dataesr/scientific-paragraphs-categorization huggingface dataset:
Next, we compared the genes identified in differential regions using Enrichr webtool, which identify the protein-protein interactions based on previously described experimental datasets. The analysis revealed that the targets of jumonji AT-rich interactive domain 2 (JARID2) and estrogen receptor 1 (ESR1) were significantly enriched in our differential peaks  (Fig 2E). For example, ESR1 has 43 targets in our differential peaks (adj. P-value = 1. 51 × 10 -03 ), out of the 871 targets identified in the protein-protein interactions database. Among the targets of ESR1 that are found in the vicinity of differential peaks, there are genes involved in chromosome organization such as H2B clustered histone 5 (H2BC5), lysine demethylase 5A (KDM5A), X-ray repair cross complementing 5 (XRCC5), or the DNA repair gene BRCA1 (Table  S3 ).
0 × 10 -04 ). Second, we chose the gene promoters located in our differential peaks containing the ESR1-binding motif. Third, we designed ChIP-qPCR primers as described in the Materials and Methods section. For example, Find Individual Motif Occurrence analysis predicted an ESR1 binding site within the altered H3K4me3 peak located in the vicinity of lysine methyltransferase 2E (KMT2E) (Fig  3C ). We performed ChIP-qPCR analysis using Ke-37 cells, and we measured 1. 6 times decrease in ESR1 binding at the KMT2E promoter. Besides KMT2E, ESR1 binding has decreased in ETS protooncogene 1, transcription factor (ETS1), signal transducer and activator of transcription 2 (STAT2), and E3 ubiquitin protein ligase RAD18 genes by 1. 60 and 1. 41 and 1. 42 times, respectively (Fig  3D ). Our immunofluorescence data showed that CD exposure leads to a global decrease in H3K9me3 levels in the nucleus (Fig  1 ).
To determine whether telomere length is altered in CD-exposed newborns, we analyzed the average telomere copies ratio compared with a singlecopy gene (T/S) in genomic DNA extracted from umbilical cord blood using qPCR as described in the Materials and Methods section. We found that T/S is significantly increased by 1. 7 times in exposed samples compared with controls (Fig  4B ). To reveal the potential candidate genes involved in telomere length changes, we extracted all the telomere maintenance function target genes from AMIGO database. 145 genes are relevant to telomere maintenance function (GO:0000723). We found that 15 genes in our datasets have altered H3K4me3 occupancy including ATM serine/threonine kinase (ATM), protection of telomeres 1 (POT1), cAMP-dependent protein kinase inhibitor beta (PKIB), DNA primase subunit 1 (PRIM1), DNA primase subunit 2 (PRIM2), replication factor C subunit 3 (RFC3), XRCC5, BRCA2 DNA repair-associated (BRCA2) and 59-39 exoribonuclease 1 (XRN1) genes (Fig  4C ).
LINE-1 activation in CD-exposed newborns Because we observed a decrease in H3K9me3 amounts, we asked whether retroelements activity could be induced in CD-exposed cells. To determine whether methylation of L1 promoters could be altered by CD exposure, we performed qPCR on HpaII enzymatically digested and undigested genomic DNA from umbilical cord blood. For the analysis of L1 promoter methylation, we chose previously published sequence of L1. 12 element (accession number U93565), the sequence of the promoter we studied (354 bp) is presented in Fig 5A. Blast analysis of this promoter compared with the human reference genome showed that this sequence is present 116 times (alignment length ≥ 350 bp, % of identity >95%, maximum five mismatches). Out of the three CpGs tested, we observed that methylation of CpG 3 was significantly decreased by 21.
3% in CD-exposed samples compared with control, *P < 0. 05 (Fig 5A ), suggesting that CD exposure could lead to decreased methylation at least at some targets. Next, we asked whether DNA methylation at CpG is associated with changes in H3K4me3 in L1s of CD-exposed newborns. To this end, we extracted coordinates of full-length L1s using L1Base (v2), a database containing putatively active LINE-1 (http://l1base. charite. de/index. php). We remapped the sequencing reads of H3K4me3 to the reference human genome without filtering multimapped reads (i. e. , reads mapping to multiple positions on the genome). We detected 186 L1s having H3K4me3 peaks out of 13,671 L1s. Of these 186 L1 elements, 90 have altered H3K4me3 occupancy in the CD group compared with control (FC ≥ 1. 2, 22 increased, 78 decreased, Table  S4 ).
