study of Zhang et al. [9] , a selection of ciRNAs including at least 300 nt was applied and consequently, we considered at first large-ciRNAs (300-1,000 nt) from midi-introns (330-1,100 bp). We retained twenty porcine midi-introns associated with 25 large-ciRNAs and six bovine midi-introns associated with seven large-ciRNAs. Among these 32 large-ciRNAs, 29 derived from a lariat obtained with a "C" at the branch point position, one from a "T" and two from an "A" (Table  2 ). The two ciRNAs that derive from a lariat built around an "A" are the respective unique form of porcine ciRNA-SF3B2 and bovine ciRNA-HNRNPU (for details, see Suppl. Doc. [16] [17] [18]. No difference from the porcine reference sequence was detected in the sequence of the porcine intron of SF3B2 (dataset SRR8237163) and the bovine intron of HNRNPU (dataset SRR775528-530).
reported 8/20  [9]. Finally, parent introns of/and intermediated size ciRNAs were analyzed (Suppl. Doc. [19] [20]. We retained 28 porcine mini-introns associated with 41 ciRNAs and 8 bovine midi-introns associated with 10 ciRNAs (Table  2 ). Among these 51 ciRNAs, 45 derived from a lariat obtained with a "C" at the branch point position, three from a "T", two from a "G" and one from an "A". We noticed that bovine ciRNA-SIN3B derives from a lariat built around a "tnA". No difference from the bovine reference sequence was detected in the concerned intron sequence (dataset SRR775527). Among these 36 mini-introns, eight mini-introns contained a "tnA" in the usual branching regions (Table  2 ). To improve the understanding of the ciRNAs genesis, the branching regions of 34 other mini-and 60 midi-introns not concerned by ciRNA were analyzed.
Examination of sequences at the circular junction Zhang et al. reported conserved motifs around the circular junction of ciRNAs. Thus, finally, we examined if we could find respective sequences present at the circular junctions of our datasets. As Zhang et al. [9] , we considered the eleven nucleotides upstream the branch point and the seven nucleotides near the 5 ′ SS to propose a consensus sequence for four sets of ciRNAs (Figure  8 ). The first set consists of all porcine ciRNAs with very small (size <166 nt corresponding to the group ciRNA Figure  6. Use frequency of the different nucleotide trio present at the circular junction. In the trio, the nucleotide used as a branch point is written in capital letter. Ten categories of nucleotide trio were considered but among the 32 large-and 37-micro-ciRNA selected, only nine were represented.
In comparison to the two other porcine consensus sequences, we noticed the presence of a "G" at the position +5, i. e. in the 5 ′ SS. The fourth set of ciRNAs consists of the 18 bovine ciRNAs and does not lead to a logo very different from porcine logos (Figure  8D ). Our data demonstrate that there is no conserved motif around the circular junction regardless of species or ciRNA size category within species. These four consensus sequences were constructed with ciRNAs detected in the testes and the tissue criterion could be important. The consensus sequence proposed by Zhang et al. [9]  was based on large ciRNAs expressed in mouse RPC cells. Talhouarne and Gall  [14]  had already observed differences between mouse and human RPCs  [14]. In addition, we would like to point out that no RNase R treatment to discard linear RNA was used to generate the datasets considered here.
Population We conducted a monocentric observational study in the University Hospital of Nantes, France, regional referral center for systemic and rare auto-immune diseases. We retrospectively analyzed the files of patients with dSSc seen in our center between 2004 and 2019. Patients had to be over 18, with a diagnosis of SSc according to the ACR/EULAR 2013 criteria  [19] , in its diffuse cutaneous form, and with at least one thoracic MDCT available in the follow-up. This study was done in accordance with the Declaration of Helsinki, and the French Data Protection Authority and Legislation concerning retrospective studies.
Data collection The collection period trended between April 2018 and June 2019. General patient's characteristics were collected, including epidemiological, clinical, biological, and pulmonary function tests. We collected the presence of thoracic LAP, their size and localization at the time of the first MDCT available. We looked for the presence of ILD pattern and extension according to the Goh score and different interstitial abnormalities according to a semiquantitative method. Esophagus dilatation was noticed as well. We also collected positron emission tomography-computed tomodentisometry (PET-CT) characteristics and thoracic lymph nodes histological data when available. When available, we analyzed thoracic MDCT during the follow-up in order to describe the evolution of thoracic LAP and ILD over time.
