dataesr/scientific-paragraphs-categorization huggingface dataset:
FUNDING  French Government 'Investissements d'Avenir' programmes OCEANOMICS  [ ANR-11-BTBR-0008 ];  FRANCE GENOMIQUE  [ ANR-10-INBS-09-08 ];  Institut Francais de Bioinformatique (IFB)  [ ANR-11-INBS-0013 ];  SeqDigger  [ ANR-19-CE45-0008 ];  AO-EMBRC  [ ANR-21-ESRE-0038 ]. Funding for open access charge:  ANR  [ ANR-19-CE45-0008 ].
Time-course of Hox gene activation in stembryos In gastrulating mouse embryos, Wnt signaling contributes to the formation of the primitive streak from epiblast cells, which thus allows the emergence of germ layers that will shape the elongating body axis. Likewise, in those stembryos cultured as initially described in  (Beccari et al. , 2018) , a pulse of the Wnt agonist chiron (Chi) 48 hours (h) AA (after aggregation; thereafter '48h') of murine ES cells (mES) i. e. , between 48h and 72h, triggers the differentiation of these epiblast-like cells to form a multi-layered structure resembling the posterior part of the elongating body axis. We generated a 12h resolution time-course ChIP-sequencing (ChIP-seq) dataset for H3K27ac, a chromatin mark found at active enhancers and associated with transcription (Fig.
Dynamic of Chromatin topology We looked for modifications in chromatin structure during this dynamic process and produced a 24h time-series of Capture Hi-C (CHi-C) datasets, starting from 48h, when the activation of the cluster hasn't yet started (Fig. 3A ). At this stage, before Wnt signaling, interactions were observed between the Hoxd genes themselves, most likely as a result of their general coverage by polycomb marks  (Noordermeer et al. , 2011 (Noordermeer et al. , , 2014) )  leading to a 'negative' micro-TAD (Fig. 3A ). At this stage, the cluster displayed several interactions with T-DOM, particularly with sub-TAD1 (Fig. 3A, 48h , arrows). Contacts established between CBSs in the cluster and sub-TAD1 were quantified (Fig. S6A, B , respectively) and CBS1 unambiguously established the strongest contacts when compared to other CBSs located within the HoxD cluster (Fig.
e. , involving now more posterior CBSs. This was supported by a quantification of contacts within this boxed area (Fig. 3C ) with the curves below showing the distributions of contacts frequencies along with time, with a shift from the main use of CBS1 to the recruitment of more posterior CBSs (Fig. 3C , compare e. g. , 48h with 120h). This shift can also be observed with the animation of this time-course dataset (Supplementary Movie 2). These directional and stepwise increases in contact frequency with sub-TAD1 (or part thereof) also applied to the Hoxd genes bodies. Hoxd1 to Hoxd4 increased contact frequency with the whole sub-TAD1 at 96h (Fig. S6D ). Hoxd9 did not, but increased its interactions with the 3' region of sub-TAD1 (Fig. S6E ), a region rich in H3K27ac marks potentially reflecting enhancer-promoter contacts.
These interactions were also documented by a HiChIP dataset for H3K27ac using 120h stembryos (Fig. 3D ), a stage when anterior genes were activated and cohesin loading was already initiated. The contacts map produced a stripe illustrating the preferential interactions between the sub-TAD1 region and the active, H3K27ac-positive segment of the HoxD cluster, whereas contacts with sub-TAD2 were much less robust (Fig. 3D ). The hotspots detected within the stripe matched either the sub-TAD1 CBSs (Fig. 3D , arrows), or acetylated peaks within sub-TAD1 (Fig. 3D , arrowheads), consistent with previous reports that had identified such stripes at active enhancers or within NIPBL-enriched regions  (Kraft et al. , 2019; Vian et al. , 2018).