RNA extraction and qRT-PCR Total RNA was extracted using the RNeasy Plus Mini Kit (QIAGEN), which includes the DNA elimination step. The snap-frozen Ke-37 cells were lysed, DNA eliminated, and DNA-free RNA was placed on column, washed, and eluted according to the manufacturer's instructions. For qRT-PCR analysis, a minimum of six biological replicates was used for both control and CD-exposed cells. Reverse transcription was performed with 1 μg of RNA using the iScript Reverse Transcription kit (Invitrogen) according to the manufacturer's instructions. The resulting cDNA was diluted 10 times to a final volume of 200 μl, and 4 μl was used for quantitative PCR. QPCR was performed using a Bio-Rad 384 plate machine using iTaq Universal SYBR Green Supermix (1725124; Bio-Rad). Ct values for RPL37A, a housekeeping gene, were used for normalization using CFX manager software provided with Bio-Rad 384 plate machine.
The primer sequences used for qPCR are shown in Table  S4. The data were analyzed and are presented as mean values of FC compared with control ±SD. Minimum six replicates was used for control and exposed groups. The Wilcoxon-Mann-Whitney test was used to assess the statistical significance. Chromatin immunoprecipitation (ChIP), ChIP-seq, and ChIP-qPCR ChIP from cells was performed as previously described (  71 ) with small modifications. We performed ChIP-seq experiments using six replicates for control or CD-exposed samples. 200 μl of cord blood cells was resuspended in 10 ml of PBS, fixed in 1% paraformaldehyde for 10 min at room temperature, quenched with 0. 125 M glycine, and pelleted at 900g for 10 min. Centrifugation at 900g during 10 min precipitate mainly intact nuclei; we verified the cell suspension under microscope, and we observed that most nuclei were intact.
From the aligned reads, H3K4me3 peaks were identified using 12 biological replicates and the corresponding input by MACS2 (v2. 1. 1) algorithm; the following parameters were applied: a shift-size window of 73 bp, no model, and a P-value threshold <10 × 10 -05. To compare the H3K4me3 ChIP datasets of the CD-exposed and control samples, differential peaks were identified using the following steps: first, from all of the peaks called using MACS2, we retained only peaks with average values above the median; second, we selected peaks with fold changes above 1. 5 and FDR < 0. 1. Statistical significance was calculated using R package Limma v3. 38. 3 that was designed to work with a limited number of replicates. We performed functional annotation of the differential peaks using the web-based tool GREAT v3.
0. 0 (default parameters). The differential peaks were visualized using the Integrative Genomics Viewer (v2. 4. 2). In umbilical cord blood, there are several cell types; the granulocytes and lymphocytes are the major cell types. Because each cell type have cell-specific marker expression, it is feasible to verify the proportion of given cell type  (72). For example, there are a large number of commonly used macrophage markers such as CD14, CD16, CD64, CD68, CD71, and CCR5. We detected in our ChIP-seq data that cell-specific markers for different cell types have no altered promoter H3K4me3 occupancy in control and exposed samples, suggesting that cell population is not altered by CD exposure, Fig S9. For retroelement LINE L1 studies, genome-wide alignment and peak calling of the sequencing reads were mapped to the reference genome hg38 using Bowtie2 v2.
For ChIP-qPCR, six biological replicates were used for Ke-37 cells treated with 300 nM of CD. For ChIP-qPCR of cells treated with 100 nM of CD, we used 18 biological replicates for control and 20 biological replicates for CD. The primer sequences used for ChIP-qPCR are shown in Table  S5. QPCR was carried out as described in the previous section. The internal region (far from promoter) of the GAPDH or RPLP0 gene was used for normalization. We assessed the efficiency of ChIP by analyzing ChIP/input ratio. In most of the cases, we observed an enrichment of immunoprecipitated regions compared to input. The data were analyzed and are presented as mean values of FC compared with control ±SD. The Wilcoxon-Mann-Whitney test was used to assess the statistical significance.