In patients who underwent a PET-CT, we collected LAP metabolism data with the evaluation of « Standardized Uptake Value» (SUV) for the more hypermetabolic lymph node. Evolution was assessed using the first and the last thoracic MDCT available. Thoracic LAP number was considered stable when it was identical at 1 lymph node near, increased when 2 or more lymph nodes were added and decreased when 2 or more lymph nodes disappeared. In cases of ILD, the MDCT pattern was evaluated: UIP pattern was defined according to current diagnostic criteria  [21] , and NSIP when ground glass was the dominant characteristic. All other situations were considered as undetermined patterns. ILD extension was evaluated with two different methods. A detailed semi-quantitative method according to Kanzerooni  [22] : ground glass alone, mixed ground glass and reticulation, reticulations alone, honey-combing, and all abnormalities in global were separately analyzed.
Cases were only included if there was certainty of the status with regards to unilateral or bilateral disease at latest follow up, as this was the key dependent outcome variable in the analysis. The primary objective was to explore whether the ratio of unilateral versus bilateral retinoblastoma was influenced by birth order, maternal age or income status of the mother's country. Data were imported into SPSS version 26. Univariate analysis was performed to determine differences in the maternal characteristics between mothers with unilateral versus bilateral retinoblastoma, with respect to birth order, maternal age, and GDP per capita of mother's residence. GDP per capita was chosen as it allowed us to assign each country a continuous variable which can be incorporated into a regression model.
336, p = 0. 563). In addition, there was no significant difference in means of GDP per capita between the groups (F = 1. 872, p = 0. 172) (Table  2 ). In the forward stepwise conditional logistic regression, with a probability cut off for entry p < 0. 05 for removal 0. 1, none of the 3 inputted predictive factors entered the model, and only the constant entered the model (see Table  3 ). Collinearity diagnostics (SPSS) for maternal age (as Log10), birth order, and GDP per capita was performed. All three variables could reasonably be assumed to be independent of each other, using a Variance Inflation Factor cut-off of <3. The ratio of unilateral versus bilateral disease, by country (GDP per capita) is presented in Table  4. The chi-square test showed no significant differences in the unilateral versus bilateral retinoblastoma between countries.
kamiyai  [16]. Like surrounding regions, Luxembourg is known as an endemic area for E. multilocularis; a prevalence in foxes of 23. 8% (based on 84 examined animals) was reported in 2018  [17]. No published data are available for IH infection with E. multilocularis in this country. For the present study, trapped muskrats were obtained from two distinct river habitats in northern Luxembourg, where the muskrat population is routinely controlled to protect populations of freshwater pearl mussels (Margaritifera margaritifera) and thick-shelled river mussels (Unio crassus) (LIFE Resto-unio project). The aims of this study were  (1)  to estimate the prevalence of E. multilocularis and other taeniids, (2) to identify possible risk factors for infection, (3) to assess parasite reproductive potential, (4) to verify the hypothesis of a lobe preference for the metacestodes settlement and their development in the liver and, and (5) to assess the genetic diversity of E.
Data Analyses Animal age was estimated using the weight as a proxy  [3]  and grouped into juveniles (<600 g), subadults (600-1000 g), and adults (>1000 g). The collected carcasses were then grouped according to their river basin of origin (Sauer and Our) and to the period of collection (from 2013 to 2015 and 2016/2017). Univariate analysis  [18]  was preliminarily performed to identify potential risk factors for E. multilocularis and the distribution of lesions in the different liver lobes. Pearson's Chi-square test with a simulated p-value according to the Monte Carlo method based on 2000 replicates was used to study the relationships between the presence of infection and risk categories (area, season, sex, period, age, and co-infections by the other cestodes) and to further evaluate the distribution of E.
The presence of lesions in the liver lobes was treated as binary response variable, whereas each lobe per animal was settled as an independent fixed variable. Considering that each subject contained a group of five values (one per lobe), we set muskrat ID as a random factor  [22]. All statistical analyses were performed using R statistical software version 3. 5. 1  [23]. To run the univariate analysis, chisq. test function was used, and in the case of the presence of lesions in the five liver lobes, we used the same function with simulate. p. value (package stats). The first linear models (LM) were formulated by using the lm function, whereas the binomial generalized linear model with a logit link was formulated using the glm function in the package stats. A general linear mixed-effects model was developed using the glmer function in the package lme4  [24].