6A ). RNA-seq of mutant stembryos showed a marked decrease in transcripts level for Hoxd8 and Hoxd9, both at 96h and 120h (Fig. 6B ). To see whether this decrease in transcription was associated with a change in the contacts established between the various intra-cluster CBSs and sub-TAD1, we produced a CHi-C dataset of Del(Hoxd1-Hoxd4) stembryos at 96h (Fig. 6C ). While the micro-TAD observed over the anterior region of the cluster under normal condition had expectedly disappeared along with the deletion (Fig. 6C , arrow; compare with Fig. S7A  at 96h), a quantification of contacts between the various CBS1-5 and the CS38-40 sub-TAD1 region revealed important modifications. In 96h control stembryos, CBS2 showed a higher contact frequency with the CBSs located within the CS38-40 region, when compared to CBS1 (Fig.
7C ; compare 120h to 144h). We produced a time-series of CHi-C datasets with Del(CBS1-5) stembryos and noticed several changes in the dynamic of chromatin architecture when compared to controls (Fig. 7D ). First, the Wnt-dependent translocation of the gene cluster into two separated domains was no longer observed, supporting a requirement of CTCF for this process. Second, at 96h, the progression of contacts towards the more posterior (5') part of the cluster was not observed in mutant stembryos, indicating again that it was normally driven by occupied CTCF sites. Instead, after activation of the Wnt pathway, the anterior part did not clearly segregate from the central and posterior regions of the cluster and contacts remained distributed throughout (Fig. 7D ), reflecting a diffused but general activation at a time when controls display two well-ordered negative and positive domains, with a dynamic transition from the former to the latter.
NGS analysis and figure generation: All NGS analyses were performed on a local installation of galaxy  (Afgan et al. , 2016). All command lines and scripts to regenerate figures are available on https://github. com/lldelisle/scriptsForRekaikEtAl2022. Most of genomic tracks were plotted using pyGenomeTracks version 3. 7  (Lopez-Delisle et al. , 2021; Ramirez et al. , 2016)  and modified with illustrator. All boxplots and barplots were plotted using Prism, heatmaps were plotted with R (www. r-project. org). Mutant genomes: Chromosome 2 matching the 3 mutants (Ins(2xCBS-d4d8), Del(d1-d4) and Del(sub-TAD1) were generated manually using the chromosome 2 sequence from UCSC of mm10 and results from sanger sequencing of the breakpoints  (Rekaik and Lopez-Delisle, 2022). The mutant chromosome 2 sequence was concatenated with the sequences of all other autosomes, chrX, chrY and mitochondrial DNA from mm10 (UCSC).
RNA-seq: Stembryos for each condition were collected and pooled in a 2 ml Eppendorf tube. After centrifugation and medium removal, pelleted stembryos were stored at -80°C until RNA extraction. RNeasy Plus Micro kit (Qiagen) with on-column DNase digestion was used for RNA extraction following the manufacturer's instructions. RNA-seq library preparation with Poly-A selection was performed with 1 µg of purified RNA using the TruSeq Stranded mRNA kit from Illumina and following the manufacturer's protocol. Library quality was assessed with Fragment analyzer before sequencing on a NextSeq 500 sequencer as paired-end, 75 bp reads. For data analysis, adapter sequences were trimmed from reads using cutadapt  (Martin, 2011)   2021). Only uniquely mapped reads (tag NH:i:1) were kept using bamFilter version 2.
ChIP and ChIPmentation (ChIP-M): ChIP and ChIP-M experiments were performed according to the protocol described in  (Darbellay et al. , 2019; Rodriguez-Carballo et al. , 2017)   Laboratories)], DNA fragments bound to the antibody-bead complex were tegmented using the Nextera Tegmentation kit. Beads were resuspended in the tegmentation buffer and incubated at 37° for 2 min with 1 µl of the Tn5 transposase. Fragment were then eluted and purified as described previously, and amplified using Nextera primers. Final DNA libraries were purified and size selected using AMPure XP magnetic beads (Beckman Coulter), and a fragment analysis was performed before sequencing on a NextSeq 500 sequencer as paired-end, 75 bp reads. For data analysis, fastqs of the 3 replicates of wt_144h_pSer2PolII were concatenated before any processing.
