dataesr/scientific-paragraphs-categorization huggingface dataset:
Regulation of stem cell and lineage TFs involves the Pcenriched PcG-M and PcG-repressive states Because lineage decisions are driven by expression of key TFs, we assessed the chromatin states of TF-encoding genes using a list of 885 annotated TFs from the Gene List Annotation for Drosophila (GLAD) database. 54  TF-encoding genes were significantly enriched in the Pc-associated PcG state compared with the distribution of all genes in chromatin states in all cell types (Figure  4A ). Focusing on the TFs that underwent chromatin changes during differentiation, the main TFs known for their role in the regulation of ISCs (esg, wor, Sox100B, Ets21c, and klu) were found in active states in ISCs and/or EBs and were marked by the PcG or PcG-M state in ECs (Figure  4B ). This trend was also found with genes of the E(spl)-Complex (Figure  3H ).
Data and code availability d DamID data, RNA-seq data, and ATAC-seq data have been deposited at GEO and are publicly available, accession numbers are listed in the key resources table. Chromatin state tracks for each cell type are at https://github. com/gervlouis/ Josserand_et_al and are publicly available. DamID data for RNA Pol II, Brm, Pc, H1 and HP1 in ISC were part of a previous study of ours  32  and have been deposited at GEO. This study analyzes other existing, publicly available data. These accession numbers for the datasets are listed in the key resources table. Microscopy data reported in this paper will be shared by the lead contact upon request. d The code used in this study, derived from Delandre et al. 46  is at github. com/marshall-lab/universal_chromatin_hmm and is publicly available.
Two or three biological replicates were performed for each experiment. Following tissue dissociation, genomic DNA was extracted from the guts and digested with DpnI to cut GATC methylated sites. DNA was purified and ligated with PCR adaptors specific to methylated fragments, before digestion with DpnII and PCR amplification. Then, DNA samples were purified and sonicated to reduce the fragment size to approximately 300 bp, followed by Alw1 digestion in order to remove DamID adaptors and initial GATC sequences. Digested DNA was purified with magnetic Seramag beads (FisherScientific) and end-repaired, adenylated, and ligated with Illumina TruSeq adaptors. Samples were subsequently cleaned twice with Seramag beads and enriched by 6 cycles of PCR, and cleaned again with Seramag beads. Sequencing and data analysis Samples were sequenced by a HiSeq 2500 (rapid run) to generate 50 bp single-end reads.
The damidseq_pipeline script was used to align the NGS reads on the Drosophila reference genome and normalize them (log 2 ratio of Dam-fusion/Dam alone). 85  Correlations between the ratio files were calculated with the Pearson correlation coefficient. Replicates that correlated less than 0. 8 with other replicates were discarded, leading to two or three replicates per experiment, that were averaged for further processing. Significant peaks were obtained by processing the ratio files with find_peaks (https://github. com/owenjm/find_peaks). All analyses were performed in the Galaxy server  94  and the visualization of alignments was performed using the IGV software. 99  CATaDa analysis Chromatin accessibility profiling was performed using Dam-only reference sequences generated for Targeted DamID (3 replicates per cell type).
Fastq files were processed for CATaDa as previously described. 47  Reads were aligned to the Drosophila melanogaster reference genome version 6 and assigned to bins delimited by GATC sites using damidseq_pipeline_output_Dam-only_data. Peaks were called and mapped to genes, using Peak_Calling_for_CATaDa Perl program (FDR<0,01) and a consensus peak list was determined between each replicate. Scripts for CATaDa can be found at https://github. com/tonysouthall.
Chromatin state modeling The log 2 ratio files obtained from damidseq_pipeline for the five proteins in bedgraph format were averaged and scaled by the standard deviation as previously described. 11  HMM modeling was performed using Universal Chromatin HMM script version 0. 9. 10 (github. com/marshall-lab/universal_chromatin_hmm) derived from ChroMATIC. 46  HMM models were trained on chromosomes 2L and 2R via 2000 random initializations of 10 iterations, followed by a full model fitted from the best-fitting random initialization parameters (as assessed via loglikelihood) and Viterbi paths determined, using the RHMM R package (https://r-forge. rproject. org/projects/ rhmm/). Models with 5-40 states were fitted independently for each cell type, in order to allow the detection of chromatin state differences between cell types.