Acknowledgements Sequencing was performed by the GenomEast platform, a member of the " France G énomique" consortium  ( ANR-10-INBS-0009 ). Funding: This work was supported by funding from  Atip-Avenir  to  F Smagulova  ( R13139NS ). L Legoff  was supported by  Allocations de Recherche Doctorale (ARED) /l' Institut National de la Sant é et de la Recherche M édicale (INSERM)  fellowship. This work was also supported by the  French National Health Directorate  Grant (Grant  R20024NN ) to  Luc Multigner. However , the funder has no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Zhang et al. [29]  used a variance-based approach to locate center pivot irrigation systems, with preprocessing to reduce false detections. U-Net can locate the borders of center pivot irrigation systems based on pixel segmentation, but essentially extracts border pixels rather than detecting shapes. Given prior knowledge of the circular shape of center pivot irrigation systems, Tang  [34]  proposed a combined method of PVANET-Hough to automatically detect their accurate location. The proposed method first relied on a lightweight real-time object detection network PVANET  [35]  to detect circles and then applied a Hough transform to delineate the shape of center pivot irrigation systems and eliminate false detections of PVANET. The method was fast, and it was less demanding to build the sample database, which only required annotated bounding boxes of center pivot irrigation systems.
Image Data TCI (True Color Image) images of Sentinel-2 are used to detect the center pivot irrigation systems. The spatial resolution is 10 meters. The images used in the study cover three major Amazon watersheds in Mato Grosso (Juruena, Teles Pires and Xingu river), covering an area of 750,000 square kilometers and 2/3 of Mato Grosso. We analyzed a total of image 77 tiles, acquired between June and August 2017, filtered to select the images with low cloud cover. The image size is 10,980 × 10,980 pixels.
Training Datasets and Training of PVANET and GoogLeNet A set of images with center pivot irrigation systems were sampled to be the training and validation data for PVANET. In this study, images with the size of 500 × 500 were randomly cropped from a tile of Sentinel-2 images of Mato Grosso whose acquisition time differed from the test image tiles acquired in July 2017. In total, 613 images with center pivot irrigation systems were selected from the cropped images, and subsequently annotated, which were then used as the dataset to train and validate PVANET. Examples of the annotated samples are shown in Figure  7 , both complete and incomplete footprints of center pivot irrigation were included in the sample images, and these were randomly located in the images. Each sample image may contain a different number of center pivot irrigation systems.
Ninety percent of the dataset was used as training data and ten percent was reversed for use as validation data. The dataset used to train and validate GoogLeNet consisted of images with and without center pivot irrigation systems, since GoogLeNet classifies the detections of PVANET into 2 classes: center pivot irrigation systems and non-center pivot irrigation systems. Images with center pivot irrigation systems were cropped from the training samples of PVANET according to the bounding box annotation (Figure  8a ). For the non-center irrigation system samples, we used images with objects that have a similar shape to center pivot irrigation systems, such as forest patches for training (Figure  8b ), because PVANET tends to mistakenly detect these objects as center pivot irrigation systems.
To obtain the noncenter irrigation system samples, image patches with the size of 250 × 250 were randomly cropped from a Sentinel-2 image tile of Mato Grosso whose acquisition time is different from the test image tiles acquired in July 2017. The image patches with objects that have a similar shape to center pivot irrigation systems were selected and cropped as samples of non-center pivot irrigation system. Examples of the sample images are shown in Figure  8   With the prepared datasets, we fine-tuned the pre-trained model of PVANET from ILSVRC2012 training images for 1000-class image classification. The learning rate was set to be 0. 001. For GoogLeNet, we fine-tuned the pre-trained model of GoogLeNet from ImageNet  [44]. The learning rate was set to be 0. 0002. PVANET and GoogLeNet were implemented using caffe  [45].
The Size of Center Pivot Irrigation Systems Center pivot irrigation systems have variable sizes. Typical center pivot irrigation systems in Mato Grosso have the radii of 500-800 m  [46]. With the Inception module and multi-scale features concatenation in PVANET, our method can detect center pivot irrigation systems with different sizes. The radii of the detected center pivot irrigation systems range from 31-87 pixels (310-870 m), which shows the ability of our method to detect center pivot irrigation systems with different sizes. Although our method missed some smaller center pivot irrigation systems, most of the mid-size ones and big ones were successfully detected.
