dataesr/scientific-paragraphs-categorization huggingface dataset:
Transcriptome Analysis The RNAs were extracted using the RNeasy Mini Kit (Qiagen) following the manufacturer's protocol and qualified using the Agilent 2100 Bioanalyzer platform (Agilent Technologies). Qualified RNAs were sent to the MGX platform in Montpellier for RNASeq analysis. Briefly, libraries were produced using a TruSeq Stranded mRNA Sample Preparation Kit (Illumina, San Diego, CA, USA) and paired-end sequenced 150nt on NovaSeq sequencer (Illumina). The RNAseq data (fastq files) have been deposited in the Sequence Read Archive database under the accession number PRJNA781814. Raw data were collected and processed using the bioinformatic strategy of genome-guided alignment  [21]. The mapping tool used was Hisat2, the counting tool used was FeatureCounts and the tools used for the determination of differential expression were DESeq2.
Statistical Analysis Statistics of qualitative and quantitative data were performed using GraphPad Prism 6 software (San Diego, CA, USA). All experiments were performed at least 3 times and histograms represent the mean ± SEM. Statistical significance was evaluated by Kruskal-Wallis, Mann-Whitney, Fisher exact test (* p < 0. 05; ** p < 0. 01; *** p < 0. 001).
Conceptualization, L. K. -T. and P. -O. G. ; data curation, G. T. , K. M. and P. -O. G. ; formal analysis, G. T. , P. -O. G. and L. K. -T. ; funding acquisition, G. T. and L. K. -T. ; investigation, G. T. , K. M. , P. R. , A. D. and E. G. ; resources: S. M. and M. W. ; supervision, L. K. -T. and P. -O. G. ; visualization and writing, G. T. ; writing-review and editing, K. M. , L. K. -T. and P. -O. G. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Ligue contre le Cancer of Grand Ouest from the comity of Vienne and Deux-Sèvres departments. MGX acknowledges financial support from France Génomique National infrastructure, funded as part of "Investissement d'Avenir" program managed by Agence Nationale pour la Recherche (contract ANR-10-INBS-09).
Author Contributions: Conceptualization, L. K. -T. and P. -O. G. ; data curation, G. T. , K. M. and P. -O. G. ; formal analysis, G. T. , P. -O. G. and L. K. -T. ; funding acquisition, G. T. and L. K. -T. ; investigation, G. T. , K. M. , P. R. , A. D. and E. G. ; resources: S. M. and M. W. ; supervision, L. K. -T. and P. -O. G. ; visualization and writing, G. T. ; writing-review and editing, K. M. , L. K. -T. and P. -O. G. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Ligue contre le Cancer of Grand Ouest from the comity of Vienne and Deux-Sèvres departments. MGX acknowledges financial support from France Génomique National infrastructure, funded as part of "Investissement d'Avenir" program managed by Agence Nationale pour la Recherche (contract ANR-10-INBS-09).
The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities. From this first experience, a few avenues for improvements have been identified regarding data size, head diversity, and label reliability. To address these issues, the 2020 dataset has been reexamined, relabeled, and complemented by adding 1722 images from 5 additional countries, allowing for 81,553 additional wheat heads. We now release in 2021 a new version of the Global Wheat Head Detection dataset, which is bigger, more diverse, and less noisy than the GWHD_2020 version.
Introduction Quality training data is essential for the deployment of deep learning (DL) techniques to get a general model that can scale on all the possible cases. Increasing dataset size, diversity, and quality is expected to be more efficient than increasing network complexity and depth  [1]. Datasets like ImageNet  [2]  for classification or MS COCO  [3]  for instance detection are crucial for researchers to develop and rigorously benchmark new DL methods. Similarly, the importance of getting plant-or crop-specific datasets is recognized within the plant phenotyping community (  [4] [5] [6] [7] [8] [9] [10] , p. 2,  [11] [12] [13] ). These datasets allow benchmarking the algorithm performances used to estimate phenotyping traits while encouraging computer vision experts to further improvement (  [10] , p.
2,  [14] [15] [16] [17] ). The emergence of affordable RGB cameras and platforms, including UAVs and smartphones, makes in-field image acquisition easily accessible. These high-throughput methods are progressively replacing manual measurement of important traits such as wheat head density. Wheat is a crop grown worldwide, and the number of heads per unit area is one of the main components of yield potential. Creating a robust deep learning model performing over all the situations requires a dataset of images covering a wide range of genotypes, sowing density and pattern, plant state and stage, and acquisition conditions. To answer this need for a large and diverse wheat head dataset with consistent and quality labeling, we developed in 2020 the Global Wheat Head Detection (GWHD_2020)  [18]  that was used to benchmark methods proposed in the computer vision community and recommend best practices to acquire images and keep track of the metadata.