HMM states were clustered based on their transition probabilities, and the clustering dendrogram was cut into 8 clusters. Each cluster was then assigned a chromatin state based on the strongest Pearson correlation between the mean state emissions of the cluster and the predefined intensities for 7 chromatin states (unpublished data provided by OJM). Finally, simplified Viterbi paths for the assigned chromatin states were created from this clustering. Manual adjustments to chromatin state calls were performed to correct some group misassignments. The optimum number of HMM states was determined via the Akaike information criterion (AIC) and manual inspection of the clustering dendrogram. Heatmaps of HMM state emissions were generated via the ComplexHeatmap R package. 95  Calculations of genome coverage and intersections with other datasets were performed using BEDtools in Galaxy (https://usegalaxy.
eu/). The karyotype view in Figure  S3C  was generated using the R package karyoploteR (Rstudio version 2921. 09. 2). 96  Gene chromatin state analysis As per Marshall and Brand,  11  each gene was assigned a single chromatin state based on the prevalent state covering the gene length. Chromatin state transition plots were generated using SankeyMATIC (https://sankeymatic. com/). Cell-type specific RNA-seq data published in Dutta et al. can be found on (http://flygutseq. buchonlab. com/). On the graphs presented in Figure  S3 , null values of rpkm were excluded for easier visualization. The list of gene-encoding transcription factors comes from https://www. flyrnai. org/ tools/glad/web/. The list of EE-enriched genes was obtained as follows: gene ontology analysis enrichment was performed on a list of 827 EE-enriched genes (from  48  ), then only the genes associated with the enriched GO terms were kept (316 genes) and some-well known EE markers (from Guo et al.
Sequencing and data analysis Sequencing was carried out on the NovaSeq 6000 instrument from Illumina based on a 2*100 cycle mode (paired-end reads, 100 bases) to obtain around 44 million clusters (88 million raw paired-end reads) per sample. The Nextflow pipeline for RNA-seq data processing developed by the Curie Bioinformatics Core facility was used (https://doi. org/10. 5281/zenodo. 7443721). FastQ files were generated with bcl2Fastq, demultiplexed and trimmed to remove adaptors using the Raw-QC and RNA-seq analysis pipelines. Reads were aligned to the Drosophila reference genome (dm6) to generate raw counts tables. Differential expression analysis was carried out with DESeq2 88 in Galaxy. Genes were filtered for p-adjusted value<0. 05 to be considered as significantly deregulated genes.
Sequencing and data analysis Libraries were pooled and sequenced on the NovaSeq 6000 instrument from Illumina based on a 2*50 cycle mode (paired-end reads, 50 bases) using a SP flow cell in order to obtain around 67 million clusters (134 milion raw paired-end reads) per sample. Bioinfo-pfcurie/ATAC-seq: v1. 0. 2 pipeline for ATAC-seq data processing developed by the Curie Bioinformatics Core facility was used (https:// doi. org/10. 5281/zenodo. 7576558). Raw reads were trimmed for adapters using trimgalore and aligned to the Drosophila melanogaster reference genome (dm6) using bowtie2. 86  Duplicated reads were removed from the aligned results with Picard, and filtered to discard low mapping quality reads <20, aligned to mitochondrial chromosome reads as well as singleton reads. Only valid pairs were considered.
Imaging and quantifications Imaging was conducted using a Zeiss LSM900 confocal microscope and the ZEN software at the Curie Institute imaging facility (PICT). Images were acquired with a 40X oil objective. For stack images, z-step was set at 1 to 1. 5 mm. For each midgut, four stacks were taken corresponding to four different regions (R5, R4, R3, R2). Depending on the experiment, the number of GFP + , Pros + cells, LTK2 + cells, ECs, and total cells were quantified in each stack. Image analysis was performed using Fiji and QuPath 98 software followed by image assembly using Adobe Photoshop. Enterocytes (ECs) were identified and counted based on their nucleus size (nucleus diameter > 7mm). H1 and DAPI intensities in the nuclei of GFP + cells were quantified using QuPath. H1 intensity was then normalized to DAPI intensity.