All amplification products were purified with a High Pure PCR Product Purification Kit (Roche, Mannheim, Germany), following the instructions of the manufacturer. The sequencing of each purified product was performed by GATC (Konstanz, Germany). The obtained DNA sequences were compared with existing sequences in GenBank databases using the NCBI BLAST search tool (www. blast. ncbi. nlm. nih. gov). Sequences of H. kamiyai were further screened for genetic variants, and phylogenetic analysis with previously published haplotypes was performed  [16]. For this purpose, the sequences obtained in the present study and the haplotypes B1-B22 (KT693076-KT693094; EU544596; JQ663994;  [16] ) were trimmed to a uniform consensus length of 318 nucleotides. A Maximum Likelihood tree was constructed with MEGA X using the GTR+R+I substitution model  [29].
kamiyai, 23 cysticerci of T. martis, 6 cysticerci of T. polyacantha, and 2 cysticerci of V. mustelae were identified by sequencing a fragment of the cox1 gene. Obtained sequences were compared to reference sequences in GenBank. The acquired sequences of E. multilocularis varied in length from 300 to 1,590 base pairs, and no exchanges between them could be observed. The longest sequence with 1590 bp showed 100% identity with the European haplotypes E2, E4, and E5, which share the same cox1 sequence  [31]. All sequences of H. kamiyai (212-385 bp) had 98. 96-100% identity with NC037071  [15]. Taenia martis sequences (405 bp) showed 99. 75 -100% identity with AB731758  [15]. The T. polyacantha sequences (385 bp) were 100% identical to EU544581  [32] , and V. mustelae (407 bp) showed 99. 75% identity with AB732957  [15].
kamiyai (each from different hosts), 26 gave sequences of sufficient length of 396 bp for the analysis of the genetic diversity and comparison with already published haplotypes. In total, nine different haplotypes could be identified. Four of these haplotypes are already known from deposited sequences, and five have not been described and were submitted to GenBank (OP363933-OP363937; Table  4 ). For the phylogenetic analysis, the present sequences, as well as the reference sequences, had to be shortened to a common length of 318 bp. The shortening of the length of the 22 reference haplotypes B1-B22  [16]  resulted in a reduction of the number of haplotypes to 19. This affected five of the original haplotypes. B3, B6, and B19 constituted a single haplotype with a shorter sequence, as did B7 and B15.
Patients' characteristics and representative images from hematoxylin and eosin staining of the tissue are provided in Suppl. Fig. 1A  and 1B , respectively. Each tissue sample was reviewed by an experienced lung pathologist, who confirmed that all samples were tumor-free and evaluated COPD relevant phenotypes, like emphysema, airway thickening and immune infiltration. Only ex-smokers with preserved lung function and no indication of emphysema or fibrosis in the test results or patient history were included as control samples. Importantly, we included 2 samples from COPD (GOLD II-IV) donors (HLD38 and HLD39), which originated from lung resections, ensuring that the observed changes are present in COPD tissue without cancer background. There were no significant differences between control and COPD donors regarding age, body mass index, smoking status, and smoking history, but the control and COPD group could be clearly separated based on lung function data (Figure  1A  and Suppl.
Emphysema score index (ESI) determination Lung and emphysema segmentation were performed to calculate the ESI from clinically indicated preoperative CT scans taken with mixed technical parameters. After automated lung segmentation using the YACTA-software, a threshold of -950 HU was used with a noise-correction range between -910 and-950 HU to calculate the relative amount of emphysema in % of the respective lung portion  (Lim et al. 2016). While usually global ESI was measured, only the contralateral non-affected lung side was used if one lung was severely affected by the tumor.
RNA-seq read alignment and transcript abundance quantification Single-end reads were mapped to the human genome version 37 (hg19) and the reference gene annotation (release 70, Ensembl) using STAR v2. 5. 0a  (Dobin et al. 2013)  with following parameters: --outFilterType BySJout --outFilterMultimapNmax 20 --alignSJoverhangMin 8 --alignSJDBoverhangMin 1 --outFilterMismatchNmax 999 --alignIntronMin 20 --alignIntronMax 100000 --outFilterMismatchNoverReadLmax 0. 04 --outSAMtype BAM SortedByCoordinate --outSAMmultNmax 1 --outMultimapperOrder Random. Contamination of PCR duplication artefacts in the RNA-seq data was controlled using the R package dupRadar  (Sayols et al. 2016). The featureCounts script  (Liao et al. 2014 ) of the Subread package v1. 5. 3 was used to assign and count mapped reads to annotated protein-coding and lncRNA genes with default settings.
