dataesr/scientific-paragraphs-categorization huggingface dataset:
FUNDING This researched was funded by the  Fondation de France , the  Ecole Normale Supérieure , the  Laboratoire de Sciences Cognitives , the  Laboratoire de Linguistique Formelle  ( UMR 7110 ) and the following grants from l' Agence Nationale de la Recherche :  ANR-13-APPR-0012   LangLearn ,  ANR-17-CE28-0007-01   LangAge , and  ANR-17-EURE-0017  FrontCog. PB was supported by the  ESRC International Centre for Language and Communicative Development (LuCiD)  while writing the last version of this manuscript [ ES/L008955/1 ].
Abstract The behavioural variant of frontotemporal dementia is a clinical syndrome characterised by changes in behaviour, cognition and functional ability. Although atrophy in frontal and temporal regions would appear to be a defining feature, neuroimaging studies have identified volumetric differences distributed across large parts of the cortex, giving rise to a classification into distinct neuroanatomical subtypes. Here, we extended these neuroimaging studies to examine how distributed patterns of cortical atrophy map onto brain network hubs. We used baseline structural magnetic resonance imaging data collected from 213 behavioural variant of frontotemporal dementia patients meeting consensus diagnostic criteria and having definite evidence of frontal and/or temporal lobe atrophy from a global clinical trial conducted in 70 sites in Canada, United States of America, Australia, Asia and Europe.
We have previously reported the results of a voxel-based morphometric (VBM) comparison of baseline MRI scans of patients in a large randomised controlled clinical trial in bvFTD (TRx-237-007, NCT01626378) with those randomised to a comparable study of mild AD. This showed that the bvFTD group was clearly distinguishable from the mild AD group with a similar overall level of cognitive impairment  (Shiells et al. , 2020; Vuksanović et al. , 2019). As expected, the bvFTD patients had significantly more atrophy in frontal cortex and anterior temporal cortex, and significantly less atrophy in hippocampus, middle temporal gyrus, cuneus and insula. We undertook the present study to determine how the apparent cortical heterogeneity of bvFTD subtypes relates to atrophy in central brain hubs and whether the subtypes are associated with distinctive clinical profiles.
Baseline MRI scans were evaluated by a single independent neuroradiologist out of a pool of trained neuroradiologists to determine eligibility (RadMD, NY). In addition, MRI scans were obtained from 244 age-matched healthy controls from the well characterised Aberdeen 1936 Birth Cohort (ABC36) brain imaging database held in the Aberdeen Biomedical Imaging Centre at the University of Aberdeen. The ABC36 project has been described elsewhere  (Murray et al. , 2011; Whalley et al. , 2011). Demographics and clinical characteristics of the study groups are given in Table  1.
MRI data collection The acquisition protocol was standardised across sites using 1. 5T and 3T scanners manufactured by General Electric, Philips or Siemens. All data were centrally collected, quality-controlled and analysed by the imaging core laboratory (Bioclinica). MRI data acquisition included a 3D sagittal T1-weighted sequence which we used in our analysis here. 3DT1 images were acquired using a 3D MPRAGE sequence (Siemens) or the specific manufacturer equivalent sequence (General Electric 3D IR-prepped Fast SPGR, Philips 3D TFE), covering the whole brain with a resolution of 1. 25×1. 25×1. 2 mm 3. FreeSurfer version 5. 3. 0 (http://freesurfer. net/) was used to extract regional volumes for the clustering analysis. FreeSurfer automated segmentation parcelates the brain into 76 regions according to the Desikan-Killiany Atlas  (Desikan et al.
, 2006). For the purpose of this study, we selected 68 regional volumes (34 from each hemisphere) of the frontal, temporal or parietal lobe and additional sub-lobar regions (limbic lobes, basal ganglia, amygdala and thalamus) previously identified as locations of atrophy in bvFTD and/or as anatomical correlates of clinical symptoms. A full list of regions is given in the Supplementary Table  1. Hierarchical agglomerative clustering, implemented in SPSS v. 23. 0, was used to classify differences/similarities in the 68 regional volumes. The bottom-up hierarchical agglomerative clustering is based on similarities and linkages between data points (subjectwise region of interest [ROI] volumes on MRI), with successive agglomeration of pairs of clusters until all clusters are merged into a single cluster containing all subjects.