ACKNOWLEDGMENTS We thank members of the  Bardin team ,  M. van den Beek ,  C. Maurange ,  P. -A. Defossez ,  M. Wassef , and  D. Bourc 'his for comments on the manuscript. We thank  A. Brand ,  A. I. Skoultchi ,  Bloomington, Vienna , and  NIG, Kyoto  stock centers for fly lines, and  J. Veenstra  for the LTK2 antibody. We thank  S. Baulande  and  V. Raynal  for help with high-throughput sequencing performed by the  ICGex NGS  platform of the  Institut Curie , supported by the grants  ANR-10-EQPX-03  ( Equipex ) and  ANR-10-INBS-09-08  (France Genomique Consortium) from the  Agence Nationale de la Recherche  ('' Investissements d'Avenir'' program ), by the  Canceropole Ilede-France , and by the  SiRIC-Curie program-SiRIC Grant  '' INCa-DGOS4654. '' Microscopy training and access has been provided by The  Cell and Tissue Imaging Facility /UMR3215 ( PICT-IBiSA ) of the  Institut Curie , members of the  French National Research Infrastructure France -Bio-Imaging,  ANR10-INBS-04.
O. J. M. was supported by  NHMRC  grants  APP1185220  and  APP1128784. Work in the Bardin lab is supported by  Fondation pour la Recherche  Medicale (A. J. B. ,  EQ202003010251 ),  Fondation ARC  (L. G. ,  PJA20191209548 ), the program '' Investissements d'Avenir' ' launched by the  French Government  and implemented by  ANR ,  ANR   SoMuSeq-STEM  (A. J. B. ,  ANR-16-CE13-0012-01 ),  ANR ChronoDamage  (A. J. B. ,  ANR-20-CE13-0013_01 ),  Labex DEEP  ( ANR11-LBX-0044 ), and  IDEX PSL  ( ANR-10-IDEX-0001-02 PSL ). Salary support for M. J. was from  Ministere de l'Enseignement Superieur  (doctoral grant) and  Fondation pour la Recherche  Medicale ( FDT202106012783 ); for A. J. B. and L. G. salary support was from the  CNRS.
Key message The TreeTrace_spruce database contains images and measurements of 100 Norway spruce (Picea abies (L. ) H. Karst. ) logs from Northeastern France, each about 4. 5 m long. The image database includes RGB images of large and small ends of the logs and hyperspectral and computed tomography (CT) images of wood discs sampled at both log ends. The 100 logs were also fully X-ray scanned with a CT device for roundwoods and their top surface was scanned with a terrestrial LiDAR device. The measurements performed on discs include wood local density, growth ring widths and pith location. This database is complementary to another one (TreeTrace_Douglas) resulting from the same ANR project TreeTrace, but if the objectives are similar, the protocols and conditions of acquisition are not the same for these two databases.
in the project. In France, they represent the largest standing volumes and the highest annual growth, as well as the largest volumes produced by sawmills  (FCBA 2022). In Austria and Germany, too, spruce is the most important wood species  (BFW 2022)  and Douglas fir is one of the promising climate change species in both countries  (BFW 2008). This article and the corresponding database are complementary to the article already published on the Douglas fir part of the project  (Longuetaud et al. 2022a ) and the associated database TreeTrace_Douglas fir (https:// doi. org/ 10. 15454/ YUNEGL). The general objectives described below are similar but the protocols, data types and acquisition conditions differ between the two databases and it was important to distinguish the two samplings. The main differences between the two databases are as follows: • TreeTrace_Douglas: The database was collected in an industrial context with RGB images taken on the log yard of a sawmill and then on the sawing line with an industrial camera installed for the project.
Moreover, as this was a strong originality of this database, boards were collected and sent to the LaBoMaP laboratory (Arts et Métiers Sciences et Technologies, Cluny) to be analysed in terms of mechanical quality with the final objective of linking the quality information obtained on the logs and then on the boards; • TreeTrace_spruce: The database was collected at the felling site and then at the FVA (Forest Research Institute Baden-Württemberg, Freiburg, Germany), with acquisition of terrestrial LiDAR images of the surface of the logs and CT images on the whole length of the 4. 5 m logs that were scanned through the Microtec X-ray scanner at FVA. This point constitutes a strong originality of this database with the possibility of linking the external observations with the internal structure of the logs.