Adapter sequences and bad quality bases were trimmed from reads using cutadapt  (Martin, 2011)  version 1. 16 (-a 'GATCGGAAGAGCACACGTCTGAACTCCAGTCAC' -A 'GATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCAT T' for ChIP and -a 'CTGTCTCTTATACACATCTCCGAGCCCACGAGAC' -A 'CTGTCTCTTATACACATCTGACGCTGCCGACGA' for ChIPmentation --quality-cutoff=30). Trimmed reads were mapped on mm10 genome (or the mutant genome Ins(2xCBS-d4d8) for the corresponding mutant CTCF ChIPmentation) using bowtie2 version 2. 3. 4. 1  (Langmead and Salzberg, 2012). Only alignments with proper pairs and mapping quality above 30 were kept using samtools 1. 8. Peaks and coverage were obtained with macs2 version 2. 1. 1. 20160309 (--format BAMPE --gsize 1870000000 --call-summits --bdg). In order to better compare ChIP tracks, ChIP samples for the same protein with a time-course were normalized together using a custom python script (available in the github repository).
Similarly, this normalization was used when mutant and control were performed in parallel (see github repository). ChIP quantifications were performed with multiBigWigSummary from deeptools version 3. 0. 0 (Ramirez et al. , 2016). The cumulative H3K27ac profile of figure  2A  was computed with a custom python script available on the github repository. In order to reduce the impact of variations in gastruloid growth speed for the H3K27ac and H3K27me3 datasets in supplementary figure  S9A  and supplementary figure S12A, the profiles of mutant and corresponding wild-type were corrected using the time-course experiment and the profile at the HoxA cluster as a guide (see github repository for more details). The profiles were then averaged for the two replicates and differences were performed using bash script available in github repository.
, 2015)  with default paramters on mm10 or on the corresponding mutant genome. The output BAM was converted to Juicebox format thanks to a custom python script available in (https://testtoolshed. g2. bx. psu. edu/repository/download?repository_id=be5040251cd4afb7&chan geset_revision=44365a4feb3b&file_type=gz). Only pairs with both mates falling into the capture region (mm10:chr2:72402000-77000000) and with both mates with quality above 30 were kept. Filtered pairs were loaded into 2kb and 5kb matrices with cooler version 0. 7. 4  (Abdennur and Mirny, 2020). The matrices were balanced with cooler balance (--cis-only). For the Del(sub-TAD1) mutant, the balancing was done with the option --ignore-diags 5 instead of --ignore-diags 2 to discard the bias due to the presence of a BAC containing the region (mm10:chr2:74747769-74913171).
On figure  3C , the histograms were produced with Plot profile on imageJ version 2. 1. 0/1. 53c. Virtual 4C were computed using the same script as in  (Amândio et al. , 2021)  inspired by  (Despang et al. , 2019). The output bedgraph were then used for the quantifications. HiChIP: Wild type stembryos were collected at 120h and fixed for 10 min in 1% formaldehyde at room temperature before proceeding with the library generation. The protocol for H3K27ac HiChIP was adapted from  (Mumbach et al. , 2016). Briefly, after lysis, DpnII digestion, biotin fill-in and blunt-end ligation (see Capture Hi-C), the samples were sonicated in a Covaris E220 instrument for 10 min (duty cycle 2%, peak incident power 105 W) and incubated overnight at 4° with magnetic beads conjugated to the H3K27ac antibody (Abcam, ab4729).
ACKNOWLEDGMENTS We thank  Dr. Fabrice Darbellay  for advice with ChIPmentation experiments,  Chase Bolt  for his assistance with Capture Hi-C experiments,  Marie Anselmet  for her help with RNA-seq experiments as well as other colleagues from the  Duboule laboratories  for discussions. We are grateful to  Anne-Catherine Cossy  for her technical assistance with cell culture. This work was supported in part using the resources and services of the  Gene Expression Research Core Facility (GECF) at the School of Life Sciences of EPFL.