The GWHD_2020 dataset results from the harmonization of several datasets coming from nine different institutions across seven countries and three continents. There are already 27 publications  (accessed July 2021) that have reported their wheat head detection model using the GWHD_2020 dataset as the standard for training/testing data. A "Global Wheat Detection" competition hosted by Kaggle was also organized, attracting 2245 teams across the world  [14] , leading to improvements in wheat head detection models  [23, 25, 31, 41]. However, issues with the GWHD_2020 dataset were detected during the competition, including labeling noise and an unbalanced test dataset. To provide a better benchmark dataset for the community, the GWHD_2021 dataset was organized with the following improvements: (1) the GWHD_2020 dataset was checked again to eliminate few poor-quality images, (2) images were re-labeled to avoid consistency issues, (3) a wider range of developmental stages from the GWHD_ 2020 sites was included, and (4) datasets from 5 new countries (the USA, Mexico, Republic of Sudan, Norway, and Belgium) were added.
Materials and Methods The first version of GWHD_2020, used for the Kaggle competition, was divided into several subdatasets. Each subdataset represented all images from one location, acquired with one sensor while mixing several stages. However, wheat head detection models may be sensitive to the developmental stage and acquisition conditions: at the beginning of head emergence, a part of the head is barely visible because it is still not fully out from the last leaf sheath and possibly masked by the awns. Further, during ripening, wheat heads tend to bend and overlap, leading to more erratic labeling. A redefinition of the subdataset was hence necessary to help investigate the effect of the developmental stage on model performances. The new definition of a subdataset was then formulated as "a consistent set of images acquired over the same experimental unit, during the same acquisition session with the same vector and sensor.
" A subdataset defines therefore a domain. This new definition forced to split the original GWHD_2020 subdatasets into several smaller ones. The UQ_1 was split into 6 much smaller subdatasets, Arvalis_1 was split into 3 subdatasets, Arvalis_3 into 2 subdatasets, and utokyo_1 into 2 subdatasets. However, in the case of utokyo_2 which was a collection of images taken by farmers at different stages and in different fields, the original subdataset was kept. Overall, the 11 original subdatasets in GWHD_2020 were distributed into 19 subdatasets for GWHD_2021. Almost 2000 new images were added to GWHD_2020, constituting a major improvement. A part of the new images comes from the institutions already contributing to GWHD_2020 and was collected during a different year and/or at a different location.
This was the case for Arvalis (Arvalis_7 to Arvalis_12), University of Queensland (UQ_7 to UQ_11), Nanjing Agricultural University (NAU_2 and NAU_3), and University of Tokyo (Utokyo_1). In addition, 14 new subdatasets were included, coming from 5 new countries: Norway (NMBU), Belgium (Université of Liège  [46] ), United States of America (Kansas State University  [47] , TERRA-REF  [7] ), Mexico (CIMMYT), and Republic of Sudan (Agricultural Research Council). All these images were acquired at a ground sampling distance between 0. 2 2 Plant Phenomics and 0. 4 mm, i. e. , similar to that of the images in GWHD_ 2020. Because none of them was already labeled, a sample was selected by taking no more than one image per microplot, which was randomly cropped to 1024 × 1024 px patches that will be called images in the following for the sake of simplicity.
With the addition of 1722 images and 86,000 wheat heads, the GWHD_2021 dataset contains 6500 images and 275,000 wheat heads. The increase in the number of subdatasets from 18 to 47 leads to a larger diversity between them which can be observed on Figure  1. The subdatasets are described in Table  1. However, the new definition of a subdataset led also to more unbalanced subdatasets: the smallest (Arvalis_8) contains only 20 images, while the biggest (ETHZ_1) contains 747 images. This provides the opportunity to possibly take advantage of the data distribution to improve model training. Each subdataset has been visually assigned to several development stage classes depending on the respective color of leaves and heads (Figure  2 ): postflowering, filling, filling-ripening, and ripening. Examples of the different stages are presented in Figure  2.