In addition, hyperspectral images are also available for this species. In the context of climate change and strong pressure on resources, the optimisation of the forest and wood sectors is a challenge. In order to make the best use of wood resources and ensure supplies to industries in terms of both quality and quantity, it is necessary to be able to describe the quality of the resource as early as possible in the processing chain and to develop the traceability of this resource. Moreover, in the context of illegal logging, there is an increasing need to trace logs. The data collected from the spruce sample of the TreeTrace project can be used for several purposes: • Wood traceability along the forest wood chain, from the forest to the sawmill, by image processing of cross-sections only, potentially based on techniques such as those used for fingerprint recognition.
• Assessment of the wood quality based on crosssection analysis mainly, applicable in the forest, on a log sorting platform or at the sawmill. The information available on a cross-section (geometric centre, pith location, juvenile wood area, number and width of the annual growth rings) is complementary to what could be obtained from the analysis of the external log shape by laser sensors installed at the entrance of the sawing line (in practice, only tapering information is generally used). These application cases share many aspects. A common dataset for experimental validation can be used, ground-truth data established by annotating images can be shared and many software components implementing preprocessing (e. g. pith detection, cross-section segmentation, contrast optimization) as well as feature extraction techniques (e.
2021a). A second application is to estimate the pith location. Norell and Borgefors (2008)  have estimated the pith location in order to further detect tree rings  (Norell 2011). Schraml and Uhl (2013)  and  Kurdthongmee et al. (2018)  proposed algorithms for pith detection on digital images of untreated wood cross-sections. For  Schraml and Uhl (2013) , the objective was to retrieve cross-section characteristics in order to trace log ends. Recently, Deep Neural Networks (DNNs) were trained in order to address the problem of the pith detection in cross-section images of logs  (Kurdthongmee 2020). The work already carried out with the TreeTrace_ spruce database is listed in Section 5.
It should be noted that the bark of the logs was relatively damaged by the harvester and transportation. Point clouds registration was performed using Faro Scene 5. 4 software taking position 4 as reference. In the end, five merged point clouds, one for each set of 20 logs, are available on the data repository in TreeTrace_ spruce/log_images/tlidar. The cloud files are in text format with XYZ coordinates (in meters, Z corresponding to the altitude) and a reflectance value (given as an RGB triplet). The log_order_and_position. txt file gives the position of the logs in each cloud and.
3nm. The second scanner was a Specim FX17 which provides scans between 990 and 1665nm also with a bandwidth of approx. 3nm. Therefore, the Specim FX10 uses mainly the visible light (VIS) spectra and the Specim FX17 uses mainly parts of the NIR spectra. For the scanning setup, a resolution of 640 × 640 pixel was chosen. Each wood disc was scanned with both cameras. For this purpose, each disc was pushed through the system by hand and the speed was synchronized with a trigger. The hyperspectral data, i. e. the translation from line scanning data to a hyperspectral cube, was performed by the acquisition software Perception Studio. After multispectral scanning with the first camera, the same side was captured using a Canon 70D, 35mm lens. At least four images with different rotations were taken of each disc.
After hyperspectral scanning and RGB imaging the rough discs were transported to Kuchl, Austria, where each disc was sanded with a mesh width of 120 to reduce the influence of manual chainsaw cuts at scanning. This Fig. 8  Installation of a log for CT scanning Fig. 9  sensor system shows the line scanner Specim FX10 which was mounted on a metal frame and the slice is moved perpendicular to the scanned line manually. The stored samples were closed airtight to avoid surface changes. Each hyperspectral cube was cropped to reduce the massive amount of data mesh width was chosen as it gave good results for the wet samples. In Kuchl RGB images were taken using the same setup as in Munich-at least six images with different rotations were taken. In total, 2077 images (4320 × 3240 pixels) taken with the Canon camera at Munich and Kuchl were collected (Table  1 ).
The images are available on the data repository in TreeTrace_spruce/log_end_images/RGB/ Munich_Canon. The hyperspectral data (captured with the FX10 or FX17 camera) from each disc were stored in a special format (HSD -hyperspectral data) utilized by Perception Studio. A HSD file contains all spectral bands of a disc in kind of a hyperspectral cube. It was necessary to convert the data to the ENVI format for which open source image processing libraries are available. The ENVI files for the 200 discs are provided in the repository under the following path: TreeTrace_spruce/ log_end_images/hyperspectral.