Differential Gene Expression Analysis Statistical analysis of differential gene expression was performed with the DESeq2 Bioconductor package  (Love et al. 2014). For exploratory RNA-seq data analysis the data needs to be homoscedastic. Therefore, the raw counts were transformed by the regularized-logarithm transformation rlog. Genes with less than 32 counts in at least 5 samples were excluded from further analysis. Ex-smokers with preserved lung function (no COPD) were considered as ground state and differential gene expression in COPD patients classified as GOLD Grade II-IV was identified as a significant change in expression by an FDR (false discovery rate) < 0. 05 and an absolute log2 fold change > 0. 5 (corresponding to fold change > 1. 4), after fold change correction with the built-in lfcShrink function.
Read Alignment The whole genome bisulfite sequencing mapping pipeline MethylCtools with modifications to adapt for the T-WGBS data was used (https://github. com/hovestadt/methylCtools) (Hovestadt et al. 2014). Briefly, the hg19 reference genome (37d5) was transformed in silico for both the top strand (C to T) and bottom strand (G to A). Before alignment, adaptor sequences were trimmed using Trimmomatic (release 0. 35)  (Bolger et al. 2014 ). The first read in each read pair was then C-to-T converted and the 2nd read in the pair was G-to-A converted. The converted reads were aligned to a combined reference of the transformed top (C to T) and bottom (G to A) strands using BWA MEM (bwa-0. 7. 8) with default parameters, yet, disabling the quality threshold for read output (-T 0)  (Li and Durbin 2009).
DMR calling Differences in CpG methylation profiles of no COPD donors and patients diagnosed with COPD were analyzed using the R/Bioconductor package bsseq  (Hansen et al. 2012). First, the data was smoothed using the built-in BSmooth function with default settings. Only CpG sites with a coverage of at least 4x were kept for subsequent analysis. A t-statistic was calculated between no COPD and COPD (II-IV) samples using the BSmooth. tstat function with following parameters: local. correct=TRUE, maxGap=300, estimate. var="same". Differentially methylated regions (DMRs) were called by 1) selecting the regions with the 5% most extreme t-statistics in the data (lower and upper 2. 5% quantile; default paramters of the dmrFinder function), 2) filtering for regions exhibiting at least 10% methylation difference between no COPD and COPD (II-IV) and containing at least 3 CpGs.
ChIP-seq Data Histone modification ChIP-seq data of human lung fibroblasts were obtained from the ENCODE portal (https://www. encodeproject. org/) (Davis et al. 2018)  with the following identifiers: ENCFF354IJB, ENCFF070CZY, ENCFF377BNX, ENCFF227WSF, ENCFF208SHP, ENCFF386FDQ, ENCFF102BGI, ENCFF843AYT. Chromatin states for adult human lung fibroblasts (accession: ENCFF001TDQ) were obtained from ENCODE data base  (Ernst and Kellis 2017). The number of bp of each DMR coinciding with a chromatin state was calculated and the chromatin state with the largest overlap was assigned to the DMR. To assess the genomic background, 10. 000 regions with matching size and CpG distribution were randomly selected.
Acknowledgments We would like to thank  Lung Biobank  ( Heidelberg, Germany )a member of the  Biomaterial bank Heidelberg (BMBH) , the tissue bank of the  National Center for Tumor Diseases (NCT)  and the  Biobank platform of the German Center for Lung Research  (DZL), as well as the  Asklepios Biobank for Lung Diseases , member of the  German Center for Lung Research (DZL)  for providing Biomaterials and Data. We also thank  Christa Stolp  for help with collecting primary material. We acknowledge excellent sequencing service and helpful discussions from the  Genomics core facility  ( GeneCore, EMBL, Germany ) for RNA-seq and from  NGX Bio (San Francisco, USA ) for T-WGBS sequencing, as well as support from the  ZMBH imaging facility  ( Heidelberg, Germany ) for immunofluorescence. We thank  Morphisto GmbH  ( Frankfurt, Germany ) for excellent histological service.