Dataset Diversity Analysis In comparison to GWHD_2020, the GWHD_2021 dataset puts emphasis on metadata documentation of the different subdatasets, as described in the discussion section of David et al. [18]. Alongside the acquisition platform, each subdataset has been reviewed and a development stage was assigned to each, except for Utokyo_3 (formerly utokyo_2) as it is a collection of images from various farmer fields and development stages. Globally, the GWHD_2021 dataset covers well all development stages ranging from postanthesis to ripening (Figure  2 ). The diversity between images within the GWHD_2021 dataset was documented using the method proposed by Tolias et al. [48]. The deep learning image features were first extracted from the VGG-16 deep network pretrained on the ImageNet dataset that is considered representing well the general features of RGB images.
We then selected the last layer which has a size of 14 × 14 × 512 and summed it into a unique vector of 512 channels, which is then normalized. Then, the UMAP dimentionality reduction algorithm  [49]  was used to project representations into a 2D space. The UMAP algorithm is used to keep the existing clusters during the projection to a low-dimension space. This 2D space is expected to capture the main features of the images. Results (Figure  3 ) demonstrate that the test dataset used for GWHD_2020 was biased in comparison to the training dataset. The subdatasets added in 2021 populate more evenly the 2D space which is expected to improve the robustness of the models.
Presentation of Global Wheat Challenge 2021 (GWC 2021) The results from the Kaggle challenge based on GWHD_ 2020 have been analyzed by the authors  [14]. The findings emphasize that the design of a competition is critical to enable solutions that improve the robustness of the wheat head detection models. The Kaggle competition was based on a metric that was averaged across all test images, without  4 Plant Phenomics distinction for the subdatasets, and it was biased toward a strict match of the labelling. This artificially enhances the influence on the global score of the largest datasets such as utokyo_1 (now split into Utokyo_1 and Utokyo_2). Further, the metrics used to score the agreement with the labeled heads and largely used for big datasets, such as MS COCO, appear to be less efficient when some heads are labeled in a more uncertain way as it was the case in several situations depending on the development stage, illumination conditions, and head density.
As a result, the weighted domain    5 Plant Phenomics accuracy is proposed as a new metric  [14]. The accuracy computed over image i belonging to domain d, AI d ðiÞ, is classically defined as AI d i ð Þ = TP TP + FN + FP , ð1Þ where TP, FN, and FP are, respectively, the number of true positive, false negative, and false positive found in image i. The weighted domain accuracy (WDA) is the weighted average of all domain accuracies: WDA = 1 D 〠 D d=1 1 n d * 〠 n d i=1 AI di , ð2Þ where D is the number of domains (subdatasets) and n d is the number of images in domain d. The training, validation, and test datasets used are presented in Section 5. The results of the Global Wheat Challenge 2021 are summarized in Table  2. The reference method is a faster-RCN with the same parameters than in the research paper GWHD_2020  [18]  and trained on the GWHD_2021 (Global Wheat Challenge 2021 split) training dataset.
How to Use/FAQ (i) How to download? The dataset can be download on Zenodo: https://zenodo. org/record/5092309 (ii) What is the license of the dataset? The dataset is under the MIT license, allowing for reuse without restriction (iii) How to cite the dataset? The present paper can be cited when using the GWHD_2021 dataset. However, cite preferentially  [18]  for wheat head detection challenges or when discussing the difficulty to constitute a large datasets (iv) How to benchmark? Depending on the objectives of the study, we recommend two sets of training, validation, and test (Table  3 ): (a) The Global Wheat Challenge 2021 split when the dataset is used for phenotyping purpose, to allow direct comparison with the winning solutions (b) The "GlobalWheat-WILDS" split is the one used for the WILDS paper  [50].
Conclusion The second edition of the Global Wheat Head Detection, GWHD_2021, alongside the organization of a second Global Wheat Challenge is an important step for illustrating the usefulness of open and shared data across organizations to further improve high-throughput phenotyping methods. In comparison to the GWHD_2020 dataset, it represents five new countries, 22 new subdatasets, 1200 new images, and 120,000 new-labeled wheat heads. Its revised organization and additional diversity are more representative of the type of images researchers and agronomists can acquire across the world. The revised metrics used to evaluate the models during the Global Wheat Challenge 2021 can help researchers to benchmark one-class localization models on a large range of acquisition conditions. GWHD_2021 is expected to accelerate the building of robust solutions.
