dataesr/scientific-paragraphs-categorization huggingface dataset:
Flow cytometry MCF10a cells were fixed in 4% Paraformaldehyde for 10 min at room temperature followed by a 15 min permeabilization step in 0. 5% Tween20. Cells were resuspended in Blocking Buffer (PBS, 3% BSA, 0. 1% Tween20) for 30 min on rotating wheel at room temperature and incubated with conjugated antibodies EPCAM-PE  and MCAM-APC (MACS Miltenyi 130-120-771) for 1h30 on rotating wheel at room temperature protected from light. Cells were harvested and analyzed using a MACS Quant 10 (MACS Miltenyi Biotec). PE was excited by a 488-nm laser line (laser DPSS) and its emission was collected through 655/605 nm; APC was excited by a 640-nm laser line and its emission was collected through 655/730 nm. The data were analyzed using Flowing software (Perttu Terho, Turku Centre for Biotechnology).
Cell sorting -FACS Cells were resuspended in Blocking Buffer (PBS, 3% BSA) for 30 min on rotating wheel at room temperature and successively incubated with CTNND1 exon 2 primary antibody (Santa Cruz sc-23873) for 1h30 on rotating wheel at room temperature, and PE-Cy7 secondary antibody (Thermo Fisher 25-4015-82) for 30 min on rotating wheel at room temperature protected from light. Cells were harvested and analyzed using a BD FACS Melody (BD Biosciences-US). PE-Cy7 was excited by a 561-nm laser line and its emission was collected through 783/56BD. The data were analyzed using BD FACS Chorus software (BD Biosciences-US) and only cells with protein levels above the no primary antibody background signal were collected.
Polymerase II elongation measurement A DRB treatment (Sigma D1916) of 100 mM for 6 h was necessary in order to fully block endogenous CFTR transcription. Cells were washed and the kinetic  (0, 5, 10, 15, 20, 30, 45, 60, 90 min)  was started by adding complete medium. For each time point of the kinetic, cells are scraped and cell pellets are snap frozen in liquid nitrogen. Total RNA was extracted as mentioned above in RNA Extraction and RT-qPCR. Reverse transcriptase reaction was initiated with random hexamers. Quantification of the pre-mRNAs was performed by real-time PCR with amplicons spanning the intron-exon junctions. For each biological replicate, quantitative PCR reactions were e6 Cell Reports 38, 110357, February 15, 2022 Report performed in technical duplicates using the iTaq Universal Syber Green supermix (Bio-Rad) on the Bio-Rad CFX-96 Touch Real-Time PCR System, and the data normalized by tRNA.
3 (http://rbpdb. ccbr. utoronto. ca), RBPMAP v1. 1 (http://rbpmap. technion. ac. il), SFMAP v1. 8 (http://sfmap. technion. ac. il/), Spliceaid (http://www. introni. it/splicing. html). All softwares were used with the default parameter settings, except for some exceptions. For RBPDB the threshold 0. 8 was applied. For RBPMAP, the Stringency level used was ''High stringency'' with all motifs available from Human/ mouse. For SFMAP both ''Perfect match'' and ''High stringency'' stringency levels were used. For catRAPID, the following settings were used: ''Full-length proteins'', ''RNA and DNA binding'' and including ''disordered proteins. We prioritized the RNA motifs predicted by more than one database and expressed in MCF10a cells.
ACKNOWLEDGMENTS We are thankful to  Dr. Haber  for the EMT cellular model, to  Drs. Bertrand ,  Salton, Duckett, Pradeepa, and Ge  for plasmids, and to  Paola Scaffidi  and  Bernard de Massy  for critical reading and discussion of the manuscript. This work was supported by the  ANR program Labex EpiGenMed ,  La Ligue  contre le Cancer, the  French National Institute of Cancer (INCa   PLBIO18-156 ), and the  ANR   Young Investigator grant  ( ANR-16-CE12-0012-01 ) to the R. F. L. lab and by the  Wellcome Trust  ( 104175/Z/14/Z ), the  Sir Henry Dale Fellowship , and by funding from the  European Research Council  ( ERC-STG no. 639253 ) to the P. V. lab. We are grateful to Montpellier's  MRI imaging facility and Edinburgh's Protein Production Facility  for their support.