A custom template was created from a data set of all participants in the study. Finally, images were normalized to the Montreal Neurological Institute (MNI) space and smoothed with a Gaussian kernel (8 mm FWHM). Each group identified by the clustering technique was compared to the healthy control group. Regions showing atrophy in the bvFTD group were identified from the MNI coordinates of the voxels within the areas that were significantly different using maximum difference t-test statistics. All image processing steps and statistical analysis were implemented in the Statistical Parametric Mapping (SPM12) software package available at http://www. fil. ion. ucl. ac. uk/spm/. The t-tests were performed on each pair of voxels/volumes corrected for age, gender and either estimated total intracranial volume  (Whitwell et al.
Statistical analyses Statistical analyses were performed using SPSS v. 23. 0, employing paired samples ttests to compare males and females in Table  1. One-way ANOVA was used to test differences between bvFTD subtypes in cognitive and behavioural sub-scores given in  Table 2. A significance level of p < 0. 05 was used. In order to generate a summary variable accounting for the multiple regional volumes affected by neurodegeneration, we employed a post hoc principal component analysis to reduce the volume measurements into a manageable number of factors. We examined clinical-anatomical correlations using general linear models (GLM). In these we tested for associations between clinical measures and the first volumetric summary variable accounting for 40% of the variance. The models used included adjustments for known and potential confounders: age, sex, head size and anatomical subtype.
Demographic and clinical features of the populations studied A total of 213 bvFTD patients of 220 randomised to the trial were included in the present study based on baseline MRI scan quality and the complete clinical data required for the present study at baseline. Baseline demographic and clinical data are provided in Table  1. Mean (± SD) age was 63 ± 7 years for both males (136) and females (77). Total years in education were 15. 4 ± 0. 5, with no difference between males and females. The estimated total intracranial volume (eTIV) was significantly larger in males (1600 ± 220 cm 3 ) than in females (1400 ± 150 cm 3 ), although there was no difference in brain parenchymal fraction (BF, 0. 67 ± 0. 06). The MMSE score was significantly higher in males (25. 4 ± 3. 5) than in females (22. 9 ± 4.
Pectin remodeling genes show specific expression pattern during rootlet emergence Cell wall properties of primordium-overlaying tissues greatly impact the emergence process. Previous studies discovered a large subset of cell wall remodeling genes involved in lateral root emergence in Arabidopsis. We established a list of orthologs for important known regulators of cell wall loosening during lateral root morphogenesis including the closest lupin orthologs of EXPANSIN A1 (EXPA1 -At1g69530,  Ramakrishna et al. , 2019)   3a ). Next, we assessed the expression patterns of the selected candidates in lupin hairy roots transformed with promoter::GUS fusion constructs (Extended Data Fig. 3b  and Extended Data Fig. 4 ). Some of the selected candidate genes that were not identified in our transcriptomic dataset displayed overlapping gene expression patterns.
RNA sequencing Four independent biological replicates were used to prepare RNA-sequencing libraries with Illumina TruSeq Stranded Total RNA Kit with Ribo-Zero. The libraries were sequenced as 150bp pair-end reads using Illumina HiSeq3000 at Get-PlaGe core facility (INRA, Toulouse, France). A total of 2,110,906,218 paired-end reads were generated. The following procedure was applied to each paired read dataset. Cutadapt (version 1. 15) was used to remove Illumina Truseq adapters from the sequencing data and to remove bases with a quality score lower than 30, in both 5' and 3' end of the reads. Reads with a length lower than 60 were discarded. The quality checked RNA-seq reads were then used to quantify white lupin transcript abundance using salmon (version 0. 12. 0) with the options validateMappings, gcBias and seqBias turned on.
Data were imported to DESeq2 using tximpot and normalized using DESeq2 method. Then normalized counts were extracted for further analyses. Differential expression was performed using the following method: given a gene, if the mean count of the less expressed condition was more than twice inferior to the mean count of the most expressed condition, and if the mean of the most expressed condition was superior to 100, the gene was considered differentially expressed (DE). The DE gene count data for relevant samples were "log regularized", using the Variance Stabilizing Transformation (vst) function from DESeq2. Then the degPatterns function from the DEGReport package was used to group genes into clusters based on their expression profile using the divisive hierarchical clustering method diana from the cluster package.
Phylogenetic trees Protein sequences from white lupin were retrieved from the white lupin genome database (www. whitelupin. fr -Hufnagel et al. , 2020). Sequences producing significant alignments after blastp analysis of full length protein sequences of Arabidopsis as queries (Total score > 700; Evalue=0) were considered as putative lupin orthologous proteins for each gene family. Sequences were aligned by MUSCLE and evolutionary history was inferred using the Neighbor-Joining method (bootstrap replication number: 1000). Phylogenetic trees were then exported from the software MEGA X (version 10. 0. 4).