Finally, animal studies were performed. The optimized hydrogel was placed on rats skins wound, and the wound healing process was observed over 19 days. (Designed by Photoshop CC 2022; http:// www. adobe. com/ produ cts/ photo shop. html). ing, and conversion to the molar ellipticity of CD data were performed using CDSD software. Corresponding controls containing the applied alginate concentrations were prepared to correct for the buffer. Furthermore, CDNN software was applied to deconvolute the data and obtain the contents of various secondary structure elements  30, 31. Thermal UV -Vis spectroscopy. Thermal stability of 13. 2 µM fibrinogen in the presence and absence of 1. 6 mM alginate (proportional to the ratio of protein to ligand concentration in the synthesized hydrogel) was studied by UV-visible spectroscopy.
The pore sizes were determined by ImageJ (version 1. 53a) software  33. Fourier-transform infrared spectroscopy. Fourier-transform infrared spectroscopy (FT-IR) was performed on an FT-IR spectrophotometer (AVATAR, range from 4000 to 400 cm -1 , Thermo, USA) with resolution of 2 cm -1 using KBr pellets  34. Swelling ratio. This test was performed to determine the maximum percentage of hydrogel water absorption in simulated wound fluid (SWF). The SWF contained 2. 0% BSA, 0. 02 M CaCl 2 , 0. 4 M NaCl, and 0. 08 M Tris buffer in deionized water (pH 7. 5). The weight changes of the Alg-Fib@Nis-EDTA hydrogel were measured every 20 min for a period of 240 min. The swollen hydrogel was carefully removed every 20 min and placed on a filter paper to eliminate the surrounding water, and then weighed.
Thermal UV-Vis spectroscopy. Thermal stability of fibrinogen was studied in the presence of alginate ligand using UV-vis spectroscopy. Based on the data from the ultraviolet-visible spectrophotometry (Fig. S5 ), it was found that fibrinogen remained conformationally stable up to 42 °C, which is the maximum temperature of the chronic wound  [47] [48] [49]. Since the first melting temperature (T m ) of the protein begins from about 59 °C, it is expected that the protein maintains its conformational stability when placed in the wound site.
Hydrogel characterization. Visual evaluation and scanning electron microscopy. The nisin containing al- ginate-fibrinogen (Alg-Fib@Nis-EDTA) hydrogel cross-linked with CaCl 2 was uniform, soft, and semi-opaque. The hydrogel was easily removed from the glass plate and showed smooth surface. Figure  2A, B  displays an example of the prepared hydrogel. The morphology of alginate-fibrinogen hydrogel was studied using scanning electron microscopy (SEM) (Fig. 2C, D ). The SEM image revealed a highly porous structure, rough surface, and large irregular wrinkles for the hydrogel, which presented a good agreement with published reports on the prepared hydrogels for wound dressings  37. The diameters of the pores were measured in the range of 14-198 μm using Image J software. This pore size range is suitable for cell attachment, based on the previous studies  [50] [51] [52].
Regarding technical limitations presented by the hydrogel, we could not detect significant differences in growth inhibition imposed by various concentrations of nisin. Therefore, MIC and MBC were determined to obtain more accurate data. The data obtained from MIC and MBC tests are reported in Table  2. According to our findings, presence of nisin in the studied concentrations in the Alg-Fib@Nis-EDTA hydrogel killed and inhibited the growth of both gram-positive and gram-negative bacteria. As expected, alginate and fibrinogen did not show any antibacterial effect in agreement with Alboofetileh et al. 68  who reported that alginate had no antibacterial effect on either gram-positive or gram-negative bacteria. In another study, Påhlman et al. 69  reported a similar lack of antibacterial properties for fibrinogen.