However, progress on the representation of developing countries is still expected and we are open to new contributions from South America, Africa, and South Asia. We started to include nadir view photos from smartphones, to get a more comprehensive dataset and train reliable models for such affordable devices. Additional works are required to adapt such an approach to other vectors such as a camera mounted on unmanned aerial vehicle, or other high-resolution cameras working in other spectral domains. Further, it is planned to release wheat head masks alongside the bounding box given the very large number of boxes that already exists and provides more associated metadata. Ethz_1, Rres_1, Inrae_1, Arvalis (all), NMBU (all), ULiège-GxABT (all) UQ_1 to UQ_6, Utokyo (all), NAU_1, Usask_1 UQ_7 to UQ_12, Ukyoto_1, NAU_2 and NAU_3, ARC_1, CIMMYT (all), KSU (all), Terraref (all) GlobalWheat-WILDS Ethz_1, Rres_1, Inrae_1, Arvalis (all), NMBU (all), ULiège-GxABT (all) UQ (all), Utokyo (all), Ukyoto_1, NAU (all) CIMMYT (all), KSU (all); Terraref (all), Usask_1, ARC_1 7 Plant Phenomics.
6515 275187 * VLB: Villiers le Bâcle; VSC: Villers-Saint-Christophe. * * Utokyo_1 and Utokyo_2 were taken at the same location with different sensors. * * * Utokyo_3 is a special subdataset made from images coming from a large variety of farmers in Hokaido between 2016 and 2019. Italic: Europe: bold: North America; underline: Asia; bold italic: Oceania; bold underline: Africa.
Acknowledgments We would like to thank the company "Human in the loop", which corrected and labeled the new datasets. The help of  Frederic Venault (INRAe Avignon ) was also precious to check the labelled images. The work received support from  ANRT  for the  CIFRE grant of Etienne David , cofunded by  Arvalis  for the project management. The labelling work was supported by several companies and projects, including Canada: The  Global Institute Food Security, University of Saskatchewan  which supported the organization of the competition. France: This work was supported by the  French National Research Agency  under the Investments for the Future Program, referred as  ANR-16-CONV-0004   PIA #Digitag. Institut Convergences Agriculture Numérique, Hiphen  supported the organization of the competition.
Immunofluorescence DIvA cells were plated on glass coverslips and fixed with 4% paraformaldehyde during 15 min at room temperature, permeabilized with 0. 5% Triton X-100 in PBS for 10 min then blocked with 3% BSA in PBS for 30min. Cells were then incubated with the primary antibody (referenced in Extended Data Table  1 ) diluted in PBS-BSA overnight at 4°C, washed with 1X PBS and incubated with the appropriate anti-mouse or anti-rabbit secondary antibodies (conjugated to Alexa 594 or Alexa 488, Invitrogen), diluted 1:1000 in PBS-BSA, for 1h at room temperature, followed by DAPI staining. Coverslips were mounted in Citifluor (Citifluor, AF-1). Image acquisition was performed with MetaMorph on a widefield microscope (Leica, DM6000) equipped with a camera (DR-328G-C01-SIL-505, ANDOR Technology) using 40x or 100x objectives.
Membranes were blocked in TBS containing 0. 1% Tween 20 (Sigma, P1379) and 3% nonfat dry milk for 1h followed by an overnight incubation at 4°C with primary antibodies (referenced in Extended Data Table  1 ). The appropriate horseradish peroxidase-coupled secondary antibodies were used to reveal the proteins (anti-mouse at 1:10,000 (Sigma, A2554) and anti-rabbit at 1: 10,000 (Sigma, A0545)) using a luminol-based enhanced chemiluminescence HRP substrate (Super Signal West Dura Extended Duration Substrate, Thermo Scientific). Pictures of the membranes were acquired with the ChemiDoc™ Touch Imaging System and were visualized using Image Lab Touch software. Uncropped blots are presented in Supplementary Information Figure  1.
Biotin-ligated DNA was precipitated with streptavidin-coupled magnetic beads (included in the kit). Hi-C library was prepared on beads using the NEBNext® Ultra™ II DNA Library Prep Kit for Illumina and NEBNext® Multiplex Oligos for Illumina (New England Biolabs) following instructions from the Arima Hi-C kit. The final libraries were subjected to 75 bp paired-end sequencing on a Nextseq500 platform at the EMBL Genomics core facility (Heidelberg, Germany). Hi-C reads were mapped to hg19 and processed with Juicer using default settings (https://github. com/ aidenlab/juicer). Matrix-balanced Hi-C count matrices were generated at multiple resolutions: 250kb, 100kb, 50kb, 25kb, 10kb and 5kb and visualized on Juicebox and on Hi-Glass.