Artificial microRNA cloning Artificial microRNA (amiR) targeting LaPG3 transcripts was designed using the online tool WMD3 (http://wmd3. weigelworld. org/cgi-bin/webapp. cgi) and the pRS300 backbone vector according to the protocol of  Schwab et al. , 2006. The amiR-LaPG3 and the nlsYFP coding sequence (transformation control) were inserted into the entry vector pDONR201 (P1P2 recombination sites) and the promoter of LaPG3 was inserted into the pDONRP4P1r (sequences listed in Supplemental table  3 ). The two fragments were recombined into the multisite Gateway vector pK7m24GW_CR, specifically designed for hairy root transient expression and cluster root phenotype analysis in this study, using Gateway LR clonase II enzyme mix. The same procedure was repeated to generate the DR5::nlsYFP construct in pK7m24GW_CR.
Microscopic analysis GUS-stained, DR5::nlsYFP and LaPG3::nlsYFP transformed cluster roots were embedded in 4% agarose (m/v) and cut with a vibratome to produce thick sections of 70 μm, (VT1000S, Leica Microsystems). The root sections were mounted on slides in 50% glycerol. Wild type (cv. AMIGA) cluster root thin sections of 6 μm were produced using a microtome (RM2165, Leica Microsystems). They were counterstained for 5 min either with 0. 05% toluidine blue or with 0. 1% ruthenium red in 1X phosphate buffer saline (1X PBS pH 7. 4, Sigma Aldrich, P3813). GUS-stained and wild type cluster root sections were observed with a color camera on an Olympus BX61 epifluorescence microscope (Tokyo, Japan) with Camera ProgRes ® C5 Jenoptik and controlled by ProgRes Capture software (Jenoptik, Jena, Germany).
Confocal microscopy Immunostained root sections were imaged on a confocal microscope (Zeiss LSM 780). Autofluorescence observation was performed using an argon laser at 405 nm and secondary antibodies were excited at 561 nm. Both were detected at a 566-679 nm window using the same settings (gain, offset, resolution) to allow quantification measurements. For LaPG3::nlsYFP, DR5::nlsYFP and screening of transformed cluster roots, mCherry internal control and nlsYFP were excited at 561 nm and 514 nm respectively and detected at 583-696 nm and 519-583 nm windows, respectively. Observations were made using Plan-Apochromat 10x/0. 45 M27 and 20x/0. 8 M27 objectives. Image acquisition was performed with the Zeiss ZEN black 2010 software and image analysis was conducted using the ZEN blue 2. 3 lite software (Carl Zeiss Microscopy).
Homology modeling The LaPG3 model was created using I-TASSER software for protein structure and function prediction (https://zhanglab. ccmb. med. umich. edu/I-TASSER/; Zhang, 2008)  with Aspergillus aculeatus rhamnogalacturonase A (PDB: 1RMG,  Petersen et al. , 1997)  as the best template. While these enzymes share rather low sequence identity (24. 93%), they have high structural homology with estimated RMSD 6. 6±4. 0Å. The LaPG3 model consisted of 425 AA excluding the 24 AA of the signal peptide. The modeled structure was compared with that of Pectobacterium carotovorum PG (PDB:1BHE), Aspergillus niger PGII (PDB:1CZF) and Aspergillus aculeatus PG (PDB:1IA5). UCSF Chimera (http://www. cgl. ucsf. edu/chimera/) was used for creation of graphics  (Pettersen et al. , 2004).
2) are shown on the right. Extended Data Fig. 5: Auxin-responsive Lupinus albus POLYGALACTURONASE3 (LaPG3) expression. RNAseq expression level of LaPG3, an auxin-responsive gene from the expression cluster 3. Extended Data Fig. 6: LaPOLYGALACTURONASE3 in silico characterization. a, Protein alignment of AtPGLR (AT5g14650) and LaPG3 (Lalb_Chr02g0160451) using clustal omega (https://www. ebi. ac. uk/Tools/msa/clustalo/. version 1. 2. 4). Results are shown using MView, b, Surface and ribbon representation of LaPG3 modeled structure (gray) with amino acid of the active site highlighted (blue). c, LaPG3 modelled structure (grey) superimposed to the structure of Pectobacterium carotovorum PG (PDB:1BHE, yellow), Aspergillus niger PGII (PDB:1CZF, green) and Aspergillus acuelatus PG (PDB:1IA5, purple).