Statistics Descriptive statistics for the categorical and continuous data were given as frequency (percentage) and median (minimum-maximum), respectively. Changes in the correct answer percentage regarding each question were evaluated using the McNemar test, and pre-post differences in the total score were compared with the Wilcoxon Signed Rank Test. All statistical analyses were performed with Statistical Package for Social Sciences (SPSS Version 15. 0, Chicago, IL), and the level of statistical significance was set to 0. 05.
Slides were also acquired with a confocal SP8 T. C. S. Leica microscope (×40, Confocal Scan Head Leica SP8, Wetzlar, Germany) in fluorescent mode (488 nm line of argon lasers), according to the protocol explained by Steenkeste et al. [21]. Images acquired with the NanoZoomer were analyzed with NDP. view2 software  [29]  and images acquired with the SP8 confocal were analyzed with Fiji software  [30].
Quantification Software Perifluo QCG [31] is a software that has been specially created to determine the ratio of the PMF to cytoplasm fluorescence while deducting the background from a urine cytology or a cell line cytology that was acquired with the NanoZoomer in transmission and fluorescent mode (Video S1). The cytology that was acquired with the Nanozoomer was saved in an ". ndpis" format. In this format, the transmission and fluorescent parts were merged into a single image. The conversion of the ". ndpis" format into the ". tif" format and the separation of the transmission mode from the fluorescent mode into two distinct images were mandatory steps for the continuation of the software. To separate fluorescence from transmission, the image was exported in ". tif" format with the NDP.
view2 software (Hamamatsu) where it was possible to choose the channel of the image to export. The image was zoomed to the ×0. 9 objective to obtain a visual of the whole cytology and recorded digitally magnified 10 times. The fluorescence image was renamed "fluo. tif" and the transmission image "bf. tif". Both images were saved in the same folder so that the Perifluo QCG software could find them. Perifluo QCG is composed of two sub-software developed under Fiji. The first one merges the "fluo. tif" and "bf. tif" images into a single ". tif" image with both channels. It was then required for the operator to delimit a square of 12,000/12,000 pixels that encompasses the cytology. With this square, the software can divide the merged image into 16 sub-images of 3000/3000 pixels in order to simplify the software's calculation load.
Cells Death Assay and Flow Cytometry For cells death assay, cells were cultured as described in "Induction of cellular stress" and stopped with trypsinization after 1 h of treatment with etoposide for J + 0, after 3 days of treatment for J + 3, after 6 days of treatment for J + 6, and after 9 days of treatment for J + 9. Cells were washed 3 times with cold PBS and were centrifuged at 700 G for 4 min. Annexin V staining was performed with FITC Annexin V Apoptosis Detection Kit I (BD biosciences, Inc. , Franklin Lakes, NJ, USA) according to the manufacturer's instructions. Cells were then acquired on a Gallios flow cytometer (Beckman Coulter, Brea, CA, USA) and the data were analyzed using Kaluza software. Total cell death was determined by summing the percentage of cells positive for annexin V with a percentage of cells positive for propidium iodide and with a percentage of cells positive for both markers.
To validate whether all cells exhibit identical PMF or cell type-dependent PMF, it was important to examine the fluorescence in more detail and move from a qualitative to a quantitative analysis. The advantage of a quantitative analysis is that if there is a variation in fluorescence between different cell lines that express a PMF, it will be possible to distinguish it. This allows to determine which type of PMF the cells express according to their physiological state or tissue origin. To determine the PMF in a quantitative way, a software capable of determining precisely the membrane and cytoplasmic fluorescence was developed. This software, Perifluo QCG  [31] , is able to detect every cell present in the sample. When a cell is detected, its cytoplasmic and peripheral/membrane fluorescence is quantified while deducting the background.
According to this study, EA50 and OG6 are essential to visualize the PMF and hematoxylin and EA50 for the intensity of the PMF (data not shown). As we could not characterize the chemical origin of the PMF nor find a cell line without PMF, we decided to set up a software able to give the precise PMF of a cell in order to determine if the cells already tested showed a variation of PMF or not, depending on the origin of the cell. This software, Perifluo QCG, offered us the necessary elements to go from a qualitative analysis where we were looking for a cell model without PMF to compare with a model with PMF, to a quantitative analysis where we could significantly determine a variation in PMF. As a result, our software can quantify every cell on the cytology extremely quickly, giving us a great amount of information.