Description of S2D2 S2D2 combines a classic data mining algorithm as DBSCAN with a statistically sound, theoretically rooted method to choose its parameters so that it searches for the smallest scale significant structures in a sample. Its basis was developed and extensively discussed in J17 and J18, where it was successfully applied to Taurus, a characteristic example of a very young and structured SFR. There, the small scale significant structures (that we will also call NESTs, or Nested Elementary Structure, following their nomenclature) had a high likelihood of being the pristine imprints of the stellar formation process. In the following, we briefly explain the procedure and the slight modifications and additions that allow its robust application in a general case.
It is important to mention that there are cases where some structure can still be detected by the procedure in homogeneous regions, due to the projection of 3D structures. The retrieval of this spurious structure in S2D2 is not frequent, but projection effects should not be confused with substructure to avoid the over-interpretation of results. We note that the Q values and thresholds used in this work correspond to the 2D analysis of 3D data and, thus, take into account projection effects. A cut in the Q parameter of 0. 7 will discard some quasi-homogeneous structured regions, but gives more than 2σ certainty that we are dealing with a structured region, considering CW04,  Cartwright (2009) ;  Sánchez & Alfaro (2009) , covering an ample range of sample sizes. Analogously, regions with Q between 0.
Each image is then analyzed to extract quantitative morphological measurements that describe the respective cellular phenotype. Some tools are commonly used for this numerical feature extraction step  (McQuin et al. , 2018; Pau et al. , 2010) , and recent deep learning approaches attempt to replace expert-curated measurements with data-driven discriminative features  (Ando et al. , 2017; Lu et al. , 2019; Pawlowski et al. , 2016). While the analytical tasks of HCS experiments vary between applications, they involve common data normalization and filtering steps, and guidelines have been proposed for computing informative representations of cellular phenotypes, usually referred to as morphological profiles  (Bougen-Zhukov et al. , 2017; Caicedo et al. , 2017). An analysis pipeline suitable to facilitate morphological profiling should meet several criteria.
The few actively maintained HCS analysis tools attempting to fulfill these needs include CellProfiler Analyst and its graphical user interface, designed to handle CellProfiler measurements  (Jones et al. , 2008; McQuin et al. , 2018) , as well as the general-purpose cytominer and Pycytominer in the R and Python languages, respectively  (Becker et al. , 2021; Way et al. , 2021b). There are also packages addressing similar challenges but focusing on other modalities (which generally provide less spatial information or less throughput than high-content imaging screens) such as the R packages cellHTS2, optimized for measurements from plate readers  (Boutros et al. , 2006) , and more recently cytomapper for analyzing imaging mass cytometry experiments  (Eling et al. , 2021). Despite the existence of these tools, HCS analysts still heavily rely on custom implementations of morphological profile curation for each study to account for different imaging modalities and analytical goals  (Ziegler et al.
, 2021). Julia is a high-performance, high-level open-source programming language specifically designed for scientific computing and data science  (Bezanson et al. , 2017). It is increasingly adopted by researchers in bioinformatics and biomedical research  (Roesch et al. , 2021) , with applications ranging from protein sequence analysis  (Zea et al. , 2016)  to structural bioinformatics  (Greener et al. , 2020)  and flux balance analysis  (Heirendt et al. , 2017). Julia is also ideal for tackling the challenges of morphological analyses, as they are both computationally demanding and inherently high-level. In this article, we introduce BioProfiling. jl, the first Julia library for efficient and convenient morphological profiling that (i) handles noisy data through systematic filtering and robust statistics, (ii) provides dedicated functions to normalize data and mitigate layout effects and (iii) implements statistical tests for quantifying the strength of morphological changes that take the variability of morphological profiles into account.
Package implementation and features We created BioProfiling. jl, a package for the Julia programming language that compiles over 30 methods and data structures for all steps in assembling and curating morphological profiles. To enable the bioimage analysis community to apply BioProfiling. jl to their own data, a complete documentation and a set of notebooks reproducing the analyses described in this paper are provided. In brief, the whole process of morphological profiling is conceptually simplified by defining an Experiment object that includes both quantitative data and metadata in a tabular format, and methods able to interact with these objects directly to curate, transform and visualize the corresponding profiles. After creating the Experiment object from a table of morphological features, such as measurements obtained from CellProfiler  (McQuin et al.