08 mM) ----Alg-Fib@Nis-EDTA(0. 15 mM) ----Figure  6. In vitro blood coagulation test. The absorption of the hemoglobin in the rinsing water was measured in 540 nm. The images of the rinsing water from the three samples of gauze dressing, Alg hydrogel, and Alg-Fib@Nis-EDTA hydrogel are displayed to imply the remarkable differences. Data are mean ± SD (n = 3). are displayed in Fig. 7A. In the group receiving Alg hydrogel dressing, some infection and inflammation were observed and the healing process was relatively slow. The same situation was observed in the control group, which was bandaged only with sterile gauze. While the group treated with Alg-Fib@Nis-EDTA hydrogel did not show any sign of infection and presented improved recovery process compared to the control and Alg hydrogel groups.
AUF is an orally administered, gold (Au)-containing old drug approved by the FDA in 1985 for treatment of rheumatoid arthritis  [3]. Interestingly, AUF has emerged as a candidate for multiple repurposed therapies including microbial infections and cancers  [4, 5]. The anticancer activity of AUF has been observed in a variety of human cancer cell lines and tumor models  [6]. These studies have led to the clinical trials of AUF in different types of cancers (ClinicalTrials. gov-www. clinicaltrials. org, accessed on 10 September 2022). The anticancer mechanism of AUF is not fully understood. The best-studied mechanism is the inhibition of thioredoxin reductase (TrxR) activity through AUF's high affinity for selenol-containing residues of the active site of both cytoplasmic TrxR1 and mitochondrial TrxR2, resulting in modifications of the cellular redox state, over-production of ROS, oxidative damage and apoptosis  [4, 7, 8].
Protein extraction and analysis of oxidized and reduced forms of HyPer sensors by redox Western blots were performed according to our previously reported method  [21]. HyPer fluorescent imaging in living cells was acquired Cancers 2022, 14, 4864 4 of 18 with a Nikon inverted spinning-disk microscope using excitation laser lines 405 nm and 491 nm and an emission range set from 500 to 530 nm according to the method described by Mishina et al. [22]. Time-lapse imaging was recorded every 1 min using multi-channel 4D (x,y,z,t). Images were exported to the ImageJ software, and the ratio (491/405 nm) was computed by dividing the 491 nm by the 405 nm image pixel by pixel. The final 491/405 nm ratio images after Z projection used the ImageJ look-up table 'Fire' for creating false color ratio images.
Quantitative Redoxome Analysis MDA-MB-231 cells were seeded in 6-well plates at a density of 3. 5 × 10 5 cells/well (2 mL volume of medium) and allowed to attach for 24 h before being subjected to defined treatments. Proteins containing oxidized thiols were extracted according to our previously published protocol  [23]. Digests of oxidized proteins were analyzed in triplicate with an Orbitrap Fusion Tribrid equipped with an EASY-Spray ion source. Label-free liquidchromatography tandem mass spectrometry (LC-MS/MS) acquisition was performed. Peptides from MS/MS data were processed with Proteome Discoverer Software (v 2. 1) using the Sequest search node. All results were 1% FDR filtered before exporting. The resulting files were imported into Progenesis-Qi software for report edition. Variations of protein abundance were considered as validated if their Anova p values were < 0.
05. The PANTHER classification system (http://www. pantherdb. org, accessed on 10 September 2022)  [24]  and DAVID software (https://david. ncifcrf. gov, version 6. 8, accessed on 10 September 2022)  [25]  were used to determine functional protein classes and biological processes enriched from Gene Ontology (GO) database. Functional annotations with p < 0. 05 (EASE score) (modified Fisher's exact test) were selected. The proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  [26]  partner repository with the dataset identifier PXD036558.