Libraries were purified with the QIAquick PCR Purification Kit (Qiagen), pooled and subjected to 75bp single-end sequencing on a Nextseq500 platform at the I2BC Next Generation Sequencing Core Facility (Gif-sur-Yvette, France). Each sample was then demultiplexed using a specific python script from the FourCSeq R package  47  thus assigning each read to a specific viewpoint based to its primer sequence into separate fastQ files. bwa mem was then used for mapping and samtools for sorting and indexing. A custom R script (https://github. com/bbcf/ bbcfutils/blob/master/R/smoothData. R)  48  was used to build the coverage file in bedGraph format, to normalize using the average coverage and to exclude the nearest region from each viewpoint (viewpoint-containing restriction fragment and the two adjacent restriction fragments).
Input exp × reads ctrl where Input ctrl is the total number of reads mapped in ES Input (Mouse) and Input exp the total number of reads in DIvA Input. reads ctrl and reads exp were respectively the number of reads from immunoprecipitated samples mapped on the mm10 genome and hg19 genome. For calibrated SCC1 ChIP-qPCR, the immunoprecipitated samples from DIvA cells were normalized by the signal of the immunoprecipitated sample from ES cells on a mouse cohesin positive site (using primers provided in Extended Data Table  2 ). Data were analyzed using the Bio-Rad CFX manager software. For the ChIP-chip experiments, the immunoprecipitated samples of γH2AX, pSMC1 S966, pSMC3 S1083 and input samples were amplified as in  10  , labeled, and hybridized on Affymetrix tiling arrays covering human chromosomes 1 and 6 (at the Genotoul GeTbiopuces facility, Toulouse, France)).
Scanned array data were normalized using Tiling Affymetrix Software (TAS) (quantile normalization, scale set to 500) and analyzed as described in  10, 12  and converted to. wig files using R/Bioconductor software, when necessary, for visualization using the Integrated Genome Browser (bioviz. org). For the ChIP experiment in yeast, individual colonies of yFZ014 and yFZ016 were grown in YEP + 3% lactic acid (YEP-Lac) until log phase growth with a final cell concentration between 5 × 106 cells/ml and 8 × 106 cells/ml. Degradation of Pds5::9myc-AID in yFZ016 was induced by addition of auxin (indole-3-acetic acid (IAA) Sigma Aldrich # I3750) at a final concentration of 1 mM and confirmed by Western blotting. For chromatin immunoprecipitation, 45 ml of culture was fixed and cross-linked with 1% formaldehyde for 10 min, after which 2.
Hi-C, 4C-seq and ChIP-seq analyses Hi-C heatmaps-Hi-C heatmaps screenshots were generated using Juicebox stand-alone program (https://github. com/aidenlab/Juicebox/wiki/Download). To build the average heatmaps, sub-matrix for cis interactions around DSBs were extracted using Juicer, both for observed matrix and observed over expected matrix. Log2(ratio) after vs before DSB were computed, using both Hi-C replicates, and averaged for each bin of the final matrix. Insulation score and TAD calling-Insulation score was computed using Hi-C matrices at 50 kb resolution with matrix2insulation. pl (https://github. com/dekkerlab/cranenature-2015). As parameters, we used is=800000 and ids=100000. TADs were called using Hi-C matrices at 50 kb resolution with TopDom R package and window size parameter of 10 (https://github.
com/HenrikBengtsson/TopDom). In order to filter out very weak TAD borders (corresponding to sub-TAD borders), we filtered TAD borders with an insulation score below a threshold of -0. 05. For Extended Data Fig. 2d , 80 TADs were also randomly selected from TopDom output, which didn't contain any of the best 80 cleaved DSBs, to be used as controls. Loops anchors and APA-Loops were called using Juicer Tools HiCCUPS program at 10 kb and 25 kb resolutions (https://github. com/aidenlab/juicer/wiki/HiCCUPS). Aggregate Peak Analysis (APA) was done using Juicer Tools APA program at 10 kb resolution (https:// github. com/aidenlab/juicer/wiki/APA). 525 loops were retrieved between the 174 best cleaved DSBs and nearby loop anchors (<1Mb) for replicate 1 (Fig. 2c ), and 552 for replicate 2 (Extended Data Fig.