We acknowledge  Carine Alcon  for technical support at the imaging facility MRI (Montpellier, France), a member of the national infrastructure  France-BioImaging  infrastructure supported by the  French National Research Agency  ( ANR-10-INBS-04 ,  «Investments  for the future») and the microscopy facility at  UPSC (Umeå, Sweden). We thank  Serge Pilard  ( Analytical Platfrom, UPJV, France ) for the LC-MS/MS analyses. This work was supported by the  Kempestiftelserna  (Scholarship SMK 1759, F. J. ), the  Swedish Research Council Vetenskapsrådet  grant  VR-2020-03420  (F. J. ), the  Knut and Alice Wallenberg Foundation and Vinnova (Verket för Innovationssystem)  (F. J. , S. R. ). This project has received funding. CC-BY-NC-ND 4. peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
Quality control and alignment Raw data quality was verified with FastQC (v0. 11. 5). Low-quality reads, Illumina adapters and reads smaller than 20 nucleotides were removed with Trimmomatic (v0. 39)  (22) , using default values for other options. Reads were aligned on GRCh37 (hg19) human reference genome from Ensembl using STAR (v2. 5. 4b)  (23) , specifying that the reads are 2x150 nucleotides (paired-end). Alignment quality was checked by BAMQC (qualimap v2. 2. 1)  (24). Gene quantification was done with featureCounts (v2. 0. 0)  (25) , using the exons as features and the genes as attributes. All the bioinformatics steps are arranged in a Snakemake (v5. 7. 4)  (26)  workflow and full reproductibility is ensure though a Docker image, available at this address (github link).
Differential expression analysis Statistical analyses were performed using R version 3. 6. 2. Because there is a large infiltrate of immune cells in the heart tissue, and therefore in the samples studies, genes related to immunoglobulins and T-cell receptor (TCR) were excluded of the analyses. The DESeq2 package (v1. 26. 0) was used for data normalization and differential gene expression analyses  (27). Log2 fold change (log2(FC)) were corrected using the DESeq2 shrinkage function. Multi testing correction was performed using the Benjamini-Hochberg method to obtain False Discovery Rate (FDR) for each gene. Genes with an FDR value lower than 0. 05 and an absolute log2(FC) greater than 1. 5 were considered as differentially expressed (DEG.
Functional enrichment Functional enrichment was computed using two strategies: (i) using ClueGO cytoscape plugin for Gene Ontology Biological Process annotations (GO BP) (29) and using GAGE package (v2. 36. 0) and pathview package (v1. 26. 0) for KEGG pathways annotations  (30, 31). The interaction between differentially expressed ncRNA and protein-coding genes was analyzed with LncRNA2Target and LncTarD databases  (32, 33). Considering the lack of annotation of lncRNA function, the LncRNADisease database was also used  (34).
Evaluation of cell types in heart tissue RNAseq deconvolution was performed using ADAPTS R package  (35). Cell type signatures coming from microarray and single-cell RNA-seq datasets were used: a first one containing 22 cell types of immune cells from PBMC samples  (36) , and a second one containing 5 cell types from the left ventricle of healthy heart tissue  (37). Signature matrices were generated with ADAPTS, and then the proportion of each cell type was estimated on our total normalized RNA-seq data (with the Ig and TR genes). Finally, for each cell type, a Wilcoxon test (FDR < 0. 05) was applied to identify which ones have significantly different amount between cases and controls. To confirm these results, we performed the same analysis using Cybersortx Fractions and we obtained the same qualitative results (data not shown).
Tissue DNA methylation analysis Analysis of DNA methylation data was performed with the ChAMP package  (38). The beta values were normalized with the BMIQ method  (39) , and the batch effect was corrected with the ComBat method  (40). The analysis of variation of methylation on genomic positions was done using the ChAMP package and multitesting correction was performed using the Benjamini-Hochberg method (FDR, False Discovery Rate). Only genomic positions with an FDR < 0. 05 and a |Δβ| > 0. 2 (41) were selected. A DMP (Differentially Methylated Position) is associated with a gene when the DMP is inside the gene body or in its promoter region (from gene TSS to 1. 5kb upstream), according to Illumina annotations. The functional analysis was done in the same manner as for DEGs (see «Functional enrichment» paragraph).