Perifluo QCG uses a simple process whereby it calculates the cytoplasmic and membrane fluorescence (up to 5 pixels around the membrane), while deducting the background. Once the data is obtained, the PMF value is calculated by operating FR. If this FR value is greater than 1, the membrane fluorescence is more important and a PMF is therefore present; conversely, if the FR value is less than 1. Thanks to this tool, the determination of the FR of cells already tested was facilitated, allowing us to easily distinguish the variations of PMF according to the cell lines used. The use of the Perifluo QCG software did not reveal any significant differences in PMF and confirmed that all cells, after Papanicolaou staining, constitutively express this fluorescence. The software also allowed us to confirm the hypothesis that UCUs would have an FR lower than 1 and therefore would not express PMF, unlike UCs recovered from the UroThelium (UCUT) which have an FR higher than 1.
Acknowledgments: We thank the photonic facility of the  Microscopy Rennes Imaging Center (MRic-Photonics) of Biosit, Université de Rennes 1. MRic is member of the national infrastructure  France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ). We thank the  Animalerie Rennaise Centre d'Hébergement et d'Expérimentation (Arche) of Biosit, Université de Rennes 1 , for graciously giving us rats and for the irradiation of the cells.
General information The reactions were performed by employing standard protocols and techniques. Schiff base condensation reactions were carried out on an  Using ESI as the source, mass spectral data were assembled on Bruker high-resolution mass spectrometer (micrOTOF QII) equipment. 4. 1. 2. General procedure for the synthesis of conjugates 12-13 K 2 CO 3 (1. 2 mmol) was added to a well-stirred solution of 4-hydroxybenzaldehyde/vanillin 8 (1 mmol) in acetone, and the resulting mixture was stirred for 30 min at room temperature to generate anion. On addition of propargyl bromide (1 mmol), the reaction mixture was refluxed for 12 h. After completion of the reaction as monitored by TLC, K 2 CO 3 was filtered off, and acetone was evaporated under reduced pressure to yield O-propargylated benzaldehyde/O-propargylated vanillin 9 in excellent yields.
78 μg/mL) were added to the cells, which were further incubated at 37 • C with 5% CO 2 for 24 h or 72 h after which 10% (vol/ vol) resazurin dye was added to each well and left to incubate for 4 h at 37 • C with 5% CO 2. INH and RFB were included as reference drugs. Data were acquired using a fluorescent plate reader (excitation 540 nm, emission 590 nm).
Whole-genome sequencing DNA was extracted using NucleoBond AXG 100 DNA kit (Macherey-Nagel, Germany) according to the manufacturer's instructions. DNA was quantified using Qubit™ dsDNA BR Assay Kit and Qubit™ 4 fluorometer (Invitrogen, Thermo Fischer Scientific). Library preparation and Illumina sequencing was performed by Novogene (Cambridge, UK) on IlluminaNovaSeq platform (2x150bp). The short-read data were first trimmed using fastp v0. 23. 2  [58] , based on a Phred scale base quality threshold of Q30 and removing reads of less than 21 bp. Kraken2 v2. 0. 9-beta  [59]  with the bacteria database was used to perform taxonomic identification at the read level in order to check for potential contamination. All reads assigned to the Mycobacteriaceae family were extracted and then assembled using SPAdes v3.
Acknowledgments The authors wish to thank  A. Speer  for the generous gift of pSMT3-katG, the  Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation for funding MA. VK thanks Council of Scientific and Industrial Research (CSIR)  for providing financial support (grant no. 02  ( 0400)/21/EMR-II ). B. S. acknowledges the  Council of Scientific and Industrial Research, New Delhi, India , for providing  CSIR-SRF  fellowship ( CSIR Ref No. 09/254(0257)/2016-EMR-I ).
Study population Baseline data were collected from 1982 to 2002 and were derived from 38 population-based cohorts in 11 European countries (Supplemental Table  1 ). The cohorts in the MORGAM Project had either been part of the World Health Organization's MONICA Project (MONitoring trends and determinants In CArdiovascular disease) or had used the same standardized MONICA survey procedures for data collection as described in the MORGAM manual  (MORGAM Project, 2001 ). An exception to this is the ESTHER cohort, where weight and height and were self-reported. A total of 17,552 individuals were excluded because of missing information related to the following variables: history of diabetes mellitus (n = 1753), history of ASCVD (n = 781), use of antihypertensive medication (n = 4224), the CV risk factors included in SCORE (n = 7618)  (Conroy et al.