Potential Conflict of Interest RZJ, MLP, VM, US, RT, MR,  SP, OS and BM as employees of BioMed X Institute  received research funding by  Boehringer Ingelheim Pharma GmbH & Co KG. HS, BJ, MSchu, CK, KQ and DWy are employees of  Boehringer Ingelheim Pharma GmbH & Co KG  and receive compensation as such. TM received a research grant, non-financial support and has patent applications with  Roche Diagnostics GmbH  outside of the described work. CPH has stock ownership in GSK; received research funding from  Siemens ,  Pfizer ,  MeVis and Boehringer Ingelheim ; consultation fees from  Schering-Plough ,  Pfizer ,  Basilea ,  Boehringer Ingelheim ,  Novartis ,  Roche ,  Astellas ,  Gilead ,  MSD ,  Lilly Intermune and Fresenius , and speaker fees from  Gilead ,  Essex ,  Schering-Plough ,  AstraZeneca ,  Lilly ,  Roche ,  MSD ,  Pfizer ,  Bracco ,  MEDA Pharma ,  Intermune ,  Chiesi ,  Siemens ,  Covidien ,  Boehringer Ingelheim ,  Grifols ,  Novartis ,  Basilea  and  Bayer , outside the submitted work.
Funding This study was supported by  Boehringer Ingelheim. The work was partly funded by the  School of Biosciences (Cardiff University ) to RZJ and the  German Center for Lung Research (DZL)  to CP, TM, MS, FH, CPH, HW, AW and  MLP. Asklepios Biobank  as part of the DZL is partly funded by  BMBF.
Data acquisition of 8-color mAb combinations was performed in a FACSCanto II flow cytometer using FACSDiva 8. 0. 1 software (both from Becton Dickinson Biosciences, San Jose, CA). Data acquisition of 8 and 17 markers (Tubes 4 and 5) was performed in a Cyto-Flex LX flow cytometer using CytExpert software (both from Beckman Coulter Life Sciences, Indianapolis, IN). Data acquisition of a 24-mAb combination (Tube 6) was performed by using an Aurora flow cytometer and SpectroFlo software (both from Cytek Biosciences, Fremont, CA).
Data analysis and system requirements Immunophenotypic data were analyzed using R version 4. 0. 0 software. All phases of the workflow were performed on laptops equipped with an i7 8565u central processing unit and 16 GB DDR4 random access memory, except the analysis of 150 patients with SMM and 100 patients with MM that was run on an x86_64redhat-linux-gnu platform (under CentOS Linux 7 [Core]). When indicated, other flow cytometry software was used (Infinicyt v2. 0; Cytognos SL, Salamanca, Spain). FlowCT can be installed from GitHub and a detailed tutorial is available at https://github. com/ paiva-s-lab/FlowCT/blob/main/tutorial. md. Table  1  describes the algorithms embedded in FlowCT and a list of the main software packages included in FlowCT is available in supplemental Table  2. Those interested in using FlowCT should have a basic knowledge of R software to understand and use its script, which is described in the tutorial.
RNA-seq data were analyzed as described elsewere. 10 Briefly, raw sequencing data were demultiplexed using bcl2fastq software (version 2. 20. 0; Illumina) and aligned to the GRCh37 human genome using STAR  35  aligner (version 2. 6. 1c). Matrixes with gene counts were generated with quant3p (a wrapper based on HTSeq 36 dynamics). Analysis of differentially expressed genes was performed using DESeq2  37  and DEGreports 38 R packages available in version 3. 5. 1. A likelihood-ratio test was used to select genes differentially expressed across all cell types identified by using FlowCT (Bonferroni-adjusted P ,. 01), which were then used to perform a supervised hierarchical clustering of different samples.
At this stage, cluster annotation can be achieved by visualizing expression levels of each marker on uniform manifold approximation and projection (UMAP) (supplemental Figure  3C ) and on 2-dimensional biaxial plots using either FlowCT (supplemental Figure  3D ) or standard flow cytometry software on saved flow cytometry standard (FCS) files (supplemental Figure  4 ). Lymphocytes were selected for downstream subclustering after supervised annotation (Figure  1A ). Batch effect was removed with canonical correlation analysis (supplemental Figure  5 ) and lymphocytes were subclustered using phenotyping by accelerated refined community partitioning (PARC). Automated clustering identified 25 subsets (Figure  1B ; supplemental Figure  6 ), and a significant correlation between PB and BM was observed in 20 of the 25 subsets (Figure  1C ).