Transcription factor characterization An analysis of differentially methylated regions (DMR) was done with the ChAMP package, using the DMRCate method, with parameters lambda=400 and C=2  (42). A DMR of interest was defined as a region containing at least 1 DMP located in TSS ([TSS + 1500bp; TSS]), 1 st Exon or 5'UTR region of DEGs; and having a FDR less than or equal to 0. 05. In order to identify transcription factor binding sites (TFBS) affected by a difference in methylation, the ReMap database was used  (43). A total of 84 cell lines was selected (Supplementary Table  1 ), containing immune and heart-related cells, and including 151 transcription factors (TF). First, the transcription factors specifically associated with DMRs were identified with the OLOGRAM tool  (44)  in a pairwise analysis, meaning we identify the individual TFs enriched with the DMRs.
Blood biomarkers search For this analysis, the cohorts were mixed and divided in two groups: a training group, containing 70% of the data and a validation group containing the remaining samples. Two cases were studied: the differentially methylated sites between controls and all the CCC (indicator of disease presence), and the differentially methylated sites between moderate and severe CCCs (indicator of disease severity). After DMP selection, 3 different machine learning (ML) models were tested with our dataset: a decision tree, a logistic regression, and a random forest. On one hand, for the tree-based models, the following options were fitted: number of estimators, maximal depth, maximal features and minimal samples per leaf. On the other hand, the penalty and the parameter of regularization C (a method which reduce the risk of overfitting by favoring the simplest models) were optimized in logistic regression.
For each case, the analysis was made in two steps: a low number of DMPs were selected according to the statistical test included in ChAMP package, and then different models of ML were applied on this data. The models were adjusted depending on the parameters mentioned above (number of DMP, number of estimators, maximal depth. ), and the set of modelparameters providing the better accuracy (TP (True Positive) + TN (True Negative) / All) was kept. If the accuracy was still low, the analysis was restarted and relaunched from the beginning, with a bigger number of DMPs (classified by FDR). All the ML analyses were done on Python3 using the python pandas (https://pandas. pydata. org/) and scikit-learn packages (http://citebay. com/how-to-cite/scikit-learn/). Cross validations were performed using the training group only.
Description of datasets Gene expression analysis was conducted on left ventricular free wall myocardial tissue from 8 severe CCC patients and 6 healthy organ donors (see workflow: Figure  1 ). Gene expression analysis was done by sequencing total RNA (paired end 2x150bp) after ribosomal RNAs depletion. For each sample, we obtained between 40 and 75 million sequencing reads. Reads were aligned to the human reference genome GRCh37/hg19 using STAR. The average mappable rate of the raw reads reached 90% (± 2%). Tissue sample DNA methylation analysis was conducted on the same samples as gene expression. This analysis was done using Illumina EPIC kit, allowing the study of 850. 000 methylation sites. After quality control, 721. 803 CpGs were retained for analysis. For blood DNA methylation analysis, the same tool as for tissue DNA methylation was used.
Gene expression profile To understand the pathogenic processes driving CCC, we have studied the genomic deregulation of patients at several scales. Gene expression data were obtained from 43533 transcripts. These include protein coding units (43. 03%); pseudogenes (20. 71%); long noncoding RNA (lincRNA) (11. 54%); antisense sequences (10. 12%); micro RNA (miRNA) (3. 64%); miscellaneous RNA (miscRNA) (3. 03%); small nuclear RNA (snRNA) (2. 45%); small nucleolar RNA (snoRNA) (1. 99%). sense intronic sequences (1. 54%); among others (Supplementary Table  2 ). A small fraction of these transcripts (1407/43533 (3. 23%)) were considered as differentially expressed (FDR ≤ 0. 05 and absolute log2FoldChange ≥ 1. 5) between the two groups of samples (Supplementary Table  3 ). Most of these differentially All rights reserved.
6% of the full dataset variance (Supplementary Figure  1B ), and to separate the CCC and control samples. A heatmap of the same DMP methylation levels with HCA (Figure  2B ) confirmed the clustering of the samples according to their phenotypes. The sex and the age of the patients seems to have no impact on this clustering (Supplementary Figure  2C-2D   3%). Knowing the impact of DNA methylation on gene regulation, and in particular the association between promoter undermethylation and gene overexpression  (46) , we retained these regions for further analysis. In this article, we will call "regulatory region" the region between the TSS and 1500bp among the TSS as well as 5'UTR and gene 1 st exon.