, 2018)  as implemented in UMAP. jl. The resulting feature profiles can be visually inspected by highlighting images and individual cells matching a Filter with the diagnostic_images method, currently implemented for TIFF images in any accessible folder. Up to three distinct files can be specified to produce an RGB image. Finally, robust_morphologi-cal_perturbation_value and efficient implementations of statistical distances, described in detail below, are available for quantifying the significance of morphological changes induced by a particular perturbation. Freely available from GitHub and the Julia package registry under the MIT license, BioProfiling. jl is part of a growing opensource software ecosystem ensuring that it stays flexible, maintainable and interoperable. To ensure its stability, the package is thoroughly validated with more than 120 tests, systematically run on multiple environments using GitHub Actions for continuous integration.
To compare our results with other approaches that could be adopted with BioProfiling. jl, we quantified the Mahalanobis distance between the centroid (arithmetic mean of all points) and the centroid of the DMSO controls, either from unreduced profiles or after PCA transformation to two dimensions, which preserved 97. 8% of the dataset variance. These distances were used in a permutation test as previously described to obtain FDR-corrected Pvalues describing how likely it is to observe such distances in the absence of a compound effect.
Morphological and network distances We integrated morphological profile information with publicly available data about each compound. First, we collected mechanisms of action (MOAs) and molecular targets from the LINCS perturbation database  (Stathias et al. , 2020). We queried the Application Programming Interface ( API) for exact name matches or removed pharmaceutical salts or chirality when necessary to find the correct compound. All annotations are presented in Supplementary Table  S1. In total, 141 compounds had known targets and 112 were annotated with one or several MOAs. In particular, 23 MOAs were associated with 2 or more compounds and considered for downstream analysis. The largest changes induced for several MOAs were obtained using the characteristic_features method (Supplementary Table  S3 ).
To compare morphological profiles between pairs of MOAs, we projected the profiles of the 59 hit compounds in four dimensions using UMAP and computed pairwise RHDs as described above. The morphological distance between two MOAs was then defined as the average pairwise distance between compounds annotated to each MOA. We also obtained all human protein-protein interactions (PPIs) from the HIPPIE database (Alanis-Lobato et al. , 2017), filtered out those with a confidence score below 0. 63 (median of the score distribution), and assembled them into a PPI network. The conversion between gene symbols and ENTREZ identifiers of the targets was done with MyGene. info (Xin et al. , 2016). We define the targets of an MOA as all known targets of the hit compounds associated with this MOA. We then assessed the network separation between the targets of each MOA using the s AB score, which was previously found to be a good metric to study disease module and drug module separation  (Caldera et al.
Profiling overexpression in Cell Painting experiments We used a dataset from the Cell Painting Image Collection, a resource made publicly available under the CC0 1. 0 license for the CytoData Hackathon 2018 and compiling several HCS experiments  (Caicedo et al. , 2018). We retrieved CellProfiler measurements aggregated per well from two plates in an experiment characterizing the overexpression of 135 genes in A549 cells using the Cell Painting assay  (Bray et al. , 2016) , identified as 'BBBC041-Caicedo'. From the untransformed measurements, we filtered out metadata and features related to object localization, excluded genetic targets with less than four replicates per plate, log-transformed the data and decorrelated the features by decreasing MAD as previously described. Finally, we reduced these profiles to two dimensions using UMAP, with the spread parameter set to 10 and other values left to default.
Profiling chemical perturbations with BioProfiling. jl We conducted and analyzed a chemical HCS experiment to study the morphological effect of small molecules in human osteosarcoma cells and demonstrate the applicability of BioProfiling. jl. In brief, we selected 311 compounds at a single concentration based on their morphological activity, and on their wide range of MOAs and disease associations. U-2-OS cells were seeded on top of drug plates, and were fixed and stained to display nuclei, F-actin and total protein. Fluorescence images were acquired at a 20Â magnification (Fig. 2a ). The images were analyzed with CellProfiler  (McQuin et al. , 2018)  and morphological descriptors were measured for each cell. These measurements were imported in Julia and used to define an Experiment object to be processed with BioProfiling.