Acknowledgements The  MORGAM  Project has received funding from  EU  projects  MORGAM (Biomed ,  BMH4-CT98-3183 ),  GenomEUtwin  ( FP5 ,  QLG2- CT-2002-01254 ),  ENGAGE  ( FP7 ,  HEALTH-F4-2007-201413 ), CHANCES ( FP7 ,  HEALTH-F3-2010-242244 ),  BiomarCaRE  ( FP7 ,  HEALTH-F2-2011-278913 ), euCanSHare ( Horizon 2020 , No. 825903 ) and  AFFECT-EU (Horizon 2020 , No. 847770 ); and  Medical Research Council, London  ( G0601463 , No. 80983: Biomarkers in the MORGAM Populations). This has supported central coordination, workshops and part of the activities of the  MORGAM Data Centre , the  MORGAM Laboratories  and the  MORGAM Participating Centres. PRIME/Belfast, Queenś University Belfast , Belfast, Norhern Ireland:  F. Kee  (principal investigator); Former  MORGAM Coordinating Centre, Queen's University Belfast, Belfast, Northern Ireland :  A.
Related research The growing number of NGS data from symptomatic SVs/CNVs carriers led to the emergence of multiple databases inspiring further studies regarding the role of gene position effect (GPE) in the patient phenotype. For example, Zepada et. al. applied the haploinsufficiency and triplosensivity scores to characterize GPE derived from balanced translocations in 17 subjects from the Developmental Genome Anatomy Project (GDAP)  (23). Ruifeng et al. using data from ClinVar database  (24)  introduced the Structure Influence score to prioritize and designate SVs that are likely to disturb gene regulation through TADs disorganization. Salem et al. (25)  based on the DECIPHER database (containing nearly one thousand deletions)  (26)  showed that only 4. 5% of analyzed deletions can disrupt TAD boundaries leading to the gene misexpression due to enhancer adoption.
Our results To address existing challenges and provide a competitive tool, we have developed TADeus2--a web server for a quick evaluation of SVs/CNVs that provides a visual framework to aid the medical expert in the interpretation of variant pathogenicity in the context of changes in the TADs organization (see Graphical Abstract). Based on the type of a variant TADeus2 allows to visualize the affected region in two modes: (i) syntenic mode dedicated for the analysis of deletions and duplications; (ii) breakpoint mode designed for translocations and inversions. The former mode allows visualization of one genomic region, while the latter enables analysis of two different genome loci joined together during the rearrangement event. Both modes integrate multiple datasets (e. g. Hi-C or ChIPseq) either available on the server or provided by the user.
TADeus2 rank gene table The table is ordered by the aforementioned ranking score in the descending order. Genes with the equal scores are secondary sorted by the number of the disrupted enhancer-promoter interactions and the distance from the rearrangement breakpoints. For the convenience of the user the final importance ranking is color-coded with a dark-pink to white scale. Additionally, for a broader perspective, the disease and inheritance type from Online Mendelian Inheritance in Man (OMIM)  (45)  and pLI score from Genome Aggregation Database (gnomAD)  (46) , were added.
SVs/CNVs pathogenicity assessment To evaluate the pathogenicity of a rearrangement TADeus2 uses the state-of-the-art, third-party software: TADA (available only for autosomes)  (47)  and Classi-fyCNV  (48)  as well as an original statistical significance p-value. TADA automatically ranks Copy Number Variants (CNVs) based on a extensive catalogue of functional annotations supported by enrichment analysis. The software is based on a machine-learning classifier to accurately predict and prioritize pathogenic deletion or duplication to produce a well-calibrated pathogenicity score. ClassifyCNV, uses pre-parsed publicly available databases to calculate a pathogenicity score for each duplication and deletion. Importantly, the tool is an implementation of the 2019 ACMG guidelines for variant interpretation that provide a set of criteria to score variants and place them into one of the five classification tiers  (49).
Implementation and architecture of TADeus2 web server TADeus2 is implemented as a Django application using MySQL database as data storage. Fragments of code from HiCExplorer  (51)  are reused in the track plot module. All the presented command-line tools (e. g. TADA, classify-CNV) incorporated in TADeus2 are accompanied with a responsive and user-friendly graphical interface developed with Bootstrap v4. Additionally, java-script snippets enable customization of forms and export of any user-defined plots that are produced using the python matplotlib package. Finally, TADeus2 provides the rest-API that can be utilized by external applications.