Notes on the hyperspectral data Figure  10  provides exemplary images for one wood disc (#E001B). In the first and second row RGB images taken from the raw and sanded disc surface are shown. In the subsequent rows, for each hyperspectral camera, images for selected bands are illustrated for the same disc, raw and sanded. It can be recognized that the width of the wood disc for the FX10 and FX17 images differs. We assume that this is caused by the acquisition software and an inaccurate trigger synchronization. Furthermore, a set of cubes got lost during file storage and are thus missing in the repository. Table  2  shows an overview of the number of captured cubes for each hyperspectral camera. For the FX10 and the raw discs all cubes are available.
Measurements on wood discs Finally, the 200 discs were transported to INRAE Nancy, France, in March 2019, for further analyses. Ring widths were measured on four orthogonal radii and are provided in the disc-radius-ring-level data file (TreeTrace_spruce/tables/discs_radii_ rings. txt). From these values, the average profile of annual ring widths of a disc was obtained by subtracting the successive quadratic means of the four radii (i. e. root mean square of external radii of a given ring minus root mean square of internal radii of the same ring). The corresponding disc-ring-level data file (TreeTrace_spruce/tables/discs_rings. txt) provides cambial age (cambialAge), growth year (growthYear), distance to pith (externalRadius_mm) and ring width (ringWidth_mm). Then, the discs were scanned in air-dried state with the medical CT scanner described in  Freyburger et al.
txt). The radial profile of ring widths was coupled with the radial profile of density to assess the mean density of each annual growth ring (airDryDensity_kgpm3). The variable was added to the disc-ring-level data file. The method is described in  Ravoajanahary et al. (2022). These disc-ring-level data were used to compute the data table at the disc level (TreeTrace_spruce/ tables/discs_measurements. txt). In this table, the average density of a disc (airDryDensity_kgpm3) is the average of all pixels corresponding to wood (excluding bark) in the calibrated CT image. The number of these pixels was used to compute the under-bark area of the discs (areaUBScanner_cm2). The quadratic mean of the four radii on which ring widths were measured (meanRadiusUBOptical_mm) is also reported. The height in the stem of each disc (estimatedHeight_m) was estimated based on the number of rings as explained in Section 2.
Access to the data and metadata description The images and the database  (Longuetaud et al. 2022b ) are available at Recherche Data Gouv repository: https:// doi. org/ 10. 57745/ WKLTJI. Associated metadata access is at https:// metad ata-afs. nancy. inra. fr/ geone twork/ srv/ fre/ catal og. searc h#/ metad ata/ cffee 2f1-18e1-4b53-9f5b-6cc4c 66f1c b8. The arborescence of the TreeTrace_spruce directory was detailed in the previous section and is summarized in Fig. 11. The main numeric data are in the TreeTrace_ spruce/tables directory. There are five tables, all in the form of an array with variables in columns and a first line indicating the variable names. Each data table is accompanied with a variable table (with the same name followed by _variable) describing each variable. The data tables are the following with the different levels of measurements:.
Reuse potential and limits All data tables are in plain text format with tabulations as separator. They can be imported easily with R or with any spreadsheet software. Sometimes information is missing for some variables, which is translated in "NA" value in the tables. The RGB images are in standard JPEG or PNG formats. A scale included in each image allows to perform spatial calibration. For a subset of RGB images (one image per log taken at the large end and per site and device), the black and white scale was manually measured in the images and the image resolution is provided in a Table. The CT images of the discs (in the TreeTrace_ spruce/log_end_images/xray directory) and logs (in the TreeTrace_spruce/log_images/xray directory) can be read with ImageJ software (https:// imagej. nih. gov/ ij/).
The registered T-LiDAR clouds (in the TreeTrace_ spruce/log_images/tlidar directory) can be visualised with the 3D point cloud processing software CloudCompare (https:// www. danie lgm. net/ cc/). The hyperspectral images (in the TreeTrace_ spruce/log_end_images/hyperspectral directory) require Matlab to be read. The image database TreeTrace_spruce, providing both raw images and ground truth, can be used to develop and to validate image analysis algorithms for detecting various features (e. g. pith location, cross-sectional area, annual ring widths) on untreated (i. e. freshly cut) crosssections of logs. The main objectives for the further development of algorithms are (1) to trace the logs from the forest to the sawmill and/or (2) to estimate the wood quality directly in the forest, on the log yard or on the sawmill line before sawing.