4, 150 mM NaCl, 0. 5% Tween 20 and 0. 5% BSA. Finally, cells were washed in wash solution, resuspended and incubated in wash solution containing 10 µg/mL of PI and 0. 5 mg/mL of RNAse A for 30 min at room temperature. Samples were analyzed using a Cytoflex flow cytometer (Beckman Coulter), and data were analyzed using CyExpert software. For experiments with a reverse scheme in which MDA-MB-231 cells were first labeled with 10 µM BrdU for 1 h, and then were left untreated or exposed to AUF 6 µM for 30 min, analyses were performed at 0, 4, 8 and 24 h during the post-treatment recovery. Cell collection, fixation, DNA hydrolysis, FITC anti-BrdU antibody and PI staining were proceeded as above. Samples were analyzed using a FACSCanto flow cytometer (Becton Dickinson) and data were analyzed using Flowjo software.
Images were obtained using a Leica SP8 upright confocal microscope. Z-stacks (about 15 images taken at different z-planes) encompassing the entire volume of the cells were taken. The merged z-stacks were obtained using the ImageJ software. Actin filament intensity per cell area was determined indirectly by measuring the intensity of fluorescence (phalloidin staining) using the ImageJ software, and more than 30 cells per sample were measured.
According to molecular function, most of the proteins belonged to the subcategories of binding and catalytic activity (Figure  4A ), whereas most biological process proteins belonged to the subcategories of cellular process, metabolic process, cellular component organization or biogenesis (Figure  4B ). According to protein class, most of the proteins belonged to the subcategory of nucleic acid binding (Figure  4C ), including several ribosomal proteins and DNA helicases such as 60S ribosomal protein L11 (RPL11), 40S ribosomal protein S5, ATP-dependent DNA helicase Q1 (RECQL), and DNA replication licensing factor MCM6. These 161 proteins with significant fold change were also subjected to analysis by DAVID software to explore the biological process involved. The results indicated that 50 GO-biological process terms were significantly enriched (p values < 0.
Acknowledgments: We thank  Charlène Lasgi  ( Institut Curie ) for technical assistance;  Thibaut Leger  and  Camille Garcia  ( Université Paris Cité, CNRS, Institut Jacques Monod, Mass Spectrometry Laboratory ) for redoxome analysis. The present work has also benefited from  Imagerie-Gif core facility  ( Mickaël Bourge ,  Romain Le Bars ,  Sandrine Lecart  and  Mébarek Temagoult ) supported by the  Agence Nationale de la Recherche  ( ANR-11-EQPX-0029 /Morphoscope ,  ANR-10-INBS-04 / FranceBioImaging ;  ANR-11-IDEX-0003-02/Saclay Plant Sciences ).
Preprocessing For MK and G1, using afni_proc. py, all echo timeseries were slice-time corrected, motion correction was computed on the first echo and applied to all echos, and echo images were merged using t2smap. py from the meica. py toolbox provided with AFNI: http://afni. nimh. nih. gov/afni/. Then, using SPM12 (Wellcome Institute of Imaging Neuroscience, London, UK) implemented in Matlab, functional time-series were co-registered to the anatomical volume, normalized, resampled to 3 mm cubic voxels, and spatially smoothed (6 mm FWHM). For G2, time-series were preprocessed only with SPM12, following the same steps: slice-timing, realignment, co-registration to the anatomy, normalization, resampling to 3 mm cubic voxels, smoothing (6mm FWHM).
MRI acquisition, preprocessing, and statistical analysis Acquisition and preprocessing were the same as for multi-echo data in Experiment 1. Univariate analyses and thresholding followed the same method as in Experiment 1, apart from the definition of regressors of interest: here we defined regressors for low-frequency words, highfrequency words, pseudowords, targets and responses to targets. As controls were all younger than MK, an age regressor would have absorbed any difference between the two. Hence no age regressor was included in second-level analyses. Moreover, multivariate pattern analyses (MVPA) were conducted using The Decoding Toolbox  (Hebart et al. , 2015) , in order to decode words from pseudowords. To train and test the support vector machine, we performed 8 leaveone-block-out cross validation folds, across the 8 blocks of the experiment.