The latter integrates two genomic fragments from different loci (e. g. located at distinct chromosomes) as if it was a continuous genome chunk, allowing visualization of the new rearrangement. To use this mode the user should specify the coordinates of the breakpoints and the direction of the fused strands (e. g. in the case of translocation between P and q arms, a forward reverse strand fusion occurs). The breakpoint view also includes wild-type regions that compose the rearrangement. Further analysis (in both modes) includes an integration of additional tracks containing publicly available or self-uploaded datasets. After the selection of disease causing putative genes (based on the TADeus2rank gene table or the interpretation of particular tracks e. g. gnomAD pLI) the user should focus on the characterization of the cis-regulatory landscape within the region of interest.
Based on the clinical picture that the patient displayed we decided to conduct the next steps of the analysis using data Hi-C from neural progenitor cells (NPC). The following tracks have been added to the breakpoint view: Genes coloured by pLI score, Virtual 4C for promoter regions (±2. 5 kb from transcription start site (TSS)) for DSP and FOXG1 and experimentally validated functional elements from NCBI database (Figure  2 ). The result indicate that FOXG1 lost contact with 4 experimentally verified enhancers, while DSP did not lose any, making FOXG1 the most probable cause of the disease (see the red box in Figure  2 ). Indeed, at least 11 translocations located in proximity of FOXG1 in patients with Rett-like syndrome have been described. Furthermore, it has been proposed that loss of only one active FOXG1 enhancer is not clinically relevant  (52).
Indeed, the spatiotemporal expression analysis of CNTNAP2 during mouse embryonic development revealed no expression in the majority of tissues linked to the patient's phenotype  (54). Moreover, the CNTNAP2-/-mice present no abnormalities in craniofacial and inner ear development  (55). Therefore, for further analysis we focused on the 7q21. 3 breakpoint region. The TADeus2 ranking list pointed DLX5 and DLX6 as genes with a high probability of being responsible for  these developmental abnormalities (top two genes with rank score: 300), with the pLI score equal to 0. 2184 and 0. 9213, respectively (Supplementary Table  S1 ). Based on these results we extended our analysis by implementing the TADeus2 browser tracks with: experimentally validated functional elements from NCBI database and virtual 4C for DLX5 and DLX6 promoter regions (±2.
DATA AVAILABILITY TADeus2 is publicly available at https://tadeus2. mimuw. edu. pl. Detailed tutorial, help pages, short videos presenting functionality of application, and a FAQ section are provided. The tool is free and open to all users and there is no login requirement. TADeus2 source code and installation instructions are available at https://github. com/ bposzewiecka/tadeus2. The software is distributed under a GNU General Public License v3. 0.
Supporting Data The MEAM1/B B. tabaci genome of 615 Mb available in the NCBI database (assembly ASM185493v1) was used to identify the TE content of this insect genome and TE insertions in connection with the defensome genes involved in insecticide resistance. This genome was sequenced by Chen et al. (2016) using both Illumina short reads and PacBio long reads approaches, then assembled into 19,751 scaffolds with a 3,232,964 kbp N50 length  [6].
TE Identification The identification of B. tabaci TEs was conducted using the REPET v3. 0 package with default parameters  [27, 28] , which integrates two main pipelines: TEdenovo for repeats identification and TEannot for their annotation. TEdenovo analysis is based on three main steps. First, the B. tabaci genome was cut into batches in order to be compared to itself by the "all against all" method using BLASTER. Second, the detected repetitive High Scoring Pairs were grouped into clusters of repeats using GROUPER and RECON, which are clustering programs specific for interspersed repeats. Then, the results were combined and redundancy was eliminated in order to create a multiple alignment for each cluster from which a consensus sequence was produced. Finally, the consensuses were categorized based on TE characteristics (LTR, TIR, polyA tail, Open Reading Frame, etc.
TE Insertion Sites The bedtools-closest tool v2. 26. 0  [34, 35]  was used to look for possible TE insertions within the genes (from transcription start site to stop codon) and 2 kb up and downstream  [36]  by combining TEs' Generic File Format annotations from TEannot with those of the genes obtained from NCBI database. This program looks for features in the genome that are close to and/or overlap with TE copies. The TE insertion locations in the genes, their orientations, and the distance between genes and TEs were used to classify the insertions. Then, we searched for TE insertions that may be involved in insecticide resistance using Chen et al. , 2016's annotated defensome genes associated with detoxification and insecticide resistance in B. tabaci in both susceptible and resistant populations in response to insecticide treatment  [6].